def train(self):
log.info('Training Model')
self.init_train_data()
self.init_image_callback()
sl = SaveLoss(self.conf.folder)
cl = CSVLogger(self.conf.folder + '/training.csv')
cl.on_train_begin()
es = EarlyStopping('val_loss_mod2_fused', min_delta=0.01, patience=60)
es.model = self.model.Segmentor
es.on_train_begin()
loss_names = self.get_loss_names()
total_loss = {n: [] for n in loss_names}
progress_bar = Progbar(target=self.batches * self.conf.batch_size)
for self.epoch in range(self.conf.epochs):
log.info('Epoch %d/%d' % (self.epoch, self.conf.epochs))
epoch_loss = {n: [] for n in loss_names}
epoch_loss_list = []
for self.batch in range(self.batches):
self.train_batch(epoch_loss)
progress_bar.update((self.batch + 1) * self.conf.batch_size)
self.validate(epoch_loss)
for n in loss_names:
epoch_loss_list.append((n, np.mean(epoch_loss[n])))
total_loss[n].append(np.mean(epoch_loss[n]))
log.info(str('Epoch %d/%d: ' + ', '.join([l + ' Loss = %.3f' for l in loss_names])) %
((self.epoch, self.conf.epochs) + tuple(total_loss[l][-1] for l in loss_names)))
logs = {l: total_loss[l][-1] for l in loss_names}
cl.model = self.model.D_Mask
cl.model.stop_training = False
cl.on_epoch_end(self.epoch, logs)
sl.on_epoch_end(self.epoch, logs)
# Plot some example images
self.img_callback.on_epoch_end(self.epoch)
self.model.save_models()
if self.stop_criterion(es, logs):
log.info('Finished training from early stopping criterion')
break
def train(self):
log.info('Training Model')
self.init_train_data()
self.init_image_callback()
sl = SaveLoss(self.conf.folder)
cl = CSVLogger(self.conf.folder + '/training.csv')
cl.on_train_begin()
es = EarlyStopping('val_loss_mod2_fused', min_delta=0.01, patience=60)
es.model = self.model.Segmentor
es.on_train_begin()
loss_names = self.get_loss_names()
total_loss = {n: [] for n in loss_names}
progress_bar = Progbar(target=self.batches * self.conf.batch_size)
for self.epoch in range(self.conf.epochs):
log.info('Epoch %d/%d' % (self.epoch, self.conf.epochs))
epoch_loss = {n: [] for n in loss_names}
epoch_loss_list = []
for self.batch in range(self.batches):
self.train_batch(epoch_loss)
progress_bar.update((self.batch + 1) * self.conf.batch_size)
self.set_swa_model_weights()
for swa_m in self.get_swa_models():
swa_m.on_epoch_end(self.epoch)
self.validate(epoch_loss)
for n in loss_names:
epoch_loss_list.append((n, np.mean(epoch_loss[n])))
total_loss[n].append(np.mean(epoch_loss[n]))
log.info(
str('Epoch %d/%d: ' +
', '.join([l + ' Loss = %.5f' for l in loss_names])) %
((self.epoch, self.conf.epochs) + tuple(total_loss[l][-1]
for l in loss_names)))
logs = {l: total_loss[l][-1] for l in loss_names}
cl.model = self.model.D_Mask
cl.model.stop_training = False
cl.on_epoch_end(self.epoch, logs)
sl.on_epoch_end(self.epoch, logs)
# print images
self.img_callback.on_epoch_end(self.epoch)
self.save_models()
if self.stop_criterion(es, logs):
log.info('Finished training from early stopping criterion')
es.on_train_end(logs)
cl.on_train_end(logs)
for swa_m in self.get_swa_models():
swa_m.on_train_end()
# Set final model parameters based on SWA
self.model.D_Mask = self.swa_D_Mask.model
self.model.D_Image1 = self.swa_D_Image1.model
self.model.D_Image2 = self.swa_D_Image2.model
self.model.Encoders_Anatomy[0] = self.swa_Enc_Anatomy1.model
self.model.Encoders_Anatomy[1] = self.swa_Enc_Anatomy2.model
