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Tecogan work

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Its training!  There's still probably plenty of bugs though..
This commit is contained in:
James Betker 2020-10-07 09:03:30 -06:00
parent e9d7371a61
commit 1c44d395af
3 changed files with 119 additions and 77 deletions
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14
codes/models/networks.py
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@ -1,3 +1,4 @@
import munch
import torch
import logging
from munch import munchify
@ -14,7 +15,6 @@ import models.archs.rcan as rcan
from collections import OrderedDict
import torchvision
import functools
from models.flownet2.models import FlowNet2
logger = logging.getLogger('base')
@ -86,20 +86,24 @@ def define_G(opt, net_key='network_G', scale=None):
init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
elif which_model == 'stacked_switches':
xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
netG = ssg.StackedSwitchGenerator(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
in_nc = opt_net['in_nc'] if 'in_nc' in opt_net.keys() else 3
netG = ssg.StackedSwitchGenerator(in_nc=in_nc, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
elif which_model == 'stacked_switches_5lyr':
xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
netG = ssg.StackedSwitchGenerator5Layer(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
in_nc = opt_net['in_nc'] if 'in_nc' in opt_net.keys() else 3
netG = ssg.StackedSwitchGenerator5Layer(in_nc=in_nc, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
elif which_model == 'ssg_deep':
xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
netG = ssg.SSGDeep(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
elif which_model == "flownet2":
args_dict = {}
args = munchify(args_dict)
from models.flownet2.models import FlowNet2
ld = torch.load(opt_net['load_path'])
args = munch.Munch({'fp16': False, 'rgb_max': 1.0})
netG = FlowNet2(args)
netG.load_state_dict(ld['state_dict'])
elif which_model == "backbone_encoder":
netG = SwitchedGen_arch.BackboneEncoder(pretrained_backbone=opt_net['pretrained_spinenet'])
elif which_model == "backbone_encoder_no_ref":

24
codes/models/steps/losses.py
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@ -28,6 +28,8 @@ def create_loss(opt_loss, env):
return TranslationInvarianceLoss(opt_loss, env)
elif type == 'recursive':
return RecursiveInvarianceLoss(opt_loss, env)
elif type == 'recurrent':
return RecurrentLoss(opt_loss, env)
else:
raise NotImplementedError
@ -372,3 +374,25 @@ class RecursiveInvarianceLoss(ConfigurableLoss):
else:
return self.criterion(compare_real, compare_fake)
# Loss that pulls tensors from dim 1 of the input and repeatedly feeds them into the
# 'subtype' loss.
class RecurrentLoss(ConfigurableLoss):
def __init__(self, opt, env):
super(RecurrentLoss, self).__init__(opt, env)
o = opt.copy()
o['type'] = opt['subtype']
o['fake'] = '_fake'
o['real'] = '_real'
self.loss = create_loss(o, self.env)
def forward(self, net, state):
total_loss = 0
st = state.copy()
real = state[self.opt['real']]
for i in range(real.shape[1]):
st['_real'] = real[:, i]
st['_fake'] = state[self.opt['fake']][i]
total_loss += self.loss(net, st)
return total_loss

158
codes/models/steps/tecogan_losses.py
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@ -1,29 +1,52 @@
from models.steps.losses import ConfigurableLoss, GANLoss, extract_params_from_state
from models.steps.losses import ConfigurableLoss, GANLoss, extract_params_from_state, get_basic_criterion_for_name
from models.layers.resample2d_package.resample2d import Resample2d
from models.steps.recurrent import RecurrentController
from models.steps.injectors import Injector
import torch
import torch.nn.functional as F
import os
import os.path as osp
import torchvision
def create_teco_loss(opt, env):
type = opt['type']
if type == 'teco_generator_gan':
return TecoGanGeneratorLoss(opt, env)
elif type == 'teco_discriminator_gan':
return TecoGanDiscriminatorLoss(opt, env)
if type == 'teco_gan':
return TecoGanLoss(opt, env)
elif type == "teco_pingpong":
return PingPongLoss(opt, env)
return None
def create_teco_discriminator_sextuplet(input_list, index, flow_gen, resampler):
triplet = input_list[index:index+3]
first_flow = flow_gen(triplet[0], triplet[1])
last_flow = flow_gen(triplet[2], triplet[1])
flow_triplet = [resampler(triplet[0], first_flow), triplet[1], resampler(triplet[2], last_flow)]
return torch.cat(triplet + flow_triplet, dim=1)
def create_teco_injector(opt, env):
type = opt['type']
if type == 'teco_recurrent_generated_sequence_injector':
return RecurrentImageGeneratorSequenceInjector(opt, env)
return None
def create_teco_discriminator_sextuplet(input_list, lr_imgs, scale, index, flow_gen, resampler):
triplet = input_list[:, index:index+3]
