915 lines
28 KiB
Python
915 lines
28 KiB
Python
import math
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import multiprocessing
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import random
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from contextlib import contextmanager, ExitStack
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from functools import partial
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from math import log2, floor
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from pathlib import Path
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from random import random
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import torch
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import torch.nn.functional as F
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from gsa_pytorch import GSA
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import trainer.losses as L
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import torchvision
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from PIL import Image
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from einops import rearrange, reduce
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from kornia import filter2d
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from torch import nn, einsum
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from torch.utils.data import Dataset
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from torchvision import transforms
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from models.stylegan.stylegan2_lucidrains import gradient_penalty
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from trainer.networks import register_model
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from utils.util import opt_get
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def DiffAugment(x, types=[]):
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for p in types:
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for f in AUGMENT_FNS[p]:
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x = f(x)
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return x.contiguous()
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# """
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# Augmentation functions got images as `x`
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# where `x` is tensor with this dimensions:
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# 0 - count of images
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# 1 - channels
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# 2 - width
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# 3 - height of image
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# """
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def rand_brightness(x):
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x = x + (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) - 0.5)
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return x
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def rand_saturation(x):
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x_mean = x.mean(dim=1, keepdim=True)
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x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) * 2) + x_mean
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return x
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def rand_contrast(x):
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x_mean = x.mean(dim=[1, 2, 3], keepdim=True)
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x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) + 0.5) + x_mean
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return x
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def rand_translation(x, ratio=0.125):
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shift_x, shift_y = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5)
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translation_x = torch.randint(-shift_x, shift_x + 1, size=[x.size(0), 1, 1], device=x.device)
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translation_y = torch.randint(-shift_y, shift_y + 1, size=[x.size(0), 1, 1], device=x.device)
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grid_batch, grid_x, grid_y = torch.meshgrid(
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torch.arange(x.size(0), dtype=torch.long, device=x.device),
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torch.arange(x.size(2), dtype=torch.long, device=x.device),
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torch.arange(x.size(3), dtype=torch.long, device=x.device),
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)
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grid_x = torch.clamp(grid_x + translation_x + 1, 0, x.size(2) + 1)
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grid_y = torch.clamp(grid_y + translation_y + 1, 0, x.size(3) + 1)
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x_pad = F.pad(x, [1, 1, 1, 1, 0, 0, 0, 0])
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x = x_pad.permute(0, 2, 3, 1).contiguous()[grid_batch, grid_x, grid_y].permute(0, 3, 1, 2)
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return x
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def rand_offset(x, ratio=1, ratio_h=1, ratio_v=1):
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w, h = x.size(2), x.size(3)
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imgs = []
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for img in x.unbind(dim = 0):
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max_h = int(w * ratio * ratio_h)
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max_v = int(h * ratio * ratio_v)
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value_h = random.randint(0, max_h) * 2 - max_h
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value_v = random.randint(0, max_v) * 2 - max_v
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if abs(value_h) > 0:
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img = torch.roll(img, value_h, 2)
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if abs(value_v) > 0:
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img = torch.roll(img, value_v, 1)
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imgs.append(img)
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return torch.stack(imgs)
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def rand_offset_h(x, ratio=1):
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return rand_offset(x, ratio=1, ratio_h=ratio, ratio_v=0)
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def rand_offset_v(x, ratio=1):
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return rand_offset(x, ratio=1, ratio_h=0, ratio_v=ratio)
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def rand_cutout(x, ratio=0.5):
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cutout_size = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5)
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offset_x = torch.randint(0, x.size(2) + (1 - cutout_size[0] % 2), size=[x.size(0), 1, 1], device=x.device)
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offset_y = torch.randint(0, x.size(3) + (1 - cutout_size[1] % 2), size=[x.size(0), 1, 1], device=x.device)
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grid_batch, grid_x, grid_y = torch.meshgrid(
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torch.arange(x.size(0), dtype=torch.long, device=x.device),
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torch.arange(cutout_size[0], dtype=torch.long, device=x.device),
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torch.arange(cutout_size[1], dtype=torch.long, device=x.device),
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)
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grid_x = torch.clamp(grid_x + offset_x - cutout_size[0] // 2, min=0, max=x.size(2) - 1)
