From 0209264f75f7e5ef70ab0a4b049b19adff136d1f Mon Sep 17 00:00:00 2001
From: Neta Zmora <neta.zmora@intel.com>
Date: Thu, 30 May 2019 00:49:59 +0300
Subject: [PATCH] Added support from the MNIST dataset
Also added a simple network model for MNIST, under
distiller/models/mnist.
---
distiller/apputils/data_loaders.py | 65 ++++++++++++++++---
distiller/models/__init__.py | 25 +++++--
distiller/models/mnist/__init__.py | 19 ++++++
distiller/models/mnist/simplenet_mnist.py | 49 ++++++++++++++
.../compress_classifier.py | 4 +-
5 files changed, 143 insertions(+), 19 deletions(-)
create mode 100755 distiller/models/mnist/__init__.py
create mode 100755 distiller/models/mnist/simplenet_mnist.py
diff --git a/distiller/apputils/data_loaders.py b/distiller/apputils/data_loaders.py
index 874dbdc..381454d 100755
--- a/distiller/apputils/data_loaders.py
+++ b/distiller/apputils/data_loaders.py
@@ -25,10 +25,32 @@ import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data.sampler import Sampler
import numpy as np
-
import distiller
-DATASETS_NAMES = ['imagenet', 'cifar10']
+
+DATASETS_NAMES = ['imagenet', 'cifar10', 'mnist']
+
+
+def classification_dataset_str_from_arch(arch):
+ if 'cifar' in arch:
+ dataset = 'cifar10'
+ elif 'mnist' in arch:
+ dataset = 'mnist'
+ else:
+ dataset = 'imagenet'
+ return dataset
+
+
+def classification_num_classes(dataset):
+ return {'cifar10': 10,
+ 'mnist': 10,
+ 'imagenet': 1000}.get(dataset, None)
+
+
+def __dataset_factory(dataset):
+ return {'cifar10': cifar10_get_datasets,
+ 'mnist': mnist_get_datasets,
+ 'imagenet': imagenet_get_datasets}.get(dataset, None)
def load_data(dataset, data_dir, batch_size, workers, validation_split=0.1, deterministic=False,
@@ -45,20 +67,43 @@ def load_data(dataset, data_dir, batch_size, workers, validation_split=0.1, dete
deterministic: set to True if you want the data loading process to be deterministic.
Note that deterministic data loading suffers from poor performance.
effective_train/valid/test_size: portion of the datasets to load on each epoch.
- The subset is chosen randomly each time. For the training and validation sets, this is applied AFTER
- the split to those sets according to the validation_split parameter
- fixed_subset: set to True to keep the same subset of data throughout the run (the size of the subset
- is still determined according to the effective_train/valid/test_size args)
+ The subset is chosen randomly each time. For the training and validation sets,
+ this is applied AFTER the split to those sets according to the validation_split parameter
+ fixed_subset: set to True to keep the same subset of data throughout the run
+ (the size of the subset is still determined according to the effective_train/valid/test
+ size args)
"""
if dataset not in DATASETS_NAMES:
raise ValueError('load_data does not support dataset %s" % dataset')
- datasets_fn = cifar10_get_datasets if dataset == 'cifar10' else imagenet_get_datasets
- return get_data_loaders(datasets_fn, data_dir, batch_size, workers, validation_split=validation_split,
- deterministic=deterministic, effective_train_size=effective_train_size,
- effective_valid_size=effective_valid_size, effective_test_size=effective_test_size,
+ datasets_fn = __dataset_factory(dataset)
+ return get_data_loaders(datasets_fn, data_dir, batch_size, workers,
+ validation_split=validation_split,
+ deterministic=deterministic,
+ effective_train_size=effective_train_size,
+ effective_valid_size=effective_valid_size,
+ effective_test_size=effective_test_size,
fixed_subset=fixed_subset)
+def mnist_get_datasets(data_dir):
+ """Load the MNIST dataset."""
+ train_transform = transforms.Compose([
+ transforms.ToTensor(),
+ transforms.Normalize((0.1307,), (0.3081,))
+ ])
+ train_dataset = datasets.MNIST(root=data_dir, train=True,
+ download=True, transform=train_transform)
+
+ test_transform = transforms.Compose([
+ transforms.ToTensor(),
+ transforms.Normalize((0.1307,), (0.3081,))
+ ])
+ test_dataset = datasets.MNIST(root=data_dir, train=False,
+ transform=test_transform)
+
+ return train_dataset, test_dataset
+
+
def cifar10_get_datasets(data_dir):
"""Load the CIFAR10 dataset.
