diff --git a/hpvm/projects/onnx/README.md b/hpvm/projects/onnx/README.md
deleted file mode 100644
index eba07cd9d4102283362bbd021f28ff59b3755b0e..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
-## Importing Conda Environment:
-
-conda env create -f onnx\_environment.yml
-
-## Activate/deactivate Conda Environment
-
-conda activate onnx\_frontend
-
-## Building and Installing Frontend for ONNX:
-
-python setup.py build
-
-python setup.py install
diff --git a/hpvm/projects/onnx/models/alexnet/alexnet.onnx b/hpvm/projects/onnx/models/alexnet/alexnet.onnx
deleted file mode 100644
index d87fa5fe75eddf8ae2823bd0ace120bbacd50cdf..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/alexnet/alexnet.onnx and /dev/null differ
diff --git a/hpvm/projects/onnx/models/alexnet/test_data_0.npz b/hpvm/projects/onnx/models/alexnet/test_data_0.npz
deleted file mode 100644
index 0089f591dc76a98182e0384c70874fca701d3469..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/alexnet/test_data_0.npz and /dev/null differ
diff --git a/hpvm/projects/onnx/models/alexnet/test_data_1.npz b/hpvm/projects/onnx/models/alexnet/test_data_1.npz
deleted file mode 100644
index 30b9d311981525cff5ef71ad588a763eb403c1b4..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/alexnet/test_data_1.npz and /dev/null differ
diff --git a/hpvm/projects/onnx/models/alexnet/test_data_2.npz b/hpvm/projects/onnx/models/alexnet/test_data_2.npz
deleted file mode 100644
index f73145dc9154aacf13455de9a865889d54efe263..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/alexnet/test_data_2.npz and /dev/null differ
diff --git a/hpvm/projects/onnx/models/alexnet/test_data_set_0/input_0.pb b/hpvm/projects/onnx/models/alexnet/test_data_set_0/input_0.pb
deleted file mode 100644
index 0dbdb3ecde433c70d5201cce0343b396261ca9e0..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/alexnet/test_data_set_0/input_0.pb and /dev/null differ
diff --git a/hpvm/projects/onnx/models/alexnet/test_data_set_0/output_0.pb b/hpvm/projects/onnx/models/alexnet/test_data_set_0/output_0.pb
deleted file mode 100644
index c9a100142c98c684ce9358966c6ae4b99e62521d..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/alexnet/test_data_set_0/output_0.pb and /dev/null differ
diff --git a/hpvm/projects/onnx/models/alexnet/test_data_set_1/input_0.pb b/hpvm/projects/onnx/models/alexnet/test_data_set_1/input_0.pb
deleted file mode 100644
index 968e6125f1ab45d6d3a1c2067309f05a2aab689a..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/alexnet/test_data_set_1/input_0.pb and /dev/null differ
diff --git a/hpvm/projects/onnx/models/alexnet/test_data_set_1/output_0.pb b/hpvm/projects/onnx/models/alexnet/test_data_set_1/output_0.pb
deleted file mode 100644
index c841190b7499f8eded7a484e691254a5aafbc4db..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/alexnet/test_data_set_1/output_0.pb and /dev/null differ
diff --git a/hpvm/projects/onnx/models/alexnet/test_data_set_2/input_0.pb b/hpvm/projects/onnx/models/alexnet/test_data_set_2/input_0.pb
deleted file mode 100644
index 189e616b49d2fa21fae9a32425e24cd263ca0203..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/alexnet/test_data_set_2/input_0.pb and /dev/null differ
diff --git a/hpvm/projects/onnx/models/alexnet/test_data_set_2/output_0.pb b/hpvm/projects/onnx/models/alexnet/test_data_set_2/output_0.pb
deleted file mode 100644
index ec17185491fa73d344cb46415185422bc7f416bc..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/alexnet/test_data_set_2/output_0.pb and /dev/null differ
diff --git a/hpvm/projects/onnx/models/alexnet/test_data_set_3/input_0.pb b/hpvm/projects/onnx/models/alexnet/test_data_set_3/input_0.pb
deleted file mode 100644
index 3d91313b14fb444c268f57e8bb05f8c38421107d..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/alexnet/test_data_set_3/input_0.pb and /dev/null differ
diff --git a/hpvm/projects/onnx/models/alexnet/test_data_set_3/output_0.pb b/hpvm/projects/onnx/models/alexnet/test_data_set_3/output_0.pb
deleted file mode 100644
index 49e4e4f83c8384886ceb08283a6b46d2430ac1b9..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/alexnet/test_data_set_3/output_0.pb and /dev/null differ
diff --git a/hpvm/projects/onnx/models/alexnet/test_data_set_4/input_0.pb b/hpvm/projects/onnx/models/alexnet/test_data_set_4/input_0.pb
deleted file mode 100644
index 72d611c195d20032b4953562f4722b4e1ddedff8..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/alexnet/test_data_set_4/input_0.pb and /dev/null differ
diff --git a/hpvm/projects/onnx/models/alexnet/test_data_set_4/output_0.pb b/hpvm/projects/onnx/models/alexnet/test_data_set_4/output_0.pb
deleted file mode 100644
index dfe5b65c8260d684171cc66e3759cb66e02a1ed7..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/alexnet/test_data_set_4/output_0.pb and /dev/null differ
diff --git a/hpvm/projects/onnx/models/alexnet/test_data_set_5/input_0.pb b/hpvm/projects/onnx/models/alexnet/test_data_set_5/input_0.pb
deleted file mode 100644
index f737585597ce2b56253e37f75f3defbbfce30cec..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/alexnet/test_data_set_5/input_0.pb and /dev/null differ
diff --git a/hpvm/projects/onnx/models/alexnet/test_data_set_5/output_0.pb b/hpvm/projects/onnx/models/alexnet/test_data_set_5/output_0.pb
deleted file mode 100644
index 6374baf7869b08c98c3b43aebbe58e3d15e99774..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/alexnet/test_data_set_5/output_0.pb and /dev/null differ
diff --git a/hpvm/projects/onnx/models/keras/alexnet.onnx b/hpvm/projects/onnx/models/keras/alexnet.onnx
deleted file mode 100644
index 452d9f846fad21b5fd89321e86b8a7945c625d2d..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/keras/alexnet.onnx and /dev/null differ
diff --git a/hpvm/projects/onnx/models/keras/alexnet2.onnx b/hpvm/projects/onnx/models/keras/alexnet2.onnx
deleted file mode 100644
index e1147a8e589243004411b2b4f958a2240f3df94d..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/keras/alexnet2.onnx and /dev/null differ
diff --git a/hpvm/projects/onnx/models/keras/alexnet8k.onnx b/hpvm/projects/onnx/models/keras/alexnet8k.onnx
deleted file mode 100644
index 49f74d5f2f4dfb74f266b174a5a72d5c7add0cc1..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/keras/alexnet8k.onnx and /dev/null differ
diff --git a/hpvm/projects/onnx/models/keras/alexnet_last.onnx b/hpvm/projects/onnx/models/keras/alexnet_last.onnx
deleted file mode 100644
index f9f82e3cbfd79df383afe3c61540fdfded45a976..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/keras/alexnet_last.onnx and /dev/null differ
diff --git a/hpvm/projects/onnx/models/keras/lenet.onnx b/hpvm/projects/onnx/models/keras/lenet.onnx
deleted file mode 100644
index 659764f2f46d05dad62c2187c13c665839fe3058..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/keras/lenet.onnx and /dev/null differ
diff --git a/hpvm/projects/onnx/models/keras/mobilenet.onnx b/hpvm/projects/onnx/models/keras/mobilenet.onnx
deleted file mode 100644
index 947ab70daf9653af5f7d84349f92aca79d31c131..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/keras/mobilenet.onnx and /dev/null differ
diff --git a/hpvm/projects/onnx/models/keras/mobilenet_cifar10.onnx b/hpvm/projects/onnx/models/keras/mobilenet_cifar10.onnx
deleted file mode 100644
index 998d7f05b540b97e0fa36daab280aebbb3574eff..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/keras/mobilenet_cifar10.onnx and /dev/null differ
diff --git a/hpvm/projects/onnx/models/keras/resnet.onnx b/hpvm/projects/onnx/models/keras/resnet.onnx
deleted file mode 100644
index b9ad6bc843e3167151e74ad1a720130ef8421b4e..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/keras/resnet.onnx and /dev/null differ
diff --git a/hpvm/projects/onnx/models/keras/vgg16-cifar10.onnx b/hpvm/projects/onnx/models/keras/vgg16-cifar10.onnx
deleted file mode 100644
index 2395a9770aaf0df87d8e2acdad0935a8058fed3b..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/keras/vgg16-cifar10.onnx and /dev/null differ
diff --git a/hpvm/projects/onnx/models/keras/vgg16_cifar10.onnx b/hpvm/projects/onnx/models/keras/vgg16_cifar10.onnx
deleted file mode 100644
index a96ddf4afffca4528d27b4f4fd482aeddc8ae165..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/keras/vgg16_cifar10.onnx and /dev/null differ
diff --git a/hpvm/projects/onnx/models/mnist/mnist.onnx b/hpvm/projects/onnx/models/mnist/mnist.onnx
deleted file mode 100644
index fc1a3f733c6e6243dd23dacb125b7a372de55a50..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/mnist/mnist.onnx and /dev/null differ
diff --git a/hpvm/projects/onnx/models/mnist/test_data_set_0/input_0.pb b/hpvm/projects/onnx/models/mnist/test_data_set_0/input_0.pb
deleted file mode 100644
index f0072d51a480af615e92312608f75993be9f1136..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/mnist/test_data_set_0/input_0.pb and /dev/null differ
diff --git a/hpvm/projects/onnx/models/mnist/test_data_set_0/output_0.pb b/hpvm/projects/onnx/models/mnist/test_data_set_0/output_0.pb
deleted file mode 100644
index a6f4cdf92e27aaab21e44098b5b28e16048098e2..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/models/mnist/test_data_set_0/output_0.pb
+++ /dev/null
@@ -1,2 +0,0 @@
-
-J(ãêsDU®ÄŒtÍEÚ'DWQeÄYôÐÄQôÄ3vÂNKBÄñ³Ä
\ No newline at end of file
diff --git a/hpvm/projects/onnx/models/mnist/test_data_set_1/input_0.pb b/hpvm/projects/onnx/models/mnist/test_data_set_1/input_0.pb
deleted file mode 100644
index b40ca9538b7b03111268892ca8d7c71a5e376d06..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/mnist/test_data_set_1/input_0.pb and /dev/null differ
diff --git a/hpvm/projects/onnx/models/mnist/test_data_set_1/output_0.pb b/hpvm/projects/onnx/models/mnist/test_data_set_1/output_0.pb
deleted file mode 100644
index 786b006694a575f64fa212cb60d60d962fc042b0..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/models/mnist/test_data_set_1/output_0.pb
+++ /dev/null
@@ -1,2 +0,0 @@
-
-J( E _ÅÓ;ùÒÄXê“Äy›Ä„*_DHԺÓ!‘Ã9ZÞÂ
\ No newline at end of file
diff --git a/hpvm/projects/onnx/models/mnist/test_data_set_2/input_0.pb b/hpvm/projects/onnx/models/mnist/test_data_set_2/input_0.pb
deleted file mode 100644
index d84a2064893ba68ad633391af74744dfc991a7cd..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/mnist/test_data_set_2/input_0.pb and /dev/null differ
diff --git a/hpvm/projects/onnx/models/mnist/test_data_set_2/output_0.pb b/hpvm/projects/onnx/models/mnist/test_data_set_2/output_0.pb
deleted file mode 100644
index cb22e7b4ff7c12b9ec73ab832e34c3a566a51077..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/models/mnist/test_data_set_2/output_0.pb
+++ /dev/null
@@ -1,2 +0,0 @@
-
-J(láÅ4‹Ä’†DMWÄ ‘ÆDË¿Ä>á'Åìa¤&•B6hE
\ No newline at end of file
diff --git a/hpvm/projects/onnx/models/resnet50/resnet50.onnx b/hpvm/projects/onnx/models/resnet50/resnet50.onnx
deleted file mode 100644
index 09992ccb764246e182874eda716d535c83a82123..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/resnet50/resnet50.onnx and /dev/null differ
diff --git a/hpvm/projects/onnx/models/resnet50/test_data_set_0/input_0.pb b/hpvm/projects/onnx/models/resnet50/test_data_set_0/input_0.pb
deleted file mode 100644
index 846b581f7683fe007535309dd7bba5e356e524f6..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/resnet50/test_data_set_0/input_0.pb and /dev/null differ
diff --git a/hpvm/projects/onnx/models/resnet50/test_data_set_0/output_0.pb b/hpvm/projects/onnx/models/resnet50/test_data_set_0/output_0.pb
deleted file mode 100644
index 5c357e69d8f002e509525cbf8bbb470fa6bc575e..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/resnet50/test_data_set_0/output_0.pb and /dev/null differ
diff --git a/hpvm/projects/onnx/models/resnet50/test_data_set_1/input_0.pb b/hpvm/projects/onnx/models/resnet50/test_data_set_1/input_0.pb
deleted file mode 100644
index 0454c0cd3e58115a8805a1f770ea5406b5a72dbb..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/resnet50/test_data_set_1/input_0.pb and /dev/null differ
diff --git a/hpvm/projects/onnx/models/resnet50/test_data_set_1/output_0.pb b/hpvm/projects/onnx/models/resnet50/test_data_set_1/output_0.pb
deleted file mode 100644
index 631cf2cad41b3d582b4d792ef480f8fcdec261df..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/resnet50/test_data_set_1/output_0.pb and /dev/null differ
diff --git a/hpvm/projects/onnx/models/resnet50/test_data_set_2/input_0.pb b/hpvm/projects/onnx/models/resnet50/test_data_set_2/input_0.pb
deleted file mode 100644
index 665be39c8ee7136b9341f5ecdb370433d8afc910..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/resnet50/test_data_set_2/input_0.pb and /dev/null differ
diff --git a/hpvm/projects/onnx/models/resnet50/test_data_set_2/output_0.pb b/hpvm/projects/onnx/models/resnet50/test_data_set_2/output_0.pb
deleted file mode 100644
index ac41dadf34359eb8ea9d185ad181138b32e09cec..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/models/resnet50/test_data_set_2/output_0.pb and /dev/null differ
diff --git a/hpvm/projects/onnx/profile/alexnet_keras.py b/hpvm/projects/onnx/profile/alexnet_keras.py
deleted file mode 100644
index bcbf35f0c632eb27d300a94fde5882761701c53b..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/profile/alexnet_keras.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import numpy as np
-from keras.datasets import cifar10
-from keras.models import Sequential
-from keras.layers.core import Dense, Dropout, Flatten, Activation
-from keras.layers.convolutional import Conv2D
-from keras.optimizers import Adam
-from keras.layers.pooling import MaxPooling2D
-from keras.utils.np_utils import to_categorical
-from keras.preprocessing.image import ImageDataGenerator
-from keras import backend as K
-from keras import regularizers
-from keras.callbacks import LearningRateScheduler
-import sys
-import struct
-import keras
-import numpy as np
-import os
-os.environ['CUDA_VISIBLE_DEVICES'] = '1'
-
-def buildModel2():
-
- activation_type = "tanh"
- weight_decay = 1e-4
-
- model = Sequential()
- model.add(Conv2D(64, kernel_size=(11, 11), activation=activation_type,
- input_shape=(3, 32, 32), padding = 'same',
- kernel_regularizer=regularizers.l2(weight_decay) ))
- model.add(MaxPooling2D(pool_size=(2, 2), strides=(2,2) ))
- model.add(Dropout(0.2))
- model.add(Conv2D(192, kernel_size=(5, 5), activation=activation_type, padding = 'same',
- kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(MaxPooling2D(pool_size=(2, 2), strides=(2,2) ))
- model.add(Dropout(0.3))
-
- model.add(Conv2D(384, kernel_size=(3, 3), activation=activation_type, padding = 'same',
- kernel_regularizer=regularizers.l2(weight_decay) ))
- model.add(Conv2D(256, kernel_size=(3, 3), activation=activation_type, padding = 'same',
- kernel_regularizer=regularizers.l2(weight_decay) ))
- model.add(Conv2D(256, kernel_size=(3, 3), activation=activation_type, padding = 'same',
- kernel_regularizer=regularizers.l2(weight_decay) ))
- model.add(MaxPooling2D(pool_size=(2, 2), strides=(2,2) ))
- model.add(Dropout(0.4))
-
- model.add(Flatten())
- #model.add(Flatten())
- #model.add(Dense(256))
- model.add(Dense(10))
- model.add(Activation('softmax'))
-
- return model
-
-def lr_schedule(epoch):
- lrate = 0.001
- if epoch > 20:
- lrate = 0.0005
- if epoch > 40:
- lrate = 0.0003
- if epoch > 60:
- lrate = 0.0001
- if epoch > 80:
- lrate = 0.00005
-
- return lrate
-
-def trainModel(model):
- #dir_prefix = "/home/hsharif3/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10/"
-
- #opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)
- # Compile the model
- model.compile(loss='categorical_crossentropy',
- optimizer=Adam(lr=0.0001, decay=1e-6),
- #optimizer = opt_rms,
- metrics=['accuracy'])
-
- #print to_categorical(Y_train, 10)
- print (to_categorical(Y_train))
-
-
- datagen = ImageDataGenerator(
- rotation_range=15,
- width_shift_range=0.1,
- height_shift_range=0.1,
- horizontal_flip=True,
- )
- datagen.fit(X_train)
-
-
- model.fit(X_train, to_categorical(Y_train, 10),
- batch_size=128,
- shuffle=True,
- epochs = 1,
- #epochs=100,
- validation_data=(X_test, to_categorical(Y_test, 10)), callbacks=[LearningRateScheduler(lr_schedule)])
-
- # Evaluate the model
- scores = model.evaluate(X_test.astype('float32'), to_categorical(Y_test, 10))
-
- print('Loss: %.3f' % scores[0])
- print('Accuracy: %.3f' % scores[1])
-
- print ("*** TRAINED MODEL ****\n")
-
-K.set_image_data_format('channels_first')
-(X_train, Y_train), (X_test, Y_test) = cifar10.load_data()
-test_labels = Y_test
-train_labels = Y_train
-
-#X_train = X_train.astype('float32')
-#X_test = X_test.astype('float32')
-X_train = X_train / 255.0
-X_test = X_test / 255.0
-
-mean = np.mean(X_train,axis=(0,1,2,3))
-std = np.std(X_train,axis=(0,1,2,3))
-X_train = (X_train-mean)/(std+1e-7)
-X_test = (X_test-mean)/(std+1e-7)
-model = buildModel2()
-trainModel(model)
-import time
-start = time.time()
-keras_result = model.predict(X_test[:8000])
-print("time:", time.time() - start)
\ No newline at end of file
diff --git a/hpvm/projects/onnx/profile/alexnet_onnx.py b/hpvm/projects/onnx/profile/alexnet_onnx.py
deleted file mode 100644
index 1bde4eaab39d901ef131f7cda59810721d701e1b..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/profile/alexnet_onnx.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import numpy as np
-from keras.datasets import cifar10
-from keras import backend as K
-import numpy as np
-import os
-import keras2onnx
-import onnx
-import onnxruntime
-import time
-os.environ['CUDA_VISIBLE_DEVICES'] = '1'
-
-K.set_image_data_format('channels_first')
-(X_train, Y_train), (X_test, Y_test) = cifar10.load_data()
-test_labels = Y_test
-train_labels = Y_train
-
-#X_train = X_train.astype('float32')
-#X_test = X_test.astype('float32')
-X_train = X_train / 255.0
-X_test = X_test / 255.0
-
-mean = np.mean(X_train,axis=(0,1,2,3))
-std = np.std(X_train,axis=(0,1,2,3))
-X_train = (X_train-mean)/(std+1e-7)
-X_test = (X_test-mean)/(std+1e-7)
-
-sess = onnxruntime.InferenceSession("../models/keras/alexnet.onnx")
-
-input_name = sess.get_inputs()[0].name
-print("Input name :", input_name)
-input_shape = sess.get_inputs()[0].shape
-print("Input shape :", input_shape)
-input_type = sess.get_inputs()[0].type
-print("Input type :", input_type)
-
-output_name = sess.get_outputs()[0].name
-print("Output name :", output_name)
-output_shape = sess.get_outputs()[0].shape
-print("Output shape :", output_shape)
-output_type = sess.get_outputs()[0].type
-print("Output type :", output_type)
-
-start_time = time.time()
-ort_result = sess.run([output_name], {input_name: X_test.astype('float32')[:8000]})
-print("time: ", time.time() - start_time)
\ No newline at end of file
diff --git a/hpvm/projects/onnx/profile/alexnet_tvm.py b/hpvm/projects/onnx/profile/alexnet_tvm.py
deleted file mode 100644
index 5a3847335f43e0d9af9a5a51fd1ba90582f22489..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/profile/alexnet_tvm.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import numpy as np
-from keras.datasets import cifar10
-from keras import backend as K
-import sys
-import struct
-import numpy as np
-import os
-import tvm
-import tvm.relay as relay
-from tvm.contrib import graph_runtime
-os.environ['CUDA_VISIBLE_DEVICES'] = '1'
-
-K.set_image_data_format('channels_last')
-(X_train, Y_train), (X_test, Y_test) = cifar10.load_data()
-X_test = X_test / 255.0
-mean = np.mean(X_train,axis=(0,1,2,3))
-std = np.std(X_train,axis=(0,1,2,3))
-X_test = (X_test-mean)/(std+1e-7)
-
-import onnx
-onnx_model = onnx.load("../models/keras/alexnet_last.onnx")
-
-input_name = 'conv2d_8_input'
-input_size = 8000
-shape_dict = {input_name: X_test[:input_size].shape}
-mod, params = relay.frontend.from_onnx(onnx_model, shape_dict)
-target = 'cuda -libs=cudnn,cublas'
-with relay.build_config(opt_level=3):
- graph, lib, params = relay.build(mod, target, params=params)
-
-import time
-ctx = tvm.gpu()
-#data = np.random.uniform(-1, 1, size=data_shape).astype("float32")
-# create module
-module = graph_runtime.create(graph, lib, ctx)
-# set input and parameters
-module.set_input("conv2d_8_input", X_test[:input_size].astype("float32"))
-module.set_input(**params)
-# run
-start_time = time.time()
-module.run()
-out_shape = (input_size, 10)
-# get output
-out = module.get_output(0, tvm.nd.empty(out_shape)).asnumpy()
-print("Time:", time.time() - start_time)
\ No newline at end of file
diff --git a/hpvm/projects/onnx/profile/prof.nvvp b/hpvm/projects/onnx/profile/prof.nvvp
deleted file mode 100644
index be5cbd996b6980e9e4bad773fe9f3abea1ccb0f6..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/onnx/profile/prof.nvvp and /dev/null differ
diff --git a/hpvm/projects/onnx/specs.dump b/hpvm/projects/onnx/specs.dump
deleted file mode 100644
index 3016325b9bb2f94c3d66f0185408cccfa7cf9a43..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/specs.dump
+++ /dev/null
@@ -1,1051 +0,0 @@
-
-Computer
-********
-
-
-Summary
--------
-
--Computer-
-Processor : 40x Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz
-Memory : 65830MB (28703MB used)
-Operating System : Ubuntu 16.04.6 LTS
-User Name : ys26 (Yuanjing Shi)
-Date/Time : Tue 31 Mar 2020 07:33:16 PM CDT
--Display-
-Resolution : 0x0 pixels
-OpenGL Renderer : Unknown
-X11 Vendor : (null)
--Multimedia-
-Audio Adapter : HDA-Intel - HDA Intel PCH
-Audio Adapter : HDA-Intel - HDA NVidia
-Audio Adapter : HDA-Intel - HDA NVidia
--Input Devices-
- Power Button
- Power Button
- HDA Intel PCH Front Mic
- HDA Intel PCH Rear Mic
- HDA Intel PCH Line
- HDA Intel PCH Line Out Front
- HDA Intel PCH Line Out Surround
- HDA Intel PCH Line Out CLFE
- HDA Intel PCH Front Headphone
- HDA NVidia HDMI/DP,pcm : 3=
- HDA NVidia HDMI/DP,pcm : 7=
- HDA NVidia HDMI/DP,pcm : 8=
- HDA NVidia HDMI/DP,pcm : 9=
- HDA NVidia HDMI/DP,pcm : 3=
- HDA NVidia HDMI/DP,pcm : 7=
- HDA NVidia HDMI/DP,pcm : 8=
- HDA NVidia HDMI/DP,pcm : 9=
- Dell Dell USB Keyboard
- Logitech USB-PS/2 Optical Mouse
--Printers-
-No printers found
--SCSI Disks-
-ATA Samsung SSD 850
-ATA Samsung SSD 850
-Generic Ultra HS-SD/MMC
-
-Operating System
-----------------
-
--Version-
-Kernel : Linux 4.15.0-66-generic (x86_64)
-Compiled : #75~16.04.1-Ubuntu SMP Tue Oct 1 14:01:08 UTC 2019
-C Library : Unknown
-Default C Compiler : GNU C Compiler version 5.4.0 20160609 (Ubuntu 5.4.0-6ubuntu1~16.04.11)
-Distribution : Ubuntu 16.04.6 LTS
--Current Session-
-Computer Name : tyler
-User Name : ys26 (Yuanjing Shi)
-Home Directory : /home/ys26
-Desktop Environment : Terminal
--Misc-
-Uptime : 67 days, 16 hours and 1 minute
-Load Average : 19.85, 20.81, 20.51
-
-Kernel Modules
---------------
-
--Loaded Modules-
-nvidia_uvm
-nfsv3
-nfs_acl
-nfs
-lockd : NFS file locking service version 0.5.
-grace
-fscache : FS Cache Manager
-nvram
-video : ACPI Video Driver
-msr : x86 generic MSR driver
-xt_tcpudp : Xtables: TCP, UDP and UDP-Lite match
-iptable_filter : iptables filter table
-ip_tables : IPv4 packet filter
-x_tables : {ip,ip6,arp,eb}_tables backend module
-input_leds : Input -> LEDs Bridge
-snd_hda_codec_hdmi : HDMI HD-audio codec
-binfmt_misc
-xfs : SGI XFS with ACLs, security attributes, realtime, no debug enabled
-nvidia_drm
-nvidia_modeset
-nvidia
-intel_rapl : Driver for Intel RAPL (Running Average Power Limit)
-sb_edac : MC Driver for Intel Sandy Bridge and Ivy Bridge memory controllers - Ver: 1.1.2
-x86_pkg_temp_thermal : X86 PKG TEMP Thermal Driver
-intel_powerclamp : Package Level C-state Idle Injection for Intel CPUs
-coretemp : Intel Core temperature monitor
-snd_hda_codec_realtek : Realtek HD-audio codec
-kvm_intel
-snd_hda_codec_generic : Generic HD-audio codec parser
-kvm
-snd_hda_intel : Intel HDA driver
-snd_hda_codec : HDA codec core
-snd_hda_core : HD-audio bus
-snd_hwdep : Hardware dependent layer
-irqbypass : IRQ bypass manager utility module
-snd_pcm : Midlevel PCM code for ALSA.
-intel_wmi_thunderbolt : Intel WMI Thunderbolt force power driver
-intel_cstate
-snd_seq_midi : Advanced Linux Sound Architecture sequencer MIDI synth.
-intel_rapl_perf
-snd_seq_midi_event : MIDI byte <-> sequencer event coder
-drm_kms_helper : DRM KMS helper
-snd_rawmidi : Midlevel RawMidi code for ALSA.
-drm : DRM shared core routines
-snd_seq : Advanced Linux Sound Architecture sequencer.
-snd_seq_device : ALSA sequencer device management
-ipmi_devintf : Linux device interface for the IPMI message handler.
-snd_timer : ALSA timer interface
-ipmi_msghandler : Incoming and outgoing message routing for an IPMI interface.
-fb_sys_fops : Generic file read (fb in system RAM)
-snd : Advanced Linux Sound Architecture driver for soundcards.
-syscopyarea : Generic copyarea (sys-to-sys)
-sysfillrect : Generic fill rectangle (sys-to-sys)
-sysimgblt : 1-bit/8-bit to 1-32 bit color expansion (sys-to-sys)
-lpc_ich : LPC interface for Intel ICH
-soundcore : Core sound module
-ioatdma
-shpchp : Standard Hot Plug PCI Controller Driver
-mac_hid
-ib_iser : iSER (iSCSI Extensions for RDMA) Datamover
-rdma_cm : Generic RDMA CM Agent
-iw_cm : iWARP CM
-ib_cm : InfiniBand CM
-ib_core : core kernel InfiniBand API
-iscsi_tcp : iSCSI/TCP data-path
-libiscsi_tcp : iSCSI/TCP data-path
-libiscsi : iSCSI library functions
-scsi_transport_iscsi : iSCSI Transport Interface
-parport_pc : PC-style parallel port driver
-ppdev
-lp
-parport
-sunrpc
-autofs4
-btrfs
-zstd_compress : Zstd Compressor
-raid10 : RAID10 (striped mirror) personality for MD
-raid456 : RAID4/5/6 (striping with parity) personality for MD
-async_raid6_recov : asynchronous RAID-6 recovery api
-async_memcpy : asynchronous memcpy api
-async_pq : asynchronous raid6 syndrome generation/validation
-async_xor : asynchronous xor/xor-zero-sum api
-async_tx : Asynchronous Bulk Memory Transactions API
-xor
-raid6_pq : RAID6 Q-syndrome calculations
-libcrc32c : CRC32c (Castagnoli) calculations
-raid1 : RAID1 (mirroring) personality for MD
-raid0 : RAID0 (striping) personality for MD
-multipath : simple multi-path personality for MD
-linear : Linear device concatenation personality for MD
-uas
-usb_storage : USB Mass Storage driver for Linux
-hid_generic : HID generic driver
-usbhid : USB HID core driver
-hid
-mxm_wmi : MXM WMI Driver
-crct10dif_pclmul : T10 DIF CRC calculation accelerated with PCLMULQDQ.
