diff --git a/Examples/Image/Classification/VGG/Python/README.md b/Examples/Image/Classification/VGG/Python/README.md deleted file mode 100644 index 839500972700..000000000000 --- a/Examples/Image/Classification/VGG/Python/README.md +++ /dev/null @@ -1,27 +0,0 @@ -# CNTK Examples: Image/Classification/VGG - -## Python - -### VGG16_ImageNet_Distributed.py - -This is the VGG model that contains 16 layers, which was referred as `ConvNet configuration D` in the [original paper](https://arxiv.org/pdf/1409.1556v6.pdf). - -Run the example from the current folder using: - -`python VGG16_ImageNet_Distributed.py` - -To run it in a distributed manner, please check [here](https://github.com/Microsoft/CNTK/wiki/Multiple-GPUs-and-machines#32-python). For example, the command for distributed training on the same machine (with multiple GPUs) with Windows is: - -`mpiexec -n <#workers> python VGG16_ImageNet_Distributed.py` - -### VGG19_ImageNet_Distributed.py - -This is the VGG model that contains 19 layers, which was referred as `ConvNet configuration E` in the [original paper](https://arxiv.org/pdf/1409.1556v6.pdf). - -Run the example from the current folder using: - -`python VGG19_ImageNet_Distributed.py` - -To run it in a distributed manner, please check [here](https://github.com/Microsoft/CNTK/wiki/Multiple-GPUs-and-machines#32-python). For example, the command for distributed training on the same machine (with multiple GPUs) with Windows is: - -`mpiexec -n <#workers> python VGG19_ImageNet_Distributed.py` diff --git a/Examples/Image/Classification/VGG/Python/VGG16_ImageNet_Distributed.py b/Examples/Image/Classification/VGG/Python/VGG16_ImageNet_Distributed.py deleted file mode 100644 index 39dc0884a728..000000000000 --- a/Examples/Image/Classification/VGG/Python/VGG16_ImageNet_Distributed.py +++ /dev/null @@ -1,236 +0,0 @@ -# Copyright (c) Microsoft. All rights reserved. - -# Licensed under the MIT license. See LICENSE.md file in the project root -# for full license information. -# ============================================================================== - -from __future__ import print_function -import os -import math -import argparse -import numpy as np -import _cntk_py -import cntk.io.transforms as xforms - -import cntk as C -from cntk.logging import * -from cntk.ops import * -from cntk.distributed import data_parallel_distributed_learner, Communicator -from cntk.io import ImageDeserializer, MinibatchSource, StreamDef, StreamDefs, FULL_DATA_SWEEP -from cntk.layers import Placeholder, Convolution2D, Activation, MaxPooling, Dense, Dropout, default_options, Sequential, For -from cntk.initializer import normal -from cntk.training_session import * - -# default Paths relative to current python file. -abs_path = os.path.dirname(os.path.abspath(__file__)) -data_path = os.path.join(abs_path, "..", "..", "..", "DataSets", "ImageNet") -model_path = os.path.join(abs_path, "Models") -log_dir = None - -# model dimensions -image_height = 224 -image_width = 224 -num_channels = 3 # RGB -num_classes = 1000 -model_name = "VGG16.model" - -# Create a minibatch source. -def create_image_mb_source(map_file, is_training, total_number_of_samples): - if not os.path.exists(map_file): - raise RuntimeError("File '%s' does not exist." %map_file) - - # transformation pipeline for the features has jitter/crop only when training - transforms = [] - if is_training: - transforms += [ - xforms.crop(crop_type='randomside', side_ratio=0.4375:0.875, jitter_type='uniratio') # train uses jitter - ] - else: - transforms += [ - xforms.crop(crop_type='center', side_ratio=0.5833333) # test has no jitter - ] - - transforms += [ - xforms.scale(width=image_width, height=image_height, channels=num_channels, interpolations='linear'), - ] - - # deserializer - return MinibatchSource( - ImageDeserializer(map_file, StreamDefs( - features = StreamDef(field='image', transforms=transforms), # first column in map file is