self.model.Enc_Modality = self.swa_Enc_Modality.model
self.model.Anatomy_Fuser = self.swa_Anatomy_Fuser.model
self.model.Segmentor = self.swa_Segmentor.model
self.model.Decoder = self.swa_Decoder.model
self.model.Balancer = self.swa_Balancer.model
self.save_models()
break
def train(self):
self.init_train_data()
# make genetrated data
gen_dict = self.get_datagen_params()
p_gen = ImageDataGenerator(**gen_dict).flow(x=self.p_images, y=self.p_masks, batch_size=self.conf.batch_size)
h_gen = ImageDataGenerator(**gen_dict).flow(x=self.h_images, y=self.h_masks, batch_size=self.conf.batch_size)
random_p_masks = ImageDataGenerator(**gen_dict).flow(x= self.p_masks, batch_size=self.conf.batch_size)
# initialize training
batches = int(np.ceil(self.conf.data_len/self.conf.batch_size))
progress_bar = Progbar(target=batches * self.conf.batch_size)
sl = SaveLoss(self.conf.folder)
cl = CSVLogger(self.conf.folder+'/training.csv')
cl.on_train_begin()
img_clb = ImageCallback(self.conf, self.model, self.comet_exp)
loss_names = self.get_loss_names()
total_loss = {n: [] for n in loss_names}
# start training
for epoch in range(self.conf.epochs):
log.info("Train epoch %d/%d"%(epoch, self.conf.epochs))
epoch_loss = {n: [] for n in loss_names}
epoch_loss_list = []
pool_to_print_p_img, pool_to_print_p_msk, pool_to_print_h_img, pool_to_print_h_msk = [], [], [], []
for batch in range(batches):
p_img, p_msk = next(p_gen)
h_img, h_msk = next(h_gen)
r_p_msk = next(random_p_masks)
if len(pool_to_print_p_img)<30:
pool_to_print_p_img.append(p_img[0])
pool_to_print_p_msk.append(p_msk[0])
if len(pool_to_print_h_img)<30:
pool_to_print_h_img.append(h_img[0])
pool_to_print_h_msk.append(h_msk[0])
# Adversarial ground truths
real_pred = -np.ones((h_img.shape[0],1))
fake_pred = np.ones((h_img.shape[0],1))
dummy = np.zeros((h_img.shape[0],1))
dummy_Img = np.ones(h_img.shape)
if self.conf.self_rec:
h_test_sr = self.model.train_self_rec.fit([h_img, h_msk], [h_img, h_img], epochs=1, verbose=0)
epoch_loss["test_self_rec_loss"].append(np.mean(h_test_sr.history["loss"]))
else:
epoch_loss["test_self_rec_loss"].append(0)
# ---------------------
# Train Discriminator
# ---------------------
# Get a group of synthetic msks and imgs
cy1_pse_h_img = self.model.G_d_to_h.predict(p_img)
cy1_seg_d_msk = self.model.S_d_to_msk.predict(p_img)
cy2_fake_h_img = self.model.G_h_to_d.predict([h_img, h_msk])
if epoch<25:
for _ in range(self.conf.ncritic[0]):
cy1_epsilon = np.random.uniform(0,1, size=(h_img.shape[0],1,1,1))
cy1_average = cy1_epsilon * h_img +(1-cy1_epsilon) * cy1_pse_h_img
cy1_epsilon_msk = np.random.uniform(0, 1, size=(h_img.shape[0], 1, 1, 1))
cy1_average_msk = cy1_epsilon_msk * r_p_msk + (1 - cy1_epsilon) * cy1_seg_d_msk
cy2_epsilon = np.random.uniform(0,1, size=(h_img.shape[0],1,1,1))
cy2_average = cy2_epsilon * h_img +(1-cy2_epsilon) * cy2_fake_h_img
h_d = self.model.critic_model.fit([h_img, cy1_pse_h_img, cy1_average,
r_p_msk, cy1_seg_d_msk, cy1_average_msk,
h_img, cy2_fake_h_img, cy2_average],
[real_pred, fake_pred, dummy, real_pred, fake_pred, dummy,
real_pred, fake_pred, dummy],
epochs=1, verbose=0)
else:
for _ in range(self.conf.ncritic[1]):
cy1_epsilon = np.random.uniform(0,1, size=(h_img.shape[0],1,1,1))
cy1_average = cy1_epsilon * h_img +(1-cy1_epsilon) * cy1_pse_h_img
cy1_epsilon_msk = np.random.uniform(0, 1, size=(h_img.shape[0], 1, 1, 1))
