Merging

llvm/projects/keras/src/vgg16_imagenet.py

@@ -4,102 +4,105 @@ import glob
 import scipy
 import scipy.io
 import cv2
+import numpy as np
 
 import keras
-from keras.models import Model
+from keras.models import Sequential, Model
 from keras.layers import *
 from keras.applications.vgg16 import VGG16, preprocess_input
 from keras.utils import to_categorical
+from keras import backend as K
 
 from frontend.approxhpvm_translator import translate_to_approxhpvm
 from frontend.weight_utils import dumpCalibrationData
 
+
+np.random.seed(0)
+
+os.environ["CUDA_VISIBLE_DEVICES"] = "1"
+
+K.set_image_data_format('channels_first')
+
+
+num_images = 5000
+val_size = 100
+
 data_format = 'channels_first'
 
+
+
+
 def get_vgg16_nchw_keras():
     img_input = Input(shape=(3, 224, 224))
     
     # Block 1
-    x = ZeroPadding2D(padding=(1, 1), data_format=data_format)(img_input)
     x = Conv2D(64, (3, 3),
-                      padding='valid',
-                      data_format=data_format)(x)
+                      padding='same',
+                      data_format=data_format)(img_input)
     x = Activation('relu')(x)
-    x = ZeroPadding2D(padding=(1, 1), data_format=data_format)(x)
     x = Conv2D(64, (3, 3),
-                      padding='valid',
+                      padding='same',
                       data_format=data_format)(x)
     x = Activation('relu')(x)
     x = MaxPooling2D((2, 2), strides=(2, 2), data_format=data_format)(x)
 
     # Block 2
-    x = ZeroPadding2D(padding=(1, 1), data_format=data_format)(x)
     x = Conv2D(128, (3, 3),
-                      padding='valid',
+                      padding='same',
                       data_format=data_format)(x)
     x = Activation('relu')(x)
-    x = ZeroPadding2D(padding=(1, 1), data_format=data_format)(x)
     x = Conv2D(128, (3, 3),
-                      padding='valid',
+                      padding='same',
                       data_format=data_format)(x)
     x = Activation('relu')(x)
     x = MaxPooling2D((2, 2), strides=(2, 2), data_format=data_format)(x)
 
     # Block 3
-    x = ZeroPadding2D(padding=(1, 1), data_format=data_format)(x)
     x = Conv2D(256, (3, 3),
-                      padding='valid',
+                      padding='same',
                       data_format=data_format)(x)
     x = Activation('relu')(x)
-    x = ZeroPadding2D(padding=(1, 1), data_format=data_format)(x)
     x = Conv2D(256, (3, 3),
-                      padding='valid',
+                      padding='same',
                       data_format=data_format)(x)
     x = Activation('relu')(x)
-    x = ZeroPadding2D(padding=(1, 1), data_format=data_format)(x)
     x = Conv2D(256, (3, 3),
-                      padding='valid',
+                      padding='same',
                       data_format=data_format)(x)
     x = Activation('relu')(x)
     x = MaxPooling2D((2, 2), strides=(2, 2), data_format=data_format)(x)
 
     # Block 4
-    x = ZeroPadding2D(padding=(1, 1), data_format=data_format)(x)
     x = Conv2D(512, (3, 3),
-                      padding='valid',
+                      padding='same',
                       data_format=data_format)(x)
     x = Activation('relu')(x)
-    x = ZeroPadding2D(padding=(1, 1), data_format=data_format)(x)
     x = Conv2D(512, (3, 3),
-                      padding='valid',
+                      padding='same',
                       data_format=data_format)(x)
     x = Activation('relu')(x)
-    x = ZeroPadding2D(padding=(1, 1), data_format=data_format)(x)
     x = Conv2D(512, (3, 3),
-                      padding='valid',
+                      padding='same',
                       data_format=data_format)(x)
     x = Activation('relu')(x)
     x = MaxPooling2D((2, 2), strides=(2, 2), data_format=data_format)(x)
 