referred to as 'image' - labels = StreamDef(field='label', shape=num_classes))), # and second as 'label' - randomize = is_training, - max_samples=total_number_of_samples, - multithreaded_deserializer = True) - -# Create the network. -def create_vgg16(): - - # Input variables denoting the features and label data - feature_var = C.input_variable((num_channels, image_height, image_width)) - label_var = C.input_variable((num_classes)) - - # apply model to input - # remove mean value - input = minus(feature_var, constant([[[104]], [[117]], [[124]]]), name='mean_removed_input') - - with default_options(activation=None, pad=True, bias=True): - z = Sequential([ - # we separate Convolution and ReLU to name the output for feature extraction (usually before ReLU) - For(range(2), lambda i: [ - Convolution2D((3,3), 64, name='conv1_{}'.format(i)), - Activation(activation=relu, name='relu1_{}'.format(i)), - ]), - MaxPooling((2,2), (2,2), name='pool1'), - - For(range(2), lambda i: [ - Convolution2D((3,3), 128, name='conv2_{}'.format(i)), - Activation(activation=relu, name='relu2_{}'.format(i)), - ]), - MaxPooling((2,2), (2,2), name='pool2'), - - For(range(3), lambda i: [ - Convolution2D((3,3), 256, name='conv3_{}'.format(i)), - Activation(activation=relu, name='relu3_{}'.format(i)), - ]), - MaxPooling((2,2), (2,2), name='pool3'), - - For(range(3), lambda i: [ - Convolution2D((3,3), 512, name='conv4_{}'.format(i)), - Activation(activation=relu, name='relu4_{}'.format(i)), - ]), - MaxPooling((2,2), (2,2), name='pool4'), - - For(range(3), lambda i: [ - Convolution2D((3,3), 512, name='conv5_{}'.format(i)), - Activation(activation=relu, name='relu5_{}'.format(i)), - ]), - MaxPooling((2,2), (2,2), name='pool5'), - - Dense(4096, name='fc6'), - Activation(activation=relu, name='relu6'), - Dropout(0.5, name='drop6'), - Dense(4096, name='fc7'), - Activation(activation=relu, name='relu7'), - Dropout(0.5, name='drop7'), - Dense(num_classes, name='fc8') - ])(input) - - # loss and metric - ce = cross_entropy_with_softmax(z, label_var) - pe = classification_error(z, label_var) - pe5 = classification_error(z, label_var, topN=5) - - log_number_of_parameters(z) ; print() - - return { - 'feature': feature_var, - 'label': label_var, - 'ce' : ce, - 'pe' : pe, - 'pe5': pe5, - 'output': z - } - -# Create trainer -def create_trainer(network, epoch_size, num_quantization_bits, progress_printer): - # Set learning parameters - lr_per_mb = [0.01]*20 + [0.001]*20 + [0.0001]*20 + [0.00001]*10 + [0.000001] - lr_schedule = C.learning_rate_schedule(lr_per_mb, unit=C.learner.UnitType.minibatch, epoch_size=epoch_size) - mm_schedule = C.learner.momentum_schedule(0.9) - l2_reg_weight = 0.0005 # CNTK L2 regularization is per sample, thus same as Caffe - - # Create learner - local_learner = C.learner.momentum_sgd(network['output'].parameters, lr_schedule, mm_schedule, unit_gain=False, l2_regularization_weight=l2_reg_weight) - # Since we reuse parameter settings (learning rate, momentum) from Caffe, we set unit_gain to False to ensure consistency - parameter_learner = data_parallel_distributed_learner( - local_learner, - num_quantization_bits=num_quantization_bits, - distributed_after=0) - - # Create trainer - return C.Trainer(network['output'], (network['ce'], network['pe']), parameter_learner, progress_printer) - -# Train and test -def train_and_test(network, trainer, train_source, test_source, minibatch_size, epoch_size, restore): - - # define mapping from intput streams to network inputs - input_map = { - network['feature']: train_source.streams.features, - network['label']: train_source.streams.labels - } - - mb_size_schedule = C.minibatch_size_schedule(minibatch_size) - - # Train all minibatches - training_session( - trainer=trainer, mb_source = train_source, - model_inputs_to_streams = input_map, - mb_size_schedule = mb_size_schedule, - progress_frequency=epoch_size, - checkpoint_config = CheckpointConfig(filename = os.path.join(model_path, model_name), restore=restore), - test_config = TestConfig(source=test_source, mb_size=mb_size_schedule) - ).train() - -# Train and evaluate the network. -def vgg16_train_and_eval(train_data, test_data, num_quantization_bits=32, minibatch_size=128, epoch_size = 1281167, max_epochs=80, - restore=True, log_to_file=None, num_mbs_per_log=None, gen_heartbeat=False): - _cntk_py.set_computation_network_trace_level(0) - - progress_printer = ProgressPrinter( - freq=num_mbs_per_log, - tag='Training', - log_to_file=log_to_file, - rank=Communicator.rank(), - gen_heartbeat=gen_heartbeat, - num_epochs=max_epochs) - - network = create_vgg16() - trainer = create_trainer(network, epoch_size, num_quantization_bits, progress_printer) - train_source = create_image_mb_source(train_data, True, total_number_of_samples=max_epochs * epoch_size) - test_source = create_image_mb_source(test_data, False, total_number_of_samples=FULL_DATA_SWEEP) - train_and_test(network, trainer, train_source, test_source, minibatch_size, epoch_size, restore) - - -if __name__=='__main__': - - parser = argparse.ArgumentParser() - - parser.add_argument('-datadir', '--datadir', help='Data directory where the ImageNet dataset is located', required=False, default=data_path) - parser.add_argument('-outputdir', '--outputdir', help='Output directory for checkpoints and models', required=False, default=None) - parser.add_argument('-logdir', '--logdir', help='Log file', required=False, default=None) - parser.add_argument('-n', '--num_epochs', help='Total number of epochs to train', type=int, required=False, default='80') - parser.add_argument('-m', '--minibatch_size', help='Minibatch size', type=int, required=False, default='128') - parser.add_argument('-e', '--epoch_size', help='Epoch size', type=int, required=False, default='1281167') - parser.add_argument('-q', '--quantized_bits', help='Number of quantized bits used for gradient aggregation', type=int, required=False, default='32') - parser.add_argument('-r', '--restart', help='Indicating whether to restart from scratch (instead of restart from checkpoint file by default)', action='store_true') - parser.add_argument('-device', '--device', type=int, help="Force to run the script on a specified device", required=False, default=None) - - args = vars(parser.parse_args()) - - if args['outputdir'] is not None: - model_path = args['outputdir'] + "/models" - if args['datadir'] is not None: - data_path = args['datadir'] - if args['logdir'] is not None: - log_dir = args['logdir'] - if args['device'] is not None: - C.device.try_set_default_device(C.device.gpu(args['device'])) - - if not os.path.isdir(data_path): - raise RuntimeError("Directory %s does not exist" % data_path) - - train_data=os.path.join(data_path, 'train_map.txt') - test_data=os.path.join(data_path, 'val_map.txt') - - vgg16_train_and_eval(train_data, test_data, - minibatch_size=args['minibatch_size'], - epoch_size=args['epoch_size'], - num_quantization_bits=args['quantized_bits'], - max_epochs=args['num_epochs'], - restore=not args['restart'], - log_to_file=args['logdir'], - num_mbs_per_log=200, - gen_heartbeat=True) - # Must call MPI finalize when process exit without exceptions - cntk.distributed.Communicator.finalize() diff --git a/Examples/Image/Classification/VGG/Python/VGG19_ImageNet_Distributed.py b/Examples/Image/Classification/VGG/Python/VGG19_ImageNet_Distributed.py deleted file mode 100644 index 5b2f09ba4378..000000000000 --- a/Examples/Image/Classification/VGG/Python/VGG19_ImageNet_Distributed.py +++ /dev/null @@ -1,230 +0,0 @@ -# Copyright (c) Microsoft. All rights reserved. - -# Licensed under the MIT license. See LICENSE.md file in the project root -# for full license information. -# ============================================================================== - -from __future__ import print_function -import os -import math -import argparse -import numpy as np -import