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Ray Wong
2020-09-25 18:31:12 +08:00
parent fbea96f6d7
commit acf243cb12
11 changed files with 542 additions and 115 deletions
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49
engine/base/i2i.py
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@@ -189,34 +189,33 @@ def get_trainer(config, kernel: EngineKernel):
for i in range(len(image_list)):
test_images[k].append([])
with torch.no_grad():
g = torch.Generator()
g.manual_seed(config.misc.random_seed + engine.state.epoch
if config.handler.test.random else config.misc.random_seed)
random_start = \
torch.randperm(len(engine.state.test_dataset) - config.handler.test.images, generator=g).tolist()[0]
for i in range(random_start, random_start + config.handler.test.images):
batch = convert_tensor(engine.state.test_dataset[i], idist.device())
for k in batch:
if isinstance(batch[k], torch.Tensor):
batch[k] = batch[k].unsqueeze(0)
elif isinstance(batch[k], dict):
for kk in batch[k]:
if isinstance(batch[k][kk], torch.Tensor):
batch[k][kk] = batch[k][kk].unsqueeze(0)
g = torch.Generator()
g.manual_seed(config.misc.random_seed + engine.state.epoch
if config.handler.test.random else config.misc.random_seed)
random_start = \
torch.randperm(len(engine.state.test_dataset) - config.handler.test.images, generator=g).tolist()[0]
for i in range(random_start, random_start + config.handler.test.images):
batch = convert_tensor(engine.state.test_dataset[i], idist.device())
for k in batch:
if isinstance(batch[k], torch.Tensor):
batch[k] = batch[k].unsqueeze(0)
elif isinstance(batch[k], dict):
for kk in batch[k]:
if isinstance(batch[k][kk], torch.Tensor):
batch[k][kk] = batch[k][kk].unsqueeze(0)
generated = kernel.forward(batch)
images = kernel.intermediate_images(batch, generated)
generated = kernel.forward(batch, inference=True)
images = kernel.intermediate_images(batch, generated)
for k in test_images:
for j in range(len(images[k])):
test_images[k][j].append(images[k][j])
for k in test_images:
tensorboard_handler.writer.add_image(
f"test/{k}",
make_2d_grid([torch.cat(ti) for ti in test_images[k]], range=(-1, 1)),
engine.state.iteration * pairs_per_iteration
)
for j in range(len(images[k])):
test_images[k][j].append(images[k][j])
for k in test_images:
tensorboard_handler.writer.add_image(
f"test/{k}",
make_2d_grid([torch.cat(ti) for ti in test_images[k]], range=(-1, 1)),
engine.state.iteration * pairs_per_iteration
)
return trainer