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| import torch | |
| import torch.nn as nn | |
| from functools import partial | |
| from timm.models.layers import DropPath, to_2tuple, trunc_normal_ | |
| from timm.models.registry import register_model | |
| import math | |
| from config import Config | |
| config = Config() | |
| class Mlp(nn.Module): | |
| def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): | |
| super().__init__() | |
| out_features = out_features or in_features | |
| hidden_features = hidden_features or in_features | |
| self.fc1 = nn.Linear(in_features, hidden_features) | |
| self.dwconv = DWConv(hidden_features) | |
| self.act = act_layer() | |
| self.fc2 = nn.Linear(hidden_features, out_features) | |
| self.drop = nn.Dropout(drop) | |
| 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) | |
| elif isinstance(m, nn.Conv2d): | |
| fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels | |
| fan_out //= m.groups | |
| m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) | |
| if m.bias is not None: | |
| m.bias.data.zero_() | |
| def forward(self, x, H, W): | |
| x = self.fc1(x) | |
| x = self.dwconv(x, H, W) | |
| x = self.act(x) | |
| x = self.drop(x) | |
| x = self.fc2(x) | |
| x = self.drop(x) | |
| return x | |
| class Attention(nn.Module): | |
| def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1): | |
| super().__init__() | |
| assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}." | |
| self.dim = dim | |
| self.num_heads = num_heads | |
| head_dim = dim // num_heads | |
| self.scale = qk_scale or head_dim ** -0.5 | |
| self.q = nn.Linear(dim, dim, bias=qkv_bias) | |
| self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias) | |
| self.attn_drop_prob = attn_drop | |
| self.attn_drop = nn.Dropout(attn_drop) | |
| self.proj = nn.Linear(dim, dim) | |
| self.proj_drop = nn.Dropout(proj_drop) | |
| self.sr_ratio = sr_ratio | |
| if sr_ratio > 1: | |
| self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio) | |
| self.norm = nn.LayerNorm(dim) | |
| 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) | |
| elif isinstance(m, nn.Conv2d): | |
| fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels | |
| fan_out //= m.groups | |
| m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) | |
| if m.bias is not None: | |
| m.bias.data.zero_() | |
| def forward(self, x, H, W): | |
| B, N, C = x.shape | |
| q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) | |
| if self.sr_ratio > 1: | |
| x_ = x.permute(0, 2, 1).reshape(B, C, H, W) | |
| x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1) | |
| x_ = self.norm(x_) | |
| kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) | |
| else: | |
| kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) | |
| k, v = kv[0], kv[1] | |
| if config.SDPA_enabled: | |
| x = torch.nn.functional.scaled_dot_product_attention( | |
| q, k, v, | |
| attn_mask=None, dropout_p=self.attn_drop_prob, is_causal=False | |
| ).transpose(1, 2).reshape(B, N, C) | |
| else: | |
| attn = (q @ k.transpose(-2, -1)) * self.scale | |
| attn = attn.softmax(dim=-1) | |
| attn = self.attn_drop(attn) | |
| x = (attn @ v).transpose(1, 2).reshape(B, N, C) | |
| x = self.proj(x) | |
| x = self.proj_drop(x) | |
| return x | |
| class Block(nn.Module): | |
| def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., | |
| drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1): | |
| super().__init__() | |
| self.norm1 = norm_layer(dim) | |
| self.attn = Attention( | |
| dim, | |
| num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, | |
| attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio) | |
| # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here | |
| self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() | |
| self.norm2 = norm_layer(dim) | |
| mlp_hidden_dim = int(dim * mlp_ratio) | |
| self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) | |
| 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) | |
| elif isinstance(m, nn.Conv2d): | |
| fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels | |
| fan_out //= m.groups | |
| m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) | |
| if m.bias is not None: | |
| m.bias.data.zero_() | |
| def forward(self, x, H, W): | |
| x = x + self.drop_path(self.attn(self.norm1(x), H, W)) | |
| x = x + self.drop_path(self.mlp(self.norm2(x), H, W)) | |
| return x | |
| class OverlapPatchEmbed(nn.Module): | |
| """ Image to Patch Embedding | |
| """ | |
