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Allow tecogan to be used in process_video

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This commit is contained in:
James Betker 2020-10-09 19:21:43 -06:00
parent 58d8bf8f69
commit 7e777ea34c
4 changed files with 59 additions and 14 deletions
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19
codes/data_scripts/use_discriminator_as_filter.py
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@ -61,19 +61,20 @@ if __name__ == "__main__":
tq = tqdm(test_loader)
removed = 0
means = []
dataset_mean = -7.133
for data in tq:
model.feed_data(data, need_GT=True)
model.test()
results = model.eval_state['discriminator_out'][0]
print(torch.mean(results), torch.max(results), torch.min(results))
means.append(torch.mean(results).item())
print(sum(means)/len(means), torch.mean(results), torch.max(results), torch.min(results))
for i in range(results.shape[0]):
if results[i] < .8:
os.remove(data['GT_path'][i])
removed += 1
#imname = osp.basename(data['GT_path'][i])
#if results[i] > .8:
# torchvision.utils.save_image(data['GT'][i], osp.join(good_path, imname))
#else:
# torchvision.utils.save_image(data['GT'][i], osp.join(bin_path, imname))
#if results[i] < .8:
# os.remove(data['GT_path'][i])
# removed += 1
imname = osp.basename(data['GT_path'][i])
if results[i]-dataset_mean > 1:
torchvision.utils.save_image(data['GT'][i], osp.join(bin_path, imname))
print("Removed %i/%i images" % (removed, len(test_set)))

5
codes/models/ExtensibleTrainer.py
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@ -30,8 +30,9 @@ class ExtensibleTrainer(BaseModel):
self.env = {'device': self.device,
'rank': self.rank,
'opt': opt,
'step': 0,
'base_path': os.path.join(opt['path']['models'])}
'step': 0}
if opt['path']['models'] is not None:
self.env['base_path'] = os.path.join(opt['path']['models'])
self.mega_batch_factor = 1
if self.is_train:

19
codes/models/steps/tecogan_losses.py
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@ -21,6 +21,8 @@ def create_teco_injector(opt, env):
type = opt['type']
if type == 'teco_recurrent_generated_sequence_injector':
return RecurrentImageGeneratorSequenceInjector(opt, env)
elif type == 'teco_flow_adjustment':
return FlowAdjustment(opt, env)
return None
def create_teco_discriminator_sextuplet(input_list, lr_imgs, scale, index, flow_gen, resampler, margin):
@ -132,6 +134,23 @@ class RecurrentImageGeneratorSequenceInjector(Injector):
torchvision.utils.save_image(gen_input[:, 3:], osp.join(base_path, "%s_recurrent.png" % (it,)))
class FlowAdjustment(Injector):
def __init__(self, opt, env):
super(FlowAdjustment, self).__init__(opt, env)
self.resample = Resample2d()
self.flow = opt['flow_network']
self.flow_target = opt['flow_target']
self.flowed = opt['flowed']
def forward(self, state):
flow = self.env['generators'][self.flow]
flow_target = state[self.flow_target]
flowed = state[self.flowed]
flow_input = torch.stack([flow_target, flowed], dim=2)
flowfield = flow(flow_input)
return {self.output: self.resample(flowed.float(), flowfield.float())}
# This is the temporal discriminator loss from TecoGAN.
#
# It has a strict contract for 'real' and 'fake' inputs:

30
codes/process_video.py
View File

@ -28,6 +28,7 @@ class FfmpegBackedVideoDataset(data.Dataset):
self.frame_rate = self.opt['frame_rate']
self.start_at = self.opt['start_at_seconds']
self.end_at = self.opt['end_at_seconds']
self.force_multiple = self.opt['force_multiple']
self.frame_count = (self.end_at - self.start_at) * self.frame_rate
# The number of (original) video frames that will be stored on the filesystem at a time.
self.max_working_files = 20
@ -69,6 +70,18 @@ class FfmpegBackedVideoDataset(data.Dataset):
mask = torch.ones(1, img_LQ.shape[1], img_LQ.shape[2])
ref = torch.cat([img_LQ, mask], dim=0)
if self.force_multiple > 1:
assert self.vertical_splits <= 1 # This is not compatible with vertical splits for now.
_, h, w = img_LQ.shape
height_removed = h % self.force_multiple
width_removed = w % self.force_multiple
if height_removed != 0:
img_LQ = img_LQ[:, :-height_removed, :]
ref = ref[:, :-height_removed, :]
if width_removed != 0:
img_LQ = img_LQ[:, :, :-width_removed]
ref = ref[:, :, :-width_removed]
return {'LQ': img_LQ, 'lq_fullsize_ref': ref,
'lq_center': torch.tensor([img_LQ.shape[1] // 2, img_LQ.shape[2] // 2], dtype=torch.long) }
@ -128,18 +141,30 @@ if __name__ == "__main__":
vid_output = opt['mini_vid_output_folder'] if 'mini_vid_output_folder' in opt.keys() else dataset_dir
vid_counter = opt['minivid_start_no'] if 'minivid_start_no' in opt.keys() else 0
img_index = opt['generator_img_index']
recurrent_mode = opt['recurrent_mode']
first_frame = True
ffmpeg_proc = None
tq = tqdm(test_loader)
for data in tq:
need_GT = False if test_loader.dataset.opt['dataroot_GT'] is None else True
if recurrent_mode and first_frame:
recurrent_entry = data['LQ'].detach().clone()
first_frame = False
if recurrent_mode:
data['recurrent'] = recurrent_entry
model.feed_data(data, need_GT=need_GT)
model.test()
if isinstance(model.fake_H, tuple):
visuals = model.fake_H[img_index].detach().float().cpu()
visuals = model.fake_H[img_index].detach()
else:
visuals = model.fake_H.detach().float().cpu()
visuals = model.fake_H.detach()
if recurrent_mode:
recurrent_entry = torch.nn.functional.interpolate(visuals, scale_factor=1/opt['scale'], mode='bicubic')
visuals = visuals.cpu().float()
for i in range(visuals.shape[0]):
sr_img = util.tensor2img(visuals[i]) # uint8
@ -148,7 +173,6 @@ if __name__ == "__main__":
util.save_img(sr_img, save_img_path)
frame_counter += 1
if frame_counter % frames_per_vid == 0:
if ffmpeg_proc is not None:
print("Waiting for last encode..")