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grid_y = torch.clamp(grid_y + offset_y - cutout_size[1] // 2, min=0, max=x.size(3) - 1)
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mask = torch.ones(x.size(0), x.size(2), x.size(3), dtype=x.dtype, device=x.device)
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mask[grid_batch, grid_x, grid_y] = 0
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x = x * mask.unsqueeze(1)
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return x
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AUGMENT_FNS = {
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'color': [rand_brightness, rand_saturation, rand_contrast],
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'offset': [rand_offset],
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'offset_h': [rand_offset_h],
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'offset_v': [rand_offset_v],
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'translation': [rand_translation],
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'cutout': [rand_cutout],
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}
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# constants
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NUM_CORES = multiprocessing.cpu_count()
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EXTS = ['jpg', 'jpeg', 'png']
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# helpers
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def exists(val):
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return val is not None
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@contextmanager
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def null_context():
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yield
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def combine_contexts(contexts):
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@contextmanager
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def multi_contexts():
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with ExitStack() as stack:
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yield [stack.enter_context(ctx()) for ctx in contexts]
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return multi_contexts
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def is_power_of_two(val):
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return log2(val).is_integer()
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def default(val, d):
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return val if exists(val) else d
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def set_requires_grad(model, bool):
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for p in model.parameters():
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p.requires_grad = bool
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def cycle(iterable):
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while True:
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for i in iterable:
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yield i
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def raise_if_nan(t):
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if torch.isnan(t):
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raise NanException
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def gradient_accumulate_contexts(gradient_accumulate_every, is_ddp, ddps):
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if is_ddp:
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num_no_syncs = gradient_accumulate_every - 1
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head = [combine_contexts(map(lambda ddp: ddp.no_sync, ddps))] * num_no_syncs
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tail = [null_context]
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contexts = head + tail
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else:
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contexts = [null_context] * gradient_accumulate_every
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for context in contexts:
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with context():
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yield
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def hinge_loss(real, fake):
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return (F.relu(1 + real) + F.relu(1 - fake)).mean()
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def evaluate_in_chunks(max_batch_size, model, *args):
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split_args = list(zip(*list(map(lambda x: x.split(max_batch_size, dim=0), args))))
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chunked_outputs = [model(*i) for i in split_args]
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if len(chunked_outputs) == 1:
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return chunked_outputs[0]
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return torch.cat(chunked_outputs, dim=0)
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def slerp(val, low, high):
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low_norm = low / torch.norm(low, dim=1, keepdim=True)
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high_norm = high / torch.norm(high, dim=1, keepdim=True)
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omega = torch.acos((low_norm * high_norm).sum(1))
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so = torch.sin(omega)
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res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high
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return res
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def safe_div(n, d):
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try:
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res = n / d
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except ZeroDivisionError:
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prefix = '' if int(n >= 0) else '-'
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res = float(f'{prefix}inf')
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return res
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# helper classes
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class NanException(Exception):
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pass
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class EMA():
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def __init__(self, beta):
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super().__init__()
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self.beta = beta
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def update_average(self, old, new):
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if not exists(old):
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return new
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return old * self.beta + (1 - self.beta) * new
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class EMAWrapper(nn.Module):
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def __init__(self, wrapped_module, following_module, rate=.995, steps_per_ema=10, steps_per_reset=1000, steps_after_no_reset=25000, reset=True):
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super().__init__()
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self.wrapped = wrapped_module
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self.following = following_module
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self.ema_updater = EMA(rate)
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self.steps_per_ema = steps_per_ema
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self.steps_per_reset = steps_per_reset
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self.steps_after_no_reset = steps_after_no_reset
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if reset:
