def create_bn_inception():
# Input variables denoting the features and label data
feature_var = input_variable((NUM_CHANNELS, IMAGE_HEIGHT, IMAGE_WIDTH))
label_var = input_variable((NUM_CLASSES))
bn_time_const = 4096
z = bn_inception_cifar_model(feature_var, NUM_CLASSES, bn_time_const)
# 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
}
def train_lm(testing=False):
data = DataReader(token_to_id_path, segment_sepparator)
# Create model nodes for the source and target inputs
input_sequence, label_sequence = create_inputs(data.vocab_dim)
# Create the model. It has three output nodes
# z: the input to softmax that provides the latent representation of the next token
# cross_entropy: this is used training criterion
# error: this a binary indicator if the model predicts the correct token
z, cross_entropy, error = create_model(input_sequence, label_sequence, data.vocab_dim, hidden_dim)
# For measurement we use the (build in) full softmax.
full_ce = C.cross_entropy_with_softmax(z, label_sequence)
# print out some useful training information
log_number_of_parameters(z) ; print()
# Run the training loop
num_trained_samples = 0
num_trained_samples_since_last_report = 0
# Instantiate the trainer object to drive the model training
lr_schedule = C.learning_parameter_schedule_per_sample(learning_rate)
momentum_schedule = C.momentum_schedule_per_sample(momentum_per_sample)
gradient_clipping_with_truncation = True
learner = momentum_sgd(z.parameters, lr_schedule, momentum_schedule,
gradient_clipping_threshold_per_sample=clipping_threshold_per_sample,
gradient_clipping_with_truncation=gradient_clipping_with_truncation)
trainer = Trainer(z, (cross_entropy, error), learner)
last_avg_ce = 0
for epoch_count in range(num_epochs):
for features, labels, token_count in data.minibatch_generator(train_file_path, sequence_length, sequences_per_batch):
arguments = ({input_sequence : features, label_sequence : labels})
t_start = timeit.default_timer()
trainer.train_minibatch(arguments)
t_end = timeit.default_timer()
samples_per_second = token_count / (t_end - t_start)
# Print progress report every num_samples_between_progress_report samples
if num_trained_samples_since_last_report >= num_samples_between_progress_report or num_trained_samples == 0:
av_ce = average_cross_entropy(full_ce, input_sequence, label_sequence, data)
print_progress(samples_per_second, av_ce, num_trained_samples, t_start)
num_trained_samples_since_last_report = 0
last_avg_ce = av_ce
num_trained_samples += token_count
num_trained_samples_since_last_report += token_count
if not testing:
# after each epoch save the model
model_filename = "models/lm_epoch%d.dnn" % epoch_count
z.save(model_filename)
print("Saved model to '%s'" % model_filename)
return last_avg_ce
def train_lm(training_file, epochs, max_num_minibatches):
# load the data and vocab
data, char_to_ix, ix_to_char, data_size, vocab_dim = load_data_and_vocab(training_file)
# Model the source and target inputs to the model
input_sequence, label_sequence = create_inputs(vocab_dim)
# create the model
model = create_model(vocab_dim)
# and apply it to the input sequence
z = model(input_sequence)
# setup the criterions (loss and metric)
ce = cross_entropy_with_softmax(z, label_sequence)
errs = classification_error(z, label_sequence)
# Instantiate the trainer object to drive the model training
lr_per_sample = learning_parameter_schedule_per_sample(0.001)
momentum_schedule = momentum_schedule_per_sample(0.9990913221888589)
clipping_threshold_per_sample = 5.0
gradient_clipping_with_truncation = True
learner = momentum_sgd(z.parameters, lr_per_sample, momentum_schedule,
gradient_clipping_threshold_per_sample=clipping_threshold_per_sample,
gradient_clipping_with_truncation=gradient_clipping_with_truncation)
progress_printer = ProgressPrinter(freq=100, tag='Training')
trainer = Trainer(z, (ce, errs), learner, progress_printer)
sample_freq = 1000
minibatches_per_epoch = min(data_size // minibatch_size, max_num_minibatches // epochs)
# print out some useful training information
log_number_of_parameters(z)
print ("Running %d epochs with %d minibatches per epoch" % (epochs, minibatches_per_epoch))
print()
for e in range(0, epochs):
# Specify the mapping of input variables in the model to actual minibatch data to be trained with
# If it's the start of the data, we specify that we are looking at a new sequence (True)
mask = [True]
for b in range(0, minibatches_per_epoch):
# get the data
features, labels = get_data(b, minibatch_size, data, char_to_ix, vocab_dim)
arguments = ({input_sequence : features, label_sequence : labels}, mask)
mask = [False]
trainer.train_minibatch(arguments)
global_minibatch = e*minibatches_per_epoch + b
if global_minibatch % sample_freq == 0:
print(sample(z, ix_to_char, vocab_dim, char_to_ix))
model_filename = "models/shakespeare_epoch%d.dnn" % (e+1)
z.save(model_filename)
print("Saved model to '%s'" % model_filename)
def finalize_network(reader, model_details, max_amount_of_epochs,
samples_per_epoch, samples_per_minibatch,
pixel_dimensions, classes, learning_rate):
features = input_variable(shape=(pixel_dimensions['depth'],
pixel_dimensions['height'],
pixel_dimensions['width']))
label = input_variable(shape=len(classes))
# speeds up training
normalized_features = element_times(1.0 / 256.0, features)
model = create_tf_model(model_details,
num_classes=len(classes),
input_features=normalized_features,
freeze=True)
loss = cross_entropy_with_softmax(model, label)
metric = classification_error(model, label)
learner = momentum_sgd(parameters=model.parameters,
lr=learning_rate_schedule(learning_rate,
UnitType.minibatch),
momentum=0.9,
l2_regularization_weight=0.0005)
reporter = ProgressPrinter(tag='training', num_epochs=max_amount_of_epochs)
trainer = Trainer(model=model,
criterion=(loss, metric),
parameter_learners=[learner],
progress_writers=[reporter])
log_number_of_parameters(model)
map_input_to_streams_train = {
features: reader.streams.features,
label: reader.streams.labels
}
training_session(trainer=trainer,
mb_source=reader,
model_inputs_to_streams=map_input_to_streams_train,
mb_size=samples_per_minibatch,
progress_frequency=samples_per_epoch,
checkpoint_config=CheckpointConfig(
frequency=samples_per_epoch,
filename=os.path.join("./checkpoints",
"ConvNet_Lego_VisiOn"),
restore=True)).train()
network = {'features': features, 'label': label, 'model': softmax(model)}
model_name = f"CNN-3200-224-resnet-18.model"
export_path = os.path.abspath(
os.path.join("..", "..", "Final models", "CNN", model_name))
model.save(export_path)
return network
def main():
prepare_dir() # create the vocab dir and model dir
network = create_model(vocab_sqrt)
if opt.pre_model:
network['model'].restore(opt.pre_model)
log_number_of_parameters(network['model'])
location_path = os.path.join(opt.vocabdir, opt.alloc_file)
for i in range(len(opt.epochs)):
train(network, location_path, i)
location_path = get_k_round_location_path(i + 1)
Communicator.finalize()
def train_fast_rcnn(debug_output=False):
if debug_output:
print("Storing graphs and intermediate models to %s." % os.path.join(abs_path, "Output"))
# Create the minibatch source
minibatch_source = create_mb_source(image_height, image_width, num_channels,
num_classes, num_rois, base_path, "train")
# Input variables denoting features, rois and label data
image_input = input((num_channels, image_height, image_width))
roi_input = input((num_rois, 4))
label_input = input((num_rois, num_classes))
# define mapping from reader streams to network inputs
input_map = {
image_input: minibatch_source.streams.features,
roi_input: minibatch_source.streams.rois,
label_input: minibatch_source.streams.roiLabels
}
# Instantiate the Fast R-CNN prediction model and loss function
frcn_output = frcn_predictor(image_input, roi_input, num_classes)
ce = cross_entropy_with_softmax(frcn_output, label_input, axis=1)
pe = classification_error(frcn_output, label_input, axis=1)
if debug_output:
plot(frcn_output, os.path.join(abs_path, "Output", "graph_frcn.png"))
# Set learning parameters
l2_reg_weight = 0.0005
lr_per_sample = [0.00001] * 10 + [0.000001] * 5 + [0.0000001]
lr_schedule = learning_rate_schedule(lr_per_sample, unit=UnitType.sample)
mm_schedule = momentum_as_time_constant_schedule(momentum_time_constant)
# Instantiate the trainer object
learner = momentum_sgd(frcn_output.parameters, lr_schedule, mm_schedule, l2_regularization_weight=l2_reg_weight)
progress_printer = ProgressPrinter(tag='Training', num_epochs=max_epochs)
trainer = Trainer(frcn_output, (ce, pe), learner, progress_printer)
# Get minibatches of images and perform model training
print("Training Fast R-CNN model for %s epochs." % max_epochs)
log_number_of_parameters(frcn_output)
for epoch in range(max_epochs): # loop over epochs
sample_count = 0
while sample_count < epoch_size: # loop over minibatches in the epoch
data = minibatch_source.next_minibatch(min(mb_size, epoch_size-sample_count), input_map=input_map)
trainer.train_minibatch(data) # update model with it
sample_count += trainer.previous_minibatch_sample_count # count samples processed so far
trainer.summarize_training_progress()
if debug_output:
frcn_output.save(os.path.join(abs_path, "Output", "frcn_py_%s.model" % (epoch+1)))
return frcn_output
def train_fast_rcnn(debug_output=False, model_path=model_file):
if debug_output:
print("Storing graphs and intermediate models to %s." % os.path.join(abs_path, "Output"))
# Create the minibatch source
minibatch_source = create_mb_source(image_height, image_width, num_channels,
num_classes, num_rois, base_path, "train")
# Input variables denoting features, rois and label data
image_input = C.input_variable((num_channels, image_height, image_width))
roi_input = C.input_variable((num_rois, 4))
label_input = C.input_variable((num_rois, num_classes))
# define mapping from reader streams to network inputs
input_map = {
image_input: minibatch_source.streams.features,
roi_input: minibatch_source.streams.rois,
label_input: minibatch_source.streams.roiLabels
}
# Instantiate the Fast R-CNN prediction model and loss function
frcn_output = frcn_predictor(image_input, roi_input, num_classes, model_path)
