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allow for rectangular images for efficient adapter

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Phil Wang
2022-01-31 08:55:31 -08:00
parent 25b384297d
commit 1bae5d3cc5
2 changed files with 7 additions and 3 deletions
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2
setup.py
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@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
setup(
name = 'vit-pytorch',
packages = find_packages(exclude=['examples']),
version = '0.26.6',
version = '0.26.7',
license='MIT',
description = 'Vision Transformer (ViT) - Pytorch',
author = 'Phil Wang',

8
vit_pytorch/efficient.py
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@@ -3,12 +3,16 @@ from torch import nn
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
def pair(t):
return t if isinstance(t, tuple) else (t, t)
class ViT(nn.Module):
def __init__(self, *, image_size, patch_size, num_classes, dim, transformer, pool = 'cls', channels = 3):
super().__init__()
assert image_size % patch_size == 0, 'image dimensions must be divisible by the patch size'
image_size_h, image_size_w = pair(image_size)
assert image_size_h % patch_size == 0 and image_size_w % patch_size == 0, 'image dimensions must be divisible by the patch size'
assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'
num_patches = (image_size // patch_size) ** 2
num_patches = (image_size_h // patch_size) * (image_size_w // patch_size)
patch_dim = channels * patch_size ** 2
self.to_patch_embedding = nn.Sequential(