diff --git a/pvt.py b/pvt.py deleted file mode 100644 index de7ac77..0000000 --- a/pvt.py +++ /dev/null @@ -1,401 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from functools import partial - -from timm.models.layers import DropPath, to_2tuple, trunc_normal_ -from timm.models.registry import register_model -from timm.models.vision_transformer import _cfg -import math - - -class Mlp(nn.Module): - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0., linear=False): - 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.linear = linear - if self.linear: - self.relu = nn.ReLU(inplace=True) - 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) - if self.linear: - x = self.relu(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, linear=False): - 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 = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - self.proj_drop = nn.Dropout(proj_drop) - - self.linear = linear - self.sr_ratio = sr_ratio - if not linear: - if sr_ratio > 1: - self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio) - self.norm = nn.LayerNorm(dim) - else: - self.pool = nn.AdaptiveAvgPool2d(7) - self.sr = nn.Conv2d(dim, dim, kernel_size=1, stride=1) - self.norm = nn.LayerNorm(dim) - self.act = nn.GELU() - 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 not self.linear: - 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) - else: - x_ = x.permute(0, 2, 1).reshape(B, C, H, W) - x_ = self.sr(self.pool(x_)).reshape(B, C, -1).permute(0, 2, 1) - x_ = self.norm(x_) - x_ = self.act(x_) - 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] - - 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, linear=False): - 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, linear=linear) - # 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, linear=linear) - - 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_chans=3, embed_dim=768): - super().__init__() - - img_size = to_2tuple(img_size) - patch_size = to_2tuple(patch_size) - - assert max(patch_size) > stride, "Set larger patch_size than stride" - - self.img_size = img_size - self.patch_size = patch_size - self.H, self.W = img_size[0] // stride, img_size[1] // stride - self.num_patches = self.H * self.W - self.proj = nn.Conv2d(in_chans, 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 PyramidVisionTransformerV2(nn.Module): - def __init__(self, img_size=224, patch_size=16, in_chans=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], num_stages=4, linear=False): - super().__init__() - self.num_classes = num_classes - self.depths = depths - self.num_stages = num_stages - - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule - cur = 0 - - for i in range(num_stages): - patch_embed = OverlapPatchEmbed(img_size=img_size if i == 0 else img_size // (2 ** (i + 1)), - patch_size=7 if i == 0 else 3, - stride=4 if i == 0 else 2, - in_chans=in_chans if i == 0 else embed_dims[i - 1], - embed_dim=embed_dims[i]) - - block = nn.ModuleList([Block( - dim=embed_dims[i], num_heads=num_heads[i], mlp_ratio=mlp_ratios[i], qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + j], norm_layer=norm_layer, - sr_ratio=sr_ratios[i], linear=linear) - for j in range(depths[i])]) - norm = norm_layer(embed_dims[i]) - cur += depths[i] - - setattr(self, f"patch_embed{i + 1}", patch_embed) - setattr(self, f"block{i + 1}", block) - setattr(self, f"norm{i + 1}", norm) - - # 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 freeze_patch_emb(self): - self.patch_embed1.requires_grad = False - - @torch.jit.ignore - 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] - - for i in range(self.num_stages): - patch_embed = getattr(self, f"patch_embed{i + 1}") - block = getattr(self, f"block{i + 1}") - norm = getattr(self, f"norm{i + 1}") - x, H, W = patch_embed(x) - for blk in block: - x = blk(x, H, W) - x = norm(x) - if i != self.num_stages - 1: - x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() - - 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) - 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 -def pvt_v2_b0(pretrained=False, **kwargs): - model = PyramidVisionTransformerV2( - 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], - **kwargs) - model.default_cfg = _cfg() - - return model - - -@register_model -def pvt_v2_b1(pretrained=False, **kwargs): - model = PyramidVisionTransformerV2( - 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], - **kwargs) - model.default_cfg = _cfg() - - return model - - -@register_model -def pvt_v2_b2(pretrained=False, **kwargs): - model = PyramidVisionTransformerV2( - 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], **kwargs) - model.default_cfg = _cfg() - - return model - - -@register_model -def pvt_v2_b3(pretrained=False, **kwargs): - model = PyramidVisionTransformerV2( - 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], - **kwargs) - model.default_cfg = _cfg() - - return model - - -@register_model -def pvt_v2_b4(pretrained=False, **kwargs): - model = PyramidVisionTransformerV2( - 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], - **kwargs) - model.default_cfg = _cfg() - - return model - - -@register_model -def pvt_v2_b5(pretrained=False, **kwargs): - model = PyramidVisionTransformerV2( - 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], - **kwargs) - model.default_cfg = _cfg() - - return model - - -@register_model -def pvt_v2_b2_li(pretrained=False, **kwargs): - model = PyramidVisionTransformerV2( - 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], linear=True, **kwargs) - model.default_cfg = _cfg() - - return model \ No newline at end of file