405 lines
16 KiB
Python
405 lines
16 KiB
Python
import os
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import random
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import torch.nn
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import torchvision
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from torch.cuda.amp import autocast
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from utils.weight_scheduler import get_scheduler_for_opt
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from trainer.losses import extract_params_from_state
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from trainer.inject import Injector
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# Uses a generator to synthesize an image from [in] and injects the results into [out]
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# Note that results are *not* detached.
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class GeneratorInjector(Injector):
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def __init__(self, opt, env):
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super(GeneratorInjector, self).__init__(opt, env)
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self.grad = opt['grad'] if 'grad' in opt.keys() else True
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def forward(self, state):
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gen = self.env['generators'][self.opt['generator']]
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with autocast(enabled=self.env['opt']['fp16']):
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if isinstance(self.input, list):
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params = extract_params_from_state(self.input, state)
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else:
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params = [state[self.input]]
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if self.grad:
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results = gen(*params)
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else:
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with torch.no_grad():
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results = gen(*params)
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new_state = {}
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if isinstance(self.output, list):
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# Only dereference tuples or lists, not tensors.
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assert isinstance(results, list) or isinstance(results, tuple)
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for i, k in enumerate(self.output):
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new_state[k] = results[i]
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else:
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new_state[self.output] = results
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return new_state
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# Injects a result from a discriminator network into the state.
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class DiscriminatorInjector(Injector):
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def __init__(self, opt, env):
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super(DiscriminatorInjector, self).__init__(opt, env)
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def forward(self, state):
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with autocast(enabled=self.env['opt']['fp16']):
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d = self.env['discriminators'][self.opt['discriminator']]
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if isinstance(self.input, list):
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params = [state[i] for i in self.input]
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results = d(*params)
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else:
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results = d(state[self.input])
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new_state = {}
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if isinstance(self.output, list):
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# Only dereference tuples or lists, not tensors.
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assert isinstance(results, list) or isinstance(results, tuple)
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for i, k in enumerate(self.output):
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new_state[k] = results[i]
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else:
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new_state[self.output] = results
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return new_state
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# Injects a scalar that is modulated with a specified schedule. Useful for increasing or decreasing the influence
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# of something over time.
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class ScheduledScalarInjector(Injector):
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def __init__(self, opt, env):
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super(ScheduledScalarInjector, self).__init__(opt, env)
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self.scheduler = get_scheduler_for_opt(opt['scheduler'])
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def forward(self, state):
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return {self.opt['out']: self.scheduler.get_weight_for_step(self.env['step'])}
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# Adds gaussian noise to [in], scales it to [0,[scale]] and injects into [out]
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class AddNoiseInjector(Injector):
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def __init__(self, opt, env):
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super(AddNoiseInjector, self).__init__(opt, env)
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self.mode = opt['mode'] if 'mode' in opt.keys() else 'normal'
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def forward(self, state):
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# Scale can be a fixed float, or a state key (e.g. from ScheduledScalarInjector).
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if isinstance(self.opt['scale'], str):
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scale = state[self.opt['scale']]
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else:
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scale = self.opt['scale']
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if scale is None:
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scale = 1
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ref = state[self.opt['in']]
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if self.mode == 'normal':
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noise = torch.randn_like(ref) * scale
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elif self.mode == 'uniform':
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noise = torch.FloatTensor(ref.shape).uniform_(0.0, scale).to(ref.device)
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return {self.opt['out']: state[self.opt['in']] + noise}
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# Averages the channel dimension (1) of [in] and saves to [out]. Dimensions are
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# kept the same, the average is simply repeated.
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class GreyInjector(Injector):
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def __init__(self, opt, env):
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super(GreyInjector, self).__init__(opt, env)
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def forward(self, state):
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mean = torch.mean(state[self.opt['in']], dim=1, keepdim=True)
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mean = mean.repeat(1, 3, 1, 1)
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return {self.opt['out']: mean}
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class InterpolateInjector(Injector):
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def __init__(self, opt, env):
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super(InterpolateInjector, self).__init__(opt, env)
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if 'scale_factor' in opt.keys():
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self.scale_factor = opt['scale_factor']
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self.size = None
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else:
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self.scale_factor = None
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self.size = (opt['size'], opt['size'])
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def forward(self, state):
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scaled = torch.nn.functional.interpolate(state[self.opt['in']], scale_factor=self.opt['scale_factor'],
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size=self.opt['size'], mode=self.opt['mode'])
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return {self.opt['out']: scaled}
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# Extracts four patches from the input image, each a square of 'patch_size'. The input images are taken from each
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# of the four corners of the image. The intent of this loss is that each patch shares some part of the input, which
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# can then be used in the translation invariance loss.
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#
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# This injector is unique in that it does not only produce the specified output label into state. Instead it produces five
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# outputs for the specified label, one for each corner of the input as well as the specified output, which is the top left
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# corner. See the code below to find out how this works.
