with tf.GradientTape() as tape:
loss = train_one_step(model, emb, seq, pos, neg)
grads = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
cos_loss(model(seq), emb(pos))
tbcb.on_train_batch_end(step)
loss_history.append(loss.numpy())
cos_loss_history.append(cos_loss.result())
# print('========== loss: {:03f} ============'.format(loss_history[-1]))
print('Epoch: {:03d}, Loss: {:.3f}, CosineSimilarity: {:.3f}'.format(
epoch, loss_history[-1], cos_loss.result()))
logs = {'train_loss': loss, 'cosine_similarity': cos_loss.result()}
tbcb.on_epoch_end(epoch, logs=logs)
if epoch % 5 == 0:
t1 = time.time() - t0
T += t1
print('========== Evaluating ==========')
t_test = evaluate(model, emb, dataset, max_len)
t_valid = evaluate_valid(model, emb, dataset, max_len)
print(
'Epoch: {:03d}, Time: {:f}, valid (NDCG@10: {:.4f}, HR@10: {:.4f}), test (NDCG@10: {:.4f}, HR@10: {:.4f})'
.format(epoch, T, t_valid[0], t_valid[1], t_test[0],
t_test[1]))
f.write(str(t_valid) + ' ' + str(t_test) + '\n')
f.flush()
t0 = time.time()
class SelectorNetwork:
def __init__(self, mask_batch_size, tensorboard_logs_dir=""):
self.batch_size = mask_batch_size
self.mask_batch_size = mask_batch_size
self.tr_loss_history = []
self.te_loss_history = []
self.y_pred_std_history = []
self.y_true_std_history = []
self.tf_logs = tensorboard_logs_dir
self.epoch_counter = 0
self.data_masks = None
self.data_targets = None
self.best_performing_mask = None
self.sample_weights = None
def set_label_input_params(self, y_shape, y_input_layer):
self.label_input_layer = y_input_layer
self.label_shape = y_shape
def create_dense_model(self, input_shape, dense_arch):
input_mask_layer = Input(shape=input_shape)
x = Flatten()(input_mask_layer)
for i in range(len(dense_arch[:-1])):
x = Dense(dense_arch[i], activation="sigmoid")(x)
x = Dense(dense_arch[-1], activation="linear")(x)
self.model = Model(inputs=[input_mask_layer], outputs=x)
print("Subject Network model built:")
#self.model.summary()
def named_logs(self, model, logs):
result = {}
try:
iterator = iter(logs)
except TypeError:
logs = [logs]
for l in zip(model.metrics_names, logs):
result[l[0]] = l[1]
return result
def compile_model(self):
self.model.compile(loss='mae',
optimizer='adam',
metrics=[
self.get_y_std_metric(True),
self.get_y_std_metric(False)
])
if self.tf_logs != "":
log_path = './logs'
self.tb_clbk = TensorBoard(self.tf_logs)
self.tb_clbk.set_model(self.model)
def train_one(self, epoch_number,
apply_weights): # train on data in object memory
if apply_weights == False:
curr_loss = self.model.train_on_batch(x=self.data_masks,
y=self.data_targets)
else:
curr_loss = self.model.train_on_batch(
x=self.data_masks,
y=self.data_targets,
sample_weight=self.sample_weights)
self.best_performing_mask = self.data_masks[np.argmin(
self.data_targets, axis=0)]
self.tr_loss_history.append(curr_loss)
self.epoch_counter = epoch_number
if self.tf_logs != "":
self.tb_clbk.on_epoch_end(self.epoch_counter,
self.named_logs(self.model, curr_loss))
self.data_masks = None
self.data_targets = None
def append_data(self, x, y):
if self.data_masks is None:
self.data_masks = x
self.data_targets = y
else:
self.data_masks = np.concatenate([self.data_masks, x], axis=0)
self.data_targets = tf.concat([self.data_targets, y], axis=0)
def test_one(self, x, y):
y_pred = self.model.predict(x=x)
curr_loss = self.model.test_on_batch(x=x, y=y)
self.te_loss_history.append(curr_loss)
# print("SN test loss: "+str(curr_loss))
# print("SN prediction: "+str(np.squeeze(curr_loss)))
# print("SN targets: "+str(np.squeeze(y_pred)))
return curr_loss
def predict(self, x):
y_pred = self.model.predict(x=x)
return y_pred
def get_y_std_metric(self, ifpred=True):
def y_pred_std_metric(y_true, y_pred):
y_pred_std = K.std(y_pred)
self.y_pred_std_history.append(y_pred_std)
return y_pred_std
def y_true_std_metric(y_true, y_pred):
y_true_std = K.std(y_true)
self.y_true_std_history.append(y_true_std)
return y_true_std
if (ifpred == True):
return y_pred_std_metric
else:
return y_true_std_metric
class Trainer:
"""Class object to setup and carry the training.
Takes as input a generator that produces SR images.
Conditionally, also a discriminator network and a feature extractor
to build the components of the perceptual loss.
Compiles the model(s) and trains in a GANS fashion if a discriminator is provided, otherwise
carries a regular ISR training.
Args:
generator: Keras model, the super-scaling, or generator, network.
discriminator: Keras model, the discriminator network for the adversarial
component of the perceptual loss.
feature_extractor: Keras model, feature extractor network for the deep features
component of perceptual loss function.
lr_train_dir: path to the directory containing the Low-Res images for training.
hr_train_dir: path to the directory containing the High-Res images for training.
lr_valid_dir: path to the directory containing the Low-Res images for validation.
hr_valid_dir: path to the directory containing the High-Res images for validation.
learning_rate: float.
loss_weights: dictionary, use to weigh the components of the loss function.
Contains 'generator' for the generator loss component, and can contain 'discriminator' and 'feature_extractor'
for the discriminator and deep features components respectively.
logs_dir: path to the directory where the tensorboard logs are saved.
weights_dir: path to the directory where the weights are saved.
dataname: string, used to identify what dataset is used for the training session.
weights_generator: path to the pre-trained generator's weights, for transfer learning.
weights_discriminator: path to the pre-trained discriminator's weights, for transfer learning.
n_validation:integer, number of validation samples used at training from the validation set.
flatness: dictionary. Determines determines the 'flatness' threshold level for the training patches.
See the TrainerHelper class for more details.
lr_decay_frequency: integer, every how many epochs the learning rate is reduced.
lr_decay_factor: 0 < float <1, learning rate reduction multiplicative factor.
Methods:
train: combines the networks and triggers training with the specified settings.
"""
def __init__(
self,
generator,
discriminator,
feature_extractor,
lr_train_dir,
hr_train_dir,
lr_valid_dir,
hr_valid_dir,
loss_weights={
'generator': 1.0,
'discriminator': 0.003,
'feature_extractor': 1 / 12
},
log_dirs={
'logs': 'logs',
'weights': 'weights'
},
fallback_save_every_n_epochs=2,
dataname=None,
weights_generator=None,
weights_discriminator=None,
n_validation=None,
flatness={
'min': 0.0,
'increase_frequency': None,
'increase': 0.0,
'max': 0.0
},
learning_rate={
'initial_value': 0.0004,
'decay_frequency': 100,
'decay_factor': 0.5
},
adam_optimizer={
'beta1': 0.9,
'beta2': 0.999,
'epsilon': None
},
losses={
'generator': 'mae',
'discriminator': 'binary_crossentropy',
'feature_extractor': 'mse',
},
metrics={'generator': 'PSNR_Y'},
):
self.generator = generator
self.discriminator = discriminator
self.feature_extractor = feature_extractor
self.scale = generator.scale
self.lr_patch_size = generator.patch_size
self.learning_rate = learning_rate
self.loss_weights = loss_weights
self.weights_generator = weights_generator
self.weights_discriminator = weights_discriminator
self.adam_optimizer = adam_optimizer
self.dataname = dataname
self.flatness = flatness
self.n_validation = n_validation
self.losses = losses
self.log_dirs = log_dirs
self.metrics = metrics
if self.metrics['generator'] == 'PSNR_Y':
self.metrics['generator'] = PSNR_Y
elif self.metrics['generator'] == 'PSNR':
self.metrics['generator'] = PSNR
self._parameters_sanity_check()
self.model = self._combine_networks()
self.settings = {}
self.settings['training_parameters'] = locals()
self.settings['training_parameters'][
'lr_patch_size'] = self.lr_patch_size
self.settings = self.update_training_config(self.settings)
self.logger = get_logger(__name__)
self.helper = TrainerHelper(
generator=self.generator,
weights_dir=log_dirs['weights'],
logs_dir=log_dirs['logs'],
lr_train_dir=lr_train_dir,
feature_extractor=self.feature_extractor,
discriminator=self.discriminator,
dataname=dataname,
weights_generator=self.weights_generator,
weights_discriminator=self.weights_discriminator,
fallback_save_every_n_epochs=fallback_save_every_n_epochs,
)
self.train_dh = DataHandler(
lr_dir=lr_train_dir,
hr_dir=hr_train_dir,
patch_size=self.lr_patch_size,
scale=self.scale,
n_validation_samples=None,
)
self.valid_dh = DataHandler(
lr_dir=lr_valid_dir,
hr_dir=hr_valid_dir,
patch_size=self.lr_patch_size,
scale=self.scale,
n_validation_samples=n_validation,
)
def _parameters_sanity_check(self):
""" Parameteres sanity check. """
if self.discriminator:
assert self.lr_patch_size * self.scale == self.discriminator.patch_size
self.adam_optimizer
if self.feature_extractor:
assert self.lr_patch_size * self.scale == self.feature_extractor.patch_size
check_parameter_keys(
self.learning_rate,
needed_keys=['initial_value'],
optional_keys=['decay_factor', 'decay_frequency'],
default_value=None,
)
check_parameter_keys(
self.flatness,
needed_keys=[],
optional_keys=['min', 'increase_frequency', 'increase', 'max'],
default_value=0.0,
)
check_parameter_keys(
self.adam_optimizer,
needed_keys=['beta1', 'beta2'],
optional_keys=['epsilon'],
default_value=None,
)
check_parameter_keys(self.log_dirs, needed_keys=['logs', 'weights'])
def _combine_networks(self):
"""
Constructs the combined model which contains the generator network,
as well as discriminator and geature extractor, if any are defined.