ce = cross_entropy_with_softmax(frcn_output, label_input, axis=1)
pe = classification_error(frcn_output, label_input, axis=1)
if debug_output:
plot(frcn_output, os.path.join(abs_path, "Output", "graph_frcn.png"))
# Set learning parameters
l2_reg_weight = 0.0005
lr_per_sample = [0.00001] * 10 + [0.000001] * 5 + [0.0000001]
lr_schedule = learning_rate_schedule(lr_per_sample, unit=UnitType.sample)
mm_schedule = momentum_as_time_constant_schedule(momentum_time_constant)
# Instantiate the trainer object
learner = momentum_sgd(frcn_output.parameters, lr_schedule, mm_schedule, l2_regularization_weight=l2_reg_weight)
progress_printer = ProgressPrinter(tag='Training', num_epochs=max_epochs)
trainer = Trainer(frcn_output, (ce, pe), learner, progress_printer)
# Get minibatches of images and perform model training
print("Training Fast R-CNN model for %s epochs." % max_epochs)
log_number_of_parameters(frcn_output)
for epoch in range(max_epochs): # loop over epochs
sample_count = 0
while sample_count < epoch_size: # loop over minibatches in the epoch
data = minibatch_source.next_minibatch(min(mb_size, epoch_size-sample_count), input_map=input_map)
trainer.train_minibatch(data) # update model with it
sample_count += trainer.previous_minibatch_sample_count # count samples processed so far
trainer.summarize_training_progress()
if debug_output:
frcn_output.save(os.path.join(abs_path, "Output", "frcn_py_%s.model" % (epoch+1)))
return frcn_output
def main():
prepare_dir() # create the vocab dir and model dir
network = create_model(vocab_sqrt)
if opt.pre_model:
network['model'].restore(opt.pre_model)
log_number_of_parameters(network['model'])
location_path = os.path.join(opt.vocabdir, opt.alloc_file)
for i in range(len(opt.epochs)):
train(network, location_path, i)
location_path = get_k_round_location_path(i + 1)
Communicator.finalize()
def train_model(base_model_file, feature_node_name, last_hidden_node_name,
image_width, image_height, num_channels, num_classes, train_map_file,
num_epochs, max_images=-1, freeze=False):
epoch_size = sum(1 for line in open(train_map_file))
if max_images > 0:
epoch_size = min(epoch_size, max_images)
# Create the minibatch source and input variables
minibatch_source = create_mb_source(train_map_file, image_width, image_height, num_channels, num_classes)
image_input = C.input_variable((num_channels, image_height, image_width))
label_input = C.input_variable(num_classes)
# Define mapping from reader streams to network inputs
input_map = {
image_input: minibatch_source[features_stream_name],
label_input: minibatch_source[label_stream_name]
}
# Instantiate the transfer learning model and loss function
tl_model = create_model(base_model_file, feature_node_name, last_hidden_node_name, num_classes, image_input, freeze)
ce = cross_entropy_with_softmax(tl_model, label_input)
pe = classification_error(tl_model, label_input)
# Instantiate the trainer object
lr_schedule = learning_rate_schedule(lr_per_mb, unit=UnitType.minibatch)
mm_schedule = momentum_schedule(momentum_per_mb)
learner = momentum_sgd(tl_model.parameters, lr_schedule, mm_schedule, l2_regularization_weight=l2_reg_weight)
progress_printer = ProgressPrinter(tag='Training', num_epochs=num_epochs)
trainer = Trainer(tl_model, (ce, pe), learner, progress_printer)
# Get minibatches of images and perform model training
print("Training transfer learning model for {0} epochs (epoch_size = {1}).".format(num_epochs, epoch_size))
log_number_of_parameters(tl_model)
for epoch in range(num_epochs): # loop over epochs
sample_count = 0
while sample_count < epoch_size: # loop over minibatches in the epoch
data = minibatch_source.next_minibatch(min(mb_size, epoch_size-sample_count), input_map=input_map)
trainer.train_minibatch(data) # update model with it
sample_count += trainer.previous_minibatch_sample_count # count samples processed so far
if sample_count % (100 * mb_size) == 0:
print("Processed {0} samples".format(sample_count))
trainer.summarize_training_progress()
return tl_model
def train_model(base_model_file, feature_node_name, last_hidden_node_name,
image_width, image_height, num_channels, num_classes, train_map_file,
num_epochs, max_images=-1, freeze=False):
epoch_size = sum(1 for line in open(train_map_file))
if max_images > 0:
epoch_size = min(epoch_size, max_images)
# Create the minibatch source and input variables
minibatch_source = create_mb_source(train_map_file, image_width, image_height, num_channels, num_classes)
image_input = C.input_variable((num_channels, image_height, image_width))
label_input = C.input_variable(num_classes)
# Define mapping from reader streams to network inputs
input_map = {
image_input: minibatch_source[features_stream_name],
label_input: minibatch_source[label_stream_name]
}
# Instantiate the transfer learning model and loss function
tl_model = create_model(base_model_file, feature_node_name, last_hidden_node_name, num_classes, image_input, freeze)
ce = cross_entropy_with_softmax(tl_model, label_input)
pe = classification_error(tl_model, label_input)
# Instantiate the trainer object
lr_schedule = learning_rate_schedule(lr_per_mb, unit=UnitType.minibatch)
mm_schedule = momentum_schedule(momentum_per_mb)
learner = momentum_sgd(tl_model.parameters, lr_schedule, mm_schedule, l2_regularization_weight=l2_reg_weight)
progress_printer = ProgressPrinter(tag='Training', num_epochs=num_epochs)
trainer = Trainer(tl_model, (ce, pe), learner, progress_printer)
# Get minibatches of images and perform model training
print("Training transfer learning model for {0} epochs (epoch_size = {1}).".format(num_epochs, epoch_size))
log_number_of_parameters(tl_model)
for epoch in range(num_epochs): # loop over epochs
sample_count = 0
while sample_count < epoch_size: # loop over minibatches in the epoch
data = minibatch_source.next_minibatch(min(mb_size, epoch_size-sample_count), input_map=input_map)
trainer.train_minibatch(data) # update model with it
sample_count += trainer.previous_minibatch_sample_count # count samples processed so far
if sample_count % (100 * mb_size) == 0:
print ("Processed {0} samples".format(sample_count))
trainer.summarize_training_progress()
return tl_model
def train_fast_rcnn(debug_output=False, model_path=model_file):
if debug_output:
print("Storing graphs and intermediate models to %s." %
os.path.join(abs_path, "Output"))
# Create the minibatch source
minibatch_source = create_mb_source(image_height, image_width,
num_channels, num_classes, num_rois,
base_path, "train")
# Input variables denoting features, rois and label data
image_input = C.input_variable((num_channels, image_height, image_width))
roi_input = C.input_variable((num_rois, 4))
label_input = C.input_variable((num_rois, num_classes))
# define mapping from reader streams to network inputs
input_map = {
image_input: minibatch_source.streams.features,
roi_input: minibatch_source.streams.rois,
label_input: minibatch_source.streams.roiLabels
}
# Instantiate the Fast R-CNN prediction model and loss function
frcn_output = frcn_predictor(image_input, roi_input, num_classes,
model_path)
ce = cross_entropy_with_softmax(frcn_output, label_input, axis=1)
pe = classification_error(frcn_output, label_input, axis=1)
if debug_output:
plot(frcn_output, os.path.join(abs_path, "Output", "graph_frcn.png"))
# Set learning parameters
l2_reg_weight = 0.0005
lr_per_sample = [0.00001] * 10 + [0.000001] * 5 + [0.0000001]
lr_schedule = learning_parameter_schedule_per_sample(lr_per_sample)
mm_schedule = momentum_schedule_per_sample(momentum_per_sample)
# Instantiate the trainer object as default
learner = momentum_sgd(frcn_output.parameters,
lr_schedule,
mm_schedule,
l2_regularization_weight=l2_reg_weight)
# Preparation for distributed learning, which is compatible for normal learner
learner = distributed.data_parallel_distributed_learner(
learner=learner,
num_quantization_bits=
num_quantization_bits, # non-quantized gradient accumulation
distributed_after=warm_up) # no warm start as default
progress_printer = ProgressPrinter(tag='Training',
num_epochs=max_epochs,
rank=distributed.Communicator.rank())
trainer = Trainer(frcn_output, (ce, pe), learner, progress_printer)
# Get minibatches of images and perform model training
print("Training Fast R-CNN model for %s epochs." % max_epochs)
log_number_of_parameters(frcn_output)
for epoch in range(max_epochs): # loop over epochs
sample_count = 0
while sample_count < epoch_size: # loop over minibatches in the epoch
data = minibatch_source.next_minibatch(
min(mb_size * C.Communicator.num_workers(),
epoch_size - sample_count),
input_map=input_map,
num_data_partitions=C.Communicator.num_workers(),
partition_index=C.Communicator.rank())
trainer.train_minibatch(data) # update model with it
sample_count += trainer.previous_minibatch_sample_count # count samples processed so far
trainer.summarize_training_progress()
if debug_output:
frcn_output.save(
os.path.join(abs_path, "Output",
"frcn_py_%s.model" % (epoch + 1)))
if distributed_flg:
distributed.Communicator.finalize()
return frcn_output
def train(train_reader, valid_reader, vocab, i2w, s2smodel, max_epochs,
epoch_size):
# Note: We would like to set the signature of 's2smodel' (s2smodel.update_signature()), but that will cause
# an error since the training criterion uses a reduced sequence axis for the labels.
# This is because it removes the initial <s> symbol. Hence, we must leave the model
# with unspecified input shapes and axes.
# create the training wrapper for the s2smodel, as well as the criterion function
model_train = create_model_train(s2smodel)
criterion = create_criterion_function(model_train)
# also wire in a greedy decoder so that we can properly log progress on a validation example
# This is not used for the actual training process.
model_greedy = create_model_greedy(s2smodel)
# This does not need to be done in training generally though
# Instantiate the trainer object to drive the model training
minibatch_size = 72
lr = 0.001 if use_attention else 0.005 # TODO: can we use the same value for both?
