574 lines
22 KiB
Python
574 lines
22 KiB
Python
import random
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import torch.nn
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from kornia.augmentation import RandomResizedCrop
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from torch.cuda.amp import autocast
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from trainer.inject import Injector, create_injector
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from trainer.losses import extract_params_from_state
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from utils.util import opt_get
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from utils.weight_scheduler import get_scheduler_for_opt
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class PadInjector(Injector):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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self.multiple = opt['multiple']
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def forward(self, state):
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ldim = state[self.input].shape[-1]
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mod = self.multiple-(ldim % self.multiple)
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t = state[self.input]
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if mod != 0:
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t = torch.nn.functional.pad(t, (0, mod))
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return {self.output: t}
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class SqueezeInjector(Injector):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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self.dim = opt['dim']
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def forward(self, state):
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return {self.output: state[self.input].squeeze(dim=self.dim)}
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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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self.method = opt_get(opt, ['method'], None) # If specified, this method is called instead of __call__()
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self.args = opt_get(opt, ['args'], {})
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def forward(self, state):
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gen = self.env['generators'][self.opt['generator']]
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if self.method is not None and hasattr(gen, 'module'):
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gen = gen.module # Dereference DDP wrapper.
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method = gen if self.method is None else getattr(gen, self.method)
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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 = method(*params, **self.args)
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else:
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with torch.no_grad():
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results = method(*params, **self.args)
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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. IF YOU REACH THIS ERROR, REMOVE THE BRACES AROUND YOUR OUTPUTS IN THE YAML CONFIG
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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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# Randomly performs a uniform resize & crop from a base image.
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# Never resizes below input resolution or messes with the aspect ratio.
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class RandomCropInjector(Injector):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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dim_in = opt['dim_in']
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dim_out = opt['dim_out']
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scale = dim_out / dim_in
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self.operator = RandomResizedCrop(size=(dim_out, dim_out), scale=(scale, 1),
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ratio=(.99,1), # An aspect ratio range is required, but .99,1 is effectively "none".
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resample='NEAREST')
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def forward(self, state):
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return {self.output: self.operator(state[self.input])}
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class Stylegan2NoiseInjector(Injector):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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self.mix_prob = opt_get(opt, ['mix_probability'], .9)
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self.latent_dim = opt_get(opt, ['latent_dim'], 512)
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def make_noise(self, batch, latent_dim, n_noise, device):
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return torch.randn(n_noise, batch, latent_dim, device=device).unbind(0)
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def forward(self, state):
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i = state[self.input]
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if self.mix_prob > 0 and random.random() < self.mix_prob:
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return {self.output: self.make_noise(i.shape[0], self.latent_dim, 2, i.device)}
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else:
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return {self.output: self.make_noise(i.shape[0], self.latent_dim, 1, i.device)}
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class NoiseInjector(Injector):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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self.shape = tuple(opt['shape'])
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def forward(self, state):
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shape = (state[self.input].shape[0],) + self.shape
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return {self.output: torch.randn(shape, device=state[self.input].device)}
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# Incorporates the specified dimension into the batch dimension.
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class DecomposeDimensionInjector(Injector):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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self.dim = opt['dim']
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self.cutoff_dim = opt_get(opt, ['cutoff_dim'], -1)
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assert self.dim != 0 # Cannot decompose the batch dimension
|
|
|
|
def forward(self, state):
|
|
inp = state[self.input]
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|
dims = list(range(len(inp.shape))) # Looks like [0,1,2,3]
|
|
shape = list(inp.shape)
|
|
del dims[self.dim]
|
|
del shape[self.dim]
|
|
|
|
# Compute the reverse permutation and shape arguments needed to undo this operation.
|
|
rev_shape = [inp.shape[self.dim]] + shape.copy()
|
|
rev_permute = list(range(len(inp.shape)))[1:] # Looks like [1,2,3]
|
|
rev_permute = rev_permute[:self.dim] + [0] + (rev_permute[self.dim:] if self.dim < len(rev_permute) else [])
|
|
|
|
out = inp.permute([self.dim] + dims).reshape((-1,) + tuple(shape[1:]))
|
|
if self.cutoff_dim > -1:
|
|
out = out[:self.cutoff_dim]