-crc32_pclmul
-ghash_clmulni_intel : GHASH Message Digest Algorithm, acclerated by PCLMULQDQ-NI
-pcbc : PCBC block cipher algorithm
-igb : Intel(R) Gigabit Ethernet Network Driver
-aesni_intel : Rijndael (AES) Cipher Algorithm, Intel AES-NI instructions optimized
-dca
-i2c_algo_bit : I2C-Bus bit-banging algorithm
-aes_x86_64 : Rijndael (AES) Cipher Algorithm, asm optimized
-ahci : AHCI SATA low-level driver
-crypto_simd
-ptp : PTP clocks support
-glue_helper
-cryptd : Software async crypto daemon
-pps_core : LinuxPPS support (RFC 2783) - ver. 5.3.6
-libahci : Common AHCI SATA low-level routines
-wmi : ACPI-WMI Mapping Driver
-
-Boots
------
-
--Boots-
-Fri Jan 24 02:31 : 44..15.0-66-generi|still
-Mon Nov 18 18:04 : 44..15.0-66-generi|still
-Fri Nov 1 1:52 : 44..15.0-66-generi|-
-Fri Nov 1 1:25 : 44..15.0-66-generi|-
-
-Languages
----------
-
--Available Languages-
-en_US.utf8 : English locale for the USA
-
-Filesystems
------------
-
--Mounted File Systems-
-udev /dev 0.00 % (31.4 GiB of 31.4 GiB)
-tmpfs /run 7.15 % (5.8 GiB of 6.3 GiB)
-/dev/mapper/tyler--vg-root / 5.81 % (1720.1 GiB of 1826.1 GiB)
-tmpfs /dev/shm 0.00 % (31.4 GiB of 31.4 GiB)
-tmpfs /run/lock 0.08 % (5.0 MiB of 5.0 MiB)
-tmpfs /sys/fs/cgroup 0.00 % (31.4 GiB of 31.4 GiB)
-/dev/sdb1 /srv/local 9.99 % (1676.1 GiB of 1862.1 GiB)
-/dev/sdb1 /nix 9.99 % (1676.1 GiB of 1862.1 GiB)
-/dev/sda2 /boot 11.01 % (3.3 GiB of 3.7 GiB)
-vadve-file-01.cs.illinois.edu:/srv/home/deepanv2 /home/deepanv2 84.56 % (1580.7 GiB of 10238.0 GiB)
-tmpfs /run/user/242233 0.00 % (6.3 GiB of 6.3 GiB)
-vadve-file-01.cs.illinois.edu:/srv/home/nz11 /home/nz11 84.56 % (1580.7 GiB of 10238.0 GiB)
-tmpfs /run/user/1091162 0.00 % (6.3 GiB of 6.3 GiB)
-vadve-file-01.cs.illinois.edu:/srv/home/hsharif3 /home/hsharif3 84.56 % (1580.7 GiB of 10238.0 GiB)
-tmpfs /run/user/641600 0.00 % (6.3 GiB of 6.3 GiB)
-vadve-file-01.cs.illinois.edu:/srv/home/bjschre2 /home/bjschre2 84.56 % (1580.7 GiB of 10238.0 GiB)
-tmpfs /run/user/373498 0.00 % (6.3 GiB of 6.3 GiB)
-/etc/autofs/cs_vadve/auto.direct /shared 84.56 % (1580.7 GiB of 10238.0 GiB)
-engr-linux-siebl1.engr.illinois.edu:/srv/software/megacli-8.07 /software/megacli-8.07 95.35 % (92.6 GiB of 1992.1 GiB)
-vadve-file-01.cs.illinois.edu:/srv/shared /shared 84.56 % (1580.7 GiB of 10238.0 GiB)
-engr-linux-siebl1.engr.illinois.edu:/srv/software/cuda-9.1 /software/cuda-9.1 95.35 % (92.6 GiB of 1992.1 GiB)
-vadve-file-01.cs.illinois.edu:/srv/home/yifanz16 /home/yifanz16 84.56 % (1580.7 GiB of 10238.0 GiB)
-tmpfs /run/user/1086025 0.00 % (6.3 GiB of 6.3 GiB)
-vadve-file-01.cs.illinois.edu:/srv/home/ys26 /home/ys26 84.56 % (1580.7 GiB of 10238.0 GiB)
-tmpfs /run/user/1055463 0.00 % (6.3 GiB of 6.3 GiB)
-
-Display
--------
-
--Display-
-Resolution : 0x0 pixels
-Vendor : (null)
-Version : (null)
--Monitors-
--Extensions-
-
-Environment Variables
----------------------
-
--Environment Variables-
-MANPATH : /usr/share/lmod/lmod/share/man::
-NIX_PROFILES : /nix/var/nix/profiles/default /home/ys26/.nix-profile
-XDG_SESSION_ID : 14482
-TERM : xterm-256color
-SHELL : /bin/bash
-DISTARCH : Linux-x86_64
-MODULEPATH_ROOT : /etc/modulefiles/software
-SSH_CLIENT : 10.251.9.206 64268 22
-CONDA_SHLVL : 1
-CONDA_PROMPT_MODIFIER : (base)
-LMOD_PKG : /usr/share/lmod/lmod
-LMOD_VERSION : 7.7.14
-GTK_MODULES : gail:atk-bridge
-SSH_TTY : /dev/pts/7
-USER : ys26
-LMOD_sys : Linux
-CONDA_EXE : /home/ys26/anaconda3/bin/conda
-_CE_CONDA
-MAIL : /var/mail/ys26
-PATH : /home/ys26/anaconda3/bin:/home/ys26/anaconda3/condabin:/home/ys26/.nix-profile/bin:/nix/var/nix/profiles/default/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/home/ys26/.local/bin:/home/ys26/bin
-CONDA_PREFIX : /home/ys26/anaconda3
-NIX_PATH : nixpkgs=/nix/var/nix/profiles/per-user/root/channels/nixpkgs:/nix/var/nix/profiles/per-user/root/channels
-PWD : /home/ys26
-LANG : en_US.UTF-8
-MODULEPATH : /etc/modulefiles/software/Linux:/etc/modulefiles/software/Core:/usr/share/lmod/lmod/modulefiles/Core:/etc/modulefiles/env:/etc/modulefiles/class:/etc/modulefiles/software
-NIX_SSL_CERT_FILE : /etc/ssl/certs/ca-certificates.crt
-LMOD_CMD : /usr/share/lmod/lmod/libexec/lmod
-_CE_M
-KRB5CCNAME : FILE:/tmp/krb5cc_1055463_itHNAb
-SHLVL : 1
-HOME : /home/ys26
-LANGUAGE : en_US:
-SHOST : tyler
-BASH_ENV : /usr/share/lmod/lmod/init/bash
-CONDA_PYTHON_EXE : /home/ys26/anaconda3/bin/python
-LMOD_arch : x86_64
-LOGNAME : ys26
-XDG_DATA_DIRS : /usr/local/share:/usr/share:/var/lib/snapd/desktop
-SOFTPATH : /software
-SSH_CONNECTION : 10.251.9.206 64268 130.126.136.179 22
-MODULESHOME : /usr/share/lmod/lmod
-CONDA_DEFAULT_ENV : base
-LMOD_SETTARG_FULL_SUPPORT : no
-ARCH : x86_64
-XDG_RUNTIME_DIR : /run/user/1055463
-LMOD_DIR : /usr/share/lmod/lmod/libexec
-NIX_USER_PROFILE_DIR : /nix/var/nix/profiles/per-user/ys26
-BASH_FUNC_module%% : () { eval $($LMOD_CMD bash "$@") && eval $(${LMOD_SETTARG_CMD:-:} -s sh)
-
-Users
------
-
--Users-
-root : root
-daemon : daemon
-bin : bin
-sys : sys
-sync : sync
-games : games
-man : man
-lp : lp
-mail : mail
-news : news
-uucp : uucp
-proxy : proxy
-www-data : www-data
-backup : backup
-list : Mailing List Manager
-irc : ircd
-gnats : Gnats Bug-Reporting System (admin)
-nobody : nobody
-systemd-timesync : systemd Time Synchronization
-systemd-network : systemd Network Management
-systemd-resolve : systemd Resolver
-systemd-bus-proxy : systemd Bus Proxy
-syslog
-_apt
-messagebus
-lxd
-dnsmasq : dnsmasq
-sshd
-statd
-postfix
-colord : colord colour management daemon
-ntp
-clamav
-sensu : Sensu Monitoring Framework
-telegraf
-lightdm : Light Display Manager
-whoopsie
-avahi-autoipd : Avahi autoip daemon
-avahi : Avahi mDNS daemon
-speech-dispatcher : Speech Dispatcher
-hplip : HPLIP system user
-kernoops : Kernel Oops Tracking Daemon
-pulse : PulseAudio daemon
-rtkit : RealtimeKit
-saned
-usbmux : usbmux daemon
-nixbld1 : Nix build user 1
-nixbld2 : Nix build user 2
-nixbld3 : Nix build user 3
-nixbld4 : Nix build user 4
-nixbld5 : Nix build user 5
-nixbld6 : Nix build user 6
-nixbld7 : Nix build user 7
-nixbld8 : Nix build user 8
-nixbld9 : Nix build user 9
-nixbld10 : Nix build user 10
-nixbld11 : Nix build user 11
-nixbld12 : Nix build user 12
-nixbld13 : Nix build user 13
-nixbld14 : Nix build user 14
-nixbld15 : Nix build user 15
-nixbld16 : Nix build user 16
-nixbld17 : Nix build user 17
-nixbld18 : Nix build user 18
-nixbld19 : Nix build user 19
-nixbld20 : Nix build user 20
-nixbld21 : Nix build user 21
-nixbld22 : Nix build user 22
-nixbld23 : Nix build user 23
-nixbld24 : Nix build user 24
-nixbld25 : Nix build user 25
-nixbld26 : Nix build user 26
-nixbld27 : Nix build user 27
-nixbld28 : Nix build user 28
-nixbld29 : Nix build user 29
-nixbld30 : Nix build user 30
-nixbld31 : Nix build user 31
-nixbld32 : Nix build user 32
-
-Devices
-*******
-
-
-Processor
----------
-
--Processors-
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2599.98MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2602.97MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2456.45MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2601.63MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2600.04MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2600.07MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2600.02MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2600.04MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2600.03MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2600.19MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2695.21MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2715.75MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2715.46MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 1424.73MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 1787.70MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2711.77MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 1353.80MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2710.79MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2709.20MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2642.89MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2600.04MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2600.04MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2449.84MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2600.04MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2600.05MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2600.04MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2600.04MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2599.95MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2600.04MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2600.04MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2706.53MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2675.88MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2792.21MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 1358.61MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 1748.81MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2611.28MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 1373.20MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2705.52MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2736.24MHz
-Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz : 2702.87MHz
-
-Memory
-------
-
--Memory-
-Total Memory : 65830480 kB
-Free Memory : 14031276 kB
-MemAvailable : 38320820 kB
-Buffers : 398312 kB
-Cached : 23096348 kB
-Cached Swap : 126052 kB
-Active : 33001128 kB
-Inactive : 14469604 kB
-Active(anon) : 23204468 kB
-Inactive(anon) : 830232 kB
-Active(file) : 9796660 kB
-Inactive(file) : 13639372 kB
-Unevictable : 3684 kB
-Mlocked : 3684 kB
-Virtual Memory : 3997692 kB
-Free Virtual Memory : 2660556 kB
-Dirty : 136 kB
-Writeback : 8 kB
-AnonPages : 23971776 kB
-Mapped : 1338980 kB
-Shmem : 63004 kB
-Slab : 3122640 kB
-SReclaimable : 1516712 kB
-SUnreclaim : 1605928 kB
-KernelStack : 30624 kB
-PageTables : 108936 kB
-NFS_Unstable : 0 kB
-Bounce : 0 kB
-WritebackTmp : 0 kB
-CommitLimit : 36912932 kB
-Committed_AS : 64285764 kB
-VmallocTotal : 34359738367 kB
-VmallocUsed : 0 kB
-VmallocChunk : 0 kB
-HardwareCorrupted : 0 kB
-AnonHugePages : 2934784 kB
-ShmemHugePages : 0 kB
-ShmemPmdMapped : 0 kB
-CmaTotal : 0 kB
-CmaFree : 0 kB
-HugePages_Total : 0
-HugePages_Free : 0
-HugePages_Rsvd : 0
-HugePages_Surp : 0
-Hugepagesize : 2048 kB
-DirectMap4k : 63504064 kB
-DirectMap2M : 3469312 kB
-DirectMap1G : 2097152 kB
-
-PCI Devices
------------
-
--PCI Devices-
-Host bridge : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D DMI2 (rev 01)
-PCI bridge : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D PCI Express Root Port 1 (rev 01) (prog-if 00 [Normal decode])
-PCI bridge : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D PCI Express Root Port 3 (rev 01) (prog-if 00 [Normal decode])
-System peripheral : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-System peripheral : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 1 (rev 01)
-System peripheral : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 2 (rev 01)
-System peripheral : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 3 (rev 01)
-System peripheral : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 4 (rev 01)
-System peripheral : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 5 (rev 01)
-System peripheral : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 6 (rev 01)
-System peripheral : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 7 (rev 01)
-System peripheral : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Map/VTd_Misc/System Management (rev 01)
-System peripheral : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D IIO Hot Plug (rev 01)
-System peripheral : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D IIO RAS/Control Status/Global Errors (rev 01)
-PIC : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D I/O APIC (rev 01) (prog-if 20 [IO(X)-APIC])
-
-USB Devices
------------
-
-
-Printers
---------
-
--Printers-
-No printers found
-
-Battery
--------
-
--No batteries-
-No batteries found on this system
-
-Sensors
--------
-
--Cooling Fans-
--Temperatures-
--Voltage Values-
-
-Input Devices
--------------
-
--Input Devices-
- Power Button
- Power Button
- HDA Intel PCH Front Mic
- HDA Intel PCH Rear Mic
- HDA Intel PCH Line
- HDA Intel PCH Line Out Front
- HDA Intel PCH Line Out Surround
- HDA Intel PCH Line Out CLFE
- HDA Intel PCH Front Headphone
- HDA NVidia HDMI/DP,pcm : 3=
- HDA NVidia HDMI/DP,pcm : 7=
- HDA NVidia HDMI/DP,pcm : 8=
- HDA NVidia HDMI/DP,pcm : 9=
- HDA NVidia HDMI/DP,pcm : 3=
- HDA NVidia HDMI/DP,pcm : 7=
- HDA NVidia HDMI/DP,pcm : 8=
- HDA NVidia HDMI/DP,pcm : 9=
- Dell Dell USB Keyboard
- Logitech USB-PS/2 Optical Mouse
-
-Storage
--------
-
--SCSI Disks-
-ATA Samsung SSD 850
-ATA Samsung SSD 850
-Generic Ultra HS-SD/MMC
-
-DMI
----
-
--BIOS-
-Date : 11/09/2016
-Vendor : American Megatrends Inc. (www.ami.com)
-Version : 2.0a
--Board-
-Name : X10DAI
-Vendor : Supermicro
-
-Resources
----------
-
--I/O Ports-
-<tt>0000-0000 </tt> : PCI Bus 0000:80
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1) (prog-if 00 [VGA controller])
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1) (prog-if 00 [VGA controller])
-<tt> 0000-0000 </tt> : NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1) (prog-if 00 [VGA controller])
-<tt> 0000-0000 </tt> : NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1) (prog-if 00 [VGA controller])
-<tt> 0000-0000 </tt> : NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1) (prog-if 00 [VGA controller])
-<tt> 0000-0000 </tt> : NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1) (prog-if 00 [VGA controller])
-<tt> 0000-0000 </tt> : NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1) (prog-if 00 [VGA controller])
-<tt> 0000-0000 </tt> : NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1) (prog-if 00 [VGA controller])
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1) (prog-if 00 [VGA controller])
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt>0000-0000 </tt> : PCI Bus 0000:80
-<tt>0000-0000 </tt> : PCI Bus 0000:80
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1) (prog-if 00 [VGA controller])
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1) (prog-if 00 [VGA controller])
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1) (prog-if 00 [VGA controller])
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1) (prog-if 00 [VGA controller])
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1) (prog-if 00 [VGA controller])
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1) (prog-if 00 [VGA controller])
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1) (prog-if 00 [VGA controller])
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1) (prog-if 00 [VGA controller])
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1) (prog-if 00 [VGA controller])
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1) (prog-if 00 [VGA controller])
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1) (prog-if 00 [VGA controller])
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1) (prog-if 00 [VGA controller])
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1) (prog-if 00 [VGA controller])
-<tt>0000-0000 </tt> : PCI Bus 0000:80
-<tt> 0000-0000 </tt> : PCI Bus 0000:81
-<tt> 0000-0000 </tt> : NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1) (prog-if 00 [VGA controller])
--Memory-
-<tt>00000000-00000000 </tt> : PCI Bus 0000:80
-<tt>00000000-00000000 </tt> : PCI Bus 0000:80
-<tt>00000000-00000000 </tt> : PCI Bus 0000:80
-<tt>00000000-00000000 </tt> : PCI Bus 0000:80
-<tt>00000000-00000000 </tt> : PCI Bus 0000:80
-<tt>00000000-00000000 </tt> : PCI Bus 0000:80
-<tt>00000000-00000000 </tt> : PCI Bus 0000:80
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt>00000000-00000000 </tt> : PCI Bus 0000:80
-<tt>00000000-00000000 </tt> : PCI Bus 0000:80
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt>00000000-00000000 </tt> : PCI Bus 0000:80
-<tt>00000000-00000000 </tt> : PCI Bus 0000:80
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt>00000000-00000000 </tt> : PCI Bus 0000:80
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : ICH HD audio
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : ICH HD audio
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : ICH HD audio
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : ICH HD audio
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : ICH HD audio
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : ICH HD audio
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt>00000000-00000000 </tt> : PCI Bus 0000:80
-<tt>00000000-00000000 </tt> : PCI Bus 0000:80
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : ICH HD audio
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : ICH HD audio
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt>00000000-00000000 </tt> : PCI Bus 0000:80
-<tt>00000000-00000000 </tt> : PCI Bus 0000:80
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt>00000000-00000000 </tt> : PCI Bus 0000:80
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt>00000000-00000000 </tt> : PCI Bus 0000:80
-<tt>00000000-00000000 </tt> : PCI Bus 0000:80
-<tt>00000000-00000000 </tt> : PCI Bus 0000:80
-<tt>00000000-00000000 </tt> : PCI Bus 0000:80
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt>00000000-00000000 </tt> : PCI Bus 0000:80
-<tt>00000000-00000000 </tt> : PCI Bus 0000:80
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt>00000000-00000000 </tt> : PCI Bus 0000:80
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt>00000000-00000000 </tt> : PCI Bus 0000:80
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt>00000000-00000000 </tt> : PCI Bus 0000:80
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt>00000000-00000000 </tt> : PCI Bus 0000:80
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
-<tt> 00000000-00000000 </tt> : Intel Corporation Xeon E7 v4/Xeon E5 v4/Xeon E3 v4/Xeon D Crystal Beach DMA Channel 0 (rev 01)
-<tt> 00000000-00000000 </tt> : ioatdma
--DMA-
-<tt> 4</tt> : cascade
-
-Network
-*******
-
-
-Interfaces
-----------
-
--Network Interfaces-
-eth0 4338573.20MiB 13789563.94MiB 130.126.136.179
-eth1 0.00MiB 0.00MiB
-lo 4132.02MiB 4132.02MiB 127.0.0.1
-
-IP Connections
---------------
-
--Connections-
-0.0.0.0:22 LISTEN 0.0.0.0:* tcp
-127.0.0.1:3030 0.0.0.0:* udp
-127.0.0.1:631 LISTEN 0.0.0.0:* tcp
-127.0.0.1:3031 LISTEN 0.0.0.0:* tcp
-127.0.0.1:8888 LISTEN 0.0.0.0:* tcp
-127.0.0.1:25 LISTEN 0.0.0.0:* tcp
-127.0.0.1:6010 ESTABLISHED 127.0.0.1:59498 tcp
-0.0.0.0:445 LISTEN 0.0.0.0:* tcp
-127.0.0.1:34409 LISTEN 0.0.0.0:* tcp
-0.0.0.0:139 LISTEN 0.0.0.0:* tcp
-0.0.0.0:47279 LISTEN 0.0.0.0:* tcp
-0.0.0.0:111 0.0.0.0:* udp
-0.0.0.0:39059 LISTEN 0.0.0.0:* tcp
-127.0.1.1:53 0.0.0.0:* udp
-127.0.0.1:6010 ESTABLISHED 127.0.0.1:59498 tcp
-127.0.0.1:59498 ESTABLISHED 127.0.0.1:6010 tcp
-130.126.136.179:765 ESTABLISHED 172.22.98.29:33920 tcp
-130.126.136.179:40932 ESTABLISHED 172.22.246.11:4505 tcp
-130.126.136.179:789 ESTABLISHED 130.126.112.49:2049 tcp
-130.126.136.179:58444 ESTABLISHED 172.22.6.74:5672 tcp
-130.126.136.179:37674 ESTABLISHED 172.22.6.146:8086 tcp
-130.126.136.179:58446 ESTABLISHED 172.22.6.74:5672 tcp
-130.126.136.179:22 ESTABLISHED 10.251.18.206:65464 tcp
-130.126.136.179:22 ESTABLISHED 10.251.18.206:65464 tcp
-130.126.136.179:45112 TIME_WAIT 172.22.98.29:111 tcp
-130.126.136.179:58442 ESTABLISHED 172.22.6.74:5672 tcp
-130.126.136.179:33326 ESTABLISHED 172.22.246.13:4505 tcp
-130.126.136.179:59164 ESTABLISHED 172.22.246.140:4505 tcp
-130.126.136.179:22 ESTABLISHED 10.251.18.206:65464 tcp
-130.126.136.179:58440 ESTABLISHED 172.22.6.74:5672 tcp
-130.126.136.179:745 ESTABLISHED 172.22.98.29:2049 tcp
-130.126.136.179:51970 ESTABLISHED 172.22.246.14:10514 tcp
-130.126.136.179:22 ESTABLISHED 10.251.18.206:65464 tcp
-:::22 LISTEN :::* tcp6
-::1:631 LISTEN :::* tcp6
-::1:6010 LISTEN :::* tcp6
-:::445 LISTEN :::* tcp6
-:::44513 LISTEN :::* tcp6
-:::139 LISTEN :::* tcp6
-:::111 :::* udp6
-:::35349 LISTEN :::* tcp6
-0.0.0.0:59373 0.0.0.0:* udp
-127.0.1.1:53 0.0.0.0:* udp
-0.0.0.0:68 0.0.0.0:* udp
-0.0.0.0:111 0.0.0.0:* udp
-130.126.136.179:123 0.0.0.0:* udp
-127.0.0.1:123 0.0.0.0:* udp
-0.0.0.0:123 0.0.0.0:* udp
-130.126.139.255:137 0.0.0.0:* udp
-130.126.136.179:137 0.0.0.0:* udp
-0.0.0.0:137 0.0.0.0:* udp
-130.126.139.255:138 0.0.0.0:* udp
-130.126.136.179:138 0.0.0.0:* udp
-0.0.0.0:138 0.0.0.0:* udp
-0.0.0.0:631 0.0.0.0:* udp
-127.0.0.1:762 0.0.0.0:* udp
-0.0.0.0:773 0.0.0.0:* udp
-0.0.0.0:34156 0.0.0.0:* udp
-0.0.0.0:34688 0.0.0.0:* udp
-127.0.0.1:3030 0.0.0.0:* udp
-0.0.0.0:37696 0.0.0.0:* udp
-0.0.0.0:5353 0.0.0.0:* udp
-0.0.0.0:42410 0.0.0.0:* udp
-:::111 :::* udp6
-2620:0:e00:550a:5c4:123 :::* udp6
-2620:0:e00:550a:4cd:123 :::* udp6
-2620:0:e00:550a:f5e:123 :::* udp6
-2620:0:e00:550a:c5b:123 :::* udp6
-2620:0:e00:550a:cd1:123 :::* udp6
-2620:0:e00:550a:dcf:123 :::* udp6
-2620:0:e00:550a:d8d:123 :::* udp6
-fe80::ec4:7aff:fedc:123 :::* udp6
-2620:0:e00:550a:ec4:123 :::* udp6
-::1:123 :::* udp6
-:::123 :::* udp6
-:::773 :::* udp6
-:::37708 :::* udp6
-:::5353 :::* udp6
-:::39262 :::* udp6
-:::47288 :::* udp6
-:::55076 :::* udp6
-
-Routing Table
--------------
-
--IP routing table-
-0.0.0.0 / 130.126.136.1 0.0.0.0 UG eth0
-130.126.136.0 / 0.0.0.0 255.255.252.0 U eth0
-169.254.0.0 / 0.0.0.0 255.255.0.0 U eth0
-192.17.2.10 / 130.126.136.1 255.255.255.255 UGH eth0
-
-ARP Table
----------
-
--ARP Table-
-130.126.136.121 90:b1:1c:2d:31:0e eth0
-130.126.136.198 40:a8:f0:47:38:97 eth0
-130.126.136.1 00:fe:c8:5d:df:ff eth0
-130.126.136.110 98:10:e8:f3:a2:4f eth0
-130.126.136.247 00:11:32:c3:57:3f eth0
-130.126.136.159 c4:34:6b:5e:26:eb eth0
-130.126.136.243 00:4e:01:bf:b6:ea eth0
-130.126.136.77 bc:30:5b:e0:f9:06 eth0
-130.126.139.44 98:90:96:dc:55:d9 eth0
-130.126.136.14 4c:d9:8f:17:1c:e2 eth0
-130.126.136.73 18:66:da:31:06:6b eth0
-130.126.136.63 18:66:da:3d:e2:68 eth0
-169.254.169.254 00:fe:c8:5d:df:ff eth0
-130.126.136.126 20:04:0f:f4:66:74 eth0
-130.126.139.118 70:85:c2:83:33:0b eth0
-130.126.136.210 2c:4d:54:46:4f:ed eth0
-130.126.136.195 48:4d:7e:f5:03:b2 eth0
-130.126.136.55 40:a8:f0:5e:b9:9f eth0
-130.126.136.118 90:b1:1c:1a:02:d6 eth0
-130.126.136.156 04:d4:c4:5b:50:f4 eth0
-130.126.136.240 e4:54:e8:79:44:68 eth0
-130.126.136.162 88:51:fb:58:dc:b2 eth0
-130.126.136.148 6c:2b:59:d5:f9:d2 eth0
-130.126.136.11 8c:dc:d4:29:8d:28 eth0
-130.126.136.74 c8:2a:14:1e:91:67 eth0
-130.126.138.79 74:e6:e2:da:8b:93 eth0
-130.126.136.7 18:03:73:3c:2e:f1 eth0
-130.126.136.129 00:25:00:ed:73:7d eth0
-130.126.136.245 00:04:4b:dd:43:42 eth0
-130.126.136.119 90:b1:1c:2d:2a:90 eth0
-130.126.136.182 90:b1:1c:5e:2c:72 eth0
-130.126.136.241 e4:54:e8:79:52:be eth0
-130.126.136.100 64:00:6a:5f:2e:25 eth0
-130.126.139.197 6c:3b:e5:14:6c:8f eth0
-130.126.136.12 54:bf:64:99:59:02 eth0
-130.126.136.23 8c:ec:4b:b2:4f:c9 eth0
-130.126.136.201 fc:aa:14:2f:a7:ab eth0
-130.126.136.191 40:6c:8f:24:b1:a4 eth0
-130.126.138.206 18:66:da:0d:6f:d0 eth0
-130.126.136.120 90:b1:1c:2d:2e:f8 eth0
-130.126.136.46 48:4d:7e:e6:a4:16 eth0
-130.126.138.135 38:c9:86:02:0b:99 eth0
-130.126.136.105 d0:94:66:7b:67:c0 eth0
-130.126.136.221 f8:b1:56:c6:32:86 eth0
-130.126.136.95 00:24:e8:44:aa:a7 eth0
-130.126.136.28 ac:1f:6b:48:8a:84 eth0
-130.126.136.242 00:4e:01:bf:6c:ca eth0
-130.126.136.76 18:66:da:00:bb:74 eth0
-130.126.136.97 98:90:96:bc:6f:66 eth0
-130.126.136.160 ac:87:a3:1f:4e:ca eth0
-130.126.136.9 6c:2b:59:c7:ad:19 eth0
-130.126.136.72 50:9a:4c:59:c3:5a eth0
-130.126.136.209 18:66:da:30:64:45 eth0
-130.126.136.83 00:13:3b:11:6a:a1 eth0
-130.126.136.5 4c:d9:8f:0c:1e:02 eth0
-
-DNS Servers
------------
-
--Name servers-
-127.0.1.1
-
-Statistics
-----------
-
--IP-
-3340235186 : Total packets received
-99269 : With invalid addresses
-0 : Incoming packets discarded
-0 : Incoming packets discarded
-3340132854 : Incoming packets delivered
-1109643438 : Requests sent out
-16 : Outgoing packets dropped
-248929 : Dropped because of missing route
-6126 : Reassemblies required
-3063 : Packets reassembled ok
--ICMP-
-1355 : ICMP messages received
-19 : Input ICMP message failed.
-7599 : ICMP messages sent
-0 : ICMP messages failed
--ICMPMSG-
--TCP-
-26139644 : Active connections openings
-3919 : Passive connection openings
-25871410 : Failed connection attempts
-4562 : Connection resets received
-18 : Connections established
-3302346907 : Segments received
-10359040645 : Segments send out
-2844431 : Segments retransmited
-1 : Bad segments received.
-25878380 : Resets sent
--UDP-
-24979118 : Packets received
-6362 : Packets to unknown port received.