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#
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# Another note: this injector operates differently in eval mode (e.g. when env['training']=False) - in this case, it
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# simply sets all the output state variables to the input. This is so that you can feed the output of this injector
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# directly into your generator in training without affecting test performance.
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class ImagePatchInjector(Injector):
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def __init__(self, opt, env):
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super(ImagePatchInjector, self).__init__(opt, env)
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self.patch_size = opt['patch_size']
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self.resize = opt[
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'resize'] if 'resize' in opt.keys() else None # If specified, the output is resized to a square with this size after patch extraction.
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def forward(self, state):
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im = state[self.opt['in']]
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if self.env['training']:
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res = {self.opt['out']: im[:, :3, :self.patch_size, :self.patch_size],
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'%s_top_left' % (self.opt['out'],): im[:, :, :self.patch_size, :self.patch_size],
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'%s_top_right' % (self.opt['out'],): im[:, :, :self.patch_size, -self.patch_size:],
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'%s_bottom_left' % (self.opt['out'],): im[:, :, -self.patch_size:, :self.patch_size],
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'%s_bottom_right' % (self.opt['out'],): im[:, :, -self.patch_size:, -self.patch_size:]}
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else:
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res = {self.opt['out']: im,
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'%s_top_left' % (self.opt['out'],): im,
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'%s_top_right' % (self.opt['out'],): im,
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'%s_bottom_left' % (self.opt['out'],): im,
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'%s_bottom_right' % (self.opt['out'],): im}
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if self.resize is not None:
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res2 = {}
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for k, v in res.items():
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res2[k] = torch.nn.functional.interpolate(v, size=(self.resize, self.resize), mode="nearest")
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res = res2
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return res
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# Concatenates a list of tensors on the specified dimension.
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class ConcatenateInjector(Injector):
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def __init__(self, opt, env):
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super(ConcatenateInjector, self).__init__(opt, env)
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self.dim = opt['dim']
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def forward(self, state):
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input = [state[i] for i in self.input]
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return {self.opt['out']: torch.cat(input, dim=self.dim)}
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# Removes margins from an image.
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class MarginRemoval(Injector):
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def __init__(self, opt, env):
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super(MarginRemoval, self).__init__(opt, env)
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self.margin = opt['margin']
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self.random_shift_max = opt['random_shift_max'] if 'random_shift_max' in opt.keys() else 0
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def forward(self, state):
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input = state[self.input]
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if self.random_shift_max > 0:
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output = []
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# This is a really shitty way of doing this. If it works at all, I should reconsider using Resample2D, for example.
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for b in range(input.shape[0]):
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shiftleft = random.randint(-self.random_shift_max, self.random_shift_max)
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shifttop = random.randint(-self.random_shift_max, self.random_shift_max)
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output.append(input[b, :, self.margin + shiftleft:-(self.margin - shiftleft),
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self.margin + shifttop:-(self.margin - shifttop)])
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output = torch.stack(output, dim=0)
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else:
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output = input[:, :, self.margin:-self.margin,
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self.margin:-self.margin]
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return {self.opt['out']: output}
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# Produces an injection which is composed of applying a single injector multiple times across a single dimension.
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class ForEachInjector(Injector):
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def __init__(self, opt, env):
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super(ForEachInjector, self).__init__(opt, env)
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o = opt.copy()
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o['type'] = opt['subtype']
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o['in'] = '_in'
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o['out'] = '_out'
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self.injector = create_injector(o, self.env)
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self.aslist = opt['aslist'] if 'aslist' in opt.keys() else False
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def forward(self, state):
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injs = []
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st = state.copy()
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inputs = state[self.opt['in']]
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for i in range(inputs.shape[1]):
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st['_in'] = inputs[:, i]
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injs.append(self.injector(st)['_out'])
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if self.aslist:
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return {self.output: injs}
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else:
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return {self.output: torch.stack(injs, dim=1)}
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class ConstantInjector(Injector):
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def __init__(self, opt, env):
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super(ConstantInjector, self).__init__(opt, env)
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self.constant_type = opt['constant_type']
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self.like = opt['like'] # This injector uses this tensor to determine what batch size and device to use.
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def forward(self, state):
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like = state[self.like]
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if self.constant_type == 'zeroes':
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out = torch.zeros_like(like)
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else:
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raise NotImplementedError
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return {self.opt['out']: out}
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class IndicesExtractor(Injector):
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def __init__(self, opt, env):
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super(IndicesExtractor, self).__init__(opt, env)
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self.dim = opt['dim']
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assert self.dim == 1 # Honestly not sure how to support an abstract dim here, so just add yours when needed.
