Upload modelling_uniformer.py with huggingface_hub

modelling_uniformer.py

@@ -0,0 +1,412 @@
+from collections import OrderedDict
+from functools import partial
+from typing import Optional, Tuple, Union
+from math import isqrt
+
+import torch
+import torch.nn as nn
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+from transformers import ViTConfig
+from transformers.modeling_outputs import ModelOutput
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+layer_scale = False
+init_value = 1e-6
+
+
+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.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class CMlp(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.Conv2d(in_features, hidden_features, 1)
+        self.act = act_layer()
+        self.fc2 = nn.Conv2d(hidden_features, out_features, 1)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        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.):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]
+
+        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 CBlock(nn.Module):
+    def __init__(self, dim, mlp_ratio=4., drop=0., drop_path=0., act_layer=nn.GELU):
+        super().__init__()
+        self.pos_embed = nn.Conv2d(dim, dim, 3, padding=1, groups=dim)
+        self.norm1 = nn.BatchNorm2d(dim)
+        self.conv1 = nn.Conv2d(dim, dim, 1)
+        self.conv2 = nn.Conv2d(dim, dim, 1)
+        self.attn = nn.Conv2d(dim, dim, 5, padding=2, groups=dim)
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = nn.BatchNorm2d(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = CMlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+    def forward(self, x):
+        x = x + self.pos_embed(x)
+        x = x + self.module_1(x)
+        x = x + self.module_2(x)
+        return x
+
+    def module_1(self, x):
+        x = self.norm1(x.to(dtype=self.norm1.weight.dtype))  # Won't autocast to the dtype of the parameters of nn.BatchNorm2d.
+        x = self.conv1(x)
+        x = self.attn(x)
+        x = self.conv2(x)
+        x = self.drop_path(x)
+        return x
+    
+    def module_2(self, x):
+        x = self.norm2(x.to(dtype=self.norm2.weight.dtype))  # Won't autocast to the dtype of the parameters of nn.BatchNorm2d.
+        x = self.mlp(x)
+        x = self.drop_path(x)
+        return x
+
+class SABlock(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):
+        super().__init__()
+        self.pos_embed = nn.Conv2d(dim, dim, 3, padding=1, groups=dim)
+        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)
+        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)
+        global layer_scale
+        self.ls = layer_scale
+        if self.ls:
+            global init_value
+            print(f"Use layer_scale: {layer_scale}, init_values: {init_value}")
+            self.gamma_1 = nn.Parameter(init_value * torch.ones((dim)),requires_grad=True)
+            self.gamma_2 = nn.Parameter(init_value * torch.ones((dim)),requires_grad=True)
+
+    def forward(self, x):
+        x = x + self.pos_embed(x)
+        B, N, H, W = x.shape
+        x = x.flatten(2).transpose(1, 2)
+        if self.ls:
+            x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x)))
+            x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
+        else:
+            x = x + self.drop_path(self.attn(self.norm1(x)))
+            x = x + self.drop_path(self.mlp(self.norm2(x)))
+        x = x.transpose(1, 2).reshape(B, N, H, W)
+        return x        
+   
+
+class HeadEmbedding(nn.Module):
+    def __init__(self, in_channels, out_channels):
+        super(HeadEmbedding, self).__init__()
+
+        self.proj = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels // 2, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
+            nn.BatchNorm2d(out_channels // 2),
+            nn.GELU(),
+            nn.Conv2d(out_channels // 2, out_channels, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
+            nn.BatchNorm2d(out_channels),
+        )
+
+    def forward(self, x):
+        x = self.proj(x)
+        return x
+
+
+class MiddleEmbedding(nn.Module):
+    def __init__(self, in_channels, out_channels):
+        super(MiddleEmbedding, self).__init__()
+
+        self.proj = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
+            nn.BatchNorm2d(out_channels),
+        )
+
+    def forward(self, x):
+        x = self.proj(x)
+        return x
+
+
+class PatchEmbed(nn.Module):
+    def __init__(self, image_size=224, patch_size=16, in_chans=3, embed_dim=768):
+        super().__init__()
+        image_size = to_2tuple(image_size)
+        patch_size = to_2tuple(patch_size)
+        num_patches_height = image_size[0] // patch_size[0]
+        num_patches_width = image_size[1] // patch_size[1]
+        num_patches = num_patches_height * num_patches_width
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_patches = num_patches
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+        self.norm = nn.LayerNorm(embed_dim)
+
+    def forward(self, x):
+        _, _, H, W = x.shape
+        assert H == self.image_size[0] and W == self.image_size[1], \
+            f"Input image size ({H}*{W}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})."
