def test_cifar_resnet_error(device_id):
if cntk_device(device_id).type() != DeviceKind_GPU:
pytest.skip('test only runs on GPU')
set_default_device(cntk_device(device_id))
try:
base_path = os.path.join(os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY'],
*"Image/CIFAR/v0/cifar-10-batches-py".split("/"))
# N.B. CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY has {train,test}_map.txt
# and CIFAR-10_mean.xml in the base_path.
except KeyError:
base_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
*"../../../../Examples/Image/DataSets/CIFAR-10".split("/"))
base_path = os.path.normpath(base_path)
os.chdir(os.path.join(base_path, '..'))
from _cntk_py import set_computation_network_trace_level, set_fixed_random_seed, force_deterministic_algorithms
set_computation_network_trace_level(1)
set_fixed_random_seed(1) # BUGBUG: has no effect at present # TODO: remove debugging facilities once this all works
#force_deterministic_algorithms()
# TODO: do the above; they lead to slightly different results, so not doing it for now
reader_train = create_reader(os.path.join(base_path, 'train_map.txt'), os.path.join(base_path, 'CIFAR-10_mean.xml'), True)
reader_test = create_reader(os.path.join(base_path, 'test_map.txt'), os.path.join(base_path, 'CIFAR-10_mean.xml'), False)
test_error = train_and_evaluate(reader_train, reader_test, max_epochs=5)
expected_test_error = 0.282
assert np.allclose(test_error, expected_test_error,
atol=TOLERANCE_ABSOLUTE)
def test_cifar_resnet_error(device_id):
if cntk_device(device_id).type() != DeviceKind_GPU:
pytest.skip('test only runs on GPU')
set_default_device(cntk_device(device_id))
try:
base_path = os.path.join(
os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY'],
*"Image/CIFAR/v0/cifar-10-batches-py".split("/"))
# N.B. CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY has {train,test}_map.txt
# and CIFAR-10_mean.xml in the base_path.
except KeyError:
base_path = os.path.join(
os.path.dirname(os.path.abspath(__file__)),
*"../../../../Examples/Image/DataSets/CIFAR-10".split("/"))
base_path = os.path.normpath(base_path)
os.chdir(os.path.join(base_path, '..'))
from _cntk_py import set_computation_network_trace_level, set_fixed_random_seed, force_deterministic_algorithms
set_computation_network_trace_level(1)
set_fixed_random_seed(
1
) # BUGBUG: has no effect at present # TODO: remove debugging facilities once this all works
#force_deterministic_algorithms()
# TODO: do the above; they lead to slightly different results, so not doing it for now
reader_train = create_reader(os.path.join(base_path, 'train_map.txt'),
os.path.join(base_path, 'CIFAR-10_mean.xml'),
True)
reader_test = create_reader(os.path.join(base_path, 'test_map.txt'),
os.path.join(base_path, 'CIFAR-10_mean.xml'),
False)
test_error = train_and_evaluate(reader_train, reader_test, max_epochs=5)
expected_test_error = 0.384
assert np.allclose(test_error,
expected_test_error,
atol=TOLERANCE_ABSOLUTE)
def cifar_resnet_distributed(data_path, run_test, num_epochs, communicator=None, save_model_filename=None, load_model_filename=None, debug_output=False):
image_height = 32
image_width = 32
num_channels = 3
num_classes = 10
feats_stream_name = 'features'
labels_stream_name = 'labels'
minibatch_source = create_reader(os.path.join(data_path, 'train_map.txt'), os.path.join(data_path, 'CIFAR-10_mean.xml'), True,
distributed_communicator = communicator)
features_si = minibatch_source[feats_stream_name]
labels_si = minibatch_source[labels_stream_name]
# Instantiate the resnet classification model, or load from file
if load_model_filename:
print("Loading model:", load_model_filename)
classifier_output = persist.load_model(load_model_filename)
image_input = classifier_output.arguments[0]
else:
image_input = input_variable(
(num_channels, image_height, image_width), features_si.m_element_type)
classifier_output = create_resnet_model(image_input, num_classes)
# Input variables denoting the features and label data
label_var = input_variable((num_classes), features_si.m_element_type)
ce = cross_entropy_with_softmax(classifier_output, label_var)
pe = classification_error(classifier_output, label_var)
# Instantiate the trainer object to drive the model training
mb_size = 128
num_mb_per_epoch = 100
num_mbs = num_mb_per_epoch * num_epochs
lr_per_sample = [1/mb_size]*80+[0.1/mb_size]*40+[0.01/mb_size]
lr_schedule = learning_rate_schedule(lr_per_sample, units = mb_size * num_mb_per_epoch)
momentum_time_constant = -mb_size/np.log(0.9)
# create data parallel distributed trainer if needed
dist_trainer = distributed.data_parallel_distributed_trainer(communicator, False) if communicator else None
# Instantiate the trainer object to drive the model training
trainer = Trainer(classifier_output, ce, pe,
[momentum_sgd(classifier_output.parameters, lr_schedule, momentum_time_constant, l2_regularization_weight=0.0001)],
