1215 lines
52 KiB
Python
1215 lines
52 KiB
Python
import os
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import random
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import cv2
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import torch
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import numpy as np
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import torch.fft as fft
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from lama_cleaner.schema import Config
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from lama_cleaner.helper import load_model, get_cache_path_by_url, norm_img, boxes_from_mask, resize_max_size
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from lama_cleaner.model.base import InpaintModel
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from torch import conv2d, nn
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import torch.nn.functional as F
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from lama_cleaner.model.utils import setup_filter, _parse_scaling, _parse_padding, Conv2dLayer, FullyConnectedLayer, \
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MinibatchStdLayer, activation_funcs, conv2d_resample, bias_act, upsample2d, normalize_2nd_moment, downsample2d
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def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl='cuda'):
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assert isinstance(x, torch.Tensor)
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return _upfirdn2d_ref(x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain)
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def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1):
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"""Slow reference implementation of `upfirdn2d()` using standard PyTorch ops.
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"""
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# Validate arguments.
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assert isinstance(x, torch.Tensor) and x.ndim == 4
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if f is None:
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f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
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assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
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assert f.dtype == torch.float32 and not f.requires_grad
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batch_size, num_channels, in_height, in_width = x.shape
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upx, upy = _parse_scaling(up)
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downx, downy = _parse_scaling(down)
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padx0, padx1, pady0, pady1 = _parse_padding(padding)
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# Upsample by inserting zeros.
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x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1])
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x = torch.nn.functional.pad(x, [0, upx - 1, 0, 0, 0, upy - 1])
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x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx])
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# Pad or crop.
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x = torch.nn.functional.pad(x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)])
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x = x[:, :, max(-pady0, 0): x.shape[2] - max(-pady1, 0), max(-padx0, 0): x.shape[3] - max(-padx1, 0)]
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# Setup filter.
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f = f * (gain ** (f.ndim / 2))
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f = f.to(x.dtype)
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if not flip_filter:
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f = f.flip(list(range(f.ndim)))
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# Convolve with the filter.
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f = f[np.newaxis, np.newaxis].repeat([num_channels, 1] + [1] * f.ndim)
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if f.ndim == 4:
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x = conv2d(input=x, weight=f, groups=num_channels)
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else:
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x = conv2d(input=x, weight=f.unsqueeze(2), groups=num_channels)
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x = conv2d(input=x, weight=f.unsqueeze(3), groups=num_channels)
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# Downsample by throwing away pixels.
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x = x[:, :, ::downy, ::downx]
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return x
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class EncoderEpilogue(torch.nn.Module):
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def __init__(self,
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in_channels, # Number of input channels.
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cmap_dim, # Dimensionality of mapped conditioning label, 0 = no label.
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z_dim, # Output Latent (Z) dimensionality.
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resolution, # Resolution of this block.
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img_channels, # Number of input color channels.
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architecture='resnet', # Architecture: 'orig', 'skip', 'resnet'.
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mbstd_group_size=4, # Group size for the minibatch standard deviation layer, None = entire minibatch.
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mbstd_num_channels=1, # Number of features for the minibatch standard deviation layer, 0 = disable.
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activation='lrelu', # Activation function: 'relu', 'lrelu', etc.
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conv_clamp=None, # Clamp the output of convolution layers to +-X, None = disable clamping.
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):
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assert architecture in ['orig', 'skip', 'resnet']
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super().__init__()
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self.in_channels = in_channels
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self.cmap_dim = cmap_dim
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self.resolution = resolution
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self.img_channels = img_channels
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self.architecture = architecture
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if architecture == 'skip':
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self.fromrgb = Conv2dLayer(self.img_channels, in_channels, kernel_size=1, activation=activation)
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self.mbstd = MinibatchStdLayer(group_size=mbstd_group_size,
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num_channels=mbstd_num_channels) if mbstd_num_channels > 0 else None
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self.conv = Conv2dLayer(in_channels + mbstd_num_channels, in_channels, kernel_size=3, activation=activation,
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conv_clamp=conv_clamp)
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self.fc = FullyConnectedLayer(in_channels * (resolution ** 2), z_dim, activation=activation)
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self.dropout = torch.nn.Dropout(p=0.5)
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def forward(self, x, cmap, force_fp32=False):
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_ = force_fp32 # unused
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dtype = torch.float32
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memory_format = torch.contiguous_format
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# FromRGB.
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x = x.to(dtype=dtype, memory_format=memory_format)
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# Main layers.
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if self.mbstd is not None:
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x = self.mbstd(x)
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const_e = self.conv(x)
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x = self.fc(const_e.flatten(1))
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x = self.dropout(x)
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# Conditioning.
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if self.cmap_dim > 0:
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x = (x * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim))
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assert x.dtype == dtype
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return x, const_e
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class EncoderBlock(torch.nn.Module):
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def __init__(self,
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in_channels, # Number of input channels, 0 = first block.
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tmp_channels, # Number of intermediate channels.
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out_channels, # Number of output channels.
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resolution, # Resolution of this block.
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img_channels, # Number of input color channels.
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first_layer_idx, # Index of the first layer.
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architecture='skip', # Architecture: 'orig', 'skip', 'resnet'.
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activation='lrelu', # Activation function: 'relu', 'lrelu', etc.
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resample_filter=[1, 3, 3, 1], # Low-pass filter to apply when resampling activations.
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conv_clamp=None, # Clamp the output of convolution layers to +-X, None = disable clamping.
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use_fp16=False, # Use FP16 for this block?
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fp16_channels_last=False, # Use channels-last memory format with FP16?
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freeze_layers=0, # Freeze-D: Number of layers to freeze.
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):
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assert in_channels in [0, tmp_channels]
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assert architecture in ['orig', 'skip', 'resnet']
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super().__init__()
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self.in_channels = in_channels
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self.resolution = resolution
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self.img_channels = img_channels + 1
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self.first_layer_idx = first_layer_idx
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self.architecture = architecture
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self.use_fp16 = use_fp16
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self.channels_last = (use_fp16 and fp16_channels_last)
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self.register_buffer('resample_filter', setup_filter(resample_filter))
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self.num_layers = 0
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def trainable_gen():
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while True:
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layer_idx = self.first_layer_idx + self.num_layers
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trainable = (layer_idx >= freeze_layers)
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self.num_layers += 1
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yield trainable
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trainable_iter = trainable_gen()
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if in_channels == 0:
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self.fromrgb = Conv2dLayer(self.img_channels, tmp_channels, kernel_size=1, activation=activation,
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trainable=next(trainable_iter), conv_clamp=conv_clamp,
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channels_last=self.channels_last)
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self.conv0 = Conv2dLayer(tmp_channels, tmp_channels, kernel_size=3, activation=activation,
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trainable=next(trainable_iter), conv_clamp=conv_clamp,
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channels_last=self.channels_last)
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self.conv1 = Conv2dLayer(tmp_channels, out_channels, kernel_size=3, activation=activation, down=2,
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trainable=next(trainable_iter), resample_filter=resample_filter, conv_clamp=conv_clamp,
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channels_last=self.channels_last)
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if architecture == 'resnet':
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self.skip = Conv2dLayer(tmp_channels, out_channels, kernel_size=1, bias=False, down=2,
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trainable=next(trainable_iter), resample_filter=resample_filter,
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channels_last=self.channels_last)
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def forward(self, x, img, force_fp32=False):
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# dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
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dtype = torch.float32
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memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format
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# Input.
