416 lines
16 KiB
Python
416 lines
16 KiB
Python
import random
|
|
|
|
import torch.nn
|
|
from kornia.augmentation import RandomResizedCrop
|
|
from torch.cuda.amp import autocast
|
|
|
|
from trainer.inject import Injector
|
|
from trainer.losses import extract_params_from_state
|
|
from utils.util import opt_get
|
|
from utils.weight_scheduler import get_scheduler_for_opt
|
|
|
|
|
|
# Uses a generator to synthesize an image from [in] and injects the results into [out]
|
|
# Note that results are *not* detached.
|
|
class GeneratorInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super(GeneratorInjector, self).__init__(opt, env)
|
|
self.grad = opt['grad'] if 'grad' in opt.keys() else True
|
|
|
|
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)
|
|
else:
|
|
params = [state[self.input]]
|
|
if self.grad:
|
|
results = gen(*params)
|
|
else:
|
|
with torch.no_grad():
|
|
results = gen(*params)
|
|
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
|
|
|
|
|
|
# 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']
|
|
if scale is None:
|
|
scale = 1
|
|
|
|
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)
|
|
self.aslist = opt['aslist'] if 'aslist' in opt.keys() else False
|
|
|
|
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'])
|
|
if self.aslist:
|
|
return {self.output: injs}
|
|
else:
|
|
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 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 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)}
|
|
|
|
|
|
# Injector used to work with image deltas used in diff-SR
|
|
class SrDiffsInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super(SrDiffsInjector, self).__init__(opt, env)
|
|
self.mode = opt['mode']
|
|
assert self.mode in ['recombine', 'produce_diff']
|
|
self.lq = opt['lq']
|
|
self.hq = opt['hq']
|
|
if self.mode == 'produce_diff':
|
|
self.diff_key = opt['diff']
|
|
self.include_combined = opt['include_combined']
|
|
|
|
def forward(self, state):
|
|
resampled_lq = state[self.lq]
|
|
hq = state[self.hq]
|
|
if self.mode == 'produce_diff':
|
|
diff = hq - resampled_lq
|
|
if self.include_combined:
|
|
res = torch.cat([resampled_lq, diff, hq], dim=1)
|
|
else:
|
|
res = torch.cat([resampled_lq, diff], dim=1)
|
|
return {self.output: res,
|
|
self.diff_key: diff}
|
|
elif self.mode == 'recombine':
|
|
combined = resampled_lq + hq
|
|
return {self.output: combined}
|
|
|
|
|
|
class MultiFrameCombiner(Injector):
|
|
def __init__(self, opt, env):
|
|
super().__init__(opt, env)
|
|
self.mode = opt['mode']
|
|
self.dim = opt['dim'] if 'dim' in opt.keys() else None
|
|
self.flow = opt['flow']
|
|
self.in_lq_key = opt['in']
|
|
self.in_hq_key = opt['in_hq']
|
|
self.out_lq_key = opt['out']
|
|
self.out_hq_key = opt['out_hq']
|
|
from models.flownet2.networks import Resample2d
|
|
self.resampler = Resample2d()
|
|
|
|
def combine(self, state):
|
|
flow = self.env['generators'][self.flow]
|
|
lq = state[self.in_lq_key]
|
|
hq = state[self.in_hq_key]
|
|
b, f, c, h, w = lq.shape
|
|
center = f // 2
|
|
center_img = lq[:, center, :, :, :]
|
|
imgs = [center_img]
|
|
with torch.no_grad():
|
|
for i in range(f):
|
|
if i == center:
|
|
continue
|
|
nimg = lq[:, i, :, :, :]
|
|
flowfield = flow(torch.stack([center_img, nimg], dim=2).float())
|
|
nimg = self.resampler(nimg, flowfield)
|
|
imgs.append(nimg)
|
|
hq_out = hq[:, center, :, :, :]
|
|
return {self.out_lq_key: torch.cat(imgs, dim=1),
|
|
self.out_hq_key: hq_out,
|
|
self.out_lq_key + "_flow_sample": torch.cat(imgs, dim=0)}
|
|
|
|
def synthesize(self, state):
|
|
lq = state[self.in_lq_key]
|
|
return {
|
|
self.out_lq_key: lq.repeat(1, self.dim, 1, 1)
|
|
}
|
|
|
|
def forward(self, state):
|
|
if self.mode == "synthesize":
|
|
return self.synthesize(state)
|
|
elif self.mode == "combine":
|
|
return self.combine(state)
|
|
else:
|
|
raise NotImplementedError
|
|
|
|
|
|
# Combines data from multiple different sources and mixes them along the batch dimension. Labels are then emitted
|
|
# according to how the mixing was performed.
|
|
class MixAndLabelInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super().__init__(opt, env)
|
|
self.out_labels = opt['out_labels']
|
|
|
|
def forward(self, state):
|
|
input_tensors = [state[i] for i in self.input]
|
|
num_inputs = len(input_tensors)
|
|
bs = input_tensors[0].shape[0]
|
|
labels = torch.randint(0, num_inputs, (bs,), device=input_tensors[0].device)
|
|
# Still don't know of a good way to do this in torch.. TODO make it better..
|
|
res = []
|
|
for b in range(bs):
|
|
res.append(input_tensors[labels[b]][b, :, :, :])
|
|
output = torch.stack(res, dim=0)
|
|
return {self.out_labels: labels, self.output: output}
|
|
|
|
|
|
# Randomly performs a uniform resize & crop from a base image.
|
|
# Never resizes below input resolution or messes with the aspect ratio.
|
|
class RandomCropInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super().__init__(opt, env)
|
|
dim_in = opt['dim_in']
|
|
dim_out = opt['dim_out']
|
|
scale = dim_out / dim_in
|
|
self.operator = RandomResizedCrop(size=(dim_out, dim_out), scale=(scale, 1),
|
|
ratio=(.99,1), # An aspect ratio range is required, but .99,1 is effectively "none".
|
|
resample='NEAREST')
|
|
|
|
def forward(self, state):
|
|
return {self.output: self.operator(state[self.input])}
|
|
|
|
|
|
class Stylegan2NoiseInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super().__init__(opt, env)
|
|
self.mix_prob = opt_get(opt, ['mix_probability'], .9)
|
|
self.latent_dim = opt_get(opt, ['latent_dim'], 512)
|
|
|
|
def make_noise(self, batch, latent_dim, n_noise, device):
|
|
return torch.randn(n_noise, batch, latent_dim, device=device).unbind(0)
|
|
|
|
def forward(self, state):
|
|
i = state[self.input]
|
|
if self.mix_prob > 0 and random.random() < self.mix_prob:
|
|
return {self.output: self.make_noise(i.shape[0], self.latent_dim, 2, i.device)}
|
|
else:
|
|
return {self.output: self.make_noise(i.shape[0], self.latent_dim, 1, i.device)}
|
|
|
|
|
|
class NoiseInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super().__init__(opt, env)
|
|
self.shape = tuple(opt['shape'])
|
|
|
|
def forward(self, state):
|
|
shape = (state[self.input].shape[0],) + self.shape
|
|
return {self.output: torch.randn(shape, device=state[self.input].device)}
|