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self.wrapped.load_state_dict(self.following.state_dict())
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for p in self.wrapped.parameters():
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p.DO_NOT_TRAIN = True
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def reset_parameter_averaging(self):
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self.wrapped.load_state_dict(self.following.state_dict())
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def update_moving_average(self):
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for current_params, ma_params in zip(self.following.parameters(), self.wrapped.parameters()):
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old_weight, up_weight = ma_params.data, current_params.data
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ma_params.data = self.ema_updater.update_average(old_weight, up_weight)
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for current_buffer, ma_buffer in zip(self.following.buffers(), self.wrapped.buffers()):
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new_buffer_value = self.ema_updater.update_average(ma_buffer, current_buffer)
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ma_buffer.copy_(new_buffer_value)
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def after_step(self, step):
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if step % self.steps_per_ema == 0:
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self.update_moving_average()
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if step % self.steps_per_reset and step < self.steps_after_no_reset:
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self.reset_parameter_averaging()
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def forward(self, x):
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with torch.no_grad():
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return self.wrapped(x)
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class RandomApply(nn.Module):
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def __init__(self, prob, fn, fn_else=lambda x: x):
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super().__init__()
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self.fn = fn
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self.fn_else = fn_else
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self.prob = prob
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def forward(self, x):
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fn = self.fn if random() < self.prob else self.fn_else
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return fn(x)
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class Rezero(nn.Module):
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def __init__(self, fn):
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super().__init__()
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self.fn = fn
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self.g = nn.Parameter(torch.tensor(1e-3))
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def forward(self, x):
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return self.g * self.fn(x)
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class Residual(nn.Module):
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def __init__(self, fn):
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super().__init__()
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self.fn = fn
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def forward(self, x):
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return self.fn(x) + x
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class SumBranches(nn.Module):
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def __init__(self, branches):
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super().__init__()
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self.branches = nn.ModuleList(branches)
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def forward(self, x):
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return sum(map(lambda fn: fn(x), self.branches))
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class Blur(nn.Module):
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def __init__(self):
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super().__init__()
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f = torch.Tensor([1, 2, 1])
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self.register_buffer('f', f)
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def forward(self, x):
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f = self.f
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f = f[None, None, :] * f[None, :, None]
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return filter2d(x, f, normalized=True)
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# dataset
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def convert_image_to(img_type, image):
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if image.mode != img_type:
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return image.convert(img_type)
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return image
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class identity(object):
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def __call__(self, tensor):
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return tensor
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class expand_greyscale(object):
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def __init__(self, transparent):
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self.transparent = transparent
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def __call__(self, tensor):
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channels = tensor.shape[0]
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num_target_channels = 4 if self.transparent else 3
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if channels == num_target_channels:
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return tensor
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alpha = None
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if channels == 1:
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color = tensor.expand(3, -1, -1)
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elif channels == 2:
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color = tensor[:1].expand(3, -1, -1)
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alpha = tensor[1:]
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else:
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raise Exception(f'image with invalid number of channels given {channels}')
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if not exists(alpha) and self.transparent:
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alpha = torch.ones(1, *tensor.shape[1:], device=tensor.device)
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return color if not self.transparent else torch.cat((color, alpha))
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def resize_to_minimum_size(min_size, image):
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if max(*image.size) < min_size:
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return torchvision.transforms.functional.resize(image, min_size)
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return image
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class ImageDataset(Dataset):
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def __init__(
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self,
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folder,
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image_size,
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transparent=False,
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greyscale=False,
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aug_prob=0.