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if x is not None:
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x = x.to(dtype=dtype, memory_format=memory_format)
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# FromRGB.
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if self.in_channels == 0:
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img = img.to(dtype=dtype, memory_format=memory_format)
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y = self.fromrgb(img)
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x = x + y if x is not None else y
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img = downsample2d(img, self.resample_filter) if self.architecture == 'skip' else None
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# Main layers.
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if self.architecture == 'resnet':
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y = self.skip(x, gain=np.sqrt(0.5))
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x = self.conv0(x)
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feat = x.clone()
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x = self.conv1(x, gain=np.sqrt(0.5))
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x = y.add_(x)
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else:
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x = self.conv0(x)
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feat = x.clone()
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x = self.conv1(x)
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assert x.dtype == dtype
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return x, img, feat
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class EncoderNetwork(torch.nn.Module):
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def __init__(self,
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c_dim, # Conditioning label (C) dimensionality.
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z_dim, # Input latent (Z) dimensionality.
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img_resolution, # Input resolution.
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img_channels, # Number of input color channels.
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architecture='orig', # Architecture: 'orig', 'skip', 'resnet'.
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channel_base=16384, # Overall multiplier for the number of channels.
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channel_max=512, # Maximum number of channels in any layer.
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num_fp16_res=0, # Use FP16 for the N highest resolutions.
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conv_clamp=None, # Clamp the output of convolution layers to +-X, None = disable clamping.
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cmap_dim=None, # Dimensionality of mapped conditioning label, None = default.
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block_kwargs={}, # Arguments for DiscriminatorBlock.
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mapping_kwargs={}, # Arguments for MappingNetwork.
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epilogue_kwargs={}, # Arguments for EncoderEpilogue.
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):
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super().__init__()
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self.c_dim = c_dim
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self.z_dim = z_dim
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self.img_resolution = img_resolution
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self.img_resolution_log2 = int(np.log2(img_resolution))
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self.img_channels = img_channels
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self.block_resolutions = [2 ** i for i in range(self.img_resolution_log2, 2, -1)]
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channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions + [4]}
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fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8)
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if cmap_dim is None:
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cmap_dim = channels_dict[4]
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if c_dim == 0:
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cmap_dim = 0
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common_kwargs = dict(img_channels=img_channels, architecture=architecture, conv_clamp=conv_clamp)
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cur_layer_idx = 0
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for res in self.block_resolutions:
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in_channels = channels_dict[res] if res < img_resolution else 0
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tmp_channels = channels_dict[res]
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out_channels = channels_dict[res // 2]
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use_fp16 = (res >= fp16_resolution)
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use_fp16 = False
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block = EncoderBlock(in_channels, tmp_channels, out_channels, resolution=res,
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first_layer_idx=cur_layer_idx, use_fp16=use_fp16, **block_kwargs, **common_kwargs)
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setattr(self, f'b{res}', block)
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cur_layer_idx += block.num_layers
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if c_dim > 0:
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self.mapping = MappingNetwork(z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None,
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**mapping_kwargs)
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self.b4 = EncoderEpilogue(channels_dict[4], cmap_dim=cmap_dim, z_dim=z_dim * 2, resolution=4, **epilogue_kwargs,
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**common_kwargs)
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def forward(self, img, c, **block_kwargs):
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x = None
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feats = {}
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for res in self.block_resolutions:
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block = getattr(self, f'b{res}')
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x, img, feat = block(x, img, **block_kwargs)
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feats[res] = feat
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cmap = None
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if self.c_dim > 0:
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cmap = self.mapping(None, c)
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x, const_e = self.b4(x, cmap)
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feats[4] = const_e
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B, _ = x.shape
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z = torch.zeros((B, self.z_dim), requires_grad=False, dtype=x.dtype,
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device=x.device) ## Noise for Co-Modulation
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return x, z, feats
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def fma(a, b, c): # => a * b + c
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return _FusedMultiplyAdd.apply(a, b, c)
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class _FusedMultiplyAdd(torch.autograd.Function): # a * b + c
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@staticmethod
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def forward(ctx, a, b, c): # pylint: disable=arguments-differ
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out = torch.addcmul(c, a, b)
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ctx.save_for_backward(a, b)
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ctx.c_shape = c.shape
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return out
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@staticmethod
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def backward(ctx, dout): # pylint: disable=arguments-differ
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a, b = ctx.saved_tensors
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c_shape = ctx.c_shape
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da = None
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db = None
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dc = None
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if ctx.needs_input_grad[0]:
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da = _unbroadcast(dout * b, a.shape)
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if ctx.needs_input_grad[1]:
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db = _unbroadcast(dout * a, b.shape)
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if ctx.needs_input_grad[2]:
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dc = _unbroadcast(dout, c_shape)
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return da, db, dc
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def _unbroadcast(x, shape):
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extra_dims = x.ndim - len(shape)
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assert extra_dims >= 0
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dim = [i for i in range(x.ndim) if x.shape[i] > 1 and (i < extra_dims or shape[i - extra_dims] == 1)]
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if len(dim):
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x = x.sum(dim=dim, keepdim=True)
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if extra_dims:
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x = x.reshape(-1, *x.shape[extra_dims + 1:])
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assert x.shape == shape
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return x
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def modulated_conv2d(
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x, # Input tensor of shape [batch_size, in_channels, in_height, in_width].
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weight, # Weight tensor of shape [out_channels, in_channels, kernel_height, kernel_width].
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styles, # Modulation coefficients of shape [batch_size, in_channels].
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noise=None, # Optional noise tensor to add to the output activations.
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up=1, # Integer upsampling factor.
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down=1, # Integer downsampling factor.
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padding=0, # Padding with respect to the upsampled image.
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resample_filter=None,
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# Low-pass filter to apply when resampling activations. Must be prepared beforehand by calling upfirdn2d.setup_filter().
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demodulate=True, # Apply weight demodulation?
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flip_weight=True, # False = convolution, True = correlation (matches torch.nn.functional.conv2d).
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fused_modconv=True, # Perform modulation, convolution, and demodulation as a single fused operation?
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):
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batch_size = x.shape[0]
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out_channels, in_channels, kh, kw = weight.shape
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# Pre-normalize inputs to avoid FP16 overflow.
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if x.dtype == torch.float16 and demodulate:
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weight = weight * (1 / np.sqrt(in_channels * kh * kw) / weight.norm(float('inf'), dim=[1, 2, 3],
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keepdim=True)) # max_Ikk
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styles = styles / styles.norm(float('inf'), dim=1, keepdim=True) # max_I
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# Calculate per-sample weights and demodulation coefficients.