learner = adam_sgd(
model_train.parameters,
lr=learning_rate_schedule([lr] * 2 + [lr / 2] * 3 + [lr / 4],
UnitType.sample, epoch_size),
momentum=momentum_as_time_constant_schedule(1100),
gradient_clipping_threshold_per_sample=2.3,
gradient_clipping_with_truncation=True)
trainer = Trainer(None, criterion, learner)
# Get minibatches of sequences to train with and perform model training
total_samples = 0
mbs = 0
eval_freq = 100
# print out some useful training information
log_number_of_parameters(model_train)
print()
progress_printer = ProgressPrinter(freq=30, tag='Training')
#progress_printer = ProgressPrinter(freq=30, tag='Training', log_to_file=model_path_stem + ".log") # use this to log to file
sparse_to_dense = create_sparse_to_dense(input_vocab_dim)
for epoch in range(max_epochs):
print("Saving model to '%s'" % model_path(epoch))
s2smodel.save(model_path(epoch))
while total_samples < (epoch + 1) * epoch_size:
# get next minibatch of training data
mb_train = train_reader.next_minibatch(minibatch_size)
#trainer.train_minibatch(mb_train[train_reader.streams.features], mb_train[train_reader.streams.labels])
trainer.train_minibatch({
criterion.arguments[0]:
mb_train[train_reader.streams.features],
criterion.arguments[1]:
mb_train[train_reader.streams.labels]
})
progress_printer.update_with_trainer(
trainer, with_metric=True) # log progress
# every N MBs evaluate on a test sequence to visually show how we're doing
if mbs % eval_freq == 0:
mb_valid = valid_reader.next_minibatch(1)
# run an eval on the decoder output model (i.e. don't use the groundtruth)
e = model_greedy(mb_valid[valid_reader.streams.features])
print(
format_sequences(
sparse_to_dense(
mb_valid[valid_reader.streams.features]), i2w))
print("->")
print(format_sequences(e, i2w))
# debugging attention
if use_attention:
debug_attention(model_greedy,
mb_valid[valid_reader.streams.features])
total_samples += mb_train[train_reader.streams.labels].num_samples
mbs += 1
# log a summary of the stats for the epoch
progress_printer.epoch_summary(with_metric=True)
# done: save the final model
print("Saving final model to '%s'" % model_path(max_epochs))
s2smodel.save(model_path(max_epochs))
print("%d epochs complete." % max_epochs)
def train_model(base_model_file,
train_map_file,
test_map_file,
input_resolution,
num_epochs,
mb_size,
max_train_images,
lr_per_mb,
momentum_per_mb,
l2_reg_weight,
dropout_rate,
freeze_weights,
num_channels=3):
#init
image_width = input_resolution
image_height = input_resolution
epoch_size_test = len(readTable(test_map_file))
epoch_size_train = len(readTable(train_map_file))
epoch_size_train = min(epoch_size_train, max_train_images)
num_classes = max(toIntegers(getColumn(readTable(train_map_file), 1))) + 1
# Create the minibatch source
minibatch_source_train = create_mb_source(train_map_file, image_width,
image_height, num_channels,
num_classes, True)
minibatch_source_test = create_mb_source(test_map_file, image_width,
image_height, num_channels,
num_classes, False)
# Define mapping from reader streams to network inputs
label_input = input_variable(num_classes)
image_input = input_variable((num_channels, image_height, image_width),
name="input")
input_map = {
image_input: minibatch_source_train['features'],
label_input: minibatch_source_train['labels']
}
# Instantiate the transfer learning model and loss function
cntkModel = create_model(base_model_file, image_input, num_classes,
dropout_rate, freeze_weights)
ce = cross_entropy_with_softmax(cntkModel, label_input)
pe = classification_error(cntkModel, label_input)
# Instantiate the trainer object
lr_schedule = learning_rate_schedule(lr_per_mb, unit=UnitType.minibatch)
mm_schedule = momentum_schedule(momentum_per_mb)
learner = momentum_sgd(cntkModel.parameters,
lr_schedule,
mm_schedule,
l2_regularization_weight=l2_reg_weight)
progress_writers = [ProgressPrinter(tag='Training', num_epochs=num_epochs)]
trainer = Trainer(cntkModel, (ce, pe), learner, progress_writers)
# Run training epochs
print(
"Training transfer learning model for {0} epochs (epoch_size_train = {1})."
.format(num_epochs, epoch_size_train))
errsTest = []
errsTrain = []
log_number_of_parameters(cntkModel)
for epoch in range(num_epochs):
err_numer = 0
sample_counts = 0
while sample_counts < epoch_size_train: # Loop over minibatches in the epoch
sample_count = min(mb_size, epoch_size_train - sample_counts)
data = minibatch_source_train.next_minibatch(sample_count,
input_map=input_map)
trainer.train_minibatch(data) # Update model with it
sample_counts += sample_count # Count samples processed so far
err_numer += trainer.previous_minibatch_evaluation_average * sample_count
if sample_counts % (100 * mb_size) == 0:
print("Training: processed {0} samples".format(sample_counts))
# Visualize training images
# img_data = data[image_input].asarray()
# for i in range(len(img_data)):
# debugImg = img_data[i].squeeze().swapaxes(0, 1).swapaxes(1, 2) / 255.0
# imshow(debugImg)
# Compute accuracy on training and test sets
errsTrain.append(err_numer / float(sample_counts))
trainer.summarize_training_progress()
errsTest.append(
cntkComputeTestError(trainer, minibatch_source_test, mb_size,
epoch_size_test, input_map))
trainer.summarize_test_progress()
# Plot training progress
plt.plot(errsTrain, 'b-', errsTest, 'g-')
plt.xlabel('Epoch number')
plt.ylabel('Error')
plt.title('Training error (blue), test error (green)')
plt.draw()
return cntkModel
errs = classification_error(z, label_sequence)
lr_per_sample = learning_rate_schedule(0.001, UnitType.sample)
momentum_time_constant = momentum_as_time_constant_schedule(1100)
clipping_threshold_per_sample = 5.0
gradient_clipping_with_truncation = True
learner = momentum_sgd(
z.parameters,
lr_per_sample,
momentum_time_constant,
gradient_clipping_threshold_per_sample=clipping_threshold_per_sample,
gradient_clipping_with_truncation=gradient_clipping_with_truncation)
progress_printer = ProgressPrinter(freq=100, tag='Training')
trainer = Trainer(z, (ce, errs), learner, progress_printer)
log_number_of_parameters(z)
def sample(net, prime_text='', use_hardmax=True, length=100, temperature=1.0):
# Применяем температуру: T < 1 - сглаживание; T=1.0 - без изменений; T > 1 - выделение пиков
def apply_temp(p):
p = np.power(p, (temperature))
# повторно нормализуем
return (p / np.sum(p))
def sample_word(p):
if use_hardmax:
w = np.argmax(p, axis=2)[0, 0]
else:
# выбираем случайным образом исходя из вероятностей
def train_model(image_input,
roi_input,
dims_input,
loss,
pred_error,
lr_per_sample,
mm_schedule,
l2_reg_weight,
epochs_to_train,
rpn_rois_input=None,
buffered_rpn_proposals=None):
if isinstance(loss, cntk.Variable):
loss = combine([loss])
params = loss.parameters
biases = [p for p in params if '.b' in p.name or 'b' == p.name]
others = [p for p in params if not p in biases]
bias_lr_mult = cfg["CNTK"].BIAS_LR_MULT
if cfg["CNTK"].DEBUG_OUTPUT:
print("biases")
for p in biases:
print(p)
print("others")
for p in others:
print(p)
print("bias_lr_mult: {}".format(bias_lr_mult))
# Instantiate the learners and the trainer object
lr_schedule = learning_rate_schedule(lr_per_sample, unit=UnitType.sample)
learner = momentum_sgd(others,
lr_schedule,
mm_schedule,
l2_regularization_weight=l2_reg_weight,
unit_gain=False,
use_mean_gradient=cfg["CNTK"].USE_MEAN_GRADIENT)
bias_lr_per_sample = [v * bias_lr_mult for v in lr_per_sample]
bias_lr_schedule = learning_rate_schedule(bias_lr_per_sample,
unit=UnitType.sample)
bias_learner = momentum_sgd(
biases,
bias_lr_schedule,
mm_schedule,
l2_regularization_weight=l2_reg_weight,
unit_gain=False,
use_mean_gradient=cfg["CNTK"].USE_MEAN_GRADIENT)
trainer = Trainer(None, (loss, pred_error), [learner, bias_learner])
# Get minibatches of images and perform model training
print("Training model for %s epochs." % epochs_to_train)
log_number_of_parameters(loss)
# Create the minibatch source
od_minibatch_source = ObjectDetectionMinibatchSource(
globalvars['train_map_file'],
globalvars['train_roi_file'],
max_annotations_per_image=cfg["CNTK"].INPUT_ROIS_PER_IMAGE,
pad_width=image_width,
pad_height=image_height,
pad_value=img_pad_value,
randomize=True,
use_flipping=cfg["TRAIN"].USE_FLIPPED,
max_images=cfg["CNTK"].NUM_TRAIN_IMAGES,
buffered_rpn_proposals=buffered_rpn_proposals)
# define mapping from reader streams to network inputs
input_map = {
od_minibatch_source.image_si: image_input,
od_minibatch_source.roi_si: roi_input,
od_minibatch_source.dims_si: dims_input
}
use_buffered_proposals = buffered_rpn_proposals is not None
progress_printer = ProgressPrinter(tag='Training',
num_epochs=epochs_to_train,
gen_heartbeat=True)
for epoch in range(epochs_to_train): # loop over epochs
sample_count = 0
while sample_count < epoch_size: # loop over minibatches in the epoch
data, proposals = od_minibatch_source.next_minibatch_with_proposals(