| def __init__(self, img_size=224, patch_size=7, stride=4, in_channels=3, embed_dim=768): | |
| super().__init__() | |
| img_size = to_2tuple(img_size) | |
| patch_size = to_2tuple(patch_size) | |
| self.img_size = img_size | |
| self.patch_size = patch_size | |
| self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1] | |
| self.num_patches = self.H * self.W | |
| self.proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=stride, | |
| padding=(patch_size[0] // 2, patch_size[1] // 2)) | |
| self.norm = nn.LayerNorm(embed_dim) | |
| 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) | |
| elif isinstance(m, nn.Conv2d): | |
| fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels | |
| fan_out //= m.groups | |
| m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) | |
| if m.bias is not None: | |
| m.bias.data.zero_() | |
| def forward(self, x): | |
| x = self.proj(x) | |
| _, _, H, W = x.shape | |
| x = x.flatten(2).transpose(1, 2) | |
| x = self.norm(x) | |
| return x, H, W | |
| class PyramidVisionTransformerImpr(nn.Module): | |
| def __init__(self, img_size=224, patch_size=16, in_channels=3, num_classes=1000, embed_dims=[64, 128, 256, 512], | |
| num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0., | |
| attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm, | |
| depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1]): | |
| super().__init__() | |
| self.num_classes = num_classes | |
| self.depths = depths | |
| # patch_embed | |
| self.patch_embed1 = OverlapPatchEmbed(img_size=img_size, patch_size=7, stride=4, in_channels=in_channels, | |
| embed_dim=embed_dims[0]) | |
| self.patch_embed2 = OverlapPatchEmbed(img_size=img_size // 4, patch_size=3, stride=2, in_channels=embed_dims[0], | |
| embed_dim=embed_dims[1]) | |
| self.patch_embed3 = OverlapPatchEmbed(img_size=img_size // 8, patch_size=3, stride=2, in_channels=embed_dims[1], | |
| embed_dim=embed_dims[2]) | |
| self.patch_embed4 = OverlapPatchEmbed(img_size=img_size // 16, patch_size=3, stride=2, in_channels=embed_dims[2], | |
| embed_dim=embed_dims[3]) | |
| # transformer encoder | |
| dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule | |
| cur = 0 | |
| self.block1 = nn.ModuleList([Block( | |
| dim=embed_dims[0], num_heads=num_heads[0], mlp_ratio=mlp_ratios[0], qkv_bias=qkv_bias, qk_scale=qk_scale, | |
| drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, | |
| sr_ratio=sr_ratios[0]) | |
| for i in range(depths[0])]) | |
| self.norm1 = norm_layer(embed_dims[0]) | |
| cur += depths[0] | |
| self.block2 = nn.ModuleList([Block( | |
| dim=embed_dims[1], num_heads=num_heads[1], mlp_ratio=mlp_ratios[1], qkv_bias=qkv_bias, qk_scale=qk_scale, | |
| drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, | |
| sr_ratio=sr_ratios[1]) | |
| for i in range(depths[1])]) | |
| self.norm2 = norm_layer(embed_dims[1]) | |
| cur += depths[1] | |
| self.block3 = nn.ModuleList([Block( | |
| dim=embed_dims[2], num_heads=num_heads[2], mlp_ratio=mlp_ratios[2], qkv_bias=qkv_bias, qk_scale=qk_scale, | |
| drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, | |
| sr_ratio=sr_ratios[2]) | |
| for i in range(depths[2])]) | |
| self.norm3 = norm_layer(embed_dims[2]) | |
| cur += depths[2] | |
| self.block4 = nn.ModuleList([Block( | |
| dim=embed_dims[3], num_heads=num_heads[3], mlp_ratio=mlp_ratios[3], qkv_bias=qkv_bias, qk_scale=qk_scale, | |
| drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, | |
| sr_ratio=sr_ratios[3]) | |
| for i in range(depths[3])]) | |
| self.norm4 = norm_layer(embed_dims[3]) | |
| # classification head | |
| # self.head = nn.Linear(embed_dims[3], num_classes) if num_classes > 0 else nn.Identity() | |
| 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) | |
| elif isinstance(m, nn.Conv2d): | |
| fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels | |
| fan_out //= m.groups | |
| m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) | |
| if m.bias is not None: | |
| m.bias.data.zero_() | |
| def init_weights(self, pretrained=None): | |
| if isinstance(pretrained, str): | |
| logger = 1 | |
| #load_checkpoint(self, pretrained, map_location='cpu', strict=False, logger=logger) | |
| def reset_drop_path(self, drop_path_rate): | |
| dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(self.depths))] | |
| cur = 0 | |
| for i in range(self.depths[0]): | |
| self.block1[i].drop_path.drop_prob = dpr[cur + i] | |
| cur += self.depths[0] | |
| for i in range(self.depths[1]): | |
| self.block2[i].drop_path.drop_prob = dpr[cur + i] | |
| cur += self.depths[1] | |