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w = None
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dcoefs = None
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if demodulate or fused_modconv:
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w = weight.unsqueeze(0) # [NOIkk]
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w = w * styles.reshape(batch_size, 1, -1, 1, 1) # [NOIkk]
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if demodulate:
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dcoefs = (w.square().sum(dim=[2, 3, 4]) + 1e-8).rsqrt() # [NO]
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if demodulate and fused_modconv:
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w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1) # [NOIkk]
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# Execute by scaling the activations before and after the convolution.
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if not fused_modconv:
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x = x * styles.to(x.dtype).reshape(batch_size, -1, 1, 1)
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x = conv2d_resample.conv2d_resample(x=x, w=weight.to(x.dtype), f=resample_filter, up=up, down=down,
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padding=padding, flip_weight=flip_weight)
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if demodulate and noise is not None:
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x = fma(x, dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1), noise.to(x.dtype))
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elif demodulate:
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x = x * dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1)
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elif noise is not None:
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x = x.add_(noise.to(x.dtype))
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return x
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# Execute as one fused op using grouped convolution.
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batch_size = int(batch_size)
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x = x.reshape(1, -1, *x.shape[2:])
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w = w.reshape(-1, in_channels, kh, kw)
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x = conv2d_resample(x=x, w=w.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding,
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groups=batch_size, flip_weight=flip_weight)
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x = x.reshape(batch_size, -1, *x.shape[2:])
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if noise is not None:
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x = x.add_(noise)
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return x
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class SynthesisLayer(torch.nn.Module):
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def __init__(self,
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in_channels, # Number of input channels.
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out_channels, # Number of output channels.
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w_dim, # Intermediate latent (W) dimensionality.
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resolution, # Resolution of this layer.
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kernel_size=3, # Convolution kernel size.
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up=1, # Integer upsampling factor.
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use_noise=True, # Enable noise input?
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activation='lrelu', # Activation function: 'relu', 'lrelu', etc.
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resample_filter=[1, 3, 3, 1], # Low-pass filter to apply when resampling activations.
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conv_clamp=None, # Clamp the output of convolution layers to +-X, None = disable clamping.
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channels_last=False, # Use channels_last format for the weights?
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):
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super().__init__()
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self.resolution = resolution
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self.up = up
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self.use_noise = use_noise
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self.activation = activation
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self.conv_clamp = conv_clamp
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self.register_buffer('resample_filter', setup_filter(resample_filter))
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self.padding = kernel_size // 2
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self.act_gain = activation_funcs[activation].def_gain
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self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1)
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memory_format = torch.channels_last if channels_last else torch.contiguous_format
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self.weight = torch.nn.Parameter(
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torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format))
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if use_noise:
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self.register_buffer('noise_const', torch.randn([resolution, resolution]))
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self.noise_strength = torch.nn.Parameter(torch.zeros([]))
|
|
self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
|
|
|
|
def forward(self, x, w, noise_mode='none', fused_modconv=True, gain=1):
|
|
assert noise_mode in ['random', 'const', 'none']
|
|
in_resolution = self.resolution // self.up
|
|
styles = self.affine(w)
|
|
|
|
noise = None
|
|
if self.use_noise and noise_mode == 'random':
|
|
noise = torch.randn([x.shape[0], 1, self.resolution, self.resolution],
|
|
device=x.device) * self.noise_strength
|
|
if self.use_noise and noise_mode == 'const':
|
|
noise = self.noise_const * self.noise_strength
|
|
|
|
flip_weight = (self.up == 1) # slightly faster
|
|
x = modulated_conv2d(x=x, weight=self.weight, styles=styles, noise=noise, up=self.up,
|
|
padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight,
|
|
fused_modconv=fused_modconv)
|
|
|
|
act_gain = self.act_gain * gain
|
|
act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
|
|
x = F.leaky_relu(x, negative_slope=0.2, inplace=False)
|
|
if act_gain != 1:
|
|
x = x * act_gain
|
|
if act_clamp is not None:
|
|
x = x.clamp(-act_clamp, act_clamp)
|
|
return x
|
|
|
|
|
|
class ToRGBLayer(torch.nn.Module):
|
|
def __init__(self, in_channels, out_channels, w_dim, kernel_size=1, conv_clamp=None, channels_last=False):
|
|
super().__init__()
|
|
self.conv_clamp = conv_clamp
|
|
self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1)
|
|
memory_format = torch.channels_last if channels_last else torch.contiguous_format
|
|
self.weight = torch.nn.Parameter(
|
|
torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format))
|
|
self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
|
|
self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2))
|
|
|
|
def forward(self, x, w, fused_modconv=True):
|
|
styles = self.affine(w) * self.weight_gain
|
|
x = modulated_conv2d(x=x, weight=self.weight, styles=styles, demodulate=False, fused_modconv=fused_modconv)
|
|
x = bias_act(x, self.bias.to(x.dtype), clamp=self.conv_clamp)
|
|
return x
|
|
|
|
|
|
class SynthesisForeword(torch.nn.Module):
|
|
def __init__(self,
|
|
z_dim, # Output Latent (Z) dimensionality.
|
|
resolution, # Resolution of this block.
|
|
in_channels,
|
|
img_channels, # Number of input color channels.
|
|
architecture='skip', # Architecture: 'orig', 'skip', 'resnet'.
|
|
activation='lrelu', # Activation function: 'relu', 'lrelu', etc.
|
|
|
|
):
|
|
super().__init__()
|
|
self.in_channels = in_channels
|
|
self.z_dim = z_dim
|
|
self.resolution = resolution
|
|
self.img_channels = img_channels
|
|
self.architecture = architecture
|
|
|
|
self.fc = FullyConnectedLayer(self.z_dim, (self.z_dim // 2) * 4 * 4, activation=activation)
|
|
self.conv = SynthesisLayer(self.in_channels, self.in_channels, w_dim=(z_dim // 2) * 3, resolution=4)
|
|
|
|
if architecture == 'skip':
|
|
self.torgb = ToRGBLayer(self.in_channels, self.img_channels, kernel_size=1, w_dim=(z_dim // 2) * 3)
|
|
|
|
def forward(self, x, ws, feats, img, force_fp32=False):
|
|
_ = force_fp32 # unused
|
|
dtype = torch.float32
|
|
memory_format = torch.contiguous_format
|
|
|
|
x_global = x.clone()
|
|
# ToRGB.
|
|
x = self.fc(x)
|
|
x = x.view(-1, self.z_dim // 2, 4, 4)
|
|
x = x.to(dtype=dtype, memory_format=memory_format)