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):
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super().__init__()
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self.folder = folder
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self.image_size = image_size
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self.paths = [p for ext in EXTS for p in Path(f'{folder}').glob(f'**/*.{ext}')]
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assert len(self.paths) > 0, f'No images were found in {folder} for training'
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if transparent:
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num_channels = 4
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pillow_mode = 'RGBA'
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expand_fn = expand_greyscale(transparent)
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elif greyscale:
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num_channels = 1
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pillow_mode = 'L'
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expand_fn = identity()
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else:
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num_channels = 3
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pillow_mode = 'RGB'
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expand_fn = expand_greyscale(transparent)
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convert_image_fn = partial(convert_image_to, pillow_mode)
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self.transform = transforms.Compose([
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transforms.Lambda(convert_image_fn),
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transforms.Lambda(partial(resize_to_minimum_size, image_size)),
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transforms.Resize(image_size),
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RandomApply(aug_prob, transforms.RandomResizedCrop(image_size, scale=(0.5, 1.0), ratio=(0.98, 1.02)),
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transforms.CenterCrop(image_size)),
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transforms.ToTensor(),
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transforms.Lambda(expand_fn)
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])
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def __len__(self):
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return len(self.paths)
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def __getitem__(self, index):
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path = self.paths[index]
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img = Image.open(path)
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return self.transform(img)
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# augmentations
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def random_hflip(tensor, prob):
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if prob > random():
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return tensor
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return torch.flip(tensor, dims=(3,))
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class AugWrapper(nn.Module):
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def __init__(self, D, image_size, prob, types):
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super().__init__()
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self.D = D
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self.prob = prob
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self.types = types
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def forward(self, images, detach=False, **kwargs):
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context = torch.no_grad if detach else null_context
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with context():