-4203 : Packet receive errors
-662177 : Packets sent
--UDPLITE-
--TCPEXT-
-1 : Resets received for embryonic SYN_RECV sockets
-235858 : TCP sockets finished time wait in fast timer
-171 : Time wait sockets recycled by time stamp
-1825 : Packets rejects in established connections because of timestamp
-6984717 : Delayed acks sent
-3194 : Delayed acks further delayed because of locked socket
-527487290 : Packet headers predicted
-543016884 : Acknowledgments not containing data payload received
-1893599141 : Predicted acknowledgments
-554129 : Times recovered from packet loss by selective acknowledgements
-119 : Congestion windows fully recovered without slow start
-85 : Congestion windows partially recovered using Hoe heuristic
-6456 : Congestion windows recovered without slow start by DSACK
-1683 : Congestion windows recovered without slow start after partial ack
-101 : Timeouts after SACK recovery
-136 : Timeouts in loss state
-2738885 : Fast retransmits
-2730 : Retransmits in slow start
-3141 : Other TCP timeouts
-820 : SACK retransmits failed
-32589 : DSACKs sent for old packets
-607 : DSACKs sent for out of order packets
-84876 : DSACKs received
-567 : DSACKs for out of order packets received
-650 : Connections reset due to unexpected data
-4384 : Connections reset due to early user close
-286 : Connections aborted due to timeout
-3 : Times unabled to send RST due to no memory
--IPEXT-
-
-Shared Directories
-------------------
-
--SAMBA-
--NFS-
-
-Benchmarks
-**********
-
-
-CPU Blowfish
-------------
-
--CPU Blowfish-
-<big><b>This Machine</b></big> 2600 MHz 0.665
-Intel(R) Celeron(R) M processor 1.50GHz (null) 26.1876862
-PowerPC 740/750 (280.00MHz) (null) 172.816713
-
-CPU CryptoHash
---------------
-
--CPU CryptoHash-
-<big><b>This Machine</b></big> 2600 MHz 1697.719
-
-CPU Fibonacci
--------------
-
--CPU Fibonacci-
-<big><b>This Machine</b></big> 2600 MHz 2.166
-Intel(R) Celeron(R) M processor 1.50GHz (null) 8.1375674
-PowerPC 740/750 (280.00MHz) (null) 58.07682
-
-CPU N-Queens
-------------
-
--CPU N-Queens-
-<big><b>This Machine</b></big> 2600 MHz 0.592
-
-FPU FFT
--------
-
--FPU FFT-
-<big><b>This Machine</b></big> 2600 MHz 1.072
-
-FPU Raytracing
---------------
-
--FPU Raytracing-
-<big><b>This Machine</b></big> 2600 MHz 6.178
-Intel(R) Celeron(R) M processor 1.50GHz (null) 40.8816714
-PowerPC 740/750 (280.00MHz) (null) 161.312647
diff --git a/hpvm/projects/onnx/src/.ipynb_checkpoints/mnist-checkpoint.ipynb b/hpvm/projects/onnx/src/.ipynb_checkpoints/mnist-checkpoint.ipynb
deleted file mode 100644
index bbac5d491d80f5f4aaa9286b2323704e55555b48..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/src/.ipynb_checkpoints/mnist-checkpoint.ipynb
+++ /dev/null
@@ -1,105 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "import sys\n",
- "import numpy as np\n",
- "import onnx\n",
- "import glob\n",
- "from onnxruntime.backend.backend import OnnxRuntimeBackend as backend\n",
- "\n",
- "from onnx import numpy_helper\n",
- "\n",
- "# onnx2hpvm modules\n",
- "from onnx2hpvm.onnx_translator import from_onnx_to_hpvm"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "model = onnx.load('../models/mnist/mnist.onnx')\n",
- "test_data_dir = '../models/mnist/test_data_set_0'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1\n"
- ]
- }
- ],
- "source": [
- "# Load inputs\n",
- "inputs = []\n",
- "inputs_num = len(glob.glob(os.path.join(test_data_dir, 'input_*.pb')))\n",
- "print(inputs_num)\n",
- "for i in range(inputs_num):\n",
- " input_file = os.path.join(test_data_dir, 'input_{}.pb'.format(i))\n",
- " tensor = onnx.TensorProto()\n",
- " with open(input_file, 'rb') as f:\n",
- " tensor.ParseFromString(f.read())\n",
- " inputs.append(numpy_helper.to_array(tensor))\n",
- "\n",
- "# Load reference outputs\n",
- "ref_outputs = []\n",
- "ref_outputs_num = len(glob.glob(os.path.join(test_data_dir, 'output_*.pb')))\n",
- "for i in range(ref_outputs_num):\n",
- " output_file = os.path.join(test_data_dir, 'output_{}.pb'.format(i))\n",
- " tensor = onnx.TensorProto()\n",
- " with open(output_file, 'rb') as f:\n",
- " tensor.ParseFromString(f.read())\n",
- " ref_outputs.append(numpy_helper.to_array(tensor))\n",
- "\n",
- "# Run the model on the backend\n",
- "outputs = list(backend.run_model(model, inputs))\n",
- "\n",
- "#from_onnx_to_hpvm(model)\n",
- "# Compare the results with reference outputs.\n",
- "#for ref_o, o in zip(ref_outputs, outputs):\n",
- "# np.testing.assert_almost_equal(ref_o, o)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/hpvm/projects/onnx/src/alexnet_keras.ipynb b/hpvm/projects/onnx/src/alexnet_keras.ipynb
deleted file mode 100644
index 3e46e220eb5438188d19e06934c6a9ef780cfa21..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/src/alexnet_keras.ipynb
+++ /dev/null
@@ -1,356 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Using TensorFlow backend.\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "from keras.datasets import cifar10\n",
- "from keras.models import Sequential\n",
- "from keras.layers.core import Dense, Dropout, Flatten, Activation\n",
- "from keras.layers.convolutional import Conv2D\n",
- "from keras.optimizers import Adam\n",
- "from keras.layers.pooling import MaxPooling2D\n",
- "from keras.utils.np_utils import to_categorical\n",
- "from keras.preprocessing.image import ImageDataGenerator\n",
- "from keras import backend as K\n",
- "from keras import regularizers\n",
- "from keras.callbacks import LearningRateScheduler\n",
- "import sys\n",
- "import struct\n",
- "import keras\n",
- "import numpy as np\n",
- "import os\n",
- "os.environ['CUDA_VISIBLE_DEVICES'] = '1'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "def buildModel2():\n",
- "\n",
- " activation_type = \"tanh\"\n",
- " weight_decay = 1e-4\n",
- " \n",
- " model = Sequential()\n",
- " model.add(Conv2D(64, kernel_size=(11, 11), activation=activation_type,\n",
- " input_shape=(3, 32, 32), padding = 'same',\n",
- " kernel_regularizer=regularizers.l2(weight_decay) ))\n",
- " model.add(MaxPooling2D(pool_size=(2, 2), strides=(2,2) ))\n",
- " model.add(Dropout(0.2))\n",
- " model.add(Conv2D(192, kernel_size=(5, 5), activation=activation_type, padding = 'same',\n",
- " kernel_regularizer=regularizers.l2(weight_decay)))\n",
- " model.add(MaxPooling2D(pool_size=(2, 2), strides=(2,2) ))\n",
- " model.add(Dropout(0.3))\n",
- "\n",
- " model.add(Conv2D(384, kernel_size=(3, 3), activation=activation_type, padding = 'same',\n",
- " kernel_regularizer=regularizers.l2(weight_decay) )) \n",
- " model.add(Conv2D(256, kernel_size=(3, 3), activation=activation_type, padding = 'same',\n",
- " kernel_regularizer=regularizers.l2(weight_decay) ))\n",
- " model.add(Conv2D(256, kernel_size=(3, 3), activation=activation_type, padding = 'same',\n",
- " kernel_regularizer=regularizers.l2(weight_decay) ))\n",
- " model.add(MaxPooling2D(pool_size=(2, 2), strides=(2,2) ))\n",
- " model.add(Dropout(0.4))\n",
- "\n",
- " model.add(Flatten())\n",
- " #model.add(Flatten())\n",
- " #model.add(Dense(256))\n",
- " model.add(Dense(10))\n",
- " model.add(Activation('softmax'))\n",
- " \n",
- " return model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "def lr_schedule(epoch):\n",
- " lrate = 0.001\n",
- " if epoch > 20:\n",
- " lrate = 0.0005\n",
- " if epoch > 40:\n",
- " lrate = 0.0003\n",
- " if epoch > 60:\n",
- " lrate = 0.0001\n",
- " if epoch > 80:\n",
- " lrate = 0.00005 \n",
- " \n",
- " return lrate"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "def trainModel(model): \n",
- " #dir_prefix = \"/home/hsharif3/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10/\"\n",
- "\n",
- " #opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
- " # Compile the model\n",
- " model.compile(loss='categorical_crossentropy',\n",
- " optimizer=Adam(lr=0.0001, decay=1e-6),\n",
- " #optimizer = opt_rms,\n",
- " metrics=['accuracy'])\n",
- "\n",
- " #print to_categorical(Y_train, 10)\n",
- " print (to_categorical(Y_train))\n",
- "\n",
- "\n",
- " datagen = ImageDataGenerator(\n",
- " rotation_range=15,\n",
- " width_shift_range=0.1,\n",
- " height_shift_range=0.1,\n",
- " horizontal_flip=True,\n",
- " )\n",
- " datagen.fit(X_train)\n",
- "\n",
- " \n",
- " model.fit(X_train, to_categorical(Y_train, 10),\n",
- " batch_size=128,\n",
- " shuffle=True,\n",
- " epochs = 1,\n",
- " #epochs=100,\n",
- " validation_data=(X_test, to_categorical(Y_test, 10)), callbacks=[LearningRateScheduler(lr_schedule)])\n",
- "\n",
- " # Evaluate the model\n",
- " scores = model.evaluate(X_test.astype('float32'), to_categorical(Y_test, 10))\n",
- "\n",
- " print('Loss: %.3f' % scores[0])\n",
- " print('Accuracy: %.3f' % scores[1])\n",
- " \n",
- " print (\"*** TRAINED MODEL ****\\n\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "K.set_image_data_format('channels_first')\n",
- "(X_train, Y_train), (X_test, Y_test) = cifar10.load_data()\n",
- "test_labels = Y_test\n",
- "train_labels = Y_train\n",
- "\n",
- "#X_train = X_train.astype('float32')\n",
- "#X_test = X_test.astype('float32')\n",
- "X_train = X_train / 255.0\n",
- "X_test = X_test / 255.0\n",
- "\n",
- "mean = np.mean(X_train,axis=(0,1,2,3))\n",
- "std = np.std(X_train,axis=(0,1,2,3)) \n",
- "X_train = (X_train-mean)/(std+1e-7)\n",
- "X_test = (X_test-mean)/(std+1e-7)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "X_train = X_train[:8000]\n",
- "X_test = X_test[:8000]\n",
- "Y_train = Y_train[:8000]\n",
- "Y_test = Y_test[:8000]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[0. 0. 0. ... 0. 0. 0.]\n",
- " [0. 0. 0. ... 0. 0. 1.]\n",
- " [0. 0. 0. ... 0. 0. 1.]\n",
- " ...\n",
- " [0. 0. 0. ... 0. 0. 1.]\n",
- " [0. 1. 0. ... 0. 0. 0.]\n",
- " [0. 1. 0. ... 0. 0. 0.]]\n",
- "WARNING:tensorflow:Variable *= will be deprecated. Use `var.assign(var * other)` if you want assignment to the variable value or `x = x * y` if you want a new python Tensor object.\n",
- "Train on 50000 samples, validate on 10000 samples\n",
- "Epoch 1/1\n",
- "50000/50000 [==============================] - 12s 232us/step - loss: 2.0118 - acc: 0.3704 - val_loss: 1.8003 - val_acc: 0.4312\n",
- "10000/10000 [==============================] - 1s 150us/step\n",
- "Loss: 1.800\n",
- "Accuracy: 0.431\n",
- "*** TRAINED MODEL ****\n",
- "\n"
- ]
- }
- ],
- "source": [
- "model = buildModel2()\n",
- "trainModel(model)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "time: 1.0208089351654053\n"
- ]
- }
- ],
- "source": [
- "import time\n",
- "start = time.time()\n",
- "keras_result = model.predict(X_test[:8000])\n",
- "print(\"time:\", time.time() - start)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import keras2onnx\n",
- "onnx_model = keras2onnx.convert_keras(model, model.name, target_opset=10)\n",
- "import onnx\n",
- "onnx.save(onnx_model, \"../models/keras/alexnet8k.onnx\")\n",
- "import pickle\n",
- "with open('keras_dump', 'wb') as fp:\n",
- " pickle.dump(keras_result, fp)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "#onnx_model = keras2onnx.convert_keras(model, model.name)\n",
- "import onnxruntime\n",
- "sess = onnxruntime.InferenceSession(\"../models/keras/alexnet.onnx\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "input_name = sess.get_inputs()[0].name\n",
- "print(\"Input name :\", input_name)\n",
- "input_shape = sess.get_inputs()[0].shape\n",
- "print(\"Input shape :\", input_shape)\n",
- "input_type = sess.get_inputs()[0].type\n",
- "print(\"Input type :\", input_type)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "output_name = sess.get_outputs()[0].name\n",
- "print(\"Output name :\", output_name) \n",
- "output_shape = sess.get_outputs()[0].shape\n",
- "print(\"Output shape :\", output_shape)\n",
- "output_type = sess.get_outputs()[0].type\n",
- "print(\"Output type :\", output_type)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "\n",
- "#K.clear_session()\n",
- "start = time.time()\n",
- "ort_result = sess.run([output_name], {input_name: X_test.astype('float32')})\n",
- "import pickle\n",
- "\n",
- "with open('dumps/ort_dump', 'wb') as fp:\n",
- " pickle.dump(ort_result, fp)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pickle\n",
- "import numpy as np\n",
- "with open ('ort_dump', 'rb') as fp:\n",
- " ort_res = pickle.load(fp)\n",
- "with open ('keras_dump', 'rb') as fp:\n",
- " keras_res = pickle.load(fp)\n",
- "\n",
- "for ref_o, o in zip(ort_res[0], keras_res):\n",
- " np.testing.assert_almost_equal(ref_o, o, 5) #using decimal of 3 would pass test"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "print(ort_res[0])\n",
- "print(\"--------------\")\n",
- "print(keras_res)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/hpvm/projects/onnx/src/alexnet_onnx.ipynb b/hpvm/projects/onnx/src/alexnet_onnx.ipynb
deleted file mode 100644
index 73755ecf1ccc27eedc5f1319c20e30473936c3e8..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/src/alexnet_onnx.ipynb
+++ /dev/null
@@ -1,192 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Using TensorFlow backend.\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "from keras.datasets import cifar10\n",
- "from keras import backend as K\n",
- "import numpy as np\n",
- "import os\n",
- "import keras2onnx\n",
- "import onnx\n",
- "import onnxruntime\n",
- "import time\n",
- "os.environ['CUDA_VISIBLE_DEVICES'] = '1'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "K.set_image_data_format('channels_first')\n",
- "(X_train, Y_train), (X_test, Y_test) = cifar10.load_data()\n",
- "test_labels = Y_test\n",
- "train_labels = Y_train\n",
- "\n",
- "#X_train = X_train.astype('float32')\n",
- "#X_test = X_test.astype('float32')\n",
- "X_train = X_train / 255.0\n",
- "X_test = X_test / 255.0\n",
- "\n",
- "mean = np.mean(X_train,axis=(0,1,2,3))\n",
- "std = np.std(X_train,axis=(0,1,2,3)) \n",
- "X_train = (X_train-mean)/(std+1e-7)\n",
- "X_test = (X_test-mean)/(std+1e-7)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "sess = onnxruntime.InferenceSession(\"../models/keras/alexnet.onnx\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Input name : conv2d_1_input\n",
- "Input shape : [None, 3, 32, 32]\n",
- "Input type : tensor(float)\n"
- ]
- }
- ],
- "source": [
- "input_name = sess.get_inputs()[0].name\n",
- "print(\"Input name :\", input_name)\n",
- "input_shape = sess.get_inputs()[0].shape\n",
- "print(\"Input shape :\", input_shape)\n",
- "input_type = sess.get_inputs()[0].type\n",
- "print(\"Input type :\", input_type)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Output name : activation_1\n",
- "Output shape : [None, 10]\n",
- "Output type : tensor(float)\n"
- ]
- }
- ],
- "source": [
- "output_name = sess.get_outputs()[0].name\n",
- "print(\"Output name :\", output_name) \n",
- "output_shape = sess.get_outputs()[0].shape\n",
- "print(\"Output shape :\", output_shape)\n",
- "output_type = sess.get_outputs()[0].type\n",
- "print(\"Output type :\", output_type)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "time: 1.3165459632873535\n"
- ]
- }
- ],
- "source": [
- "#X_test = np.moveaxis(X_test, -1, 1)\n",
- "X_test.shape\n",
- "#K.clear_session()\n",
- "start_time = time.time()\n",
- "ort_result = sess.run([output_name], {input_name: X_test.astype('float32')[:8000]})\n",
- "print(\"time: \", time.time() - start_time)\n",
- "import pickle\n",
- "\n",
- "with open('dumps/ort_dump', 'wb') as fp:\n",
- " pickle.dump(ort_result, fp)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "ename": "FileNotFoundError",
- "evalue": "[Errno 2] No such file or directory: 'dumps/keras_dump'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-20-a59ffee1b5dd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'dumps/ort_dump'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mort_res\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'dumps/keras_dump'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mkeras_res\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'dumps/keras_dump'"
- ]
- }
- ],
- "source": [
- "import pickle\n",
- "import numpy as np\n",
- "with open ('dumps/ort_dump', 'rb') as fp:\n",
- " ort_res = pickle.load(fp)\n",
- "with open ('dumps/keras_dump', 'rb') as fp:\n",
- " keras_res = pickle.load(fp)\n",
- "\n",
- "for ref_o, o in zip(ort_res[0], keras_res):\n",
- " np.testing.assert_almost_equal(ref_o, o, 3) #using decimal of 3 would pass test"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/hpvm/projects/onnx/src/alexnet_tvm.ipynb b/hpvm/projects/onnx/src/alexnet_tvm.ipynb
deleted file mode 100644
index eaa92800e5ef55e1de64cbbde9395872727a4e5b..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/src/alexnet_tvm.ipynb
+++ /dev/null
@@ -1,225 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Using TensorFlow backend.\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "from keras.datasets import cifar10\n",
- "from keras import backend as K\n",
- "import sys\n",
- "import struct\n",
- "import numpy as np\n",
- "import os\n",
- "import tvm\n",
- "import tvm.relay as relay\n",
- "from tvm.contrib import graph_runtime\n",
- "os.environ['CUDA_VISIBLE_DEVICES'] = '1'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "K.set_image_data_format('channels_last')\n",
- "(X_train, Y_train), (X_test, Y_test) = cifar10.load_data()\n",
- "X_test = X_test / 255.0\n",
- "mean = np.mean(X_train,axis=(0,1,2,3))\n",
- "std = np.std(X_train,axis=(0,1,2,3)) \n",
- "X_test = (X_test-mean)/(std+1e-7)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "#print(X_test.shape)\n",
- "#X_test = X_test[:8000]\n",
- "#print(X_test.shape)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "import onnx\n",
- "onnx_model = onnx.load(\"../models/keras/alexnet_last.onnx\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "target='cuda -libs=cudnn,cublas'\n",
- "input_name = 'conv2d_8_input'\n",
- "shape_dict = {input_name: X_test[:1000].shape}\n",
- "mod, params = relay.frontend.from_onnx(onnx_model, shape_dict)\n",
- "ctx = tvm.gpu()\n",
- "with relay.build_config(opt_level=3):\n",
- " executor = relay.build_module.create_executor('graph', mod, ctx, target)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# LLVM EXECUTE SUCCEEDED\n",
- "import time\n",
- "start_time = time.time()\n",
- "tvm_out = executor.evaluate()(tvm.nd.array(X_test.astype('float32')[:1000]), **params)\n",
- "print(\"Time:\", time.time() - start_time)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "top1_tvm = np.argmax(tvm_out.asnumpy()[0])\n",
- "import pickle\n",
- "with open('dumps/tvm_dump', 'wb') as fp:\n",
- " pickle.dump(tvm_output, fp)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.max_pool2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.max_pool2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.max_pool2d\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('dense', (8000, 4096, 'float32'), (10, 4096, 'float32'), 0, 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (8000, 256, 8, 8, 'float32'), (256, 256, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (8000, 384, 8, 8, 'float32'), (256, 384, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (8000, 192, 8, 8, 'float32'), (384, 192, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (8000, 64, 16, 16, 'float32'), (192, 64, 5, 5, 'float32'), (1, 1), (2, 2), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (8000, 3, 32, 32, 'float32'), (64, 3, 11, 11, 'float32'), (1, 1), (5, 5), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n"
- ]
- }
- ],
- "source": [
- "input_name = 'conv2d_8_input'\n",
- "input_size = 8000\n",
- "shape_dict = {input_name: X_test[:input_size].shape}\n",
- "mod, params = relay.frontend.from_onnx(onnx_model, shape_dict)\n",
- "target = 'cuda -libs=cudnn,cublas'\n",
- "with relay.build_config(opt_level=3):\n",
- " graph, lib, params = relay.build(mod, target, params=params)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "ename": "TVMError",
- "evalue": "Traceback (most recent call last):\n [bt] (7) /home/ys26/tvm/build/libtvm.so(TVMFuncCall+0x61) [0x7f664ddc32b1]\n [bt] (6) /home/ys26/tvm/build/libtvm.so(+0xbecd74) [0x7f664de16d74]\n [bt] (5) /home/ys26/tvm/build/libtvm.so(+0xbecc19) [0x7f664de16c19]\n [bt] (4) /home/ys26/tvm/build/libtvm.so(tvm::runtime::GraphRuntimeCreate(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, tvm::runtime::Module const&, std::vector<DLContext, std::allocator<DLContext> > const&)+0xcf) [0x7f664de16a1f]\n [bt] (3) /home/ys26/tvm/build/libtvm.so(tvm::runtime::GraphRuntime::Init(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, tvm::runtime::Module, std::vector<DLContext, std::allocator<DLContext> > const&)+0x258) [0x7f664de16698]\n [bt] (2) /home/ys26/tvm/build/libtvm.so(tvm::runtime::GraphRuntime::SetupStorage()+0x65f) [0x7f664de1334f]\n [bt] (1) /home/ys26/tvm/build/libtvm.so(tvm::runtime::NDArray::Empty(std::vector<long, std::allocator<long> >, DLDataType, DLContext)+0x1ef) [0x7f664dd9fcaf]\n [bt] (0) /home/ys26/tvm/build/libtvm.so(tvm::runtime::CUDADeviceAPI::AllocDataSpace(DLContext, unsigned long, unsigned long, DLDataType)+0xde1) [0x7f664de226d1]\n File \"/home/ys26/tvm/src/runtime/cuda/cuda_device_api.cc\", line 119\nCUDA: Check failed: e == cudaSuccess || e == cudaErrorCudartUnloading: out of memory",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mTVMError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-6-0bd1741a62f4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m#data = np.random.uniform(-1, 1, size=data_shape).astype(\"float32\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# create module\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mmodule\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgraph_runtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;31m# set input and parameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"conv2d_8_input\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0minput_size\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"float32\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/tvm/python/tvm/contrib/graph_runtime.py\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(graph_json_str, libmod, ctx)\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0mfcreate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_global_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"tvm.graph_runtime.create\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mGraphModule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph_json_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlibmod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mdevice_type_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 60\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_device_ctx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlibmod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/tvm/python/tvm/_ffi/_ctypes/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 205\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtcodes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc_int\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 206\u001b[0m ctypes.byref(ret_val), ctypes.byref(ret_tcode)) != 0:\n\u001b[0;32m--> 207\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mget_last_ffi_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 208\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtemp_args\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 209\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mTVMError\u001b[0m: Traceback (most recent call last):\n [bt] (7) /home/ys26/tvm/build/libtvm.so(TVMFuncCall+0x61) [0x7f664ddc32b1]\n [bt] (6) /home/ys26/tvm/build/libtvm.so(+0xbecd74) [0x7f664de16d74]\n [bt] (5) /home/ys26/tvm/build/libtvm.so(+0xbecc19) [0x7f664de16c19]\n [bt] (4) /home/ys26/tvm/build/libtvm.so(tvm::runtime::GraphRuntimeCreate(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, tvm::runtime::Module const&, std::vector<DLContext, std::allocator<DLContext> > const&)+0xcf) [0x7f664de16a1f]\n [bt] (3) /home/ys26/tvm/build/libtvm.so(tvm::runtime::GraphRuntime::Init(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, tvm::runtime::Module, std::vector<DLContext, std::allocator<DLContext> > const&)+0x258) [0x7f664de16698]\n [bt] (2) /home/ys26/tvm/build/libtvm.so(tvm::runtime::GraphRuntime::SetupStorage()+0x65f) [0x7f664de1334f]\n [bt] (1) /home/ys26/tvm/build/libtvm.so(tvm::runtime::NDArray::Empty(std::vector<long, std::allocator<long> >, DLDataType, DLContext)+0x1ef) [0x7f664dd9fcaf]\n [bt] (0) /home/ys26/tvm/build/libtvm.so(tvm::runtime::CUDADeviceAPI::AllocDataSpace(DLContext, unsigned long, unsigned long, DLDataType)+0xde1) [0x7f664de226d1]\n File \"/home/ys26/tvm/src/runtime/cuda/cuda_device_api.cc\", line 119\nCUDA: Check failed: e == cudaSuccess || e == cudaErrorCudartUnloading: out of memory"
- ]
- }
- ],
- "source": [
- "import time\n",
- "ctx = tvm.gpu()\n",
- "#data = np.random.uniform(-1, 1, size=data_shape).astype(\"float32\")\n",
- "# create module\n",
- "module = graph_runtime.create(graph, lib, ctx)\n",
- "# set input and parameters\n",
- "module.set_input(\"conv2d_8_input\", X_test[:input_size].astype(\"float32\"))\n",
- "module.set_input(**params)\n",
- "# run\n",
- "start_time = time.time()\n",
- "module.run()\n",
- "out_shape = (input_size, 10)\n",
- "# get output\n",
- "out = module.get_output(0, tvm.nd.empty(out_shape)).asnumpy()\n",
- "print(\"Time:\", time.time() - start_time)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pickle\n",
- "import numpy as np\n",
- "with open ('dumps/tvm_dump', 'rb') as fp:\n",
- " tvm_res = pickle.load(fp)\n",
- "with open ('dumps/keras_dump', 'rb') as fp:\n",
- " keras_res = pickle.load(fp)\n",
- "\n",
- "for ref_o, o in zip(tvm_res, keras_res):\n",
- " np.testing.assert_almost_equal(ref_o, o, 3) #using decimal of 3 would pass test\n",
- "print(\"Accuracy matched!\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/hpvm/projects/onnx/src/alexnet_tvm.py b/hpvm/projects/onnx/src/alexnet_tvm.py
deleted file mode 100644
index dbbe8c6f8cb5f4e0cb98a467784ded7cfb384d31..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/src/alexnet_tvm.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import numpy as np
-from keras.datasets import cifar10
-from keras import backend as K
-import sys
-import struct
-import numpy as np
-import os
-import tvm
-import tvm.relay as relay
-from tvm.contrib import graph_runtime
-os.environ['CUDA_VISIBLE_DEVICES'] = '1'
-
-K.set_image_data_format('channels_last')
-(X_train, Y_train), (X_test, Y_test) = cifar10.load_data()
-test_labels = Y_test
-train_labels = Y_train
-
-#X_train = X_train.astype('float32')
-#X_test = X_test.astype('float32')
-X_train = X_train / 255.0
-X_test = X_test / 255.0
-
-mean = np.mean(X_train,axis=(0,1,2,3))
-std = np.std(X_train,axis=(0,1,2,3))
-X_train = (X_train-mean)/(std+1e-7)
-X_test = (X_test-mean)/(std+1e-7)
-
-import onnx
-onnx_model = onnx.load("../models/keras/alexnet_last.onnx")
-
-target = tvm.target.cuda()
-input_name = 'conv2d_8_input'
-shape_dict = {input_name: X_test.shape}
-mod, params = relay.frontend.from_onnx(onnx_model, shape_dict)
-ctx = tvm.gpu(0)
-with relay.build_config(opt_level=3):
- executor = relay.build_module.create_executor('graph', mod, ctx, target)
-
-dtype = 'float32'
-tvm_out = executor.evaluate()(tvm.nd.array(X_test.astype('float32')), **params)
\ No newline at end of file
diff --git a/hpvm/projects/onnx/src/keras_models/alexnet.py b/hpvm/projects/onnx/src/keras_models/alexnet.py
deleted file mode 100644
index b562c7d658969c8efe8dca20a0502475268a7af2..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/src/keras_models/alexnet.py