|
|
|
|
# Main layers.
|
|
x_skip = feats[4].clone()
|
|
x = x + x_skip
|
|
|
|
mod_vector = []
|
|
mod_vector.append(ws[:, 0])
|
|
mod_vector.append(x_global.clone())
|
|
mod_vector = torch.cat(mod_vector, dim=1)
|
|
|
|
x = self.conv(x, mod_vector)
|
|
|
|
mod_vector = []
|
|
mod_vector.append(ws[:, 2 * 2 - 3])
|
|
mod_vector.append(x_global.clone())
|
|
mod_vector = torch.cat(mod_vector, dim=1)
|
|
|
|
if self.architecture == 'skip':
|
|
img = self.torgb(x, mod_vector)
|
|
img = img.to(dtype=torch.float32, memory_format=torch.contiguous_format)
|
|
|
|
assert x.dtype == dtype
|
|
return x, img
|
|
|
|
|
|
class SELayer(nn.Module):
|
|
def __init__(self, channel, reduction=16):
|
|
super(SELayer, self).__init__()
|
|
self.avg_pool = nn.AdaptiveAvgPool2d(1)
|
|
self.fc = nn.Sequential(
|
|
nn.Linear(channel, channel // reduction, bias=False),
|
|
nn.ReLU(inplace=False),
|
|
nn.Linear(channel // reduction, channel, bias=False),
|
|
nn.Sigmoid()
|
|
)
|
|
|
|
def forward(self, x):
|
|
b, c, _, _ = x.size()
|
|
y = self.avg_pool(x).view(b, c)
|
|
y = self.fc(y).view(b, c, 1, 1)
|
|
res = x * y.expand_as(x)
|
|
return res
|
|
|
|
|
|
class FourierUnit(nn.Module):
|
|
|
|
def __init__(self, in_channels, out_channels, groups=1, spatial_scale_factor=None, spatial_scale_mode='bilinear',
|
|
spectral_pos_encoding=False, use_se=False, se_kwargs=None, ffc3d=False, fft_norm='ortho'):
|
|
# bn_layer not used
|
|
super(FourierUnit, self).__init__()
|
|
self.groups = groups
|
|
|
|
self.conv_layer = torch.nn.Conv2d(in_channels=in_channels * 2 + (2 if spectral_pos_encoding else 0),
|
|
out_channels=out_channels * 2,
|
|
kernel_size=1, stride=1, padding=0, groups=self.groups, bias=False)
|
|
self.relu = torch.nn.ReLU(inplace=False)
|
|
|
|
# squeeze and excitation block
|
|
self.use_se = use_se
|
|
if use_se:
|
|
if se_kwargs is None:
|
|
se_kwargs = {}
|
|
self.se = SELayer(self.conv_layer.in_channels, **se_kwargs)
|
|
|
|
self.spatial_scale_factor = spatial_scale_factor
|
|
self.spatial_scale_mode = spatial_scale_mode
|
|
self.spectral_pos_encoding = spectral_pos_encoding
|
|
self.ffc3d = ffc3d
|
|
self.fft_norm = fft_norm
|
|
|
|
def forward(self, x):
|
|
batch = x.shape[0]
|
|
|
|
if self.spatial_scale_factor is not None:
|
|
orig_size = x.shape[-2:]
|
|
x = F.interpolate(x, scale_factor=self.spatial_scale_factor, mode=self.spatial_scale_mode,
|
|
align_corners=False)
|
|
|
|
r_size = x.size()
|
|
# (batch, c, h, w/2+1, 2)
|
|
fft_dim = (-3, -2, -1) if self.ffc3d else (-2, -1)
|
|
ffted = fft.rfftn(x, dim=fft_dim, norm=self.fft_norm)
|
|
ffted = torch.stack((ffted.real, ffted.imag), dim=-1)
|
|
ffted = ffted.permute(0, 1, 4, 2, 3).contiguous() # (batch, c, 2, h, w/2+1)
|
|
ffted = ffted.view((batch, -1,) + ffted.size()[3:])
|
|
|
|
if self.spectral_pos_encoding:
|
|
height, width = ffted.shape[-2:]
|
|
coords_vert = torch.linspace(0, 1, height)[None, None, :, None].expand(batch, 1, height, width).to(ffted)
|
|
coords_hor = torch.linspace(0, 1, width)[None, None, None, :].expand(batch, 1, height, width).to(ffted)
|
|
ffted = torch.cat((coords_vert, coords_hor, ffted), dim=1)
|
|
|
|
if self.use_se:
|
|
ffted = self.se(ffted)
|
|
|
|
ffted = self.conv_layer(ffted) # (batch, c*2, h, w/2+1)
|
|
ffted = self.relu(ffted)
|
|
|
|
ffted = ffted.view((batch, -1, 2,) + ffted.size()[2:]).permute(
|
|
0, 1, 3, 4, 2).contiguous() # (batch,c, t, h, w/2+1, 2)
|
|
ffted = torch.complex(ffted[..., 0], ffted[..., 1])
|
|
|
|
ifft_shape_slice = x.shape[-3:] if self.ffc3d else x.shape[-2:]
|
|
output = torch.fft.irfftn(ffted, s=ifft_shape_slice, dim=fft_dim, norm=self.fft_norm)
|
|
|
|
if self.spatial_scale_factor is not None:
|
|
output = F.interpolate(output, size=orig_size, mode=self.spatial_scale_mode, align_corners=False)
|
|
|
|
return output
|
|
|
|
|
|
class SpectralTransform(nn.Module):
|
|
|
|
def __init__(self, in_channels, out_channels, stride=1, groups=1, enable_lfu=True, **fu_kwargs):
|
|
# bn_layer not used
|
|
super(SpectralTransform, self).__init__()
|
|
self.enable_lfu = enable_lfu
|
|
if stride == 2:
|
|
self.downsample = nn.AvgPool2d(kernel_size=(2, 2), stride=2)
|
|
else:
|
|
self.downsample = nn.Identity()
|
|
|
|
self.stride = stride
|
|
self.conv1 = nn.Sequential(
|
|
nn.Conv2d(in_channels, out_channels //
|
|
2, kernel_size=1, groups=groups, bias=False),
|
|
# nn.BatchNorm2d(out_channels // 2),
|
|
nn.ReLU(inplace=True)
|
|
)
|
|
self.fu = FourierUnit(
|
|
out_channels // 2, out_channels // 2, groups, **fu_kwargs)
|
|
if self.enable_lfu:
|
|
self.lfu = FourierUnit(
|
|
out_channels // 2, out_channels // 2, groups)
|
|
self.conv2 = torch.nn.Conv2d(
|
|
out_channels // 2, out_channels, kernel_size=1, groups=groups, bias=False)
|
|
|
|
def forward(self, x):
|
|
|
|
x = self.downsample(x)
|
|
x = self.conv1(x)
|
|
output = self.fu(x)
|
|
|
|
if self.enable_lfu:
|
|
n, c, h, w = x.shape
|
|
split_no = 2
|
|
split_s = h // split_no
|
|
xs = torch.cat(torch.split(
|
|
x[:, :c // 4], split_s, dim=-2), dim=1).contiguous()
|
|
xs = torch.cat(torch.split(xs, split_s, dim=-1),
|
|
dim=1).contiguous()
|
|
xs = self.lfu(xs)
|
|
xs = xs.repeat(1, 1, split_no, split_no).contiguous()
|
|
else:
|
|
xs = 0
|
|
|
|
output = self.conv2(x + output + xs)
|
|
|
|
return output
|
|
|
|
|
|
class FFC(nn.Module):
|
|
|
|
def __init__(self, in_channels, out_channels, kernel_size,
|
|
ratio_gin, ratio_gout, stride=1, padding=0,
|
|
dilation=1, groups=1, bias=False, enable_lfu=True,
|
|
padding_type='reflect', gated=False, **spectral_kwargs):
|
|
super(FFC, self).__init__()
|
|
|
|
assert stride == 1 or stride == 2, "Stride should be 1 or 2."