min(mb_size, epoch_size - sample_count), input_map=input_map)
if use_buffered_proposals:
data[rpn_rois_input] = MinibatchData(
Value(batch=np.asarray(proposals, dtype=np.float32)), 1, 1,
False)
# remove dims input if no rpn is required to avoid warnings
del data[[k for k in data if '[6]' in str(k)][0]]
trainer.train_minibatch(data) # update model with it
sample_count += trainer.previous_minibatch_sample_count # count samples processed so far
progress_printer.update_with_trainer(
trainer, with_metric=True) # log progress
if sample_count % 100 == 0:
print("Processed {} samples".format(sample_count))
progress_printer.epoch_summary(with_metric=True)
def train_model(image_input, roi_input, dims_input, loss, pred_error,
lr_per_sample, mm_schedule, l2_reg_weight, epochs_to_train, cfg,
rpn_rois_input=None, buffered_rpn_proposals=None):
if isinstance(loss, cntk.Variable):
loss = combine([loss])
params = loss.parameters
biases = [p for p in params if '.b' in p.name or 'b' == p.name]
others = [p for p in params if not p in biases]
bias_lr_mult = cfg["CNTK"].BIAS_LR_MULT
if cfg["CNTK"].DEBUG_OUTPUT:
print("biases")
for p in biases: print(p)
print("others")
for p in others: print(p)
print("bias_lr_mult: {}".format(bias_lr_mult))
# Instantiate the learners and the trainer object
lr_schedule = learning_parameter_schedule_per_sample(lr_per_sample)
learner = momentum_sgd(others, lr_schedule, mm_schedule, l2_regularization_weight=l2_reg_weight,
unit_gain=False, use_mean_gradient=True)
bias_lr_per_sample = [v * bias_lr_mult for v in lr_per_sample]
bias_lr_schedule = learning_parameter_schedule_per_sample(bias_lr_per_sample)
bias_learner = momentum_sgd(biases, bias_lr_schedule, mm_schedule, l2_regularization_weight=l2_reg_weight,
unit_gain=False, use_mean_gradient=True)
trainer = Trainer(None, (loss, pred_error), [learner, bias_learner])
# Get minibatches of images and perform model training
print("Training model for %s epochs." % epochs_to_train)
log_number_of_parameters(loss)
# Create the minibatch source
if buffered_rpn_proposals is not None:
proposal_provider = ProposalProvider.fromlist(buffered_rpn_proposals, requires_scaling=False)
else:
proposal_provider = None
od_minibatch_source = ObjectDetectionMinibatchSource(
cfg["DATA"].TRAIN_MAP_FILE, cfg["DATA"].TRAIN_ROI_FILE,
num_classes=cfg["DATA"].NUM_CLASSES,
max_annotations_per_image=cfg.INPUT_ROIS_PER_IMAGE,
pad_width=cfg.IMAGE_WIDTH,
pad_height=cfg.IMAGE_HEIGHT,
pad_value=cfg["MODEL"].IMG_PAD_COLOR,
randomize=True,
use_flipping=cfg["TRAIN"].USE_FLIPPED,
max_images=cfg["DATA"].NUM_TRAIN_IMAGES,
proposal_provider=proposal_provider)
# define mapping from reader streams to network inputs
input_map = {
od_minibatch_source.image_si: image_input,
od_minibatch_source.roi_si: roi_input,
}
if buffered_rpn_proposals is not None:
input_map[od_minibatch_source.proposals_si] = rpn_rois_input
else:
input_map[od_minibatch_source.dims_si] = dims_input
progress_printer = ProgressPrinter(tag='Training', num_epochs=epochs_to_train, gen_heartbeat=True)
for epoch in range(epochs_to_train): # loop over epochs
sample_count = 0
while sample_count < cfg["DATA"].NUM_TRAIN_IMAGES: # loop over minibatches in the epoch
data = od_minibatch_source.next_minibatch(min(cfg.MB_SIZE, cfg["DATA"].NUM_TRAIN_IMAGES-sample_count), input_map=input_map)
trainer.train_minibatch(data) # update model with it
sample_count += trainer.previous_minibatch_sample_count # count samples processed so far
progress_printer.update_with_trainer(trainer, with_metric=True) # log progress
if sample_count % 100 == 0:
print("Processed {} samples".format(sample_count))
progress_printer.epoch_summary(with_metric=True)
def train_and_evaluate(reader_train, reader_test, max_epochs, model_func):
# Input variables denoting the features and label data
input_var = input_variable((num_channels, image_height, image_width))
label_var = input_variable((num_classes))
# Normalize the input
feature_scale = 1.0 / 256.0
input_var_norm = element_times(feature_scale, input_var)
# apply model to input
z = model_func(input_var_norm, out_dims=num_classes)
#
# Training action
#
# loss and metric
ce = cross_entropy_with_softmax(z, label_var)
pe = classification_error(z, label_var)
# training config
epoch_size = 20000
minibatch_size = 64
# Set training parameters
lr_per_minibatch = learning_rate_schedule([0.01]*10 + [0.003]*10 + [0.001], UnitType.minibatch, epoch_size)
momentum_time_constant = momentum_as_time_constant_schedule(-minibatch_size/np.log(0.9))
l2_reg_weight = 0.001
# trainer object
progress_printer = ProgressPrinter(0)
learner = momentum_sgd(z.parameters,
lr = lr_per_minibatch, momentum = momentum_time_constant,
l2_regularization_weight=l2_reg_weight)
trainer = Trainer(z, (ce, pe), [learner], [progress_printer])
# define mapping from reader streams to network inputs
input_map = {
input_var: reader_train.streams.features,
label_var: reader_train.streams.labels
}
log_number_of_parameters(z) ; print()
#progress_printer = ProgressPrinter(tag='Training')
# perform model training
stop_run=False
batch_index = 0
plot_data = {'batchindex':[], 'loss':[], 'error':[]}
for epoch in range(max_epochs): # loop over epochs
sample_count = 0
while sample_count < epoch_size: # loop over minibatches in the epoch
data = reader_train.next_minibatch(min(minibatch_size, epoch_size - sample_count), input_map=input_map) # fetch minibatch.
trainer.train_minibatch(data) # update model with it
sample_count += data[label_var].num_samples # count samples processed so far
# For visualization...
plot_data['batchindex'].append(batch_index)
plot_data['loss'].append(trainer.previous_minibatch_loss_average)
plot_data['error'].append(trainer.previous_minibatch_evaluation_average)
progress_printer.update_with_trainer(trainer, with_metric=True) # log progress
batch_index += 1
if trainer.previous_minibatch_evaluation_average < 0.025:
stop_run=True
break
if stop_run:
break
progress_printer.epoch_summary(with_metric=True)
#trainer.save_checkpoint(model_temp_file)
#
# Evaluation action
#
epoch_size = 6600
minibatch_size = 32
# process minibatches and evaluate the model
metric_numer = 0
metric_denom = 0
sample_count = 0
minibatch_index = 0
input_map = {
input_var: reader_test.streams.features,
label_var: reader_test.streams.labels
}
while sample_count < epoch_size:
current_minibatch = min(minibatch_size, epoch_size - sample_count)
# Fetch next test min batch.
data = reader_test.next_minibatch(current_minibatch, input_map=input_map)
# minibatch data to be trained with
metric_numer += trainer.test_minibatch(data) * current_minibatch
metric_denom += current_minibatch
# Keep track of the number of samples processed so far.
sample_count += data[label_var].num_samples
minibatch_index += 1
print("")
print("Final Results: Minibatch[1-{}]: errs = {:0.1f}% * {}".format(minibatch_index+1, (metric_numer*100.0)/metric_denom, metric_denom))
print("")
# Visualize training result:
window_width = 32
loss_cumsum = np.cumsum(np.insert(plot_data['loss'], 0, 0))
error_cumsum = np.cumsum(np.insert(plot_data['error'], 0, 0))
# Moving average.
plot_data['batchindex'] = np.insert(plot_data['batchindex'], 0, 0)[window_width:]
plot_data['avg_loss'] = (loss_cumsum[window_width:] - loss_cumsum[:-window_width]) / window_width
plot_data['avg_error'] = (error_cumsum[window_width:] - error_cumsum[:-window_width]) / window_width
plt.figure(1)
plt.subplot(211)
plt.plot(plot_data["batchindex"], plot_data["avg_loss"], 'b--')
plt.xlabel('Minibatch number')
plt.ylabel('Loss')
plt.title('Minibatch run vs. Training loss ')
plt.show()
plt.subplot(212)
plt.plot(plot_data["batchindex"], plot_data["avg_error"], 'r--')
plt.xlabel('Minibatch number')
plt.ylabel('Label Prediction Error')
plt.title('Minibatch run vs. Label Prediction Error ')
plt.show()
return softmax(z)
def train(train_reader, valid_reader, vocab, i2w, s2smodel, max_epochs, epoch_size):
# Note: We would like to set the signature of 's2smodel' (s2smodel.update_signature()), but that will cause
# an error since the training criterion uses a reduced sequence axis for the labels.
# This is because it removes the initial <s> symbol. Hence, we must leave the model
# with unspecified input shapes and axes.
# create the training wrapper for the s2smodel, as well as the criterion function
model_train = create_model_train(s2smodel)
criterion = create_criterion_function(model_train)
# also wire in a greedy decoder so that we can properly log progress on a validation example
# This is not used for the actual training process.
model_greedy = create_model_greedy(s2smodel)
# This does not need to be done in training generally though
# Instantiate the trainer object to drive the model training
minibatch_size = 72
lr = 0.001 if use_attention else 0.005 # TODO: can we use the same value for both?
