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| """ Image to Patch Embedding using Conv2d | |
| A convolution based approach to patchifying a 2D image w/ embedding projection. | |
| Based on the impl in https://github.com/google-research/vision_transformer | |
| Hacked together by / Copyright 2020 Ross Wightman | |
| """ | |
| from torch import nn as nn | |
| from .helpers import to_2tuple | |
| class PatchEmbed(nn.Module): | |
| """ 2D Image to Patch Embedding | |
| """ | |
| def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None, flatten=True): | |
| 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.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] | |
| self.flatten = flatten | |
| self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) | |
| 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}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." | |
| x = self.proj(x) | |
| if self.flatten: | |
| x = x.flatten(2).transpose(1, 2) # BCHW -> BNC | |
| x = self.norm(x) | |
| return x | |