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