cntk as C -import _cntk_py - -from cntk.logging import * -from cntk.ops import * -from cntk.distributed import data_parallel_distributed_learner, Communicator -from cntk.io import ImageDeserializer, MinibatchSource, StreamDef, StreamDefs, FULL_DATA_SWEEP -from cntk.layers import Placeholder, Block, Convolution2D, Activation, MaxPooling, Dense, Dropout, default_options, Sequential, For -from cntk.initializer import normal -from cntk.train.training_session import * - -# default Paths relative to current python file. -abs_path = os.path.dirname(os.path.abspath(__file__)) -data_path = os.path.join(abs_path, "..", "..", "..", "DataSets", "ImageNet") -model_path = os.path.join(abs_path, "Models") -log_dir = None - -# model dimensions -image_height = 224 -image_width = 224 -num_channels = 3 # RGB -num_classes = 1000 -model_name = "VGG19.model" - -# Create a minibatch source. -def create_image_mb_source(map_file, is_training, total_number_of_samples): - if not os.path.exists(map_file): - raise RuntimeError("File '%s' does not exist." %map_file) - - # transformation pipeline for the features has jitter/crop only when training - transforms = [] - if is_training: - transforms += [ - ImageDeserializer.crop(crop_type='randomside', side_ratio='0.4375:0.875', jitter_type='uniratio') # train uses jitter - ] - else: - transforms += [ - ImageDeserializer.crop(crop_type='center', side_ratio=0.5833333) # test has no jitter - ] - - transforms += [ - ImageDeserializer.scale(width=image_width, height=image_height, channels=num_channels, interpolations='linear'), - ] - - # deserializer - return MinibatchSource( - ImageDeserializer(map_file, StreamDefs( - features = StreamDef(field='image', transforms=transforms), # first column in map file is referred to as 'image' - labels = StreamDef(field='label', shape=num_classes))), # and second as 'label' - randomize = is_training, - max_samples=total_number_of_samples, - multithreaded_deserializer = True) - -# Create the network. -def create_vgg19(): - - # Input variables denoting the features and label data - feature_var = C.input_variable((num_channels, image_height, image_width)) - label_var = C.input_variable((num_classes)) - - # apply model to input - # remove mean value - input = minus(feature_var, constant([[[104]], [[117]], [[124]]]), name='mean_removed_input') - - with default_options(activation=None, pad=True, bias=True): - z = Sequential([ - # we separate Convolution and ReLU to name the output for feature extraction (usually before ReLU) - For(range(2), lambda i: [ - Convolution2D((3,3), 64, name='conv1_{}'.format(i)), - Activation(activation=relu, name='relu1_{}'.format(i)), - ]), - MaxPooling((2,2), (2,2), name='pool1'), - - For(range(2), lambda i: [ - Convolution2D((3,3), 128, name='conv2_{}'.format(i)), - Activation(activation=relu, name='relu2_{}'.format(i)), - ]), - MaxPooling((2,2), (2,2), name='pool2'), - - For(range(4), lambda i: [ - Convolution2D((3,3), 256, name='conv3_{}'.format(i)), - Activation(activation=relu, name='relu3_{}'.format(i)), - ]), - MaxPooling((2,2), (2,2), name='pool3'), - - For(range(4), lambda i: [ - Convolution2D((3,3), 512, name='conv4_{}'.format(i)), - Activation(activation=relu, name='relu4_{}'.format(i)), - ]), - MaxPooling((2,2), (2,2), name='pool4'), - - For(range(4), lambda i: [ - Convolution2D((3,3), 512, name='conv5_{}'.format(i)), - Activation(activation=relu, name='relu5_{}'.format(i)), - ]), - MaxPooling((2,2), (2,2), name='pool5'), - - Dense(4096, name='fc6'), - Activation(activation=relu, name='relu6'), - Dropout(0.5, name='drop6'), - Dense(4096, name='fc7'), - Activation(activation=relu, name='relu7'), - Dropout(0.5, name='drop7'), - Dense(num_classes, name='fc8') - ])(input) - - # loss and metric - ce = cross_entropy_with_softmax(z, label_var) - pe = classification_error(z, label_var) - pe5 = classification_error(z, label_var, topN=5) - - log_number_of_parameters(z) ; print() - - return { - 'feature': feature_var, - 'label': label_var, - 'ce' : ce, - 'pe' : pe, - 'pe5': pe5, - 'output': z - } - -# Create trainer -def create_trainer(network, epoch_size, num_quantization_bits, progress_printer): - # Set learning parameters - lr_per_mb = [0.01]*20 + [0.001]*20 + [0.0001]*20 + [0.00001]*10 + [0.000001] - lr_schedule = C.learning_rate_schedule(lr_per_mb, unit=C.learner.UnitType.minibatch, epoch_size=epoch_size) - mm_schedule = C.learner.momentum_schedule(0.9) - l2_reg_weight = 0.0005 # CNTK L2 regularization is per sample, thus same as Caffe - - # Create learner - local_learner = C.learner.momentum_sgd(network['output'].parameters, lr_schedule, mm_schedule, unit_gain=False, l2_regularization_weight=l2_reg_weight) - # Since we reuse parameter settings (learning rate, momentum) from Caffe, we set unit_gain to False to ensure consistency - parameter_learner = data_parallel_distributed_learner( - local_learner, - num_quantization_bits=num_quantization_bits, - distributed_after=0) - - # Create trainer - return C.Trainer(network['output'], (network['ce'], network['pe']), parameter_learner, progress_printer) - -# Train and test -def train_and_test(network, trainer, train_source, test_source, minibatch_size, epoch_size, restore): - - # define mapping from intput streams to network inputs - input_map = { - network['feature']: train_source.streams.features, - network['label']: train_source.streams.labels - } - - # Train all minibatches - training_session( - trainer=trainer, mb_source = train_source, - model_inputs_to_streams = input_map, - mb_size = minibatch_size, - progress_frequency=epoch_size, - checkpoint_config = CheckpointConfig(filename = os.path.join(model_path, model_name), restore=restore), - test_config = TestConfig(source=test_source, mb_size=minibatch_size) - ).train() - -# Train and evaluate the network. -def vgg19_train_and_eval(train_data, test_data, num_quantization_bits=32, minibatch_size=128, epoch_size = 1281167, max_epochs=80, - restore=True, log_to_file=None, num_mbs_per_log=None, gen_heartbeat=False): - _cntk_py.set_computation_network_trace_level(0) - - progress_printer = ProgressPrinter( - freq=num_mbs_per_log, - tag='Training', - log_to_file=log_to_file, - rank=Communicator.rank(), - gen_heartbeat=gen_heartbeat, - num_epochs=max_epochs) - - network = create_vgg19() - trainer = create_trainer(network, epoch_size, num_quantization_bits, progress_printer) - train_source = create_image_mb_source(train_data, True, total_number_of_samples=max_epochs * epoch_size) - test_source = create_image_mb_source(test_data, False, total_number_of_samples=FULL_DATA_SWEEP) - train_and_test(network, trainer, train_source, test_source, minibatch_size, epoch_size, restore) - - -if __name__=='__main__': - - parser = argparse.ArgumentParser() - - parser.add_argument('-datadir', '--datadir', help='Data directory where the ImageNet dataset is located', required=False, default=data_path) - parser.add_argument('-outputdir', '--outputdir', help='Output directory for checkpoints and models', required=False, default=None) - parser.add_argument('-logdir', '--logdir', help='Log file', required=False, default=None) - parser.add_argument('-n', '--num_epochs', help='Total number of epochs to train', type=int, required=False, default='80') - parser.add_argument('-m', '--minibatch_size', help='Minibatch size', type=int, required=False, default='128') - parser.add_argument('-e', '--epoch_size', help='Epoch size', type=int, required=False, default='1281167') - parser.add_argument('-q', '--quantized_bits', help='Number of quantized bits used for gradient aggregation', type=int, required=False, default='32') - parser.add_argument('-r', '--restart', help='Indicating whether to restart from scratch (instead of restart from checkpoint file by default)', action='store_true') - parser.add_argument('-device', '--device', type=int, help="Force to run the script on a specified