learner = fsadagrad(model_train.parameters,
lr = learning_rate_schedule([lr]*2+[lr/2]*3+[lr/4], UnitType.sample, epoch_size),
momentum = momentum_as_time_constant_schedule(1100),
gradient_clipping_threshold_per_sample=2.3,
gradient_clipping_with_truncation=True)
trainer = Trainer(None, criterion, learner)
# Get minibatches of sequences to train with and perform model training
total_samples = 0
mbs = 0
eval_freq = 100
# print out some useful training information
log_number_of_parameters(model_train) ; print()
progress_printer = ProgressPrinter(freq=30, tag='Training')
#progress_printer = ProgressPrinter(freq=30, tag='Training', log_to_file=model_path_stem + ".log") # use this to log to file
sparse_to_dense = create_sparse_to_dense(input_vocab_dim)
for epoch in range(max_epochs):
print("Saving model to '%s'" % model_path(epoch))
s2smodel.save(model_path(epoch))
while total_samples < (epoch+1) * epoch_size:
# get next minibatch of training data
mb_train = train_reader.next_minibatch(minibatch_size)
#trainer.train_minibatch(mb_train[train_reader.streams.features], mb_train[train_reader.streams.labels])
trainer.train_minibatch({criterion.arguments[0]: mb_train[train_reader.streams.features], criterion.arguments[1]: mb_train[train_reader.streams.labels]})
progress_printer.update_with_trainer(trainer, with_metric=True) # log progress
# every N MBs evaluate on a test sequence to visually show how we're doing
if mbs % eval_freq == 0:
mb_valid = valid_reader.next_minibatch(1)
# run an eval on the decoder output model (i.e. don't use the groundtruth)
e = model_greedy(mb_valid[valid_reader.streams.features])
print(format_sequences(sparse_to_dense(mb_valid[valid_reader.streams.features]), i2w))
print("->")
print(format_sequences(e, i2w))
# debugging attention
if use_attention:
debug_attention(model_greedy, mb_valid[valid_reader.streams.features])
total_samples += mb_train[train_reader.streams.labels].num_samples
mbs += 1
# log a summary of the stats for the epoch
progress_printer.epoch_summary(with_metric=True)
# done: save the final model
print("Saving final model to '%s'" % model_path(max_epochs))
s2smodel.save(model_path(max_epochs))
print("%d epochs complete." % max_epochs)
def train_model(cntkModel, params, input_map):
log = logging.getLogger("neuralnets1.utils.train_model")
mb_size = params['mb_size']
num_epochs = params['num_epochs']
epoch_size_train = params['epoch_size_train']
epoch_size_test = params['epoch_size_test']
minibatch_source_train = params['train_mbs']
minibatch_source_valid = params['valid_mbs']
#minibatch_source_test = params['test_mbs'] ;
# Instantiate the trainer object
#lr_schedule = learning_rate_schedule(params['learn_rate'], unit=UnitType.minibatch)
lr_per_minibatch = learning_parameter_schedule(params['learn_rate'],
minibatch_size=mb_size,
epoch_size=epoch_size_train)
mm_schedule = momentum_schedule(params['beta_momentum_gd'])
learner = momentum_sgd(cntkModel.parameters,
lr_per_minibatch,
mm_schedule,
l2_regularization_weight=params['l2_reg_weight'])
progress_writers = [ProgressPrinter(tag='Training', num_epochs=num_epochs)]
trainer = Trainer(cntkModel, (params['ce'], params['pe']), learner,
progress_writers)
# Run training epochs
log.info(
'Training transfer learning model for %s epochs (epoch_size_train = %s ) .'
% (num_epochs, epoch_size_train))
# print("Training transfer learning model for {0} epochs (epoch_size_train = {1}).".format(num_epochs, epoch_size_train))
errsVal = []
errsTrain = []
log_number_of_parameters(cntkModel)
for epoch in range(num_epochs):
err_numer = 0
sample_counts = 0
while sample_counts < epoch_size_train: # Loop over minibatches in the epoch
sample_count = min(mb_size, epoch_size_train - sample_counts)
data = minibatch_source_train.next_minibatch(sample_count,
input_map=input_map)
trainer.train_minibatch(data) # Update model with it
sample_counts += sample_count # Count samples processed so far
err_numer += trainer.previous_minibatch_evaluation_average * sample_count
if sample_counts % (100 * mb_size) == 0:
log.info("Training: processed %s samples" % sample_counts)
# Compute accuracy on training and test sets
errsTrain.append(err_numer / float(sample_counts))
trainer.summarize_training_progress()
errsVal.append(
cntkComputeTestError(trainer, minibatch_source_valid, mb_size,
epoch_size_test, input_map))
trainer.summarize_test_progress()
# Plot training progress
plt.plot(errsTrain, 'b-', errsVal, 'g-')
plt.xlabel('Epoch number')
plt.ylabel('Error')
plt.title('Training error (blue), validation error (green)')
plt.draw()
return cntkModel
def train_model(image_input, roi_input, dims_input, loss, pred_error,
lr_per_sample, mm_schedule, l2_reg_weight, epochs_to_train, cfg,
rpn_rois_input=None, buffered_rpn_proposals=None):
if isinstance(loss, cntk.Variable):
loss = combine([loss])
params = loss.parameters
biases = [p for p in params if '.b' in p.name or 'b' == p.name]
others = [p for p in params if not p in biases]
bias_lr_mult = cfg["CNTK"].BIAS_LR_MULT
if cfg["CNTK"].DEBUG_OUTPUT:
print("biases")
for p in biases: print(p)
print("others")
for p in others: print(p)
print("bias_lr_mult: {}".format(bias_lr_mult))
# Instantiate the learners and the trainer object
lr_schedule = learning_parameter_schedule_per_sample(lr_per_sample)
learner = momentum_sgd(others, lr_schedule, mm_schedule, l2_regularization_weight=l2_reg_weight,
unit_gain=False, use_mean_gradient=True)
bias_lr_per_sample = [v * bias_lr_mult for v in lr_per_sample]
bias_lr_schedule = learning_parameter_schedule_per_sample(bias_lr_per_sample)
bias_learner = momentum_sgd(biases, bias_lr_schedule, mm_schedule, l2_regularization_weight=l2_reg_weight,
unit_gain=False, use_mean_gradient=True)
trainer = Trainer(None, (loss, pred_error), [learner, bias_learner])
# Get minibatches of images and perform model training
print("Training model for %s epochs." % epochs_to_train)
log_number_of_parameters(loss)
# Create the minibatch source
if buffered_rpn_proposals is not None:
proposal_provider = ProposalProvider.fromlist(buffered_rpn_proposals, requires_scaling=False)
else:
proposal_provider = None
od_minibatch_source = ObjectDetectionMinibatchSource(
cfg["DATA"].TRAIN_MAP_FILE, cfg["DATA"].TRAIN_ROI_FILE,
num_classes=cfg["DATA"].NUM_CLASSES,
max_annotations_per_image=cfg.INPUT_ROIS_PER_IMAGE,
pad_width=cfg.IMAGE_WIDTH,
pad_height=cfg.IMAGE_HEIGHT,
pad_value=cfg["MODEL"].IMG_PAD_COLOR,
randomize=True,
use_flipping=cfg["TRAIN"].USE_FLIPPED,
max_images=cfg["DATA"].NUM_TRAIN_IMAGES,
proposal_provider=proposal_provider)
# define mapping from reader streams to network inputs
input_map = {
od_minibatch_source.image_si: image_input,
od_minibatch_source.roi_si: roi_input,
}
if buffered_rpn_proposals is not None:
input_map[od_minibatch_source.proposals_si] = rpn_rois_input
else:
input_map[od_minibatch_source.dims_si] = dims_input
progress_printer = ProgressPrinter(tag='Training', num_epochs=epochs_to_train, gen_heartbeat=True)
for epoch in range(epochs_to_train): # loop over epochs
sample_count = 0
while sample_count < cfg["DATA"].NUM_TRAIN_IMAGES: # loop over minibatches in the epoch
data = od_minibatch_source.next_minibatch(min(cfg.MB_SIZE, cfg["DATA"].NUM_TRAIN_IMAGES-sample_count), input_map=input_map)
trainer.train_minibatch(data) # update model with it
sample_count += trainer.previous_minibatch_sample_count # count samples processed so far
#progress_printer.update_with_trainer(trainer, with_metric=True) # log progress
if sample_count % 100 == 0:
continue
#print("Processed {} samples".format(sample_count))
progress_printer.epoch_summary(with_metric=True)
def train(reader_train, reader_test, samples_per_epoch, max_amount_of_epochs,
samples_per_minibatch, dimensions, classes, learning_rate,
output_directory, with_tf):
features = input_variable(shape=(dimensions['depth'], dimensions['height'],
dimensions['width']))
label = input_variable(shape=len(classes))
# speeds up training
normalized_features = element_times(1.0 / 256.0, features)
if with_tf:
base_model = {
'model_file':
os.path.join("..", "..", "Pretrained Models/ResNet_18.model"),
'feature_node_name':
'features',
'last_hidden_node_name':
'z.x',
'image_dims': (3, 224, 224)
}
model = create_tf_model(base_model,
num_classes=len(classes),
input_features=normalized_features,
freeze=True)
else:
model = create_model(feature_dimensions=normalized_features,
classes=classes)
loss = cross_entropy_with_softmax(model, label)
metric = classification_error(model, label)
learner = momentum_sgd(parameters=model.parameters,
lr=learning_rate_schedule(learning_rate,
UnitType.minibatch),
momentum=0.9,
l2_regularization_weight=0.0005)
reporter = ProgressPrinter(tag='training', num_epochs=max_amount_of_epochs)
trainer = Trainer(model=model,
criterion=(loss, metric),
parameter_learners=[learner],
progress_writers=[reporter])
log_number_of_parameters(model)
map_input_to_streams_train = {
features: reader_train.streams.features,
label: reader_train.streams.labels
}
map_input_to_streams_test = {
features: reader_test.streams.features,
label: reader_test.streams.labels
}
training_session(
trainer=trainer,
mb_source=reader_train,
model_inputs_to_streams=map_input_to_streams_train,
mb_size=samples_per_minibatch,
progress_frequency=samples_per_epoch,
checkpoint_config=CheckpointConfig(frequency=samples_per_epoch,
filename=os.path.join(
output_directory,
"ConvNet_Lego_VisiOn"),
restore=False),
test_config=TestConfig(
reader_test,
minibatch_size=samples_per_minibatch,
model_inputs_to_streams=map_input_to_streams_test)).train()
network = {'features': features, 'label': label, 'model': softmax(model)}
return network
learner = momentum_sgd(z.parameters,
lr = lr_per_minibatch, momentum = momentum_time_constant,
l2_regularization_weight=l2_reg_weight)
######### RESTORE TRAINER IF NEEDED
trainer = Trainer(z, (ce, pe), [learner], [progress_printer])
# trainer.restore_from_checkpoint(model_temp_file)
# define mapping from reader streams to network inputs
input_map = {
input_var: reader_train.streams.features,
label_var: reader_train.streams.labels
}
log_number_of_parameters(z) ; print()
#progress_printer = ProgressPrinter(tag='Training')
# perform model training
batch_index = 0
plot_data = {'batchindex':[], 'loss':[], 'error':[]}
for epoch in range(max_epochs): # loop over epochs
sample_count = 0
ev_avg=0
i_count=0
while sample_count < epoch_size: # loop over minibatches in the epoch
data = reader_train.next_minibatch(min(minibatch_size, epoch_size - sample_count), input_map=input_map) # fetch minibatch.
