def train(self):
start_time = time()
pp('Start training')
pp({
'train': self.train_data_loader.dataset,
'validation': self.val_data_loader.dataset,
'test': self.test_data_loader.dataset
})
pp(vars(self.args))
# TODO(lyj):
for self.cur_epoch in range(self.args.epochs):
self.train_epoch()
v_b_i = self.results["val"].argmax()
t_b_i = self.results["test"].argmax()
test_best = self.results["test"].max()
test_select = self.results["test"][v_b_i]
# print("Best val %g, corresponding test %g - best test: %g" % (val_res.max(), test_res[idx_best], test_res.max()))
temp_dict = {
'now':
strftime("%Y-%m-%d %H:%M:%S", localtime()),
'source':
self.args.source,
'target':
self.args.target,
'param':
self.args.parameters,
'bs':
self.args.batch_size,
'lr':
self.args.learning_rate,
'Highest accuracy on validation set appears on epoch':
t_b_i.item(),
'Highest accuracy on test set appears on epoch':
v_b_i.item(),
'Accuracy on test set when the accuracy on validation set is highest':
test_select.item(),
'Highest accuracy on test set':
test_best.item(),
'duration':
time() - start_time
}
pp(temp_dict)
self.writer.w(temp_dict)
def train_epoch(self):
self.scheduler.step()
lrs = self.scheduler.get_lr()
criterion = nn.CrossEntropyLoss()
# Set the mode of the model to trainer, then the parameters can begin to be trained
self.model.train()
for i, (data, n, c_l) in enumerate(self.train_data_loader):
data, n, c_l = data.to(self.device), n.to(self.device), c_l.to(
self.device)
self.optimizer.zero_grad()
n_logit, c_l_logit = self.model(data) # , lambda_val=lambda_val)
usv_loss = criterion(n_logit, n)
sv_loss = criterion(c_l_logit[n == 0], c_l[n == 0])
_, c_l_pred = c_l_logit.max(dim=1)
_, usv_pred = n_logit.max(dim=1)
# _, domain_pred = domain_logit.max(dim=1)
loss = sv_loss + usv_loss * self.args.usvt_weight
loss.backward()
self.optimizer.step()
# record and print
acc_class = torch.sum(c_l_pred == c_l).item() / data.shape[0]
acc_u = torch.sum(usv_pred == n).item() / data.shape[0]
if i == 0:
col_n = ceil(
len(self.train_data_loader) / self.collect_per_batch)
print(
f'epoch:{self.cur_epoch}/{self.args.epochs};bs:{data.shape[0]};'
f'lr:{" ".join([str(lr) for lr in lrs])}; '
f'{len(self.train_data_loader)}/{self.collect_per_batch}={col_n}|',
end='')
if self.args.wandb and self.args.nth_repeat == 0 and i % self.collect_per_batch == 0:
print('#', end='')
wandb.log({
'acc/train/sv_task': acc_class,
'acc/train/usv_task': acc_u,
'loss/train/class': sv_loss.item(),
'loss/train/usv_task': usv_loss.item(),
'loss/train/sum': loss.item()
})
if i == len(self.train_data_loader) - 1:
print()
pp([
f'train_acc:u:{acc_class};c:{acc_u}'
f'train_loss:j:{usv_loss.item()};c:{sv_loss.item()}'
])
del loss, sv_loss, usv_loss, n_logit, c_l_logit
# eval
self.model.eval()
with torch.no_grad():
for phase, loader in self.test_loaders.items():
l_acc, _ = Trainer.test(self.model, loader, device=self.device)
pp(f'{phase}_acc:c:{l_acc}')
if self.args.wandb and self.args.nth_repeat == 0:
wandb.log({f'acc/{phase}/sv_task': l_acc})
self.results[phase][self.cur_epoch] = l_acc
def train_epoch(self):
print('******************************')
self.scheduler.step()
lrs = self.scheduler.get_lr()
criterion = nn.CrossEntropyLoss()
