def _validation(self):
logger.info("Before validation", extra={'epoch': self.epoch_flt})
self.model.eval()
metrics_values = defaultdict(int)
samples_cnt = 0
for val_it, (inputs, targets) in enumerate(self.data_loaders['val']):
inputs, targets = inputs.cuda(), targets.cuda()
outputs = self.model(inputs)
full_batch_size = inputs.size(0)
for name, metric in self.val_metrics.items():
metric_value = metric(outputs, targets)
metric_value = metric_value.item()
metrics_values[name] += metric_value * full_batch_size
samples_cnt += full_batch_size
logger.info("Validation in progress", extra={'epoch': self.epoch_flt,
'val_iter': val_it, 'val_iters': self.val_iters})
for name in metrics_values:
metrics_values[name] /= float(samples_cnt)
sly.report_metrics_validation(self.epoch_flt, metrics_values)
self.model.train()
logger.info("Validation has been finished", extra={'epoch': self.epoch_flt})
return metrics_values
def _validation(self):
sly.logger.info("Before validation", extra={'epoch': self.epoch_flt})
if self.config['validate_with_model_eval']:
self.model.eval()
metrics_values = defaultdict(int)
samples_cnt = 0
for val_it, (inputs, targets) in enumerate(self.data_loaders['val']):
inputs, targets = cuda_variable(inputs, volatile=True), cuda_variable(targets)
outputs = self.model(inputs)
full_batch_size = inputs.size(0)
for name, metric in self.val_metrics.items():
metric_value = metric(outputs, targets)
if isinstance(metric_value, torch.autograd.Variable): # for val loss
metric_value = metric_value.data[0]
metrics_values[name] += metric_value * full_batch_size
samples_cnt += full_batch_size
sly.logger.info("Validation in progress", extra={'epoch': self.epoch_flt,
'val_iter': val_it, 'val_iters': self.val_iters})
for name in metrics_values:
metrics_values[name] /= float(samples_cnt)
sly.report_metrics_validation(self.epoch_flt, metrics_values)
self.model.train()
sly.logger.info("Validation has been finished", extra={'epoch': self.epoch_flt})
return metrics_values
def _validation(self):
# Compute validation metrics.
# Switch the model to evaluation model to stop batchnorm runnning average updates.
self._model.eval()
# Initialize the totals counters.
validated_samples = 0
total_loss = 0.0
# Iterate over validation dataset batches.
for val_it, (inputs, targets) in enumerate(self._data_loaders[VAL]):
# Move the data to the GPU and run inference.
with torch.no_grad():
inputs_cuda, targets_cuda = Variable(inputs).cuda(), Variable(
targets).cuda()
outputs_cuda = self._model(inputs_cuda)
# The last betch may be smaller than the rest if the dataset does not have a whole number of full batches,
# so read the batch size from the input.
batch_size = inputs_cuda.size(0)
# Compute the loss and grab the value from GPU.
loss_value = self._loss_fn(outputs_cuda, targets_cuda).item()
# Add up the totals.
total_loss += loss_value * batch_size
validated_samples += batch_size
# Report progress.
sly.logger.info("Validation in progress",
extra={
'epoch': self.epoch_flt,
'val_iter': val_it,
'val_iters': self._val_iters
})
# Compute the average loss from the accumulated totals.
metrics_values = {LOSS: total_loss / validated_samples}
# Report progress and metric values to be plotted in the training chart and return.
sly.report_metrics_validation(self.epoch_flt, metrics_values)
sly.logger.info("Validation has been finished",
extra={'epoch': self.epoch_flt})
return metrics_values
def _validation(self, session):
overall_val_loss = 0
val_iters = len(self.data_loaders['val'])
for val_it, (batch_inputs,
batch_targets) in enumerate(self.data_loaders['val']):
feed = {self.inputs: batch_inputs, self.labels: batch_targets}
val_loss = session.run(self.val_loss, feed)
overall_val_loss += val_loss
sly.logger.info("Validation in progress",
extra={
'epoch': self.epoch_flt,
'val_iter': val_it,
'val_iters': val_iters
})
metrics_values_val = {
'loss': overall_val_loss / val_iters,
}
sly.report_metrics_validation(self.epoch_flt, metrics_values_val)
sly.logger.info("Validation has been finished",
extra={'epoch': self.epoch_flt})
return metrics_values_val
def train(datasets_dicts,
epochs,
val_every,
iters_cnt,
validate_with_eval_model,
pipeline_config,
num_clones=1,
save_cback=None,
is_transfer_learning=False):
logger.info('Start train')
configs = configs_from_pipeline(pipeline_config)
model_config = configs['model']
train_config = configs['train_config']
create_model_fn = functools.partial(model_builder.build,
model_config=model_config,
is_training=True)
detection_model = create_model_fn()
def get_next(dataset):
return dataset_util.make_initializable_iterator(
build_dataset(dataset)).get_next()
create_tensor_dict_fn = functools.partial(get_next,
datasets_dicts['train'])
create_tensor_dict_fn_val = functools.partial(get_next,
datasets_dicts['val'])
data_augmentation_options = [
preprocessor_builder.build(step)
for step in train_config.data_augmentation_options
]
with tf.Graph().as_default():