"""
lr = Input(shape=(self.lr_patch_size, ) * 2 + (3, ))
sr = self.generator.model(lr)
outputs = [sr]
losses = [self.losses['generator']]
loss_weights = [self.loss_weights['generator']]
if self.discriminator:
self.discriminator.model.trainable = False
validity = self.discriminator.model(sr)
outputs.append(validity)
losses.append(self.losses['discriminator'])
loss_weights.append(self.loss_weights['discriminator'])
if self.feature_extractor:
self.feature_extractor.model.trainable = False
sr_feats = self.feature_extractor.model(sr)
outputs.extend([*sr_feats])
losses.extend([self.losses['feature_extractor']] * len(sr_feats))
loss_weights.extend(
[self.loss_weights['feature_extractor'] / len(sr_feats)] *
len(sr_feats))
combined = Model(inputs=lr, outputs=outputs)
# https://stackoverflow.com/questions/42327543/adam-optimizer-goes-haywire-after-200k-batches-training-loss-grows
optimizer = Adam(
beta_1=self.adam_optimizer['beta1'],
beta_2=self.adam_optimizer['beta2'],
lr=self.learning_rate['initial_value'],
epsilon=self.adam_optimizer['epsilon'],
)
combined.compile(loss=losses,
loss_weights=loss_weights,
optimizer=optimizer,
metrics=self.metrics)
return combined
def _lr_scheduler(self, epoch):
""" Scheduler for the learning rate updates. """
n_decays = epoch // self.learning_rate['decay_frequency']
lr = self.learning_rate['initial_value'] * (
self.learning_rate['decay_factor']**n_decays)
# no lr below minimum control 10e-7
return max(1e-7, lr)
def _flatness_scheduler(self, epoch):
if self.flatness['increase']:
n_increases = epoch // self.flatness['increase_frequency']
else:
return self.flatness['min']
f = self.flatness['min'] + n_increases * self.flatness['increase']
return min(self.flatness['max'], f)
def _load_weights(self):
"""
Loads the pretrained weights from the given path, if any is provided.
If a discriminator is defined, does the same.
"""
if self.weights_generator:
self.model.get_layer('generator').load_weights(
self.weights_generator)
if self.discriminator:
if self.weights_discriminator:
self.model.get_layer('discriminator').load_weights(
self.weights_discriminator)
self.discriminator.model.load_weights(
self.weights_discriminator)
def _format_losses(self, prefix, losses, model_metrics):
""" Creates a dictionary for tensorboard tracking. """
return dict(zip([prefix + m for m in model_metrics], losses))
def update_training_config(self, settings):
""" Summarizes training setting. """
_ = settings['training_parameters'].pop('weights_generator')
_ = settings['training_parameters'].pop('self')
_ = settings['training_parameters'].pop('generator')
_ = settings['training_parameters'].pop('discriminator')
_ = settings['training_parameters'].pop('feature_extractor')
settings['generator'] = {}
settings['generator']['name'] = self.generator.name
settings['generator']['parameters'] = self.generator.params
settings['generator']['weights_generator'] = self.weights_generator
_ = settings['training_parameters'].pop('weights_discriminator')
if self.discriminator:
settings['discriminator'] = {}
settings['discriminator']['name'] = self.discriminator.name
settings['discriminator'][
'weights_discriminator'] = self.weights_discriminator
else:
settings['discriminator'] = None
if self.discriminator:
settings['feature_extractor'] = {}
settings['feature_extractor']['name'] = self.feature_extractor.name
settings['feature_extractor'][
'layers'] = self.feature_extractor.layers_to_extract
else:
settings['feature_extractor'] = None
return settings
def train(self, epochs, steps_per_epoch, batch_size, monitored_metrics):
"""
Carries on the training for the given number of epochs.
Sends the losses to Tensorboard.
Args:
epochs: how many epochs to train for.
steps_per_epoch: how many batches epoch.
batch_size: amount of images per batch.
monitored_metrics: dictionary, the keys are the metrics that are monitored for the weights
saving logic. The values are the mode that trigger the weights saving ('min' vs 'max').
"""
self.settings['training_parameters'][
'steps_per_epoch'] = steps_per_epoch
self.settings['training_parameters']['batch_size'] = batch_size
starting_epoch = self.helper.initialize_training(
self) # load_weights, creates folders, creates basename
self.tensorboard = TensorBoard(
log_dir=str(self.helper.callback_paths['logs']))
self.tensorboard.set_model(self.model)
# validation data
validation_set = self.valid_dh.get_validation_set(batch_size)
y_validation = [validation_set['hr']]
if self.discriminator:
discr_out_shape = list(
self.discriminator.model.outputs[0].shape)[1:4]
valid = np.ones([batch_size] + discr_out_shape)
fake = np.zeros([batch_size] + discr_out_shape)
validation_valid = np.ones([len(validation_set['hr'])] +
discr_out_shape)
y_validation.append(validation_valid)
if self.feature_extractor:
validation_feats = self.feature_extractor.model.predict(
validation_set['hr'])
y_validation.extend([*validation_feats])
for epoch in range(starting_epoch, epochs):
self.logger.info('Epoch {e}/{tot_eps}'.format(e=epoch,
tot_eps=epochs))
K.set_value(self.model.optimizer.lr,
self._lr_scheduler(epoch=epoch))
self.logger.info('Current learning rate: {}'.format(
K.eval(self.model.optimizer.lr)))
flatness = self._flatness_scheduler(epoch)
if flatness:
self.logger.info(
'Current flatness treshold: {}'.format(flatness))
epoch_start = time()
for step in tqdm(range(steps_per_epoch)):
batch = self.train_dh.get_batch(batch_size, flatness=flatness)
y_train = [batch['hr']]
training_losses = {}
## Discriminator training
if self.discriminator:
sr = self.generator.model.predict(batch['lr'])
d_loss_real = self.discriminator.model.train_on_batch(
batch['hr'], valid)
d_loss_fake = self.discriminator.model.train_on_batch(
sr, fake)
d_loss_fake = self._format_losses(
'train_d_fake_', d_loss_fake,
self.discriminator.model.metrics_names)
d_loss_real = self._format_losses(
'train_d_real_', d_loss_real,
self.discriminator.model.metrics_names)
training_losses.update(d_loss_real)
training_losses.update(d_loss_fake)
y_train.append(valid)
## Generator training
if self.feature_extractor:
hr_feats = self.feature_extractor.model.predict(
batch['hr'])
y_train.extend([*hr_feats])
model_losses = self.model.train_on_batch(batch['lr'], y_train)
model_losses = self._format_losses('train_', model_losses,
self.model.metrics_names)
training_losses.update(model_losses)
self.tensorboard.on_epoch_end(epoch * steps_per_epoch + step,
training_losses)
self.logger.debug('Losses at step {s}:\n {l}'.format(
s=step, l=training_losses))
elapsed_time = time() - epoch_start
self.logger.info('Epoch {} took {:10.1f}s'.format(
epoch, elapsed_time))
validation_losses = self.model.evaluate(validation_set['lr'],
y_validation,
batch_size=batch_size)
validation_losses = self._format_losses('val_', validation_losses,
self.model.metrics_names)
if epoch == starting_epoch:
remove_metrics = []
for metric in monitored_metrics:
if (metric not in training_losses) and (
metric not in validation_losses):
msg = ' '.join([
metric,
'is NOT among the model metrics, removing it.'