trainer.train_minibatch(data) # update model with it
sample_count += data[label_var].num_samples # count samples processed so far
def train_model(base_model_file, feature_node_name, last_hidden_node_name,
image_width, image_height, num_channels, num_classes, train_map_file,
num_epochs, max_images=-1, freeze=False):
epoch_size = sum(1 for line in open(train_map_file))
if max_images > 0:
epoch_size = min(epoch_size, max_images)
# Create the minibatch source and input variables
minibatch_source = create_mb_source(train_map_file, image_width, image_height, num_channels, num_classes)
image_input = C.input_variable((num_channels, image_height, image_width))
label_input = C.input_variable(num_classes)
# Define mapping from reader streams to network inputs
input_map = {
image_input: minibatch_source[features_stream_name],
label_input: minibatch_source[label_stream_name]
}
# Instantiate the transfer learning model and loss function
tl_model = create_model(base_model_file, feature_node_name, last_hidden_node_name, num_classes, image_input, freeze)
ce = cross_entropy_with_softmax(tl_model, label_input)
pe = classification_error(tl_model, label_input)
# Instantiate the trainer object
lr_schedule = learning_parameter_schedule(lr_per_mb)
mm_schedule = momentum_schedule(momentum_per_mb)
learner = momentum_sgd(tl_model.parameters, lr_schedule, mm_schedule, l2_regularization_weight=l2_reg_weight)
progress_printer = ProgressPrinter(tag='Training', log_to_file=log_file_name, num_epochs=num_epochs)
#progress_printer = ProgressPrinter(tag='Training', log_to_file=log_file_name, num_epochs=num_epochs)
trainer = Trainer(tl_model, (ce, pe), learner, progress_printer)
# Get minibatches of images and perform model training
print("Training transfer learning model for {0} epochs (epoch_size = {1}).".format(num_epochs, epoch_size))
batch_index = 0
plot_data = {'batchindex': list(), 'loss': list(), 'error': list()}
log_number_of_parameters(tl_model)
for epoch in range(num_epochs): # loop over epochs
sample_count = 0
while sample_count < epoch_size: # loop over minibatches in the epoch
data = minibatch_source.next_minibatch(min(mb_size, epoch_size-sample_count), input_map=input_map)
trainer.train_minibatch(data) # update model with it
sample_count += trainer.previous_minibatch_sample_count # count samples processed so far
#if sample_count % (100 * mb_size) == 0:
# print ("Processed {0} samples".format(sample_count))
# For visualization...
#print("type of plot_data:", type(plot_data), type(plot_data['batchindex']), type(plot_data['loss']),type(plot_data['error']))
plot_data['batchindex'].append(batch_index)
plot_data['loss'].append(trainer.previous_minibatch_loss_average)
plot_data['error'].append(trainer.previous_minibatch_evaluation_average)
batch_index += 1
trainer.summarize_training_progress()
# Visualize training result:
window_width = 32
loss_cumsum = np.cumsum(np.insert(plot_data['loss'], 0, 0))
error_cumsum = np.cumsum(np.insert(plot_data['error'], 0, 0))
# Moving average.
plot_data['batchindex'] = np.insert(plot_data['batchindex'], 0, 0)[window_width:]
plot_data['avg_loss'] = (loss_cumsum[window_width:] - loss_cumsum[:-window_width]) / window_width
plot_data['avg_error'] = (error_cumsum[window_width:] - error_cumsum[:-window_width]) / window_width
plt.figure(1)
#plt.subplot(211)
plt.plot(plot_data["batchindex"], plot_data["avg_loss"], 'b--')
plt.xlabel('Minibatch number')
plt.ylabel('Loss')
plt.title('Minibatch run vs. Training loss ')
#plt.show()
plt.savefig(output_figure_loss, bbox_inches='tight' )
plt.figure(2)
#plt.subplot(212)
plt.plot(plot_data["batchindex"], plot_data["avg_error"], 'r--')
plt.xlabel('Minibatch number')
plt.ylabel('Label Prediction Error')
plt.title('Minibatch run vs. Label Prediction Error ')
#plt.show()
plt.savefig(output_figure_error, bbox_inches='tight')
return tl_model
def init_train_fast_rcnn(image_height,
image_width,
num_classes,
num_rois,
mb_size,
max_epochs,
cntk_lr_per_image,
l2_reg_weight,
momentum_time_constant,
base_path,
boSkipTraining=False,
debug_output=False,
tensorboardLogDir=None):
#make sure we use GPU for training
if use_default_device().type() == 0:
print("WARNING: using CPU for training.")
else:
print("Using GPU for training.")
# Instantiate the Fast R-CNN prediction model
image_input = input_variable((3, image_height, image_width))
roi_input = input_variable((num_rois, 4))
label_input = input_variable((num_rois, num_classes))
frcn_output, frcn_penultimateLayer = frcn_predictor(
image_input, roi_input, num_classes, base_path)
if boSkipTraining:
print("Using pre-trained DNN without refinement")
return frcn_penultimateLayer
# Create the minibatch source and define mapping from reader streams to network inputs
minibatch_source, epoch_size = create_mb_source("train",
image_height,
image_width,
num_classes,
num_rois,
base_path,
randomize=True)
input_map = {
image_input: minibatch_source.streams.features,
roi_input: minibatch_source.streams.rois,
label_input: minibatch_source.streams.roiLabels
}
# set loss / error functions
ce = cross_entropy_with_softmax(frcn_output, label_input, axis=1)
pe = classification_error(frcn_output, label_input, axis=1)
if debug_output:
plot(frcn_output, "graph_frcn.png")
# set the progress printer(s)
progress_writers = [ProgressPrinter(tag='Training', num_epochs=max_epochs)]
if tensorboardLogDir != None:
tensorboard_writer = TensorBoardProgressWriter(
freq=10, log_dir=tensorboardLogDir, model=frcn_output)
progress_writers.append(tensorboard_writer)
# Set learning parameters and instantiate the trainer object
lr_per_sample = [f / float(num_rois) for f in cntk_lr_per_image]
lr_schedule = learning_rate_schedule(lr_per_sample, unit=UnitType.sample)
mm_schedule = momentum_as_time_constant_schedule(momentum_time_constant)
learner = momentum_sgd(frcn_output.parameters,
lr_schedule,
mm_schedule,
l2_regularization_weight=l2_reg_weight)
trainer = Trainer(frcn_output, (ce, pe), learner, progress_writers)
# Get minibatches of images and perform model training
print("Training Fast R-CNN model for %s epochs." % max_epochs)
log_number_of_parameters(frcn_output)
for epoch in range(max_epochs):
sample_count = 0
# loop over minibatches in the epoch
while sample_count < epoch_size:
data = minibatch_source.next_minibatch(min(
mb_size, epoch_size - sample_count),
input_map=input_map)
if sample_count % 100 == 1:
print(
"Training in progress: epoch {} of {}, sample count {} of {}"
.format(epoch, max_epochs, sample_count, epoch_size))
trainer.train_minibatch(data)
sample_count += trainer.previous_minibatch_sample_count # count samples processed so far
trainer.summarize_training_progress()
# Log mean of each parameter tensor, so that we can confirm that the parameters change indeed.
if tensorboardLogDir != None:
for parameter in frcn_output.parameters:
tensorboard_writer.write_value(parameter.uid + "/mean",
np.mean(parameter.value), epoch)
tensorboard_writer.write_value(parameter.uid + "/std",
np.std(parameter.value), epoch)
tensorboard_writer.write_value(parameter.uid + "/absSum",
np.sum(np.abs(parameter.value)),
epoch)
if debug_output:
frcn_output.save_model("frcn_py_%s.model" % (epoch + 1))
return frcn_output
def train_fast_rcnn(cfg):
# Train only if no model exists yet
model_path = cfg['MODEL_PATH']
if os.path.exists(model_path) and cfg["CNTK"].MAKE_MODE:
print("Loading existing model from %s" % model_path)
return load_model(model_path)
else:
# Input variables denoting features and labeled ground truth rois (as 5-tuples per roi)
image_input = input_variable(shape=(cfg.NUM_CHANNELS, cfg.IMAGE_HEIGHT, cfg.IMAGE_WIDTH),
dynamic_axes=[Axis.default_batch_axis()],
name=cfg["MODEL"].FEATURE_NODE_NAME)
roi_proposals = input_variable((cfg.NUM_ROI_PROPOSALS, 4), dynamic_axes=[Axis.default_batch_axis()], name = "roi_proposals")