# Build a configuration specifying multi-GPU and multi-replicas.
deploy_config = model_deploy.DeploymentConfig(
num_clones=4,
clone_on_cpu=False,
replica_id=0,
num_replicas=1,
num_ps_tasks=0,
worker_job_name='lonely_worker')
# Place the global step on the device storing the variables.
with tf.device(deploy_config.variables_device()):
global_step = slim.create_global_step()
with tf.device(deploy_config.inputs_device()):
coord = coordinator.Coordinator()
input_queue = create_input_queue(
train_config.batch_size, create_tensor_dict_fn,
train_config.batch_queue_capacity,
train_config.num_batch_queue_threads,
train_config.prefetch_queue_capacity,
data_augmentation_options)
input_queue_val = create_input_queue(
train_config.batch_size, create_tensor_dict_fn_val,
train_config.batch_queue_capacity,
train_config.num_batch_queue_threads,
train_config.prefetch_queue_capacity,
data_augmentation_options)
# create validation graph
create_model_fn_val = functools.partial(
model_builder.build,
model_config=model_config,
is_training=not validate_with_eval_model)
with tf.device(deploy_config.optimizer_device()):
training_optimizer, optimizer_summary_vars = optimizer_builder.build(
train_config.optimizer)
for var in optimizer_summary_vars:
tf.summary.scalar(var.op.name, var, family='LearningRate')
train_losses = []
grads_and_vars = []
with slim.arg_scope([slim.model_variable, slim.variable],
device='/device:CPU:0'):
for curr_dev_id in range(num_clones):
with tf.device('/gpu:{}'.format(curr_dev_id)):
with tf.name_scope(
'clone_{}'.format(curr_dev_id)) as scope:
with tf.variable_scope(
tf.get_variable_scope(),
reuse=True if curr_dev_id > 0 else None):
losses = _create_losses_val(
input_queue, create_model_fn, train_config)
clones_loss = tf.add_n(losses)
clones_loss = tf.divide(clones_loss,
1.0 * num_clones)
grads = training_optimizer.compute_gradients(
clones_loss)
train_losses.append(clones_loss)
grads_and_vars.append(grads)
if curr_dev_id == 0:
update_ops = tf.get_collection(
tf.GraphKeys.UPDATE_OPS)
val_total_loss = get_val_loss(num_clones, input_queue_val,
create_model_fn_val, train_config)
with tf.device(deploy_config.optimizer_device()):
total_loss = tf.add_n(train_losses)
grads_and_vars = model_deploy._sum_clones_gradients(grads_and_vars)
total_loss = tf.check_numerics(total_loss,
'LossTensor is inf or nan.')
# Optionally multiply bias gradients by train_config.bias_grad_multiplier.
if train_config.bias_grad_multiplier:
biases_regex_list = ['.*/biases']
grads_and_vars = variables_helper.multiply_gradients_matching_regex(
grads_and_vars,
biases_regex_list,
multiplier=train_config.bias_grad_multiplier)
# Optionally freeze some layers by setting their gradients to be zero.
if train_config.freeze_variables:
grads_and_vars = variables_helper.freeze_gradients_matching_regex(
grads_and_vars, train_config.freeze_variables)
# Optionally clip gradients
if train_config.gradient_clipping_by_norm > 0:
with tf.name_scope('clip_grads'):
grads_and_vars = slim.learning.clip_gradient_norms(
grads_and_vars, train_config.gradient_clipping_by_norm)
