forked from mrq/DL-Art-School
382 lines
15 KiB
Python
382 lines
15 KiB
Python
import random
|
|
|
|
import torch.nn
|
|
from torch.cuda.amp import autocast
|
|
|
|
from models.archs.SPSR_arch import ImageGradientNoPadding
|
|
from models.archs.pytorch_ssim import SSIM
|
|
from utils.weight_scheduler import get_scheduler_for_opt
|
|
from models.steps.losses import extract_params_from_state
|
|
|
|
# Injectors are a way to sythesize data within a step that can then be used (and reused) by loss functions.
|
|
def create_injector(opt_inject, env):
|
|
type = opt_inject['type']
|
|
if 'teco_' in type:
|
|
from models.steps.tecogan_losses import create_teco_injector
|
|
return create_teco_injector(opt_inject, env)
|
|
elif 'progressive_' in type:
|
|
from models.steps.progressive_zoom import create_progressive_zoom_injector
|
|
return create_progressive_zoom_injector(opt_inject, env)
|
|
elif 'stereoscopic_' in type:
|
|
from models.steps.stereoscopic import create_stereoscopic_injector
|
|
return create_stereoscopic_injector(opt_inject, env)
|
|
elif type == 'generator':
|
|
return ImageGeneratorInjector(opt_inject, env)
|
|
elif type == 'discriminator':
|
|
return DiscriminatorInjector(opt_inject, env)
|
|
elif type == 'scheduled_scalar':
|
|
return ScheduledScalarInjector(opt_inject, env)
|
|
elif type == 'img_grad':
|
|
return ImageGradientInjector(opt_inject, env)
|
|
elif type == 'add_noise':
|
|
return AddNoiseInjector(opt_inject, env)
|
|
elif type == 'greyscale':
|
|
return GreyInjector(opt_inject, env)
|
|
elif type == 'interpolate':
|
|
return InterpolateInjector(opt_inject, env)
|
|
elif type == 'image_patch':
|
|
return ImagePatchInjector(opt_inject, env)
|
|
elif type == 'concatenate':
|
|
return ConcatenateInjector(opt_inject, env)
|
|
elif type == 'margin_removal':
|
|
return MarginRemoval(opt_inject, env)
|
|
elif type == 'foreach':
|
|
return ForEachInjector(opt_inject, env)
|
|
elif type == 'constant':
|
|
return ConstantInjector(opt_inject, env)
|
|
elif type == 'fft':
|
|
return ImageFftInjector(opt_inject, env)
|
|
elif type == 'extract_indices':
|
|
return IndicesExtractor(opt_inject, env)
|
|
elif type == 'random_shift':
|
|
return RandomShiftInjector(opt_inject, env)
|
|
elif type == 'psnr':
|
|
return PsnrInjector(opt_inject, env)
|
|
elif type == 'batch_rotate':
|
|
return BatchRotateInjector(opt_inject, env)
|
|
else:
|
|
raise NotImplementedError
|
|
|
|
|
|
class Injector(torch.nn.Module):
|
|
def __init__(self, opt, env):
|
|
super(Injector, self).__init__()
|
|
self.opt = opt
|
|
self.env = env
|
|
if 'in' in opt.keys():
|
|
self.input = opt['in']
|
|
self.output = opt['out']
|
|
|
|
# This should return a dict of new state variables.
|
|
def forward(self, state):
|
|
raise NotImplementedError
|
|
|
|
# Uses a generator to synthesize an image from [in] and injects the results into [out]
|
|
# Note that results are *not* detached.
|
|
class ImageGeneratorInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super(ImageGeneratorInjector, self).__init__(opt, env)
|
|
|
|
def forward(self, state):
|
|
gen = self.env['generators'][self.opt['generator']]
|
|
with autocast(enabled=self.env['opt']['fp16']):
|
|
if isinstance(self.input, list):
|
|
params = extract_params_from_state(self.input, state)
|
|
results = gen(*params)
|
|
else:
|
|
results = gen(state[self.input])
|
|
new_state = {}
|
|
if isinstance(self.output, list):
