74 lines
3.8 KiB
Python
74 lines
3.8 KiB
Python
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from models.steps.losses import ConfigurableLoss, GANLoss, extract_params_from_state
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from models.layers.resample2d_package.resample2d import Resample2d
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import torch
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from apex import amp
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def create_teco_discriminator_sextuplet(input_list, index, flow_gen, resampler, detach=True):
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triplet = input_list[index:index+3]
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first_flow = flow_gen(triplet[1], triplet[0])
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last_flow = flow_gen(triplet[1], triplet[2])
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if detach:
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first_flow = first_flow.detach()
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last_flow = last_flow.detach()
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flow_triplet = [resampler(triplet[0], first_flow), triplet[1], resampler(triplet[2], last_flow)]
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return torch.cat(triplet + flow_triplet, dim=1)
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# This is the temporal discriminator loss from TecoGAN.
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#
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# It has a strict contact for 'real' and 'fake' inputs:
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# 'real' - Must be a list of arbitrary images (len>3) drawn from the dataset
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# 'fake' - The output of the RecurrentImageGeneratorSequenceInjector for the same set of images.
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#
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# This loss does the following:
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# 1) Picks an image triplet, starting with the first '3' elements in 'real' and 'fake'.
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# 2) Uses the image flow generator (specified with 'image_flow_generator') to create detached flow fields for the first and last images in the above sequence.
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# 3) Warps the first and last images according to the flow field.
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# 4) Composes the three base image and the 2 warped images and middle image into a tensor concatenated at the filter dimension for both real and fake, resulting in a bx18xhxw shape tensor.
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# 5) Feeds the catted real and fake image sets into the discriminator, computes a loss, and backward().
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# 6) Repeat from (1) until all triplets from the real sequence have been exhausted.
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#
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# Note: All steps before 'discriminator_flow_after' do not use triplets. Instead, they use a single image repeated 6 times across the filter dimension.
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class TecoGanDiscriminatorLoss(ConfigurableLoss):
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def __init__(self, opt, env):
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super(TecoGanDiscriminatorLoss, self).__init__(opt, env)
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self.opt = opt
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self.criterion = GANLoss(opt['gan_type'], 1.0, 0.0).to(env['device'])
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self.discriminator_flow_after = opt['discriminator_flow_after']
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self.image_flow_generator = opt['image_flow_generator']
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self.resampler = Resample2d()
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def forward(self, net, state):
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self.metrics = []
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flow_gen = self.env['generators'][self.image_flow_generator]
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real = state[self.opt['real']]
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fake = state[self.opt['fake']]
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backwards_count = range(len(real)-2)
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for i in range(len(real) - 2):
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if self.env['']
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real_sext = create_teco_discriminator_sextuplet(real, i, flow_gen, self.resampler)
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fake_sext = create_teco_discriminator_sextuplet(fake, i, flow_gen, self.resampler)
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d_real = net(real_sext)
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d_fake = net(fake_sext)
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if self.opt['gan_type'] in ['gan', 'pixgan']:
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self.metrics.append(("d_fake", torch.mean(d_fake)))
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self.metrics.append(("d_real", torch.mean(d_real)))
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l_real = self.criterion(d_real, True)
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l_fake = self.criterion(d_fake, False)
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l_total = l_real + l_fake
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elif self.opt['gan_type'] == 'ragan':
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d_fake_diff = d_fake - torch.mean(d_real)
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self.metrics.append(("d_fake_diff", torch.mean(d_fake_diff)))
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l_total = (self.criterion(d_real - torch.mean(d_fake), True) +
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self.criterion(d_fake_diff, False))
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else:
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raise NotImplementedError
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l_total = l_total / backwards_count
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if self.env['amp']:
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with amp.scale_loss(l_total, self.env['current_step_optimizers'][0], self.env['amp_loss_id']) as loss:
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loss.backward()
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else:
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l_total.backward()
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