def validation_epoch_end(self, outputs):
loss = torch.stack([x["val_loss"] for x in outputs]).mean()
acc = torch.stack([x["val_acc"] for x in outputs]).mean()
result = EvalResult(checkpoint_on=loss)
result.log("val/loss", loss, on_step=False, on_epoch=True)
result.log("val/acc", acc, on_step=False, on_epoch=True)
return result
def training_step(self, batch, idx, val=False):
batch = maybe_combine_frames_and_channels(self.hparams, batch)
# unpack
c = batch["cloth"]
im_c = batch["im_cloth"]
im_g = batch["grid_vis"]
person_inputs = get_and_cat_inputs(batch, self.hparams.person_inputs)
cloth_inputs = get_and_cat_inputs(batch, self.hparams.cloth_inputs)
# forward
grid, theta = self.forward(person_inputs, cloth_inputs)
self.warped_cloth = F.grid_sample(c, grid, padding_mode="border")
self.warped_grid = F.grid_sample(im_g, grid, padding_mode="zeros")
# loss
loss = F.l1_loss(self.warped_cloth, im_c)
# Logging
if not val and self.global_step % self.hparams.display_count == 0:
self.visualize(batch)
val_ = "val_" if val else ""
result = EvalResult(checkpoint_on=loss) if val else TrainResult(loss)
result.log(f"{val_}loss/G", loss, prog_bar=True)
return result
def test_step(self, batch, batch_idx) -> EvalResult:
output = self.forward(**batch)
result = EvalResult(checkpoint_on=output.loss, early_stop_on=output.loss)
result.log(
"test_loss",
output.loss,
prog_bar=True,
)
return result
def validation_step_result_only_epoch_metrics(self, batch, batch_idx):
"""
Only track epoch level metrics
"""
acc = self.step(batch, batch_idx)
result = EvalResult(checkpoint_on=acc, early_stop_on=acc)
# step only metrics
result.log('no_val_no_pbar',
torch.tensor(11 + batch_idx).type_as(acc),
prog_bar=False,
logger=False)
result.log('val_step_log_acc',
torch.tensor(11 + batch_idx).type_as(acc),
prog_bar=False,
logger=True)
result.log('val_step_log_pbar_acc',
torch.tensor(12 + batch_idx).type_as(acc),
prog_bar=True,
logger=True)
result.log('val_step_pbar_acc',
torch.tensor(13 + batch_idx).type_as(acc),
prog_bar=True,
logger=False)
self.validation_step_called = True
return result
def eval_step_full_loop_result_obj_dp(self, batch, batch_idx, optimizer_idx=None):
"""
Full loop flow train step (result obj + dp)
"""
x, y = batch
x = x.view(x.size(0), -1)
y_hat = self(x.to(self.device))
loss_val = y_hat.sum()
result = EvalResult(checkpoint_on=loss_val, early_stop_on=loss_val)
eval_name = 'validation' if not self.trainer.testing else 'test'
result.log(f'{eval_name}_step_metric', loss_val + 1, on_step=True)
setattr(self, f'{eval_name}_step_called', True)
return result
def validation_step_for_epoch_end_result(self, batch, batch_idx):
"""
EvalResult flows to epoch end (without step_end)
"""
acc = self.step(batch, batch_idx)
result = EvalResult(checkpoint_on=acc, early_stop_on=acc)
# step only metrics
result.log('val_step_metric',
torch.tensor(batch_idx).type_as(acc),
prog_bar=True,
logger=True,
on_epoch=True,
on_step=False)
result.log('batch_idx',
torch.tensor(batch_idx).type_as(acc),
prog_bar=True,
logger=True,
on_epoch=True,
on_step=False)
self.validation_step_called = True
return result
def test_step_result_preds(self, batch, batch_idx, optimizer_idx=None):
x, y = batch
x = x.view(x.size(0), -1)
y_hat = self(x)
loss_test = self.loss(y, y_hat)
# acc
labels_hat = torch.argmax(y_hat, dim=1)
test_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
test_acc = torch.tensor(test_acc)
test_acc = test_acc.type_as(x)
# Do regular EvalResult Logging
result = EvalResult(checkpoint_on=loss_test)
result.log('test_loss', loss_test)
result.log('test_acc', test_acc)
batch_size = x.size(0)
lst_of_str = [random.choice(['dog', 'cat']) for i in range(batch_size)]
lst_of_int = [random.randint(500, 1000) for i in range(batch_size)]
lst_of_lst = [[x] for x in lst_of_int]
lst_of_dict = [{k: v} for k, v in zip(lst_of_str, lst_of_int)]
# This is passed in from pytest via parameterization
option = getattr(self, 'test_option', 0)
prediction_file = getattr(self, 'prediction_file', 'predictions.pt')
lazy_ids = torch.arange(batch_idx * self.batch_size,
batch_idx * self.batch_size + x.size(0))
# Base
if option == 0:
self.write_prediction('idxs', lazy_ids, prediction_file)
self.write_prediction('preds', labels_hat, prediction_file)
# Check mismatching tensor len
elif option == 1:
self.write_prediction('idxs', torch.cat((lazy_ids, lazy_ids)),
