274 lines
13 KiB
Python
274 lines
13 KiB
Python
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
|
|
import torch.distributed as dist
|
|
|
|
def create_teco_loss(opt, env):
|
|
type = opt['type']
|
|
if type == 'teco_gan':
|
|
return TecoGanLoss(opt, env)
|
|
elif type == "teco_pingpong":
|
|
return PingPongLoss(opt, env)
|
|
return None
|
|
|
|
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):
|
|
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([triplet[:,1], triplet[:,0]], dim=2).float())
|
|
#first_flow = F.interpolate(first_flow, scale_factor=scale, mode='bicubic')
|
|
last_flow = flow_gen(torch.stack([triplet[:,1], triplet[:,2]], dim=2).float())
|
|
#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=1)
|
|
combined = torch.cat([triplet, flow_triplet], dim=1)
|
|
b, f, c, h, w = combined.shape
|
|
combined = combined.view(b, 3*6, h, w) # 3*6 is essentially an assertion here.
|
|
# Apply margin
|
|
return combined[:, :, margin:-margin, margin:-margin]
|
|
|
|
|
|
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).
|
|
# All computation is done with torch.no_grad().
|
|
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_index' in opt.keys() else 0
|
|
self.recurrent_index = opt['recurrent_index']
|
|
self.scale = opt['scale']
|
|
self.resample = Resample2d()
|
|
self.first_inputs = opt['first_inputs'] if 'first_inputs' in opt.keys() else opt['in'] # Use this to specify inputs that will be used in the first teco iteration, the rest will use 'in'.
|
|
self.do_backwards = opt['do_backwards'] if 'do_backwards' in opt.keys() else True
|
|
self.hq_recurrent = opt['hq_recurrent'] if 'hq_recurrent' in opt.keys() else False # When True, recurrent_index is not touched for the first iteration, allowing you to specify what is fed in. When False, zeros are fed into the recurrent index.
|
|
|
|
def forward(self, state):
|
|
gen = self.env['generators'][self.opt['generator']]
|
|
flow = self.env['generators'][self.flow]
|
|
first_inputs = extract_params_from_state(self.first_inputs, state)
|
|
inputs = extract_params_from_state(self.input, state)
|
|
if not isinstance(inputs, list):
|
|
inputs = [inputs]
|
|
|
|
if not isinstance(self.output, list):
|
|
self.output = [self.output]
|
|
results = {}
|
|
for out_key in self.output:
|
|
results[out_key] = []
|
|
|
|
# Go forward in the sequence first.
|
|
first_step = True
|
|
b, f, c, h, w = inputs[self.input_lq_index].shape
|
|
debug_index = 0
|
|
for i in range(f):
|
|
if first_step:
|
|
input = extract_inputs_index(first_inputs, i)
|
|
if self.hq_recurrent:
|
|
recurrent_input = input[self.recurrent_index]
|
|
else:
|
|
recurrent_input = torch.zeros_like(input[self.recurrent_index])
|
|
first_step = False
|
|
else:
|
|
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 = F.interpolate(flow(flow_input), scale_factor=self.scale, mode='bicubic')
|
|
# Resample does not work in FP16.
|
|
recurrent_input = self.resample(recurrent_input.float(), flowfield.float())
|
|
input[self.recurrent_index] = recurrent_input
|
|
if self.env['step'] % 50 == 0:
|
|
self.produce_teco_visual_debugs(input[self.input_lq_index], input[self.recurrent_index], debug_index)
|
|
debug_index += 1
|
|
gen_out = gen(*input)
|
|
if isinstance(gen_out, torch.Tensor):
|
|
gen_out = [gen_out]
|
|
for i, out_key in enumerate(self.output):
|
|
results[out_key].append(gen_out[i])
|
|
recurrent_input = gen_out[self.output_hq_index]
|
|
|
|
# Now go backwards, skipping the last element (it's already stored in recurrent_input)
|
|
if self.do_backwards:
|
|
it = reversed(range(f - 1))
|
|
for i in it:
|
|
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 = F.interpolate(flow(flow_input), scale_factor=self.scale, mode='bicubic')
|
|
recurrent_input = self.resample(recurrent_input.float(), flowfield.float())
|
|
input[self.recurrent_index
|
|
] = recurrent_input
|
|
if self.env['step'] % 50 == 0:
|
|
self.produce_teco_visual_debugs(input[self.input_lq_index], input[self.recurrent_index], debug_index)
|
|
debug_index += 1
|
|
gen_out = gen(*input)
|
|
if isinstance(gen_out, torch.Tensor):
|
|
gen_out = [gen_out]
|
|
for i, out_key in enumerate(self.output):
|
|
results[out_key].append(gen_out[i])
|
|
recurrent_input = gen_out[self.output_hq_index]
|
|
|
|
for k, v in results.items():
|
|
results[k] = torch.stack(v, dim=1)
|
|
return results
|
|
|
|
def produce_teco_visual_debugs(self, gen_input, gen_recurrent, it):
|
|
if self.env['rank'] > 0:
|
|
return
|
|
base_path = osp.join(self.env['base_path'], "..", "visual_dbg", "teco_geninput", str(self.env['step']))
|
|
os.makedirs(base_path, exist_ok=True)
|
|
torchvision.utils.save_image(gen_input, osp.join(base_path, "%s_img.png" % (it,)))
|
|
torchvision.utils.save_image(gen_recurrent, 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:
|
|
# 'real' - Must be a list of arbitrary images (len>3) drawn from the dataset
|
|
# 'fake' - The output of the RecurrentImageGeneratorSequenceInjector for the same set of images.
|
|
#
|
|
# This loss does the following:
|
|
# 1) Picks an image triplet, starting with the first '3' elements in 'real' and 'fake'.
|
|
# 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.
|
|
# 3) Warps the first and last images according to the flow field.
|
|
# 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 TecoGanLoss(ConfigurableLoss):
|
|
def __init__(self, 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']
|
|
self.min_loss = opt['min_loss'] if 'min_loss' in opt.keys() else 0
|
|
self.margin = opt['margin'] # Per the tecogan paper, the GAN loss only pays attention to an inner part of the image with the margin removed, to get rid of artifacts resulting from flow errors.
|
|
|
|
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']]
|
|
sequence_len = real.shape[1]
|
|
lr = state[self.opt['lr_inputs']]
|
|
l_total = 0
|
|
for i in range(sequence_len - 2):
|
|
real_sext = create_teco_discriminator_sextuplet(real, lr, self.scale, i, flow_gen, self.resampler, self.margin)
|
|
fake_sext = create_teco_discriminator_sextuplet(fake, lr, self.scale, i, flow_gen, self.resampler, self.margin)
|
|
d_fake = net(fake_sext)
|
|
d_real = net(real_sext)
|
|
self.metrics.append(("d_fake", torch.mean(d_fake)))
|
|
self.metrics.append(("d_real", torch.mean(d_real)))
|
|
|
|
if self.for_generator and self.env['step'] % 50 == 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']:
|
|
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_step = l_fake + l_real
|
|
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_step = (self.criterion(d_real - torch.mean(d_fake), not self.for_generator) +
|
|
self.criterion(d_fake_diff, self.for_generator))
|
|
else:
|
|
raise NotImplementedError
|
|
if l_step > self.min_loss:
|
|
l_total += l_step
|
|
|
|
return l_total
|
|
|
|
def produce_teco_visual_debugs(self, sext, lbl, it):
|
|
if self.env['rank'] > 0:
|
|
return
|
|
base_path = osp.join(self.env['base_path'], "..", "visual_dbg", "teco_sext", str(self.env['step']), lbl)
|
|
os.makedirs(base_path, exist_ok=True)
|
|
lbls = ['img_a', 'img_b', 'img_c', 'flow_a', 'flow_b', 'flow_c']
|
|
for i in range(6):
|
|
torchvision.utils.save_image(sext[:, i*3:(i+1)*3, :, :], osp.join(base_path, "%s_%s.png" % (it, lbls[i])))
|
|
|
|
|
|
# This loss doesn't have a real entry - only fakes are used.
|
|
class PingPongLoss(ConfigurableLoss):
|
|
def __init__(self, opt, env):
|
|
super(PingPongLoss, self).__init__(opt, env)
|
|
self.opt = opt
|
|
self.criterion = get_basic_criterion_for_name(opt['criterion'], env['device'])
|
|
|
|
def forward(self, _, state):
|
|
fake = state[self.opt['fake']]
|
|
l_total = 0
|
|
img_count = fake.shape[1]
|
|
for i in range((img_count - 1) // 2):
|
|
early = fake[:, i]
|
|
late = fake[:, -i]
|
|
l_total += self.criterion(early, late)
|
|
|
|
if self.env['step'] % 50 == 0:
|
|
self.produce_teco_visual_debugs(fake)
|
|
|
|
return l_total
|
|
|
|
def produce_teco_visual_debugs(self, imglist):
|
|
if self.env['rank'] > 0:
|
|
return
|
|
base_path = osp.join(self.env['base_path'], "..", "visual_dbg", "teco_pingpong", str(self.env['step']))
|
|
os.makedirs(base_path, exist_ok=True)
|
|
cnt = imglist.shape[1]
|
|
for i in range(cnt):
|
|
img = imglist[:, i]
|
|
torchvision.utils.save_image(img, osp.join(base_path, "%s.png" % (i, )))
|