# Create gradient updates.
grad_updates = training_optimizer.apply_gradients(
grads_and_vars, global_step=global_step)
update_ops.append(grad_updates)
update_op = tf.group(*update_ops, name='update_barrier')
with tf.control_dependencies([update_op]):
train_tensor = tf.identity(total_loss, name='train_op')
config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
coord.clear_stop()
sess = tf.Session(config=config)
saver = tf.train.Saver()
graph = ops.get_default_graph()
with graph.as_default():
with ops.name_scope('init_ops'):
init_op = variables.global_variables_initializer()
ready_op = variables.report_uninitialized_variables()
local_init_op = control_flow_ops.group(
variables.local_variables_initializer(),
lookup_ops.tables_initializer())
# graph.finalize()
sess.run([init_op, ready_op, local_init_op])
queue_runners = graph.get_collection(ops.GraphKeys.QUEUE_RUNNERS)
threads = []
for qr in queue_runners:
threads.extend(
qr.create_threads(sess, coord=coord, daemon=True, start=True))
logger.info('Start restore')
if train_config.fine_tune_checkpoint:
var_map = detection_model.restore_map(
fine_tune_checkpoint_type=train_config.
fine_tune_checkpoint_type,
load_all_detection_checkpoint_vars=(
train_config.load_all_detection_checkpoint_vars
and (not is_transfer_learning)))
available_var_map = (
variables_helper.get_variables_available_in_checkpoint(
var_map, train_config.fine_tune_checkpoint))
if 'global_step' in available_var_map:
del available_var_map['global_step']
init_saver = tf.train.Saver(available_var_map)
logger.info('Restoring model weights from previous checkpoint.')
init_saver.restore(sess, train_config.fine_tune_checkpoint)
logger.info('Model restored.')
eval_planner = EvalPlanner(epochs, val_every)
progress = sly.Progress('Model training: ',
epochs * iters_cnt['train'])
best_val_loss = float('inf')
epoch_flt = 0
for epoch in range(epochs):
logger.info("Before new epoch", extra={'epoch': epoch_flt})
for train_it in range(iters_cnt['train']):
total_loss, np_global_step = sess.run(
[train_tensor, global_step])
metrics_values_train = {
'loss': total_loss,
}
progress.iter_done_report()
epoch_flt = epoch_float(epoch, train_it + 1,
iters_cnt['train'])
sly.report_metrics_training(epoch_flt, metrics_values_train)
if eval_planner.need_validation(epoch_flt):
logger.info("Before validation",
extra={'epoch': epoch_flt})
overall_val_loss = 0
for val_it in range(iters_cnt['val']):
overall_val_loss += sess.run(val_total_loss)
logger.info("Validation in progress",
extra={
'epoch': epoch_flt,
'val_iter': val_it,
'val_iters': iters_cnt['val']
})
metrics_values_val = {
'loss': overall_val_loss / iters_cnt['val'],
}
sly.report_metrics_validation(epoch_flt,
metrics_values_val)
logger.info("Validation has been finished",
extra={'epoch': epoch_flt})
eval_planner.validation_performed()
val_loss = metrics_values_val['loss']
model_is_best = val_loss < best_val_loss
if model_is_best:
best_val_loss = val_loss
logger.info(
'It\'s been determined that current model is the best one for a while.'
)
save_cback(saver,
sess,
model_is_best,
opt_data={
'epoch': epoch_flt,
'val_metrics': metrics_values_val,
})
logger.info("Epoch was finished", extra={'epoch': epoch_flt})
coord.request_stop()
coord.join(threads)
def train(self):