])
self.logger.error(msg)
remove_metrics.append(metric)
for metric in remove_metrics:
_ = monitored_metrics.pop(metric)
# should average train metrics
end_losses = {}
end_losses.update(validation_losses)
end_losses.update(training_losses)
self.helper.on_epoch_end(
epoch=epoch,
losses=end_losses,
generator=self.model.get_layer('generator'),
discriminator=self.discriminator,
metrics=monitored_metrics,
)
self.tensorboard.on_epoch_end(epoch, validation_losses)
self.tensorboard.on_train_end(None)
def train_srgan(
self,
epochs,
batch_size,
dataname,
datapath_train,
datapath_validation=None,
steps_per_validation=1000,
datapath_test=None,
workers=4,
max_queue_size=10,
first_epoch=0,
print_frequency=1,
crops_per_image=2,
log_weight_frequency=None,
log_weight_path='./data/weights/',
log_tensorboard_path='./data/logs/',
log_tensorboard_name='SRGAN',
log_tensorboard_update_freq=10000,
log_test_frequency=500,
log_test_path="./images/samples/",
):
"""Train the SRGAN network
:param int epochs: how many epochs to train the network for
:param str dataname: name to use for storing model weights etc.
:param str datapath_train: path for the image files to use for training
:param str datapath_test: path for the image files to use for testing / plotting
:param int print_frequency: how often (in epochs) to print progress to terminal. Warning: will run validation inference!
:param int log_weight_frequency: how often (in epochs) should network weights be saved. None for never
:param int log_weight_path: where should network weights be saved
:param int log_test_frequency: how often (in epochs) should testing & validation be performed
:param str log_test_path: where should test results be saved
:param str log_tensorboard_path: where should tensorflow logs be sent
:param str log_tensorboard_name: what folder should tf logs be saved under
"""
# Create train data loader
loader = DataLoader(datapath_train, batch_size, self.height_hr,
self.width_hr, self.upscaling_factor,
crops_per_image)
# Validation data loader
if datapath_validation is not None:
validation_loader = DataLoader(datapath_validation, batch_size,
self.height_hr, self.width_hr,
self.upscaling_factor,
crops_per_image)
# Use several workers on CPU for preparing batches
enqueuer = OrderedEnqueuer(loader,
use_multiprocessing=True,
shuffle=True)
enqueuer.start(workers=workers, max_queue_size=max_queue_size)
output_generator = enqueuer.get()
# Callback: tensorboard
if log_tensorboard_path:
tensorboard = TensorBoard(log_dir=os.path.join(
log_tensorboard_path, log_tensorboard_name),
histogram_freq=0,
batch_size=batch_size,
write_graph=False,
write_grads=False,
update_freq=log_tensorboard_update_freq)
tensorboard.set_model(self.srgan)
else:
print(
">> Not logging to tensorboard since no log_tensorboard_path is set"
)
# Callback: format input value
def named_logs(model, logs):
"""Transform train_on_batch return value to dict expected by on_batch_end callback"""
result = {}
for l in zip(model.metrics_names, logs):
result[l[0]] = l[1]
return result
# Shape of output from discriminator
disciminator_output_shape = list(self.discriminator.output_shape)
disciminator_output_shape[0] = batch_size
disciminator_output_shape = tuple(disciminator_output_shape)
# VALID / FAKE targets for discriminator
real = np.ones(disciminator_output_shape)
fake = np.zeros(disciminator_output_shape)
# Each epoch == "update iteration" as defined in the paper
print_losses = {"G": [], "D": []}
start_epoch = datetime.datetime.now()
# Random images to go through
idxs = np.random.randint(0, len(loader), epochs)
# Loop through epochs / iterations
for epoch in range(first_epoch, int(epochs) + first_epoch):
# Start epoch time
if epoch % (print_frequency + 1) == 0:
start_epoch = datetime.datetime.now()
# Train discriminator
imgs_lr, imgs_hr = next(output_generator)
generated_hr = self.generator.predict(imgs_lr)
real_loss = self.discriminator.train_on_batch(imgs_hr, real)
fake_loss = self.discriminator.train_on_batch(generated_hr, fake)
discriminator_loss = 0.5 * np.add(real_loss, fake_loss)
# Train generator
features_hr = self.vgg.predict(self.preprocess_vgg(imgs_hr))
generator_loss = self.srgan.train_on_batch(imgs_lr,
[real, features_hr])
# Callbacks
logs = named_logs(self.srgan, generator_loss)
tensorboard.on_epoch_end(epoch, logs)
# Save losses
print_losses['G'].append(generator_loss)
print_losses['D'].append(discriminator_loss)
# Show the progress
if epoch % print_frequency == 0:
g_avg_loss = np.array(print_losses['G']).mean(axis=0)
d_avg_loss = np.array(print_losses['D']).mean(axis=0)
print(
"\nEpoch {}/{} | Time: {}s\n>> Generator/GAN: {}\n>> Discriminator: {}"
.format(
epoch, epochs + first_epoch,
(datetime.datetime.now() - start_epoch).seconds,
", ".join([
"{}={:.4f}".format(k, v) for k, v in zip(
self.srgan.metrics_names, g_avg_loss)
]), ", ".join([
"{}={:.4f}".format(k, v) for k, v in zip(
self.discriminator.metrics_names, d_avg_loss)
])))
print_losses = {"G": [], "D": []}
# Run validation inference if specified
if datapath_validation:
validation_losses = self.generator.evaluate_generator(
validation_loader,
steps=steps_per_validation,
use_multiprocessing=workers > 1,
workers=workers)
print(">> Validation Losses: {}".format(", ".join([
"{}={:.4f}".format(k, v) for k, v in zip(
self.generator.metrics_names, validation_losses)
])))
# If test images are supplied, run model on them and save to log_test_path
if datapath_test and epoch % log_test_frequency == 0:
plot_test_images(self, loader, datapath_test, log_test_path,
epoch)
# Check if we should save the network weights
if log_weight_frequency and epoch % log_weight_frequency == 0:
# Save the network weights
self.save_weights(os.path.join(log_weight_path, dataname))
class MLP:
def __init__(self,
x_batch_size,
mask_batch_size,
tensorboard_logs_dir="",
add_mopt_perf_metric=True,
use_early_stopping=True):
self.batch_size = mask_batch_size * x_batch_size
self.mask_batch_size = mask_batch_size
self.x_batch_size = x_batch_size
self.losses_per_sample = None
self.tr_loss_history = []
self.te_loss_history = []
self.tf_logs = tensorboard_logs_dir
self.epoch_counter = 0
self.add_mopt_perf_metric = add_mopt_perf_metric
self.useEarlyStopping = use_early_stopping
def create_dense_model(self,
input_shape,
dense_arch,
last_activation="linear"):
self.x_shape = input_shape
self.y_shape = dense_arch[-1]
input_data_layer = Input(shape=input_shape)
x = Flatten()(input_data_layer)
input_mask_layer = Input(shape=input_shape)
mask = Flatten()(input_mask_layer)
x = tf.keras.layers.Concatenate(axis=1)([x, mask])
for units in dense_arch[:-1]:
x = Dense(units, activation="sigmoid")(x)
x = Dense(dense_arch[-1], activation=last_activation)(x)
self.model = Model(inputs=[input_data_layer, input_mask_layer],
outputs=x)
# self.model.summary()
def create_batch(self, x, masks, y):
"""
x = [[1,2],[3,4]] -> [[1,2],[1,2],[1,2],[3,4],[3,4],[3,4]]
masks = [[0,0],[1,0],[1,1]] -> [[0,0],[1,0],[1,1],[0,0],[1,0],[1,1]]
y = [1,3] -> [1 ,1 ,1 ,3 ,3 ,3 ]
"""
x_prim = np.repeat(x, len(masks), axis=0)
y_prim = np.repeat(y, len(masks), axis=0)