+++ /dev/null
@@ -1,174 +0,0 @@
-
-import numpy as np
-from keras.datasets import cifar10
-from keras.models import Sequential
-from keras.layers.core import Dense, Dropout, Flatten, Activation
-from keras.layers.convolutional import Conv2D
-from keras.optimizers import Adam
-from keras.layers.pooling import MaxPooling2D
-from keras.utils.np_utils import to_categorical
-from keras.preprocessing.image import ImageDataGenerator
-from keras import backend as K
-from keras import regularizers
-from keras.callbacks import LearningRateScheduler
-import sys
-import struct
-import keras
-import numpy as np
-import os
-
-
-
-def lr_schedule(epoch):
- lrate = 0.001
- if epoch > 20:
- lrate = 0.0005
- if epoch > 40:
- lrate = 0.0003
- if epoch > 60:
- lrate = 0.0001
- if epoch > 80:
- lrate = 0.00005
-
- return lrate
-
-
-
-def buildModel2():
-
- activation_type = "tanh"
- weight_decay = 1e-4
-
- model = Sequential()
- model.add(Conv2D(64, kernel_size=(11, 11), activation=activation_type,
- input_shape=(3, 32, 32), padding = 'same',
- kernel_regularizer=regularizers.l2(weight_decay) ))
- model.add(MaxPooling2D(pool_size=(2, 2), strides=(2,2) ))
- model.add(Dropout(0.2))
- model.add(Conv2D(192, kernel_size=(5, 5), activation=activation_type, padding = 'same',
- kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(MaxPooling2D(pool_size=(2, 2), strides=(2,2) ))
- model.add(Dropout(0.3))
-
- model.add(Conv2D(384, kernel_size=(3, 3), activation=activation_type, padding = 'same',
- kernel_regularizer=regularizers.l2(weight_decay) ))
- model.add(Conv2D(256, kernel_size=(3, 3), activation=activation_type, padding = 'same',
- kernel_regularizer=regularizers.l2(weight_decay) ))
- model.add(Conv2D(256, kernel_size=(3, 3), activation=activation_type, padding = 'same',
- kernel_regularizer=regularizers.l2(weight_decay) ))
- model.add(MaxPooling2D(pool_size=(2, 2), strides=(2,2) ))
- model.add(Dropout(0.4))
-
- model.add(Flatten())
- #model.add(Flatten())
- #model.add(Dense(256))
- model.add(Dense(10))
- model.add(Activation('softmax'))
-
- return model
-
-
-
-def buildModel():
-
- model = Sequential()
- model.add(Conv2D(128, kernel_size=(3, 3), activation='tanh', input_shape=(3, 32, 32), padding = 'same'))
- model.add(Conv2D(256, kernel_size=(3, 3), activation='tanh', padding = 'same'))
- model.add(MaxPooling2D(pool_size=(2, 2)))
- #model.add(Dropout(0.25))
-
- model.add(Conv2D(256, kernel_size=(3, 3), activation='tanh', padding = 'same'))
- model.add(MaxPooling2D(pool_size=(2, 2)))
-
- model.add(Conv2D(256, kernel_size=(3, 3), activation='tanh', padding = 'same'))
- model.add(MaxPooling2D(pool_size=(2, 2)))
- model.add(Conv2D(256, kernel_size=(3, 3), activation='tanh', padding = 'same'))
- model.add(MaxPooling2D(pool_size=(2, 2)))
- #model.add(Dropout(0.25))
-
- model.add(Flatten())
- #model.add(Flatten())
- model.add(Dense(4096, activation='tanh'))
- #model.add(Dropout(0.5))
- model.add(Dense(2048, activation='tanh'))
- model.add(Dense(10, activation='tanh'))
- model.add(Activation('softmax'))
-
- return model
-
-
-
-
-def trainModel(model):
-
- (X_train, Y_train), (X_test, Y_test) = cifar10.load_data()
- test_labels = Y_test
- train_labels = Y_train
-
- #X_train = X_train.astype('float32')
- #X_test = X_test.astype('float32')
- X_train = X_train / 255.0
- X_test = X_test / 255.0
-
- mean = np.mean(X_train,axis=(0,1,2,3))
- std = np.std(X_train,axis=(0,1,2,3))
- X_train = (X_train-mean)/(std+1e-7)
- X_test = (X_test-mean)/(std+1e-7)
-
- dir_prefix = "/home/hsharif3/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10/"
-
- #opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)
- # Compile the model
- model.compile(loss='categorical_crossentropy',
- optimizer=Adam(lr=0.0001, decay=1e-6),
- #optimizer = opt_rms,
- metrics=['accuracy'])
-
- #print to_categorical(Y_train, 10)
- print (to_categorical(Y_train))
-
-
- datagen = ImageDataGenerator(
- rotation_range=15,
- width_shift_range=0.1,
- height_shift_range=0.1,
- horizontal_flip=True,
- )
- datagen.fit(X_train)
-
-
- model.fit(X_train, to_categorical(Y_train, 10),
- batch_size=128,
- shuffle=True,
- epochs = 1,
- #epochs=100,
- validation_data=(X_test, to_categorical(Y_test, 10)), callbacks=[LearningRateScheduler(lr_schedule)])
-
- # Evaluate the model
- scores = model.evaluate(X_test, to_categorical(Y_test, 10))
-
- print('Loss: %.3f' % scores[0])
- print('Accuracy: %.3f' % scores[1])
-
- print ("*** TRAINED MODEL ****\n")
-
-
- #dumpCalibrationData("calibration_data/alexnet_calib.bin", X_train,
- # "calibration_data/alexnet_train_labels.bin", train_labels)
-
- #translate_to_approxhpvm(model, "data/alexnet_cifar10/", X_test, test_labels, 10)
-
-
-
-if __name__ == "__main__":
-
- os.environ["CUDA_VISIBLE_DEVICES"] = "0"
- # Changing to NCHW format
- K.set_image_data_format('channels_first')
-
- model = buildModel2()
- trainModel(model)
- import keras2onnx
- onnx_model = keras2onnx.convert_keras(model, model.name)
- import onnx
- onnx.save(onnx_model, "../models/keras/alexnet.onnx")
diff --git a/hpvm/projects/onnx/src/keras_models/alexnet2.py b/hpvm/projects/onnx/src/keras_models/alexnet2.py
deleted file mode 100644
index 812b212165666370092a3d55e8482643b550f830..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/src/keras_models/alexnet2.py
+++ /dev/null
@@ -1,243 +0,0 @@
-
-import keras
-from keras.models import Sequential
-from keras.utils import np_utils
-from keras.preprocessing.image import ImageDataGenerator
-from keras.layers import Dense, Activation, Flatten, Dropout, BatchNormalization
-from keras.layers import Conv2D, MaxPooling2D
-from keras.datasets import cifar10
-from keras import regularizers
-from keras.callbacks import LearningRateScheduler
-import numpy as np
-import os
-import struct
-from keras import backend as K
-from approxhpvm_translator import translate_to_approxhpvm
-
-
-
-def dumpWeights(file_name, weights, N, H, W, C):
- # NOTE: Writing the NHWC weights array as NCHW
- f = open(file_name, "wb")
- for i in range(N):
- for j in range(C):
- for k in range(H):
- for l in range(W):
- f.write(weights[i][k][l][j])
-
- f.close()
-
-
-def dumpConvWeights(file_name, weights, N, C, H, W):
-
- print (weights.shape)
-
- f = open(file_name, "wb")
- for i in range(N):
- for j in range(C):
- for k in range(H):
- for l in range(W):
- f.write(weights[k][l][j][i])
- f.close()
-
-
-
-def dumpFcWeights(file_name, weights, H, W):
-
- print (weights.shape)
-
- f = open(file_name, "wb")
- for i in range(H):
- for j in range(W):
- f.write(weights[i][j])
- f.close()
-
-
-def dumpFcBias(file_name, bias, W):
-
- print (bias.shape)
-
- f = open(file_name, "wb")
- for i in range(W):
- f.write(bias[i])
- f.close()
-
-
-def dumpLabels(file_name, Y_test):
-
- f = open(file_name, "wb")
-
- labels_map = {}
- for label in Y_test:
- label_val = np.int8(label[0])
- if label_val not in labels_map:
- labels_map[label_val] = 0
- labels_map[label_val] += 1
-
- f.write(label_val)
-
- f.close()
-
-
-
-def dumpData(X_test, file_name, N, C, H, W):
-
- print (X_test.shape)
-
- f = open(file_name, "wb")
- for i in range(N):
- for j in range(C):
- for k in range(H):
- for l in range(W):
- val = struct.unpack("f", struct.pack("f", X_test[i][j][k][l]))
- f.write(np.float32(val[0]))
-
- f.close()
-
-
-
-
-
-def lr_schedule(epoch):
- lrate = 0.001
- if epoch > 75:
- lrate = 0.0005
- if epoch > 100:
- lrate = 0.0003
- return lrate
-
-
-def lr_schedule2(epoch):
- lrate = 0.0005
- if epoch > 100:
- lrate = 0.0003
- if epoch > 200:
- lrate = 0.0002
- if epoch > 250:
- lrate = 0.0001
- if epoch > 300:
- lrate = 0.00003
-
- return lrate
-
-
-K.set_image_data_format('channels_first')
-
-(x_train, y_train), (x_test, y_test) = cifar10.load_data()
-test_labels = y_test
-x_train = x_train.astype('float32')
-x_test = x_test.astype('float32')
-
-#z-score
-mean = np.mean(x_train,axis=(0,1,2,3))
-std = np.std(x_train,axis=(0,1,2,3))
-x_train = (x_train-mean)/(std+1e-7)
-x_test = (x_test-mean)/(std+1e-7)
-
-
-# Dumping test data and test labels
-dir_prefix = "/home/hsharif3/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10/"
-
-dumpLabels(dir_prefix + "test_labels.bin", y_test)
-dumpData(x_test, dir_prefix + "norm_cifar_input.bin", 10000, 3, 32, 32)
-
-
-
-num_classes = 10
-y_train = np_utils.to_categorical(y_train,num_classes)
-y_test = np_utils.to_categorical(y_test,num_classes)
-
-weight_decay = 1e-4
-activation_type = 'tanh'
-
-
-os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-
-
-model = Sequential()
-model.add(Conv2D(32, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:]))
-model.add(Activation(activation_type))
-#model.add(BatchNormalization())
-model.add(Conv2D(32, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
-model.add(Activation(activation_type))
-#model.add(BatchNormalization())
-model.add(MaxPooling2D(pool_size=(2,2)))
-model.add(Dropout(0.2))
-
-model.add(Conv2D(64, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
-model.add(Activation(activation_type))
-#model.add(BatchNormalization())
-model.add(Conv2D(64, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
-model.add(Activation(activation_type))
-#model.add(BatchNormalization())
-model.add(MaxPooling2D(pool_size=(2,2)))
-model.add(Dropout(0.3))
-
-model.add(Conv2D(128, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
-model.add(Activation(activation_type))
-#model.add(BatchNormalization())
-model.add(Conv2D(128, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
-model.add(Activation(activation_type))
-#model.add(BatchNormalization())
-model.add(MaxPooling2D(pool_size=(2,2)))
-model.add(Dropout(0.4))
-
-model.add(Flatten())
-model.add(Dense(num_classes))
-model.add(Activation('softmax'))
-model.summary()
-
-#data augmentation
-datagen = ImageDataGenerator(
- rotation_range=15,
- width_shift_range=0.1,
- height_shift_range=0.1,
- horizontal_flip=True,
- )
-
-datagen.fit(x_train)
-
-
-#training
-batch_size = 64
-
-opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)
-model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])
-model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),\
- steps_per_epoch=x_train.shape[0] // batch_size, #epochs=350,\
- epochs=1,
- verbose=1,validation_data=(x_test,y_test),callbacks=[LearningRateScheduler(lr_schedule2)])
-#save to disk
-model_json = model.to_json()
-with open('model.json', 'w') as json_file:
- json_file.write(model_json)
- model.save_weights('model.h5')
-
-#testing
-scores = model.evaluate(x_test, y_test, batch_size=128, verbose=1)
-print('\nTest result: %.3f loss: %.3f' % (scores[1]*100,scores[0]))
-
-
-translate_to_approxhpvm(model, "alexnet2_cifar10_test/", x_test, test_labels, "alexnet2_cifar10/", y_test)
-sys.exit(0)
-
-
-
-params = model.get_weights()
-dumpConvWeights(dir_prefix + "conv1.bin", params[0], 32, 3, 3, 3)
-dumpFcBias(dir_prefix + "conv1_bias.bin", params[1], 32)
-dumpConvWeights(dir_prefix + "conv2.bin", params[2], 32, 32, 3, 3)
-dumpFcBias(dir_prefix + "conv2_bias.bin", params[3], 32)
-dumpConvWeights(dir_prefix + "conv3.bin", params[4], 64, 32, 3, 3)
-dumpFcBias(dir_prefix + "conv3_bias.bin", params[5], 64)
-dumpConvWeights(dir_prefix + "conv4.bin", params[6], 64, 64, 3, 3)
-dumpFcBias(dir_prefix + "conv4_bias.bin", params[7], 64)
-dumpConvWeights(dir_prefix + "conv5.bin", params[8], 128, 64, 3, 3)
-dumpFcBias(dir_prefix + "conv5_bias.bin", params[9], 128)
-dumpConvWeights(dir_prefix + "conv6.bin", params[10], 128, 128, 3, 3)
-dumpFcBias(dir_prefix + "conv6_bias.bin", params[11], 128)
-
-dumpFcWeights(dir_prefix + "fc1.bin", params[12], 2048, 10)
-dumpFcBias(dir_prefix + "fc1_bias.bin", params[13], 10)
-
-
diff --git a/hpvm/projects/onnx/src/keras_models/lenet.py b/hpvm/projects/onnx/src/keras_models/lenet.py
deleted file mode 100644
index c9588eef6c393457617b7fdda03c7b8222af5357..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/src/keras_models/lenet.py
+++ /dev/null
@@ -1,97 +0,0 @@
-
-import sys
-import keras
-from keras.datasets import mnist
-from keras.models import Sequential
-from keras.layers import Dense, Dropout, Flatten, Activation
-from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D
-from keras import backend as K
-from frontend.approxhpvm_translator import translate_to_approxhpvm
-
-
-batch_size = 128
-num_classes = 10
-
-
-# input image dimensions
-img_rows, img_cols = 28, 28
-
-
-if __name__ == "__main__":
-
- # Changing Keras data format to NCHW - NHWC is default
- # NOTE: ApproxHPVM requires NCHW format
- K.set_image_data_format('channels_first')
-
- # Loads Mnist dataset
- (x_train, y_train), (x_test, y_test) = mnist.load_data()
- test_labels = y_test
-
- # Reshaping data to be NCHW format
- x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
- x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
- input_shape = (1, img_rows, img_cols)
-
-
- # Data Normalization
- x_train = x_train.astype('float32')
- x_test = x_test.astype('float32')
- x_train /= 255
- x_test /= 255
-
-
- # convert class vectors to binary class matrices - required by Keras
- y_train = keras.utils.to_categorical(y_train, num_classes)
- y_test = keras.utils.to_categorical(y_test, num_classes)
-
-
-
- # Network Compostion: 3 Conv Layers, 2 Dense Layers
- model = Sequential()
-
- # ConvLayer1
- model.add(Conv2D(32, kernel_size=(5, 5),
- activation='relu',
- padding = 'same',
- input_shape=input_shape))
- model.add(MaxPooling2D(pool_size=(2, 2)))
-
- # ConvLayer2
- model.add(Conv2D(64, (5, 5), activation='relu', padding = 'same'))
-
- # ConvLayer3
- # NOTE: ZeroPading needed for ConvLayer with strides > 1
- model.add(ZeroPadding2D(padding = (1,1)))
- model.add(Conv2D(64, (3, 3), strides = (2,2), activation='relu', padding = 'valid') )
-
- model.add(Flatten())
- # DenseLayer1
- model.add(Dense(1024, activation='relu'))
- # DenseLayer2
- model.add(Dense(num_classes, activation='relu'))
- # Softmax Layer
- model.add(Activation('softmax'))
-
-
- # Configures model for training
- model.compile(loss=keras.losses.categorical_crossentropy,
- optimizer=keras.optimizers.Adadelta(),
- metrics=['accuracy'])
-
- # Training
- model.fit(x_train, y_train,
- batch_size=batch_size,
- epochs=5,
- verbose=1,
- validation_data=(x_test, y_test))
-
-
- # Inference
- score = model.evaluate(x_test, y_test, verbose=0)
- print('Test loss:', score[0])
- print('Test accuracy:', score[1])
-
-
- # NOTE: Call to ApproxHPVM Translator - Dumps weights and ApproxHPVM C src
- translate_to_approxhpvm(model, "data/lenet_hpvm_batch/", x_test, test_labels, 10)
-
diff --git a/hpvm/projects/onnx/src/keras_models/mnist.ipynb b/hpvm/projects/onnx/src/keras_models/mnist.ipynb
deleted file mode 100644
index 1964ebe3991a1eb78760ea6a1739eacfec47e666..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/src/keras_models/mnist.ipynb
+++ /dev/null
@@ -1,105 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "import sys\n",
- "import numpy as np\n",
- "import onnx\n",
- "import glob\n",
- "from onnxruntime.backend.backend import OnnxRuntimeBackend as backend\n",
- "\n",
- "from onnx import numpy_helper\n",
- "\n",
- "# onnx2hpvm modules\n",
- "from onnx2hpvm.onnx_translator import from_onnx_to_hpvm"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "model = onnx.load('../models/mnist/mnist.onnx')\n",
- "test_data_dir = '../models/mnist/test_data_set_0'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1\n"
- ]
- }
- ],
- "source": [
- "# Load inputs\n",
- "inputs = []\n",
- "inputs_num = len(glob.glob(os.path.join(test_data_dir, 'input_*.pb')))\n",
- "print(inputs_num)\n",
- "for i in range(inputs_num):\n",
- " input_file = os.path.join(test_data_dir, 'input_{}.pb'.format(i))\n",
- " tensor = onnx.TensorProto()\n",
- " with open(input_file, 'rb') as f:\n",
- " tensor.ParseFromString(f.read())\n",
- " inputs.append(numpy_helper.to_array(tensor))\n",
- "\n",
- "# Load reference outputs\n",
- "ref_outputs = []\n",
- "ref_outputs_num = len(glob.glob(os.path.join(test_data_dir, 'output_*.pb')))\n",
- "for i in range(ref_outputs_num):\n",
- " output_file = os.path.join(test_data_dir, 'output_{}.pb'.format(i))\n",
- " tensor = onnx.TensorProto()\n",
- " with open(output_file, 'rb') as f:\n",
- " tensor.ParseFromString(f.read())\n",
- " ref_outputs.append(numpy_helper.to_array(tensor))\n",
- "\n",
- "# Run the model on the backend\n",
- "outputs = list(backend.run_model(model, inputs))\n",
- "\n",
- "#from_onnx_to_hpvm(model)\n",
- "# Compare the results with reference outputs.\n",
- "#for ref_o, o in zip(ref_outputs, outputs):\n",
- "# np.testing.assert_almost_equal(ref_o, o)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/hpvm/projects/onnx/src/keras_models/mobilenet_cifar10.py b/hpvm/projects/onnx/src/keras_models/mobilenet_cifar10.py
deleted file mode 100644
index b739ed819634f30f4b33173443ac41f848f9c8f1..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/src/keras_models/mobilenet_cifar10.py
+++ /dev/null
@@ -1,161 +0,0 @@
-
-import sys
-import os
-os.environ['CUDA_VISIBLE_DEVICES'] = '1'
-
-from keras.models import Sequential
-from keras.layers import *
-from keras.datasets import cifar10
-from keras.utils import to_categorical
-from keras.callbacks import *
-from keras.preprocessing.image import ImageDataGenerator
-from keras.models import Model
-from keras import optimizers
-import keras.backend as K
-from frontend.approxhpvm_translator import translate_to_approxhpvm
-
-
-K.set_image_data_format('channels_first')
-
-(X_train, y_train), (X_test, y_test) = cifar10.load_data()
-test_labels = y_test
-
-print ("X_train.shape = ", X_train.shape)
-print ("X_test.shape = ", X_test.shape)
-
-
-X_train = X_train.astype('float32')
-X_test = X_test.astype('float32')
-
-
-mean = np.mean(X_train, axis=(0, 2, 3), keepdims=True)
-std = np.std(X_train, axis=(0, 2, 3), keepdims=True)
-
-X_train = (X_train - mean) / (std + 1e-9)
-X_test = (X_test - mean) / (std + 1e-9)
-
-y_train = to_categorical(y_train, num_classes=10)
-y_test = to_categorical(y_test, num_classes=10)
-
-
-def get_mobilenet(alpha=1, depth_multiplier=1):
- model = Sequential()
-
- def _conv_block(filters, alpha, kernel=(3, 3), strides=(1, 1)):
- channel_axis = 1
-
- model.add(Conv2D(filters, kernel,
- padding='same',
- use_bias=False,
- strides=strides,
- input_shape=(3, 32, 32)))
- model.add(BatchNormalization(axis=channel_axis))
- model.add(Activation('relu'))
-
- def _depthwise_conv_block(pointwise_conv_filters, alpha, depth_multiplier=1, strides=(1, 1)):
- channel_axis = 1
-
- model.add(ZeroPadding2D(padding = ((1,1), (1,1) )))
-
- model.add(DepthwiseConv2D((3, 3),
- padding='valid',
- #depth_multiplier=depth_multiplier,
- strides=strides,
- use_bias=False))
- model.add(BatchNormalization(axis=channel_axis))
-
- model.add(Activation('relu'))
- model.add(Conv2D(pointwise_conv_filters, (1, 1),
- padding='same',
- use_bias=False,
- strides=(1, 1)))
- model.add(BatchNormalization(axis=channel_axis))
- model.add(Activation('relu'))
-
-
- _conv_block(32, alpha, strides=(1, 1))
-
- _depthwise_conv_block(64, alpha, depth_multiplier)
-
- _depthwise_conv_block(128, alpha, depth_multiplier,
- strides=(2, 2))
- _depthwise_conv_block(128, alpha, depth_multiplier)
- model.add(Dropout(rate=0.5))
-
- _depthwise_conv_block(256, alpha, depth_multiplier,
- strides=(2, 2))
- _depthwise_conv_block(256, alpha, depth_multiplier)
- model.add(Dropout(rate=0.5))
-
- _depthwise_conv_block(512, alpha, depth_multiplier,
- strides=(2, 2))
- _depthwise_conv_block(512, alpha, depth_multiplier)
- _depthwise_conv_block(512, alpha, depth_multiplier)
- model.add(Dropout(rate=0.5))
-
- _depthwise_conv_block(512, alpha, depth_multiplier)
- _depthwise_conv_block(512, alpha, depth_multiplier)
- _depthwise_conv_block(512, alpha, depth_multiplier)
- model.add(Dropout(rate=0.5))
-
- _depthwise_conv_block(1024, alpha, depth_multiplier,
- strides=(2, 2))
- _depthwise_conv_block(1024, alpha, depth_multiplier)
- model.add(Dropout(rate=0.5))
-
- model.add(AveragePooling2D(pool_size=2))
- model.add(Flatten())
- model.add(Dense(10, activation='softmax'))
-
- return model
-
-
-# data augmentation, horizontal flips only
-datagen = ImageDataGenerator(
- featurewise_center=False,
- featurewise_std_normalization=False,
- rotation_range=0.0,
- width_shift_range=0.0,
- height_shift_range=0.0,
- vertical_flip=False,
- horizontal_flip=True)
-datagen.fit(X_train)
-
-
-model = get_mobilenet()
-
-learning_rates=[]
-for i in range(5):
- learning_rates.append(2e-2)
-for i in range(50-5):
- learning_rates.append(1e-2)
-for i in range(100-50):
- learning_rates.append(8e-3)
-for i in range(150-100):
- learning_rates.append(4e-3)
-for i in range(200-150):
- learning_rates.append(2e-3)
-for i in range(300-200):
- learning_rates.append(1e-3)
-
-callbacks = [
- LearningRateScheduler(lambda epoch: float(learning_rates[epoch]))
-]
-
-model.compile(optimizer=optimizers.SGD(lr=learning_rates[0], momentum=0.9, decay=0.0, nesterov=False),
- loss='categorical_crossentropy',
- metrics=['accuracy'])
-
-model.fit_generator(
- datagen.flow(X_train, y_train, batch_size=128),
- steps_per_epoch=int(np.ceil(50000 / 128)),
- validation_data=(X_test, y_test),
- #epochs=300,
- epochs=50,
- callbacks=callbacks
-)
-
-model.summary()
-
-translate_to_approxhpvm(model, "data/mobilenet_hpvm/", X_test, test_labels, 10)
-
diff --git a/hpvm/projects/onnx/src/keras_models/mobilenet_shallow.py b/hpvm/projects/onnx/src/keras_models/mobilenet_shallow.py
deleted file mode 100644
index 64df7f98174f22a59f3382ed4337d23e29900051..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/src/keras_models/mobilenet_shallow.py
+++ /dev/null
@@ -1,158 +0,0 @@
-import sys
-import os
-os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-
-from keras.models import Sequential
-from keras.layers import *
-from keras.datasets import cifar10
-from keras.utils import to_categorical
-from keras.callbacks import *
-from keras.preprocessing.image import ImageDataGenerator
-from keras.models import Model
-from keras import optimizers
-import keras.backend as K
-from frontend.approxhpvm_translator import translate_to_approxhpvm
-
-
-K.set_image_data_format('channels_first')
-
-(X_train, y_train), (X_test, y_test) = cifar10.load_data()
-test_labels = y_test
-
-print ("X_train.shape = ", X_train.shape)
-print ("X_test.shape = ", X_test.shape)
-
-
-X_train = X_train.astype('float32')
-X_test = X_test.astype('float32')
-
-
-mean = np.mean(X_train, axis=(0, 2, 3), keepdims=True)
-std = np.std(X_train, axis=(0, 2, 3), keepdims=True)
-
-X_train = (X_train - mean) / (std + 1e-9)
-X_test = (X_test - mean) / (std + 1e-9)
-
-y_train = to_categorical(y_train, num_classes=10)
-y_test = to_categorical(y_test, num_classes=10)
-
-
-def get_mobilenet(alpha=1, depth_multiplier=1):
- model = Sequential()
-
- def _conv_block(filters, alpha, kernel=(3, 3), strides=(1, 1)):
- channel_axis = 1
- filters = int(filters * alpha)
- model.add(Conv2D(filters, kernel,
- padding='same',
- use_bias=False,
- strides=strides,
- input_shape=(3, 32, 32)))
- model.add(BatchNormalization(axis=channel_axis))
- model.add(Activation('relu'))
-
- def _depthwise_conv_block(pointwise_conv_filters, alpha, depth_multiplier=1, strides=(1, 1)):
- channel_axis = 1
- pointwise_conv_filters = int(pointwise_conv_filters * alpha)
-
- model.add(DepthwiseConv2D((3, 3),
- padding='same',
- depth_multiplier=depth_multiplier,
- strides=strides,
- use_bias=False))
- model.add(BatchNormalization(axis=channel_axis))
- model.add(Activation('relu'))
- model.add(Conv2D(pointwise_conv_filters, (1, 1),
- padding='same',
- use_bias=False,
- strides=(1, 1)))
- model.add(BatchNormalization(axis=channel_axis))
- model.add(Activation('relu'))
-
-
- _conv_block(32, alpha, strides=(1, 1))
-
- _depthwise_conv_block(64, alpha, depth_multiplier)
-
- _depthwise_conv_block(128, alpha, depth_multiplier,
- strides=(2, 2))
- _depthwise_conv_block(128, alpha, depth_multiplier)
- model.add(Dropout(rate=0.5))
-
- _depthwise_conv_block(256, alpha, depth_multiplier,
- strides=(2, 2))
- _depthwise_conv_block(256, alpha, depth_multiplier)
- model.add(Dropout(rate=0.5))
-
- _depthwise_conv_block(512, alpha, depth_multiplier,
- strides=(2, 2))
-# _depthwise_conv_block(512, alpha, depth_multiplier)
-# _depthwise_conv_block(512, alpha, depth_multiplier)
-# model.add(Dropout(rate=0.5))
-
-# _depthwise_conv_block(512, alpha, depth_multiplier)
-# _depthwise_conv_block(512, alpha, depth_multiplier)
-# _depthwise_conv_block(512, alpha, depth_multiplier)
-# model.add(Dropout(rate=0.5))
-
-# _depthwise_conv_block(1024, alpha, depth_multiplier,
-# strides=(2, 2))
-# _depthwise_conv_block(1024, alpha, depth_multiplier)
-# model.add(Dropout(rate=0.5))
-
- model.add(AveragePooling2D(pool_size=2))
- model.add(Flatten())
- model.add(Dense(10, activation='softmax'))
-
- return model
-
-
-# data augmentation, horizontal flips only
-datagen = ImageDataGenerator(
- featurewise_center=False,
- featurewise_std_normalization=False,
- rotation_range=0.0,
- width_shift_range=0.2,
- height_shift_range=0.2,
- vertical_flip=False,
- horizontal_flip=True)
-datagen.fit(X_train)
-
-
-model = get_mobilenet()
-
-learning_rates=[]
-for i in range(5):
- learning_rates.append(5e-2)
-for i in range(50-5):
- learning_rates.append(2e-2)
-for i in range(100-50):
- learning_rates.append(8e-3)
-for i in range(150-100):
- learning_rates.append(4e-3)
-for i in range(200-150):
- learning_rates.append(2e-3)
-for i in range(250-200):
- learning_rates.append(1e-3)
-
-callbacks = [
- LearningRateScheduler(lambda epoch: float(learning_rates[epoch]))
-]
-
-model.compile(optimizer=optimizers.SGD(lr=learning_rates[0], momentum=0.9, decay=0.0, nesterov=False),
- loss='categorical_crossentropy',
- metrics=['accuracy'])
-
-model.fit_generator(
- datagen.flow(X_train, y_train, batch_size=128),
- steps_per_epoch=int(np.ceil(50000 / 128)),
- validation_data=(X_test, y_test),
- #epochs=300,
- epochs=250,
- callbacks=callbacks
-)
-
-model.summary()
-
-translate_to_approxhpvm(model, "data/mobilenet_shallow/", X_test, test_labels, 10)
-
diff --git a/hpvm/projects/onnx/src/keras_models/mobilenetv2_cifar10.py b/hpvm/projects/onnx/src/keras_models/mobilenetv2_cifar10.py
deleted file mode 100644
index 2fbed4623d0e57d7a0dd948fa0894127fea72324..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/src/keras_models/mobilenetv2_cifar10.py
+++ /dev/null
@@ -1,176 +0,0 @@
-import sys
-import os
-os.environ['CUDA_VISIBLE_DEVICES'] = '1'
-
-from keras.models import Sequential
-from keras.layers import *
-from keras.datasets import cifar10
-from keras.utils import to_categorical
-from keras.callbacks import *
-from keras.preprocessing.image import ImageDataGenerator
-from keras.models import Model
-from keras import optimizers
-import keras.backend as K
-
-
-K.set_image_data_format('channels_first')
-
-(X_train, y_train), (X_test, y_test) = cifar10.load_data()
-test_labels = y_test
-
-print ("X_train.shape = ", X_train.shape)
-print ("X_test.shape = ", X_test.shape)
-
-
-X_train = X_train.astype('float32')
-X_test = X_test.astype('float32')
-
-mean = np.mean(X_train, axis=(0, 1, 2), keepdims=True)
-std = np.std(X_train, axis=(0, 1, 2), keepdims=True)
-X_train = (X_train - mean) / (std + 1e-9)
-X_test = (X_test - mean) / (std + 1e-9)
-
-y_train = to_categorical(y_train, num_classes=10)
-y_test = to_categorical(y_test, num_classes=10)
-
-
-def _make_divisible(v, divisor, min_value=None):
- if min_value is None:
- min_value = divisor
- new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
- # Make sure that round down does not go down by more than 10%.
