def save_images(self, nets, epoch, curr_tr, images_i, images_j):
"""
Saves input and output images.
"""
nets = utils.set_mode(nets, "eval")
exists_or_mkdir(self._images_dir)
if curr_tr > 0:
donorm = False
else:
donorm = True
names = [
'inputA_', 'inputB_', 'fakeA_', 'fakeB_', 'cycA_', 'cycB_',
'mask_a', 'mask_b'
]
with open(
os.path.join(self._output_dir,
'epoch_' + str(epoch) + '.html'), 'w') as v_html:
input_iter = minibatches(images_i,
images_j,
batch_size=1,
shuffle=True)
for i in range(0, self._num_imgs_to_save):
#pdb.set_trace()
print("Saving image {}/{}...".format(i,
self._num_imgs_to_save))
self.image_a, self.image_b = next(input_iter)
tmp_imgA = self.get_fake_image_pool(self.num_fake_inputs,
self.fake_images_A)
self.fake_pool_A_mask = tmp_imgA["mask"]
self.fake_pool_A = tmp_imgA["im"]
tmp_imgB = self.get_fake_image_pool(self.num_fake_inputs,
self.fake_images_B)
self.fake_pool_B_mask = tmp_imgB["mask"]
self.fake_pool_B = tmp_imgB["im"]
self.transition_rate = np.array([curr_tr], dtype=np.float32)
self.donorm = np.array([donorm], dtype=np.float32)
self.output_converter(
model.get_outputs(self.input_converter(), nets))
figures_to_save = [
self.image_a, self.image_b, self.fake_images_b,
self.fake_images_a, self.cycle_images_a,
self.cycle_images_b, self.masks[0], self.masks[1]
]
self.figure_writer(figures_to_save,
names,
v_html,
epoch,
i,
html_mode=0)
def main():
args = parse_args()
if args is None:
exit()
to_train = args.to_train
log_dir = args.log_dir
config_filename = args.config_filename
checkpoint_dir = args.checkpoint_dir
skip = args.skip
switch = args.switch
threshold_fg = args.threshold
exists_or_mkdir(log_dir)
with open(config_filename) as config_file:
config = json.load(config_file)
lambda_a = float(config['_LAMBDA_A']) if '_LAMBDA_A' in config else 10.0
lambda_b = float(config['_LAMBDA_B']) if '_LAMBDA_B' in config else 10.0
pool_size = int(config['pool_size']) if 'pool_size' in config else 50
to_restore = (to_train == 2)
base_lr = float(config['base_lr']) if 'base_lr' in config else 0.0002
max_step = int(config['max_step']) if 'max_step' in config else 200
dataset_name = str(config['dataset_name'])
do_flipping = bool(config['do_flipping'])
checkpoint_name = args.checkpoint_name
if checkpoint_name == '' and to_train != 1:
print("Error: please provide the latest checkpoint name.")
exit()
cyclegan_model = CycleGAN(pool_size, lambda_a, lambda_b, log_dir,
to_restore, checkpoint_name, base_lr, max_step,
dataset_name, checkpoint_dir, do_flipping, skip,
switch, threshold_fg)
if to_train > 0:
cyclegan_model.train()
else:
cyclegan_model.test()
def find_top_model(self, sess, sort=None, model_name='model', **kwargs):
"""Finds and returns a model architecture and its parameters from the database which matches the requirement.
Parameters
----------
sess : Session
TensorFlow session.
sort : List of tuple
PyMongo sort comment, search "PyMongo find one sorting" and `collection level operations <http://api.mongodb.com/python/current/api/pymongo/collection.html>`__ for more details.
model_name : str or None
The name/key of model.
kwargs : other events
Other events, such as name, accuracy, loss, step number and etc (optinal).
Examples
---------
- see ``save_model``.
Returns
---------
network : TensorLayer layer
Note that, the returned network contains all information of the document (record), e.g. if you saved accuracy in the document, you can get the accuracy by using ``net._accuracy``.