cy1_average_msk = cy1_epsilon_msk * r_p_msk + (1 - cy1_epsilon) * cy1_seg_d_msk
cy2_epsilon = np.random.uniform(0,1, size=(h_img.shape[0],1,1,1))
cy2_average = cy2_epsilon * h_img +(1-cy2_epsilon) * cy2_fake_h_img
h_d = self.model.critic_model.fit([h_img, cy1_pse_h_img, cy1_average,
r_p_msk, cy1_seg_d_msk, cy1_average_msk,
h_img, cy2_fake_h_img, cy2_average],
[real_pred, fake_pred, dummy, real_pred, fake_pred, dummy,
real_pred, fake_pred, dummy],
epochs=1, verbose=0)
# print(h_d.history)
d_dis_pse_image_loss = np.mean([h_d.history['dis_cy1_I_pse_h_loss'], h_d.history['dis_cy2_I_pse_h_loss']])
d_dis_r_image_loss = np.mean([h_d.history['dis_cy1_I_h_loss'], h_d.history['dis_cy2_I_h_loss']])
d_dis_d_mask_loss = np.mean([h_d.history['dis_cy1_M_d_loss'], h_d.history['dis_cy1_M_seg_d_loss']])
d_gp_loss = np.mean([h_d.history['gp_cy1_I_h_loss'], h_d.history['gp_cy2_I_h_loss'], h_d.history['gp_cy1_M_d_loss']])
epoch_loss['d_dis_pse_image_loss'].append(d_dis_pse_image_loss)
epoch_loss['d_dis_r_image_loss'].append(d_dis_r_image_loss)
epoch_loss['d_dis_d_mask_loss'].append(d_dis_d_mask_loss)
epoch_loss['d_gp_loss'].append(d_gp_loss)
# --------------------
# Train Generator
# --------------------
h_g = self.model.gan.fit([p_img, h_img, h_msk],[real_pred, real_pred, p_img, real_pred, h_img, h_msk], epochs=1, verbose=0)
g_dis_pse_image_loss = np.mean([h_g.history['cy1_dis_I_pse_h_loss'], h_g.history['cy2_dis_I_pse_d_loss']])
g_rec_image_loss = np.mean([h_g.history['cy2_I_rec_h_loss'], h_g.history['cy1_I_rec_d_loss']])
g_dis_d_mask_loss = np.mean(h_g.history['cy1_dis_M_seg_d_loss'])
epoch_loss['g_dis_pse_image_loss'].append(g_dis_pse_image_loss)
epoch_loss['g_rec_image_loss'].append(g_rec_image_loss)
epoch_loss['g_dis_d_mask_loss'].append(g_dis_d_mask_loss)
# print(h_g.history)
# Plot the progress
progress_bar.update((batch + 1) * self.conf.batch_size)
for n in loss_names:
epoch_loss_list.append((n, np.mean(epoch_loss[n])))
total_loss[n].append(np.mean(epoch_loss[n]))
for n in loss_names:
epoch_loss_list.append((n, np.mean(epoch_loss[n])))
total_loss[n].append(np.mean(epoch_loss[n]))
log.info(str('Epoch %d/%d: ' + ', '.join([l + ' Loss = %.3f' for l in loss_names])) %
((epoch, self.conf.epochs) + tuple(total_loss[l][-1] for l in loss_names)))
logs = {l: total_loss[l][-1] for l in loss_names}
cl.model = self.model.D_pse_h
cl.model.stop_training = False
cl.on_epoch_end(epoch, logs)
sl.on_epoch_end(epoch, logs)
pool_to_print_p_img = np.asarray(pool_to_print_p_img)
pool_to_print_p_msk = np.asarray(pool_to_print_p_msk)
pool_to_print_h_img = np.asarray(pool_to_print_h_img)
pool_to_print_h_msk = np.asarray(pool_to_print_h_msk)
print("pool_to_print_p_img: ", np.shape(pool_to_print_p_img))
img_clb.on_epoch_end(epoch, pool_to_print_p_img, pool_to_print_p_msk,
pool_to_print_h_img, pool_to_print_h_msk)
def fit(self):
"""
Train SDNet
"""
log.info('Training SDNet')
# Load data
self.init_train()
# Initialise callbacks
sl = SaveLoss(self.conf.folder)
cl = CSVLogger(self.conf.folder + '/training.csv')
cl.on_train_begin()
si = SDNetCallback(self.conf.folder, self.conf.batch_size, self.sdnet)
es = EarlyStopping('val_loss', min_delta=0.001, patience=20)
es.on_train_begin()
loss_names = [
'adv_M', 'adv_X', 'rec_X', 'rec_M', 'rec_Z', 'dis_M', 'dis_X',
'mask', 'image', 'val_loss'