|
|
self.stride = stride
|
|
|
|
in_cg = int(in_channels * ratio_gin)
|
|
in_cl = in_channels - in_cg
|
|
out_cg = int(out_channels * ratio_gout)
|
|
out_cl = out_channels - out_cg
|
|
# groups_g = 1 if groups == 1 else int(groups * ratio_gout)
|
|
# groups_l = 1 if groups == 1 else groups - groups_g
|
|
|
|
self.ratio_gin = ratio_gin
|
|
self.ratio_gout = ratio_gout
|
|
self.global_in_num = in_cg
|
|
|
|
module = nn.Identity if in_cl == 0 or out_cl == 0 else nn.Conv2d
|
|
self.convl2l = module(in_cl, out_cl, kernel_size,
|
|
stride, padding, dilation, groups, bias, padding_mode=padding_type)
|
|
module = nn.Identity if in_cl == 0 or out_cg == 0 else nn.Conv2d
|
|
self.convl2g = module(in_cl, out_cg, kernel_size,
|
|
stride, padding, dilation, groups, bias, padding_mode=padding_type)
|
|
module = nn.Identity if in_cg == 0 or out_cl == 0 else nn.Conv2d
|
|
self.convg2l = module(in_cg, out_cl, kernel_size,
|
|
stride, padding, dilation, groups, bias, padding_mode=padding_type)
|
|
module = nn.Identity if in_cg == 0 or out_cg == 0 else SpectralTransform
|
|
self.convg2g = module(
|
|
in_cg, out_cg, stride, 1 if groups == 1 else groups // 2, enable_lfu, **spectral_kwargs)
|
|
|
|
self.gated = gated
|
|
module = nn.Identity if in_cg == 0 or out_cl == 0 or not self.gated else nn.Conv2d
|
|
self.gate = module(in_channels, 2, 1)
|
|
|
|
def forward(self, x, fname=None):
|
|
x_l, x_g = x if type(x) is tuple else (x, 0)
|
|
out_xl, out_xg = 0, 0
|
|
|
|
if self.gated:
|
|
total_input_parts = [x_l]
|
|
if torch.is_tensor(x_g):
|
|
total_input_parts.append(x_g)
|
|
total_input = torch.cat(total_input_parts, dim=1)
|
|
|
|
gates = torch.sigmoid(self.gate(total_input))
|
|
g2l_gate, l2g_gate = gates.chunk(2, dim=1)
|
|
else:
|
|
g2l_gate, l2g_gate = 1, 1
|
|
|
|
spec_x = self.convg2g(x_g)
|
|
|
|
if self.ratio_gout != 1:
|
|
out_xl = self.convl2l(x_l) + self.convg2l(x_g) * g2l_gate
|
|
if self.ratio_gout != 0:
|
|
out_xg = self.convl2g(x_l) * l2g_gate + spec_x
|
|
|
|
return out_xl, out_xg
|
|
|
|
|
|
class FFC_BN_ACT(nn.Module):
|
|
|
|
def __init__(self, in_channels, out_channels,
|
|
kernel_size, ratio_gin, ratio_gout,
|
|
stride=1, padding=0, dilation=1, groups=1, bias=False,
|
|
norm_layer=nn.SyncBatchNorm, activation_layer=nn.Identity,
|
|
padding_type='reflect',
|
|
enable_lfu=True, **kwargs):
|
|
super(FFC_BN_ACT, self).__init__()
|
|
self.ffc = FFC(in_channels, out_channels, kernel_size,
|
|
ratio_gin, ratio_gout, stride, padding, dilation,
|
|
groups, bias, enable_lfu, padding_type=padding_type, **kwargs)
|
|
lnorm = nn.Identity if ratio_gout == 1 else norm_layer
|
|
gnorm = nn.Identity if ratio_gout == 0 else norm_layer
|
|
global_channels = int(out_channels * ratio_gout)
|
|
# self.bn_l = lnorm(out_channels - global_channels)
|
|
# self.bn_g = gnorm(global_channels)
|
|
|
|
lact = nn.Identity if ratio_gout == 1 else activation_layer
|
|
gact = nn.Identity if ratio_gout == 0 else activation_layer
|
|
self.act_l = lact(inplace=True)
|
|
self.act_g = gact(inplace=True)
|
|
|
|
def forward(self, x, fname=None):
|
|
x_l, x_g = self.ffc(x, fname=fname, )
|
|
x_l = self.act_l(x_l)
|
|
x_g = self.act_g(x_g)
|
|
return x_l, x_g
|
|
|
|
|
|
class FFCResnetBlock(nn.Module):
|
|
def __init__(self, dim, padding_type, norm_layer, activation_layer=nn.ReLU, dilation=1,
|
|
spatial_transform_kwargs=None, inline=False, ratio_gin=0.75, ratio_gout=0.75):
|
|
super().__init__()
|
|
self.conv1 = FFC_BN_ACT(dim, dim, kernel_size=3, padding=dilation, dilation=dilation,
|
|
norm_layer=norm_layer,
|
|
activation_layer=activation_layer,
|
|
padding_type=padding_type,
|
|
ratio_gin=ratio_gin, ratio_gout=ratio_gout)
|
|
self.conv2 = FFC_BN_ACT(dim, dim, kernel_size=3, padding=dilation, dilation=dilation,
|
|
norm_layer=norm_layer,
|
|
activation_layer=activation_layer,
|
|
padding_type=padding_type,
|
|
ratio_gin=ratio_gin, ratio_gout=ratio_gout)
|
|
self.inline = inline
|
|
|
|
def forward(self, x, fname=None):
|
|
if self.inline:
|
|
x_l, x_g = x[:, :-self.conv1.ffc.global_in_num], x[:, -self.conv1.ffc.global_in_num:]
|
|
else:
|
|
x_l, x_g = x if type(x) is tuple else (x, 0)
|
|
|
|
id_l, id_g = x_l, x_g
|
|
|
|
x_l, x_g = self.conv1((x_l, x_g), fname=fname)
|
|
x_l, x_g = self.conv2((x_l, x_g), fname=fname)
|
|
|
|
x_l, x_g = id_l + x_l, id_g + x_g
|
|
out = x_l, x_g
|
|
if self.inline:
|
|
out = torch.cat(out, dim=1)
|
|
return out
|
|
|
|
|
|
class ConcatTupleLayer(nn.Module):
|
|
def forward(self, x):
|
|
assert isinstance(x, tuple)
|
|
x_l, x_g = x
|
|
assert torch.is_tensor(x_l) or torch.is_tensor(x_g)
|
|
if not torch.is_tensor(x_g):
|
|
return x_l
|
|
return torch.cat(x, dim=1)
|
|
|
|
|
|
class FFCBlock(torch.nn.Module):
|
|
def __init__(self,
|
|
dim, # Number of output/input channels.