label_targets = input_variable((cfg.NUM_ROI_PROPOSALS, cfg["DATA"].NUM_CLASSES), dynamic_axes=[Axis.default_batch_axis()])
bbox_targets = input_variable((cfg.NUM_ROI_PROPOSALS, 4*cfg["DATA"].NUM_CLASSES), dynamic_axes=[Axis.default_batch_axis()])
bbox_inside_weights = input_variable((cfg.NUM_ROI_PROPOSALS, 4*cfg["DATA"].NUM_CLASSES), dynamic_axes=[Axis.default_batch_axis()])
# Instantiate the Fast R-CNN prediction model and loss function
loss, pred_error = create_fast_rcnn_model(image_input, roi_proposals, label_targets, bbox_targets, bbox_inside_weights, cfg)
if isinstance(loss, cntk.Variable):
loss = combine([loss])
if cfg["CNTK"].DEBUG_OUTPUT:
print("Storing graphs and models to %s." % cfg.OUTPUT_PATH)
plot(loss, os.path.join(cfg.OUTPUT_PATH, "graph_frcn_train." + cfg["CNTK"].GRAPH_TYPE))
# Set learning parameters
lr_factor = cfg["CNTK"].LR_FACTOR
lr_per_sample_scaled = [x * lr_factor for x in cfg["CNTK"].LR_PER_SAMPLE]
mm_schedule = momentum_schedule(cfg["CNTK"].MOMENTUM_PER_MB)
l2_reg_weight = cfg["CNTK"].L2_REG_WEIGHT
epochs_to_train = cfg["CNTK"].MAX_EPOCHS
print("Using base model: {}".format(cfg["MODEL"].BASE_MODEL))
print("lr_per_sample: {}".format(lr_per_sample_scaled))
# --- train ---
# Instantiate the learners and the trainer object
params = loss.parameters
biases = [p for p in params if '.b' in p.name or 'b' == p.name]
others = [p for p in params if not p in biases]
bias_lr_mult = cfg["CNTK"].BIAS_LR_MULT
lr_schedule = learning_rate_schedule(lr_per_sample_scaled, unit=UnitType.sample)
learner = momentum_sgd(others, lr_schedule, mm_schedule, l2_regularization_weight=l2_reg_weight, unit_gain=False, use_mean_gradient=True)
bias_lr_per_sample = [v * bias_lr_mult for v in cfg["CNTK"].LR_PER_SAMPLE]
bias_lr_schedule = learning_rate_schedule(bias_lr_per_sample, unit=UnitType.sample)
bias_learner = momentum_sgd(biases, bias_lr_schedule, mm_schedule, l2_regularization_weight=l2_reg_weight, unit_gain=False, use_mean_gradient=True)
trainer = Trainer(None, (loss, pred_error), [learner, bias_learner])
# Get minibatches of images and perform model training
print("Training model for %s epochs." % epochs_to_train)
log_number_of_parameters(loss)
# Create the minibatch source
if cfg.USE_PRECOMPUTED_PROPOSALS:
proposal_provider = ProposalProvider.fromfile(cfg["DATA"].TRAIN_PRECOMPUTED_PROPOSALS_FILE, cfg.NUM_ROI_PROPOSALS)
else:
proposal_provider = ProposalProvider.fromconfig(cfg)
od_minibatch_source = ObjectDetectionMinibatchSource(
cfg["DATA"].TRAIN_MAP_FILE, cfg["DATA"].TRAIN_ROI_FILE,
max_annotations_per_image=cfg.INPUT_ROIS_PER_IMAGE,
pad_width=cfg.IMAGE_WIDTH,
pad_height=cfg.IMAGE_HEIGHT,
pad_value=cfg["MODEL"].IMG_PAD_COLOR,
randomize=True,
use_flipping=cfg["TRAIN"].USE_FLIPPED,
max_images=cfg["DATA"].NUM_TRAIN_IMAGES,
num_classes=cfg["DATA"].NUM_CLASSES,
proposal_provider=proposal_provider,
provide_targets=True,
proposal_iou_threshold = cfg.BBOX_THRESH,
normalize_means = None if not cfg.BBOX_NORMALIZE_TARGETS else cfg.BBOX_NORMALIZE_MEANS,
normalize_stds = None if not cfg.BBOX_NORMALIZE_TARGETS else cfg.BBOX_NORMALIZE_STDS)
# define mapping from reader streams to network inputs
input_map = {
od_minibatch_source.image_si: image_input,
od_minibatch_source.proposals_si: roi_proposals,
od_minibatch_source.label_targets_si: label_targets,
od_minibatch_source.bbox_targets_si: bbox_targets,
od_minibatch_source.bbiw_si: bbox_inside_weights
}
progress_printer = ProgressPrinter(tag='Training', num_epochs=epochs_to_train, gen_heartbeat=True)
for epoch in range(epochs_to_train): # loop over epochs
sample_count = 0
while sample_count < cfg["DATA"].NUM_TRAIN_IMAGES: # loop over minibatches in the epoch
data = od_minibatch_source.next_minibatch(min(cfg.MB_SIZE, cfg["DATA"].NUM_TRAIN_IMAGES - sample_count), input_map=input_map)
trainer.train_minibatch(data) # update model with it
sample_count += trainer.previous_minibatch_sample_count # count samples processed so far
progress_printer.update_with_trainer(trainer, with_metric=True) # log progress
if sample_count % 100 == 0:
print("Processed {} samples".format(sample_count))
progress_printer.epoch_summary(with_metric=True)
eval_model = create_fast_rcnn_eval_model(loss, image_input, roi_proposals, cfg)
eval_model.save(cfg['MODEL_PATH'])
return eval_model
def train_model(image_input, roi_input, dims_input, loss, pred_error,
lr_per_sample, mm_schedule, l2_reg_weight, epochs_to_train,
rpn_rois_input=None, buffered_rpn_proposals=None):
if isinstance(loss, cntk.Variable):
loss = combine([loss])
params = loss.parameters
biases = [p for p in params if '.b' in p.name or 'b' == p.name]
others = [p for p in params if not p in biases]
bias_lr_mult = cfg["CNTK"].BIAS_LR_MULT
if cfg["CNTK"].DEBUG_OUTPUT:
print("biases")
for p in biases: print(p)
print("others")
for p in others: print(p)
print("bias_lr_mult: {}".format(bias_lr_mult))
# Instantiate the learners and the trainer object
lr_schedule = learning_rate_schedule(lr_per_sample, unit=UnitType.sample)
learner = momentum_sgd(others, lr_schedule, mm_schedule, l2_regularization_weight=l2_reg_weight,
unit_gain=False, use_mean_gradient=cfg["CNTK"].USE_MEAN_GRADIENT)
bias_lr_per_sample = [v * bias_lr_mult for v in lr_per_sample]
bias_lr_schedule = learning_rate_schedule(bias_lr_per_sample, unit=UnitType.sample)
bias_learner = momentum_sgd(biases, bias_lr_schedule, mm_schedule, l2_regularization_weight=l2_reg_weight,
unit_gain=False, use_mean_gradient=cfg["CNTK"].USE_MEAN_GRADIENT)
trainer = Trainer(None, (loss, pred_error), [learner, bias_learner])
# Get minibatches of images and perform model training
print("Training model for %s epochs." % epochs_to_train)
log_number_of_parameters(loss)
# Create the minibatch source
od_minibatch_source = ObjectDetectionMinibatchSource(
globalvars['train_map_file'], globalvars['train_roi_file'],
max_annotations_per_image=cfg["CNTK"].INPUT_ROIS_PER_IMAGE,
pad_width=image_width, pad_height=image_height, pad_value=img_pad_value,
randomize=True, use_flipping=cfg["TRAIN"].USE_FLIPPED,
max_images=cfg["CNTK"].NUM_TRAIN_IMAGES,
buffered_rpn_proposals=buffered_rpn_proposals)
# define mapping from reader streams to network inputs
input_map = {
od_minibatch_source.image_si: image_input,
od_minibatch_source.roi_si: roi_input,
od_minibatch_source.dims_si: dims_input
}
use_buffered_proposals = buffered_rpn_proposals is not None
progress_printer = ProgressPrinter(tag='Training', num_epochs=epochs_to_train, gen_heartbeat=True)
for epoch in range(epochs_to_train): # loop over epochs
sample_count = 0
while sample_count < epoch_size: # loop over minibatches in the epoch
data, proposals = od_minibatch_source.next_minibatch_with_proposals(min(mb_size, epoch_size-sample_count), input_map=input_map)
if use_buffered_proposals:
data[rpn_rois_input] = MinibatchData(Value(batch=np.asarray(proposals, dtype=np.float32)), 1, 1, False)
# remove dims input if no rpn is required to avoid warnings
del data[[k for k in data if '[6]' in str(k)][0]]
trainer.train_minibatch(data) # update model with it
sample_count += trainer.previous_minibatch_sample_count # count samples processed so far
progress_printer.update_with_trainer(trainer, with_metric=True) # log progress
if sample_count % 100 == 0:
print("Processed {} samples".format(sample_count))
progress_printer.epoch_summary(with_metric=True)
def train_lm():
data = DataReader(token_to_id_path, segment_sepparator)
# Create model nodes for the source and target inputs
input_sequence, label_sequence = create_inputs(data.vocab_dim)
# Create the model. It has three output nodes
# z: the input to softmax that provides the latent representation of the next token
# cross_entropy: this is used training criterion
# error: this a binary indicator if the model predicts the correct token
z, cross_entropy, error = create_model(input_sequence, label_sequence,
data.vocab_dim, hidden_dim)