"""
# print(kwargs) # {}
kwargs.update({'model_name': model_name})
self._fill_project_info(kwargs)
s = time.time()
d = self.db.Model.find_one(filter=kwargs, sort=sort)
_temp_file_name = '_find_one_model_ztemp_file'
if d is not None:
params_id = d['params_id']
graphs = d['architecture']
_datetime = d['time']
exists_or_mkdir(_temp_file_name, False)
with open(os.path.join(_temp_file_name, 'graph.pkl'), 'wb') as file:
pickle.dump(graphs, file, protocol=pickle.HIGHEST_PROTOCOL)
else:
print("[Database] FAIL! Cannot find model: {}".format(kwargs))
return False
try:
params = self._deserialization(self.model_fs.get(params_id).read())
np.savez(os.path.join(_temp_file_name, 'params.npz'), params=params)
network = load_graph_and_params(name=_temp_file_name, sess=sess)
del_folder(_temp_file_name)
pc = self.db.Model.find(kwargs)
print(
"[Database] Find one model SUCCESS. kwargs:{} sort:{} save time:{} took: {}s".
format(kwargs, sort, _datetime, round(time.time() - s, 2))
)
# put all informations of model into the TL layer
for key in d:
network.__dict__.update({"_%s" % key: d[key]})
# check whether more parameters match the requirement
params_id_list = pc.distinct('params_id')
n_params = len(params_id_list)
if n_params != 1:
print(" Note that there are {} models match the kwargs".format(n_params))
return network
except Exception as e:
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
logging.info("{} {} {} {} {}".format(exc_type, exc_obj, fname, exc_tb.tb_lineno, e))
return False
def find_top_model(self, sess, sort=None, model_name='model', **kwargs):
"""Finds and returns a model architecture and its parameters from the database which matches the requirement.
Parameters
----------
sess : Session
TensorFlow session.
sort : List of tuple
PyMongo sort comment, search "PyMongo find one sorting" and `collection level operations <http://api.mongodb.com/python/current/api/pymongo/collection.html>`__ for more details.
model_name : str or None
The name/key of model.
kwargs : other events
Other events, such as name, accuracy, loss, step number and etc (optinal).
Examples
---------
- see ``save_model``.
Returns
---------
network : TensorLayer layer
Note that, the returned network contains all information of the document (record), e.g. if you saved accuracy in the document, you can get the accuracy by using ``net._accuracy``.
"""
# print(kwargs) # {}
kwargs.update({'model_name': model_name})
self._fill_project_info(kwargs)
s = time.time()
d = self.db.Model.find_one(filter=kwargs, sort=sort)
_temp_file_name = '_find_one_model_ztemp_file'
if d is not None:
params_id = d['params_id']
graphs = d['architecture']
_datetime = d['time']
exists_or_mkdir(_temp_file_name, False)
with open(os.path.join(_temp_file_name, 'graph.pkl'),
'wb') as file:
pickle.dump(graphs, file, protocol=pickle.HIGHEST_PROTOCOL)
else:
print("[Database] FAIL! Cannot find model: {}".format(kwargs))
return False
try:
params = self._deserialization(self.model_fs.get(params_id).read())
np.savez(os.path.join(_temp_file_name, 'params.npz'),
params=params)
network = load_graph_and_params(name=_temp_file_name, sess=sess)
del_folder(_temp_file_name)
pc = self.db.Model.find(kwargs)
print(
"[Database] Find one model SUCCESS. kwargs:{} sort:{} save time:{} took: {}s"
.format(kwargs, sort, _datetime, round(time.time() - s, 2)))
# put all informations of model into the TL layer
for key in d:
network.__dict__.update({"_%s" % key: d[key]})
# check whether more parameters match the requirement
params_id_list = pc.distinct('params_id')
n_params = len(params_id_list)
if n_params != 1:
print(" Note that there are {} models match the kwargs".
format(n_params))
return network
except Exception as e:
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
logging.info("{} {} {} {} {}".format(exc_type, exc_obj, fname,
exc_tb.tb_lineno, e))
return False
def train(self):
"""
Training Function.