masks_prim = np.tile(masks, (len(x), 1))
x_prim *= masks_prim
return x_prim, masks_prim, y_prim
def named_logs(self, model, logs, mode="train"):
result = {}
try:
iterator = iter(logs)
except TypeError:
logs = [logs]
metricNames = (mode + "_" + i for i in model.metrics_names)
for l in zip(metricNames, logs):
result[l[0]] = l[1]
return result
def compile_model(self,
loss_per_sample,
combine_losses,
combine_mask_losses,
metrics=None):
self.mask_loss_combine_function = combine_mask_losses
if self.add_mopt_perf_metric is True:
if metrics is None:
metrics = [self.get_mopt_perf_metric()]
else:
metrics.append(self.get_mopt_perf_metric())
def logging_loss_function(y_true, y_pred):
losses = loss_per_sample(y_true, y_pred)[:, 0]
# print(losses)
self.losses_per_sample = losses
return combine_losses(losses)
self.model.compile(loss=logging_loss_function,
optimizer='nadam',
metrics=metrics,
run_eagerly=True)
if self.tf_logs != "":
log_path = './logs'
self.tb_clbk = TensorBoard(self.tf_logs)
self.tb_clbk.set_model(self.model)
def get_per_mask_loss(self, used_target_shape=None):
if used_target_shape is None:
used_target_shape = (self.x_batch_size, self.mask_batch_size)
losses = tf.reshape(self.losses_per_sample, used_target_shape)
losses = self.mask_loss_combine_function(losses)
return losses
def get_per_mask_loss_with_custom_batch(self, losses, new_x_batch_size,
new_mask_batch_size):
losses = np.reshape(losses,
newshape=(new_x_batch_size, new_mask_batch_size))
losses = np.apply_along_axis(self.mask_loss_combine_function, 0,
losses)
return losses
def train_one(self, x, masks, y):
x_prim, masks_prim, y_prim = self.create_batch(x, masks, y)
curr_loss = self.model.train_on_batch(x=[x_prim, masks_prim], y=y_prim)
self.tr_loss_history.append(curr_loss)
self.epoch_counter += 1
if self.tf_logs != "":
self.tb_clbk.on_epoch_end(self.epoch_counter,
self.named_logs(self.model, curr_loss))
return x_prim, masks_prim, y_prim
def test_one(self, x, masks, y):
x_prim, masks_prim, y_prim = self.create_batch(x, masks, y)
feature = self.model.predict(x=[x_prim, masks_prim])
return feature
def get_mopt_perf_metric(self):
def m_opt_loss(y_pred, y_true):
if self.losses_per_sample.shape[0] % self.mask_batch_size != 0:
return 0.0
else:
losses = tf.reshape(self.losses_per_sample,
(-1, self.mask_batch_size))
self.last_m_opt_perf = np.mean(
losses[:, int(0.5 * self.mask_batch_size)])
return self.last_m_opt_perf
return m_opt_loss
def set_early_stopping_params(self,
starting_epoch,
patience_batches=800,
minimize=True):
self.ES_patience = patience_batches
self.ES_minimize = minimize
if minimize is True:
self.ES_best_perf = 1000000.0
else:
self.ES_best_perf = -1000000.0
self.ES_best_epoch = starting_epoch
self.ES_stop_training = False
self.ES_start_epoch = starting_epoch
self.ES_best_weights = None
return
def train_vsrganplus(self,
epochs=None,
batch_size=16,
modelname=None,
datapath_train=None,
datapath_validation=None,
steps_per_validation=1000,
datapath_test=None,
workers=4,
max_queue_size=10,
first_epoch=0,
print_frequency=1,
crops_per_image=2,
log_weight_frequency=None,
log_weight_path='./model/',
log_tensorboard_path='./data/logs/',
log_tensorboard_update_freq=10,
log_test_frequency=500,
log_test_path="./images/samples/",
media_type='i'):
"""Train the VSRGANplus network
:param int epochs: how many epochs to train the network for
:param str modelname: name to use for storing model weights etc.
:param str datapath_train: path for the image files to use for training
:param str datapath_test: path for the image files to use for testing / plotting
:param int print_frequency: how often (in epochs) to print progress to terminal. Warning: will run validation inference!
:param int log_weight_frequency: how often (in epochs) should network weights be saved. None for never
:param int log_weight_path: where should network weights be saved
:param int log_test_frequency: how often (in epochs) should testing & validation be performed
:param str log_test_path: where should test results be saved
:param str log_tensorboard_path: where should tensorflow logs be sent
"""
# Create data loaders
train_loader = DataLoader(datapath_train, batch_size, self.height_hr,
self.width_hr, self.upscaling_factor,
crops_per_image, media_type, self.channels,
self.colorspace)
# Validation data loader
validation_loader = None
if datapath_validation is not None:
validation_loader = DataLoader(datapath_validation, batch_size,
self.height_hr, self.width_hr,
self.upscaling_factor,
crops_per_image, 'i', self.channels,
self.colorspace)
test_loader = None
if datapath_test is not None:
test_loader = DataLoader(datapath_test, 1, self.height_hr,
self.width_hr, self.upscaling_factor, 1,
'i', self.channels, self.colorspace)
# Use several workers on CPU for preparing batches
enqueuer = OrderedEnqueuer(train_loader,
use_multiprocessing=True,
shuffle=True)
enqueuer.start(workers=workers, max_queue_size=max_queue_size)
output_generator = enqueuer.get()
# Callback: tensorboard
if log_tensorboard_path:
tensorboard = TensorBoard(log_dir=os.path.join(
log_tensorboard_path, modelname),
histogram_freq=0,
write_graph=True,
update_freq=log_tensorboard_update_freq)
tensorboard.set_model(self.vsrganplus)
else:
print(
">> Not logging to tensorboard since no log_tensorboard_path is set"
)
# Learning rate scheduler
def lr_scheduler(epoch, lr):
factor = 0.1
decay_step = [50000, 100000, 200000, 300000]
if epoch in decay_step and epoch:
return lr * factor
return lr
lr_scheduler_gan = LearningRateScheduler(lr_scheduler, verbose=1)
lr_scheduler_gan.set_model(self.vsrganplus)
lr_scheduler_gen = LearningRateScheduler(lr_scheduler, verbose=0)
lr_scheduler_gen.set_model(self.generator)
lr_scheduler_dis = LearningRateScheduler(lr_scheduler, verbose=0)
lr_scheduler_dis.set_model(self.discriminator)
lr_scheduler_ra = LearningRateScheduler(lr_scheduler, verbose=0)
lr_scheduler_ra.set_model(self.ra_discriminator)
# Callback: format input value
def named_logs(model, logs):
"""Transform train_on_batch return value to dict expected by on_batch_end callback"""
result = {}
for l in zip(model.metrics_names, logs):
result[l[0]] = l[1]
return result
# Shape of output from discriminator
disciminator_output_shape = list(self.ra_discriminator.output_shape)
disciminator_output_shape[0] = batch_size
disciminator_output_shape = tuple(disciminator_output_shape)
# VALID / FAKE targets for discriminator
real = np.ones(disciminator_output_shape)
fake = np.zeros(disciminator_output_shape)
# Each epoch == "update iteration" as defined in the paper
print_losses = {"GAN": [], "D": []}
start_epoch = datetime.datetime.now()
# Random images to go through
#idxs = np.random.randint(0, len(train_loader), epochs)
# Loop through epochs / iterations
for epoch in range(first_epoch, int(epochs) + first_epoch):
lr_scheduler_gan.on_epoch_begin(epoch)
lr_scheduler_ra.on_epoch_begin(epoch)
lr_scheduler_dis.on_epoch_begin(epoch)
lr_scheduler_gen.on_epoch_begin(epoch)
print("\nEpoch {}/{}:".format(epoch + 1, epochs + first_epoch))
# Start epoch time
if epoch % print_frequency == 0:
start_epoch = datetime.datetime.now()
# Train discriminator
self.discriminator.trainable = True
self.ra_discriminator.trainable = True
imgs_lr, imgs_hr = next(output_generator)
generated_hr = self.generator.predict(imgs_lr)
real_loss = self.ra_discriminator.train_on_batch(
[imgs_hr, generated_hr], real)
#print("Real: ",real_loss)
fake_loss = self.ra_discriminator.train_on_batch(
[generated_hr, imgs_hr], fake)
#print("Fake: ",fake_loss)
discriminator_loss = 0.5 * np.add(real_loss, fake_loss)
# Train generator
self.discriminator.trainable = False
self.ra_discriminator.trainable = False
#for _ in tqdm(range(10),ncols=60,desc=">> Training generator"):
imgs_lr, imgs_hr = next(output_generator)
gan_loss = self.vsrganplus.train_on_batch([imgs_lr, imgs_hr],
[imgs_hr, real, imgs_hr])
# Callbacks
logs = named_logs(self.vsrganplus, gan_loss)
tensorboard.on_epoch_end(epoch, logs)
# Save losses
print_losses['GAN'].append(gan_loss)
print_losses['D'].append(discriminator_loss)
# Show the progress
if epoch % print_frequency == 0:
g_avg_loss = np.array(print_losses['GAN']).mean(axis=0)
d_avg_loss = np.array(print_losses['D']).mean(axis=0)
print(">> Time: {}s\n>> GAN: {}\n>> Discriminator: {}".format(
(datetime.datetime.now() - start_epoch).seconds,
", ".join([
"{}={:.4f}".format(k, v) for k, v in zip(
self.vsrganplus.metrics_names, g_avg_loss)
]), ", ".join([
"{}={:.4f}".format(k, v) for k, v in zip(
self.discriminator.metrics_names, d_avg_loss)
])))
print_losses = {"GAN": [], "D": []}
# Run validation inference if specified
if datapath_validation:
validation_losses = self.generator.evaluate_generator(
validation_loader,
steps=steps_per_validation,
use_multiprocessing=workers > 1,
workers=workers)
print(">> Validation Losses: {}".format(", ".join([
"{}={:.4f}".format(k, v) for k, v in zip(
self.generator.metrics_names, validation_losses)
])))
# If test images are supplied, run model on them and save to log_test_path
if datapath_test and epoch % log_test_frequency == 0:
plot_test_images(self.generator,
test_loader,
datapath_test,
log_test_path,
epoch,
modelname,
channels=self.channels,
colorspace=self.colorspace)
# Check if we should save the network weights
if log_weight_frequency and epoch % log_weight_frequency == 0:
# Save the network weights
self.save_weights(os.path.join(log_weight_path, modelname))
def _run(game, network_params, memory_params, ops):
"""Sets up and runs the gaming simulation.
Initializes TensorFlow, the training agent, and the game environment.
The agent plays the game from the starting state for a number of
episodes set by the user.
Args:
args: The arguments from the command line parsed by_parse_arguments.
"""
# Setup TensorBoard Writer.
trial_id = json.loads(os.environ.get('TF_CONFIG',
'{}')).get('task',
{}).get('trial', '')
output_path = ops.job_dir if not trial_id else ops.job_dir + '/'
tensorboard = TensorBoard(log_dir=output_path)
hpt = hypertune.HyperTune()
graph = tf.Graph()
with graph.as_default():
env = gym.make(game)
agent = _create_agent(env, network_params, memory_params)
rewards = []
tensorboard.set_model(agent.policy)
def _train_or_evaluate(print_score, training=False):
"""Runs a gaming simulation and writes results for tensorboard.
Args:
print_score (bool): True to print a score to the console.
training (bool): True if the agent is training, False to eval.
Returns:
loss if training, else reward for evaluating.
"""
reward = _play(agent, env, training)
if print_score:
print(
'Train - ',
'Episode: {}'.format(episode),
'Total reward: {}'.format(reward),
)
return reward
for episode in range(1, ops.episodes + 1):
print_score = ops.print_rate and episode % ops.print_rate == 0
get_summary = ops.eval_rate and episode % ops.eval_rate == 0
rewards.append(_train_or_evaluate(print_score, training=True))
if get_summary:
avg_reward = sum(rewards) / len(rewards)
summary = {'eval_reward': avg_reward}
tensorboard.on_epoch_end(episode, summary)
hpt.report_hyperparameter_tuning_metric(
hyperparameter_metric_tag='avg_reward',
metric_value=avg_reward,
global_step=episode)
print(
'Eval - ',
'Episode: {}'.format(episode),
'Average Reward: {}'.format(avg_reward),
)
rewards = []
tensorboard.on_train_end(None)
_record_video(env, agent, output_path)
agent.policy.save(output_path, save_format='tf')
def train(train_lbld_trios,
val_lbls_trios,
network,
weights,
model_path,
n_epochs,
init_lr,
optmzr_name,
imagenet=False,
freeze_until=None):
"""Training function: train a model of type 'network' over the data.
Args:
network (str): String identifying the network architecture to use.
weights (str): Path string to a .cpkt weights file.
model_path (str): Path string to a directory to save models in.
n_epochs (int): Integer representing the number of epochs
to run training.
"""
# Create a folder for saving trained models
if os.path.isdir(model_path) is False:
logging.info("Creating a folder to save models at: " + str(model_path))
os.mkdir(model_path)
starting_epoch = 0
if network == 'SiameseNetTriplet':
siamese_net = SiameseNetTriplet((128, 128, 3),
arch='resnet18',
sliding=True,
imagenet=imagenet,
freeze_until=freeze_until)
optimizer = Adam(lr=0.0006)
model = siamese_net.build_model()
loss_model = siamese_net.loss_model
single_model = siamese_net.single_model
if weights:
print("Loading model at: " + str(weights))
starting_epoch = int(weights.split('-')[1]) + 1
model.load_weights(weights)
model.compile(loss=triplet_loss,
optimizer=optimizer,
metrics=[cos_sim_pos, cos_sim_neg])
# Load data and create data generator:
train_ds = tf.data.Dataset.from_generator(
image_trio_generator,
args=[train_lbld_trios, True, False, False, imagenet],
output_types=((tf.float32, tf.float32, tf.float32), tf.float32,
tf.string),
output_shapes=(((TARGET_WIDTH, TARGET_HEIGHT, 3), (TARGET_WIDTH,
TARGET_HEIGHT, 3),
(TARGET_WIDTH, TARGET_HEIGHT, 3)), (1, None), (3)))
batched_train_ds = train_ds.batch(BATCH_SIZE) # shuffle(10000).batch
valid_ds = tf.data.Dataset.from_generator(
image_trio_generator,
args=[val_lbld_trios, False, False, False, imagenet],
output_types=((tf.float32, tf.float32, tf.float32), tf.float32,
tf.string),
# output_shapes=(((TARGET_WIDTH, TARGET_HEIGHT, 3), (TARGET_WIDTH, TARGET_HEIGHT, 3), (TARGET_WIDTH, TARGET_HEIGHT, 3)), (1, None)))
output_shapes=(((TARGET_WIDTH, TARGET_HEIGHT, 3), (TARGET_WIDTH,
TARGET_HEIGHT, 3),
(TARGET_WIDTH, TARGET_HEIGHT, 3)), (1, None), (3)))
batched_valid_ds = valid_ds.batch(BATCH_SIZE)
logdir = "logs/scalars/" + datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = TensorBoard(log_dir=logdir,
histogram_freq=0,
batch_size=BATCH_SIZE,
write_graph=True,
write_grads=True)
tensorboard_callback.set_model(model)
def named_logs(metrics_names, logs):
result = {}
for l in zip(metrics_names, logs):
result[l[0]] = l[1]
return result
# Train model:
steps_per_epoch = len(train_lbld_trios) // BATCH_SIZE
val_steps_per_epoch = len(
val_lbld_trios) // BATCH_SIZE # steps_per_epoch//3
best_val_loss = 1000
best_train_loss = 1000
batched_train_iter = iter(batched_train_ds)
batched_val_iter = iter(batched_valid_ds)
