""" HAT (Hybrid Attention Transformer) main model implementation. """ import torch import torch.nn as nn import math from .components import ( RHAG, PatchEmbed, PatchUnEmbed, Upsample, trunc_normal_, window_partition, to_2tuple ) class HAT(nn.Module): def __init__(self, img_size=64, patch_size=1, in_chans=3, embed_dim=96, depths=(6, 6, 6, 6), num_heads=(6, 6, 6, 6), window_size=7, compress_ratio=3, squeeze_factor=30, conv_scale=0.01, overlap_ratio=0.5, mlp_ratio=4., qkv_bias=True, qk_scale=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, norm_layer=nn.LayerNorm, ape=False, patch_norm=True, use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv', **kwargs): super(HAT, self).__init__() self.window_size = window_size self.shift_size = window_size // 2 self.overlap_ratio = overlap_ratio num_in_ch = in_chans num_out_ch = in_chans num_feat = 64 self.img_range = img_range if in_chans == 3: rgb_mean = (0.4488, 0.4371, 0.4040) self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) else: self.mean = torch.zeros(1, 1, 1, 1) self.upscale = upscale self.upsampler = upsampler # relative position index relative_position_index_SA = self.calculate_rpi_sa() relative_position_index_OCA = self.calculate_rpi_oca() self.register_buffer('relative_position_index_SA', relative_position_index_SA) self.register_buffer('relative_position_index_OCA', relative_position_index_OCA) # shallow feature extraction self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) # deep feature extraction self.num_layers = len(depths) self.embed_dim = embed_dim self.ape = ape self.patch_norm = patch_norm self.num_features = embed_dim self.mlp_ratio = mlp_ratio # split image into non-overlapping patches self.patch_embed = PatchEmbed( img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim, norm_layer=norm_layer if self.patch_norm else None) num_patches = self.patch_embed.num_patches patches_resolution = self.patch_embed.patches_resolution self.patches_resolution = patches_resolution # merge non-overlapping patches into image self.patch_unembed = PatchUnEmbed( img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim, norm_layer=norm_layer if self.patch_norm else None) # absolute position embedding if self.ape: self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) trunc_normal_(self.absolute_pos_embed, std=.02) self.pos_drop = nn.Dropout(p=drop_rate) # stochastic depth dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # build Residual Hybrid Attention Groups (RHAG) self.layers = nn.ModuleList() for i_layer in range(self.num_layers): layer = RHAG( dim=embed_dim, input_resolution=(patches_resolution[0], patches_resolution[1]), depth=depths[i_layer], num_heads=num_heads[i_layer], window_size=window_size, compress_ratio=compress_ratio, squeeze_factor=squeeze_factor, conv_scale=conv_scale, overlap_ratio=overlap_ratio, mlp_ratio=self.mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], norm_layer=norm_layer, downsample=None, use_checkpoint=use_checkpoint, img_size=img_size, patch_size=patch_size, resi_connection=resi_connection) self.layers.append(layer) self.norm = norm_layer(self.num_features) # build the last conv layer in deep feature extraction if resi_connection == '1conv': self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) elif resi_connection == 'identity': self.conv_after_body = nn.Identity() # high quality image reconstruction if self.upsampler == 'pixelshuffle': self.conv_before_upsample = nn.Sequential( nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)) self.upsample = Upsample(upscale, num_feat) self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) def calculate_rpi_sa(self): coords_h = torch.arange(self.window_size) coords_w = torch.arange(self.window_size) coords = torch.stack(torch.meshgrid([coords_h, coords_w])) coords_flatten = torch.flatten(coords, 1) relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] relative_coords = relative_coords.permute(1, 2, 0).contiguous() relative_coords[:, :, 0] += self.window_size - 1 relative_coords[:, :, 1] += self.window_size - 1 relative_coords[:, :, 0] *= 2 * self.window_size - 1 relative_position_index = relative_coords.sum(-1) return relative_position_index def calculate_rpi_oca(self): window_size_ori = self.window_size window_size_ext = self.window_size + int(self.overlap_ratio * self.window_size) coords_h = torch.arange(window_size_ori) coords_w = torch.arange(window_size_ori) coords_ori = torch.stack(torch.meshgrid([coords_h, coords_w])) coords_ori_flatten = torch.flatten(coords_ori, 1) coords_h = torch.arange(window_size_ext) coords_w = torch.arange(window_size_ext) coords_ext = torch.stack(torch.meshgrid([coords_h, coords_w])) coords_ext_flatten = torch.flatten(coords_ext, 1) relative_coords = coords_ext_flatten[:, None, :] - coords_ori_flatten[:, :, None] relative_coords = relative_coords.permute(1, 2, 0).contiguous() relative_coords[:, :, 0] += window_size_ori - window_size_ext + 1 relative_coords[:, :, 1] += window_size_ori - window_size_ext + 1 relative_coords[:, :, 0] *= window_size_ori + window_size_ext - 1 relative_position_index = relative_coords.sum(-1) return relative_position_index def calculate_mask(self, x_size): h, w = x_size img_mask = torch.zeros((1, h, w, 1)) h_slices = (slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None)) w_slices = (slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None)) cnt = 0 for h in h_slices: for w in w_slices: img_mask[:, h, w, :] = cnt cnt += 1 mask_windows = window_partition(img_mask, self.window_size) mask_windows = mask_windows.view(-1, self.window_size * self.window_size) attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) return attn_mask @torch.jit.ignore def no_weight_decay(self): return {'absolute_pos_embed'} @torch.jit.ignore def no_weight_decay_keywords(self): return {'relative_position_bias_table'} def forward_features(self, x): x_size = (x.shape[2], x.shape[3]) attn_mask = self.calculate_mask(x_size).to(x.device) params = {'attn_mask': attn_mask, 'rpi_sa': self.relative_position_index_SA, 'rpi_oca': self.relative_position_index_OCA} x = self.patch_embed(x) if self.ape: x = x + self.absolute_pos_embed x = self.pos_drop(x) for layer in self.layers: x = layer(x, x_size, params) x = self.norm(x) x = self.patch_unembed(x, x_size) return x def forward(self, x): self.mean = self.mean.type_as(x) x = (x - self.mean) * self.img_range if self.upsampler == 'pixelshuffle': x = self.conv_first(x) x = self.conv_after_body(self.forward_features(x)) + x x = self.conv_before_upsample(x) x = self.conv_last(self.upsample(x)) x = x / self.img_range + self.mean return x