We use batch size = 1 for training
"""
# Build the network and compute losses
nets = self.model_setup()
summary_writer = tf.summary.create_file_writer(
os.path.join(self._output_dir, "log"))
summary_writer.set_as_default()
max_images = cyclegan_datasets.DATASET_TO_SIZES[self._dataset_name]
half_training_ep = int(self._max_step / 2)
# Restore the model to run the model from last checkpoint
print("Loading the latest checkpoint...")
if self._to_restore:
checkpoint_name = os.path.join(self._checkpoint_dir,
self._checkpoint_name)
utils.load(checkpoint_name, nets=nets)
self.global_step = int(checkpoint_name[-2:])
else:
self.global_step = 0
exists_or_mkdir(self._output_dir)
self.upd_fake_image_pool(self.num_fake_inputs, self.fake_pool_A,
self.fake_pool_A_mask, self.fake_images_A)
self.upd_fake_image_pool(self.num_fake_inputs, self.fake_pool_B,
self.fake_pool_B_mask, self.fake_images_B)
self.num_fake_inputs += 1
# Training Loop
for epoch in range(self.global_step, self._max_step):
print("In the epoch ", epoch)
print("Saving the latest checkpoint...")
utils.save(nets,
os.path.join(self._output_dir, "AGGAN_%02d" % epoch))
# Setting lr
curr_lr = self._base_lr
if epoch >= half_training_ep:
curr_lr -= self._base_lr * (
epoch - half_training_ep) / half_training_ep
self.g_A_trainer.learning_rate = curr_lr
self.g_B_trainer.learning_rate = curr_lr
self.g_A_trainer_bis.learning_rate = curr_lr
self.g_B_trainer_bis.learning_rate = curr_lr
self.d_A_trainer.learning_rate = curr_lr
self.d_B_trainer.learning_rate = curr_lr
if epoch < self._switch:
curr_tr = 0
donorm = True
to_train_A = self.g_A_trainer
to_train_B = self.g_B_trainer
to_train_A_vars = self.g_A_vars + self.g_Ae_vars
to_train_B_vars = self.g_B_vars + self.g_Be_vars
else:
curr_tr = self._threshold_fg
donorm = False
to_train_A = self.g_A_trainer_bis
to_train_B = self.g_B_trainer_bis
to_train_A_vars = self.g_A_vars
to_train_B_vars = self.g_B_vars
print("Loading data...")
tot_inputs = data_loader.load_data(self._dataset_name,
self._size_before_crop, False,
self._do_flipping)
self.inputs_img_i = tot_inputs['images_i']
self.inputs_img_j = tot_inputs['images_j']
assert (len(self.inputs_img_i) == len(self.inputs_img_j)
and max_images == len(self.inputs_img_i))
self.save_images(nets, epoch, curr_tr, self.inputs_img_i,
self.inputs_img_j)
nets = utils.set_mode(nets, "train")
input_iter = minibatches(self.inputs_img_i,
self.inputs_img_j,
batch_size=1,
shuffle=True)
for i in range(max_images):
print("Processing batch {}/{} in {}th epoch".format(
i, max_images, epoch))
self.image_a, self.image_b = next(input_iter)
tmp_imgA = self.get_fake_image_pool(self.num_fake_inputs,
self.fake_images_A)
self.fake_pool_A_mask = tmp_imgA["mask"]
self.fake_pool_A = tmp_imgA["im"]
tmp_imgB = self.get_fake_image_pool(self.num_fake_inputs,
self.fake_images_B)
self.fake_pool_B_mask = tmp_imgB["mask"]
self.fake_pool_B = tmp_imgB["im"]
self.transition_rate = np.array([curr_tr], dtype=np.float32)
self.donorm = np.array([donorm], dtype=np.float32)