|
|
# Only dereference tuples or lists, not tensors.
|
|
assert isinstance(results, list) or isinstance(results, tuple)
|
|
for i, k in enumerate(self.output):
|
|
new_state[k] = results[i]
|
|
else:
|
|
new_state[self.output] = results
|
|
|
|
return new_state
|
|
|
|
|
|
# Injects a result from a discriminator network into the state.
|
|
class DiscriminatorInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super(DiscriminatorInjector, self).__init__(opt, env)
|
|
|
|
def forward(self, state):
|
|
with autocast(enabled=self.env['opt']['fp16']):
|
|
d = self.env['discriminators'][self.opt['discriminator']]
|
|
if isinstance(self.input, list):
|
|
params = [state[i] for i in self.input]
|
|
results = d(*params)
|
|
else:
|
|
results = d(state[self.input])
|
|
new_state = {}
|
|
if isinstance(self.output, list):
|
|
# Only dereference tuples or lists, not tensors.
|
|
assert isinstance(results, list) or isinstance(results, tuple)
|
|
for i, k in enumerate(self.output):
|
|
new_state[k] = results[i]
|
|
else:
|
|
new_state[self.output] = results
|
|
|
|
return new_state
|
|
|
|
|
|
# Creates an image gradient from [in] and injects it into [out]
|
|
class ImageGradientInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super(ImageGradientInjector, self).__init__(opt, env)
|
|
self.img_grad_fn = ImageGradientNoPadding().to(env['device'])
|
|
|
|
def forward(self, state):
|
|
return {self.opt['out']: self.img_grad_fn(state[self.opt['in']])}
|
|
|
|
|
|
# Injects a scalar that is modulated with a specified schedule. Useful for increasing or decreasing the influence
|
|
# of something over time.
|
|
class ScheduledScalarInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super(ScheduledScalarInjector, self).__init__(opt, env)
|
|
self.scheduler = get_scheduler_for_opt(opt['scheduler'])
|
|
|
|
def forward(self, state):
|
|
return {self.opt['out']: self.scheduler.get_weight_for_step(self.env['step'])}
|
|
|
|
|
|
# Adds gaussian noise to [in], scales it to [0,[scale]] and injects into [out]
|
|
class AddNoiseInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super(AddNoiseInjector, self).__init__(opt, env)
|
|
self.mode = opt['mode'] if 'mode' in opt.keys() else 'normal'
|
|
|
|
def forward(self, state):
|
|
# Scale can be a fixed float, or a state key (e.g. from ScheduledScalarInjector).
|
|
if isinstance(self.opt['scale'], str):
|
|
scale = state[self.opt['scale']]
|
|
else:
|
|
scale = self.opt['scale']
|
|
|
|
ref = state[self.opt['in']]
|
|
if self.mode == 'normal':
|
|
noise = torch.randn_like(ref) * scale
|
|
elif self.mode == 'uniform':
|
|
noise = torch.FloatTensor(ref.shape).uniform_(0.0, scale).to(ref.device)
|
|
return {self.opt['out']: state[self.opt['in']] + noise}
|
|
|
|
|
|
# Averages the channel dimension (1) of [in] and saves to [out]. Dimensions are
|
|
# kept the same, the average is simply repeated.
|
|
class GreyInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super(GreyInjector, self).__init__(opt, env)
|
|
|
|
def forward(self, state):
|
|
mean = torch.mean(state[self.opt['in']], dim=1, keepdim=True)
|
|
mean = mean.repeat(1, 3, 1, 1)
|
|
return {self.opt['out']: mean}
|
|
|
|
|
|
class InterpolateInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super(InterpolateInjector, self).__init__(opt, env)
|
|
if 'scale_factor' in opt.keys():
|
|
self.scale_factor = opt['scale_factor']
|
|
self.size = None
|
|
else:
|
|
self.scale_factor = None
|
|
self.size = (opt['size'], opt['size'])
|
|
|
|
def forward(self, state):
|
|
scaled = torch.nn.functional.interpolate(state[self.opt['in']], scale_factor=self.opt['scale_factor'],
|
|
size=self.opt['size'], mode=self.opt['mode'])