|
|
kernel_size, # Width and height of the convolution kernel.
|
|
padding,
|
|
ratio_gin=0.75,
|
|
ratio_gout=0.75,
|
|
activation='linear', # Activation function: 'relu', 'lrelu', etc.
|
|
):
|
|
super().__init__()
|
|
if activation == 'linear':
|
|
self.activation = nn.Identity
|
|
else:
|
|
self.activation = nn.ReLU
|
|
self.padding = padding
|
|
self.kernel_size = kernel_size
|
|
self.ffc_block = FFCResnetBlock(dim=dim,
|
|
padding_type='reflect',
|
|
norm_layer=nn.SyncBatchNorm,
|
|
activation_layer=self.activation,
|
|
dilation=1,
|
|
ratio_gin=ratio_gin,
|
|
ratio_gout=ratio_gout)
|
|
|
|
self.concat_layer = ConcatTupleLayer()
|
|
|
|
def forward(self, gen_ft, mask, fname=None):
|
|
x = gen_ft.float()
|
|
|
|
x_l, x_g = x[:, :-self.ffc_block.conv1.ffc.global_in_num], x[:, -self.ffc_block.conv1.ffc.global_in_num:]
|
|
id_l, id_g = x_l, x_g
|
|
|
|
x_l, x_g = self.ffc_block((x_l, x_g), fname=fname)
|
|
x_l, x_g = id_l + x_l, id_g + x_g
|
|
x = self.concat_layer((x_l, x_g))
|
|
|
|
return x + gen_ft.float()
|
|
|
|
|
|
class FFCSkipLayer(torch.nn.Module):
|
|
def __init__(self,
|
|
dim, # Number of input/output channels.
|
|
kernel_size=3, # Convolution kernel size.
|
|
ratio_gin=0.75,
|
|
ratio_gout=0.75,
|
|
):
|
|
super().__init__()
|
|
self.padding = kernel_size // 2
|
|
|
|
self.ffc_act = FFCBlock(dim=dim, kernel_size=kernel_size, activation=nn.ReLU,
|
|
padding=self.padding, ratio_gin=ratio_gin, ratio_gout=ratio_gout)
|
|
|
|
def forward(self, gen_ft, mask, fname=None):
|
|
x = self.ffc_act(gen_ft, mask, fname=fname)
|
|
return x
|
|
|
|
|
|
class SynthesisBlock(torch.nn.Module):
|
|
def __init__(self,
|
|
in_channels, # Number of input channels, 0 = first block.
|
|
out_channels, # Number of output channels.
|
|
w_dim, # Intermediate latent (W) dimensionality.
|
|
resolution, # Resolution of this block.
|
|
img_channels, # Number of output color channels.
|
|
is_last, # Is this the last block?
|
|
architecture='skip', # Architecture: 'orig', 'skip', 'resnet'.
|
|
resample_filter=[1, 3, 3, 1], # Low-pass filter to apply when resampling activations.
|
|
conv_clamp=None, # Clamp the output of convolution layers to +-X, None = disable clamping.
|
|
use_fp16=False, # Use FP16 for this block?
|
|
fp16_channels_last=False, # Use channels-last memory format with FP16?
|
|
**layer_kwargs, # Arguments for SynthesisLayer.
|
|
):
|
|
assert architecture in ['orig', 'skip', 'resnet']
|
|
super().__init__()
|
|
self.in_channels = in_channels
|
|
self.w_dim = w_dim
|
|
self.resolution = resolution
|
|
self.img_channels = img_channels
|
|
self.is_last = is_last
|
|
self.architecture = architecture
|
|
self.use_fp16 = use_fp16
|
|
self.channels_last = (use_fp16 and fp16_channels_last)
|
|
self.register_buffer('resample_filter', setup_filter(resample_filter))
|
|
self.num_conv = 0
|
|
self.num_torgb = 0
|
|
self.res_ffc = {4: 0, 8: 0, 16: 0, 32: 1, 64: 1, 128: 1, 256: 1, 512: 1}
|
|
|
|
if in_channels != 0 and resolution >= 8:
|
|
self.ffc_skip = nn.ModuleList()
|
|
for _ in range(self.res_ffc[resolution]):
|
|
self.ffc_skip.append(FFCSkipLayer(dim=out_channels))
|
|
|
|
if in_channels == 0:
|
|
self.const = torch.nn.Parameter(torch.randn([out_channels, resolution, resolution]))
|
|
|
|
if in_channels != 0:
|
|
self.conv0 = SynthesisLayer(in_channels, out_channels, w_dim=w_dim * 3, resolution=resolution, up=2,
|
|
resample_filter=resample_filter, conv_clamp=conv_clamp,
|
|
channels_last=self.channels_last, **layer_kwargs)
|
|
self.num_conv += 1
|
|
|
|
self.conv1 = SynthesisLayer(out_channels, out_channels, w_dim=w_dim * 3, resolution=resolution,
|
|
conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs)
|
|
self.num_conv += 1
|
|
|
|
if is_last or architecture == 'skip':
|
|
self.torgb = ToRGBLayer(out_channels, img_channels, w_dim=w_dim * 3,
|
|
conv_clamp=conv_clamp, channels_last=self.channels_last)
|
|
self.num_torgb += 1
|
|
|
|
if in_channels != 0 and architecture == 'resnet':
|
|
self.skip = Conv2dLayer(in_channels, out_channels, kernel_size=1, bias=False, up=2,
|
|
resample_filter=resample_filter, channels_last=self.channels_last)
|
|
|
|
def forward(self, x, mask, feats, img, ws, fname=None, force_fp32=False, fused_modconv=None, **layer_kwargs):
|
|
dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
|
|
dtype = torch.float32
|
|
memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format
|
|
if fused_modconv is None:
|
|
fused_modconv = (not self.training) and (dtype == torch.float32 or int(x.shape[0]) == 1)
|
|
|
|
x = x.to(dtype=dtype, memory_format=memory_format)
|
|
x_skip = feats[self.resolution].clone().to(dtype=dtype, memory_format=memory_format)
|
|
|
|
# Main layers.
|
|
if self.in_channels == 0:
|
|
x = self.conv1(x, ws[1], fused_modconv=fused_modconv, **layer_kwargs)
|
|
elif self.architecture == 'resnet':
|
|
y = self.skip(x, gain=np.sqrt(0.5))
|
|
x = self.conv0(x, ws[0].clone(), fused_modconv=fused_modconv, **layer_kwargs)
|
|
if len(self.ffc_skip) > 0:
|
|
mask = F.interpolate(mask, size=x_skip.shape[2:], )
|
|
z = x + x_skip
|
|
for fres in self.ffc_skip:
|
|
z = fres(z, mask)
|
|
x = x + z
|
|
else:
|
|
x = x + x_skip
|
|
x = self.conv1(x, ws[1].clone(), fused_modconv=fused_modconv, gain=np.sqrt(0.5), **layer_kwargs)
|
|
x = y.add_(x)
|
|
else:
|
|
x = self.conv0(x, ws[0].clone(), fused_modconv=fused_modconv, **layer_kwargs)
|
|
if len(self.ffc_skip) > 0:
|
|
mask = F.interpolate(mask, size=x_skip.shape[2:], )
|
|
z = x + x_skip
|
|
for fres in self.ffc_skip:
|
|
z = fres(z, mask)
|
|
x = x + z
|
|
else:
|
|
x = x + x_skip
|
|
x = self.conv1(x, ws[1].clone(), fused_modconv=fused_modconv, **layer_kwargs)