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if random() < self.prob:
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images = random_hflip(images, prob=0.5)
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images = DiffAugment(images, types=self.types)
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return self.D(images, **kwargs)
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# modifiable global variables
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norm_class = nn.BatchNorm2d
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def upsample(scale_factor=2):
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return nn.Upsample(scale_factor=scale_factor)
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# squeeze excitation classes
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# global context network
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# https://arxiv.org/abs/2012.13375
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# similar to squeeze-excite, but with a simplified attention pooling and a subsequent layer norm
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class GlobalContext(nn.Module):
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def __init__(
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self,
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*,
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chan_in,
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chan_out
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):
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super().__init__()
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self.to_k = nn.Conv2d(chan_in, 1, 1)
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chan_intermediate = max(3, chan_out // 2)
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self.net = nn.Sequential(
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nn.Conv2d(chan_in, chan_intermediate, 1),
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nn.LeakyReLU(0.1),
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nn.Conv2d(chan_intermediate, chan_out, 1),
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nn.Sigmoid()
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)
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def forward(self, x):
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context = self.to_k(x)
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context = context.flatten(2).softmax(dim=-1)
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out = einsum('b i n, b c n -> b c i', context, x.flatten(2))
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out = out.unsqueeze(-1)
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return self.net(out)
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# frequency channel attention
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# https://arxiv.org/abs/2012.11879
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def get_1d_dct(i, freq, L):
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result = math.cos(math.pi * freq * (i + 0.5) / L) / math.sqrt(L)
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return result * (1 if freq == 0 else math.sqrt(2))
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def get_dct_weights(width, channel, fidx_u, fidx_v):
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dct_weights = torch.zeros(1, channel, width, width)
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c_part = channel // len(fidx_u)
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for i, (u_x, v_y) in enumerate(zip(fidx_u, fidx_v)):
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for x in range(width):
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for y in range(width):
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coor_value = get_1d_dct(x, u_x, width) * get_1d_dct(y, v_y, width)
