DL-Art-School/codes/models/steps/injectors.py
2020-09-03 11:32:47 -06:00

117 lines
4.2 KiB
Python

import torch.nn
from models.archs.SPSR_arch import ImageGradientNoPadding
from data.weight_scheduler import get_scheduler_for_opt
# 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 type == 'generator':
return ImageGeneratorInjector(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)
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']]
if isinstance(self.input, list):
params = [state[i] for i in self.input]
results = gen(*params)
else:
results = gen(state[self.input])
new_state = {}
if isinstance(self.output, list):
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)
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']
noise = torch.randn_like(state[self.opt['in']], device=self.env['device']) * scale
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)
def forward(self, state):
scaled = torch.nn.functional.interpolate(state[self.opt['in']], scale_factor=self.opt['scale_factor'],
mode=self.opt['mode'])
return {self.opt['out']: scaled}