- if new_v < 0.9 * v:
- new_v += divisor
- return new_v
-
-def _inverted_res_block(inputs, expansion, stride, alpha, filters, block_id):
- channel_axis = 1
-
- in_channels = inputs.shape[1]
- pointwise_conv_filters = int(filters * alpha)
- pointwise_filters = _make_divisible(pointwise_conv_filters, 8)
- x = inputs
-
- if block_id:
- x = Conv2D(int(expansion * in_channels), kernel_size=1, strides=1, padding='valid', use_bias=False)(x)
- x = BatchNormalization(axis=channel_axis)(x)
- x = Activation('relu')(x)
-
- if stride == 2:
- x = ZeroPadding2D(padding=(1, 1))(x)
- else:
- x = ZeroPadding2D(padding=(1, 1))(x)
-
- x = DepthwiseConv2D(kernel_size=3, strides=stride, use_bias=False, padding='valid')(x)
- x = BatchNormalization(axis=channel_axis)(x)
- x = Activation('relu')(x)
-
- x = Conv2D(pointwise_filters, kernel_size=1, strides=1, padding='valid', use_bias=False)(x)
- x = BatchNormalization(axis=channel_axis)(x)
-
-
- if in_channels == pointwise_filters and stride == 1:
- return Add()([inputs, x])
- return x
-
-def get_mobilenetv2(alpha=1.0, depth_multiplier=1):
-
- channel_axis = 1
-
- first_block_filters = _make_divisible(32 * alpha, 8)
- img_input = Input(shape=(3, 32, 32))
-
- x = ZeroPadding2D(padding=(1, 1))(img_input)
- x = Conv2D(first_block_filters, kernel_size=3, strides=1, padding='valid', use_bias=False)(x)
- #x = BatchNormalization(axis=channel_axis)(x)
- #x = Activation('relu')(x)
-
- x = _inverted_res_block(x, filters=16, alpha=alpha, stride=1, expansion=1, block_id=0 )
-
- x = _inverted_res_block(x, filters=24, alpha=alpha, stride=1, expansion=6, block_id=1 )
- x = _inverted_res_block(x, filters=24, alpha=alpha, stride=1, expansion=6, block_id=2 )
-
- x = _inverted_res_block(x, filters=32, alpha=alpha, stride=2, expansion=6, block_id=3 )
- x = _inverted_res_block(x, filters=32, alpha=alpha, stride=1, expansion=6, block_id=4 )
- x = _inverted_res_block(x, filters=32, alpha=alpha, stride=1, expansion=6, block_id=5 )
-
- x = _inverted_res_block(x, filters=64, alpha=alpha, stride=2, expansion=6, block_id=6 )
- x = _inverted_res_block(x, filters=64, alpha=alpha, stride=1, expansion=6, block_id=7 )
- x = _inverted_res_block(x, filters=64, alpha=alpha, stride=1, expansion=6, block_id=8 )
- x = _inverted_res_block(x, filters=64, alpha=alpha, stride=1, expansion=6, block_id=9 )
- x = Dropout(rate=0.25)(x)
-
- x = _inverted_res_block(x, filters=96, alpha=alpha, stride=1, expansion=6, block_id=10)
- x = _inverted_res_block(x, filters=96, alpha=alpha, stride=1, expansion=6, block_id=11)
- x = _inverted_res_block(x, filters=96, alpha=alpha, stride=1, expansion=6, block_id=12)
- x = Dropout(rate=0.25)(x)
-
- x = _inverted_res_block(x, filters=160, alpha=alpha, stride=2, expansion=6, block_id=13)
- x = _inverted_res_block(x, filters=160, alpha=alpha, stride=1, expansion=6, block_id=14)
- x = _inverted_res_block(x, filters=160, alpha=alpha, stride=1, expansion=6, block_id=15)
- x = Dropout(rate=0.25)(x)
-
- x = _inverted_res_block(x, filters=320, alpha=alpha, stride=1, expansion=6, block_id=16)
- x = Dropout(rate=0.25)(x)
-
- if alpha > 1.0:
- last_block_filters = _make_divisible(1280 * alpha, 8)
- else:
- last_block_filters = 1280
-
- x = Conv2D(last_block_filters, kernel_size=1, use_bias=False)(x)
- x = BatchNormalization(axis=channel_axis)(x)
- x = Activation('relu')(x)
-
- x = AveragePooling2D()(x)
- x = Flatten()(x)
- x = Dense(10, activation='softmax')(x)
-
- model = Model(inputs=img_input, outputs=x)
- return model
-
-
-# data augmentation, horizontal flips only
-datagen = ImageDataGenerator(
- featurewise_center=False,
- featurewise_std_normalization=False,
- rotation_range=0.0,
- width_shift_range=0.0,
- height_shift_range=0.0,
- vertical_flip=False,
- horizontal_flip=True)
-datagen.fit(X_train)
-
-
-model = get_mobilenetv2()
-
-learning_rates=[]
-for i in range(5):
- learning_rates.append(2e-2)
-for i in range(50-5):
- learning_rates.append(1e-2)
-for i in range(100-50):
- learning_rates.append(8e-3)
-for i in range(150-100):
- learning_rates.append(4e-3)
-for i in range(200-150):
- learning_rates.append(2e-3)
-for i in range(300-200):
- learning_rates.append(1e-3)
-
-callbacks = [
- LearningRateScheduler(lambda epoch: float(learning_rates[epoch]))
-]
-
-model.compile(optimizer=optimizers.SGD(lr=learning_rates[0], momentum=0.9, decay=0.0, nesterov=False),
- loss='categorical_crossentropy',
- metrics=['accuracy'])
-
-model.fit_generator(
- datagen.flow(X_train, y_train, batch_size=128),
- steps_per_epoch=int(np.ceil(50000 / 128)),
- validation_data=(X_test, y_test),
- epochs=300,
- callbacks=callbacks
-)
-
diff --git a/hpvm/projects/onnx/src/keras_models/resnet.py b/hpvm/projects/onnx/src/keras_models/resnet.py
deleted file mode 100644
index 3a9e092170c767d433e438b16fdc38bd9bb9d966..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/src/keras_models/resnet.py
+++ /dev/null
@@ -1,576 +0,0 @@
-"""
-#Trains a ResNet on the CIFAR10 dataset.
-
-ResNet v1:
-[Deep Residual Learning for Image Recognition
-](https://arxiv.org/pdf/1512.03385.pdf)
-
-ResNet v2:
-[Identity Mappings in Deep Residual Networks
-](https://arxiv.org/pdf/1603.05027.pdf)
-
-
-Model|n|200-epoch accuracy|Original paper accuracy |sec/epoch GTX1080Ti
-:------------|--:|-------:|-----------------------:|---:
-ResNet20 v1| 3| 92.16 %| 91.25 %|35
-ResNet32 v1| 5| 92.46 %| 92.49 %|50
-ResNet44 v1| 7| 92.50 %| 92.83 %|70
-ResNet56 v1| 9| 92.71 %| 93.03 %|90
-ResNet110 v1| 18| 92.65 %| 93.39+-.16 %|165
-ResNet164 v1| 27| - %| 94.07 %| -
-ResNet1001 v1|N/A| - %| 92.39 %| -
-
-
-
-Model|n|200-epoch accuracy|Original paper accuracy |sec/epoch GTX1080Ti
-:------------|--:|-------:|-----------------------:|---:
-ResNet20 v2| 2| - %| - %|---
-ResNet32 v2|N/A| NA %| NA %| NA
-ResNet44 v2|N/A| NA %| NA %| NA
-ResNet56 v2| 6| 93.01 %| NA %|100
-ResNet110 v2| 12| 93.15 %| 93.63 %|180
-ResNet164 v2| 18| - %| 94.54 %| -
-ResNet1001 v2|111| - %| 95.08+-.14 %| -
-"""
-
-from __future__ import print_function
-import keras
-from keras.layers import Dense, Conv2D, BatchNormalization, Activation
-from keras.layers import AveragePooling2D, Input, Flatten, ZeroPadding2D
-from keras.optimizers import Adam
-from keras.callbacks import ModelCheckpoint, LearningRateScheduler
-from keras.callbacks import ReduceLROnPlateau
-from keras.preprocessing.image import ImageDataGenerator
-from keras.regularizers import l2
-from keras import backend as K
-from keras.models import Model
-from keras.datasets import cifar10
-from keras import backend as K
-import numpy as np
-import os
-import sys
-#from approxhpvm_translator import translate_to_approxhpvm
-#from weight_utils import dumpCalibrationData
-
-
-
-os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-
-
-K.set_image_data_format('channels_first')
-
-
-# Training parameters
-batch_size = 32 # orig paper trained all networks with batch_size=128
-#---- epochs = 200
-epochs = 2
-data_augmentation = True
-num_classes = 10
-
-# Subtracting pixel mean improves accuracy
-subtract_pixel_mean = True
-
-# Model parameter
-# ----------------------------------------------------------------------------
-# | | 200-epoch | Orig Paper| 200-epoch | Orig Paper| sec/epoch
-# Model | n | ResNet v1 | ResNet v1 | ResNet v2 | ResNet v2 | GTX1080Ti
-# |v1(v2)| %Accuracy | %Accuracy | %Accuracy | %Accuracy | v1 (v2)
-# ----------------------------------------------------------------------------
-# ResNet20 | 3 (2)| 92.16 | 91.25 | ----- | ----- | 35 (---)
-# ResNet32 | 5(NA)| 92.46 | 92.49 | NA | NA | 50 ( NA)
-# ResNet44 | 7(NA)| 92.50 | 92.83 | NA | NA | 70 ( NA)
-# ResNet56 | 9 (6)| 92.71 | 93.03 | 93.01 | NA | 90 (100)
-# ResNet110 |18(12)| 92.65 | 93.39+-.16| 93.15 | 93.63 | 165(180)
-# ResNet164 |27(18)| ----- | 94.07 | ----- | 94.54 | ---(---)
-# ResNet1001| (111)| ----- | 92.39 | ----- | 95.08+-.14| ---(---)
-# ---------------------------------------------------------------------------
-n = 3
-
-# Model version
-# Orig paper: version = 1 (ResNet v1), Improved ResNet: version = 2 (ResNet v2)
-version = 1
-
-# Computed depth from supplied model parameter n
-if version == 1:
- depth = n * 6 + 2
-elif version == 2:
- depth = n * 9 + 2
-
-# Model name, depth and version
-model_type = 'ResNet%dv%d' % (depth, version)
-
-# Load the CIFAR10 data.
-(x_train, y_train), (x_test, y_test) = cifar10.load_data()
-test_labels = y_test
-train_labels = y_train
-
-# Input image dimensions.
-input_shape = x_train.shape[1:]
-
-# Normalize data.
-x_train = x_train.astype('float32') / 255
-x_test = x_test.astype('float32') / 255
-
-# If subtract pixel mean is enabled
-if subtract_pixel_mean:
- x_train_mean = np.mean(x_train, axis=0)
- x_train -= x_train_mean
- x_test -= x_train_mean
-
-print('x_train shape:', x_train.shape)
-print(x_train.shape[0], 'train samples')
-print(x_test.shape[0], 'test samples')
-print('y_train shape:', y_train.shape)
-
-# Convert class vectors to binary class matrices.
-y_train = keras.utils.to_categorical(y_train, num_classes)
-y_test = keras.utils.to_categorical(y_test, num_classes)
-
-
-
-
-
-
-def lr_schedule(epoch):
- """Learning Rate Schedule
-
- Learning rate is scheduled to be reduced after 80, 120, 160, 180 epochs.
- Called automatically every epoch as part of callbacks during training.
-
- # Arguments
- epoch (int): The number of epochs
-
- # Returns
- lr (float32): learning rate
- """
- lr = 1e-3
- if epoch > 180:
- lr *= 0.5e-3
- elif epoch > 160:
- lr *= 1e-3
- elif epoch > 120:
- lr *= 1e-2
- elif epoch > 80:
- lr *= 1e-1
- print('Learning rate: ', lr)
- return lr
-
-
-def resnet_layer(inputs,
- num_filters=16,
- kernel_size=3,
- strides=1,
- activation='relu',
- batch_normalization=True,
- conv_first=True):
- """2D Convolution-Batch Normalization-Activation stack builder
-
- # Arguments
- inputs (tensor): input tensor from input image or previous layer
- num_filters (int): Conv2D number of filters
- kernel_size (int): Conv2D square kernel dimensions
- strides (int): Conv2D square stride dimensions
- activation (string): activation name
- batch_normalization (bool): whether to include batch normalization
- conv_first (bool): conv-bn-activation (True) or
- bn-activation-conv (False)
-
- # Returns
- x (tensor): tensor as input to the next layer
- """
- conv = Conv2D(num_filters,
- kernel_size=kernel_size,
- strides=strides,
- padding='valid', # NOTE: using valid convs with explicit pad operation
- kernel_initializer='he_normal',
- kernel_regularizer=l2(1e-4))
-
- padding_value = int((kernel_size - 1) / 2)
- zero_padding = ZeroPadding2D(padding = (padding_value, padding_value))
-
- # FIXME: Temporarily disabled batch normalization
- batch_normalization = False
-
- x = inputs
- x = zero_padding(x)
- if conv_first:
- x = conv(x)
- if batch_normalization:
- x = BatchNormalization()(x)
- if activation is not None:
- x = Activation(activation)(x)
- else:
- if batch_normalization:
- x = BatchNormalization()(x)
- if activation is not None:
- x = Activation(activation)(x)
- x = conv(x)
- return x
-
-
-
-def resnet_v0(input_shape, depth, num_classes=10):
- """ResNet Version 1 Model builder [a]
-
- Stacks of 2 x (3 x 3) Conv2D-BN-ReLU
- Last ReLU is after the shortcut connection.
- At the beginning of each stage, the feature map size is halved (downsampled)
- by a convolutional layer with strides=2, while the number of filters is
- doubled. Within each stage, the layers have the same number filters and the
- same number of filters.
- Features maps sizes:
- stage 0: 32x32, 16
- stage 1: 16x16, 32
- stage 2: 8x8, 64
- The Number of parameters is approx the same as Table 6 of [a]:
- ResNet20 0.27M
- ResNet32 0.46M
- ResNet44 0.66M
- ResNet56 0.85M
- ResNet110 1.7M
-
- # Arguments
- input_shape (tensor): shape of input image tensor
- depth (int): number of core convolutional layers
- num_classes (int): number of classes (CIFAR10 has 10)
-
- # Returns
- model (Model): Keras model instance
- """
- if (depth - 2) % 6 != 0:
- raise ValueError('depth should be 6n+2 (eg 20, 32, 44 in [a])')
- # Start model definition.
- num_filters = 16
- num_res_blocks = int((depth - 2) / 6)
-
- inputs = Input(shape=input_shape)
- x = resnet_layer(inputs=inputs)
- # Instantiate the stack of residual units
- for stack in range(3):
- for res_block in range(num_res_blocks):
- strides = 1
- if stack > 0 and res_block == 0: # first layer but not first stack
- strides = 2 # downsample
- y = resnet_layer(inputs=x,
- num_filters=num_filters,
- strides=strides)
- y = resnet_layer(inputs=y,
- num_filters=num_filters,
- activation=None)
- if stack > 0 and res_block == 0: # first layer but not first stack
- # linear projection residual shortcut connection to match
- # changed dims
- x = resnet_layer(inputs=x,
- num_filters=num_filters,
- kernel_size=1,
- strides=strides,
- activation=None,
- batch_normalization=False)
- x = keras.layers.add([x, y])
- x = Activation('relu')(x)
- num_filters *= 1
-
- # Add classifier on top.
- # v1 does not use BN after last shortcut connection-ReLU
- #-- x = AveragePooling2D(pool_size=8)(x)
- y = Flatten()(x)
- x = Dense(64)(y)
- outputs = Dense(num_classes,
- activation='softmax',
- kernel_initializer='he_normal')(x)
-
- # Instantiate model.
- model = Model(inputs=inputs, outputs=outputs)
- return model
-
-
-def resnet_v1_1(input_shape, depth, num_classes=10):
- """ResNet Version 1 Model builder [a]
-
- Stacks of 2 x (3 x 3) Conv2D-BN-ReLU
- Last ReLU is after the shortcut connection.
- At the beginning of each stage, the feature map size is halved (downsampled)
- by a convolutional layer with strides=2, while the number of filters is
- doubled. Within each stage, the layers have the same number filters and the
- same number of filters.
- Features maps sizes:
- stage 0: 32x32, 16
- stage 1: 16x16, 32
- stage 2: 8x8, 64
- The Number of parameters is approx the same as Table 6 of [a]:
- ResNet20 0.27M
- ResNet32 0.46M
- ResNet44 0.66M
- ResNet56 0.85M
- ResNet110 1.7M
-
- # Arguments
- input_shape (tensor): shape of input image tensor
- depth (int): number of core convolutional layers
- num_classes (int): number of classes (CIFAR10 has 10)
-
- # Returns
- model (Model): Keras model instance
- """
- if (depth - 2) % 6 != 0:
- raise ValueError('depth should be 6n+2 (eg 20, 32, 44 in [a])')
- # Start model definition.
- num_filters = 16
- num_res_blocks = int((depth - 2) / 6)
-
- inputs = Input(shape=input_shape)
- x = resnet_layer(inputs=inputs)
- # Instantiate the stack of residual units
- for stack in range(3):
- for res_block in range(num_res_blocks):
- strides = 1
- if stack > 0 and res_block == 0: # first layer but not first stack
- strides = 2 # downsample
- y = resnet_layer(inputs=x,
- num_filters=num_filters,
- strides=strides)
- y = resnet_layer(inputs=y,
- num_filters=num_filters,
- activation=None)
- if stack > 0 and res_block == 0: # first layer but not first stack
- # linear projection residual shortcut connection to match
- # changed dims
- x = resnet_layer(inputs=x,
- num_filters=num_filters,
- kernel_size=1,
- strides=strides,
- activation=None,
- batch_normalization=False)
- x = keras.layers.add([x, y])
- x = Activation('relu')(x)
- num_filters *= 2
-
-
- x = AveragePooling2D(pool_size=8)(x)
- y = Flatten()(x)
- outputs = Dense(num_classes,
- #activation='softmax',
- kernel_initializer='he_normal')(y)
-
- outputs = Activation('softmax')(outputs)
-
-
- # Instantiate model.
- model = Model(inputs=inputs, outputs=outputs)
- return model
-
-
-
-def resnet_v2(input_shape, depth, num_classes=10):
- """ResNet Version 2 Model builder [b]
-
- Stacks of (1 x 1)-(3 x 3)-(1 x 1) BN-ReLU-Conv2D or also known as
- bottleneck layer
- First shortcut connection per layer is 1 x 1 Conv2D.
- Second and onwards shortcut connection is identity.
- At the beginning of each stage, the feature map size is halved (downsampled)
- by a convolutional layer with strides=2, while the number of filter maps is
- doubled. Within each stage, the layers have the same number filters and the
- same filter map sizes.
- Features maps sizes:
- conv1 : 32x32, 16
- stage 0: 32x32, 64
- stage 1: 16x16, 128
- stage 2: 8x8, 256
-
- # Arguments
- input_shape (tensor): shape of input image tensor
- depth (int): number of core convolutional layers
- num_classes (int): number of classes (CIFAR10 has 10)
-
- # Returns
- model (Model): Keras model instance
- """
- if (depth - 2) % 9 != 0:
- raise ValueError('depth should be 9n+2 (eg 56 or 110 in [b])')
- # Start model definition.
- num_filters_in = 16
- num_res_blocks = int((depth - 2) / 9)
-
- inputs = Input(shape=input_shape)
- # v2 performs Conv2D with BN-ReLU on input before splitting into 2 paths
- x = resnet_layer(inputs=inputs,
- num_filters=num_filters_in,
- conv_first=True)
-
- # Instantiate the stack of residual units
- for stage in range(3):
- for res_block in range(num_res_blocks):
- activation = 'relu'
- batch_normalization = True
- strides = 1
- if stage == 0:
- num_filters_out = num_filters_in * 4
- if res_block == 0: # first layer and first stage
- activation = None
- batch_normalization = False
- else:
- num_filters_out = num_filters_in * 2
- if res_block == 0: # first layer but not first stage
- strides = 2 # downsample
-
- # bottleneck residual unit
- y = resnet_layer(inputs=x,
- num_filters=num_filters_in,
- kernel_size=1,
- strides=strides,
- activation=activation,
- batch_normalization=batch_normalization,
- conv_first=False)
- y = resnet_layer(inputs=y,
- num_filters=num_filters_in,
- conv_first=False)
- y = resnet_layer(inputs=y,
- num_filters=num_filters_out,
- kernel_size=1,
- conv_first=False)
- if res_block == 0:
- # linear projection residual shortcut connection to match
- # changed dims
- x = resnet_layer(inputs=x,
- num_filters=num_filters_out,
- kernel_size=1,
- strides=strides,
- activation=None,
- batch_normalization=False)
- x = keras.layers.add([x, y])
-
- num_filters_in = num_filters_out
-
- # Add classifier on top.
- # v2 has BN-ReLU before Pooling
- x = BatchNormalization()(x)
- x = Activation('relu')(x)
- x = AveragePooling2D(pool_size=8)(x)
- y = Flatten()(x)
- outputs = Dense(num_classes,
- activation='softmax',
- kernel_initializer='he_normal')(y)
-
- # Instantiate model.
- model = Model(inputs=inputs, outputs=outputs)
- return model
-
-depth = 20
-
-if version == 2:
- model = resnet_v2(input_shape=input_shape, depth=depth)
-else:
- model = resnet_v1_1(input_shape=input_shape, depth=depth)
-
-
-model.compile(loss='categorical_crossentropy',
- optimizer=Adam(lr=lr_schedule(0)),
- metrics=['accuracy'])
-model.summary()
-print(model_type)
-
-# Prepare model model saving directory.
-save_dir = os.path.join(os.getcwd(), 'saved_models')
-model_name = 'cifar10_%s_model.{epoch:03d}.h5' % model_type
-if not os.path.isdir(save_dir):
- os.makedirs(save_dir)
-filepath = os.path.join(save_dir, model_name)
-
-# Prepare callbacks for model saving and for learning rate adjustment.
-checkpoint = ModelCheckpoint(filepath=filepath,
- monitor='val_acc',
- verbose=1,
- save_best_only=True)
-
-lr_scheduler = LearningRateScheduler(lr_schedule)
-
-lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.1),
- cooldown=0,
- patience=5,
- min_lr=0.5e-6)
-
-callbacks = [checkpoint, lr_reducer, lr_scheduler]
-
-# Run training, with or without data augmentation.
-if not data_augmentation:
- print('Not using data augmentation.')
- model.fit(x_train, y_train,
- batch_size=batch_size,
- epochs=epochs,
- validation_data=(x_test, y_test),
- shuffle=True,
- callbacks=callbacks)
-else:
- print('Using real-time data augmentation.')
- # This will do preprocessing and realtime data augmentation:
- datagen = ImageDataGenerator(
- # set input mean to 0 over the dataset
- featurewise_center=False,
- # set each sample mean to 0
- samplewise_center=False,
- # divide inputs by std of dataset
- featurewise_std_normalization=False,
- # divide each input by its std
- samplewise_std_normalization=False,
- # apply ZCA whitening
- zca_whitening=False,
- # epsilon for ZCA whitening
- zca_epsilon=1e-06,
- # randomly rotate images in the range (deg 0 to 180)
- rotation_range=0,
- # randomly shift images horizontally
- width_shift_range=0.1,
- # randomly shift images vertically
- height_shift_range=0.1,
- # set range for random shear
- shear_range=0.,
- # set range for random zoom
- zoom_range=0.,
- # set range for random channel shifts
- channel_shift_range=0.,
- # set mode for filling points outside the input boundaries
- fill_mode='nearest',
- # value used for fill_mode = "constant"
- cval=0.,
- # randomly flip images
- horizontal_flip=True,
- # randomly flip images
- vertical_flip=False,
- # set rescaling factor (applied before any other transformation)
- rescale=None,
- # set function that will be applied on each input
- preprocessing_function=None,
- # image data format, either "channels_first" or "channels_last"
- data_format="channels_first",
- # fraction of images reserved for validation (strictly between 0 and 1)
- validation_split=0.0)
-
- # Compute quantities required for featurewise normalization
- # (std, mean, and principal components if ZCA whitening is applied).
- datagen.fit(x_train)
-
- # Fit the model on the batches generated by datagen.flow().
- model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),
- validation_data=(x_test, y_test),
- epochs=epochs, verbose=1, workers=4,
- callbacks=callbacks)
-
-# Score trained model.
-scores = model.evaluate(x_test, y_test, verbose=1)
-print('Test loss:', scores[0])
-print('Test accuracy:', scores[1])
-
-
-#dumpCalibrationData("calibration_data/resnet18_calib.bin", x_train,
-# "calibration_data/resnet18_train_labels.bin", train_labels)
-#sys.exit(0)
-
-#translate_to_approxhpvm(model, "resnet18_cifar10_hpvm/", x_test, test_labels)
-
-#translate_to_approxhpvm(model, "resnet_test/", x_test, test_labels, 'resnet18_cifar10_promise/', y_test)
-
-import keras2onnx
-onnx_model = keras2onnx.convert_keras(model, model.name)
-import onnx
-onnx.save(onnx_model, "../models/keras/resnet.onnx")
diff --git a/hpvm/projects/onnx/src/keras_models/vgg16_cifar10.py b/hpvm/projects/onnx/src/keras_models/vgg16_cifar10.py
deleted file mode 100644
index 64e5d36e78fd728381dc864bf965ff68c4e7cf16..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/src/keras_models/vgg16_cifar10.py
+++ /dev/null
@@ -1,241 +0,0 @@
-
-
-from __future__ import print_function
-import keras
-from keras.datasets import cifar10
-from keras.preprocessing.image import ImageDataGenerator
-from keras.models import Sequential
-from keras.layers import Dense, Dropout, Activation, Flatten
-from keras.layers import Conv2D, MaxPooling2D, BatchNormalization
-from keras import optimizers
-import numpy as np
-from keras.layers.core import Lambda
-from keras import backend as K
-from keras import regularizers
-import os
-import sys
-from frontend.approxhpvm_translator import translate_to_approxhpvm
-from frontend.weight_utils import dumpCalibrationData
-
-
-
-class cifar10vgg:
- def __init__(self,train=True):
- self.num_classes = 10
- self.weight_decay = 0.0005
- self.x_shape = [3,32,32]
-
- self.model = self.build_model()
- if train:
- self.model = self.train(self.model)
- else:
- self.model.load_weights('cifar10vgg.h5')
-
-
- def build_model(self):
- # Build the network of vgg for 10 classes with massive dropout and weight decay as described in the paper.
-
- model = Sequential()
- weight_decay = self.weight_decay
-
- model.add(Conv2D(64, (3, 3), padding='same',
- input_shape=self.x_shape,kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- #model.add(BatchNormalization())
- model.add(Dropout(0.3))
-
- model.add(Conv2D(64, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- # model.add(BatchNormalization())
- model.add(MaxPooling2D(pool_size=(2, 2)))
-
- model.add(Conv2D(128, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- #model.add(BatchNormalization())
- model.add(Dropout(0.4))
-
- model.add(Conv2D(128, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- #model.add(BatchNormalization())
- model.add(MaxPooling2D(pool_size=(2, 2)))
-
- model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- #model.add(BatchNormalization())
- model.add(Dropout(0.4))
-
- model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- # model.add(BatchNormalization())
- model.add(Dropout(0.4))
-
- model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- # model.add(BatchNormalization())
- model.add(MaxPooling2D(pool_size=(2, 2)))
-
- model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- # model.add(BatchNormalization())
- model.add(Dropout(0.4))
-
- model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- # model.add(BatchNormalization())
- model.add(Dropout(0.4))
-
- model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- # model.add(BatchNormalization())
- model.add(MaxPooling2D(pool_size=(2, 2)))
-
- model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- # model.add(BatchNormalization())
- model.add(Dropout(0.4))
-
- model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- # model.add(BatchNormalization())
- model.add(Dropout(0.4))
-
- model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- # model.add(BatchNormalization())
- model.add(MaxPooling2D(pool_size=(2, 2)))
- model.add(Dropout(0.5))
-
- model.add(Flatten())
- model.add(Dense(512,kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- # model.add(BatchNormalization())
-
- model.add(Dropout(0.5))
- model.add(Dense(self.num_classes))
- model.add(Activation('softmax'))
- return model
-
-
- def normalize(self,X_train,X_test):
- #this function normalize inputs for zero mean and unit variance
- # it is used when training a model.
- # Input: training set and test set
- # Output: normalized training set and test set according to the trianing set statistics.
- mean = np.mean(X_train,axis=(0,1,2,3))
- std = np.std(X_train, axis=(0, 1, 2, 3))
- X_train = (X_train-mean)/(std+1e-7)
- X_test = (X_test-mean)/(std+1e-7)
- return X_train, X_test
-
-
- def normalize_production(self,x):
- #this function is used to normalize instances in production according to saved training set statistics
- # Input: X - a training set
- # Output X - a normalized training set according to normalization constants.
-
- #these values produced during first training and are general for the standard cifar10 training set normalization
- mean = 120.707
- std = 64.15
- return (x-mean)/(std+1e-7)
-
-
- def predict(self,x,normalize=True,batch_size=50):
- if normalize:
- x = self.normalize_production(x)
- return self.model.predict(x,batch_size)
-
-
- def train(self,model):
-
- #training parameters
- batch_size = 128
- #maxepoches = 250
- #maxepoches = 250
- maxepoches = 30
- learning_rate = 0.01
- lr_decay = 1e-6
- lr_drop = 20
- # The data, shuffled and split between train and test sets:
- (x_train, y_train), (x_test, y_test) = cifar10.load_data()
- x_train = x_train.astype('float32')
- x_test = x_test.astype('float32')
- x_train, x_test = self.normalize(x_train, x_test)
-
- y_train = keras.utils.to_categorical(y_train, self.num_classes)
- y_test = keras.utils.to_categorical(y_test, self.num_classes)
-
- def lr_scheduler(epoch):
- return learning_rate * (0.5 ** (epoch // lr_drop))
- reduce_lr = keras.callbacks.LearningRateScheduler(lr_scheduler)
-
- #data augmentation
- datagen = ImageDataGenerator(
- featurewise_center=False, # set input mean to 0 over the dataset
- samplewise_center=False, # set each sample mean to 0
- featurewise_std_normalization=False, # divide inputs by std of the dataset
- samplewise_std_normalization=False, # divide each input by its std
- zca_whitening=False, # apply ZCA whitening
- rotation_range=15, # randomly rotate images in the range (degrees, 0 to 180)
- width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)
- height_shift_range=0.1, # randomly shift images vertically (fraction of total height)
- horizontal_flip=True, # randomly flip images
- vertical_flip=False) # randomly flip images
- # (std, mean, and principal components if ZCA whitening is applied).
- datagen.fit(x_train)
-
-
-
- #optimization details
- sgd = optimizers.SGD(lr=learning_rate, decay=lr_decay, momentum=0.9, nesterov=True)
- model.compile(loss='categorical_crossentropy', optimizer=sgd,metrics=['accuracy'])
-
-
- # training process in a for loop with learning rate drop every 25 epoches.
-
- historytemp = model.fit_generator(datagen.flow(x_train, y_train,
- batch_size=batch_size),
- steps_per_epoch=x_train.shape[0] // batch_size,
- epochs=maxepoches,
- validation_data=(x_test, y_test),callbacks=[reduce_lr],verbose=2)
-
- model.save_weights('cifar10vgg.h5')
- return model
-
-
-
-if __name__ == '__main__':
-
- K.set_image_data_format('channels_first')
-
- os.environ["CUDA_VISIBLE_DEVICES"] = "1"
-
- (x_train, y_train), (x_test, y_test) = cifar10.load_data()
- test_labels = y_test
- train_labels = y_train
- x_train = x_train.astype('float32')
- x_test = x_test.astype('float32')
-
- y_train = keras.utils.to_categorical(y_train, 10)
- y_test = keras.utils.to_categorical(y_test, 10)
-
- model = cifar10vgg()
-
- predicted_x = model.predict(x_test)
-
- norm_test = model.normalize_production(x_test)
-
- # Normalizing train data before dumping
- #x_train, x_test = model.normalize(x_train, x_test)
- x_train = model.normalize_production(x_train)
-
- # dumpCalibrationData("vgg16_cifar_calib.bin", x_train, "vgg16_train_labels.bin", train_labels)
-
- translate_to_approxhpvm(model.model, "data/vgg16_cifar10/", norm_test, test_labels, 10)
-
- residuals = np.argmax(predicted_x,1)!=np.argmax(y_test,1)
-
- loss = sum(residuals)/len(residuals)
- print("the validation 0/1 loss is: ",loss)
-
-
-
diff --git a/hpvm/projects/onnx/src/keras_models/vgg16_cifar100.py b/hpvm/projects/onnx/src/keras_models/vgg16_cifar100.py
deleted file mode 100644
index 66fe6be669f984b92c8c602332c29e09968e9c8a..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/src/keras_models/vgg16_cifar100.py
+++ /dev/null
@@ -1,243 +0,0 @@
-
-from __future__ import print_function
-import os
-import keras
-from keras.datasets import cifar100
-from keras.preprocessing.image import ImageDataGenerator
-from keras.models import Sequential
-from keras.layers import Dense, Dropout, Activation, Flatten
-from keras.layers import Conv2D, MaxPooling2D
-from keras import optimizers
-import numpy as np
-from keras.layers.core import Lambda
-from keras import backend as K
-from keras import regularizers
-from approxhpvm_translator import translate_to_approxhpvm
-import sys
-from weight_utils import dumpCalibrationData
-
-
-
-class cifar100vgg:
- def __init__(self,train=True):
- self.num_classes = 100
- self.weight_decay = 0.0005
- self.x_shape = [3,32,32]
-
- self.model = self.build_model()
- if train:
- self.model = self.train(self.model)
- else:
- self.model.load_weights('cifar100vgg.h5')
-
-
- def build_model(self):
- # Build the network of vgg for 10 classes with massive dropout and weight decay as described in the paper.