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def forward(self, state):
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results = {}
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for i, o in enumerate(self.output):
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if self.dim == 1:
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results[o] = state[self.input][:, i]
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return results
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class RandomShiftInjector(Injector):
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def __init__(self, opt, env):
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super(RandomShiftInjector, self).__init__(opt, env)
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def forward(self, state):
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img = state[self.input]
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return {self.output: img}
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class BatchRotateInjector(Injector):
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def __init__(self, opt, env):
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super(BatchRotateInjector, self).__init__(opt, env)
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def forward(self, state):
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img = state[self.input]
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return {self.output: torch.roll(img, 1, 0)}
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# Injector used to work with image deltas used in diff-SR
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class SrDiffsInjector(Injector):
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def __init__(self, opt, env):
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super(SrDiffsInjector, self).__init__(opt, env)
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self.mode = opt['mode']
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assert self.mode in ['recombine', 'produce_diff']
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self.lq = opt['lq']
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self.hq = opt['hq']
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if self.mode == 'produce_diff':
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self.diff_key = opt['diff']
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self.include_combined = opt['include_combined']
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def forward(self, state):
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resampled_lq = state[self.lq]
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hq = state[self.hq]
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if self.mode == 'produce_diff':
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diff = hq - resampled_lq
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if self.include_combined:
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res = torch.cat([resampled_lq, diff, hq], dim=1)
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else:
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res = torch.cat([resampled_lq, diff], dim=1)
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return {self.output: res,
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self.diff_key: diff}
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elif self.mode == 'recombine':
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combined = resampled_lq + hq
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return {self.output: combined}
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class MultiFrameCombiner(Injector):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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self.mode = opt['mode']
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self.dim = opt['dim'] if 'dim' in opt.keys() else None
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self.flow = opt['flow']
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self.in_lq_key = opt['in']
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self.in_hq_key = opt['in_hq']
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self.out_lq_key = opt['out']
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self.out_hq_key = opt['out_hq']
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from models.flownet2.networks import Resample2d
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self.resampler = Resample2d()
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def combine(self, state):
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flow = self.env['generators'][self.flow]
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lq = state[self.in_lq_key]
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hq = state[self.in_hq_key]
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b, f, c, h, w = lq.shape
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center = f // 2
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center_img = lq[:, center, :, :, :]
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imgs = [center_img]
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with torch.no_grad():
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for i in range(f):
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if i == center:
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continue
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nimg = lq[:, i, :, :, :]
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flowfield = flow(torch.stack([center_img, nimg], dim=2).float())
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nimg = self.resampler(nimg, flowfield)
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imgs.append(nimg)
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hq_out = hq[:, center, :, :, :]
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return {self.out_lq_key: torch.cat(imgs, dim=1),
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self.out_hq_key: hq_out,
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self.out_lq_key + "_flow_sample": torch.cat(imgs, dim=0)}
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def synthesize(self, state):
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lq = state[self.in_lq_key]
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return {
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self.out_lq_key: lq.repeat(1, self.dim, 1, 1)
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}
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def forward(self, state):
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if self.mode == "synthesize":
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return self.synthesize(state)
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elif self.mode == "combine":
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return self.combine(state)
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else:
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raise NotImplementedError
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# Combines data from multiple different sources and mixes them along the batch dimension. Labels are then emitted
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# according to how the mixing was performed.
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class MixAndLabelInjector(Injector):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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self.out_labels = opt['out_labels']
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def forward(self, state):
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input_tensors = [state[i] for i in self.input]
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num_inputs = len(input_tensors)
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bs = input_tensors[0].shape[0]
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labels = torch.randint(0, num_inputs, (bs,), device=input_tensors[0].device)
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# Still don't know of a good way to do this in torch.. TODO make it better..
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res = []
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for b in range(bs):
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res.append(input_tensors[labels[b]][b, :, :, :])
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output = torch.stack(res, dim=0)
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return {self.out_labels: labels, self.output: output}
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# Doesn't inject. Rather saves images that meet a specified criteria. Useful for performing classification filtering
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# using ExtensibleTrainer.
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class SaveImages(Injector):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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self.logits = opt['logits']
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self.target = opt['target']
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self.thresh = opt['threshold']
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self.index = 0
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self.rindex = 0
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self.run_id = random.randint(0, 999999)
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self.savedir = opt['savedir']
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os.makedirs(self.savedir, exist_ok=True)
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self.rejectdir = opt['negatives']
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if self.rejectdir:
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os.makedirs(self.rejectdir, exist_ok=True)
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self.softmax = torch.nn.Softmax(dim=1)
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def forward(self, state):
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logits = self.softmax(state[self.logits])
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images = state[self.input]
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bs = images.shape[0]
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for b in range(bs):
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if logits[b][self.target] > self.thresh:
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torchvision.utils.save_image(images[b], os.path.join(self.savedir, f'{self.run_id}_{self.index}.jpg'))
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self.index += 1
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elif self.rejectdir:
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torchvision.utils.save_image(images[b],
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os.path.join(self.rejectdir, f'{self.run_id}_{self.rindex}.jpg'))
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self.rindex += 1
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return {} |