It looks like your code snippet got cut off at the end of the `PatchEmbed` class definition. Let me help you complete and clean up the entire implementation based on the original code from [rwightman/pytorch-image-models](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py), which is a well-known repository for vision transformer (ViT) models. Here’s the **complete and corrected version** of the `PatchEmbed` class, followed by the rest of the core components typically found in Vision Transformers: --- ### ✅ Complete & Corrected Code: PatchEmbed + DropPath + Utilities ```python from functools import partial from collections import OrderedDict import torch import torch.nn as nn def drop_path(x, drop_prob: float = 0., training: bool = False): """ Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). """ if drop_prob == 0. or not training: return x keep_prob = 1 - drop_prob shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) random_tensor.floor_() # binarize output = x.div(keep_prob) * random_tensor return output class DropPath(nn.Module): """ Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). """ def __init__(self, drop_prob=None): super(DropPath, self).__init__() self.drop_prob = drop_prob def forward(self, x): return drop_path(x, self.drop_prob, self.training) class PatchEmbed(nn.Module): """ 2D Image to Patch Embedding """ def __init__(self, img_size=224, patch_size=16, in_c=3, embed_dim=768, norm_layer=None): super().__init__() img_size = (img_size, img_size) patch_size = (patch_size, patch_size) self.img_size = img_size self.patch_size = patch_size self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) self.num_patches = self.grid_size[0] * self.grid_size[1] # Convolutional projection: [B, C, H, W] -> [B, embed_dim, grid_h, grid_w] self.proj = nn.Conv2d(in_c, embed_dim, kernel_size=patch_size, stride=patch_size) # Optional normalization layer after embedding self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() def forward(self, x): B, C, H, W = x.shape assert H == self.img_size[0] and W == self.img_size[1], \ f"Input image size ({H}x{W}) doesn't match model ({self.img_size[0]}x{self.img_size[1]})." # Shape: [B, C, H, W] -> [B, embed_dim, grid_h, grid_w] x = self.proj(x) # Flatten: [B, embed_dim, grid_h, grid_w] -> [B, embed_dim, num_patches] x = x.flatten(2).transpose(1, 2) # [B, num_patches, embed_dim] x = self.norm(x) return x ``` --- ### 🔧 Example Usage ```python # Example: Create a patch embedder for ImageNet-sized images patch_embed = PatchEmbed(img_size=224, patch_size=16, in_c=3, embed_dim=768) # Dummy input: batch of 1 image, 3 channels, 224x224 x = torch.randn(1, 3, 224, 224) # Forward pass out = patch_embed(x) print(out.shape) # Output: [1, 196, 768] → 196 patches (14×14), each with 768-dim embedding ``` --- ### 📌 Notes: - The `PatchEmbed` uses a **convolutional layer** (`nn.Conv2d`) to extract patches — this is standard in ViT implementations. - It outputs tokens of shape `[B, N, D]`, where: - `B`: batch size, - `N`: number of patches = `(H/W)^2 / patch_size^2`, - `D`: embedding dimension. - You can add `norm_layer=nn.LayerNorm` if you want to normalize the embeddings (common in ViT). --- ### 🔄 Next Steps (Optional) If you're building a full Vision Transformer, you’ll likely also need: - `Attention` module (with QKV projections), - `MLP` block, - `Block` (a transformer layer), - `VisionTransformer` class (with `cls_token`, `positional embeddings`, etc.). Would you like me to continue with those components? For example, here's how a typical `Block` might look: ```python class Block(nn.Module): def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): super().__init__() self.norm1 = norm_layer(dim) self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, 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) def forward(self, x): x = x + self.drop_path(self.attn(self.norm1(x))) x = x + self.drop_path(self.mlp(self.norm2(x))) return x ``` Let me know if you'd like the full `VisionTransformer` model structure!