+        x = self.proj(x)
+        B, _, H, W = x.shape
+        x = x.flatten(2).transpose(1, 2)
+        x = self.norm(x)
+        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
+        return x
+    
+    
+class UniFormer(nn.Module):
+    def __init__(self, depth=[3, 4, 8, 3], image_size=224, in_chans=3, num_classes=1000, embed_dim=[64, 128, 320, 512],
+                 head_dim=64, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None, patch_size=[4, 2, 2, 2],
+                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0., conv_stem=False, layer_norm_eps=1e-6, **kwargs):
+        super().__init__()
+        self.num_classes = num_classes
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+        norm_layer = partial(nn.LayerNorm, eps=layer_norm_eps) 
+        if conv_stem:
+            self.patch_embed1 = HeadEmbedding(in_channels=in_chans, out_channels=embed_dim[0])
+            self.patch_embed2 = MiddleEmbedding(in_channels=embed_dim[0], out_channels=embed_dim[1])
+            self.patch_embed3 = MiddleEmbedding(in_channels=embed_dim[1], out_channels=embed_dim[2])
+            self.patch_embed4 = MiddleEmbedding(in_channels=embed_dim[2], out_channels=embed_dim[3])
+        else:
+            self.patch_embed1 = PatchEmbed(
+                image_size=image_size, patch_size=patch_size[0], in_chans=in_chans, embed_dim=embed_dim[0])
+            self.patch_embed2 = PatchEmbed(
+                image_size=image_size // patch_size[0], patch_size=patch_size[1], in_chans=embed_dim[0], embed_dim=embed_dim[1])
+            self.patch_embed3 = PatchEmbed(
+                image_size=image_size // (patch_size[0]*patch_size[1]), patch_size=patch_size[2], in_chans=embed_dim[1], embed_dim=embed_dim[2])
+            self.patch_embed4 = PatchEmbed(
+                image_size=image_size // (patch_size[0]*patch_size[1]*patch_size[2]), patch_size=patch_size[3], in_chans=embed_dim[2], embed_dim=embed_dim[3])
+
+        self.pos_drop = nn.Dropout(p=drop_rate)
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depth))]  # stochastic depth decay rule
+        num_heads = [dim // head_dim for dim in embed_dim]
+        self.blocks1 = nn.ModuleList([
+            CBlock(dim=embed_dim[0], mlp_ratio=mlp_ratio, drop=drop_rate, drop_path=dpr[i])
+            for i in range(depth[0])])
+        self.blocks2 = nn.ModuleList([
+            CBlock(dim=embed_dim[1], mlp_ratio=mlp_ratio, drop=drop_rate, drop_path=dpr[i+depth[0]])
+            for i in range(depth[1])])
+        self.blocks3 = nn.ModuleList([
+            SABlock(
+                dim=embed_dim[2], num_heads=num_heads[2], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+depth[0]+depth[1]], norm_layer=norm_layer)
+            for i in range(depth[2])])
+        self.blocks4 = nn.ModuleList([
+            SABlock(
+                dim=embed_dim[3], num_heads=num_heads[3], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+depth[0]+depth[1]+depth[2]], norm_layer=norm_layer)
+        for i in range(depth[3])])
+        self.norm = nn.BatchNorm2d(embed_dim[-1])
+        
+        # Representation layer
+        if representation_size:
+            self.num_features = representation_size
+            self.pre_logits = nn.Sequential(OrderedDict([
+                ('fc', nn.Linear(embed_dim, representation_size)),
+                ('act', nn.Tanh())
+            ]))
+        else:
+            self.pre_logits = nn.Identity()
+
+    def forward_features(self, x):
+        x = self.patch_embed1(x)
+        x = self.pos_drop(x)
+        for blk in self.blocks1:
+            x = blk(x)
+        x = self.patch_embed2(x)
+        for blk in self.blocks2:
+            x = blk(x)
+        x = self.patch_embed3(x)
+        for blk in self.blocks3:
+            x = blk(x)
+        x = self.patch_embed4(x)
+        for blk in self.blocks4:
+            x = blk(x)
+        x = self.norm(x.to(dtype=self.norm.weight.dtype))  # Won't autocast to the dtype of the parameters of nn.BatchNorm2d.