# Start queue threads.
threads = tf.train.start_queue_runners(coord=self.coord, sess=self.sess)
progress = sly.progress_counter_train(self.epochs, self.iters_cnt['train'])
best_val_loss = float('inf')
internal_step = 0
for epoch in range(self.epochs):
logger.info("Before new epoch", extra={'epoch': self.epoch_flt})
for train_it in range(self.iters_cnt['train']):
feed_dict = {self.step_ph: internal_step}
internal_step += 1
loss_value, _ = self.sess.run([self.total_loss, self.train_op], feed_dict=feed_dict)
self.sess.run(self.running_vars_initializer)
# Update the running variables on new batch of samples
plh_label = self.sess.run(self.v1)
plh_prediction = self.sess.run(self.v2)
feed_dict = {self.tf_label: plh_label, self.tf_prediction: plh_prediction}
train_accuracy = self.sess.run(self.tf_metric_update, feed_dict=feed_dict) # from last GPU
metrics_values_train = {
'loss': loss_value,
'accuracy': train_accuracy
}
progress.iter_done_report()
self.epoch_flt = epoch_float(epoch, train_it + 1, self.iters_cnt['train'])
sly.report_metrics_training(self.epoch_flt, metrics_values_train)
if self.eval_planner.need_validation(self.epoch_flt):
logger.info("Before validation", extra={'epoch': self.epoch_flt})
overall_val_loss = 0
overall_val_accuracy = 0
for val_it in range(self.iters_cnt['val']):
overall_val_loss += self.sess.run(self.total_val_loss)
self.sess.run(self.running_vars_initializer)
# Update the running variables on new batch of samples
plh_label = self.sess.run(self.v1_val)
plh_prediction = self.sess.run(self.v2_val)
feed_dict = {self.tf_label: plh_label, self.tf_prediction: plh_prediction}
overall_val_accuracy += self.sess.run(self.tf_metric_update, feed_dict=feed_dict)
logger.info("Validation in progress", extra={'epoch': self.epoch_flt,
'val_iter': val_it,
'val_iters': self.iters_cnt['val']})
metrics_values_val = {
'loss': overall_val_loss / self.iters_cnt['val'],
'accuracy': overall_val_accuracy / self.iters_cnt['val']
}
sly.report_metrics_validation(self.epoch_flt, metrics_values_val)
logger.info("Validation has been finished", extra={'epoch': self.epoch_flt})
self.eval_planner.validation_performed()
val_loss = metrics_values_val['loss']
model_is_best = val_loss < best_val_loss
if model_is_best:
best_val_loss = val_loss
logger.info('It\'s been determined that current model is the best one for a while.')
self._dump_model(model_is_best, opt_data={
'epoch': self.epoch_flt,
'val_metrics': metrics_values_val,
})
logger.info("Epoch was finished", extra={'epoch': self.epoch_flt})
self.coord.request_stop()
self.coord.join(threads)
def train(data_dicts,
class_num,
input_size,
lr,
n_epochs,
num_clones,
iters_cnt,
val_every,
model_init_fn,
save_cback,
atrous_rates=[6, 12, 18],
fine_tune_batch_norm=True,
output_stride=16):
tf.logging.set_verbosity(tf.logging.INFO)
# Set up deployment (i.e., multi-GPUs and/or multi-replicas).
config = model_deploy.DeploymentConfig(num_clones=num_clones,
clone_on_cpu=clone_on_cpu,
replica_id=task,
num_replicas=num_replicas,
num_ps_tasks=num_ps_tasks)
with tf.Graph().as_default():
with tf.device(config.inputs_device()):
samples = get(data_dicts['train'],
input_size,
is_training=True,
model_variant=model_variant)
samples_val = get(data_dicts['val'],
input_size,
is_training=True,
model_variant=model_variant)
inputs_queue = prefetch_queue.prefetch_queue(samples,
capacity=128 *
config.num_clones,
dynamic_pad=True)
inputs_queue_val = prefetch_queue.prefetch_queue(samples_val,
capacity=128 *
config.num_clones,
dynamic_pad=True)
coord = tf.train.Coordinator()
# Create the global step on the device storing the variables.
with tf.device(config.variables_device()):
global_step = tf.train.create_global_step()
# Define the model and create clones.
model_fn = _build_deeplab
model_args = (inputs_queue, {
'semantic': class_num
}, input_size, atrous_rates, output_stride, fine_tune_batch_norm)
clones = model_deploy.create_clones(config,
model_fn,
args=model_args)
# Gather update_ops from the first clone. These contain, for example,
# the updates for the batch_norm variables created by model_fn.
first_clone_scope = config.clone_scope(0)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS,
first_clone_scope)
# Build the optimizer based on the device specification.
with tf.device(config.optimizer_device()):
learning_rate = lr
optimizer = tf.train.AdamOptimizer(learning_rate)
with tf.device(config.variables_device()):
total_loss, grads_and_vars = model_deploy.optimize_clones(
clones, optimizer)
total_loss = tf.check_numerics(total_loss, 'Loss is inf or nan.')