diff --git a/distiller/models/__init__.py b/distiller/models/__init__.py
index 30e2c8d..8c3969a 100755
--- a/distiller/models/__init__.py
+++ b/distiller/models/__init__.py
@@ -19,6 +19,7 @@
import torch
import torchvision.models as torch_models
from . import cifar10 as cifar10_models
+from . import mnist as mnist_models
from . import imagenet as imagenet_extra_models
import pretrainedmodels
@@ -41,8 +42,12 @@ CIFAR10_MODEL_NAMES = sorted(name for name in cifar10_models.__dict__
if name.islower() and not name.startswith("__")
and callable(cifar10_models.__dict__[name]))
+MNIST_MODEL_NAMES = sorted(name for name in mnist_models.__dict__
+ if name.islower() and not name.startswith("__")
+ and callable(mnist_models.__dict__[name]))
+
ALL_MODEL_NAMES = sorted(map(lambda s: s.lower(),
- set(IMAGENET_MODEL_NAMES + CIFAR10_MODEL_NAMES)))
+ set(IMAGENET_MODEL_NAMES + CIFAR10_MODEL_NAMES + MNIST_MODEL_NAMES)))
def create_model(pretrained, dataset, arch, parallel=True, device_ids=None):
@@ -69,9 +74,8 @@ def create_model(pretrained, dataset, arch, parallel=True, device_ids=None):
elif (arch in imagenet_extra_models.__dict__) and not pretrained:
model = imagenet_extra_models.__dict__[arch]()
elif arch in pretrainedmodels.model_names:
- model = pretrainedmodels.__dict__[arch](
- num_classes=1000,
- pretrained=(dataset if pretrained else None))
+ model = pretrainedmodels.__dict__[arch](num_classes=1000,
+ pretrained=(dataset if pretrained else None))
else:
error_message = ''
if arch not in IMAGENET_MODEL_NAMES:
@@ -80,8 +84,6 @@ def create_model(pretrained, dataset, arch, parallel=True, device_ids=None):
error_message = "Model {} (ImageNet) does not have a pretrained model".format(arch)
raise ValueError(error_message or 'Failed to find model {}'.format(arch))
- msglogger.info("=> using {p}{a} model for ImageNet".format(a=arch,
- p=('pretrained ' if pretrained else '')))
elif dataset == 'cifar10':
if pretrained:
raise ValueError("Model {} (CIFAR10) does not have a pretrained model".format(arch))
@@ -89,10 +91,19 @@ def create_model(pretrained, dataset, arch, parallel=True, device_ids=None):
model = cifar10_models.__dict__[arch]()
except KeyError:
raise ValueError("Model {} is not supported for dataset CIFAR10".format(arch))
- msglogger.info("=> creating %s model for CIFAR10" % arch)
+
+ elif dataset == 'mnist':
+ if pretrained:
+ raise ValueError("Model {} (MNIST) does not have a pretrained model".format(arch))
+ try:
+ model = mnist_models.__dict__[arch]()
+ except KeyError:
+ raise ValueError("Model {} is not supported for dataset MNIST".format(arch))
else:
raise ValueError('Could not recognize dataset {}'.format(dataset))
+ msglogger.info("=> creating a %s%s model with the %s dataset" % ('pretrained ' if pretrained else '',
+ arch, dataset))
if torch.cuda.is_available() and device_ids != -1:
device = 'cuda'
if (arch.startswith('alexnet') or arch.startswith('vgg')) and parallel:
diff --git a/distiller/models/mnist/__init__.py b/distiller/models/mnist/__init__.py
new file mode 100755
index 0000000..125515e
--- /dev/null
+++ b/distiller/models/mnist/__init__.py
@@ -0,0 +1,19 @@
+#
+# Copyright (c) 2018 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""This package contains MNIST image classification models for pytorch"""
+
+from .simplenet_mnist import *
\ No newline at end of file
diff --git a/distiller/models/mnist/simplenet_mnist.py b/distiller/models/mnist/simplenet_mnist.py
new file mode 100755
index 0000000..3851507
--- /dev/null
+++ b/distiller/models/mnist/simplenet_mnist.py
@@ -0,0 +1,49 @@
+#
+# Copyright (c) 2018 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#Â Â Â Â Â http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""An implementation of a trivial MNIST model.
+Â
+The original network definition is sourced here: https://github.com/pytorch/examples/blob/master/mnist/main.py
+"""
+
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+__all__ = ['simplenet_mnist']
+
+
+class Simplenet(nn.Module):
+ def __init__(self):
+ super().__init__()
+ self.conv1 = nn.Conv2d(1, 20, 5, 1)
+ self.conv2 = nn.Conv2d(20, 50, 5, 1)
+ self.fc1 = nn.Linear(4*4*50, 500)
+ self.fc2 = nn.Linear(500, 10)
+
+ def forward(self, x):
+ x = F.relu(self.conv1(x))
+ x = F.max_pool2d(x, 2, 2)
+ x = F.relu(self.conv2(x))
+ x = F.max_pool2d(x, 2, 2)
+ x = x.view(-1, 4*4*50)
+ x = F.relu(self.fc1(x))
+ x = self.fc2(x)
+ return F.log_softmax(x, dim=1)
+
+def simplenet_mnist():
+ model = Simplenet()
+ return model
diff --git a/examples/classifier_compression/compress_classifier.py b/examples/classifier_compression/compress_classifier.py
index b4e81f0..0f5b78c 100755
--- a/examples/classifier_compression/compress_classifier.py
+++ b/examples/classifier_compression/compress_classifier.py
@@ -138,8 +138,8 @@ def main():
torch.cuda.set_device(args.gpus[0])
# Infer the dataset from the model name
- args.dataset = 'cifar10' if 'cifar' in args.arch else 'imagenet'
- args.num_classes = 10 if args.dataset == 'cifar10' else 1000
+ args.dataset = distiller.apputils.classification_dataset_str_from_arch(args.arch)
+ args.num_classes = distiller.apputils.classification_num_classes(args.dataset)
if args.earlyexit_thresholds:
args.num_exits = len(args.earlyexit_thresholds) + 1
--
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