]
total_loss = {n: [] for n in loss_names}
progress_bar = Progbar(target=self.conf.batches * self.conf.batch_size)
for self.epoch in range(self.conf.epochs):
log.info('Epoch %d/%d' % (self.epoch, self.conf.epochs))
real_lb_pool, real_ul_pool = [], [
] # these are used only for printing images
epoch_loss = {n: [] for n in loss_names}
D_initial_weights = np.mean(
[np.mean(w) for w in self.sdnet.D_model.get_weights()])
G_initial_weights = np.mean(
[np.mean(w) for w in self.sdnet.G_model.get_weights()])
for self.batch in range(self.conf.batches):
real_lb = next(self.gen_X_L)
real_ul = next(self.gen_X_U)
# Add image/mask batch to the data pool
x, m = real_lb
real_lb_pool.extend([(x[i:i + 1], m[i:i + 1])
for i in range(x.shape[0])])
real_ul_pool.extend(real_ul)
D_weights1 = np.mean(
[np.mean(w) for w in self.sdnet.D_model.get_weights()])
self.train_batch_generator(real_lb, real_ul, epoch_loss)
D_weights2 = np.mean(
[np.mean(w) for w in self.sdnet.D_model.get_weights()])
assert D_weights1 == D_weights2
self.train_batch_discriminator(real_lb, real_ul, epoch_loss)
progress_bar.update((self.batch + 1) * self.conf.batch_size)
G_final_weights = np.mean(
[np.mean(w) for w in self.sdnet.G_model.get_weights()])
D_final_weights = np.mean(
[np.mean(w) for w in self.sdnet.D_model.get_weights()])
# Check training is altering weights
assert D_initial_weights != D_final_weights
assert G_initial_weights != G_final_weights
# Plot some example images
si.on_epoch_end(self.epoch, np.array(real_lb_pool),
np.array(real_ul_pool))
self.validate(epoch_loss)
# Calculate epoch losses
for n in loss_names:
total_loss[n].append(np.mean(epoch_loss[n]))
log.info(str('Epoch %d/%d: ' + ', '.join([l + ' Loss = %.3f' for l in loss_names])) % \
((self.epoch, self.conf.epochs) + tuple(total_loss[l][-1] for l in loss_names)))
logs = {l: total_loss[l][-1] for l in loss_names}
sl.on_epoch_end(self.epoch, logs)
# log losses to csv
cl.model = self.sdnet.D_model
cl.model.stop_training = False
cl.on_epoch_end(self.epoch, logs)
# save models
self.sdnet.save_models()
# early stopping
if self.stop_criterion(es, self.epoch, logs):
log.info('Finished training from early stopping criterion')
break
def train(self):
self.init_train_data()
# make genetrated data
gen_dict = self.get_datagen_params()
# Here we need to concatenate age and AD labels, in order to use Function ImageDataGenerator
yng_labels = np.concatenate([self.train_age_yng, self.train_AD_yng],
axis=1)
old_labels = np.concatenate([self.train_age_old, self.train_AD_old],
axis=1)
old_gen = ImageDataGenerator(**gen_dict).flow(
x=self.train_img_old,
y=old_labels,
batch_size=self.conf.batch_size)
yng_gen = ImageDataGenerator(**gen_dict).flow(
x=self.train_img_yng,
y=yng_labels,
batch_size=self.conf.batch_size)
# initialize training
batches = int(np.ceil(self.conf.data_len / self.conf.batch_size))
progress_bar = Progbar(target=batches * self.conf.batch_size)
sl = SaveLoss(self.conf.folder)
cl = CSVLogger(self.conf.folder + '/training.csv')
cl.on_train_begin()
img_clb = ImageCallback(self.conf, self.model, self.comet_exp)
# clr = CyclicLR(base_lr=self.conf.lr/5, max_lr=self.conf.lr,
# step_size=batches*4, mode='triangular')
loss_names = self.get_loss_names()
total_loss = {n: [] for n in loss_names}
# start training
for epoch in range(self.conf.epochs):