model_fn_val = _build_deeplab_val
model_args_val = (inputs_queue_val, {
'semantic': class_num
}, input_size, atrous_rates, output_stride)
val_clones, val_losses = create_val_clones(num_clones,
config,
model_fn_val,
args=model_args_val)
val_total_loss = get_clones_val_losses(val_clones, None,
val_losses)
# Modify the gradients for biases and last layer variables.
last_layers = model.get_extra_layer_scopes()
grad_mult = train_utils.get_model_gradient_multipliers(
last_layers, last_layer_gradient_multiplier)
if grad_mult:
grads_and_vars = slim.learning.multiply_gradients(
grads_and_vars, grad_mult)
# Create gradient update op.
grad_updates = optimizer.apply_gradients(grads_and_vars,
global_step=global_step)
update_ops.append(grad_updates)
update_op = tf.group(*update_ops)
with tf.control_dependencies([update_op]):
train_tensor = tf.identity(total_loss, name='train_op')
config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
coord.clear_stop()
sess = tf.Session(config=config)
graph = ops.get_default_graph()
with graph.as_default():
with ops.name_scope('init_ops'):
init_op = variables.global_variables_initializer()
ready_op = variables.report_uninitialized_variables()
local_init_op = control_flow_ops.group(
variables.local_variables_initializer(),
lookup_ops.tables_initializer())
# graph.finalize()
sess.run([init_op, ready_op, local_init_op])
queue_runners = graph.get_collection(ops.GraphKeys.QUEUE_RUNNERS)
threads = []
for qr in queue_runners:
threads.extend(
qr.create_threads(sess, coord=coord, daemon=True, start=True))
# # for i in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES):
# # print(i)
# vary_23 = [v for v in tf.global_variables() if v.name == 'xception_65/middle_flow/block1/unit_8/xception_module/separable_conv3_depthwise/BatchNorm/moving_mean:0'][0]
#
# beta_23 = [v for v in tf.global_variables() if v.name == 'xception_65/middle_flow/block1/unit_8/xception_module/separable_conv3_depthwise/BatchNorm/gamma:0'][0]
# for i in range(1000):
# train_loss = sess.run(train_tensor)
# print(train_loss)
# vary, beta = sess.run([vary_23, beta_23])
# print('mean', vary[0:3])
# print('beta', beta[0:3])
# if (i + 1) % 10 == 0:
# for i in range(10):
# val_loss = sess.run(val_total_loss)
# vary, beta = sess.run([vary_23, beta_23])
# print('mean val', vary[0:3])
# print('beta', beta[0:3])
# print('VAl_loss', val_loss)
model_init_fn(sess)
saver = tf.train.Saver()
eval_planner = EvalPlanner(n_epochs, val_every)
progress = sly.progress_counter_train(n_epochs, iters_cnt['train'])
best_val_loss = float('inf')
epoch_flt = 0
for epoch in range(n_epochs):
logger.info("Before new epoch", extra={'epoch': epoch_flt})
for train_it in range(iters_cnt['train']):
total_loss = sess.run(train_tensor)
metrics_values_train = {
'loss': total_loss,
}
progress.iter_done_report()
epoch_flt = epoch_float(epoch, train_it + 1,
iters_cnt['train'])
sly.report_metrics_training(epoch_flt, metrics_values_train)
if eval_planner.need_validation(epoch_flt):
logger.info("Before validation",
extra={'epoch': epoch_flt})
overall_val_loss = 0
for val_it in range(iters_cnt['val']):
overall_val_loss += sess.run(val_total_loss)
logger.info("Validation in progress",
extra={
'epoch': epoch_flt,
'val_iter': val_it,
'val_iters': iters_cnt['val']
})
metrics_values_val = {
'loss': overall_val_loss / iters_cnt['val'],
}
sly.report_metrics_validation(epoch_flt,
metrics_values_val)
logger.info("Validation has been finished",
extra={'epoch': epoch_flt})
eval_planner.validation_performed()
val_loss = metrics_values_val['loss']
model_is_best = val_loss < best_val_loss
if model_is_best:
best_val_loss = val_loss
logger.info(
'It\'s been determined that current model is the best one for a while.'