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dct_weights[:, i * c_part: (i + 1) * c_part, x, y] = coor_value
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return dct_weights
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class FCANet(nn.Module):
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def __init__(
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self,
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*,
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chan_in,
|
|
chan_out,
|
|
reduction=4,
|
|
width
|
|
):
|
|
super().__init__()
|
|
|
|
freq_w, freq_h = ([0] * 8), list(range(8)) # in paper, it seems 16 frequencies was ideal
|
|
dct_weights = get_dct_weights(width, chan_in, [*freq_w, *freq_h], [*freq_h, *freq_w])
|
|
self.register_buffer('dct_weights', dct_weights)
|
|
|
|
chan_intermediate = max(3, chan_out // reduction)
|
|
|
|
self.net = nn.Sequential(
|
|
nn.Conv2d(chan_in, chan_intermediate, 1),
|
|
nn.LeakyReLU(0.1),
|
|
nn.Conv2d(chan_intermediate, chan_out, 1),
|
|
nn.Sigmoid()
|
|
)
|
|
|
|
def forward(self, x):
|
|
x = reduce(x * self.dct_weights, 'b c (h h1) (w w1) -> b c h1 w1', 'sum', h1=1, w1=1)
|
|
return self.net(x)
|
|
|
|
|
|
# generative adversarial network
|
|
|
|
class Generator(nn.Module):
|
|
def __init__(
|
|
self,
|
|
*,
|
|
image_size,
|
|
latent_dim=256,
|
|
fmap_max=512,
|
|
fmap_inverse_coef=12,
|
|
transparent=False,
|
|
greyscale=False,
|
|
freq_chan_attn=False
|
|
):
|
|
super().__init__()
|
|
resolution = log2(image_size)
|
|
assert is_power_of_two(image_size), 'image size must be a power of 2'
|
|
|
|
if transparent:
|
|
init_channel = 4
|
|
elif greyscale:
|
|
init_channel = 1
|
|
else:
|
|
init_channel = 3
|
|
|
|
fmap_max = default(fmap_max, latent_dim)
|
|
|
|
self.initial_conv = nn.Sequential(
|
|
nn.ConvTranspose2d(latent_dim, latent_dim * 2, 4),
|
|
norm_class(latent_dim * 2),
|
|
nn.GLU(dim=1)
|
|
)
|
|
|
|
num_layers = int(resolution) - 2
|
|
features = list(map(lambda n: (n, 2 ** (fmap_inverse_coef - n)), range(2, num_layers + 2)))
|
|
features = list(map(lambda n: (n[0], min(n[1], fmap_max)), features))
|
|
features = list(map(lambda n: 3 if n[0] >= 8 else n[1], features))
|
|
features = [latent_dim, *features]
|
|
|
|
in_out_features = list(zip(features[:-1], features[1:]))
|
|
|
|
self.res_layers = range(2, num_layers + 2)
|
|
self.layers = nn.ModuleList([])
|
|
self.res_to_feature_map = dict(zip(self.res_layers, in_out_features))
|
|
|
|
self.sle_map = ((3, 7), (4, 8), (5, 9), (6, 10))
|
|
self.sle_map = list(filter(lambda t: t[0] <= resolution and t[1] <= resolution, self.sle_map))
|
|
self.sle_map = dict(self.sle_map)
|
|
|
|
self.num_layers_spatial_res = 1
|
|
|
|
for (res, (chan_in, chan_out)) in zip(self.res_layers, in_out_features):
|
|
attn = None
|
|
sle = None
|
|
if res in self.sle_map:
|
|
residual_layer = self.sle_map[res]
|
|
sle_chan_out = self.res_to_feature_map[residual_layer - 1][-1]
|
|
|
|
if freq_chan_attn:
|
|
sle = FCANet(
|
|
chan_in=chan_out,
|
|
chan_out=sle_chan_out,
|
|
width=2 ** (res + 1)
|
|
)
|
|
else:
|
|
sle = GlobalContext(
|
|
chan_in=chan_out,
|
|
chan_out=sle_chan_out
|
|
)
|
|
|
|
layer = nn.ModuleList([
|
|
nn.Sequential(
|
|
upsample(),
|
|
Blur(),
|
|
nn.Conv2d(chan_in, chan_out * 2, 3, padding=1),
|
|
norm_class(chan_out * 2),
|
|
nn.GLU(dim=1)
|
|
),
|
|
sle,
|
|
attn
|
|
])
|
|
self.layers.append(layer)
|
|
|
|
self.out_conv = nn.Conv2d(features[-1], init_channel, 3, padding=1)
|
|
|
|
for m in self.modules():
|
|
if type(m) in {nn.Conv2d, nn.Linear}:
|
|
nn.init.kaiming_normal_(m.weight, a=0, mode='fan_in', nonlinearity='leaky_relu')
|
|
|
|
def forward(self, x):
|
|
x = rearrange(x, 'b c -> b c () ()')
|
|
x = self.initial_conv(x)
|
|
x = F.normalize(x, dim=1)
|
|
|
|
residuals = dict()
|
|
|
|
for (res, (up, sle, attn)) in zip(self.res_layers, self.layers):
|
|
if exists(attn):
|
|
x = attn(x) + x
|
|
|
|
x = up(x)
|
|
|
|
if exists(sle):
|
|