# Flow is interpreted from the LR images so that the generator cannot learn to manipulate it.
with torch.no_grad():
first_flow = flow_gen(torch.stack([lr_imgs[:,0], lr_imgs[:,1]], dim=2))
first_flow = F.interpolate(first_flow, scale_factor=scale, mode='bicubic')
last_flow = flow_gen(torch.stack([lr_imgs[:,2], lr_imgs[:,1]], dim=2))
last_flow = F.interpolate(last_flow, scale_factor=scale, mode='bicubic')
flow_triplet = [resampler(triplet[:,0].float(), first_flow.float()),
triplet[:,1],
resampler(triplet[:,2].float(), last_flow.float())]
flow_triplet = torch.stack(flow_triplet, dim=2)
combined = torch.cat([triplet, flow_triplet], dim=2)
b, f, c, h, w = combined.shape
return combined.view(b, 3*6, h, w) # 3*6 is essentially an assertion here.
def extract_inputs_index(inputs, i):
res = []
for input in inputs:
if isinstance(input, torch.Tensor):
res.append(input[:, i])
else:
res.append(input)
return res
# Uses a generator to synthesize a sequence of images from [in] and injects the results into a list [out]
# Images are fed in sequentially forward and back, resulting in len([out])=2*len([in])-1 (last element is not repeated).
@ -32,32 +55,51 @@ class RecurrentImageGeneratorSequenceInjector(Injector):
def __init__(self, opt, env):
super(RecurrentImageGeneratorSequenceInjector, self).__init__(opt, env)
self.flow = opt['flow_network']
self.input_lq_index = opt['input_lq_index'] if 'input_lq_index' in opt.keys() else 0
self.output_hq_index = opt['output_hq_index'] if 'output_hq_index' in opt.keys() else 0
self.scale = opt['scale']
self.resample = Resample2d()
def forward(self, state):
gen = self.env['generators'][self.opt['generator']]
flow = self.env['generators'][self.flow]
results = []
recurrent_input = torch.zeros_like(state[self.input][0])
inputs = extract_params_from_state(self.input, state)
if not isinstance(inputs, list):
inputs = [inputs]
recurrent_input = torch.zeros_like(inputs[self.input_lq_index][:,0])
# Go forward in the sequence first.
first_step = True
for input in state[self.input]:
b, f, c, h, w = inputs[self.input_lq_index].shape
for i in range(f):
input = extract_inputs_index(inputs, i)
if first_step:
first_step = False
else:
flowfield = flow(recurrent_input, input)
recurrent_input = self.resample(recurrent_input, flowfield)
recurrent_input = gen(input, recurrent_input)
with torch.no_grad():
reduced_recurrent = F.interpolate(recurrent_input, scale_factor=1/self.scale, mode='bicubic')
flow_input = torch.stack([input[self.input_lq_index], reduced_recurrent], dim=2)
flowfield = flow(flow_input)
# Resample does not work in FP16.
recurrent_input = self.resample(reduced_recurrent.float(), flowfield.float())
input[self.input_lq_index] = torch.cat([input[self.input_lq_index], recurrent_input], dim=1)
gen_out = gen(*input)
recurrent_input = gen_out[self.output_hq_index]
results.append(recurrent_input)
recurrent_input = self.flow()
# Now go backwards, skipping the last element (it's already stored in recurrent_input)
it = reversed(range(len(results) - 1))
it = reversed(range(f - 1))
for i in it:
flowfield = flow(recurrent_input, results[i])
recurrent_input = self.resample(recurrent_input, flowfield)
recurrent_input = gen(results[i], recurrent_input)
input = extract_inputs_index(inputs, i)
with torch.no_grad():
reduced_recurrent = F.interpolate(recurrent_input, scale_factor=1 / self.scale, mode='bicubic')
flow_input = torch.stack([input[self.input_lq_index], reduced_recurrent], dim=2)
flowfield = flow(flow_input)
recurrent_input = self.resample(reduced_recurrent.float(), flowfield.float())
input[self.input_lq_index] = torch.cat([input[self.input_lq_index], recurrent_input], dim=1)
gen_out = gen(*input)
recurrent_input = gen_out[self.output_hq_index]
results.append(recurrent_input)
return {self.output: results}
@ -76,76 +118,48 @@ class RecurrentImageGeneratorSequenceInjector(Injector):