# Set the mode of the model to trainer, then the parameters can begin to be trained
for i, (data, n, c_l) in enumerate(self.train_data_loader):
self.model.train()
data, n, c_l = data.to(self.device), n.to(self.device), c_l.to(
self.device)
# for i in range(20):
# print(n[i])
# tf.ToPILImage()(data[i]).show()
# print()
self.optimizer.zero_grad()
n_logit, c_l_logit = self.model(data) # , lambda_val=lambda_val)
# us_loss = criterion(n_logit, n)
## temporary used
c_l_logit = n_logit
# s_loss = criterion(c_l_logit[n == 0], c_l[n == 0])
s_loss = criterion(c_l_logit, c_l)
# s_loss = torch.tensor(-1)
_, c_l_pred = c_l_logit.max(dim=1)
# _, n_pred = n_logit.max(dim=1)
# _, domain_pred = domain_logit.max(dim=1)
# self.model.eval()
n_random = torch.randint(0, 4, (len(data), ))
n_logit2, _ = self.model(data)
activations = self.input_grad.activations['out']
grad = self.input_grad.get_input_gradient(n_logit2,
create_graph=True)
abs_grad = torch.norm(grad)
mask = self.input_grad.get_mask(activations, grad).data
# k = 5
# tf.ToPILImage()(visualize_cam(mask[k], data[k])[0]).show()
# tf.ToPILImage()(visualize_cam(mask[k], data[k])[1]).show()
grad *= 100000
data_r = torch.zeros_like(data)
for j in range(len(data)):
data_r[j] = torch.rot90(data[j], -n_random[j].item(), [1, 2])
n_logit_r, _ = self.model(data_r)
activations_r = self.input_grad.activations['out']
grad_r = self.input_grad.get_input_gradient(n_logit_r,
create_graph=True)
abs_grad_r = torch.norm(grad_r)
mask_r = self.input_grad.get_mask(activations_r, grad_r)
# tf.ToPILImage()(visualize_cam(mask_r[k], data_r[k])[0]).show()
# tf.ToPILImage()(visualize_cam(mask_r[k], data_r[k])[1]).show()
for j in range(len(mask_r)):
mask_r[j] = torch.rot90(mask_r[j], n_random[j].item(), [1, 2])
grad_r_r = torch.zeros_like(grad_r)
for j in range(len(grad_r)):
grad_r_r[j] = torch.rot90(grad_r[j], n_random[j].item(),
[1, 2])
grad_r *= 100000
# grad_ori = grad_ori.reshape(grad_ori.shape[0], -1)
# print(data_ori[0] == data[0])
# print(grad_ori[0] == grad[0])
# self.model.train()
# input_gradient_loss = nn.MSELoss()(grad_r, grad)
mask_loss = nn.MSELoss()(mask_r, mask)
input_gradient_loss = nn.MSELoss()(grad_r, grad_r_r)
# loss = s_loss + us_loss * self.args.usvt_weight
# loss = us_loss * self.args.usvt_weight
# loss = s_loss
# if self.cur_epoch != 0 and 20*input_gradient_loss.item() < s_loss.item():
# loss = s_loss
# else:
# loss = s_loss + 5*input_gradient_loss
# # loss = s_loss
# if abs_grad.item()>0.4 and abs_grad.item() < 1.0:
# loss = s_loss + 5*input_gradient_loss
# else:
# loss = s_loss
loss = mask_loss
# loss = s_loss + 80 * input_gradient_loss
loss.backward()
self.optimizer.step()
# record and print
acc_s = torch.sum(c_l_pred == c_l).item() / data.shape[0]
# acc_s = -1
# acc_u = torch.sum(n_pred == n).item() / data.shape[0]
acc_u = -1
if i == 0:
col_n = ceil(
len(self.train_data_loader) / self.collect_per_batch)
print(
f'epoch:{self.cur_epoch}/{self.args.epochs};bs:{data.shape[0]};'
f'lr:{" ".join([str(lr) for lr in lrs])}; '
f'{len(self.train_data_loader)}/{self.collect_per_batch}={col_n}|',
end='')
if i % self.collect_per_batch == 0:
print('#', end='')
if self.args.wandb:
wandb.log({
'acc/train/sv_task': acc_s,
'acc/train/usv_task': acc_u,
'loss/train/class': s_loss.item(),