|
|
# ToRGB.
|
|
if img is not None:
|
|
img = upsample2d(img, self.resample_filter)
|
|
if self.is_last or self.architecture == 'skip':
|
|
y = self.torgb(x, ws[2].clone(), fused_modconv=fused_modconv)
|
|
y = y.to(dtype=torch.float32, memory_format=torch.contiguous_format)
|
|
img = img.add_(y) if img is not None else y
|
|
|
|
x = x.to(dtype=dtype)
|
|
assert x.dtype == dtype
|
|
assert img is None or img.dtype == torch.float32
|
|
return x, img
|
|
|
|
|
|
class SynthesisNetwork(torch.nn.Module):
|
|
def __init__(self,
|
|
w_dim, # Intermediate latent (W) dimensionality.
|
|
z_dim, # Output Latent (Z) dimensionality.
|
|
img_resolution, # Output image resolution.
|
|
img_channels, # Number of color channels.
|
|
channel_base=16384, # Overall multiplier for the number of channels.
|
|
channel_max=512, # Maximum number of channels in any layer.
|
|
num_fp16_res=0, # Use FP16 for the N highest resolutions.
|
|
**block_kwargs, # Arguments for SynthesisBlock.
|
|
):
|
|
assert img_resolution >= 4 and img_resolution & (img_resolution - 1) == 0
|
|
super().__init__()
|
|
self.w_dim = w_dim
|
|
self.img_resolution = img_resolution
|
|
self.img_resolution_log2 = int(np.log2(img_resolution))
|
|
self.img_channels = img_channels
|
|
self.block_resolutions = [2 ** i for i in range(3, self.img_resolution_log2 + 1)]
|
|
channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions}
|
|
fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8)
|
|
|
|
self.foreword = SynthesisForeword(img_channels=img_channels, in_channels=min(channel_base // 4, channel_max),
|
|
z_dim=z_dim * 2, resolution=4)
|
|
|
|
self.num_ws = self.img_resolution_log2 * 2 - 2
|
|
for res in self.block_resolutions:
|
|
if res // 2 in channels_dict.keys():
|
|
in_channels = channels_dict[res // 2] if res > 4 else 0
|
|
else:
|
|
in_channels = min(channel_base // (res // 2), channel_max)
|
|
out_channels = channels_dict[res]
|
|
use_fp16 = (res >= fp16_resolution)
|
|
use_fp16 = False
|
|
is_last = (res == self.img_resolution)
|
|
block = SynthesisBlock(in_channels, out_channels, w_dim=w_dim, resolution=res,
|
|
img_channels=img_channels, is_last=is_last, use_fp16=use_fp16, **block_kwargs)
|
|
setattr(self, f'b{res}', block)
|
|
|
|
def forward(self, x_global, mask, feats, ws, fname=None, **block_kwargs):
|
|
|
|
img = None
|
|
|
|
x, img = self.foreword(x_global, ws, feats, img)
|
|
|
|
for res in self.block_resolutions:
|
|
block = getattr(self, f'b{res}')
|
|
mod_vector0 = []
|
|
mod_vector0.append(ws[:, int(np.log2(res)) * 2 - 5])
|
|
mod_vector0.append(x_global.clone())
|
|
mod_vector0 = torch.cat(mod_vector0, dim=1)
|
|
|
|
mod_vector1 = []
|
|
mod_vector1.append(ws[:, int(np.log2(res)) * 2 - 4])
|
|
mod_vector1.append(x_global.clone())
|
|
mod_vector1 = torch.cat(mod_vector1, dim=1)
|
|
|
|
mod_vector_rgb = []
|
|
mod_vector_rgb.append(ws[:, int(np.log2(res)) * 2 - 3])
|
|
mod_vector_rgb.append(x_global.clone())
|
|
mod_vector_rgb = torch.cat(mod_vector_rgb, dim=1)
|
|
x, img = block(x, mask, feats, img, (mod_vector0, mod_vector1, mod_vector_rgb), fname=fname, **block_kwargs)
|
|
return img
|
|
|
|
|
|
class MappingNetwork(torch.nn.Module):
|
|
def __init__(self,
|
|
z_dim, # Input latent (Z) dimensionality, 0 = no latent.
|
|
c_dim, # Conditioning label (C) dimensionality, 0 = no label.
|
|
w_dim, # Intermediate latent (W) dimensionality.
|
|
num_ws, # Number of intermediate latents to output, None = do not broadcast.
|
|
num_layers=8, # Number of mapping layers.
|
|
embed_features=None, # Label embedding dimensionality, None = same as w_dim.
|
|
layer_features=None, # Number of intermediate features in the mapping layers, None = same as w_dim.
|
|
activation='lrelu', # Activation function: 'relu', 'lrelu', etc.
|
|
lr_multiplier=0.01, # Learning rate multiplier for the mapping layers.
|
|
w_avg_beta=0.995, # Decay for tracking the moving average of W during training, None = do not track.
|
|
):
|
|
super().__init__()
|
|
self.z_dim = z_dim
|
|
self.c_dim = c_dim
|
|
self.w_dim = w_dim
|
|
self.num_ws = num_ws
|
|
self.num_layers = num_layers
|
|
self.w_avg_beta = w_avg_beta
|
|
|
|
if embed_features is None:
|
|
embed_features = w_dim
|
|
if c_dim == 0:
|
|
embed_features = 0
|
|
if layer_features is None:
|
|
layer_features = w_dim
|
|
features_list = [z_dim + embed_features] + [layer_features] * (num_layers - 1) + [w_dim]
|
|
|
|
if c_dim > 0:
|
|
self.embed = FullyConnectedLayer(c_dim, embed_features)
|
|
for idx in range(num_layers):
|
|
in_features = features_list[idx]
|
|
out_features = features_list[idx + 1]
|
|
layer = FullyConnectedLayer(in_features, out_features, activation=activation, lr_multiplier=lr_multiplier)
|
|
setattr(self, f'fc{idx}', layer)
|
|
|
|
if num_ws is not None and w_avg_beta is not None:
|
|
self.register_buffer('w_avg', torch.zeros([w_dim]))
|
|
|
|
def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, skip_w_avg_update=False):
|
|
# Embed, normalize, and concat inputs.
|
|
x = None
|
|
with torch.autograd.profiler.record_function('input'):
|
|
if self.z_dim > 0:
|
|
x = normalize_2nd_moment(z.to(torch.float32))
|
|
if self.c_dim > 0:
|
|
y = normalize_2nd_moment(self.embed(c.to(torch.float32)))