-
- model = Sequential()
- weight_decay = self.weight_decay
-
- model.add(Conv2D(64, (3, 3), padding='same',
- input_shape=self.x_shape,kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- #model.add(BatchNormalization())
- model.add(Dropout(0.3))
-
- model.add(Conv2D(64, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- #model.add(BatchNormalization())
- model.add(MaxPooling2D(pool_size=(2, 2)))
-
- model.add(Conv2D(128, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- #model.add(BatchNormalization())
- model.add(Dropout(0.4))
-
- model.add(Conv2D(128, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- #model.add(BatchNormalization())
- model.add(MaxPooling2D(pool_size=(2, 2)))
-
- model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- #model.add(BatchNormalization())
- model.add(Dropout(0.4))
-
- model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- #model.add(BatchNormalization())
- model.add(Dropout(0.4))
-
- model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- #model.add(BatchNormalization())
- model.add(MaxPooling2D(pool_size=(2, 2)))
-
-
- model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- #model.add(BatchNormalization())
- model.add(Dropout(0.4))
-
- model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- #model.add(BatchNormalization())
- model.add(Dropout(0.4))
-
- model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- #model.add(BatchNormalization())
- model.add(MaxPooling2D(pool_size=(2, 2)))
-
-
- model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- #model.add(BatchNormalization())
- model.add(Dropout(0.4))
-
- model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- #model.add(BatchNormalization())
- model.add(Dropout(0.4))
-
- model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- #model.add(BatchNormalization())
-
- model.add(MaxPooling2D(pool_size=(2, 2)))
- model.add(Dropout(0.5))
-
- model.add(Flatten())
- model.add(Dense(512,kernel_regularizer=regularizers.l2(weight_decay)))
- model.add(Activation('relu'))
- #model.add(BatchNormalization())
-
- model.add(Dropout(0.5))
- model.add(Dense(self.num_classes))
- model.add(Activation('softmax'))
- return model
-
-
- def normalize(self,X_train,X_test):
- #this function normalize inputs for zero mean and unit variance
- # it is used when training a model.
- # Input: training set and test set
- # Output: normalized training set and test set according to the trianing set statistics.
- mean = np.mean(X_train,axis=(0,1,2,3))
- std = np.std(X_train, axis=(0, 1, 2, 3))
- print(mean)
- print(std)
- X_train = (X_train-mean)/(std+1e-7)
- X_test = (X_test-mean)/(std+1e-7)
- return X_train, X_test
-
- def normalize_production(self,x):
- #this function is used to normalize instances in production according to saved training set statistics
- # Input: X - a training set
- # Output X - a normalized training set according to normalization constants.
-
- #these values produced during first training and are general for the standard cifar10 training set normalization
- mean = 121.936
- std = 68.389
- return (x-mean)/(std+1e-7)
-
- def predict(self,x,normalize=True,batch_size=50):
- if normalize:
- x = self.normalize_production(x)
- return self.model.predict(x,batch_size)
-
- def train(self,model):
-
- #training parameters
- batch_size = 128
- #maxepoches = 250
- #maxepoches = 400
- maxepoches = 4
- learning_rate = 0.05
- lr_decay = 1e-6
- lr_drop = 20
-
- # The data, shuffled and split between train and test sets:
- (x_train, y_train), (x_test, y_test) = cifar100.load_data()
- x_train = x_train.astype('float32')
- x_test = x_test.astype('float32')
- x_train, x_test = self.normalize(x_train, x_test)
-
- y_train = keras.utils.to_categorical(y_train, self.num_classes)
- y_test = keras.utils.to_categorical(y_test, self.num_classes)
-
-
- def lr_scheduler(epoch):
- return learning_rate * (0.5 ** (epoch // lr_drop))
- reduce_lr = keras.callbacks.LearningRateScheduler(lr_scheduler)
-
-
- #data augmentation
- datagen = ImageDataGenerator(
- featurewise_center=False, # set input mean to 0 over the dataset
- samplewise_center=False, # set each sample mean to 0
- featurewise_std_normalization=False, # divide inputs by std of the dataset
- samplewise_std_normalization=False, # divide each input by its std
- zca_whitening=False, # apply ZCA whitening
- rotation_range=15, # randomly rotate images in the range (degrees, 0 to 180)
- width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)
- height_shift_range=0.1, # randomly shift images vertically (fraction of total height)
- horizontal_flip=True, # randomly flip images
- vertical_flip=False) # randomly flip images
- # (std, mean, and principal components if ZCA whitening is applied).
- datagen.fit(x_train)
-
-
-
- #optimization details
- sgd = optimizers.SGD(lr=learning_rate, decay=lr_decay, momentum=0.9, nesterov=True)
- model.compile(loss='categorical_crossentropy', optimizer=sgd,metrics=['accuracy'])
-
-
- # training process in a for loop with learning rate drop every 25 epoches.
-
- historytemp = model.fit_generator(datagen.flow(x_train, y_train,
- batch_size=batch_size),
- steps_per_epoch=x_train.shape[0] // batch_size,
- epochs=maxepoches,
- validation_data=(x_test, y_test),callbacks=[reduce_lr],verbose=2)
- model.save_weights('cifar100vgg.h5')
- return model
-
-if __name__ == '__main__':
-
- K.set_image_data_format('channels_first')
- os.environ["CUDA_VISIBLE_DEVICES"] = "1"
-
- (x_train, y_train), (x_test, y_test) = cifar100.load_data()
- test_labels = y_test
- train_labels = y_train
-
- x_train = x_train.astype('float32')
- x_test = x_test.astype('float32')
-
- y_train = keras.utils.to_categorical(y_train, 100)
- y_test = keras.utils.to_categorical(y_test, 100)
-
- model = cifar100vgg()
-
- predicted_x = model.predict(x_test)
-
- norm_test = model.normalize_production(x_test)
-
- x_train = model.normalize_production(x_train)
-
- dumpCalibrationData("calibration_data/vgg16_cifar100_calib.bin", x_train,
- "calibration_data/vgg16_cifar100_train_labels.bin", train_labels)
- sys.exit(0)
-
-
- translate_to_approxhpvm(model.model, "vgg16_cifar100_test/", norm_test, test_labels,
- "vgg16_cifar100_front", y_test)
-
-
- residuals = (np.argmax(predicted_x,1)!=np.argmax(y_test,1))
- loss = sum(residuals)/len(residuals)
- print("the validation 0/1 loss is: ",loss)
-
-
diff --git a/hpvm/projects/onnx/src/lenet_keras.ipynb b/hpvm/projects/onnx/src/lenet_keras.ipynb
deleted file mode 100644
index bd83db10050f74988dc313991d06b99e247cf011..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/src/lenet_keras.ipynb
+++ /dev/null
@@ -1,199 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Using TensorFlow backend.\n"
- ]
- }
- ],
- "source": [
- "import sys\n",
- "import keras\n",
- "from keras.datasets import mnist\n",
- "from keras.models import Sequential\n",
- "from keras.layers import Dense, Dropout, Flatten, Activation\n",
- "from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D\n",
- "from keras import backend as K"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "batch_size = 128\n",
- "num_classes = 10\n",
- "# input image dimensions\n",
- "img_rows, img_cols = 28, 28 "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Changing Keras data format to NCHW - NHWC is default\n",
- "# NOTE: ApproxHPVM requires NCHW format\n",
- "K.set_image_data_format('channels_first')\n",
- "\n",
- "# Loads Mnist dataset\n",
- "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
- "test_labels = y_test\n",
- "\n",
- "# Reshaping data to be NCHW format \n",
- "x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n",
- "x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n",
- "input_shape = (1, img_rows, img_cols)\n",
- "\n",
- "\n",
- "# Data Normalization \n",
- "x_train = x_train.astype('float32')\n",
- "x_test = x_test.astype('float32')\n",
- "x_train /= 255\n",
- "x_test /= 255\n",
- "\n",
- "\n",
- "# convert class vectors to binary class matrices - required by Keras\n",
- "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
- "y_test = keras.utils.to_categorical(y_test, num_classes)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Train on 60000 samples, validate on 10000 samples\n",
- "Epoch 1/5\n",
- "60000/60000 [==============================] - 8s 131us/step - loss: 0.2878 - acc: 0.9072 - val_loss: 0.0514 - val_acc: 0.9814\n",
- "Epoch 2/5\n",
- "60000/60000 [==============================] - 5s 83us/step - loss: 0.0459 - acc: 0.9854 - val_loss: 0.0337 - val_acc: 0.9888\n",
- "Epoch 3/5\n",
- "60000/60000 [==============================] - 8s 127us/step - loss: 0.0290 - acc: 0.9908 - val_loss: 0.0275 - val_acc: 0.9902\n",
- "Epoch 4/5\n",
- "60000/60000 [==============================] - 9s 151us/step - loss: 0.0195 - acc: 0.9940 - val_loss: 0.0367 - val_acc: 0.9883\n",
- "Epoch 5/5\n",
- "60000/60000 [==============================] - 10s 169us/step - loss: 0.0136 - acc: 0.9960 - val_loss: 0.0318 - val_acc: 0.9905\n",
- "Test loss: 0.031750623502753844\n",
- "Test accuracy: 0.9905\n"
- ]
- }
- ],
- "source": [
- "# Network Compostion: 3 Conv Layers, 2 Dense Layers\n",
- "model = Sequential()\n",
- "\n",
- "# ConvLayer1\n",
- "model.add(Conv2D(32, kernel_size=(5, 5),\n",
- " activation='relu',\n",
- " padding = 'same',\n",
- " input_shape=input_shape))\n",
- "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
- "\n",
- "# ConvLayer2\n",
- "model.add(Conv2D(64, (5, 5), activation='relu', padding = 'same'))\n",
- "\n",
- "# ConvLayer3\n",
- "# NOTE: ZeroPading needed for ConvLayer with strides > 1\n",
- "model.add(ZeroPadding2D(padding = (1,1)))\n",
- "model.add(Conv2D(64, (3, 3), strides = (2,2), activation='relu', padding = 'valid') )\n",
- "\n",
- "model.add(Flatten())\n",
- "# DenseLayer1\n",
- "model.add(Dense(1024, activation='relu'))\n",
- "# DenseLayer2\n",
- "model.add(Dense(num_classes, activation='relu'))\n",
- "# Softmax Layer\n",
- "model.add(Activation('softmax'))\n",
- "\n",
- "\n",
- "# Configures model for training \n",
- "model.compile(loss=keras.losses.categorical_crossentropy,\n",
- " optimizer=keras.optimizers.Adadelta(),\n",
- " metrics=['accuracy'])\n",
- "\n",
- "# Training\n",
- "model.fit(x_train, y_train,\n",
- " batch_size=batch_size,\n",
- " epochs=5,\n",
- " verbose=1,\n",
- " validation_data=(x_test, y_test))\n",
- "\n",
- "\n",
- "# Inference\n",
- "score = model.evaluate(x_test, y_test, verbose=0)\n",
- "print('Test loss:', score[0])\n",
- "print('Test accuracy:', score[1])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 58,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "time: 1.037928581237793\n"
- ]
- }
- ],
- "source": [
- "import time\n",
- "start = time.time()\n",
- "keras_result = model.predict(x_test[:8000])\n",
- "print(\"time:\", time.time() - start)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import keras2onnx\n",
- "onnx_model = keras2onnx.convert_keras(model, model.name, target_opset=10)\n",
- "import onnx\n",
- "onnx.save(onnx_model, \"../models/keras/lenet.onnx\")\n",
- "import pickle\n",
- "with open('dumps/lenet_keras_dump', 'wb') as fp:\n",
- " pickle.dump(keras_result, fp)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/hpvm/projects/onnx/src/lenet_onnx.ipynb b/hpvm/projects/onnx/src/lenet_onnx.ipynb
deleted file mode 100644
index fdf69ebc3cde8c1e12bee99bcde574786ffd0c18..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/src/lenet_onnx.ipynb
+++ /dev/null
@@ -1,191 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {},
- "outputs": [],
- "source": [
- "import sys\n",
- "import keras\n",
- "from keras.datasets import mnist\n",
- "from keras import backend as K\n",
- "import numpy as np\n",
- "import os\n",
- "import keras2onnx\n",
- "import onnx\n",
- "import onnxruntime\n",
- "os.environ['CUDA_VISIBLE_DEVICES'] = '1'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {},
- "outputs": [],
- "source": [
- "batch_size = 128\n",
- "num_classes = 10\n",
- "# input image dimensions\n",
- "img_rows, img_cols = 28, 28 "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Changing Keras data format to NCHW - NHWC is default\n",
- "# NOTE: ApproxHPVM requires NCHW format\n",
- "K.set_image_data_format('channels_first')\n",
- "\n",
- "# Loads Mnist dataset\n",
- "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
- "test_labels = y_test\n",
- "\n",
- "# Reshaping data to be NCHW format \n",
- "x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n",
- "x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n",
- "input_shape = (1, img_rows, img_cols)\n",
- "\n",
- "\n",
- "# Data Normalization \n",
- "x_train = x_train.astype('float32')\n",
- "x_test = x_test.astype('float32')\n",
- "x_train /= 255\n",
- "x_test /= 255\n",
- "\n",
- "\n",
- "# convert class vectors to binary class matrices - required by Keras\n",
- "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
- "y_test = keras.utils.to_categorical(y_test, num_classes)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {},
- "outputs": [],
- "source": [
- "sess = onnxruntime.InferenceSession(\"../models/keras/lenet.onnx\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Input name : conv2d_1_input\n",
- "Input shape : [None, 1, 28, 28]\n",
- "Input type : tensor(float)\n"
- ]
- }
- ],
- "source": [
- "input_name = sess.get_inputs()[0].name\n",
- "print(\"Input name :\", input_name)\n",
- "input_shape = sess.get_inputs()[0].shape\n",
- "print(\"Input shape :\", input_shape)\n",
- "input_type = sess.get_inputs()[0].type\n",
- "print(\"Input type :\", input_type)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Output name : activation_1\n",
- "Output shape : [None, 10]\n",
- "Output type : tensor(float)\n"
- ]
- }
- ],
- "source": [
- "output_name = sess.get_outputs()[0].name\n",
- "print(\"Output name :\", output_name) \n",
- "output_shape = sess.get_outputs()[0].shape\n",
- "print(\"Output shape :\", output_shape)\n",
- "output_type = sess.get_outputs()[0].type\n",
- "print(\"Output type :\", output_type)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import time\n",
- "start = time.time()\n",
- "ort_result = sess.run([output_name], {input_name: x_test[:8000]})\n",
- "print(\"time:\", time.time() - start)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pickle\n",
- "with open('dumps/lenet_ort_dump', 'wb') as fp:\n",
- " pickle.dump(ort_result, fp)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pickle\n",
- "import numpy as np\n",
- "with open ('dumps/lenet_ort_dump', 'rb') as fp:\n",
- " ort_res = pickle.load(fp)\n",
- "with open ('dumps/lenet_keras_dump', 'rb') as fp:\n",
- " keras_res = pickle.load(fp)\n",
- "\n",
- "for ref_o, o in zip(ort_res[0], keras_res):\n",
- " np.testing.assert_almost_equal(ref_o, o, 3) #using decimal of 3 would pass test"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/hpvm/projects/onnx/src/lenet_tvm.ipynb b/hpvm/projects/onnx/src/lenet_tvm.ipynb
deleted file mode 100644
index 274553dcc52d2b84f601a4480b04adc54a1ac0ea..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/src/lenet_tvm.ipynb
+++ /dev/null
@@ -1,241 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Using TensorFlow backend.\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "from keras.datasets import mnist\n",
- "from keras import backend as K\n",
- "import keras\n",
- "import sys\n",
- "import struct\n",
- "import numpy as np\n",
- "import os\n",
- "import tvm\n",
- "import tvm.relay as relay\n",
- "from tvm.contrib import graph_runtime\n",
- "os.environ['CUDA_VISIBLE_DEVICES'] = '1'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "batch_size = 128\n",
- "num_classes = 10\n",
- "# input image dimensions\n",
- "img_rows, img_cols = 28, 28 "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Changing Keras data format to NCHW - NHWC is default\n",
- "# NOTE: ApproxHPVM requires NCHW format\n",
- "K.set_image_data_format('channels_first')\n",
- "\n",
- "# Loads Mnist dataset\n",
- "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
- "test_labels = y_test\n",
- "x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n",
- "input_shape = (1, img_rows, img_cols)\n",
- "x_test = x_test.astype('float32')\n",
- "x_test /= 255"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "import onnx\n",
- "onnx_model = onnx.load(\"../models/keras/lenet.onnx\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.max_pool2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n"
- ]
- }
- ],
- "source": [
- "target='cuda -libs=cudnn,cublas'\n",
- "input_name = 'conv2d_1_input'\n",
- "shape_dict = {input_name: x_test.shape}\n",
- "mod, params = relay.frontend.from_onnx(onnx_model, shape_dict)\n",
- "ctx = tvm.gpu()\n",
- "with relay.build_config(opt_level=3):\n",
- " executor = relay.build_module.create_executor('graph', mod, ctx, target)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('dense', (10000, 1024, 'float32'), (10, 1024, 'float32'), 0, 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('dense', (10000, 3136, 'float32'), (1024, 3136, 'float32'), 0, 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (10000, 64, 14, 14, 'float32'), (64, 64, 3, 3, 'float32'), (2, 2), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (10000, 32, 14, 14, 'float32'), (64, 32, 5, 5, 'float32'), (1, 1), (2, 2), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (10000, 1, 28, 28, 'float32'), (32, 1, 5, 5, 'float32'), (1, 1), (2, 2), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n"
- ]
- }
- ],
- "source": [
- "tvm_out = executor.evaluate()(tvm.nd.array(x_test), **params)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.max_pool2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('dense', (8000, 1024, 'float32'), (10, 1024, 'float32'), 0, 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('dense', (8000, 3136, 'float32'), (1024, 3136, 'float32'), 0, 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (8000, 64, 14, 14, 'float32'), (64, 64, 3, 3, 'float32'), (2, 2), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (8000, 32, 14, 14, 'float32'), (64, 32, 5, 5, 'float32'), (1, 1), (2, 2), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (8000, 1, 28, 28, 'float32'), (32, 1, 5, 5, 'float32'), (1, 1), (2, 2), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n"
- ]
- }
- ],
- "source": [
- "input_name = 'conv2d_1_input'\n",
- "input_size = 8000\n",
- "shape_dict = {input_name: x_test[:input_size].shape}\n",
- "mod, params = relay.frontend.from_onnx(onnx_model, shape_dict)\n",
- "target = 'cuda -libs=cudnn,cublas'\n",
- "with relay.build_config(opt_level=3):\n",
- " graph, lib, params = relay.build(mod, target, params=params)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Time: 0.043964385986328125\n"
- ]
- }
- ],
- "source": [
- "import time\n",
- "ctx = tvm.gpu()\n",
- "#data = np.random.uniform(-1, 1, size=data_shape).astype(\"float32\")\n",
- "# create module\n",
- "module = graph_runtime.create(graph, lib, ctx)\n",
- "# set input and parameters\n",
- "module.set_input(\"conv2d_1_input\", x_test[:input_size])\n",
- "module.set_input(**params)\n",
- "# run\n",
- "start_time = time.time()\n",
- "module.run()\n",
- "out_shape = (input_size, 10)\n",
- "# get output\n",
- "out = module.get_output(0, tvm.nd.empty(out_shape)).asnumpy()\n",
- "print(\"Time:\", time.time() - start_time)\n",
- "#ftimer = module.module.time_evaluator(\"get_output\", ctx, number=1)\n",
- "#prof_res = np.array(ftimer().results) * 1000 # multiply 1000 for converting to millisecond\n",
- "#print(\"%-20s %-19s (%s)\" % (\"lenet\", \"%.2f ms\" % np.mean(prof_res), \"%.2f ms\" % np.std(prof_res)))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pickle\n",
- "with open('dumps/lenet_tvm_dump', 'wb') as fp:\n",
- " pickle.dump(out, fp)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pickle\n",
- "import numpy as np\n",
- "with open ('dumps/lenet_tvm_dump', 'rb') as fp:\n",
- " tvm_res = pickle.load(fp)\n",
- "with open ('dumps/lenet_keras_dump', 'rb') as fp:\n",
- " keras_res = pickle.load(fp)\n",
- "\n",
- "for ref_o, o in zip(tvm_res, keras_res):\n",
- " np.testing.assert_almost_equal(ref_o, o, 3) #using decimal of 3 would pass test"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/hpvm/projects/onnx/src/mobilenet_keras.ipynb b/hpvm/projects/onnx/src/mobilenet_keras.ipynb
deleted file mode 100644
index 5175bc2aef1403fec6a70ffb0cdd504a062d5f96..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/src/mobilenet_keras.ipynb
+++ /dev/null
@@ -1,592 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Using TensorFlow backend.\n"
- ]
- }
- ],
- "source": [
- "import sys\n",
- "import os\n",
- "\n",
- "from keras.models import Sequential\n",
- "from keras.layers import *\n",
- "from keras.datasets import cifar10\n",
- "from keras.utils import to_categorical\n",
- "from keras.callbacks import *\n",
- "from keras.preprocessing.image import ImageDataGenerator\n",
- "from keras.models import Model\n",
- "from keras import optimizers\n",
- "import keras.backend as K\n",
- "os.environ['CUDA_VISIBLE_DEVICES'] = '1'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "os.environ['CUDA_VISIBLE_DEVICES'] = '1'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "X_train.shape = (50000, 3, 32, 32)\n",
- "X_test.shape = (10000, 3, 32, 32)\n"
- ]
- }
- ],
- "source": [
- "K.set_image_data_format('channels_first')\n",
- "\n",
- "(X_train, y_train), (X_test, y_test) = cifar10.load_data()\n",
- "test_labels = y_test\n",
- "\n",
- "print (\"X_train.shape = \", X_train.shape)\n",
- "print (\"X_test.shape = \", X_test.shape)\n",
- "\n",
- "\n",
- "X_train = X_train.astype('float32')\n",
- "X_test = X_test.astype('float32')\n",
- "\n",
- "\n",
- "mean = np.mean(X_train, axis=(0, 2, 3), keepdims=True)\n",
- "std = np.std(X_train, axis=(0, 2, 3), keepdims=True)\n",
- "\n",
- "X_train = (X_train - mean) / (std + 1e-9)\n",
- "X_test = (X_test - mean) / (std + 1e-9)\n",
- "\n",
- "y_train = to_categorical(y_train, num_classes=10)\n",
- "y_test = to_categorical(y_test, num_classes=10)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "def get_mobilenet(alpha=1, depth_multiplier=1):\n",
- " model = Sequential()\n",
- " \n",
- " def _conv_block(filters, alpha, kernel=(3, 3), strides=(1, 1)):\n",
- " channel_axis = 1\n",
- " \n",
- " model.add(Conv2D(filters, kernel,\n",
- " padding='same',\n",
- " use_bias=False,\n",
- " strides=strides, \n",
- " input_shape=(3, 32, 32)))\n",
- " model.add(BatchNormalization(axis=channel_axis))\n",
- " model.add(Activation('relu'))\n",
- " \n",
- " def _depthwise_conv_block(pointwise_conv_filters, alpha, depth_multiplier=1, strides=(1, 1)):\n",
- " channel_axis = 1 \n",
- "\n",
- " model.add(ZeroPadding2D(padding = ((1,1), (1,1) )))\n",
- "\n",
- " model.add(DepthwiseConv2D((3, 3),\n",
- " padding='valid',\n",
- " #depth_multiplier=depth_multiplier,\n",
- " strides=strides,\n",
- " use_bias=False)) \n",
- " model.add(BatchNormalization(axis=channel_axis))\n",
- " \n",
- " model.add(Activation('relu'))\n",
- " model.add(Conv2D(pointwise_conv_filters, (1, 1),\n",
- " padding='same',\n",
- " use_bias=False,\n",
- " strides=(1, 1)))\n",
- " model.add(BatchNormalization(axis=channel_axis))\n",
- " model.add(Activation('relu'))\n",
- "\n",
- "\n",
- " _conv_block(32, alpha, strides=(1, 1))\n",
- " \n",
- " _depthwise_conv_block(64, alpha, depth_multiplier)\n",
- " \n",
- " _depthwise_conv_block(128, alpha, depth_multiplier,\n",
- " strides=(2, 2))\n",
- " _depthwise_conv_block(128, alpha, depth_multiplier)\n",
- " model.add(Dropout(rate=0.5))\n",
- "\n",
- " _depthwise_conv_block(256, alpha, depth_multiplier, \n",
- " strides=(2, 2))\n",
- " _depthwise_conv_block(256, alpha, depth_multiplier)\n",
- " model.add(Dropout(rate=0.5))\n",
- "\n",
- " _depthwise_conv_block(512, alpha, depth_multiplier,\n",
- " strides=(2, 2))\n",
- " _depthwise_conv_block(512, alpha, depth_multiplier)\n",
- " _depthwise_conv_block(512, alpha, depth_multiplier)\n",
- " model.add(Dropout(rate=0.5))\n",
- " \n",
- " _depthwise_conv_block(512, alpha, depth_multiplier)\n",
- " _depthwise_conv_block(512, alpha, depth_multiplier)\n",
- " _depthwise_conv_block(512, alpha, depth_multiplier)\n",
- " model.add(Dropout(rate=0.5))\n",
- " \n",
- " _depthwise_conv_block(1024, alpha, depth_multiplier,\n",
- " strides=(2, 2))\n",
- " _depthwise_conv_block(1024, alpha, depth_multiplier)\n",
- " model.add(Dropout(rate=0.5))\n",
- "\n",
- " model.add(AveragePooling2D(pool_size=2))\n",
- " model.add(Flatten())\n",
- " model.add(Dense(10, activation='softmax'))\n",
- "\n",
- " return model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/50\n",
- "391/391 [==============================] - 36s 92ms/step - loss: 2.0463 - acc: 0.2513 - val_loss: 2.2584 - val_acc: 0.2776\n",
- "Epoch 2/50\n",
- "391/391 [==============================] - 31s 80ms/step - loss: 1.6583 - acc: 0.3977 - val_loss: 1.9809 - val_acc: 0.3528\n",
- "Epoch 3/50\n",
- "391/391 [==============================] - 32s 81ms/step - loss: 1.4632 - acc: 0.4740 - val_loss: 1.6017 - val_acc: 0.4475\n",
- "Epoch 4/50\n",
- "391/391 [==============================] - 32s 82ms/step - loss: 1.3280 - acc: 0.5213 - val_loss: 1.3650 - val_acc: 0.5158\n",
- "Epoch 5/50\n",
- "391/391 [==============================] - 32s 82ms/step - loss: 1.1912 - acc: 0.5727 - val_loss: 1.2064 - val_acc: 0.5807\n",
- "Epoch 6/50\n",
- "391/391 [==============================] - 31s 80ms/step - loss: 1.0700 - acc: 0.6203 - val_loss: 1.1014 - val_acc: 0.6085\n",
- "Epoch 7/50\n",
- "391/391 [==============================] - 31s 80ms/step - loss: 1.0232 - acc: 0.6350 - val_loss: 1.0276 - val_acc: 0.6331\n",
- "Epoch 8/50\n",
- "391/391 [==============================] - 31s 79ms/step - loss: 0.9783 - acc: 0.6498 - val_loss: 1.0276 - val_acc: 0.6398\n",
- "Epoch 9/50\n",
- "391/391 [==============================] - 31s 78ms/step - loss: 0.9359 - acc: 0.6673 - val_loss: 1.0931 - val_acc: 0.6226\n",
- "Epoch 10/50\n",
- "391/391 [==============================] - 30s 76ms/step - loss: 0.9058 - acc: 0.6776 - val_loss: 1.1184 - val_acc: 0.6105\n",
- "Epoch 11/50\n",
- "391/391 [==============================] - 30s 76ms/step - loss: 0.8781 - acc: 0.6884 - val_loss: 1.0405 - val_acc: 0.6498\n",
- "Epoch 12/50\n",
- "391/391 [==============================] - 29s 74ms/step - loss: 0.8502 - acc: 0.6998 - val_loss: 1.3313 - val_acc: 0.5514\n",
- "Epoch 13/50\n",
- "391/391 [==============================] - 29s 75ms/step - loss: 0.8211 - acc: 0.7077 - val_loss: 1.0371 - val_acc: 0.6443\n",
- "Epoch 14/50\n",
- "391/391 [==============================] - 28s 73ms/step - loss: 0.7958 - acc: 0.7181 - val_loss: 0.8624 - val_acc: 0.7061\n",
- "Epoch 15/50\n",
- "391/391 [==============================] - 29s 74ms/step - loss: 0.7720 - acc: 0.7257 - val_loss: 0.8090 - val_acc: 0.7224\n",
- "Epoch 16/50\n",
- "391/391 [==============================] - 29s 74ms/step - loss: 0.7508 - acc: 0.7324 - val_loss: 0.7846 - val_acc: 0.7272\n",
- "Epoch 17/50\n",
- "391/391 [==============================] - 30s 76ms/step - loss: 0.7292 - acc: 0.7432 - val_loss: 0.7717 - val_acc: 0.7323\n",
- "Epoch 18/50\n",
- "391/391 [==============================] - 33s 85ms/step - loss: 0.7047 - acc: 0.7511 - val_loss: 0.7407 - val_acc: 0.7453\n",
- "Epoch 19/50\n",
- "391/391 [==============================] - 33s 85ms/step - loss: 0.6884 - acc: 0.7570 - val_loss: 0.7040 - val_acc: 0.7537\n",
- "Epoch 20/50\n",
- "391/391 [==============================] - 33s 85ms/step - loss: 0.6718 - acc: 0.7652 - val_loss: 0.9103 - val_acc: 0.6907\n",
- "Epoch 21/50\n",
- "391/391 [==============================] - 33s 85ms/step - loss: 0.6489 - acc: 0.7722 - val_loss: 0.7054 - val_acc: 0.7567\n",
- "Epoch 22/50\n",
- "391/391 [==============================] - 33s 84ms/step - loss: 0.6330 - acc: 0.7771 - val_loss: 0.7032 - val_acc: 0.7587\n",
- "Epoch 23/50\n",
- "391/391 [==============================] - 33s 86ms/step - loss: 0.6157 - acc: 0.7831 - val_loss: 0.6955 - val_acc: 0.7652\n",
- "Epoch 24/50\n",
- "391/391 [==============================] - 32s 83ms/step - loss: 0.6027 - acc: 0.7882 - val_loss: 0.7599 - val_acc: 0.7419\n",
- "Epoch 25/50\n",
- "391/391 [==============================] - 32s 83ms/step - loss: 0.5910 - acc: 0.7932 - val_loss: 0.6315 - val_acc: 0.7830\n",
- "Epoch 26/50\n",
- "391/391 [==============================] - 33s 84ms/step - loss: 0.5743 - acc: 0.7967 - val_loss: 0.6595 - val_acc: 0.7772\n",