     # Block 5
-    x = ZeroPadding2D(padding=(1, 1), data_format=data_format)(x)
     x = Conv2D(512, (3, 3),
-                      padding='valid',
+                      padding='same',
                       data_format=data_format)(x)
     x = Activation('relu')(x)
-    x = ZeroPadding2D(padding=(1, 1), data_format=data_format)(x)
     x = Conv2D(512, (3, 3),
-                      padding='valid',
+                      padding='same',
                       data_format=data_format)(x)
     x = Activation('relu')(x)
-    x = ZeroPadding2D(padding=(1, 1), data_format=data_format)(x)
     x = Conv2D(512, (3, 3),
-                      padding='valid',
+                      padding='same',
                       data_format=data_format)(x)
     x = Activation('relu')(x)
     x = MaxPooling2D((2, 2), strides=(2, 2), data_format=data_format)(x)
 
-    x = Flatten(data_format=data_format)(x)
+    x = Flatten(data_format='channels_last')(x)
     
     x = Dense(4096)(x)
     x = Activation('relu')(x)
@@ -114,11 +117,6 @@ def get_vgg16_nchw_keras():
 
 
 def load_image(x):
-    try:
-        x = x.decode('utf-8')
-    except:
-        pass
-    
     image = cv2.imread(x)
         
     height, width, _ = image.shape
@@ -132,88 +130,82 @@ def load_image(x):
     image = image[starty:starty + 224, startx:startx + 224]
     
     image = image[:, :, ::-1]
-    image = preprocess_input(image.astype(np.float32))
     image = np.transpose(image, (2, 0, 1))
+    image = preprocess_input(image.astype(np.float32), data_format=data_format)
         
     return image.astype(np.float32)
 
 
-if __name__ == '__main__':
 
-    os.environ["CUDA_VISIBLE_DEVICES"] = "1"
-    
-    num_images = 5000
-    val_size = 100
-    
-    model = VGG16()
-    model_nchw = get_vgg16_nchw_keras()   
-    
-    j = 0
-    for i in range(len(model_nchw.layers)):
-        if 'padding' in model_nchw.layers[i].name or 'activation' in model_nchw.layers[i].name:
-            continue
-        try:
-            model_nchw.layers[i].set_weights(model.layers[j].get_weights())
-        except:
-            print (i, model_nchw.layers[i], 'skipped')
-        j += 1
-    
-    
-    classes = os.listdir('/home/nz11/ILSVRC2012/train')
-    train_images = glob.glob('/home/nz11/ILSVRC2012/train/*/*')
-    val_images = glob.glob('/home/nz11/ILSVRC2012/val/*/*')
-    val_images = sorted(val_images, key=lambda x: x.split('/')[-1].split('_')[-1].split('.')[0])
+model = VGG16()
+model_nchw = get_vgg16_nchw_keras()
 
-    idx = np.random.permutation(len(val_images))[:num_images]
-    val_images = np.array(val_images)[idx]
+j = 0
+for i in range(len(model_nchw.layers)):
+    if 'padding' in model_nchw.layers[i].name or 'activation' in model_nchw.layers[i].name:
+        continue
+    try:
+        model_nchw.layers[i].set_weights(model.layers[j].get_weights())
+    except:
+        print (i, model_nchw.layers[i], 'skipped')
+    j += 1
 
