def train(self, arch, weight_path):
""""""
'''create loss'''
c_loss = CustomLosses()
'''create summary writer'''
summary_writer = tf.summary.create_file_writer(
"./train_logs/fit/" + datetime.now().strftime("%Y%m%d-%H%M%S"))
start_train_date = datetime.now().strftime("%Y%m%d-%H%M%S")
'''making models'''
model = self.make_model(arch=arch, w_path=weight_path)
'''create save path'''
if self.dataset_name == DatasetName.affectnet:
save_path = AffectnetConf.weight_save_path + start_train_date + '/'
elif self.dataset_name == DatasetName.rafdb:
save_path = RafDBConf.weight_save_path + start_train_date + '/'
if not os.path.exists(save_path):
os.makedirs(save_path)
'''create sample generator'''
dhp = DataHelper()
''' Train Generator'''
face_img_filenames, eyes_img_filenames, nose_img_filenames, mouth_img_filenames, exp_filenames = \
dhp.create_masked_generator_full_path(img_path=self.masked_img_path, annotation_path=self.annotation_path,
num_of_samples=self.num_of_samples)
'''create dataset'''
cds = CustomDataset()
ds = cds.create_dataset(file_names_face=face_img_filenames,
file_names_eyes=eyes_img_filenames,
file_names_nose=nose_img_filenames,
file_names_mouth=mouth_img_filenames,
anno_names=exp_filenames)
# global_accuracy, conf_mat = self._eval_model(model=model)
conf_mat = None
# if weight_path is not None:
# global_accuracy, conf_mat = self._eval_model(model=model)
# else:
# conf_mat = np.ones_like([7 * 7])
'''create train configuration'''
step_per_epoch = len(face_img_filenames) // LearningConfig.batch_size
gradients = None
virtual_step_per_epoch = LearningConfig.virtual_batch_size // LearningConfig.batch_size
'''create optimizer'''
optimizer = tf.keras.optimizers.SGD(self.lr, momentum=0.9)
# optimizer = tf.keras.optimizers.Adam(self.lr)
'''start train:'''
for epoch in range(LearningConfig.epochs):
batch_index = 0
for global_bunch, upper_bunch, middle_bunch, bottom_bunch, exp_batch in ds:
'''load annotation and images'''
# print('load data...')
# start_time = time.perf_counter()
'''squeeze'''
# print('squeeze...')
exp_batch = exp_batch[:, -1]
global_bunch = global_bunch[:, -1, :, :]
upper_bunch = upper_bunch[:, -1, :, :]
middle_bunch = middle_bunch[:, -1, :, :]
bottom_bunch = bottom_bunch[:, -1, :, :]
# [:,:,-1,:],
# self.test_print_batch(global_bunch, upper_bunch, middle_bunch, bottom_bunch, batch_index)
'''train step'''
# print("Execution time:", time.perf_counter() - start_time)
# print('train step->')
step_gradients = self.train_step(epoch=epoch,
step=batch_index,
total_steps=step_per_epoch,
global_bunch=global_bunch,
upper_bunch=upper_bunch,
middle_bunch=middle_bunch,
bottom_bunch=bottom_bunch,
anno_exp=exp_batch,
model=model,
optimizer=optimizer,
c_loss=c_loss,
summary_writer=summary_writer,
conf_mat=conf_mat)
'''apply gradients'''
# print('gradients->')
# if batch_index > 0 and batch_index % virtual_step_per_epoch == 0:
# '''apply gradient'''
# print("===============apply gradient================= ")
# optimizer.apply_gradients(zip(gradients, model.trainable_variables))
# gradients = None
# else:
# '''accumulate gradient'''
# if gradients is None:
# gradients = [self._flat_gradients(g) / LearningConfig.virtual_batch_size for g in
# step_gradients]
# else:
# for i, g in enumerate(step_gradients):
# gradients[i] += self._flat_gradients(g) / LearningConfig.virtual_batch_size
batch_index += 1
'''evaluating part'''
global_accuracy, conf_mat = self._eval_model(model=model)
'''save weights'''
model.save(save_path + '_' + str(epoch) + '_' + self.dataset_name +
'_AC_' + str(global_accuracy) + '.h5')
'''revise lr'''
if epoch > 0 and epoch % self.epochs_drop == 0:
self.lr *= self.drop
optimizer = tf.keras.optimizers.SGD(self.lr, momentum=0.9)
def test_accuracy(self, model):
dhp = DataHelper()
batch_size = LearningConfig.batch_size
# exp_pr_glob = []
# exp_gt_glob = []
# acc_per_label = []
'''create batches'''
face_img_filenames, eyes_img_filenames, nose_img_filenames, mouth_img_filenames, exp_filenames = \
dhp.create_masked_generator_full_path(
img_path=self.masked_img_path,
annotation_path=self.anno_path, label=None, num_of_samples=None)
print(len(face_img_filenames))
step_per_epoch = int(len(face_img_filenames) // batch_size)
exp_pr_lbl = []
exp_gt_lbl = []
cds = CustomDataset()
ds = cds.create_dataset(file_names_face=face_img_filenames,
file_names_eyes=eyes_img_filenames,
file_names_nose=nose_img_filenames,
file_names_mouth=mouth_img_filenames,
anno_names=exp_filenames,
is_validation=True,
ds=DatasetName.rafdb)
batch_index = 0
for global_bunch, upper_bunch, middle_bunch, bottom_bunch, exp_gt_b in tqdm(
ds):
'''predict on batch'''
exp_gt_b = exp_gt_b[:, -1]
global_bunch = global_bunch[:, -1, :, :]
upper_bunch = upper_bunch[:, -1, :, :]
middle_bunch = middle_bunch[:, -1, :, :]
bottom_bunch = bottom_bunch[:, -1, :, :]
probab_exp_pr_b, _, _, _, _ = model.predict_on_batch(
[global_bunch, upper_bunch, middle_bunch, bottom_bunch])
# scores_b = np.array([tf.nn.softmax(probab_exp_pr_b[i]) for i in range(len(probab_exp_pr_b))])
# exp_pr_b = np.array([np.argmax(scores_b[i]) for i in range(len(probab_exp_pr_b))])
exp_pr_b = np.array([
np.argmax(probab_exp_pr_b[i])
for i in range(len(probab_exp_pr_b))
])
exp_pr_lbl += np.array(exp_pr_b).tolist()
exp_gt_lbl += np.array(exp_gt_b).tolist()
batch_index += 1
exp_pr_lbl = np.float64(np.array(exp_pr_lbl))
exp_gt_lbl = np.float64(np.array(exp_gt_lbl))
global_accuracy = accuracy_score(exp_gt_lbl, exp_pr_lbl)
conf_mat = confusion_matrix(exp_gt_lbl, exp_pr_lbl)
# conf_mat = tf.math.confusion_matrix(exp_gt_lbl, exp_pr_lbl, num_classes=7)
ds = None
face_img_filenames = None
eyes_img_filenames = None
nose_img_filenames = None
mouth_img_filenames = None
exp_filenames = None
return global_accuracy, conf_mat