log.info("Train epoch %d/%d" % (epoch, self.conf.epochs))
epoch_loss = {n: [] for n in loss_names}
epoch_loss_list = []
pool_to_print_old, pool_to_print_yng = [], []
for batch in range(batches):
old_img, old_labels = next(old_gen)
yng_img, yng_labels = next(yng_gen)
# Return labels to age and AD vectors
old_age = old_labels[:, :self.conf.age_dim, :]
old_AD = old_labels[:, self.conf.age_dim:, :]
yng_age = yng_labels[:, :self.conf.age_dim, :]
yng_AD = yng_labels[:, self.conf.age_dim:, :]
if len(pool_to_print_old) < 30:
pool_to_print_old.append(old_img)
if len(pool_to_print_yng) < 30:
pool_to_print_yng.append(yng_img)
# Adversarial ground truths
real_pred = -np.ones((old_img.shape[0], 1))
fake_pred = np.ones((old_img.shape[0], 1))
dummy = np.zeros((old_img.shape[0], 1))
dummy_Img = np.ones(old_img.shape)
# ---------------------
# Train Discriminator
# ---------------------
age_gap = calculate_age_diff(yng_age, old_age)
diff_age = get_age_ord_vector(age_gap,
expand_dim=1,
con=self.conf.age_con,
ord=self.conf.age_ord,
age_dim=self.conf.age_dim)
# Get a group of synthetic msks and imgs
gen_masks = self.model.generator.predict(
[yng_img, diff_age, old_AD])
gen_old_img = np.tanh(
gen_masks +
yng_img) if self.conf.use_tanh else gen_masks + yng_img
# Need to train discriminators more iterations:
if epoch < 25:
for _ in range(self.conf.ncritic[0]):
epsilon = np.random.uniform(0,
1,
size=(old_img.shape[0], 1,
1, 1))
interpolation = epsilon * old_img + (
1 - epsilon) * gen_old_img
h_d = self.model.critic_model.fit([
old_img, old_age, old_AD, gen_old_img, old_age,
old_AD, interpolation, old_age, old_AD
], [real_pred, fake_pred, dummy],
epochs=1,
verbose=0)
# , callbacks=[clr])
# d_loss_bce = np.mean([h_real.history['binary_crossentropy'], h_fake.history['binary_crossentropy']])
else:
for _ in range(self.conf.ncritic[1]):
epsilon = np.random.uniform(0,
1,
size=(old_img.shape[0], 1,
1, 1))
interpolation = epsilon * old_img + (
1 - epsilon) * gen_old_img
h_d = self.model.critic_model.fit([
old_img, old_age, old_AD, gen_old_img, old_age,
old_AD, interpolation, old_age, old_AD
], [real_pred, fake_pred, dummy],
epochs=1,
verbose=0)
# , callbacks=[clr])
# d_loss_bce = np.mean(h_real.history['d_loss'])
print('d_real_loss', np.mean(h_d.history['d_real_loss']),
'd_fake_loss', np.mean(h_d.history['d_fake_loss']))
d_loss_bce = np.mean(
[h_d.history['d_real_loss'], h_d.history['d_fake_loss']])
d_loss_real = np.mean(h_d.history['d_real_loss'])
d_loss_fake = np.mean(h_d.history['d_fake_loss'])
d_loss_gp = np.mean(h_d.history['gp_loss'])
epoch_loss['Discriminator_loss'].append(d_loss_bce)
epoch_loss['Discriminator_real_loss'].append(d_loss_real)
epoch_loss['Discriminator_fake_loss'].append(d_loss_fake)
epoch_loss['Discriminator_gp_loss'].append(d_loss_gp)
# --------------------
# Train Generator
# --------------------
# Train the generator, want discriminator to mistake images as real
h = self.model.gan.fit(
[yng_img, old_age, diff_age, age_gap, old_AD],
[real_pred, dummy_Img],
epochs=1,
verbose=0)
# , callbacks=[clr])
# print(h.history)
g_loss_bce = h.history['discriminator_loss']
g_loss_l1 = h.history['map_l1_reg_loss']
# Deal with epoch loss
epoch_loss['Generator_fake_loss'].append(g_loss_bce)
epoch_loss['Generator_l1_reg_loss'].append(g_loss_l1)
#-----------------------------------------