distributed_trainer = dist_trainer)
# Get minibatches of images to train with and perform model training
training_progress_output_freq = 100 if communicator else 20
if debug_output:
training_progress_output_freq = training_progress_output_freq/4
for i in range(0, num_mbs):
# NOTE: depends on network, the mb_size can be changed dynamically here
mb = minibatch_source.next_minibatch(mb_size)
# Specify the mapping of input variables in the model to actual
# minibatch data to be trained with
arguments = {
image_input: mb[features_si],
label_var: mb[labels_si]
}
trainer.train_minibatch(arguments)
print_training_progress(trainer, i, training_progress_output_freq)
if save_model_filename:
print("Saving model:", save_model_filename)
persist.save_model(classifier_output, save_model_filename)
if run_test:
test_minibatch_source = create_reader(os.path.join(data_path, 'test_map.txt'), os.path.join(data_path, 'CIFAR-10_mean.xml'), False)
features_si = test_minibatch_source[feats_stream_name]
labels_si = test_minibatch_source[labels_stream_name]
mb_size = 128
num_mbs = 100
total_error = 0.0
for i in range(0, num_mbs):
mb = test_minibatch_source.next_minibatch(mb_size)
# Specify the mapping of input variables in the model to actual
# minibatch data to be trained with
arguments = {
image_input: mb[features_si],
label_var: mb[labels_si]
}
error = trainer.test_minibatch(arguments)
total_error += error
return total_error / num_mbs
else:
return 0
def cifar_resnet_distributed(data_path, run_test, num_epochs, communicator=None, save_model_filename=None, load_model_filename=None, debug_output=False):
image_height = 32
image_width = 32
num_channels = 3
num_classes = 10
feats_stream_name = 'features'
labels_stream_name = 'labels'
minibatch_source = create_reader(os.path.join(data_path, 'train_map.txt'), os.path.join(data_path, 'CIFAR-10_mean.xml'), True,
distributed_communicator = communicator)
features_si = minibatch_source[feats_stream_name]
labels_si = minibatch_source[labels_stream_name]
# Instantiate the resnet classification model, or load from file
if load_model_filename:
print("Loading model:", load_model_filename)
classifier_output = persist.load_model(load_model_filename)
image_input = classifier_output.arguments[0]
else:
image_input = input_variable(
(num_channels, image_height, image_width), features_si.m_element_type)
classifier_output = create_resnet_model(image_input, num_classes)
# Input variables denoting the features and label data
label_var = input_variable((num_classes), features_si.m_element_type)
ce = cross_entropy_with_softmax(classifier_output, label_var)
pe = classification_error(classifier_output, label_var)
# Instantiate the trainer object to drive the model training
mb_size = 128
num_mb_per_epoch = 100
num_mbs = num_mb_per_epoch * num_epochs
lr_per_minibatch = learning_rate_schedule([1]*80 + [0.1]*40 + [0.01], mb_size * num_mb_per_epoch, UnitType.minibatch)
momentum_time_constant = momentum_as_time_constant_schedule(-mb_size/np.log(0.9))
# create data parallel distributed trainer if needed
dist_trainer = distributed.data_parallel_distributed_trainer(communicator, False) if communicator else None
# Instantiate the trainer object to drive the model training
trainer = Trainer(classifier_output, ce, pe,
[momentum_sgd(classifier_output.parameters, lr=lr_per_minibatch, momentum=momentum_time_constant, l2_regularization_weight=0.0001)],
distributed_trainer = dist_trainer)
# Get minibatches of images to train with and perform model training
training_progress_output_freq = 100 if communicator else 20
if debug_output:
training_progress_output_freq = training_progress_output_freq/4
for i in range(0, num_mbs):
# NOTE: depends on network, the mb_size can be changed dynamically here
mb = minibatch_source.next_minibatch(mb_size)
# Specify the mapping of input variables in the model to actual
# minibatch data to be trained with
arguments = {
image_input: mb[features_si],
label_var: mb[labels_si]
}
trainer.train_minibatch(arguments)
print_training_progress(trainer, i, training_progress_output_freq)
if save_model_filename:
print("Saving model:", save_model_filename)
persist.save_model(classifier_output, save_model_filename)
if run_test:
test_minibatch_source = create_reader(os.path.join(data_path, 'test_map.txt'), os.path.join(data_path, 'CIFAR-10_mean.xml'), False)
features_si = test_minibatch_source[feats_stream_name]
labels_si = test_minibatch_source[labels_stream_name]
mb_size = 128
num_mbs = 100
total_error = 0.0
for i in range(0, num_mbs):
mb = test_minibatch_source.next_minibatch(mb_size)
# Specify the mapping of input variables in the model to actual
# minibatch data to be trained with
arguments = {
image_input: mb[features_si],
label_var: mb[labels_si]
}
error = trainer.test_minibatch(arguments)
total_error += error
return total_error / num_mbs
else:
return 0