with tf.GradientTape(persistent=True) as tape:
self.output_converter(
model.get_outputs(self.input_converter(), nets))
self.upd_fake_image_pool(self.num_fake_inputs,
self.fake_images_b, self.masks[0],
self.fake_images_B)
self.upd_fake_image_pool(self.num_fake_inputs,
self.fake_images_a, self.masks[1],
self.fake_images_A)
self.compute_losses()
#pdb.set_trace()
grad = tape.gradient(self.d_B_loss, self.d_B_vars)
self.d_B_trainer.apply_gradients(zip(grad, self.d_B_vars))
grad = tape.gradient(self.d_A_loss, self.d_A_vars)
self.d_A_trainer.apply_gradients(zip(grad, self.d_A_vars))
grad = tape.gradient(self.g_A_loss, to_train_A_vars)
to_train_A.apply_gradients(zip(grad, to_train_A_vars))
grad = tape.gradient(self.g_B_loss, to_train_B_vars)
to_train_B.apply_gradients(zip(grad, to_train_B_vars))
tot_loss = self.g_A_loss + self.g_B_loss + self.d_A_loss + self.d_B_loss
print("[training_info] g_A_loss = {}, g_B_loss = {}, d_A_loss = {}, d_B_loss = {}, \
tot_loss = {}, lr={}, curr_tr={}" .format(self.g_A_loss, self.g_B_loss, self.d_A_loss, \
self.d_B_loss, tot_loss, curr_lr, curr_tr))
tf.summary.scalar('g_A_loss',
self.g_A_loss,
step=self.global_step * max_images + i)
tf.summary.scalar('g_B_loss',
self.g_B_loss,
step=self.global_step * max_images + i)
tf.summary.scalar('d_A_loss',
self.d_A_loss,
step=self.global_step * max_images + i)
tf.summary.scalar('d_B_loss',
self.d_B_loss,
step=self.global_step * max_images + i)
tf.summary.scalar('learning_rate',
to_train_A.learning_rate,
step=self.global_step * max_images + i)
tf.summary.scalar('total_loss',
tot_loss,
step=self.global_step * max_images + i)
self.num_fake_inputs += 1
self.global_step = epoch + 1
summary_writer.flush()
def save_images_bis(self, nets, epoch, images_i, images_j):
"""
Saves input and output images.
"""
names = [
'input_A_', 'mask_A_', 'masked_inputA_', 'fakeB_', 'input_B_',
'mask_B_', 'masked_inputB_', 'fakeA_'
]
space = '                        ' \
'                        ' \
'         '
nets = utils.set_mode(nets, "eval")
#pdb.set_trace()
exists_or_mkdir(self._images_dir)
with open(
os.path.join(self._output_dir,
'results_' + str(epoch) + '.html'),
'w') as v_html:
v_html.write("<b>Inputs" + space + "Masks" + space +
"Masked_images" + space + "Generated_images</b>")
v_html.write("<br>")
input_iter = minibatches(images_i,
images_j,
batch_size=1,
shuffle=True)
for i in range(0, self._num_imgs_to_save):
print("Saving image {}/{}...".format(i,
self._num_imgs_to_save))
#pdb.set_trace()
self.image_a, self.image_b = next(input_iter)
tmp_imgA = self.get_fake_image_pool(self.num_fake_inputs,
self.fake_images_A)
self.fake_pool_A_mask = tmp_imgA["mask"]
self.fake_pool_A = tmp_imgA["im"]
tmp_imgB = self.get_fake_image_pool(self.num_fake_inputs,
self.fake_images_B)
self.fake_pool_B_mask = tmp_imgB["mask"]
self.fake_pool_B = tmp_imgB["im"]
self.transition_rate = np.array([0.1], dtype=np.float32)
self.donorm = np.array([True], dtype=np.float32)
self.output_converter(
model.get_outputs(self.input_converter(), nets))