# Train Generator by self-regularization
#-----------------------------------------
diff_age_zero = yng_age - yng_age
h = self.model.GAN_zero_reg([yng_img, diff_age_zero, yng_AD],
yng_img,
epochs=1,
verbose=0)
g_zero_reg = np.mean(h.history['self_reg'])
epoch_loss['Generator_zero_gre_loss'].append(g_zero_reg)
# Plot the progress
progress_bar.update((batch + 1) * self.conf.batch_size)
for n in loss_names:
epoch_loss_list.append((n, np.mean(epoch_loss[n])))
total_loss[n].append(np.mean(epoch_loss[n]))
log.info(
str('Epoch %d/%d: ' +
', '.join([l + ' Loss = %.3f' for l in loss_names])) %
((epoch, self.conf.epochs) + tuple(total_loss[l][-1]
for l in loss_names)))
logs = {l: total_loss[l][-1] for l in loss_names}
cl.model = self.model.discriminator
cl.model.stop_training = False
cl.on_epoch_end(epoch, logs)
sl.on_epoch_end(epoch, logs)
img_clb.on_epoch_end(epoch, yng_img, yng_age, old_img, old_age)
def train(self):
def _learning_rate_schedule(epoch):
return self.conf.lr * math.exp(self.lr_schedule_coef * (-epoch - 1))
if os.path.exists(os.path.join(self.conf.folder, 'test-performance.csv')):
os.remove(os.path.join(self.conf.folder, 'test-performance.csv'))
if os.path.exists(os.path.join(self.conf.folder, 'validation-performance.csv')):
os.remove(os.path.join(self.conf.folder, 'validation-performance.csv'))
log.info('Training Model')
dice_record = 0
self.eval_train_interval = int(max(1, self.conf.epochs/50))
self.init_train_data()
lr_callback = LearningRateScheduler(_learning_rate_schedule)
self.init_image_callback()
sl = SaveLoss(self.conf.folder)
cl = CSVLogger(self.conf.folder + '/training.csv')
cl.on_train_begin()
es = EarlyStopping('Validate_Dice', self.conf.min_delta, self.conf.patience)
es.model = self.model.Segmentor
es.on_train_begin()
loss_names = self.get_loss_names()
loss_names.sort()
total_loss = {n: [] for n in loss_names}
progress_bar = Progbar(target=self.conf.batches)
# self.img_clb.on_epoch_end(self.epoch)
best_performance = 0.
test_performance = 0.
total_iters = 0
for self.epoch in range(self.conf.epochs):
total_iters+=1
log.info('Epoch %d/%d' % (self.epoch+1, self.conf.epochs))
epoch_loss = {n: [] for n in loss_names}
epoch_loss_list = []
for self.batch in range(self.conf.batches):
total_iters += 1
self.train_batch(epoch_loss, lr_callback)
progress_bar.update(self.batch + 1)
val_dice = self.validate(epoch_loss)
if val_dice > dice_record:
dice_record = val_dice
cl.model = self.model.D_Reconstruction
cl.model.stop_training = False
self.model.save_models()
# Plot some example images
if self.epoch % self.eval_train_interval == 0 or self.epoch == self.conf.epochs - 1:
self.img_clb.on_epoch_end(self.epoch)
folder = os.path.join(os.path.join(self.conf.folder, 'test_during_train'),
'test_results_%s_epoch%d'
% (self.conf.test_dataset, self.epoch))
if not os.path.exists(folder):
os.makedirs(folder)
test_performance = self.test_modality(folder, self.conf.modality, 'test', False)
if test_performance > best_performance:
best_performance = test_performance
self.model.save_models('BestModel')
log.info("BestModel@Epoch%d" % self.epoch)
folder = os.path.join(os.path.join(self.conf.folder, 'test_during_train'),
'validation_results_%s_epoch%d'
% (self.conf.test_dataset, self.epoch))
if not os.path.exists(folder):
os.makedirs(folder)
validation_performance = self.test_modality(folder, self.conf.modality, 'validation', False)