|
|
x = torch.cat([x, y], dim=1) if x is not None else y
|
|
|
|
# Main layers.
|
|
for idx in range(self.num_layers):
|
|
layer = getattr(self, f'fc{idx}')
|
|
x = layer(x)
|
|
|
|
# Update moving average of W.
|
|
if self.w_avg_beta is not None and self.training and not skip_w_avg_update:
|
|
with torch.autograd.profiler.record_function('update_w_avg'):
|
|
self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta))
|
|
|
|
# Broadcast.
|
|
if self.num_ws is not None:
|
|
with torch.autograd.profiler.record_function('broadcast'):
|
|
x = x.unsqueeze(1).repeat([1, self.num_ws, 1])
|
|
|
|
# Apply truncation.
|
|
if truncation_psi != 1:
|
|
with torch.autograd.profiler.record_function('truncate'):
|
|
assert self.w_avg_beta is not None
|
|
if self.num_ws is None or truncation_cutoff is None:
|
|
x = self.w_avg.lerp(x, truncation_psi)
|
|
else:
|
|
x[:, :truncation_cutoff] = self.w_avg.lerp(x[:, :truncation_cutoff], truncation_psi)
|
|
return x
|
|
|
|
|
|
class Generator(torch.nn.Module):
|
|
def __init__(self,
|
|
z_dim, # Input latent (Z) dimensionality.
|
|
c_dim, # Conditioning label (C) dimensionality.
|
|
w_dim, # Intermediate latent (W) dimensionality.
|
|
img_resolution, # Output resolution.
|
|
img_channels, # Number of output color channels.
|
|
encoder_kwargs={}, # Arguments for EncoderNetwork.
|
|
mapping_kwargs={}, # Arguments for MappingNetwork.
|
|
synthesis_kwargs={}, # Arguments for SynthesisNetwork.
|
|
):
|
|
super().__init__()
|
|
self.z_dim = z_dim
|
|
self.c_dim = c_dim
|
|
self.w_dim = w_dim
|
|
self.img_resolution = img_resolution
|
|
self.img_channels = img_channels
|
|
self.encoder = EncoderNetwork(c_dim=c_dim, z_dim=z_dim, img_resolution=img_resolution,
|
|
img_channels=img_channels, **encoder_kwargs)
|
|
self.synthesis = SynthesisNetwork(z_dim=z_dim, w_dim=w_dim, img_resolution=img_resolution,
|
|
img_channels=img_channels, **synthesis_kwargs)
|
|
self.num_ws = self.synthesis.num_ws
|
|
self.mapping = MappingNetwork(z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs)
|
|
|
|
def forward(self, img, c, fname=None, truncation_psi=1, truncation_cutoff=None, **synthesis_kwargs):
|
|
mask = img[:, -1].unsqueeze(1)
|
|
x_global, z, feats = self.encoder(img, c)
|
|
ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff)
|
|
img = self.synthesis(x_global, mask, feats, ws, fname=fname, **synthesis_kwargs)
|
|
return img
|
|
|
|
|
|
FCF_MODEL_URL = os.environ.get(
|
|
"FCF_MODEL_URL",
|
|
"https://github.com/Sanster/models/releases/download/add_fcf/places_512_G.pth",
|
|
)
|
|
|
|
|
|
class FcF(InpaintModel):
|
|
min_size = 512
|
|
pad_mod = 512
|
|
pad_to_square = True
|
|
|
|
def init_model(self, device, **kwargs):
|
|
seed = 0
|
|
random.seed(seed)
|
|
np.random.seed(seed)
|
|
torch.manual_seed(seed)
|
|
torch.cuda.manual_seed_all(seed)
|
|
torch.backends.cudnn.deterministic = True
|
|
torch.backends.cudnn.benchmark = False
|
|
|
|
kwargs = {'channel_base': 1 * 32768, 'channel_max': 512, 'num_fp16_res': 4, 'conv_clamp': 256}
|
|
G = Generator(z_dim=512, c_dim=0, w_dim=512, img_resolution=512, img_channels=3,
|
|
synthesis_kwargs=kwargs, encoder_kwargs=kwargs, mapping_kwargs={'num_layers': 2})
|
|
self.model = load_model(G, FCF_MODEL_URL, device)
|
|
self.label = torch.zeros([1, self.model.c_dim], device=device)
|
|
|
|
@staticmethod
|
|
def is_downloaded() -> bool:
|
|
return os.path.exists(get_cache_path_by_url(FCF_MODEL_URL))
|
|
|
|
@torch.no_grad()
|
|
def __call__(self, image, mask, config: Config):
|
|
"""
|
|
images: [H, W, C] RGB, not normalized
|
|
masks: [H, W]
|
|
return: BGR IMAGE
|
|
"""
|
|
if image.shape[0] == 512 and image.shape[1] == 512:
|
|
return self._pad_forward(image, mask, config)
|
|
|
|
boxes = boxes_from_mask(mask)
|
|
crop_result = []
|
|
config.hd_strategy_crop_margin = 128
|
|
for box in boxes:
|
|
crop_image, crop_mask, crop_box = self._crop_box(image, mask, box, config)
|
|
origin_size = crop_image.shape[:2]
|
|
resize_image = resize_max_size(crop_image, size_limit=512)
|
|
resize_mask = resize_max_size(crop_mask, size_limit=512)
|
|
inpaint_result = self._pad_forward(resize_image, resize_mask, config)
|
|
|
|
# only paste masked area result
|
|
inpaint_result = cv2.resize(inpaint_result, (origin_size[1], origin_size[0]), interpolation=cv2.INTER_CUBIC)
|
|
|
|
original_pixel_indices = crop_mask < 127
|
|
inpaint_result[original_pixel_indices] = crop_image[:, :, ::-1][original_pixel_indices]
|
|
|
|
crop_result.append((inpaint_result, crop_box))
|
|
|
|
inpaint_result = image[:, :, ::-1]
|
|
for crop_image, crop_box in crop_result:
|
|
x1, y1, x2, y2 = crop_box
|
|
inpaint_result[y1:y2, x1:x2, :] = crop_image
|
|
|
|
return inpaint_result
|
|
|
|
def forward(self, image, mask, config: Config):
|
|
"""Input images and output images have same size
|
|
images: [H, W, C] RGB
|
|
masks: [H, W] mask area == 255
|
|
return: BGR IMAGE
|
|
"""
|
|
|
|
image = norm_img(image) # [0, 1]
|
|
image = image * 2 - 1 # [0, 1] -> [-1, 1]
|
|
mask = (mask > 120) * 255
|
|
mask = norm_img(mask)
|
|
|
|
image = torch.from_numpy(image).unsqueeze(0).to(self.device)
|
|
mask = torch.from_numpy(mask).unsqueeze(0).to(self.device)
|
|
|
|
erased_img = image * (1 - mask)
|
|
input_image = torch.cat([0.5 - mask, erased_img], dim=1)
|
|
|
|
output = self.model(input_image, self.label, truncation_psi=0.1, noise_mode='none')
|
|
output = (output.permute(0, 2, 3, 1) * 127.5 + 127.5).round().clamp(0, 255).to(torch.uint8)
|
|
output = output[0].cpu().numpy()
|
|
cur_res = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
|
|
return cur_res
|