)
save_cback(saver,
sess,
model_is_best,
opt_data={
'epoch': epoch_flt,
'val_metrics': metrics_values_val,
})
logger.info("Epoch was finished", extra={'epoch': epoch_flt})
def main():
# Please note that auxiliary methods from sly (supervisely_lib) use supervisely_lib.logger to format output.
# So don't replace formatters or handlers of the logger.
# One may use other loggers or simple prints for other output, but it's recommended to use supervisely_lib.logger.
logger.info('Hello ML world')
print('Glad to see u')
# TaskHelperTrain contains almost all needed to run training as Supervisely task,
# including task settings and paths to data and models.
task_helper = sly.TaskHelperTrain()
# All settings and parameters are passed to task in json file.
# Content of the file is entirely dependent on model implementation.
training_settings = task_helper.task_settings
logger.info('Task settings are read',
extra={'task_settings': training_settings})
cnt_epochs = training_settings[
"epochs"] # in the fake model we want cnt of epochs
cnt_iters_per_epoch = training_settings["iters_per_epoch"]
# Let's imitate model weights loading.
# Task acquires directory with input model weights (e.g. to continue training or to initialize some parameters).
# Content of the directory is entirely dependent on model implementation.
model_dir = task_helper.paths.model_dir
if task_helper.model_dir_is_empty():
model = create_fake_model()
logger.info('Model created from scratch')
else:
model = load_fake_model(model_dir)
logger.info('Init model weights are loaded',
extra={'model_dir': model_dir})
# We will save weights of trained model (checkpoints) into directories provided by the checkpoints_saver.
checkpoints_saver = task_helper.checkpoints_saver
logger.info('Ready to save checkpoints',
extra={'results_dir': task_helper.paths.results_dir})
# Let's imitate reading input project with training data.
# Of course in real implementations it is usually wrapped in some data loaders which are executed in parallel.
project_meta = task_helper.in_project_meta # Project meta contains list of project classes.
project_dir = task_helper.paths.project_dir
project_fs = sly.ProjectFS.from_disk_dir_project(project_dir)
# ProjectFS enlists all samples (image/annotation pairs) in input project.
for item_descr in project_fs:
logger.info('Processing input sample',
extra={
'dataset': item_descr.ds_name,
'image_name': item_descr.image_name
})
# Open some image...
img = cv2.imread(item_descr.img_path)
logger.info('Read image from input project',
extra={
'width': img.shape[1],
'height': img.shape[0]
})
# And read corresponding annotation...
ann_packed = sly.json_load(item_descr.ann_path)
ann = sly.Annotation.from_packed(ann_packed, project_meta)
logger.info('Read annotation from input project',
extra={
'object_cnt': len(ann['objects']),
'tags': ann['tags']
})
# We are to report progress of task over sly.ProgressCounter if we want to observe the progress in web panel.
# In fact one task may report progress for some sequential (not nested) subtasks,
# but here we will report training progress only.
progress = sly.progress_counter_train(cnt_epochs, cnt_iters_per_epoch)
epoch_flt = 0
for epoch in range(cnt_epochs):
logger.info("Epoch started", extra={'epoch': epoch})
for train_iter in range(cnt_iters_per_epoch):
logger.info('Some forward-backward pass...')
time.sleep(1)
progress.iter_done_report(
) # call it after every iteration to report progress
epoch_flt = sly.epoch_float(epoch, train_iter + 1,
cnt_iters_per_epoch)
# And we are to report some metrics if we want to observe those amazing charts in web panel.
# Regrettably, only the fixed metric types may be displayed now: 'loss', 'accuracy' and 'dice'.
metric_values_train = {
'loss': random.random(),
'my_metric': random.uniform(0, 100)
}
sly.report_metrics_training(epoch_flt, metric_values_train)
logger.info("Epoch finished", extra={'epoch': epoch})
# Validation is not necessary but may be performed. So let's imitate validation...
logger.info("Validation...")
time.sleep(2)
# Metrics for validation may also be reported.
metric_values_val = {
'loss': random.random(),
'my_metric': random.uniform(0, 20)
}
sly.report_metrics_validation(epoch_flt, metric_values_val)
# Save trained model weights when you want.
# Model weights (checkpoint) should be written into directory provided by sly checkpoints_saver.
# Content of the directory is entirely dependent on model implementation.
cur_checkpoint_dir = checkpoints_saver.get_dir_to_write()
dump_fake_model(cur_checkpoint_dir, model)
checkpoints_saver.saved(is_best=True,
optional_data={
'epoch': epoch_flt,
'val_metrics': metric_values_val
})
# It is necessary to call checkpoints_saver.saved after saving.
# By default new model will be created from the best checkpoint over the whole training
# (which is determined by "is_best" flag).
# Some optional info may be provided. It will be linked with the checkpoint
# and may help to distinguish checkpoints from same training.
# Thank you for your patience.
logger.info('Training finished')
