59 lines
2.0 KiB
Python
59 lines
2.0 KiB
Python
import torch.nn
|
|
from models.archs.SPSR_arch import ImageGradientNoPadding
|
|
|
|
# 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 == 'img_grad':
|
|
return ImageGradientInjector(opt_inject, env)
|
|
elif type == 'add_noise':
|
|
return AddNoiseInjector(opt_inject, env)
|
|
elif type == 'greyscale':
|
|
return GreyInjector(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
|
|
self.input = opt['in']
|
|
self.output = opt['out']
|
|
|
|
# This should return a dict of new state variables.
|
|
def forward(self, state):
|
|
raise NotImplementedError
|
|
|
|
|
|
# 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()
|
|
|
|
def forward(self, state):
|
|
return {self.opt['out']: self.img_grad_fn(state[self.opt['in']])}
|
|
|
|
|
|
# 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):
|
|
noise = torch.randn_like(state[self.opt['in']]) * self.opt['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 = torch.repeat(mean, (-1, 3, -1, -1))
|
|
return {self.opt['out']: mean} |