# 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.
# 5) Feeds the catted real and fake image sets into the discriminator, computes a loss, and backward().
# 6) Repeat from (1) until all triplets from the real sequence have been exhausted.
class TecoGanDiscriminatorLoss(ConfigurableLoss):
class TecoGanLoss(ConfigurableLoss):
def __init__(self, opt, env):
super(TecoGanDiscriminatorLoss, self).__init__(opt, env)
self.opt = opt
self.criterion = GANLoss(opt['gan_type'], 1.0, 0.0).to(env['device'])
self.noise = None if 'noise' not in opt.keys() else opt['noise']
self.image_flow_generator = opt['image_flow_generator']
self.resampler = Resample2d()
def forward(self, net, state):
self.metrics = []
flow_gen = self.env['generators'][self.image_flow_generator]
real = state[self.opt['real']]
fake = state[self.opt['fake']]
l_total = 0
for i in range(len(real) - 2):
real_sext = create_teco_discriminator_sextuplet(real, i, flow_gen, self.resampler)
fake_sext = create_teco_discriminator_sextuplet(fake, i, flow_gen, self.resampler)
d_real = net(real_sext)
d_fake = net(fake_sext)
if self.opt['gan_type'] in ['gan', 'pixgan']:
self.metrics.append(("d_fake", torch.mean(d_fake)))
self.metrics.append(("d_real", torch.mean(d_real)))
l_real = self.criterion(d_real, True)
l_fake = self.criterion(d_fake, False)
l_total += l_real + l_fake
elif self.opt['gan_type'] == 'ragan':
d_fake_diff = d_fake - torch.mean(d_real)
self.metrics.append(("d_fake_diff", torch.mean(d_fake_diff)))
l_total += (self.criterion(d_real - torch.mean(d_fake), True) +
self.criterion(d_fake_diff, False))
else:
raise NotImplementedError
return l_total
class TecoGanGeneratorLoss(ConfigurableLoss):
def __init__(self, opt, env):
super(TecoGanGeneratorLoss, self).__init__(opt, env)
super(TecoGanLoss, self).__init__(opt, env)
self.criterion = GANLoss(opt['gan_type'], 1.0, 0.0).to(env['device'])
# TecoGAN parameters
self.scale = opt['scale']
self.lr_inputs = opt['lr_inputs']
self.image_flow_generator = opt['image_flow_generator']
self.resampler = Resample2d()
self.for_generator = opt['for_generator']
def forward(self, _, state):
net = self.env['discriminators'][self.opt['discriminator']]
flow_gen = self.env['generators'][self.image_flow_generator]
real = state[self.opt['real']]
fake = state[self.opt['fake']]
fake = torch.stack(state[self.opt['fake']], dim=1)
sequence_len = real.shape[1]
lr = state[self.opt['lr_inputs']]
l_total = 0
for i in range(len(real) - 2):
real_sext = create_teco_discriminator_sextuplet(real, i, flow_gen, self.resampler)
fake_sext = create_teco_discriminator_sextuplet(fake, i, flow_gen, self.resampler)
for i in range(sequence_len - 2):
real_sext = create_teco_discriminator_sextuplet(real, lr, self.scale, i, flow_gen, self.resampler)
fake_sext = create_teco_discriminator_sextuplet(fake, lr, self.scale, i, flow_gen, self.resampler)
d_fake = net(fake_sext)
if self.env['step'] % 100 == 0:
if self.for_generator and self.env['step'] % 100 == 0:
self.produce_teco_visual_debugs(fake_sext, 'fake', i)
self.produce_teco_visual_debugs(real_sext, 'real', i)
if self.opt['gan_type'] in ['gan', 'pixgan']:
self.metrics.append(("d_fake", torch.mean(d_fake)))
l_fake = self.criterion(d_fake, True)
l_total += l_fake
l_fake = self.criterion(d_fake, self.for_generator)
if not self.for_generator:
l_real = self.criterion(d_real, True)
else:
l_real = 0
l_total += l_fake + l_real
elif self.opt['gan_type'] == 'ragan':
d_real = net(real_sext)
d_fake_diff = d_fake - torch.mean(d_real)
self.metrics.append(("d_fake_diff", torch.mean(d_fake_diff)))
l_total += (self.criterion(d_real - torch.mean(d_fake), False) +
self.criterion(d_fake_diff, True))
l_total += (self.criterion(d_real - torch.mean(d_fake), not self.for_generator) +
self.criterion(d_fake_diff, self.for_generator))
else:
raise NotImplementedError
@ -164,12 +178,12 @@ class PingPongLoss(ConfigurableLoss):
def __init__(self, opt, env):
super(PingPongLoss, self).__init__(opt, env)
self.opt = opt
self.criterion = GANLoss(opt['gan_type'], 1.0, 0.0).to(env['device'])
self.criterion = get_basic_criterion_for_name(opt['criterion'], env['device'])
def forward(self, _, state):
fake = state[self.opt['fake']]
l_total = 0
for i in range((len(fake) - 1) / 2):
for i in range((len(fake) - 1) // 2):
early = fake[i]
late = fake[-i]
l_total += self.criterion(early, late)