| for i in range(self.depths[2]): | |
| self.block3[i].drop_path.drop_prob = dpr[cur + i] | |
| cur += self.depths[2] | |
| for i in range(self.depths[3]): | |
| self.block4[i].drop_path.drop_prob = dpr[cur + i] | |
| def freeze_patch_emb(self): | |
| self.patch_embed1.requires_grad = False | |
| def no_weight_decay(self): | |
| return {'pos_embed1', 'pos_embed2', 'pos_embed3', 'pos_embed4', 'cls_token'} # has pos_embed may be better | |
| def get_classifier(self): | |
| return self.head | |
| def reset_classifier(self, num_classes, global_pool=''): | |
| self.num_classes = num_classes | |
| self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() | |
| def forward_features(self, x): | |
| B = x.shape[0] | |
| outs = [] | |
| # stage 1 | |
| x, H, W = self.patch_embed1(x) | |
| for i, blk in enumerate(self.block1): | |
| x = blk(x, H, W) | |
| x = self.norm1(x) | |
| x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() | |
| outs.append(x) | |
| # stage 2 | |
| x, H, W = self.patch_embed2(x) | |
| for i, blk in enumerate(self.block2): | |
| x = blk(x, H, W) | |
| x = self.norm2(x) | |
| x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() | |
| outs.append(x) | |
| # stage 3 | |
| x, H, W = self.patch_embed3(x) | |
| for i, blk in enumerate(self.block3): | |
| x = blk(x, H, W) | |
| x = self.norm3(x) | |
| x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() | |
| outs.append(x) | |
| # stage 4 | |
| x, H, W = self.patch_embed4(x) | |
| for i, blk in enumerate(self.block4): | |
| x = blk(x, H, W) | |
| x = self.norm4(x) | |
| x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() | |
| outs.append(x) | |
| return outs | |
| # return x.mean(dim=1) | |
| def forward(self, x): | |
| x = self.forward_features(x) | |
| # x = self.head(x) | |
| return x | |
| class DWConv(nn.Module): | |
| def __init__(self, dim=768): | |
| super(DWConv, self).__init__() | |
| self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim) | |
| def forward(self, x, H, W): | |
| B, N, C = x.shape | |
| x = x.transpose(1, 2).view(B, C, H, W).contiguous() | |
| x = self.dwconv(x) | |
| x = x.flatten(2).transpose(1, 2) | |
| return x | |
| def _conv_filter(state_dict, patch_size=16): | |
| """ convert patch embedding weight from manual patchify + linear proj to conv""" | |
| out_dict = {} | |
| for k, v in state_dict.items(): | |
| if 'patch_embed.proj.weight' in k: | |
| v = v.reshape((v.shape[0], 3, patch_size, patch_size)) | |
| out_dict[k] = v | |
| return out_dict | |
| ## @register_model | |
| class pvt_v2_b0(PyramidVisionTransformerImpr): | |
| def __init__(self, **kwargs): | |
| super(pvt_v2_b0, self).__init__( | |
| patch_size=4, embed_dims=[32, 64, 160, 256], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], | |
| qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1], | |
| drop_rate=0.0, drop_path_rate=0.1) | |
| ## @register_model | |
| class pvt_v2_b1(PyramidVisionTransformerImpr): | |
| def __init__(self, **kwargs): | |
| super(pvt_v2_b1, self).__init__( | |
| patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], | |
| qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1], | |
| drop_rate=0.0, drop_path_rate=0.1) | |
| ## @register_model | |
| class pvt_v2_b2(PyramidVisionTransformerImpr): | |
| def __init__(self, in_channels=3, **kwargs): | |
| super(pvt_v2_b2, self).__init__( | |
| patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], | |
| qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], | |
| drop_rate=0.0, drop_path_rate=0.1, in_channels=in_channels) | |
| ## @register_model | |
| class pvt_v2_b3(PyramidVisionTransformerImpr): | |
| def __init__(self, **kwargs): | |
| super(pvt_v2_b3, self).__init__( | |
| patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], | |
| qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1], | |
| drop_rate=0.0, drop_path_rate=0.1) | |
| ## @register_model | |
| class pvt_v2_b4(PyramidVisionTransformerImpr): | |
| def __init__(self, **kwargs): | |
| super(pvt_v2_b4, self).__init__( | |
| patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], | |
| qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1], | |
| drop_rate=0.0, drop_path_rate=0.1) | |
| ## @register_model | |
| class pvt_v2_b5(PyramidVisionTransformerImpr): | |
| def __init__(self, **kwargs): | |
| super(pvt_v2_b5, self).__init__( | |
| patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], | |
| qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 6, 40, 3], sr_ratios=[8, 4, 2, 1], | |
| drop_rate=0.0, drop_path_rate=0.1) | |