Merge remote-tracking branch 'origin/gan_lab' into gan_lab
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commit
54accfa693
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@ -97,9 +97,15 @@ class RecurrentImageGeneratorSequenceInjector(Injector):
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else:
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input = extract_inputs_index(inputs, i)
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with torch.no_grad() and autocast(enabled=False):
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reduced_recurrent = F.interpolate(recurrent_input, scale_factor=1/self.scale, mode='bicubic')
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flow_input = torch.stack([input[self.input_lq_index], reduced_recurrent], dim=2).float()
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flowfield = F.interpolate(flow(flow_input), scale_factor=self.scale, mode='bicubic')
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# This is a hack to workaround the fact that flownet2 cannot operate at resolutions < 64px. An assumption is
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# made here that if you are operating at 4x scale, your inputs are 32px x 32px
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if self.scale >= 4:
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flow_input = F.interpolate(input[self.input_lq_index], scale_factor=self.scale//2, mode='bicubic')
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else:
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flow_input = input[self.input_lq_index]
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reduced_recurrent = F.interpolate(recurrent_input, scale_factor=.5, mode='bicubic')
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flow_input = torch.stack([flow_input, reduced_recurrent], dim=2).float()
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flowfield = F.interpolate(flow(flow_input), scale_factor=2, mode='bicubic')
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recurrent_input = self.resample(recurrent_input.float(), flowfield)
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input[self.recurrent_index] = recurrent_input
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if self.env['step'] % 50 == 0:
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@ -122,9 +128,15 @@ class RecurrentImageGeneratorSequenceInjector(Injector):
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input = extract_inputs_index(inputs, i)
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with torch.no_grad():
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with autocast(enabled=False):
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reduced_recurrent = F.interpolate(recurrent_input, scale_factor=1 / self.scale, mode='bicubic')
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flow_input = torch.stack([input[self.input_lq_index], reduced_recurrent], dim=2).float()
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flowfield = F.interpolate(flow(flow_input), scale_factor=self.scale, mode='bicubic')
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# This is a hack to workaround the fact that flownet2 cannot operate at resolutions < 64px. An assumption is
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# made here that if you are operating at 4x scale, your inputs are 32px x 32px
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if self.scale >= 4:
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flow_input = F.interpolate(input[self.input_lq_index], scale_factor=self.scale//2, mode='bicubic')
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else:
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flow_input = input[self.input_lq_index]
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reduced_recurrent = F.interpolate(recurrent_input, scale_factor=.5, mode='bicubic')
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flow_input = torch.stack([flow_input, reduced_recurrent], dim=2).float()
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flowfield = F.interpolate(flow(flow_input), scale_factor=2, mode='bicubic')
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recurrent_input = self.resample(recurrent_input.float(), flowfield)
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input[self.recurrent_index] = recurrent_input
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if self.env['step'] % 50 == 0:
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@ -42,14 +42,11 @@ def main(master_opt, launcher):
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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#parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_imgset_chained_structured_trans_invariance.yml')
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_imgset_chained_structured_trans_invariance.yml')
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
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args = parser.parse_args()
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Loader, Dumper = OrderedYaml()
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with open(args.opt, mode='r') as f:
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opt = yaml.load(f, Loader=Loader)
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opt = {
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'trainer_options': ['../options/teco.yml', '../options/exd.yml']
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}
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main(opt, args.launcher)
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main(opt, args.launcher)
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