val_loader = DataLoader(dset,
batch_size=batch_size,
sampler=val_subsampler,
collate_fn=dset_og.custom_collate_fn)
num_data = 0
for i, (imgs, anns) in enumerate(train_loader):
num_data += len(imgs)
num_train_batches += 1
print(f'[{e + 1}/{num_epochs}] ', end='')
print(f'({fold + 1}/{num_split}) ', end='')
print(f'{num_data}/{int(len(dset) * (1 - 1 / num_split))} ',
end='')
x = make_batch(imgs).to(device)
y_age = make_batch(anns, 'age_categorical').to(device)
y_gender = make_batch(anns, 'gender_categorical').to(device)
pred_age, pred_gender = model(x)
loss_age = custom_weighted_focal_loss(pred_age, y_age,
weight_factor_age, 2)
loss_gender = custom_focal_loss(pred_gender, y_gender, 2)
loss = loss_age + loss_gender
optimizer.zero_grad()
loss_age.backward()
optimizer.step()
loss = loss.detach().cpu().item()
num_data = 0
num_batches = 0
train_loss = 0
train_acc_age = 0
train_acc_gender = 0
t_train_start = time.time()
model.train()
for i, (imgs, anns) in enumerate(train_loader):
num_data += len(imgs)
num_batches += 1
print('[{}/{}] '.format(e + 1, num_epochs), end='')
print('{}/{} '.format(num_data, n_train_data), end='')
x = make_batch(imgs).to(device)
# y_gender = make_batch(anns, 'gender').to(device)
# y_age = make_batch(anns, 'age').to(device)
y = make_batch(anns).to(device)
# pred_age, pred_gender = model(x)
# loss_age = custom_softmax_cross_entropy_loss(pred_age, y_age)
# loss_gender = custom_softmax_cross_entropy_loss(pred_gender, y_gender)
predict = model(x)
loss = loss_func(predict, y, 5, .5)
# loss = loss_age + loss_gender
optimizer.zero_grad()
loss.backward()
optimizer.step()
batch_size=batch_size,
sampler=val_subsampler,
collate_fn=custom_collate_fn)
num_datas = 0
t_train_start = time.time()
for i, (imgs, anns) in enumerate(train_loader):
t_batch_start = time.time()
num_train_batches += 1
num_datas += len(imgs)
print(f'[{e + 1}/{num_epochs}] ', end='')
print(f'({fold + 1}/{n_split} FOLD) ', end='')
print(f'{num_datas}/{int(len(dset) * (1 - 1 / n_split))} ',
end='')
x = make_batch(imgs).to(device)
predict_temp = model(x)
predict_list = []
y_list = []
for b in range(len(anns)):
ground_truth_box = anns[b]['bbox']
label_categorical = anns[b]['label_categorical']
# print(f'{label_int} {label_categorical} {name} {fn} {ground_truth_box / 32}')
h_img, w_img = 416, 416
ratio_y, ratio_x = 1 / 32, 1 / 32
ground_truth_box = torch.as_tensor(ground_truth_box)
if len(ground_truth_box.shape) < 2:
t_start = time.time()
for e in range(num_epochs):
loss_sum = 0
emr_sum = 0
num_batches = 0
model.train()
t_train_start = time.time()
for i, (images, labels) in enumerate(train_loader):
print('[{}/{}] '.format(e + 1, num_epochs), end='')
print('{}/{} '.format((i + 1) * len(images), len(dset_train)),
end='')
num_batches += 1
x = make_batch(images).to(device)
y = make_batch(labels, 'label').to(device)
predict = model(x)
predict = torch.nn.Sigmoid()(predict)
optimizer.zero_grad()
loss = loss_func(predict, y)
loss.backward()
optimizer.step()
predict = (predict > .5)
emr = exact_match_ratio(predict, y)
loss_sum += loss.item()
emr_sum += emr