out_res = self.sle_map[res]
|
|
residual = sle(x)
|
|
residuals[out_res] = residual
|
|
|
|
next_res = res + 1
|
|
if next_res in residuals:
|
|
x = x * residuals[next_res]
|
|
|
|
return self.out_conv(x)
|
|
|
|
|
|
class SimpleDecoder(nn.Module):
|
|
def __init__(
|
|
self,
|
|
*,
|
|
chan_in,
|
|
chan_out=3,
|
|
num_upsamples=4,
|
|
):
|
|
super().__init__()
|
|
|
|
self.layers = nn.ModuleList([])
|
|
final_chan = chan_out
|
|
chans = chan_in
|
|
|
|
for ind in range(num_upsamples):
|
|
last_layer = ind == (num_upsamples - 1)
|
|
chan_out = chans if not last_layer else final_chan * 2
|
|
layer = nn.Sequential(
|
|
upsample(),
|
|
nn.Conv2d(chans, chan_out, 3, padding=1),
|
|
nn.GLU(dim=1)
|
|
)
|
|
self.layers.append(layer)
|
|
chans //= 2
|
|
|
|
def forward(self, x):
|
|
for layer in self.layers:
|
|
x = layer(x)
|
|
return x
|
|
|
|
|
|
class Discriminator(nn.Module):
|
|
def __init__(
|
|
self,
|
|
*,
|
|
image_size,
|
|
fmap_max=512,
|
|
fmap_inverse_coef=12,
|
|
transparent=False,
|
|
greyscale=False,
|
|
disc_output_size=5,
|
|
attn_res_layers=[]
|
|
):
|
|
super().__init__()
|
|
self.image_size = image_size
|
|
resolution = log2(image_size)
|
|
assert is_power_of_two(image_size), 'image size must be a power of 2'
|
|
assert disc_output_size in {1, 5}, 'discriminator output dimensions can only be 5x5 or 1x1'
|
|
|
|
resolution = int(resolution)
|
|
|
|
if transparent:
|
|
init_channel = 4
|
|
elif greyscale:
|
|
init_channel = 1
|
|
else:
|
|
init_channel = 3
|
|
|
|
num_non_residual_layers = max(0, int(resolution) - 8)
|
|
num_residual_layers = 8 - 3
|
|
|
|
non_residual_resolutions = range(min(8, resolution), 2, -1)
|
|
features = list(map(lambda n: (n, 2 ** (fmap_inverse_coef - n)), non_residual_resolutions))
|
|
features = list(map(lambda n: (n[0], min(n[1], fmap_max)), features))
|
|
|
|
if num_non_residual_layers == 0:
|
|
res, _ = features[0]
|
|
features[0] = (res, init_channel)
|
|
|
|
chan_in_out = list(zip(features[:-1], features[1:]))
|
|
|
|
self.non_residual_layers = nn.ModuleList([])
|
|
for ind in range(num_non_residual_layers):
|
|
first_layer = ind == 0
|
|
last_layer = ind == (num_non_residual_layers - 1)
|
|
chan_out = features[0][-1] if last_layer else init_channel
|
|
|
|
self.non_residual_layers.append(nn.Sequential(
|
|
Blur(),
|
|
nn.Conv2d(init_channel, chan_out, 4, stride=2, padding=1),
|
|
nn.LeakyReLU(0.1)
|
|
))
|
|
|
|
self.residual_layers = nn.ModuleList([])
|
|
|
|
for (res, ((_, chan_in), (_, chan_out))) in zip(non_residual_resolutions, chan_in_out):
|
|
attn = None
|
|
self.residual_layers.append(nn.ModuleList([
|
|
SumBranches([
|
|
nn.Sequential(
|
|
Blur(),
|
|
nn.Conv2d(chan_in, chan_out, 4, stride=2, padding=1),
|
|
nn.LeakyReLU(0.1),
|
|
nn.Conv2d(chan_out, chan_out, 3, padding=1),
|
|
nn.LeakyReLU(0.1)
|
|
),
|
|
nn.Sequential(
|
|
Blur(),
|
|
nn.AvgPool2d(2),
|
|
nn.Conv2d(chan_in, chan_out, 1),
|
|
nn.LeakyReLU(0.1),
|
|
)
|
|
]),
|
|
attn
|
|
]))
|
|
|
|
last_chan = features[-1][-1]
|
|
if disc_output_size == 5:
|
|
self.to_logits = nn.Sequential(
|
|
nn.Conv2d(last_chan, last_chan, 1),
|
|
nn.LeakyReLU(0.1),
|
|
nn.Conv2d(last_chan, 1, 4)
|
|
)
|
|
elif disc_output_size == 1:
|
|
self.to_logits = nn.Sequential(
|
|
Blur(),
|
|
nn.Conv2d(last_chan, last_chan, 3, stride=2, padding=1),
|
|
nn.LeakyReLU(0.1),
|
|
nn.Conv2d(last_chan, 1, 4)
|
|
)
|
|
|
|
self.to_shape_disc_out = nn.Sequential(
|
|
nn.Conv2d(init_channel, 64, 3, padding=1),
|
|
Residual(Rezero(GSA(dim=64, norm_queries=True, batch_norm=False))),
|
|
SumBranches([
|
|
nn.Sequential(
|
|
Blur(),
|
|
nn.Conv2d(64, 32, 4, stride=2, padding=1),
|
|
nn.LeakyReLU(0.1),
|
|
nn.Conv2d(32, 32, 3, padding=1),
|
|
nn.LeakyReLU(0.1)
|
|
),
|
|
nn.Sequential(
|
|
Blur(),
|
|
nn.AvgPool2d(2),
|
|
nn.Conv2d(64, 32, 1),
|
|
nn.LeakyReLU(0.1),
|
|
)
|
|
]),
|
|
Residual(Rezero(GSA(dim=32, norm_queries=True, batch_norm=False))),
|
|
nn.AdaptiveAvgPool2d((4, 4)),
|
|
nn.Conv2d(32, 1, 4)
|
|