# 'loss/train/usv_task': us_loss.item(),
'loss/train/sum': loss.item()
})
if i == len(self.train_data_loader) - 1:
print()
pp(f'@{acc_s}:train_acc:s;{acc_u}:u')
pp(f'train_loss:s:{s_loss.item()};input_gradient_loss:{input_gradient_loss.item()}'
)
pp(f'mask_loss:{mask_loss.item()}')
pp(f'loss:{loss.item()},abs_grad:{abs_grad.item()},abs_grad_r:{abs_grad_r.item()}'
)
# pp(f'train_loss:s:{s_loss.item()};u:{us_loss.item()}')
# pp(f'@{acc_u}:train_acc:u')
# pp(f'train_loss:u:{us_loss.item()}')
# del loss, s_loss, us_loss, n_logit, c_l_logit
torch.cuda.empty_cache()
# eval
self.model.eval()
with torch.no_grad():
for phase, loader in self.test_s_loaders.items():
s_acc, us_acc = Trainer.test(self.model,
loader,
device=self.device)
pp(f'${s_acc}:{phase}_acc')
if self.args.wandb:
wandb.log({f'acc/{phase}': s_acc})
self.results[phase][self.cur_epoch] = s_acc
def train(self):
start_time = time()
pp('Start training')
pp({
'train': self.train_data_loader.dataset,
'validation': self.val_s_data_loader.dataset,
'test': self.test_s_data_loader.dataset
})
pp(vars(self.args))
# TODO(lyj):
for self.cur_epoch in range(self.args.epochs):
self.train_epoch()
r = Result()
r.v_s_b_i = self.results["val_s"].argmax()
r.t_s_b_i = self.results["test_s"].argmax()
r.val_s_best = self.results["val_s"].max()
r.test_s_best = self.results["test_s"].max()
r.test_s_select = self.results["test_s"][r.v_s_b_i]
r.u_when_s = self.results["test_us"][r.v_s_b_i]
r.v_u_b_i = self.results["val_us"].argmax()
r.t_u_b_i = self.results["test_us"].argmax()
r.val_u_best = self.results["val_us"].max()
r.test_u_best = self.results["test_us"].max()
r.test_u_select = self.results["test_us"][r.v_u_b_i]
r.s_when_u = self.results["test_s"][r.v_u_b_i]
r.v_s_means = self.results["val_s"].mean()
r.t_s_means = self.results["test_s"].mean()
r.v_u_means = self.results["val_us"].mean()
r.t_u_means = self.results["test_us"].mean()
r.v_s_std = self.results["val_s"].std()
r.t_s_std = self.results["test_s"].std()
r.v_u_std = self.results["val_us"].std()
r.t_u_std = self.results["test_us"].std()
# print("Best val %g, corresponding test %g - best test: %g" % (val_res.max(), test_res[idx_best], test_res.max()))
temp_dict = {
'now':
strftime("%Y-%m-%d %H:%M:%S", localtime()),
'source':
self.args.source,
'target':
self.args.target,
'param':
self.args.params,
'bs':
self.args.batch_size,
'lr':
self.args.learning_rate,
'Highest accuracy on validation set appears on epoch':
r.t_s_b_i.item(),
'Highest accuracy on test set appears on epoch':
r.v_s_b_i.item(),
'Accuracy on test set when the accuracy on validation set is highest':
r.test_s_select.item(),
'Highest accuracy on test set':
r.test_s_best.item(),
'duration':
time() - start_time
}
pp(temp_dict)
pp(vars(r))
self.writer.w(temp_dict)
if self.args.wandb:
# wandb.log({'r/test_select': test_s_select.item(),
# 'r/val_best': val_s_best.item(),
# 'r/test_best': test_s_best.item(),
# 'r/v_b_i': v_s_b_i,
# 'r/t_bi': t_s_b_i})
wandb.log(vars(r))
# table = wandb.Table(columns=[
# f'{self.args.source[0]}->{self.args.target}-{"-".join([str(_) for _ in self.args.params])}',
# "val_best", "test_best", "test_select"])
# table.add_data("epoch", v_b_i, t_b_i, "#")
# table.add_data("acc", val_best.item(), test_best.item(), test_select.item())
# wandb.log({"summary": table})
torch.cuda.empty_cache()