|
|
return {self.opt['out']: scaled}
|
|
|
|
|
|
# Extracts four patches from the input image, each a square of 'patch_size'. The input images are taken from each
|
|
# of the four corners of the image. The intent of this loss is that each patch shares some part of the input, which
|
|
# can then be used in the translation invariance loss.
|
|
#
|
|
# This injector is unique in that it does not only produce the specified output label into state. Instead it produces five
|
|
# outputs for the specified label, one for each corner of the input as well as the specified output, which is the top left
|
|
# corner. See the code below to find out how this works.
|
|
#
|
|
# Another note: this injector operates differently in eval mode (e.g. when env['training']=False) - in this case, it
|
|
# simply sets all the output state variables to the input. This is so that you can feed the output of this injector
|
|
# directly into your generator in training without affecting test performance.
|
|
class ImagePatchInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super(ImagePatchInjector, self).__init__(opt, env)
|
|
self.patch_size = opt['patch_size']
|
|
self.resize = opt['resize'] if 'resize' in opt.keys() else None # If specified, the output is resized to a square with this size after patch extraction.
|
|
|
|
def forward(self, state):
|
|
im = state[self.opt['in']]
|
|
if self.env['training']:
|
|
res = { self.opt['out']: im[:, :3, :self.patch_size, :self.patch_size],
|
|
'%s_top_left' % (self.opt['out'],): im[:, :, :self.patch_size, :self.patch_size],
|
|
'%s_top_right' % (self.opt['out'],): im[:, :, :self.patch_size, -self.patch_size:],
|
|
'%s_bottom_left' % (self.opt['out'],): im[:, :, -self.patch_size:, :self.patch_size],
|
|
'%s_bottom_right' % (self.opt['out'],): im[:, :, -self.patch_size:, -self.patch_size:] }
|
|
else:
|
|
res = { self.opt['out']: im,
|
|
'%s_top_left' % (self.opt['out'],): im,
|
|
'%s_top_right' % (self.opt['out'],): im,
|
|
'%s_bottom_left' % (self.opt['out'],): im,
|
|
'%s_bottom_right' % (self.opt['out'],): im }
|
|
if self.resize is not None:
|
|
res2 = {}
|
|
for k, v in res.items():
|
|
res2[k] = torch.nn.functional.interpolate(v, size=(self.resize, self.resize), mode="nearest")
|
|
res = res2
|
|
return res
|
|
|
|
|
|
# Concatenates a list of tensors on the specified dimension.
|
|
class ConcatenateInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super(ConcatenateInjector, self).__init__(opt, env)
|
|
self.dim = opt['dim']
|
|
|
|
def forward(self, state):
|
|
input = [state[i] for i in self.input]
|
|
return {self.opt['out']: torch.cat(input, dim=self.dim)}
|
|
|
|
|
|
# Removes margins from an image.
|
|
class MarginRemoval(Injector):
|
|
def __init__(self, opt, env):
|
|
super(MarginRemoval, self).__init__(opt, env)
|
|
self.margin = opt['margin']
|
|
self.random_shift_max = opt['random_shift_max'] if 'random_shift_max' in opt.keys() else 0
|
|
|
|
def forward(self, state):
|
|
input = state[self.input]
|
|
if self.random_shift_max > 0:
|
|
output = []
|
|
# This is a really shitty way of doing this. If it works at all, I should reconsider using Resample2D, for example.
|
|
for b in range(input.shape[0]):
|
|
shiftleft = random.randint(-self.random_shift_max, self.random_shift_max)
|
|
shifttop = random.randint(-self.random_shift_max, self.random_shift_max)
|
|
output.append(input[b, :, self.margin+shiftleft:-(self.margin-shiftleft),
|
|
self.margin+shifttop:-(self.margin-shifttop)])
|
|
output = torch.stack(output, dim=0)
|
|
else:
|
|
output = input[:, :, self.margin:-self.margin,
|
|
self.margin:-self.margin]