prediction_file)
self.write_prediction('preds', labels_hat, prediction_file)
# write multi-dimension
elif option == 2:
self.write_prediction('idxs', lazy_ids, prediction_file)
self.write_prediction('preds', labels_hat, prediction_file)
self.write_prediction('x', x, prediction_file)
# write str list
elif option == 3:
self.write_prediction('idxs', lazy_ids, prediction_file)
self.write_prediction('vals', lst_of_str, prediction_file)
# write int list
elif option == 4:
self.write_prediction('idxs', lazy_ids, prediction_file)
self.write_prediction('vals', lst_of_int, prediction_file)
# write nested list
elif option == 5:
self.write_prediction('idxs', lazy_ids, prediction_file)
self.write_prediction('vals', lst_of_lst, prediction_file)
# write dict list
elif option == 6:
self.write_prediction('idxs', lazy_ids, prediction_file)
self.write_prediction('vals', lst_of_dict, prediction_file)
elif option == 7:
self.write_prediction_dict({
'idxs': lazy_ids,
'preds': labels_hat
}, prediction_file)
return result
def training_step(self, batch, batch_idx, val=False):
batch = maybe_combine_frames_and_channels(self.hparams, batch)
# unpack
im = batch["image"]
prev_im = batch["prev_image"]
cm = batch["cloth_mask"]
flow = batch["flow"] if self.hparams.flow_warp else None
person_inputs = get_and_cat_inputs(batch, self.hparams.person_inputs)
cloth_inputs = get_and_cat_inputs(batch, self.hparams.cloth_inputs)
# forward. save outputs to self for visualization
(
self.p_rendereds,
self.tryon_masks,
self.p_tryons,
self.flow_masks,
) = self.forward(person_inputs, cloth_inputs, flow, prev_im)
self.p_tryons = torch.chunk(self.p_tryons,
self.hparams.n_frames_total,
dim=1)
self.p_rendereds = torch.chunk(self.p_rendereds,
self.hparams.n_frames_total,
dim=1)
self.tryon_masks = torch.chunk(self.tryon_masks,
self.hparams.n_frames_total,
dim=1)
self.flow_masks = (torch.chunk(
self.flow_masks, self.hparams.n_frames_total, dim=1)
if self.flow_masks is not None else None)
im = torch.chunk(im, self.hparams.n_frames_total, dim=1)
cm = torch.chunk(cm, self.hparams.n_frames_total, dim=1)
# loss
loss_image_l1_curr = F.l1_loss(self.p_tryons[-1], im[-1])
loss_image_l1_prev = F.l1_loss(
self.p_tryons[-2],
im[-2]) if self.hparams.n_frames_total > 1 else torch.zeros_like(
loss_image_l1_curr)
loss_image_l1 = 0.5 * (
loss_image_l1_curr + loss_image_l1_prev
) if self.hparams.n_frames_total > 1 else loss_image_l1_curr
loss_image_vgg_curr = self.criterionVGG(self.p_tryons[-1], im[-1])
loss_image_vgg_prev = self.criterionVGG(
self.p_tryons[-2],
im[-2]) if self.hparams.n_frames_total > 1 else torch.zeros_like(
loss_image_vgg_curr)
loss_image_vgg = 0.5 * (
loss_image_vgg_curr + loss_image_vgg_prev
) if self.hparams.n_frames_total > 1 else loss_image_vgg_curr
loss_tryon_mask_curr = F.l1_loss(self.tryon_masks[-1], cm[-1])
loss_tryon_mask_prev = F.l1_loss(
self.tryon_masks[-2],
cm[-2]) if self.hparams.n_frames_total > 1 else torch.zeros_like(
loss_tryon_mask_curr)
loss_tryon_mask_l1 = 0.5 * (
loss_tryon_mask_curr + loss_tryon_mask_prev
) if self.hparams.n_frames_total > 1 else loss_tryon_mask_curr
loss_flow_mask_l1 = (
self.flow_masks[-1].sum() if self.flow_masks is not None else torch
.zeros_like(loss_tryon_mask_curr)) * self.hparams.pen_flow_mask
loss = loss_image_l1 + loss_image_vgg + loss_tryon_mask_l1 + loss_flow_mask_l1
# logging
if not val and self.global_step % self.hparams.display_count == 0:
self.visualize(batch)
val_ = "val_" if val else ""
result = EvalResult(checkpoint_on=loss) if val else TrainResult(loss)
result.log(f"{val_}loss/G", loss, prog_bar=True)
result.log(f"{val_}loss/G/l1", loss_image_l1, prog_bar=True)
result.log(f"{val_}loss/G/vgg", loss_image_vgg, prog_bar=True)
result.log(f"{val_}loss/G/tryon_mask_l1",
loss_tryon_mask_l1,
prog_bar=True)
result.log(f"{val_}loss/G/flow_mask_l1",
loss_flow_mask_l1,
prog_bar=True)
if self.hparams.n_frames_total > 1:
## visualize prev frames losses
result.log(f"{val_}loss/G/l1_prev", loss_image_l1_prev)
result.log(f"{val_}loss/G/vgg_prev", loss_image_vgg_prev)
result.log(f"{val_}loss/G/tryon_mask_prev", loss_tryon_mask_prev)
## visualize curr frames losses
result.log(f"{val_}loss/G/l1_curr", loss_image_l1_curr)
result.log(f"{val_}loss/G/vgg_curr", loss_image_vgg_curr)
result.log(f"{val_}loss/G/tryon_mask_curr", loss_tryon_mask_curr)
#from IPython import embed; embed()
return result