device", required=False, default=None) - - args = vars(parser.parse_args()) - - if args['outputdir'] is not None: - model_path = args['outputdir'] + "/models" - if args['datadir'] is not None: - data_path = args['datadir'] - if args['logdir'] is not None: - log_dir = args['logdir'] - if args['device'] is not None: - C.device.try_set_default_device(C.device.gpu(args['device'])) - - train_data=os.path.join(data_path, 'train_map.txt') - test_data=os.path.join(data_path, 'val_map.txt') - - vgg19_train_and_eval(train_data, test_data, - minibatch_size=args['minibatch_size'], - epoch_size=args['epoch_size'], - num_quantization_bits=args['quantized_bits'], - max_epochs=args['num_epochs'], - restore=not args['restart'], - log_to_file=args['logdir'], - num_mbs_per_log=200, - gen_heartbeat=True) - # Must call MPI finalize when process exit without exceptions - cntk.distributed.Communicator.finalize() diff --git a/Examples/Image/Classification/VGG/README.md b/Examples/Image/Classification/VGG/README.md index 0c050d24c032..a6c39b86a99f 100644 --- a/Examples/Image/Classification/VGG/README.md +++ b/Examples/Image/Classification/VGG/README.md @@ -18,9 +18,7 @@ ILSVRC2012 datasets are not included in the CNTK distribution. You may obtain it ## Details -We give examples for both Python and BrainScript. - -### [Python](./Python) +We give examples for BrainScript. ### [BrainScript](./BrainScript) diff --git a/Examples/PythonExamples.pyproj b/Examples/PythonExamples.pyproj index b21e4c192e06..0ed1f98ced72 100644 --- a/Examples/PythonExamples.pyproj +++ b/Examples/PythonExamples.pyproj @@ -95,9 +95,6 @@ Tests\EndToEndTests\cntkv2Python\Examples\transfer_learning_test.py - - Tests\EndToEndTests\cntkv2Python\Examples\VGG16_ImageNet_Distributed_test.py - Tests\EndToEndTests\cntkv2Python\Examples\word_rnn_test.py @@ -353,4 +350,4 @@ - \ No newline at end of file + diff --git a/Tests/EndToEndTests/CNTKv2Python/Examples/VGG16_ImageNet_Distributed_test.py b/Tests/EndToEndTests/CNTKv2Python/Examples/VGG16_ImageNet_Distributed_test.py deleted file mode 100644 index ed0e1f46c772..000000000000 --- a/Tests/EndToEndTests/CNTKv2Python/Examples/VGG16_ImageNet_Distributed_test.py +++ /dev/null @@ -1,36 +0,0 @@ -# Copyright (c) Microsoft. All rights reserved. - -# Licensed under the MIT license. See LICENSE.md file in the project root -# for full license information. -# ============================================================================== - -import numpy as np -import os -import sys -import signal -import subprocess -import re -import pytest -from cntk.ops.tests.ops_test_utils import cntk_device -from cntk.cntk_py import DeviceKind_GPU -from cntk.device import try_set_default_device - -abs_path = os.path.dirname(os.path.abspath(__file__)) -sys.path.append(abs_path) -example_dir = os.path.join(abs_path, "..", "..", "..", "..", "Examples", "Image", "Classification", "VGG", "Python") -sys.path.append(example_dir) -from prepare_test_data import prepare_ImageNet_data -from distributed_common import mpiexec_test -script_under_test = os.path.join(example_dir, "VGG16_ImageNet_Distributed.py") - -#mpiexec_params = [ "-n", "2"] -# -#def test_alexnet_imagenet_distributed(device_id): -# params = [ "-n", "2", -# "-m", "1", -# "-e", "2", -# "-datadir", prepare_ImageNet_data(), -# "-q", "32", -# "-r", -# "-device", str(device_id) ] -# mpiexec_test(device_id, script_under_test, mpiexec_params, params, 0.99, True) diff --git a/Tools/samples.json b/Tools/samples.json index 941ac9d008fa..1330e714a23d 100644 --- a/Tools/samples.json +++ b/Tools/samples.json @@ -247,7 +247,7 @@ "name": "VGG", "url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Image/Classification/VGG", "description": "Deep CNN from University of Oxford. This was the winning model for the ILSVRC2014 localization task.", - "language": ["Python", "BrainScript"], + "language": ["BrainScript"], "type": ["Recipe"], "dateadded": "4/14/2017" },