- "Epoch 27/50\n",
- "391/391 [==============================] - 32s 82ms/step - loss: 0.5610 - acc: 0.8027 - val_loss: 0.6628 - val_acc: 0.7691\n",
- "Epoch 28/50\n",
- "391/391 [==============================] - 31s 80ms/step - loss: 0.5508 - acc: 0.8074 - val_loss: 0.6211 - val_acc: 0.7820\n",
- "Epoch 29/50\n",
- "391/391 [==============================] - 31s 81ms/step - loss: 0.5293 - acc: 0.8130 - val_loss: 0.6221 - val_acc: 0.7852\n",
- "Epoch 30/50\n",
- "391/391 [==============================] - 30s 78ms/step - loss: 0.5205 - acc: 0.8158 - val_loss: 0.6252 - val_acc: 0.7891\n",
- "Epoch 31/50\n",
- "391/391 [==============================] - 31s 79ms/step - loss: 0.5128 - acc: 0.8195 - val_loss: 0.6213 - val_acc: 0.7844\n",
- "Epoch 32/50\n",
- "391/391 [==============================] - 29s 75ms/step - loss: 0.4988 - acc: 0.8241 - val_loss: 0.5745 - val_acc: 0.8046\n",
- "Epoch 33/50\n",
- "391/391 [==============================] - 30s 76ms/step - loss: 0.4898 - acc: 0.8273 - val_loss: 0.5938 - val_acc: 0.8015\n",
- "Epoch 34/50\n",
- "391/391 [==============================] - 28s 72ms/step - loss: 0.4789 - acc: 0.8294 - val_loss: 0.5693 - val_acc: 0.8074\n",
- "Epoch 35/50\n",
- "391/391 [==============================] - 28s 72ms/step - loss: 0.4646 - acc: 0.8371 - val_loss: 0.5743 - val_acc: 0.8042\n",
- "Epoch 36/50\n",
- "391/391 [==============================] - 29s 74ms/step - loss: 0.4605 - acc: 0.8364 - val_loss: 0.6270 - val_acc: 0.7865\n",
- "Epoch 37/50\n",
- "391/391 [==============================] - 33s 84ms/step - loss: 0.4499 - acc: 0.8405 - val_loss: 0.6014 - val_acc: 0.7960\n",
- "Epoch 38/50\n",
- "391/391 [==============================] - 32s 83ms/step - loss: 0.4459 - acc: 0.8438 - val_loss: 0.6058 - val_acc: 0.7971\n",
- "Epoch 39/50\n",
- "391/391 [==============================] - 33s 84ms/step - loss: 0.4341 - acc: 0.8473 - val_loss: 0.5551 - val_acc: 0.8125\n",
- "Epoch 40/50\n",
- "391/391 [==============================] - 33s 83ms/step - loss: 0.4238 - acc: 0.8496 - val_loss: 0.5445 - val_acc: 0.8165\n",
- "Epoch 41/50\n",
- "391/391 [==============================] - 33s 83ms/step - loss: 0.4160 - acc: 0.8516 - val_loss: 0.5873 - val_acc: 0.8058\n",
- "Epoch 42/50\n",
- "391/391 [==============================] - 32s 83ms/step - loss: 0.4048 - acc: 0.8546 - val_loss: 0.6693 - val_acc: 0.7822\n",
- "Epoch 43/50\n",
- "391/391 [==============================] - 33s 83ms/step - loss: 0.4021 - acc: 0.8583 - val_loss: 0.5408 - val_acc: 0.8168\n",
- "Epoch 44/50\n",
- "391/391 [==============================] - 33s 84ms/step - loss: 0.3927 - acc: 0.8610 - val_loss: 0.5538 - val_acc: 0.8117\n",
- "Epoch 45/50\n",
- "391/391 [==============================] - 33s 83ms/step - loss: 0.3904 - acc: 0.8615 - val_loss: 0.5981 - val_acc: 0.8063\n",
- "Epoch 46/50\n",
- "391/391 [==============================] - 32s 81ms/step - loss: 0.3811 - acc: 0.8642 - val_loss: 0.5285 - val_acc: 0.8223\n",
- "Epoch 47/50\n",
- "391/391 [==============================] - 32s 83ms/step - loss: 0.3726 - acc: 0.8676 - val_loss: 0.5317 - val_acc: 0.8206\n",
- "Epoch 48/50\n",
- "391/391 [==============================] - 31s 79ms/step - loss: 0.3661 - acc: 0.8692 - val_loss: 0.5568 - val_acc: 0.8172\n",
- "Epoch 49/50\n",
- "391/391 [==============================] - 31s 79ms/step - loss: 0.3582 - acc: 0.8711 - val_loss: 0.5057 - val_acc: 0.8320\n",
- "Epoch 50/50\n",
- "391/391 [==============================] - 29s 75ms/step - loss: 0.3516 - acc: 0.8740 - val_loss: 0.5760 - val_acc: 0.8099\n",
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "conv2d_1 (Conv2D) (None, 32, 32, 32) 864 \n",
- "_________________________________________________________________\n",
- "batch_normalization_1 (Batch (None, 32, 32, 32) 128 \n",
- "_________________________________________________________________\n",
- "activation_1 (Activation) (None, 32, 32, 32) 0 \n",
- "_________________________________________________________________\n",
- "zero_padding2d_1 (ZeroPaddin (None, 32, 34, 34) 0 \n",
- "_________________________________________________________________\n",
- "depthwise_conv2d_1 (Depthwis (None, 32, 32, 32) 288 \n",
- "_________________________________________________________________\n",
- "batch_normalization_2 (Batch (None, 32, 32, 32) 128 \n",
- "_________________________________________________________________\n",
- "activation_2 (Activation) (None, 32, 32, 32) 0 \n",
- "_________________________________________________________________\n",
- "conv2d_2 (Conv2D) (None, 64, 32, 32) 2048 \n",
- "_________________________________________________________________\n",
- "batch_normalization_3 (Batch (None, 64, 32, 32) 256 \n",
- "_________________________________________________________________\n",
- "activation_3 (Activation) (None, 64, 32, 32) 0 \n",
- "_________________________________________________________________\n",
- "zero_padding2d_2 (ZeroPaddin (None, 64, 34, 34) 0 \n",
- "_________________________________________________________________\n",
- "depthwise_conv2d_2 (Depthwis (None, 64, 16, 16) 576 \n",
- "_________________________________________________________________\n",
- "batch_normalization_4 (Batch (None, 64, 16, 16) 256 \n",
- "_________________________________________________________________\n",
- "activation_4 (Activation) (None, 64, 16, 16) 0 \n",
- "_________________________________________________________________\n",
- "conv2d_3 (Conv2D) (None, 128, 16, 16) 8192 \n",
- "_________________________________________________________________\n",
- "batch_normalization_5 (Batch (None, 128, 16, 16) 512 \n",
- "_________________________________________________________________\n",
- "activation_5 (Activation) (None, 128, 16, 16) 0 \n",
- "_________________________________________________________________\n",
- "zero_padding2d_3 (ZeroPaddin (None, 128, 18, 18) 0 \n",
- "_________________________________________________________________\n",
- "depthwise_conv2d_3 (Depthwis (None, 128, 16, 16) 1152 \n",
- "_________________________________________________________________\n",
- "batch_normalization_6 (Batch (None, 128, 16, 16) 512 \n",
- "_________________________________________________________________\n",
- "activation_6 (Activation) (None, 128, 16, 16) 0 \n",
- "_________________________________________________________________\n",
- "conv2d_4 (Conv2D) (None, 128, 16, 16) 16384 \n",
- "_________________________________________________________________\n",
- "batch_normalization_7 (Batch (None, 128, 16, 16) 512 \n",
- "_________________________________________________________________\n",
- "activation_7 (Activation) (None, 128, 16, 16) 0 \n",
- "_________________________________________________________________\n",
- "dropout_1 (Dropout) (None, 128, 16, 16) 0 \n",
- "_________________________________________________________________\n",
- "zero_padding2d_4 (ZeroPaddin (None, 128, 18, 18) 0 \n",
- "_________________________________________________________________\n",
- "depthwise_conv2d_4 (Depthwis (None, 128, 8, 8) 1152 \n",
- "_________________________________________________________________\n",
- "batch_normalization_8 (Batch (None, 128, 8, 8) 512 \n",
- "_________________________________________________________________\n",
- "activation_8 (Activation) (None, 128, 8, 8) 0 \n",
- "_________________________________________________________________\n",
- "conv2d_5 (Conv2D) (None, 256, 8, 8) 32768 \n",
- "_________________________________________________________________\n",
- "batch_normalization_9 (Batch (None, 256, 8, 8) 1024 \n",
- "_________________________________________________________________\n",
- "activation_9 (Activation) (None, 256, 8, 8) 0 \n",
- "_________________________________________________________________\n",
- "zero_padding2d_5 (ZeroPaddin (None, 256, 10, 10) 0 \n",
- "_________________________________________________________________\n",
- "depthwise_conv2d_5 (Depthwis (None, 256, 8, 8) 2304 \n",
- "_________________________________________________________________\n",
- "batch_normalization_10 (Batc (None, 256, 8, 8) 1024 \n",
- "_________________________________________________________________\n",
- "activation_10 (Activation) (None, 256, 8, 8) 0 \n",
- "_________________________________________________________________\n",
- "conv2d_6 (Conv2D) (None, 256, 8, 8) 65536 \n",
- "_________________________________________________________________\n",
- "batch_normalization_11 (Batc (None, 256, 8, 8) 1024 \n",
- "_________________________________________________________________\n",
- "activation_11 (Activation) (None, 256, 8, 8) 0 \n",
- "_________________________________________________________________\n",
- "dropout_2 (Dropout) (None, 256, 8, 8) 0 \n",
- "_________________________________________________________________\n",
- "zero_padding2d_6 (ZeroPaddin (None, 256, 10, 10) 0 \n",
- "_________________________________________________________________\n",
- "depthwise_conv2d_6 (Depthwis (None, 256, 4, 4) 2304 \n",
- "_________________________________________________________________\n",
- "batch_normalization_12 (Batc (None, 256, 4, 4) 1024 \n",
- "_________________________________________________________________\n",
- "activation_12 (Activation) (None, 256, 4, 4) 0 \n",
- "_________________________________________________________________\n",
- "conv2d_7 (Conv2D) (None, 512, 4, 4) 131072 \n",
- "_________________________________________________________________\n",
- "batch_normalization_13 (Batc (None, 512, 4, 4) 2048 \n",
- "_________________________________________________________________\n",
- "activation_13 (Activation) (None, 512, 4, 4) 0 \n",
- "_________________________________________________________________\n",
- "zero_padding2d_7 (ZeroPaddin (None, 512, 6, 6) 0 \n",
- "_________________________________________________________________\n",
- "depthwise_conv2d_7 (Depthwis (None, 512, 4, 4) 4608 \n",
- "_________________________________________________________________\n",
- "batch_normalization_14 (Batc (None, 512, 4, 4) 2048 \n",
- "_________________________________________________________________\n",
- "activation_14 (Activation) (None, 512, 4, 4) 0 \n",
- "_________________________________________________________________\n",
- "conv2d_8 (Conv2D) (None, 512, 4, 4) 262144 \n",
- "_________________________________________________________________\n",
- "batch_normalization_15 (Batc (None, 512, 4, 4) 2048 \n",
- "_________________________________________________________________\n",
- "activation_15 (Activation) (None, 512, 4, 4) 0 \n",
- "_________________________________________________________________\n",
- "zero_padding2d_8 (ZeroPaddin (None, 512, 6, 6) 0 \n",
- "_________________________________________________________________\n",
- "depthwise_conv2d_8 (Depthwis (None, 512, 4, 4) 4608 \n",
- "_________________________________________________________________\n",
- "batch_normalization_16 (Batc (None, 512, 4, 4) 2048 \n",
- "_________________________________________________________________\n",
- "activation_16 (Activation) (None, 512, 4, 4) 0 \n",
- "_________________________________________________________________\n",
- "conv2d_9 (Conv2D) (None, 512, 4, 4) 262144 \n",
- "_________________________________________________________________\n",
- "batch_normalization_17 (Batc (None, 512, 4, 4) 2048 \n",
- "_________________________________________________________________\n",
- "activation_17 (Activation) (None, 512, 4, 4) 0 \n",
- "_________________________________________________________________\n",
- "dropout_3 (Dropout) (None, 512, 4, 4) 0 \n",
- "_________________________________________________________________\n",
- "zero_padding2d_9 (ZeroPaddin (None, 512, 6, 6) 0 \n",
- "_________________________________________________________________\n",
- "depthwise_conv2d_9 (Depthwis (None, 512, 4, 4) 4608 \n",
- "_________________________________________________________________\n",
- "batch_normalization_18 (Batc (None, 512, 4, 4) 2048 \n",
- "_________________________________________________________________\n",
- "activation_18 (Activation) (None, 512, 4, 4) 0 \n",
- "_________________________________________________________________\n",
- "conv2d_10 (Conv2D) (None, 512, 4, 4) 262144 \n",
- "_________________________________________________________________\n",
- "batch_normalization_19 (Batc (None, 512, 4, 4) 2048 \n",
- "_________________________________________________________________\n",
- "activation_19 (Activation) (None, 512, 4, 4) 0 \n",
- "_________________________________________________________________\n",
- "zero_padding2d_10 (ZeroPaddi (None, 512, 6, 6) 0 \n",
- "_________________________________________________________________\n",
- "depthwise_conv2d_10 (Depthwi (None, 512, 4, 4) 4608 \n",
- "_________________________________________________________________\n",
- "batch_normalization_20 (Batc (None, 512, 4, 4) 2048 \n",
- "_________________________________________________________________\n",
- "activation_20 (Activation) (None, 512, 4, 4) 0 \n",
- "_________________________________________________________________\n",
- "conv2d_11 (Conv2D) (None, 512, 4, 4) 262144 \n",
- "_________________________________________________________________\n",
- "batch_normalization_21 (Batc (None, 512, 4, 4) 2048 \n",
- "_________________________________________________________________\n",
- "activation_21 (Activation) (None, 512, 4, 4) 0 \n",
- "_________________________________________________________________\n",
- "zero_padding2d_11 (ZeroPaddi (None, 512, 6, 6) 0 \n",
- "_________________________________________________________________\n",
- "depthwise_conv2d_11 (Depthwi (None, 512, 4, 4) 4608 \n",
- "_________________________________________________________________\n",
- "batch_normalization_22 (Batc (None, 512, 4, 4) 2048 \n",
- "_________________________________________________________________\n",
- "activation_22 (Activation) (None, 512, 4, 4) 0 \n",
- "_________________________________________________________________\n",
- "conv2d_12 (Conv2D) (None, 512, 4, 4) 262144 \n",
- "_________________________________________________________________\n",
- "batch_normalization_23 (Batc (None, 512, 4, 4) 2048 \n",
- "_________________________________________________________________\n",
- "activation_23 (Activation) (None, 512, 4, 4) 0 \n",
- "_________________________________________________________________\n",
- "dropout_4 (Dropout) (None, 512, 4, 4) 0 \n",
- "_________________________________________________________________\n",
- "zero_padding2d_12 (ZeroPaddi (None, 512, 6, 6) 0 \n",
- "_________________________________________________________________\n",
- "depthwise_conv2d_12 (Depthwi (None, 512, 2, 2) 4608 \n",
- "_________________________________________________________________\n",
- "batch_normalization_24 (Batc (None, 512, 2, 2) 2048 \n",
- "_________________________________________________________________\n",
- "activation_24 (Activation) (None, 512, 2, 2) 0 \n",
- "_________________________________________________________________\n",
- "conv2d_13 (Conv2D) (None, 1024, 2, 2) 524288 \n",
- "_________________________________________________________________\n",
- "batch_normalization_25 (Batc (None, 1024, 2, 2) 4096 \n",
- "_________________________________________________________________\n",
- "activation_25 (Activation) (None, 1024, 2, 2) 0 \n",
- "_________________________________________________________________\n",
- "zero_padding2d_13 (ZeroPaddi (None, 1024, 4, 4) 0 \n",
- "_________________________________________________________________\n",
- "depthwise_conv2d_13 (Depthwi (None, 1024, 2, 2) 9216 \n",
- "_________________________________________________________________\n",
- "batch_normalization_26 (Batc (None, 1024, 2, 2) 4096 \n",
- "_________________________________________________________________\n",
- "activation_26 (Activation) (None, 1024, 2, 2) 0 \n",
- "_________________________________________________________________\n",
- "conv2d_14 (Conv2D) (None, 1024, 2, 2) 1048576 \n",
- "_________________________________________________________________\n",
- "batch_normalization_27 (Batc (None, 1024, 2, 2) 4096 \n",
- "_________________________________________________________________\n",
- "activation_27 (Activation) (None, 1024, 2, 2) 0 \n",
- "_________________________________________________________________\n",
- "dropout_5 (Dropout) (None, 1024, 2, 2) 0 \n",
- "_________________________________________________________________\n",
- "average_pooling2d_1 (Average (None, 1024, 1, 1) 0 \n",
- "_________________________________________________________________\n",
- "flatten_1 (Flatten) (None, 1024) 0 \n",
- "_________________________________________________________________\n",
- "dense_1 (Dense) (None, 10) 10250 \n",
- "=================================================================\n",
- "Total params: 3,239,114\n",
- "Trainable params: 3,217,226\n",
- "Non-trainable params: 21,888\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "# data augmentation, horizontal flips only\n",
- "datagen = ImageDataGenerator(\n",
- " featurewise_center=False,\n",
- " featurewise_std_normalization=False,\n",
- " rotation_range=0.0,\n",
- " width_shift_range=0.0,\n",
- " height_shift_range=0.0,\n",
- " vertical_flip=False,\n",
- " horizontal_flip=True)\n",
- "datagen.fit(X_train)\n",
- "\n",
- "model = get_mobilenet()\n",
- "\n",
- "learning_rates=[]\n",
- "for i in range(5):\n",
- " learning_rates.append(2e-2)\n",
- "for i in range(50-5):\n",
- " learning_rates.append(1e-2)\n",
- "for i in range(100-50):\n",
- " learning_rates.append(8e-3)\n",
- "for i in range(150-100):\n",
- " learning_rates.append(4e-3)\n",
- "for i in range(200-150):\n",
- " learning_rates.append(2e-3)\n",
- "for i in range(300-200):\n",
- " learning_rates.append(1e-3)\n",
- "\n",
- "callbacks = [\n",
- " LearningRateScheduler(lambda epoch: float(learning_rates[epoch]))\n",
- "]\n",
- "\n",
- "model.compile(optimizer=optimizers.SGD(lr=learning_rates[0], momentum=0.9, decay=0.0, nesterov=False), \n",
- " loss='categorical_crossentropy', \n",
- " metrics=['accuracy'])\n",
- "\n",
- "model.fit_generator(\n",
- " datagen.flow(X_train, y_train, batch_size=128),\n",
- " steps_per_epoch=int(np.ceil(50000 / 128)),\n",
- " validation_data=(X_test, y_test),\n",
- " #epochs=300,\n",
- " epochs=50,\n",
- " callbacks=callbacks\n",
- ")\n",
- "\n",
- "model.summary()\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 46,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "time: 0.012634992599487305\n"
- ]
- }
- ],
- "source": [
- "import time\n",
- "start_time = time.time()\n",
- "keras_result = model.predict(X_test[:20])\n",
- "print(\"time: \", time.time() - start_time)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [],
- "source": [
- "import keras2onnx\n",
- "onnx_model = keras2onnx.convert_keras(model, model.name, target_opset=10)\n",
- "import onnx\n",
- "onnx.save(onnx_model, \"../models/keras/mobilenet.onnx\")\n",
- "import pickle\n",
- "with open('dumps/mobilenet_keras_dump', 'wb') as fp:\n",
- " pickle.dump(keras_result, fp)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/hpvm/projects/onnx/src/mobilenet_onnx.ipynb b/hpvm/projects/onnx/src/mobilenet_onnx.ipynb
deleted file mode 100644
index 8b26d865421ca5c819b32abeb295b0169254a055..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/src/mobilenet_onnx.ipynb
+++ /dev/null
@@ -1,210 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Using TensorFlow backend.\n"
- ]
- }
- ],
- "source": [
- "import sys\n",
- "import keras\n",
- "from keras.datasets import cifar10\n",
- "from keras import backend as K\n",
- "from keras.utils import to_categorical\n",
- "import numpy as np\n",
- "import os\n",
- "import keras2onnx\n",
- "import onnx\n",
- "import onnxruntime"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "os.environ['CUDA_VISIBLE_DEVICES'] = '1'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "X_train.shape = (50000, 3, 32, 32)\n",
- "X_test.shape = (10000, 3, 32, 32)\n"
- ]
- }
- ],
- "source": [
- "K.set_image_data_format('channels_first')\n",
- "\n",
- "(X_train, y_train), (X_test, y_test) = cifar10.load_data()\n",
- "test_labels = y_test\n",
- "\n",
- "print (\"X_train.shape = \", X_train.shape)\n",
- "print (\"X_test.shape = \", X_test.shape)\n",
- "\n",
- "\n",
- "X_train = X_train.astype('float32')\n",
- "X_test = X_test.astype('float32')\n",
- "\n",
- "\n",
- "mean = np.mean(X_train, axis=(0, 2, 3), keepdims=True)\n",
- "std = np.std(X_train, axis=(0, 2, 3), keepdims=True)\n",
- "\n",
- "X_train = (X_train - mean) / (std + 1e-9)\n",
- "X_test = (X_test - mean) / (std + 1e-9)\n",
- "\n",
- "y_train = to_categorical(y_train, num_classes=10)\n",
- "y_test = to_categorical(y_test, num_classes=10)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "sess = onnxruntime.InferenceSession(\"../models/keras/mobilenet.onnx\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Input name : conv2d_1_input\n",
- "Input shape : [None, 3, 32, 32]\n",
- "Input type : tensor(float)\n"
- ]
- }
- ],
- "source": [
- "input_name = sess.get_inputs()[0].name\n",
- "print(\"Input name :\", input_name)\n",
- "input_shape = sess.get_inputs()[0].shape\n",
- "print(\"Input shape :\", input_shape)\n",
- "input_type = sess.get_inputs()[0].type\n",
- "print(\"Input type :\", input_type)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Output name : dense_1\n",
- "Output shape : [None, 10]\n",
- "Output type : tensor(float)\n"
- ]
- }
- ],
- "source": [
- "output_name = sess.get_outputs()[0].name\n",
- "print(\"Output name :\", output_name) \n",
- "output_shape = sess.get_outputs()[0].shape\n",
- "print(\"Output shape :\", output_shape)\n",
- "output_type = sess.get_outputs()[0].type\n",
- "print(\"Output type :\", output_type)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 70,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "time: 0.006893157958984375\n"
- ]
- }
- ],
- "source": [
- "import time\n",
- "start_time = time.time()\n",
- "ort_result = sess.run([output_name], {input_name: X_test[:40]})\n",
- "print(\"time: \", time.time() - start_time)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pickle\n",
- "with open('dumps/mobilenet_ort_dump', 'wb') as fp:\n",
- " pickle.dump(ort_result, fp)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pickle\n",
- "import numpy as np\n",
- "with open ('dumps/mobilenet_ort_dump', 'rb') as fp:\n",
- " ort_res = pickle.load(fp)\n",
- "with open ('dumps/mobilenet_keras_dump', 'rb') as fp:\n",
- " keras_res = pickle.load(fp)\n",
- "\n",
- "for ref_o, o in zip(ort_res[0], keras_res):\n",
- " np.testing.assert_almost_equal(ref_o, o, 3) #using decimal of 3 would pass test"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/hpvm/projects/onnx/src/mobilenet_tvm.ipynb b/hpvm/projects/onnx/src/mobilenet_tvm.ipynb
deleted file mode 100644
index c0b71588afdea134ed4a2aa675d2c6058772b0b0..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/src/mobilenet_tvm.ipynb
+++ /dev/null
@@ -1,381 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Using TensorFlow backend.\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "from keras.datasets import cifar10\n",
- "from keras import backend as K\n",
- "import sys\n",
- "import struct\n",
- "import numpy as np\n",
- "import os\n",
- "import tvm\n",
- "import tvm.relay as relay\n",
- "from tvm.contrib import graph_runtime\n",
- "os.environ['CUDA_VISIBLE_DEVICES'] = '1'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "K.set_image_data_format('channels_first')\n",
- "\n",
- "(X_train, y_train), (X_test, y_test) = cifar10.load_data()\n",
- "X_test = X_test.astype('float32')\n",
- "mean = np.mean(X_train, axis=(0, 2, 3), keepdims=True)\n",
- "std = np.std(X_train, axis=(0, 2, 3), keepdims=True)\n",
- "\n",
- "X_test = (X_test - mean) / (std + 1e-9)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "#print(X_test.shape)\n",
- "#X_test = X_test[:8000]\n",
- "print(X_test.shape)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "#import keras\n",
- "#model = keras.applications.mobilenet.MobileNet(input_shape=X_test[0].shape, include_top=False, weights=None)\n",
- "#import keras2onnx\n",
- "#onnx_model = keras2onnx.convert_keras(model, model.name, target_opset=10)\n",
- "import onnx\n",
- "onnx_model = onnx.load(\"../models/keras/mobilenet.onnx\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "target='cuda -libs=cudnn,cublas'\n",
- "input_name = 'conv2d_1_input'\n",
- "shape_dict = {input_name: X_test[:10].shape}\n",
- "mod, params = relay.frontend.from_onnx(onnx_model, shape_dict)\n",
- "ctx = tvm.gpu()\n",
- "with relay.build_config(opt_level=3):\n",
- " executor = relay.build_module.create_executor('graph', mod, ctx, target)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# LLVM EXECUTE SUCCEEDED\n",
- "import time\n",
- "start_time = time.time()\n",
- "tvm_out = executor.evaluate()(tvm.nd.array(X_test[:1000]), **params)\n",
- "print(\"Time:\", time.time() - start_time)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "top1_tvm = np.argmax(tvm_out.asnumpy()[0])\n",
- "import pickle\n",
- "with open('dumps/tvm_dump', 'wb') as fp:\n",
- " pickle.dump(tvm_output, fp)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 73,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.avg_pool2d\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('dense', (40, 1024, 'float32'), (10, 1024, 'float32'), 0, 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (40, 1024, 2, 2, 'float32'), (1024, 1024, 1, 1, 'float32'), (1, 1), (0, 0), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('depthwise_conv2d_nchw', (40, 1024, 2, 2, 'float32'), (1024, 1, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (40, 512, 2, 2, 'float32'), (1024, 512, 1, 1, 'float32'), (1, 1), (0, 0), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('depthwise_conv2d_nchw', (40, 512, 4, 4, 'float32'), (512, 1, 3, 3, 'float32'), (2, 2), (1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (40, 512, 4, 4, 'float32'), (512, 512, 1, 1, 'float32'), (1, 1), (0, 0), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('depthwise_conv2d_nchw', (40, 512, 4, 4, 'float32'), (512, 1, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (40, 256, 4, 4, 'float32'), (512, 256, 1, 1, 'float32'), (1, 1), (0, 0), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('depthwise_conv2d_nchw', (40, 256, 8, 8, 'float32'), (256, 1, 3, 3, 'float32'), (2, 2), (1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (40, 256, 8, 8, 'float32'), (256, 256, 1, 1, 'float32'), (1, 1), (0, 0), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('depthwise_conv2d_nchw', (40, 256, 8, 8, 'float32'), (256, 1, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (40, 128, 8, 8, 'float32'), (256, 128, 1, 1, 'float32'), (1, 1), (0, 0), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('depthwise_conv2d_nchw', (40, 128, 16, 16, 'float32'), (128, 1, 3, 3, 'float32'), (2, 2), (1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (40, 128, 16, 16, 'float32'), (128, 128, 1, 1, 'float32'), (1, 1), (0, 0), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('depthwise_conv2d_nchw', (40, 128, 16, 16, 'float32'), (128, 1, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (40, 64, 16, 16, 'float32'), (128, 64, 1, 1, 'float32'), (1, 1), (0, 0), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('depthwise_conv2d_nchw', (40, 64, 32, 32, 'float32'), (64, 1, 3, 3, 'float32'), (2, 2), (1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (40, 32, 32, 32, 'float32'), (64, 32, 1, 1, 'float32'), (1, 1), (0, 0), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('depthwise_conv2d_nchw', (40, 32, 32, 32, 'float32'), (32, 1, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (40, 3, 32, 32, 'float32'), (32, 3, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n"
- ]
- }
- ],
- "source": [
- "input_name = 'conv2d_1_input'\n",
- "input_size = 40\n",
- "shape_dict = {input_name: X_test[:input_size].shape}\n",
- "mod, params = relay.frontend.from_onnx(onnx_model, shape_dict)\n",
- "target = 'cuda -libs=cudnn,cublas'\n",
- "with relay.build_config(opt_level=3):\n",
- " graph, lib, params = relay.build(mod, target, params=params)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 74,
- "metadata": {},
- "outputs": [],
- "source": [
- "import time\n",
- "ctx = tvm.gpu()\n",
- "#data = np.random.uniform(-1, 1, size=data_shape).astype(\"float32\")\n",