# batched_train_iter = batched_train_ds.make_one_shot_iterator()
# batched_val_iter = batched_valid_ds.make_one_shot_iterator()
for epoch in range(starting_epoch, n_epochs):
cumm_csim_pos_tr = 0
cumm_csim_neg_tr = 0
cumm_tr_loss = 0
cumm_csim_pos_val = 0
cumm_csim_neg_val = 0
cumm_val_loss = 0
print('Epoch #' + str(epoch) + ':')
for step in tqdm(range(steps_per_epoch)):
train_inputs, train_y, train_seq_ids = next(batched_train_iter)
#train_inputs, train_y = batched_train_iter.get_next()
# tinrain_x1 = train_inputs['input_1']
# train_x2 = train_inputs['input_2']
train_x1 = train_inputs[0]
train_x2 = train_inputs[1]
train_x3 = train_inputs[2]
# train_y = train_y['output']
X_dict = {}
seq_ids = []
for idx, row in enumerate(train_seq_ids):
for idx2, class_id in enumerate(row):
class_id = class_id.numpy().decode('utf8')
if class_id not in seq_ids:
seq_ids.append(class_id)
if class_id in X_dict:
X_dict[class_id].append(train_inputs[idx2][idx])
else:
X_dict[class_id] = []
X_dict[class_id].append(train_inputs[idx2][idx])
triplets = get_batch_hard(model, train_inputs, seq_ids, BATCH_SIZE)
loss, csim_pos_tr, csim_neg_tr = model.train_on_batch(
[triplets[0], triplets[1], triplets[2]],
train_y[:BATCH_SIZE // 2])
cumm_tr_loss += loss
cumm_csim_pos_tr += csim_pos_tr
cumm_csim_neg_tr += csim_neg_tr
cumm_csim_pos_tr = cumm_csim_pos_tr / steps_per_epoch
cumm_csim_neg_tr = cumm_csim_neg_tr / steps_per_epoch
cumm_tr_loss = cumm_tr_loss / steps_per_epoch
# evaluate
for step in tqdm(range(val_steps_per_epoch)):
valid_inputs, val_y, val_seq_ids = next(batched_val_iter)
# valid_inputs, valid_y = batched_val_iter.get_next()
valid_x1 = valid_inputs[0]
valid_x2 = valid_inputs[1]
valid_x3 = valid_inputs[2]
val_loss, csim_pos_val, csim_neg_val = model.test_on_batch(
[valid_x1, valid_x2, valid_x3], val_y)
cumm_val_loss += val_loss
cumm_csim_pos_val += csim_pos_val
cumm_csim_neg_val += csim_neg_val
cumm_csim_pos_val = cumm_csim_pos_val / val_steps_per_epoch
cumm_csim_neg_val = cumm_csim_neg_val / val_steps_per_epoch
cumm_val_loss = cumm_val_loss / val_steps_per_epoch
print('Training loss: ' + str(cumm_tr_loss))
print('Validation loss: ' + str(cumm_val_loss))
print('* Cosine sim positive (train) for this epoch: %0.2f' %
(cumm_csim_pos_tr))
print('* Cosine sim negative (train) for this epoch: %0.2f' %
(cumm_csim_neg_tr))
print('* Cosine sim positive (valid) for this epoch: %0.2f' %
(cumm_csim_pos_val))
print('* Cosine sim negative (valid) for this epoch: %0.2f' %
(cumm_csim_neg_val))
metrics_names = [
'tr_loss', 'tr_csim_pos', 'tr_csim_neg', 'val_loss',
'val_csim_pos', 'val_csim_neg'
]
tensorboard_callback.on_epoch_end(
epoch,
named_logs(metrics_names, [
cumm_tr_loss, cumm_csim_pos_tr, cumm_csim_neg_tr,
cumm_val_loss, cumm_csim_pos_val, cumm_csim_neg_val
]))
model_filepath = os.path.join(
model_path, "model-{epoch:03d}-{val_loss:.4f}.hdf5".format(
epoch=epoch, val_loss=cumm_val_loss))
if cumm_val_loss < best_val_loss * 1.5:
if cumm_val_loss < best_val_loss:
best_val_loss = cumm_val_loss
model.save(model_filepath) # OR model.save_weights()
print("Best model w/ val loss {} saved to {}".format(
cumm_val_loss, model_filepath))
tensorboard_callback.on_train_end(None)
return
class DQNAgent:
def __init__(self, state_size, action_space, train=True):
self.t = 0
self.max_Q = 0
self.train = train
# Get size of state and action
self.state_size = state_size
self.action_space = action_space
self.action_size = action_space
# These are hyper parameters for the DQN
self.discount_factor = 0.99
self.learning_rate = 1e-4
if self.train:
self.epsilon = 0.5
self.initial_epsilon = 0.5
else:
self.epsilon = 1e-6
self.initial_epsilon = 1e-6
self.epsilon_min = 0.02
self.batch_size = 64
self.train_start = 100
self.explore = 10000
# Create replay memory using deque
self.memory = deque(maxlen=10000)
# Create main model and target model
if MODEL_TYPE == 1:
self.model = self.build_transfer_model_has_batchnorm()
self.target_model = self.build_transfer_model_has_batchnorm()
elif MODEL_TYPE == 2:
self.model = self.build_transfer_model_has_no_batchnorm()
self.target_model = self.build_transfer_model_has_no_batchnorm()
elif MODEL_TYPE == 3:
self.model = self.build_transfer_model_has_batchnorm_with_canny()
self.target_model = self.build_transfer_model_has_batchnorm_with_canny(
)
elif MODEL_TYPE == 4:
self.model = self.build_dc_model_without_transfer()
self.target_model = self.build_dc_model_without_transfer()
else:
self.model = self.build_og_dc_model_from_repo()
self.target_model = self.build_og_dc_model_from_repo()
# Copy the model to target model
# --> initialize the target model so that the parameters of model & target model to be same
self.update_target_model()
self.tensorboard = TensorBoard(log_dir='./logs/',
histogram_freq=0,
write_graph=True,
write_grads=True)
self.tensorboard.set_model(self.model)
def build_transfer_model_has_batchnorm(self):
source_model = load_model('./files/my_model_new.h5')
model = Sequential()
for layer in source_model.layers[:-1]:
if 'batch' not in layer.name:
model.add(layer)
for layer in model.layers:
layer.trainable = True
# model.add(Dense(512, activation="relu", name='dense'))
# model.add(Dense(512, activation="relu", name='dense_1'))
model.add(Dense(15, activation="linear", name='dense_2'))
adam = Adam(lr=self.learning_rate)
model.compile(loss='mse', optimizer=adam)
return model
def build_transfer_model_has_no_batchnorm(self):
source_model = load_model('./files/my_model_gs.h5')
model = Sequential()
for layer in source_model.layers[:-3]:
model.add(layer)
for layer in model.layers:
layer.trainable = True
model.add(Dense(512, activation="relu", name='dense'))
model.add(Dense(512, activation="relu", name='dense_1'))
model.add(Dense(15, activation="linear", name='dense_2'))
# print(model.summary())
adam = Adam(lr=self.learning_rate)
model.compile(loss='mse', optimizer=adam)
return model
def build_transfer_model_has_batchnorm_with_canny(self):
# source_model = load_model('./files/my_model_canny.h5')
# model = Sequential()
# for layer in source_model.layers[:-3]:
# model.add(layer)
# for layer in model.layers:
# layer.trainable = True
# model.add(Dense(512, activation="relu", name='dense'))
# model.add(Dense(512, activation="relu", name='dense_1'))
# model.add(Dense(15, activation="linear", name='dense_2'))
# # print(model.summary())
# adam = Adam(lr=self.learning_rate)
# model.compile(loss='mse', optimizer=adam)
# return model
#
# def build_og_dc_model_from_repo(self):
# model = Sequential()
# model.add(Conv2D(24, (5, 5), strides=(2, 2), padding="same",
# input_shape=(IMG_ROWS, IMG_COLS, IMG_CHANNELS))) # 80*80*4
# model.add(Activation('relu'))
# model.add(Conv2D(32, (5, 5), strides=(2, 2), padding="same"))
# model.add(Activation('relu'))
# model.add(Conv2D(64, (5, 5), strides=(2, 2), padding="same"))
# model.add(Activation('relu'))
# model.add(Conv2D(64, (3, 3), strides=(2, 2), padding="same"))
# model.add(Activation('relu'))
# model.add(Conv2D(64, (3, 3), strides=(1, 1), padding="same"))
# model.add(Activation('relu'))
# model.add(Flatten())
# model.add(Dense(512))
# model.add(Activation('relu'))
#
# # 15 categorical bins for Steering angles
# model.add(Dense(15, activation="linear"))
#
# adam = Adam(lr=self.learning_rate)
# model.compile(loss='mse', optimizer=adam)
#
# return model
return