# For measurement we use the (build in) full softmax.
full_ce = C.cross_entropy_with_softmax(z, label_sequence)
# print out some useful training information
log_number_of_parameters(z)
print()
# Run the training loop
num_trained_samples = 0
num_trained_samples_since_last_report = 0
# Instantiate the trainer object to drive the model training
lr_schedule = learning_rate_schedule(learning_rate, UnitType.sample)
momentum_schedule = momentum_as_time_constant_schedule(
momentum_as_time_constant)
gradient_clipping_with_truncation = True
learner = momentum_sgd(
z.parameters,
lr_schedule,
momentum_schedule,
gradient_clipping_threshold_per_sample=clipping_threshold_per_sample,
gradient_clipping_with_truncation=gradient_clipping_with_truncation)
trainer = Trainer(z, (cross_entropy, error), learner)
for epoch_count in range(num_epochs):
for features, labels, token_count in data.minibatch_generator(
train_file_path, sequence_length, sequences_per_batch):
arguments = ({input_sequence: features, label_sequence: labels})
t_start = timeit.default_timer()
trainer.train_minibatch(arguments)
t_end = timeit.default_timer()
samples_per_second = token_count / (t_end - t_start)
# Print progress report every num_samples_between_progress_report samples
if num_trained_samples_since_last_report >= num_samples_between_progress_report or num_trained_samples == 0:
av_ce = average_cross_entropy(full_ce, input_sequence,
label_sequence, data)
print_progress(samples_per_second, av_ce, num_trained_samples,
t_start)
num_trained_samples_since_last_report = 0
num_trained_samples += token_count
num_trained_samples_since_last_report += token_count
# after each epoch save the model
model_filename = "models/lm_epoch%d.dnn" % epoch_count
z.save_model(model_filename)
print("Saved model to '%s'" % model_filename)
def train_lm(training_file, epochs, max_num_minibatches):
# load the data and vocab
data, char_to_ix, ix_to_char, data_size, vocab_dim = load_data_and_vocab(
training_file)
# Model the source and target inputs to the model
input_sequence, label_sequence = create_inputs(vocab_dim)
# create the model
model = create_model(vocab_dim)
# and apply it to the input sequence
z = model(input_sequence)
# setup the criterions (loss and metric)
ce = cross_entropy_with_softmax(z, label_sequence)
errs = classification_error(z, label_sequence)
# Instantiate the trainer object to drive the model training
lr_per_sample = learning_parameter_schedule_per_sample(0.001)
momentum_schedule = momentum_schedule_per_sample(0.9990913221888589)
clipping_threshold_per_sample = 5.0
gradient_clipping_with_truncation = True
learner = momentum_sgd(
z.parameters,
lr_per_sample,
momentum_schedule,
gradient_clipping_threshold_per_sample=clipping_threshold_per_sample,
gradient_clipping_with_truncation=gradient_clipping_with_truncation)
progress_printer = ProgressPrinter(freq=100, tag='Training')
trainer = Trainer(z, (ce, errs), learner, progress_printer)
sample_freq = 1000
minibatches_per_epoch = min(data_size // minibatch_size,
max_num_minibatches // epochs)
# print out some useful training information
log_number_of_parameters(z)
print("Running %d epochs with %d minibatches per epoch" %
(epochs, minibatches_per_epoch))
print()
for e in range(0, epochs):
# Specify the mapping of input variables in the model to actual minibatch data to be trained with
# If it's the start of the data, we specify that we are looking at a new sequence (True)
mask = [True]
for b in range(0, minibatches_per_epoch):
# get the data
features, labels = get_data(b, minibatch_size, data, char_to_ix,
vocab_dim)
arguments = ({
input_sequence: features,
label_sequence: labels
}, mask)
mask = [False]
trainer.train_minibatch(arguments)
global_minibatch = e * minibatches_per_epoch + b
if global_minibatch % sample_freq == 0:
print(sample(z, ix_to_char, vocab_dim, char_to_ix))
model_filename = "models/shakespeare_epoch%d.dnn" % (e + 1)
z.save(model_filename)
print("Saved model to '%s'" % model_filename)
def train_fast_rcnn(cfg):
# Train only if no model exists yet
model_path = cfg['MODEL_PATH']
if os.path.exists(model_path) and cfg["CNTK"].MAKE_MODE:
print("Loading existing model from %s" % model_path)
return load_model(model_path)
else:
# Input variables denoting features and labeled ground truth rois (as 5-tuples per roi)
image_input = input_variable(shape=(cfg.NUM_CHANNELS, cfg.IMAGE_HEIGHT,
cfg.IMAGE_WIDTH),
dynamic_axes=[Axis.default_batch_axis()],
name=cfg["MODEL"].FEATURE_NODE_NAME)
roi_proposals = input_variable(
(cfg.NUM_ROI_PROPOSALS, 4),
dynamic_axes=[Axis.default_batch_axis()],
name="roi_proposals")
label_targets = input_variable(
(cfg.NUM_ROI_PROPOSALS, cfg["DATA"].NUM_CLASSES),
dynamic_axes=[Axis.default_batch_axis()])
bbox_targets = input_variable(
(cfg.NUM_ROI_PROPOSALS, 4 * cfg["DATA"].NUM_CLASSES),
dynamic_axes=[Axis.default_batch_axis()])
bbox_inside_weights = input_variable(
(cfg.NUM_ROI_PROPOSALS, 4 * cfg["DATA"].NUM_CLASSES),
dynamic_axes=[Axis.default_batch_axis()])
# Instantiate the Fast R-CNN prediction model and loss function
loss, pred_error = create_fast_rcnn_model(image_input, roi_proposals,
label_targets, bbox_targets,
bbox_inside_weights, cfg)
if isinstance(loss, cntk.Variable):
loss = combine([loss])
if cfg["CNTK"].DEBUG_OUTPUT:
print("Storing graphs and models to %s." % cfg.OUTPUT_PATH)
plot(
loss,
os.path.join(cfg.OUTPUT_PATH,
"graph_frcn_train." + cfg["CNTK"].GRAPH_TYPE))
# Set learning parameters
lr_factor = cfg["CNTK"].LR_FACTOR
lr_per_sample_scaled = [
x * lr_factor for x in cfg["CNTK"].LR_PER_SAMPLE
]
mm_schedule = momentum_schedule(cfg["CNTK"].MOMENTUM_PER_MB)
l2_reg_weight = cfg["CNTK"].L2_REG_WEIGHT
epochs_to_train = cfg["CNTK"].MAX_EPOCHS
print("Using base model: {}".format(cfg["MODEL"].BASE_MODEL))
print("lr_per_sample: {}".format(lr_per_sample_scaled))
# --- train ---
# Instantiate the learners and the trainer object
params = loss.parameters
biases = [p for p in params if '.b' in p.name or 'b' == p.name]
others = [p for p in params if not p in biases]
bias_lr_mult = cfg["CNTK"].BIAS_LR_MULT
lr_schedule = learning_parameter_schedule_per_sample(
lr_per_sample_scaled)
learner = momentum_sgd(others,
lr_schedule,
mm_schedule,
l2_regularization_weight=l2_reg_weight,
unit_gain=False,
use_mean_gradient=True)
bias_lr_per_sample = [
v * bias_lr_mult for v in cfg["CNTK"].LR_PER_SAMPLE
]
bias_lr_schedule = learning_parameter_schedule_per_sample(
bias_lr_per_sample)
bias_learner = momentum_sgd(biases,
bias_lr_schedule,
mm_schedule,
l2_regularization_weight=l2_reg_weight,
unit_gain=False,
use_mean_gradient=True)
trainer = Trainer(None, (loss, pred_error), [learner, bias_learner])
# Get minibatches of images and perform model training
print("Training model for %s epochs." % epochs_to_train)
log_number_of_parameters(loss)
# Create the minibatch source
if cfg.USE_PRECOMPUTED_PROPOSALS:
proposal_provider = ProposalProvider.fromfile(
cfg["DATA"].TRAIN_PRECOMPUTED_PROPOSALS_FILE,
cfg.NUM_ROI_PROPOSALS)
else:
proposal_provider = ProposalProvider.fromconfig(cfg)
od_minibatch_source = ObjectDetectionMinibatchSource(
cfg["DATA"].TRAIN_MAP_FILE,
cfg["DATA"].TRAIN_ROI_FILE,
max_annotations_per_image=cfg.INPUT_ROIS_PER_IMAGE,
pad_width=cfg.IMAGE_WIDTH,
pad_height=cfg.IMAGE_HEIGHT,
pad_value=cfg["MODEL"].IMG_PAD_COLOR,
randomize=True,
use_flipping=cfg["TRAIN"].USE_FLIPPED,
max_images=cfg["DATA"].NUM_TRAIN_IMAGES,
num_classes=cfg["DATA"].NUM_CLASSES,
proposal_provider=proposal_provider,
provide_targets=True,
proposal_iou_threshold=cfg.BBOX_THRESH,
normalize_means=None
if not cfg.BBOX_NORMALIZE_TARGETS else cfg.BBOX_NORMALIZE_MEANS,
normalize_stds=None
if not cfg.BBOX_NORMALIZE_TARGETS else cfg.BBOX_NORMALIZE_STDS)
# define mapping from reader streams to network inputs
input_map = {
od_minibatch_source.image_si: image_input,
od_minibatch_source.proposals_si: roi_proposals,
od_minibatch_source.label_targets_si: label_targets,
od_minibatch_source.bbox_targets_si: bbox_targets,
od_minibatch_source.bbiw_si: bbox_inside_weights
}
progress_printer = ProgressPrinter(tag='Training',
num_epochs=epochs_to_train,
gen_heartbeat=True)
for epoch in range(epochs_to_train): # loop over epochs
sample_count = 0
while sample_count < cfg[
"DATA"].NUM_TRAIN_IMAGES: # loop over minibatches in the epoch
data = od_minibatch_source.next_minibatch(min(
cfg.MB_SIZE, cfg["DATA"].NUM_TRAIN_IMAGES - sample_count),
input_map=input_map)
trainer.train_minibatch(data) # update model with it
sample_count += trainer.previous_minibatch_sample_count # count samples processed so far
progress_printer.update_with_trainer(
trainer, with_metric=True) # log progress
if sample_count % 100 == 0:
continue
#print("Processed {} samples".format(sample_count))
progress_printer.epoch_summary(with_metric=True)
eval_model = create_fast_rcnn_eval_model(loss, image_input,
roi_proposals, cfg)
eval_model.save(cfg['MODEL_PATH'])
return eval_model
def train_fast_rcnn(debug_output=False, model_path=model_file):
if debug_output:
print("Storing graphs and intermediate models to %s." % os.path.join(abs_path, "Output"))
# Create the minibatch source
minibatch_source = create_mb_source(image_height, image_width, num_channels,
num_classes, num_rois, base_path, "train")
# Input variables denoting features, rois and label data
image_input = C.input_variable((num_channels, image_height, image_width))
roi_input = C.input_variable((num_rois, 4))
label_input = C.input_variable((num_rois, num_classes))
# define mapping from reader streams to network inputs
input_map = {
image_input: minibatch_source.streams.features,
roi_input: minibatch_source.streams.rois,
label_input: minibatch_source.streams.roiLabels
}
# Instantiate the Fast R-CNN prediction model and loss function
frcn_output = frcn_predictor(image_input, roi_input, num_classes, model_path)
ce = cross_entropy_with_softmax(frcn_output, label_input, axis=1)
pe = classification_error(frcn_output, label_input, axis=1)
if debug_output:
plot(frcn_output, os.path.join(abs_path, "Output", "graph_frcn.png"))
# Set learning parameters
l2_reg_weight = 0.0005
lr_per_sample = [0.00001] * 10 + [0.000001] * 5 + [0.0000001]
lr_schedule = learning_parameter_schedule_per_sample(lr_per_sample)
mm_schedule = momentum_schedule_per_sample(momentum_per_sample)
# Instantiate the trainer object as default
learner = momentum_sgd(frcn_output.parameters, lr_schedule, mm_schedule, l2_regularization_weight=l2_reg_weight)
# Preparation for distributed learning, which is compatible for normal learner
learner = distributed.data_parallel_distributed_learner(
learner = learner,
num_quantization_bits = num_quantization_bits, # non-quantized gradient accumulation
distributed_after = warm_up) # no warm start as default
progress_printer = ProgressPrinter(tag='Training', num_epochs=max_epochs, rank=distributed.Communicator.rank())
trainer = Trainer(frcn_output, (ce, pe), learner, progress_printer)
# Get minibatches of images and perform model training
print("Training Fast R-CNN model for %s epochs." % max_epochs)
log_number_of_parameters(frcn_output)
for epoch in range(max_epochs): # loop over epochs
sample_count = 0
while sample_count < epoch_size: # loop over minibatches in the epoch
data = minibatch_source.next_minibatch(min(mb_size * C.Communicator.num_workers(), epoch_size-sample_count),
input_map=input_map,
num_data_partitions=C.Communicator.num_workers(),
partition_index=C.Communicator.rank())
trainer.train_minibatch(data) # update model with it
sample_count += trainer.previous_minibatch_sample_count # count samples processed so far
trainer.summarize_training_progress()
if debug_output:
frcn_output.save(os.path.join(abs_path, "Output", "frcn_py_%s.model" % (epoch+1)))
if distributed_flg:
distributed.Communicator.finalize()
return frcn_output