|
|
|
|
return {self.output: out,
|
|
f'{self.output}_reverse_shape': rev_shape,
|
|
f'{self.output}_reverse_permute': rev_permute}
|
|
|
|
|
|
# Undoes a decompose.
|
|
class RecomposeDimensionInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super().__init__(opt, env)
|
|
self.rev_shape_key = opt['reverse_shape']
|
|
self.rev_permute_key = opt['reverse_permute']
|
|
|
|
def forward(self, state):
|
|
inp = state[self.input]
|
|
rev_shape = state[self.rev_shape_key]
|
|
rev_permute = state[self.rev_permute_key]
|
|
out = inp.reshape(rev_shape)
|
|
out = out.permute(rev_permute).contiguous()
|
|
return {self.output: out}
|
|
|
|
|
|
# Performs normalization across fixed constants.
|
|
class NormalizeInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super().__init__(opt, env)
|
|
self.shift = opt['shift']
|
|
self.scale = opt['scale']
|
|
|
|
def forward(self, state):
|
|
inp = state[self.input]
|
|
out = (inp - self.shift) / self.scale
|
|
return {self.output: out}
|
|
|
|
|
|
# Performs frequency-bin normalization for spectrograms.
|
|
class FrequencyBinNormalizeInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super().__init__(opt, env)
|
|
self.shift, self.scale = torch.load(opt['stats_file'])
|
|
self.shift = self.shift.view(1,-1,1)
|
|
self.scale = self.scale.view(1,-1,1)
|
|
|
|
def forward(self, state):
|
|
inp = state[self.input]
|
|
self.shift = self.shift.to(inp.device)
|
|
self.scale = self.scale.to(inp.device)
|
|
out = (inp - self.shift) / self.scale
|
|
return {self.output: out}
|
|
|
|
|
|
# Performs normalization across fixed constants.
|
|
class DenormalizeInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super().__init__(opt, env)
|
|
self.shift = opt['shift']
|
|
self.scale = opt['scale']
|
|
|
|
def forward(self, state):
|
|
inp = state[self.input]
|
|
out = inp * self.scale + self.shift
|
|
return {self.output: out}
|
|
|
|
|
|
# Performs normalization across fixed constants.
|
|
class MelSpectrogramInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super().__init__(opt, env)
|
|
from models.tacotron2.layers import TacotronSTFT
|
|
# These are the default tacotron values for the MEL spectrogram.
|
|
filter_length = opt_get(opt, ['filter_length'], 1024)
|
|
hop_length = opt_get(opt, ['hop_length'], 256)
|
|
win_length = opt_get(opt, ['win_length'], 1024)
|
|
n_mel_channels = opt_get(opt, ['n_mel_channels'], 80)
|
|
mel_fmin = opt_get(opt, ['mel_fmin'], 0)
|
|
mel_fmax = opt_get(opt, ['mel_fmax'], 8000)
|
|
sampling_rate = opt_get(opt, ['sampling_rate'], 22050)
|
|
self.stft = TacotronSTFT(filter_length, hop_length, win_length, n_mel_channels, sampling_rate, mel_fmin, mel_fmax)
|
|
|
|
def forward(self, state):
|
|
inp = state[self.input]
|
|
if len(inp.shape) == 3: # Automatically squeeze out the channels dimension if it is present (assuming mono-audio)
|
|
inp = inp.squeeze(1)
|
|
assert len(inp.shape) == 2
|
|
self.stft = self.stft.to(inp.device)
|
|
return {self.output: self.stft.mel_spectrogram(inp)}
|
|
|
|
|
|
class RandomAudioCropInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super().__init__(opt, env)
|
|
self.crop_sz = opt['crop_size']
|
|
|
|
def forward(self, state):
|
|
inp = state[self.input]
|
|
len = inp.shape[-1]
|
|
margin = len - self.crop_sz
|
|
start = random.randint(0, margin)
|
|
return {self.output: inp[:, :, start:start+self.crop_sz]}
|
|
|
|
|
|
if __name__ == '__main__':
|
|
inj = MelSpectrogramInjector({'in': 'x', 'out': 'y'}, None)
|
|
print(inj({'x':torch.rand(10,1,40800)})['y'].shape) |