def build_dc_model_without_transfer(self):
Input_1 = Input(shape=(64, 64, 3), name='Input_1')
Convolution2D_1 = Conv2D(4,
kernel_size=3,
padding='same',
activation='relu')(Input_1)
Convolution2D_2 = Conv2D(4,
kernel_size=3,
padding='same',
activation='relu')(Convolution2D_1)
# Convolution2D_2 = BatchNormalization()(Convolution2D_2)
MaxPooling2D_1 = MaxPooling2D()(Convolution2D_2)
Convolution2D_5 = Conv2D(8,
kernel_size=3,
padding='same',
activation='relu')(MaxPooling2D_1)
Convolution2D_6 = Conv2D(8,
kernel_size=3,
padding='same',
activation='relu')(Convolution2D_5)
# Convolution2D_6 = BatchNormalization()(Convolution2D_6)
MaxPooling2D_2 = MaxPooling2D()(Convolution2D_6)
Convolution2D_7 = Conv2D(16,
kernel_size=3,
padding='same',
activation='relu')(MaxPooling2D_2)
Convolution2D_8 = Conv2D(16,
kernel_size=3,
padding='same',
activation='relu')(Convolution2D_7)
Convolution2D_11 = Conv2D(16,
kernel_size=3,
padding='same',
activation='relu')(Convolution2D_8)
# Convolution2D_11 = BatchNormalization()(Convolution2D_11)
MaxPooling2D_3 = MaxPooling2D()(Convolution2D_11)
Convolution2D_9 = Conv2D(32,
kernel_size=3,
padding='same',
activation='relu')(MaxPooling2D_3)
Convolution2D_10 = Conv2D(32,
kernel_size=3,
padding='same',
activation='relu')(Convolution2D_9)
Convolution2D_12 = Conv2D(16,
kernel_size=3,
padding='same',
activation='relu')(Convolution2D_10)
# Convolution2D_12 = BatchNormalization()(Convolution2D_12)
MaxPooling2D_4 = MaxPooling2D(name='MaxPooling2D_4')(Convolution2D_12)
Convolution2D_13 = Conv2D(32,
kernel_size=3,
padding='same',
activation='relu')(MaxPooling2D_4)
Convolution2D_14 = Conv2D(32,
kernel_size=3,
padding='same',
activation='relu')(Convolution2D_13)
Convolution2D_16 = Conv2D(16,
kernel_size=3,
padding='same',
activation='relu')(Convolution2D_14)
# Convolution2D_16 = BatchNormalization()(Convolution2D_16)
MaxPooling2D_5 = MaxPooling2D(name='MaxPooling2D_5')(Convolution2D_16)
Flatten_1 = Flatten()(MaxPooling2D_5)
Dense_1 = Dense(512, activation='relu')(Flatten_1)
# Dropout_1 = Dropout(0.2)(Dense_1)
Dense_2 = Dense(512, activation='relu')(Dense_1)
# Dropout_2 = Dropout(0.2)(Dense_2)
Dense_3 = Dense(15, activation='linear')(Dense_2)
model = Model([Input_1], [Dense_3])
model.compile(optimizer='adam', loss='mse')
return model
def rgb2gray(self, rgb):
'''
take a numpy rgb image return a new single channel image converted to greyscale
'''
return np.dot(rgb[..., :3], [0.299, 0.587, 0.114])
def process_image(self, obs):
# obs = self.rgb2gray(obs)
obs = cv2.resize(obs, (IMG_ROWS, IMG_COLS))
return obs
def process_image_for_canny(self, obs):
# obs = self.rgb2gray(obs)
obs = cv2.resize(obs, (IMG_ROWS, IMG_COLS))
obs1 = cv2.Canny(obs, 100, 200)
return obs1
def update_target_model(self):
self.target_model.set_weights(self.model.get_weights())
# Get action from model using epsilon-greedy policy
def get_action(self, s_t):
if np.random.rand() <= self.epsilon:
return self.action_space.sample()[0]
else:
print('Max Q')
# print("Return Max Q Prediction")
q_value = self.model.predict(s_t)
# Convert q array to steering value
return linear_unbin(q_value[0])
def replay_memory(self, state, action, reward, next_state, done):
self.memory.append((state, action, reward, next_state, done))
def update_epsilon(self):
if self.epsilon > self.epsilon_min:
self.epsilon -= (self.initial_epsilon -
self.epsilon_min) / self.explore
def named_logs(self, model, logs):
result = {}
for l in zip(model.metrics_names, logs):
result[l[0]] = l[1]
return result
def train_replay(self, ep_num):
if len(self.memory) < self.train_start:
return
batch_size = min(self.batch_size, len(self.memory))
minibatch = random.sample(self.memory, batch_size)
state_t, action_t, reward_t, state_t1, terminal = zip(*minibatch)
state_t = np.concatenate(state_t)
state_t1 = np.concatenate(state_t1)
targets = self.model.predict(state_t)
self.max_Q = np.max(targets[0])
target_val = self.model.predict(state_t1)
target_val_ = self.target_model.predict(state_t1)
for i in range(batch_size):
if terminal[i]:
targets[i][action_t[i]] = reward_t[i]
else:
a = np.argmax(target_val[i])
targets[i][action_t[i]] = reward_t[i] + \
self.discount_factor * (target_val_[i][a])
logs = self.model.train_on_batch(state_t, targets)
return logs
# self.tensorboard.on_epoch_end(ep_num, self.named_logs(self.model, [logs]))
def update_tensorboard(self, e, log, rew):
self.tensorboard.on_epoch_end(e, self.named_logs(self.model, [rew]))
def load_model(self, name):
self.model.load_weights(name)
# Save the model which is under training
def save_model(self, name):
self.model.save_weights(name)
class OperatorNetwork:
def __init__(self, x_batch_size, mask_batch_size, tensorboard_logs_dir="", add_mopt_perf_metric=True,
use_early_stopping=True):
self.batch_size = mask_batch_size * x_batch_size
self.mask_batch_size = mask_batch_size
self.x_batch_size = x_batch_size
self.losses_per_sample = None
# self.losses_per_sample = []
self.tr_loss_history = []
self.te_loss_history = []
self.tf_logs = tensorboard_logs_dir
self.epoch_counter = 0
self.add_mopt_perf_metric = add_mopt_perf_metric
self.useEarlyStopping = use_early_stopping
def create_dense_model(self, input_shape, dense_arch, last_activation="linear"):
self.x_shape = input_shape
self.y_shape = dense_arch[-1]
input_data_layer = Input(shape=input_shape)
x = Flatten()(input_data_layer)
input_mask_layer = Input(shape=input_shape)
mask = Flatten()(input_mask_layer)
# x = K.concatenate([x,mask])
x = tf.keras.layers.Concatenate(axis=1)([x, mask])
for units in dense_arch[:-1]:
x = Dense(units, activation="sigmoid")(x)
x = Dense(dense_arch[-1], activation=last_activation)(x)
self.model = Model(inputs=[input_data_layer, input_mask_layer], outputs=x)
print("Object network model built:")
#self.model.summary()
def create_1ch_conv_model(self, input_shape, image_shape, filter_sizes, kernel_sizes, dense_arch, padding,
last_activation="softmax"): # only for grayscale
self.x_shape = input_shape
self.y_shape = dense_arch[-1]
input_data_layer = Input(shape=input_shape)
in1 = Reshape(target_shape=(1,) + image_shape)(input_data_layer)
input_mask_layer = Input(shape=input_shape)
in2 = Reshape(target_shape=(1,) + image_shape)(input_mask_layer)
x = tf.keras.layers.Concatenate(axis=1)([in1, in2])
for i in range(len(filter_sizes)):
x = Conv2D(filters=filter_sizes[i], kernel_size=kernel_sizes[i], data_format="channels_first",
activation="relu", padding=padding)(x)
x = MaxPool2D(pool_size=(2, 2), padding=padding, data_format="channels_first")(x)
x = Flatten()(x)
for units in dense_arch[:-1]:
x = Dense(units, activation="relu")(x)
x = Dense(dense_arch[-1], activation=last_activation)(x)
self.model = Model(inputs=[input_data_layer, input_mask_layer], outputs=x)
print("Object network model built:")
#self.model.summary()