-    d = {k:v for v, k in enumerate(classes)}
-    
-    
 
-    X_test = []
-    for x in val_images:
-        X_test.append(load_image(x))
-    X_test = np.array(X_test)    
-        
-    
-    meta = scipy.io.loadmat("/home/nz11/ILSVRC2012/ILSVRC2012_devkit_t12/data/meta.mat")
-    original_idx_to_synset = {}
-    synset_to_name = {}
-
-    for i in range(1000):
-        ilsvrc2012_id = int(meta["synsets"][i,0][0][0][0])
-        synset = meta["synsets"][i,0][1][0]
-        name = meta["synsets"][i,0][2][0]
-        original_idx_to_synset[ilsvrc2012_id] = synset
-        synset_to_name[synset] = name
-
-    synset_to_keras_idx = {}
-    keras_idx_to_name = {}
-    f = open("/home/nz11/ILSVRC2012/ILSVRC2012_devkit_t12/data/synset_words.txt","r")
-    c = 0
-    for line in f:
-        parts = line.split(" ")
-        synset_to_keras_idx[parts[0]] = c
-        keras_idx_to_name[c] = " ".join(parts[1:])
-        c += 1
-    f.close()
-
-    def convert_original_idx_to_keras_idx(idx):
-        return synset_to_keras_idx[original_idx_to_synset[idx]]
-    
-    
-    f = open("/home/nz11/ILSVRC2012/ILSVRC2012_devkit_t12/data/ILSVRC2012_validation_ground_truth.txt","r")
+classes = os.listdir('/home/nz11/ILSVRC2012/train')
+train_images = glob.glob('/home/nz11/ILSVRC2012/train/*/*')
+val_images = glob.glob('/home/nz11/ILSVRC2012/val/*/*')
+val_images = sorted(val_images, key=lambda x: x.split('/')[-1].split('_')[-1].split('.')[0])
+
+idx = np.random.permutation(len(val_images))[:num_images]
+val_images = np.array(val_images)[idx]
+
+d = {k:v for v, k in enumerate(classes)}
+
+
+
+X_test = []
+for x in val_images:
+    X_test.append(load_image(x))
+X_test = np.array(X_test)    
+
+
+meta = scipy.io.loadmat("/home/nz11/ILSVRC2012/ILSVRC2012_devkit_t12/data/meta.mat")
+original_idx_to_synset = {}
+synset_to_name = {}
+
+for i in range(1000):
+    ilsvrc2012_id = int(meta["synsets"][i,0][0][0][0])
+    synset = meta["synsets"][i,0][1][0]
+    name = meta["synsets"][i,0][2][0]
+    original_idx_to_synset[ilsvrc2012_id] = synset
+    synset_to_name[synset] = name
+
+synset_to_keras_idx = {}
+keras_idx_to_name = {}
+f = open("/home/nz11/ILSVRC2012/ILSVRC2012_devkit_t12/data/synset_words.txt","r")
+c = 0
+for line in f:
+    parts = line.split(" ")
+    synset_to_keras_idx[parts[0]] = c
+    keras_idx_to_name[c] = " ".join(parts[1:])
+    c += 1
+f.close()
+
+def convert_original_idx_to_keras_idx(idx):
+    return synset_to_keras_idx[original_idx_to_synset[idx]]
+
+
+with open("/home/nz11/ILSVRC2012/ILSVRC2012_devkit_t12/data/ILSVRC2012_validation_ground_truth.txt","r") as f:
     y_true = f.read().strip().split("\n")
     y_true = list(map(int, y_true))
     y_true = np.array([convert_original_idx_to_keras_idx(idx) for idx in y_true])[idx]    
     y_true = y_true.astype(np.uint32)
-    f.close()
-        
-    
-    translate_to_approxhpvm(model_nchw, "data/vgg16_imagenet/", X_test[:val_size], y_true[:val_size], 1000)
-    
-    dumpCalibrationData("data/vgg16_imagenet/test_input.bin", X_test, "data/vgg16_imagenet/test_labels.bin", y_true)    
-    
-    
-    
+    y_true = np.expand_dims(y_true, axis=-1)
+
+
+translate_to_approxhpvm(model_nchw, "data/vgg16_imagenet/", X_test[:val_size], y_true[:val_size], 1000)
+
+dumpCalibrationData("data/vgg16_imagenet/test_input.bin", X_test, "data/vgg16_imagenet/test_labels.bin", y_true)    
+
+pred = np.argmax(model_nchw.predict(X_test), axis=1)
+print ('val accuracy', np.sum(pred == y_true.ravel()) / val_size)    
\ No newline at end of file