- "# create module\n",
- "module = graph_runtime.create(graph, lib, ctx)\n",
- "# set input and parameters\n",
- "module.set_input(\"conv2d_1_input\", X_test[:input_size])\n",
- "module.set_input(**params)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 86,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Time: 0.012343168258666992\n"
- ]
- }
- ],
- "source": [
- "# run\n",
- "start_time = time.time()\n",
- "module.run()\n",
- "out_shape = (input_size, 10)\n",
- "# get output\n",
- "out = module.get_output(0, tvm.nd.empty(out_shape)).asnumpy()\n",
- "print(\"Time:\", time.time() - start_time)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "mobilenet 17.814301 ms (0.00 ms)\n"
- ]
- }
- ],
- "source": [
- "# evaluate\n",
- "ftimer = module.module.time_evaluator(\"run\", ctx, number=1)\n",
- "prof_res = np.array(ftimer().results) * 1000 # multiply 1000 for converting to millisecond\n",
- "print(\"%-20s %-19s (%s)\" % (\"mobilenet\", \"%.6f ms\" % np.mean(prof_res), \"%.2f ms\" % np.std(prof_res)))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pickle\n",
- "import numpy as np\n",
- "with open ('dumps/tvm_dump', 'rb') as fp:\n",
- " tvm_res = pickle.load(fp)\n",
- "with open ('dumps/keras_dump', 'rb') as fp:\n",
- " keras_res = pickle.load(fp)\n",
- "\n",
- "for ref_o, o in zip(tvm_res, keras_res):\n",
- " np.testing.assert_almost_equal(ref_o, o, 3) #using decimal of 3 would pass test\n",
- "print(\"Accuracy matched!\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Use TVM's implementation of mobilenet\n",
- "import sys\n",
- "import struct\n",
- "import numpy as np\n",
- "import os\n",
- "import tvm\n",
- "import tvm.relay as relay\n",
- "from tvm.relay import testing\n",
- "from tvm.contrib import graph_runtime\n",
- "os.environ['CUDA_VISIBLE_DEVICES'] = '1'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "batch_size = 100\n",
- "dtype = 'float32'\n",
- "target='cuda -libs=cudnn,cublas'\n",
- "net, params = testing.mobilenet.get_workload(batch_size=batch_size, dtype=dtype)\n",
- "input_shape = (batch_size, 3, 224, 224)\n",
- "output_shape = (batch_size, 1000)\n",
- "with relay.build_config(opt_level=3):\n",
- " graph, lib, params = relay.build(net, target=target, params=params)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ctx = tvm.context(str(target), 0)\n",
- "module = graph_runtime.create(graph, lib, ctx)\n",
- "data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))\n",
- "module.set_input('data', data_tvm)\n",
- "module.set_input(**params)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# evaluate\n",
- "ftimer = module.module.time_evaluator(\"run\", ctx, number=1)\n",
- "prof_res = np.array(ftimer().results) * 1000 # multiply 1000 for converting to millisecond\n",
- "print(\"%-20s %-19s (%s)\" % (\"mobilenet\", \"%.2f ms\" % np.mean(prof_res), \"%.2f ms\" % np.std(prof_res)))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/hpvm/projects/onnx/src/vgg16_keras.ipynb b/hpvm/projects/onnx/src/vgg16_keras.ipynb
deleted file mode 100644
index e1d1422ead3c3bc5afa602a13b3087ecbebc8eab..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/src/vgg16_keras.ipynb
+++ /dev/null
@@ -1,392 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Using TensorFlow backend.\n"
- ]
- }
- ],
- "source": [
- "import keras\n",
- "from keras.datasets import cifar10\n",
- "from keras.preprocessing.image import ImageDataGenerator\n",
- "from keras.models import Sequential\n",
- "from keras.layers import Dense, Dropout, Activation, Flatten\n",
- "from keras.layers import Conv2D, MaxPooling2D, BatchNormalization\n",
- "from keras import optimizers\n",
- "import numpy as np\n",
- "from keras.layers.core import Lambda\n",
- "from keras import backend as K\n",
- "from keras import regularizers\n",
- "import os\n",
- "import sys"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "class cifar10vgg:\n",
- " def __init__(self,train=True):\n",
- " self.num_classes = 10\n",
- " self.weight_decay = 0.0005\n",
- " self.x_shape = [3,32,32]\n",
- "\n",
- " self.model = self.build_model()\n",
- " if train:\n",
- " self.model = self.train(self.model)\n",
- " else:\n",
- " self.model.load_weights('cifar10vgg.h5')\n",
- "\n",
- "\n",
- " def build_model(self):\n",
- " # Build the network of vgg for 10 classes with massive dropout and weight decay as described in the paper.\n",
- "\n",
- " model = Sequential()\n",
- " weight_decay = self.weight_decay\n",
- "\n",
- " model.add(Conv2D(64, (3, 3), padding='same',\n",
- " input_shape=self.x_shape,kernel_regularizer=regularizers.l2(weight_decay)))\n",
- " model.add(Activation('relu'))\n",
- " #model.add(BatchNormalization())\n",
- " model.add(Dropout(0.3))\n",
- "\n",
- " model.add(Conv2D(64, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))\n",
- " model.add(Activation('relu'))\n",
- " # model.add(BatchNormalization())\n",
- " model.add(MaxPooling2D(pool_size=(2, 2)))\n",
- "\n",
- " model.add(Conv2D(128, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))\n",
- " model.add(Activation('relu'))\n",
- " #model.add(BatchNormalization())\n",
- " model.add(Dropout(0.4))\n",
- "\n",
- " model.add(Conv2D(128, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))\n",
- " model.add(Activation('relu'))\n",
- " #model.add(BatchNormalization())\n",
- " model.add(MaxPooling2D(pool_size=(2, 2)))\n",
- "\n",
- " model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))\n",
- " model.add(Activation('relu'))\n",
- " #model.add(BatchNormalization())\n",
- " model.add(Dropout(0.4))\n",
- "\n",
- " model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))\n",
- " model.add(Activation('relu'))\n",
- " # model.add(BatchNormalization())\n",
- " model.add(Dropout(0.4))\n",
- "\n",
- " model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))\n",
- " model.add(Activation('relu'))\n",
- " # model.add(BatchNormalization())\n",
- " model.add(MaxPooling2D(pool_size=(2, 2)))\n",
- "\n",
- " model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))\n",
- " model.add(Activation('relu'))\n",
- " # model.add(BatchNormalization())\n",
- " model.add(Dropout(0.4))\n",
- "\n",
- " model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))\n",
- " model.add(Activation('relu'))\n",
- " # model.add(BatchNormalization())\n",
- " model.add(Dropout(0.4))\n",
- "\n",
- " model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))\n",
- " model.add(Activation('relu'))\n",
- " # model.add(BatchNormalization())\n",
- " model.add(MaxPooling2D(pool_size=(2, 2)))\n",
- "\n",
- " model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))\n",
- " model.add(Activation('relu'))\n",
- " # model.add(BatchNormalization())\n",
- " model.add(Dropout(0.4))\n",
- "\n",
- " model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))\n",
- " model.add(Activation('relu'))\n",
- " # model.add(BatchNormalization())\n",
- " model.add(Dropout(0.4))\n",
- "\n",
- " model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))\n",
- " model.add(Activation('relu'))\n",
- " # model.add(BatchNormalization())\n",
- " model.add(MaxPooling2D(pool_size=(2, 2)))\n",
- " model.add(Dropout(0.5))\n",
- "\n",
- " model.add(Flatten())\n",
- " model.add(Dense(512,kernel_regularizer=regularizers.l2(weight_decay)))\n",
- " model.add(Activation('relu'))\n",
- " # model.add(BatchNormalization())\n",
- "\n",
- " model.add(Dropout(0.5))\n",
- " model.add(Dense(self.num_classes))\n",
- " model.add(Activation('softmax'))\n",
- " return model\n",
- "\n",
- "\n",
- " def normalize(self,X_train,X_test):\n",
- " #this function normalize inputs for zero mean and unit variance\n",
- " # it is used when training a model.\n",
- " # Input: training set and test set\n",
- " # Output: normalized training set and test set according to the trianing set statistics.\n",
- " mean = np.mean(X_train,axis=(0,1,2,3))\n",
- " std = np.std(X_train, axis=(0, 1, 2, 3))\n",
- " X_train = (X_train-mean)/(std+1e-7)\n",
- " X_test = (X_test-mean)/(std+1e-7)\n",
- " return X_train, X_test\n",
- "\n",
- " \n",
- " def normalize_production(self,x):\n",
- " #this function is used to normalize instances in production according to saved training set statistics\n",
- " # Input: X - a training set\n",
- " # Output X - a normalized training set according to normalization constants.\n",
- "\n",
- " #these values produced during first training and are general for the standard cifar10 training set normalization\n",
- " mean = 120.707\n",
- " std = 64.15\n",
- " return (x-mean)/(std+1e-7)\n",
- "\n",
- " \n",
- " def predict(self,x,normalize=True,batch_size=50):\n",
- " if normalize:\n",
- " x = self.normalize_production(x)\n",
- " return self.model.predict(x,batch_size)\n",
- "\n",
- " \n",
- " def train(self,model):\n",
- "\n",
- " #training parameters\n",
- " batch_size = 128\n",
- " #maxepoches = 250\n",
- " #maxepoches = 250\n",
- " maxepoches = 30\n",
- " learning_rate = 0.01\n",
- " lr_decay = 1e-6\n",
- " lr_drop = 20\n",
- " # The data, shuffled and split between train and test sets:\n",
- " (x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
- " x_train = x_train.astype('float32')\n",
- " x_test = x_test.astype('float32')\n",
- " x_train, x_test = self.normalize(x_train, x_test)\n",
- "\n",
- " y_train = keras.utils.to_categorical(y_train, self.num_classes)\n",
- " y_test = keras.utils.to_categorical(y_test, self.num_classes)\n",
- "\n",
- " def lr_scheduler(epoch):\n",
- " return learning_rate * (0.5 ** (epoch // lr_drop))\n",
- " reduce_lr = keras.callbacks.LearningRateScheduler(lr_scheduler)\n",
- "\n",
- " #data augmentation\n",
- " datagen = ImageDataGenerator(\n",
- " featurewise_center=False, # set input mean to 0 over the dataset\n",
- " samplewise_center=False, # set each sample mean to 0\n",
- " featurewise_std_normalization=False, # divide inputs by std of the dataset\n",
- " samplewise_std_normalization=False, # divide each input by its std\n",
- " zca_whitening=False, # apply ZCA whitening\n",
- " rotation_range=15, # randomly rotate images in the range (degrees, 0 to 180)\n",
- " width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)\n",
- " height_shift_range=0.1, # randomly shift images vertically (fraction of total height)\n",
- " horizontal_flip=True, # randomly flip images\n",
- " vertical_flip=False) # randomly flip images\n",
- " # (std, mean, and principal components if ZCA whitening is applied).\n",
- " datagen.fit(x_train)\n",
- "\n",
- "\n",
- "\n",
- " #optimization details\n",
- " sgd = optimizers.SGD(lr=learning_rate, decay=lr_decay, momentum=0.9, nesterov=True)\n",
- " model.compile(loss='categorical_crossentropy', optimizer=sgd,metrics=['accuracy'])\n",
- "\n",
- "\n",
- " # training process in a for loop with learning rate drop every 25 epoches.\n",
- "\n",
- " historytemp = model.fit_generator(datagen.flow(x_train, y_train,\n",
- " batch_size=batch_size),\n",
- " steps_per_epoch=x_train.shape[0] // batch_size,\n",
- " epochs=maxepoches,\n",
- " validation_data=(x_test, y_test),callbacks=[reduce_lr],verbose=2)\n",
- " \n",
- " model.save_weights('cifar10vgg.h5')\n",
- " return model\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "WARNING:tensorflow:Variable *= will be deprecated. Use `var.assign(var * other)` if you want assignment to the variable value or `x = x * y` if you want a new python Tensor object.\n",
- "Epoch 1/30\n",
- " - 29s - loss: 4.3002 - acc: 0.1547 - val_loss: 4.2265 - val_acc: 0.1443\n",
- "Epoch 2/30\n",
- " - 23s - loss: 3.9048 - acc: 0.2016 - val_loss: 3.9923 - val_acc: 0.1640\n",
- "Epoch 3/30\n",
- " - 23s - loss: 3.7052 - acc: 0.2210 - val_loss: 3.8900 - val_acc: 0.1712\n",
- "Epoch 4/30\n",
- " - 22s - loss: 3.4890 - acc: 0.2839 - val_loss: 3.4656 - val_acc: 0.2731\n",
- "Epoch 5/30\n",
- " - 25s - loss: 3.2615 - acc: 0.3258 - val_loss: 3.1615 - val_acc: 0.3296\n",
- "Epoch 6/30\n",
- " - 23s - loss: 3.0814 - acc: 0.3557 - val_loss: 3.0238 - val_acc: 0.3395\n",
- "Epoch 7/30\n",
- " - 23s - loss: 2.9114 - acc: 0.3883 - val_loss: 2.8283 - val_acc: 0.4041\n",
- "Epoch 8/30\n",
- " - 23s - loss: 2.7543 - acc: 0.4239 - val_loss: 2.5891 - val_acc: 0.4622\n",
- "Epoch 9/30\n",
- " - 22s - loss: 2.6056 - acc: 0.4594 - val_loss: 2.5562 - val_acc: 0.4796\n",
- "Epoch 10/30\n",
- " - 25s - loss: 2.4590 - acc: 0.5013 - val_loss: 2.2543 - val_acc: 0.5714\n",
- "Epoch 11/30\n",
- " - 24s - loss: 2.3017 - acc: 0.5439 - val_loss: 2.1377 - val_acc: 0.5954\n",
- "Epoch 12/30\n",
- " - 22s - loss: 2.1519 - acc: 0.5811 - val_loss: 1.9902 - val_acc: 0.6356\n",
- "Epoch 13/30\n",
- " - 23s - loss: 2.0276 - acc: 0.6106 - val_loss: 1.8868 - val_acc: 0.6494\n",
- "Epoch 14/30\n",
- " - 22s - loss: 1.9065 - acc: 0.6357 - val_loss: 1.7370 - val_acc: 0.6813\n",
- "Epoch 15/30\n",
- " - 22s - loss: 1.8081 - acc: 0.6532 - val_loss: 1.7568 - val_acc: 0.6721\n",
- "Epoch 16/30\n",
- " - 22s - loss: 1.7116 - acc: 0.6736 - val_loss: 1.6687 - val_acc: 0.6959\n",
- "Epoch 17/30\n",
- " - 22s - loss: 1.6337 - acc: 0.6883 - val_loss: 1.4748 - val_acc: 0.7350\n",
- "Epoch 18/30\n",
- " - 22s - loss: 1.5609 - acc: 0.7022 - val_loss: 1.4428 - val_acc: 0.7445\n",
- "Epoch 19/30\n",
- " - 23s - loss: 1.4890 - acc: 0.7160 - val_loss: 1.3682 - val_acc: 0.7582\n",
- "Epoch 20/30\n",
- " - 22s - loss: 1.4291 - acc: 0.7298 - val_loss: 1.3004 - val_acc: 0.7717\n",
- "Epoch 21/30\n",
- " - 22s - loss: 1.3169 - acc: 0.7593 - val_loss: 1.2640 - val_acc: 0.7800\n",
- "Epoch 22/30\n",
- " - 24s - loss: 1.2843 - acc: 0.7669 - val_loss: 1.2191 - val_acc: 0.7864\n",
- "Epoch 23/30\n",
- " - 23s - loss: 1.2431 - acc: 0.7757 - val_loss: 1.1598 - val_acc: 0.7998\n",
- "Epoch 24/30\n",
- " - 22s - loss: 1.2166 - acc: 0.7813 - val_loss: 1.1435 - val_acc: 0.8097\n",
- "Epoch 25/30\n",
- " - 22s - loss: 1.1872 - acc: 0.7869 - val_loss: 1.1180 - val_acc: 0.8123\n",
- "Epoch 26/30\n",
- " - 23s - loss: 1.1634 - acc: 0.7916 - val_loss: 1.0977 - val_acc: 0.8123\n",
- "Epoch 27/30\n",
- " - 22s - loss: 1.1300 - acc: 0.7965 - val_loss: 1.0785 - val_acc: 0.8199\n",
- "Epoch 28/30\n",
- " - 22s - loss: 1.1169 - acc: 0.7983 - val_loss: 1.0927 - val_acc: 0.8120\n",
- "Epoch 29/30\n",
- " - 22s - loss: 1.0944 - acc: 0.8025 - val_loss: 1.0141 - val_acc: 0.8275\n",
- "Epoch 30/30\n",
- " - 22s - loss: 1.0686 - acc: 0.8069 - val_loss: 1.0341 - val_acc: 0.8206\n"
- ]
- }
- ],
- "source": [
- "K.set_image_data_format('channels_first')\n",
- "\n",
- "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\n",
- "\n",
- "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
- "test_labels = y_test\n",
- "train_labels = y_train\n",
- "x_train = x_train.astype('float32')\n",
- "x_test = x_test.astype('float32')\n",
- "\n",
- "y_train = keras.utils.to_categorical(y_train, 10)\n",
- "y_test = keras.utils.to_categorical(y_test, 10)\n",
- "\n",
- "model = cifar10vgg()\n",
- "\n",
- "#predicted_x = model.predict(x_test)\n",
- "\n",
- "#norm_test = model.normalize_production(x_test)\n",
- "\n",
- "# Normalizing train data before dumping\n",
- "#x_train, x_test = model.normalize(x_train, x_test)\n",
- "#x_train = model.normalize_production(x_train)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 56,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "time: 1.3477363586425781\n"
- ]
- }
- ],
- "source": [
- "import time\n",
- "start = time.time()\n",
- "result = model.predict(x_test[:8000])\n",
- "print(\"time:\", time.time() - start)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import keras2onnx\n",
- "onnx_model = keras2onnx.convert_keras(model.model, model.model.name, target_opset=10)\n",
- "import onnx\n",
- "onnx.save(onnx_model, \"../models/keras/vgg16_cifar10.onnx\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pickle\n",
- "with open('dumps/vgg16_keras_dump', 'wb') as fp:\n",
- " pickle.dump(result, fp)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/hpvm/projects/onnx/src/vgg16_onnx.ipynb b/hpvm/projects/onnx/src/vgg16_onnx.ipynb
deleted file mode 100644
index 9d0453cfb754a488d06a00afa78383ff9269e326..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/src/vgg16_onnx.ipynb
+++ /dev/null
@@ -1,158 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Using TensorFlow backend.\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "from keras.datasets import cifar10\n",
- "from keras import backend as K\n",
- "import keras\n",
- "import numpy as np\n",
- "import os\n",
- "import keras2onnx\n",
- "import onnx\n",
- "import onnxruntime\n",
- "import time\n",
- "os.environ['CUDA_VISIBLE_DEVICES'] = '1'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "K.set_image_data_format('channels_first')\n",
- "\n",
- "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
- "test_labels = y_test\n",
- "train_labels = y_train\n",
- "x_train = x_train.astype('float32')\n",
- "x_test = x_test.astype('float32')\n",
- "\n",
- "y_train = keras.utils.to_categorical(y_train, 10)\n",
- "y_test = keras.utils.to_categorical(y_test, 10)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Input name : conv2d_1_input\n",
- "Input shape : [None, 3, 32, 32]\n",
- "Input type : tensor(float)\n",
- "Output name : activation_15\n",
- "Output shape : [None, 10]\n",
- "Output type : tensor(float)\n"
- ]
- }
- ],
- "source": [
- "sess = onnxruntime.InferenceSession(\"../models/keras/vgg16_cifar10.onnx\")\n",
- "input_name = sess.get_inputs()[0].name\n",
- "print(\"Input name :\", input_name)\n",
- "input_shape = sess.get_inputs()[0].shape\n",
- "print(\"Input shape :\", input_shape)\n",
- "input_type = sess.get_inputs()[0].type\n",
- "print(\"Input type :\", input_type)\n",
- "output_name = sess.get_outputs()[0].name\n",
- "print(\"Output name :\", output_name) \n",
- "output_shape = sess.get_outputs()[0].shape\n",
- "print(\"Output shape :\", output_shape)\n",
- "output_type = sess.get_outputs()[0].type\n",
- "print(\"Output type :\", output_type)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 59,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "time: 1.4347426891326904\n"
- ]
- }
- ],
- "source": [
- "start_time = time.time()\n",
- "ort_result = sess.run([output_name], {input_name: x_test[:8000]})\n",
- "print(\"time: \", time.time() - start_time)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pickle\n",
- "with open('dumps/vgg16_ort_dump', 'wb') as fp:\n",
- " pickle.dump(ort_result, fp)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pickle\n",
- "import numpy as np\n",
- "with open ('dumps/vgg16_ort_dump', 'rb') as fp:\n",
- " ort_res = pickle.load(fp)\n",
- "with open ('dumps/vgg16_keras_dump', 'rb') as fp:\n",
- " keras_res = pickle.load(fp)\n",
- "\n",
- "for ref_o, o in zip(ort_res[0], keras_res):\n",
- " np.testing.assert_almost_equal(ref_o, o, 1) #using decimal of 3 would pass test"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/hpvm/projects/onnx/src/vgg16_tvm.ipynb b/hpvm/projects/onnx/src/vgg16_tvm.ipynb
deleted file mode 100644
index 8cea48d44615e3d407d25b943df6dbb86923a2d0..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/src/vgg16_tvm.ipynb
+++ /dev/null
@@ -1,240 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Using TensorFlow backend.\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "from keras.datasets import cifar10\n",
- "from keras import backend as K\n",
- "import sys\n",
- "import struct\n",
- "import numpy as np\n",
- "import os\n",
- "import tvm\n",
- "import tvm.relay as relay\n",
- "from tvm.contrib import graph_runtime\n",
- "os.environ['CUDA_VISIBLE_DEVICES'] = '1'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "K.set_image_data_format('channels_first')\n",
- "\n",
- "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
- "x_test = x_test.astype('float32')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "#print(X_test.shape)\n",
- "#X_test = X_test[:8000]\n",
- "#print(X_test.shape)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "import onnx\n",
- "onnx_model = onnx.load(\"../models/keras/vgg16_cifar10.onnx\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "target='cuda -libs=cudnn,cublas'\n",
- "input_name = 'conv2d_8_input'\n",
- "shape_dict = {input_name: X_test[:1000].shape}\n",
- "mod, params = relay.frontend.from_onnx(onnx_model, shape_dict)\n",
- "ctx = tvm.gpu()\n",
- "with relay.build_config(opt_level=3):\n",
- " executor = relay.build_module.create_executor('graph', mod, ctx, target)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# LLVM EXECUTE SUCCEEDED\n",
- "import time\n",
- "start_time = time.time()\n",
- "tvm_out = executor.evaluate()(tvm.nd.array(X_test.astype('float32')[:1000]), **params)\n",
- "print(\"Time:\", time.time() - start_time)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "top1_tvm = np.argmax(tvm_out.asnumpy()[0])\n",
- "import pickle\n",
- "with open('dumps/tvm_dump', 'wb') as fp:\n",
- " pickle.dump(tvm_output, fp)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 59,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.max_pool2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.max_pool2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.max_pool2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.max_pool2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.conv2d\n",
- "WARNING:root:Attribute auto_pad is ignored in relay.sym.max_pool2d\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('dense', (6000, 512, 'float32'), (10, 512, 'float32'), 0, 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('dense', (6000, 512, 'float32'), (512, 512, 'float32'), 0, 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (6000, 512, 2, 2, 'float32'), (512, 512, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (6000, 512, 4, 4, 'float32'), (512, 512, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (6000, 256, 4, 4, 'float32'), (512, 256, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (6000, 256, 8, 8, 'float32'), (256, 256, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (6000, 128, 8, 8, 'float32'), (256, 128, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (6000, 128, 16, 16, 'float32'), (128, 128, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (6000, 64, 16, 16, 'float32'), (128, 64, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (6000, 64, 32, 32, 'float32'), (64, 64, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n",
- "WARNING:autotvm:Cannot find config for target=cuda -libs=cudnn,cublas, workload=('conv2d', (6000, 3, 32, 32, 'float32'), (64, 3, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.\n"
- ]
- }
- ],
- "source": [
- "input_name = 'conv2d_1_input'\n",
- "input_size = 6000\n",
- "shape_dict = {input_name: x_test[:input_size].shape}\n",
- "mod, params = relay.frontend.from_onnx(onnx_model, shape_dict)\n",
- "target = 'cuda -libs=cudnn,cublas'\n",
- "with relay.build_config(opt_level=3):\n",
- " graph, lib, params = relay.build(mod, target, params=params)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 60,
- "metadata": {},
- "outputs": [],
- "source": [
- "import time\n",
- "ctx = tvm.gpu()\n",
- "#data = np.random.uniform(-1, 1, size=data_shape).astype(\"float32\")\n",
- "# create module\n",
- "module = graph_runtime.create(graph, lib, ctx)\n",
- "# set input and parameters\n",
- "module.set_input(\"conv2d_1_input\", x_test[:input_size])\n",
- "module.set_input(**params)\n",
- "# run\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 64,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Time: 0.2862420082092285\n"
- ]
- }
- ],
- "source": [
- "start_time = time.time()\n",
- "module.run()\n",
- "out_shape = (input_size, 10)\n",
- "# get output\n",
- "out = module.get_output(0, tvm.nd.empty(out_shape)).asnumpy()\n",
- "print(\"Time:\", time.time() - start_time)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pickle\n",
- "import numpy as np\n",
- "with open ('dumps/tvm_dump', 'rb') as fp:\n",
- " tvm_res = pickle.load(fp)\n",
- "with open ('dumps/keras_dump', 'rb') as fp:\n",
- " keras_res = pickle.load(fp)\n",
- "\n",
- "for ref_o, o in zip(tvm_res, keras_res):\n",
- " np.testing.assert_almost_equal(ref_o, o, 3) #using decimal of 3 would pass test\n",
- "print(\"Accuracy matched!\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/hpvm/projects/onnx/.gitignore b/hpvm/projects/onnx_frontend/.gitignore
similarity index 100%
rename from hpvm/projects/onnx/.gitignore
rename to hpvm/projects/onnx_frontend/.gitignore
diff --git a/hpvm/projects/onnx/frontend/README.md b/hpvm/projects/onnx_frontend/README.md
similarity index 56%
rename from hpvm/projects/onnx/frontend/README.md
rename to hpvm/projects/onnx_frontend/README.md
index 69ffe81f5ad6b1a57dda1a6410d4348ff616148a..719abc5e3c2346fd7bc68972ee4f9035af307db1 100644
--- a/hpvm/projects/onnx/frontend/README.md
+++ b/hpvm/projects/onnx_frontend/README.md
@@ -1,3 +1,17 @@
+## Importing Conda Environment:
+
+conda env create -f onnx\_environment.yml
+
+## Activate/deactivate Conda Environment
+
+conda activate onnx\_frontend
+
+## Building and Installing Frontend for ONNX:
+
+python setup.py build
+
+python setup.py install
+
### How to Run
```
python main.py
@@ -7,4 +21,5 @@ Set all your config, e.g. onnx model location, input size and emit directory for
### Resources
1. [ONNX overview](https://github.com/onnx/onnx/blob/master/docs/IR.md)
2. [ONNX operator specs](https://github.com/onnx/onnx/blob/master/docs/Operators.md)
-3. [Conversion between models - available adapters](https://github.com/onnx/onnx/blob/master/onnx/version_converter.py#L21)
\ No newline at end of file
+3. [Conversion between models - available adapters](https://github.com/onnx/onnx/blob/master/onnx/version_converter.py#L21)
+4.
\ No newline at end of file
diff --git a/hpvm/projects/onnx/TODO.md b/hpvm/projects/onnx_frontend/TODO.md
similarity index 100%
rename from hpvm/projects/onnx/TODO.md
rename to hpvm/projects/onnx_frontend/TODO.md
diff --git a/hpvm/projects/onnx/env.yaml b/hpvm/projects/onnx_frontend/env.yaml
similarity index 100%
rename from hpvm/projects/onnx/env.yaml
rename to hpvm/projects/onnx_frontend/env.yaml
diff --git a/hpvm/projects/onnx/frontend/__init__.py b/hpvm/projects/onnx_frontend/frontend/__init__.py
similarity index 100%
rename from hpvm/projects/onnx/frontend/__init__.py
rename to hpvm/projects/onnx_frontend/frontend/__init__.py
diff --git a/hpvm/projects/onnx/frontend/codegen_hpvm.py b/hpvm/projects/onnx_frontend/frontend/codegen_hpvm.py
similarity index 100%
rename from hpvm/projects/onnx/frontend/codegen_hpvm.py
rename to hpvm/projects/onnx_frontend/frontend/codegen_hpvm.py
diff --git a/hpvm/projects/onnx/frontend/codegen_tensor.py b/hpvm/projects/onnx_frontend/frontend/codegen_tensor.py
similarity index 100%
rename from hpvm/projects/onnx/frontend/codegen_tensor.py
rename to hpvm/projects/onnx_frontend/frontend/codegen_tensor.py
diff --git a/hpvm/projects/onnx/frontend/graph_builder.py b/hpvm/projects/onnx_frontend/frontend/graph_builder.py
similarity index 100%
rename from hpvm/projects/onnx/frontend/graph_builder.py
rename to hpvm/projects/onnx_frontend/frontend/graph_builder.py
diff --git a/hpvm/projects/onnx/frontend/graph_ir.py b/hpvm/projects/onnx_frontend/frontend/graph_ir.py
similarity index 100%
rename from hpvm/projects/onnx/frontend/graph_ir.py
rename to hpvm/projects/onnx_frontend/frontend/graph_ir.py
diff --git a/hpvm/projects/onnx/frontend/main.py b/hpvm/projects/onnx_frontend/frontend/main.py
similarity index 100%
rename from hpvm/projects/onnx/frontend/main.py
rename to hpvm/projects/onnx_frontend/frontend/main.py
diff --git a/hpvm/projects/onnx/frontend/onnx_attr.py b/hpvm/projects/onnx_frontend/frontend/onnx_attr.py
similarity index 100%
rename from hpvm/projects/onnx/frontend/onnx_attr.py
rename to hpvm/projects/onnx_frontend/frontend/onnx_attr.py
diff --git a/hpvm/projects/onnx/frontend/template_hpvm.cpp b/hpvm/projects/onnx_frontend/frontend/template_hpvm.cpp
similarity index 100%
rename from hpvm/projects/onnx/frontend/template_hpvm.cpp
rename to hpvm/projects/onnx_frontend/frontend/template_hpvm.cpp
diff --git a/hpvm/projects/onnx/frontend/template_tensor.cpp b/hpvm/projects/onnx_frontend/frontend/template_tensor.cpp
similarity index 100%
rename from hpvm/projects/onnx/frontend/template_tensor.cpp
rename to hpvm/projects/onnx_frontend/frontend/template_tensor.cpp
diff --git a/hpvm/projects/onnx/setup.py b/hpvm/projects/onnx_frontend/setup.py
similarity index 100%
rename from hpvm/projects/onnx/setup.py
rename to hpvm/projects/onnx_frontend/setup.py