def create_2ch_conv_model(self, input_shape, image_shape, filter_sizes, kernel_sizes, dense_arch, padding,
last_activation="softmax"): # only for grayscale
self.x_shape = input_shape
self.y_shape = dense_arch[-1]
input_data_layer = Input(shape=input_shape)
ch_data = Reshape(target_shape=(1,) + image_shape)(input_data_layer)
input_mask_layer = Input(shape=input_shape)
ch_mask = Reshape(target_shape=(1,) + image_shape)(input_mask_layer)
for i in range(len(filter_sizes)):
ch_data = Conv2D(filters=filter_sizes[i], kernel_size=kernel_sizes[i], data_format="channels_last",
activation="relu", padding=padding)(ch_data)
ch_data = MaxPool2D(pool_size=(2, 2), padding=padding, data_format="channels_last")(ch_data)
ch_mask = Conv2D(filters=filter_sizes[i], kernel_size=kernel_sizes[i], data_format="channels_last",
activation="relu", padding=padding)(ch_mask)
ch_mask = MaxPool2D(pool_size=(2, 2), padding=padding, data_format="channels_last")(ch_mask)
ch_mask = Flatten()(ch_mask)
ch_data = Flatten()(ch_data)
x = tf.keras.layers.Concatenate(axis=1)([ch_mask, ch_data])
for units in dense_arch[:-1]:
x = Dense(units, activation="relu")(x)
x = Dense(dense_arch[-1], activation=last_activation)(x)
self.model = Model(inputs=[input_data_layer, input_mask_layer], outputs=x)
print("Object network model built:")
#self.model.summary()
def create_batch(self, x, masks, y):
"""
x = [[1,2],[3,4]] -> [[1,2],[1,2],[1,2],[3,4],[3,4],[3,4]]
masks = [[0,0],[1,0],[1,1]] -> [[0,0],[1,0],[1,1],[0,0],[1,0],[1,1]]
y = [1,3] -> [1 ,1 ,1 ,3 ,3 ,3 ]
"""
# assert len(masks) == self.mask_size
x_prim = np.repeat(x, len(masks), axis=0)
y_prim = np.repeat(y, len(masks), axis=0)
masks_prim = np.tile(masks, (len(x), 1))
x_prim *= masks_prim # MASKING
# assert len(x_prim) == self.batch_size
return x_prim, masks_prim, y_prim
def named_logs(self, model, logs, mode="train"):
result = {}
try:
iterator = iter(logs)
except TypeError:
logs = [logs]
metricNames = (mode + "_" + i for i in model.metrics_names)
for l in zip(metricNames, logs):
result[l[0]] = l[1]
return result
def compile_model(self, loss_per_sample, combine_losses, combine_mask_losses, metrics=None):
self.mask_loss_combine_function = combine_mask_losses
if self.add_mopt_perf_metric is True:
if metrics is None:
metrics = [self.get_mopt_perf_metric()]
else:
metrics.append(self.get_mopt_perf_metric())
def logging_loss_function(y_true, y_pred):
losses = loss_per_sample(y_true, y_pred)
self.losses_per_sample = losses
return combine_losses(losses)
self.model.compile(loss=logging_loss_function, optimizer='nadam', metrics=metrics, run_eagerly=True)
if self.tf_logs != "":
log_path = './logs'
self.tb_clbk = TensorBoard(self.tf_logs)
self.tb_clbk.set_model(self.model)
def get_per_mask_loss(self, used_target_shape=None):
if used_target_shape is None:
used_target_shape = (self.x_batch_size, self.mask_batch_size)
losses = tf.reshape(self.losses_per_sample, used_target_shape)
# losses = np.apply_along_axis(self.mask_loss_combine_function,0,losses)
losses = self.mask_loss_combine_function(losses)
return losses
def get_per_mask_loss_with_custom_batch(self, losses, new_x_batch_size, new_mask_batch_size):
losses = np.reshape(losses, newshape=(new_x_batch_size, new_mask_batch_size))
losses = np.apply_along_axis(self.mask_loss_combine_function, 0, losses)
return losses
def train_one(self, x, masks, y):
x_prim, masks_prim, y_prim = self.create_batch(x, masks, y)
curr_loss = self.model.train_on_batch(x=[x_prim, masks_prim], y=y_prim)
self.tr_loss_history.append(curr_loss)
self.epoch_counter += 1
if self.tf_logs != "":
self.tb_clbk.on_epoch_end(self.epoch_counter, self.named_logs(self.model, curr_loss))
return x_prim, masks_prim, y_prim
def validate_one(self, x, masks, y):
x_prim, masks_prim, y_prim = self.create_batch(x, masks, y)
# print("ON: x: "+str(x_prim))
# print("ON: m: "+str(masks_prim))
# print("ON: y_true: "+str(y_prim))
curr_loss = self.model.test_on_batch(x=[x_prim, masks_prim], y=y_prim)
self.te_loss_history.append(curr_loss)
if self.tf_logs != "":
self.tb_clbk.on_epoch_end(self.epoch_counter, self.named_logs(self.model, curr_loss, "val"))
if self.useEarlyStopping is True:
self.check_ES()
# print("ON y_pred:" +str(np.squeeze(y_pred)))
# print("ON loss per sample:" +str(np.squeeze(self.losses_per_sample.numpy())))
return x_prim, masks_prim, y_prim, self.losses_per_sample.numpy()
def test_one(self, x, masks, y):
x_prim, masks_prim, y_prim = self.create_batch(x, masks, y)
curr_loss = self.model.test_on_batch(x=[x_prim, masks_prim], y=y_prim)
self.te_loss_history.append(curr_loss)
return curr_loss
def get_mopt_perf_metric(self):
# used_target_shape = (self.x_batch_size,self.mask_batch_size)
def m_opt_loss(y_pred, y_true):
if (self.losses_per_sample.shape[0] % self.mask_batch_size != 0): # when testing happens, not used anymore
return 0.0
else: # for training and validation batches
losses = tf.reshape(self.losses_per_sample, (-1, self.mask_batch_size))
self.last_m_opt_perf = np.mean(losses[:, int(0.5 * self.mask_batch_size)])
return self.last_m_opt_perf
return m_opt_loss
def set_early_stopping_params(self, starting_epoch, patience_batches=800, minimize=True):
self.ES_patience = patience_batches
self.ES_minimize = minimize
if (minimize is True):
self.ES_best_perf = 1000000.0
else:
self.ES_best_perf = -1000000.0
self.ES_best_epoch = starting_epoch
self.ES_stop_training = False
self.ES_start_epoch = starting_epoch
self.ES_best_weights = None
return
def check_ES(self, ):
if self.epoch_counter >= self.ES_start_epoch:
if self.ES_minimize is True:
if self.last_m_opt_perf < self.ES_best_perf:
self.ES_best_perf = self.last_m_opt_perf
self.ES_best_epoch = self.epoch_counter
self.ES_best_weights = self.model.get_weights()
else:
if self.last_m_opt_perf > self.ES_best_perf:
self.ES_best_perf = self.last_m_opt_perf
self.ES_best_epoch = self.epoch_counter
self.ES_best_weights = self.model.get_weights()
# print("ES patience left: "+str(self.epoch_counter-self.ES_best_epoch))
if (self.epoch_counter - self.ES_best_epoch > self.ES_patience):
self.ES_stop_training = True
test_loss, test_accuracy = model.test_on_batch(x_batch_test,
y_batch_test)
testing_acc.append(test_accuracy)
testing_loss.append(test_loss)
train_logs_dict = get_logs(train_logs_dict, epoch, model, x_train, y_train)
test_logs_dict = get_logs(test_logs_dict, epoch, model, x_test, y_test)
logs = {
'acc': np.mean(training_acc),
'loss': np.mean(training_loss),
'val_loss': np.mean(testing_loss),
'val_acc': np.mean(testing_acc)
}
modelcheckpoint.on_epoch_end(epoch, logs)
earlystop.on_epoch_end(epoch, logs)
reduce_lr.on_epoch_end(epoch, logs)
tensorboard.on_epoch_end(epoch, logs)
print(
"accuracy: {}, loss: {}, validation accuracy: {}, validation loss: {}".
format(np.mean(training_acc), np.mean(training_loss),
np.mean(testing_acc), np.mean(testing_loss)))
if model.stop_training:
break
earlystop.on_train_end()
modelcheckpoint.on_train_end()
reduce_lr.on_train_end()
tensorboard.on_train_end()
# confusion metric for training
y_train_pred = model.predict(x_train).argmax(axis=1)
conf_mat = confusion_matrix(y_train, y_train_pred)