figures_to_save = [
self.image_a, self.masks[0], self.masked_ims[0],
self.fake_images_b, self.image_b, self.masks[1],
self.masked_ims[1], self.fake_images_a
]
self.figure_writer(figures_to_save,
names,
v_html,
epoch,
i,
html_mode=1)
def train():
writer_path = './exp1/'
exists_or_mkdir(writer_path)
writer = SummaryWriter(writer_path)
# load mnist
X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(
shape=(-1, 28, 28, 1))
n = len(X_train)
# convert mnist images to 32*32
xnew = np.zeros((n, 32, 32, 1), dtype='float32')
for i in range(n):
xnew[i] = tl.prepro.imresize(X_train[i],
size=(32, 32),
interp='bicubic',
mode=None)
xnew[i] /= 128
xnew[i] -= 1
X_train = xnew
G = get_generator([None, flags.z_dim])
G.train()
# 2 different optimizers to train G and z separately
g_optimizer = tf.optimizers.SGD(lr=1e-3)
z_optimizer = tf.optimizers.SGD(lr=0.1)
# initialize z by sampling from a Gaussian distribution
z = tf.Variable(tf.random.normal([n, flags.z_dim],
stddev=np.sqrt(1.0 / flags.z_dim)),
name="z",
trainable=True)
step = 0
for epoch in trange(
flags.n_epoch,
desc='epoch loop'): ## iterate the dataset n_epoch times
start_time = time.time()
# iterate over the entire training set once
for i in range(n // flags.batch_size):
# get X_batch by indexing, without shuffling (important!)
X_batch = X_train[i * flags.batch_size:(i + 1) * flags.batch_size]
step_time = time.time()
step += 1
G.train() # enable dropout
with tf.GradientTape(persistent=True) as tape:
# compute outputs
z_batch = z[i * flags.batch_size:(i + 1) * flags.batch_size]
# tape.watch(z_batch)
fake_X = G(z_batch)
# compute loss and update model
loss = tl.cost.mean_squared_error(fake_X,
X_batch,
name='train_loss')
# compute gradient to G and z
grad = tape.gradient(loss, G.trainable_weights + [z])
# Back_propagation
grad_g = grad[:len(G.trainable_weights)]
grad_z = grad[len(G.trainable_weights):]
g_optimizer.apply_gradients(zip(grad_g, G.trainable_weights))
z_optimizer.apply_gradients(zip(grad_z, [z]))
del tape
print("Epoch: [{}/{}] [{}/{}] took: {:.3f}, loss: {:.5f}".format(
epoch, flags.n_epoch, i, n // flags.batch_size,
time.time() - step_time, loss))
# normalize z (remain unchanged for those vectors whose length is shorter than 1)
z.assign(z / tf.math.maximum(
tf.math.sqrt(tf.math.reduce_sum(z**2, axis=1))[:, tf.newaxis], 1))
# testing
G.save_weights('{}/G.npz'.format(flags.checkpoint_dir), format='npz')
G.eval()
# sampling from the z distribution
z_mean = np.mean(z.numpy(), axis=0)
z_cov = np.cov(z.numpy(), rowvar=False)
sample = np.random.multivariate_normal(z_mean, z_cov, size=(25))
result = G(sample.astype(np.float32))
G.train()
tl.visualize.save_images(
result.numpy(), [5, 5],
'{}/train_{:02d}.png'.format(flags.sample_dir, epoch))
save_res = np.tile(
tf.transpose(result, [0, 3, 1, 2]).numpy(), [1, 3, 1, 1])
save_res = torch.tensor(save_res)
save_grid = make_grid(save_res.cpu(),
nrow=5,
range=(-1, 1),
normalize=True)
writer.add_image('eval/recon_imgs', save_grid, epoch + 1)
