55 lines
1.6 KiB
Python
55 lines
1.6 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (c) Alibaba, Inc. and its affiliates.
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# All rights reserved.
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# This file contains code that is adapted from
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# timm: https://github.com/huggingface/pytorch-image-models
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# pixart: https://github.com/PixArt-alpha/PixArt-alpha
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import torch
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import torch.nn as nn
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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__(
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self,
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patch_size=16,
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in_chans=3,
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embed_dim=768,
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norm_layer=None,
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flatten=True,
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bias=True,
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):
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super().__init__()
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self.flatten = flatten
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self.proj = nn.Conv2d(in_chans,
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embed_dim,
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kernel_size=patch_size,
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stride=patch_size,
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bias=bias)
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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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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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def unpatchify(x, h, w, c, p_h, p_w):
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'''
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Args:
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x: input tensor for unpatchified with shape as (N, T, patch_size**2 * C).
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h: tokens' number align height
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w: tokens' number align width
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c: output channels
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p_h: patch size for h
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p_w: patch size for w
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Returns: unpatchified imgs with shape as (N, H, W, C)
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'''
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assert h * w == x.shape[1]
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x = x.reshape(shape=(x.shape[0], h, w, p_h, p_w, c))
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x = torch.einsum('nhwpqc->nchpwq', x)
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return x.reshape(shape=(x.shape[0], c, h * p_h, w * p_w))
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