)
|
|
|
|
self.decoder1 = SimpleDecoder(chan_in=last_chan, chan_out=init_channel)
|
|
self.decoder2 = SimpleDecoder(chan_in=features[-2][-1], chan_out=init_channel) if resolution >= 9 else None
|
|
|
|
for m in self.modules():
|
|
if type(m) in {nn.Conv2d, nn.Linear}:
|
|
nn.init.kaiming_normal_(m.weight, a=0, mode='fan_in', nonlinearity='leaky_relu')
|
|
|
|
def forward(self, x, calc_aux_loss=False):
|
|
orig_img = x
|
|
|
|
for layer in self.non_residual_layers:
|
|
x = layer(x)
|
|
|
|
layer_outputs = []
|
|
|
|
for (net, attn) in self.residual_layers:
|
|
if exists(attn):
|
|
x = attn(x) + x
|
|
|
|
x = net(x)
|
|
layer_outputs.append(x)
|
|
|
|
out = self.to_logits(x).flatten(1)
|
|
|
|
img_32x32 = F.interpolate(orig_img, size=(32, 32))
|
|
out_32x32 = self.to_shape_disc_out(img_32x32)
|
|
|
|
if not calc_aux_loss:
|
|
return out, out_32x32, None
|
|
|
|
# self-supervised auto-encoding loss
|
|
|
|
layer_8x8 = layer_outputs[-1]
|
|
layer_16x16 = layer_outputs[-2]
|
|
|
|
recon_img_8x8 = self.decoder1(layer_8x8)
|
|
|
|
aux_loss = F.mse_loss(
|
|
recon_img_8x8,
|
|
F.interpolate(orig_img, size=recon_img_8x8.shape[2:])
|
|
)
|
|
|
|
if exists(self.decoder2):
|
|
select_random_quadrant = lambda rand_quadrant, img: \
|
|
rearrange(img, 'b c (m h) (n w) -> (m n) b c h w', m=2, n=2)[rand_quadrant]
|
|
crop_image_fn = partial(select_random_quadrant, floor(random() * 4))
|
|
img_part, layer_16x16_part = map(crop_image_fn, (orig_img, layer_16x16))
|
|
|
|
recon_img_16x16 = self.decoder2(layer_16x16_part)
|
|
|
|
aux_loss_16x16 = F.mse_loss(
|
|
recon_img_16x16,
|
|
F.interpolate(img_part, size=recon_img_16x16.shape[2:])
|
|
)
|
|
|
|
aux_loss = aux_loss + aux_loss_16x16
|
|
|
|
return out, out_32x32, aux_loss
|
|
|
|
|
|
class LightweightGanDivergenceLoss(L.ConfigurableLoss):
|
|
def __init__(self, opt, env):
|
|
super().__init__(opt, env)
|
|
self.real = opt['real']
|
|
self.fake = opt['fake']
|
|
self.discriminator = opt['discriminator']
|
|
self.for_gen = opt['gen_loss']
|
|
self.gp_frequency = opt['gradient_penalty_frequency']
|
|
self.noise = opt['noise'] if 'noise' in opt.keys() else 0
|
|
# TODO: Implement generator top-k fractional loss compensation.
|
|
|
|
def forward(self, net, state):
|
|
real_input = state[self.real]
|
|
fake_input = state[self.fake]
|
|
if self.noise != 0:
|
|
fake_input = fake_input + torch.rand_like(fake_input) * self.noise
|
|
real_input = real_input + torch.rand_like(real_input) * self.noise
|
|
|
|
D = self.env['discriminators'][self.discriminator]
|
|
fake, fake32, _ = D(fake_input, detach=not self.for_gen)
|
|
if self.for_gen:
|
|
return fake.mean() + fake32.mean()
|
|
else:
|
|
real_input.requires_grad_() # <-- Needed to compute gradients on the input.
|
|
real, real32, real_aux = D(real_input, calc_aux_loss=True)
|
|
divergence_loss = hinge_loss(real, fake) + hinge_loss(real32, fake32) + real_aux
|
|
|
|
# Apply gradient penalty. TODO: migrate this elsewhere.
|
|
if self.env['step'] % self.gp_frequency == 0:
|
|
gp = gradient_penalty(real_input, real)
|
|
self.metrics.append(("gradient_penalty", gp.clone().detach()))
|
|
divergence_loss = divergence_loss + gp
|
|
|
|
real_input.requires_grad_(requires_grad=False)
|
|
return divergence_loss
|
|
|
|
|
|
@register_model
|
|
def register_lightweight_gan_g(opt_net, opt, other_nets):
|
|
gen = Generator(**opt_net['kwargs'])
|
|
if opt_get(opt_net, ['ema'], False):
|
|
following = other_nets[opt_net['following']]
|
|
return EMAWrapper(gen, following, opt_net['rate'])
|
|
return gen
|
|
|
|
|
|
@register_model
|
|
def register_lightweight_gan_d(opt_net, opt):
|
|
d = Discriminator(**opt_net['kwargs'])
|
|
if opt_net['aug']:
|
|
return AugWrapper(d, d.image_size, opt_net['aug_prob'], opt_net['aug_types'])
|
|
return d
|
|
|
|
|
|
if __name__ == '__main__':
|
|
g = Generator(image_size=256)
|
|
d = Discriminator(image_size=256)
|
|
j = torch.randn(1,256)
|
|
r = g(j)
|
|
a, b, c = d(r)
|
|
print(a.shape)
|