def train_epoch(self):
print('******************************')
self.scheduler.step()
lrs = self.scheduler.get_lr()
criterion = nn.CrossEntropyLoss()
# Set the mode of the model to trainer, then the parameters can begin to be trained
self.model.train()
for i, (data, n, c_l) in enumerate(self.train_data_loader):
data, n, c_l = data.to(self.device), n.to(self.device), c_l.to(
self.device)
# for i in range(20):
# print(n[i])
# tf.ToPILImage()(data[i]).show()
# print()
self.optimizer.zero_grad()
n_logit, c_l_logit = self.model(data) # , lambda_val=lambda_val)
us_loss = criterion(n_logit, n)
# s_loss = criterion(c_l_logit[n == 0], c_l[n == 0])
s_loss = torch.tensor(-1)
grad = self.input_grad.get_input_gradient(n_logit)
grad *= 100000
# grad = grad.reshape(grad.shape[0], -1)
data_ori = torch.zeros_like(data)
for j in range(len(data)):
data_ori[j] = torch.rot90(data[j], -n[j], [1, 2])
# data_ori = torch.tensor([torch.rot90(data[i], -n[i], [1, 2]) for i in range(len(data))])
n_logit_ori, _ = self.model(data_ori)
grad_ori = self.input_grad.get_input_gradient(n_logit_ori)
for j in range(len(grad_ori)):
grad_ori[j] = torch.rot90(grad_ori[j], n[j], [1, 2])
grad_ori *= 100000
# grad_ori = grad_ori.reshape(grad_ori.shape[0], -1)
input_gradient_loss = nn.MSELoss()(grad_ori, grad)
# _, c_l_pred = c_l_logit.max(dim=1)
_, n_pred = n_logit.max(dim=1)
# _, domain_pred = domain_logit.max(dim=1)
# loss = s_loss + us_loss * self.args.usvt_weight
# loss = us_loss * self.args.usvt_weight
loss = us_loss * self.args.usvt_weight + 0.01 * input_gradient_loss
loss.backward()
self.optimizer.step()
# record and print
# acc_s = torch.sum(c_l_pred == c_l).item() / data.shape[0]
acc_s = -1
acc_u = torch.sum(n_pred == n).item() / data.shape[0]
if i == 0:
col_n = ceil(
len(self.train_data_loader) / self.collect_per_batch)
print(
f'epoch:{self.cur_epoch}/{self.args.epochs};bs:{data.shape[0]};'
f'lr:{" ".join([str(lr) for lr in lrs])}; '
f'{len(self.train_data_loader)}/{self.collect_per_batch}={col_n}|',
end='')
if i % self.collect_per_batch == 0:
print('#', end='')
if self.args.wandb:
wandb.log({
'acc/train/sv_task': acc_s,
'acc/train/usv_task': acc_u,
'loss/train/class': s_loss.item(),
'loss/train/usv_task': us_loss.item(),
'loss/train/sum': loss.item()
})
if i == len(self.train_data_loader) - 1:
print()
# pp(f'train_acc:s:{acc_s};u:{acc_u}')
# pp(f'train_loss:s:{s_loss.item()};u:{us_loss.item()}')
pp(f'@{acc_u}:train_acc:u')
pp(f'train_loss:u:{us_loss.item()}')
pp(f'input_gradient_loss:u:{input_gradient_loss.item()}')
# del loss, s_loss, us_loss, n_logit, c_l_logit
torch.cuda.empty_cache()
# eval
self.model.eval()
with torch.no_grad():
# for phase, loader in self.test_s_loaders.items():
# s_acc, us_acc = Trainer.test(self.model, loader, device=self.device)
# pp(f'{phase}_acc:{s_acc}')
# if self.args.wandb:
# wandb.log({f'acc/{phase}': s_acc})
# self.results[phase][self.cur_epoch] = s_acc
for phase, loader in self.test_us_loaders.items():
s_acc, us_acc = Trainer.test(self.model,
loader,
device=self.device)
pp(f'${us_acc}:{phase}_acc')
if self.args.wandb:
wandb.log({f'acc/{phase}': us_acc})
self.results[phase][self.cur_epoch] = us_acc