|
|
|
|
return {self.opt['out']: output}
|
|
|
|
|
|
# Produces an injection which is composed of applying a single injector multiple times across a single dimension.
|
|
class ForEachInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super(ForEachInjector, self).__init__(opt, env)
|
|
o = opt.copy()
|
|
o['type'] = opt['subtype']
|
|
o['in'] = '_in'
|
|
o['out'] = '_out'
|
|
self.injector = create_injector(o, self.env)
|
|
|
|
def forward(self, state):
|
|
injs = []
|
|
st = state.copy()
|
|
inputs = state[self.opt['in']]
|
|
for i in range(inputs.shape[1]):
|
|
st['_in'] = inputs[:, i]
|
|
injs.append(self.injector(st)['_out'])
|
|
return {self.output: torch.stack(injs, dim=1)}
|
|
|
|
|
|
class ConstantInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super(ConstantInjector, self).__init__(opt, env)
|
|
self.constant_type = opt['constant_type']
|
|
self.like = opt['like'] # This injector uses this tensor to determine what batch size and device to use.
|
|
|
|
def forward(self, state):
|
|
like = state[self.like]
|
|
if self.constant_type == 'zeroes':
|
|
out = torch.zeros_like(like)
|
|
else:
|
|
raise NotImplementedError
|
|
return { self.opt['out']: out }
|
|
|
|
|
|
class ImageFftInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super(ImageFftInjector, self).__init__(opt, env)
|
|
self.is_forward = opt['forward'] # Whether to compute a forward FFT or backward.
|
|
self.eps = 1e-100
|
|
|
|
def forward(self, state):
|
|
if self.forward:
|
|
fftim = torch.rfft(state[self.input], signal_ndim=2, normalized=True)
|
|
b, f, h, w, c = fftim.shape
|
|
fftim = fftim.permute(0,1,4,2,3).reshape(b,-1,h,w)
|
|
# Normalize across spatial dimension
|
|
mean = torch.mean(fftim, dim=(0,1))
|
|
fftim = fftim - mean
|
|
std = torch.std(fftim, dim=(0,1))
|
|
fftim = (fftim + self.eps) / std
|
|
return {self.output: fftim,
|
|
'%s_std' % (self.output,): std,
|
|
'%s_mean' % (self.output,): mean}
|
|
else:
|
|
b, f, h, w = state[self.input].shape
|
|
# First, de-normalize the FFT.
|
|
mean = state['%s_mean' % (self.input,)]
|
|
std = state['%s_std' % (self.input,)]
|
|
fftim = state[self.input] * std + mean - self.eps
|
|
# Second, recover the FFT dimensions from the given filters.
|
|
fftim = fftim.reshape(b, f // 2, 2, h, w).permute(0,1,3,4,2)
|
|
im = torch.irfft(fftim, signal_ndim=2, normalized=True)
|
|
return {self.output: im}
|
|
|
|
|
|
class IndicesExtractor(Injector):
|
|
def __init__(self, opt, env):
|
|
super(IndicesExtractor, self).__init__(opt, env)
|
|
self.dim = opt['dim']
|
|
assert self.dim == 1 # Honestly not sure how to support an abstract dim here, so just add yours when needed.
|
|
|
|
def forward(self, state):
|
|
results = {}
|
|
for i, o in enumerate(self.output):
|
|
if self.dim == 1:
|
|
results[o] = state[self.input][:, i]
|
|
return results
|
|
|
|
|
|
class RandomShiftInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super(RandomShiftInjector, self).__init__(opt, env)
|
|
|
|
def forward(self, state):
|
|
img = state[self.input]
|
|
return {self.output: img}
|
|
|
|
|
|
class PsnrInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super(PsnrInjector, self).__init__(opt, env)
|
|
self.ssim = SSIM(size_average=False, raw=True)
|
|
self.scale = opt['output_scale_divisor']
|
|
self.exp = opt['exponent'] if 'exponent' in opt.keys() else 1
|
|
|
|
def forward(self, state):
|
|
img1, img2 = state[self.input[0]], state[self.input[1]]
|
|
ssim = self.ssim(img1, img2)
|
|
areal_se = torch.nn.functional.interpolate(ssim, scale_factor=1/self.scale,
|
|
mode="area")
|
|
return {self.output: areal_se}
|
|
|
|
|
|
class BatchRotateInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super(BatchRotateInjector, self).__init__(opt, env)
|
|
|
|
def forward(self, state):
|
|
img = state[self.input]
|
|
return {self.output: torch.roll(img, 1, 0)}
|
|
|