if self.conf.batches>check_batch_iters:
self.write_csv(os.path.join(self.conf.folder, 'test-performance.csv'),
self.epoch, self.batch, test_performance)
self.write_csv(os.path.join(self.conf.folder, 'validation-performance.csv'),
self.epoch, self.batch, validation_performance)
epoch_loss['Test_Performance_Dice'].append(test_performance)
for n in loss_names:
epoch_loss_list.append((n, np.mean(epoch_loss[n])))
total_loss[n].append(np.mean(epoch_loss[n]))
if self.epoch<5:
log.info(str('Epoch %d/%d:\n' + ''.join([l + ' Loss = %.3f\n' for l in loss_names])) %
((self.epoch, self.conf.epochs) + tuple(total_loss[l][-1] for l in loss_names)))
else:
info_str = str('Epoch %d/%d:\n' % (self.epoch, self.conf.epochs))
loss_info = ''
for l in loss_names:
loss_info = loss_info + l + ' Loss = %.3f->%.3f->%.3f->%.3f->%.3f\n' % \
(total_loss[l][-5],
total_loss[l][-4],
total_loss[l][-3],
total_loss[l][-2],
total_loss[l][-1])
log.info(info_str + loss_info)
log.info("BestTest:%f" % best_performance)
log.info('Epoch %d/%d' % (self.epoch + 1, self.conf.epochs))
logs = {l: total_loss[l][-1] for l in loss_names}
cl.on_epoch_end(self.epoch, logs)
sl.on_epoch_end(self.epoch, logs)
if self.stop_criterion(es, logs) and self.epoch > self.conf.epochs / 2:
log.info('Finished training from early stopping criterion')
self.img_clb.on_epoch_end(self.epoch)
break
def train(self):
log.info('Training Model')
self.init_train_data()
self.init_image_callback()
sl = SaveLoss(self.conf.folder)
cl = CSVLogger(self.conf.folder + '/training.csv')
cl.on_train_begin()
es = EarlyStopping('val_loss', min_delta=0.01, patience=100)
es.model = self.model.Segmentor
es.on_train_begin()
loss_names = self.get_loss_names()
total_loss = {n: [] for n in loss_names}
progress_bar = Progbar(target=self.conf.batches * self.conf.batch_size)
for self.epoch in range(self.conf.epochs):
log.info('Epoch %d/%d' % (self.epoch, self.conf.epochs))
epoch_loss = {n: [] for n in loss_names}
epoch_loss_list = []
D_initial_weights = np.mean(
[np.mean(w) for w in self.model.D_trainer.get_weights()])
G_initial_weights = np.mean(
[np.mean(w) for w in self.model.G_trainer.get_weights()])
for self.batch in range(self.conf.batches):
# real_pools = self.add_to_pool(data, real_pools)
self.train_batch(epoch_loss)
progress_bar.update((self.batch + 1) * self.conf.batch_size)
G_final_weights = np.mean(
[np.mean(w) for w in self.model.G_trainer.get_weights()])
D_final_weights = np.mean(
[np.mean(w) for w in self.model.D_trainer.get_weights()])
assert self.gen_unlabelled is None or not self.model.D_trainer.trainable \
or D_initial_weights != D_final_weights
assert G_initial_weights != G_final_weights
self.validate(epoch_loss)
for n in loss_names:
epoch_loss_list.append((n, np.mean(epoch_loss[n])))
total_loss[n].append(np.mean(epoch_loss[n]))
log.info(
str('Epoch %d/%d: ' +
', '.join([l + ' Loss = %.3f' for l in loss_names])) %
((self.epoch, self.conf.epochs) + tuple(total_loss[l][-1]
for l in loss_names)))
logs = {l: total_loss[l][-1] for l in loss_names}
cl.model = self.model.D_Mask
cl.model.stop_training = False
cl.on_epoch_end(self.epoch, logs)
sl.on_epoch_end(self.epoch, logs)
# Plot some example images
self.img_clb.on_epoch_end(self.epoch)
self.model.save_models()
if self.stop_criterion(es, logs):
log.info('Finished training from early stopping criterion')
break