+        x = self.pre_logits(x)
+        return x
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        return x
+
+
+class UniFormerPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = ViTConfig
+    base_model_prefix = "vit"
+    main_input_name = "pixel_values"
+
+    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)
+
+
+class UniFormerProjectionHead(torch.nn.Module):
+
+    def __init__(self, config) -> None:
+        super().__init__()
+
+        # Layer normalisation before projection:
+        self.layer_norm = torch.nn.LayerNorm(config.embed_dim[-1], eps=config.layer_norm_eps)
+
+        # No bias as following layer normalisation with bias:
+        self.projection = torch.nn.Linear(config.embed_dim[-1], config.projection_size, bias=False)
+
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.layer_norm(x)
+        x = self.projection(x)
+        return x
+
+
+class UniFormerModel(UniFormerPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.uniformer = UniFormer(**vars(config))
+
+        # Initialize weights and apply final processing:
+        self.post_init()
+
+    def forward(
+        self,
+        pixel_values: Optional[torch.Tensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, ModelOutput]:
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        last_hidden_state = self.uniformer(pixel_values)
+
+        # Flatten h x w:
+        last_hidden_state = torch.flatten(last_hidden_state, 2)
+
+        # Permute last hidden state:
+        last_hidden_state = torch.permute(last_hidden_state, [0, 2, 1])
+
+        # return last_hidden_state
+        if not return_dict:
+            return last_hidden_state
+
+        return ModelOutput(last_hidden_state=last_hidden_state)
+
+
+class MultiUniFormerWithProjectionHead(UniFormerPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.uniformer = UniFormer(**vars(config))
+        self.projection_head = UniFormerProjectionHead(config)
+
+        # Initialize weights and apply final processing:
+        self.post_init()
+
+    def forward(
+        self,
+        pixel_values: Optional[torch.Tensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, ModelOutput]:
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # Flatten the batch and study_id dimensions:
+        assert len(pixel_values.shape) == 5, 'pixel_values must be B, S, C, H, W, where S is the max number of images for a study in the batch.'
+        last_hidden_state = self.uniformer(pixel_values.view(-1, *pixel_values.shape[2:]))
+        # last_hidden_state = self.uniformer(pixel_values.flatten(start_dim=0, end_dim=1))
+
+        # Flatten h x w:
+        last_hidden_state = torch.flatten(last_hidden_state, 2)
+
+        # Project the features for each spatial position to the decoder's hidden size:
+        projection = self.projection_head(torch.permute(last_hidden_state, [0, 2, 1]))
+
+        # Concatenate the features for each chest X-ray:
+        projection = projection.view(pixel_values.shape[0], -1, projection.shape[-1])
+
+        # Derive the attention mask from the pixel values:
+        mask = (pixel_values[:, :, 0, 0, 0] != 0.0)[:, :, None]
+        attention_mask = torch.ones(
+            [projection.shape[0], pixel_values.shape[1], projection.shape[1] // pixel_values.shape[1]], 
+            dtype=torch.long,
+            device=mask.device,
+        )
+        attention_mask = attention_mask * mask
+        attention_mask = attention_mask.view(attention_mask.shape[0], -1)
+
+        if not return_dict:
+            return projection
+
+        return ModelOutput(last_hidden_state=projection, attention_mask=attention_mask)
+    
+
+if __name__ == '__main__':
+    y = PatchEmbed()
+    y(torch.randn(2, 3, 224, 224))