Merge branch 'xcit' of https://github.com/alexander-soare/pytorch-image-models into alexander-soare-xcit
Ross Wightman
3 years ago
commit
d3255adf8e
@ -0,0 +1,809 @@
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""" Cross-Covariance Image Transformer (XCiT) in PyTorch
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Same as the official implementation, with some minor adaptations.
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- https://github.com/facebookresearch/xcit/blob/master/xcit.py
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Paper:
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- https://arxiv.org/abs/2106.09681
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"""
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# Copyright (c) 2015-present, Facebook, Inc.
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# All rights reserved.
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import math
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from functools import partial
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import torch
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import torch.nn as nn
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from .helpers import build_model_with_cfg
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from .vision_transformer import _cfg, Mlp
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from .registry import register_model
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from .layers import DropPath, trunc_normal_, to_2tuple
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from .cait import ClassAttn
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def _cfg(url='', **kwargs):
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return {
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'url': url,
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'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
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'crop_pct': 1.0, 'interpolation': 'bicubic', 'fixed_input_size': True,
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'patch_embed.proj.0.0', 'classifier': 'head',
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**kwargs
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}
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default_cfgs = {
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# Patch size 16
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'xcit_nano_12_p16_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_nano_12_p16_224.pth'),
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'xcit_nano_12_p16_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_nano_12_p16_224_dist.pth'),
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'xcit_nano_12_p16_384_dist': _cfg(
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url='https://dl.fbaipublicfiles.com/xcit/xcit_nano_12_p16_384_dist.pth', input_size=(3, 384, 384)),
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'xcit_tiny_12_p16_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_tiny_12_p16_224.pth'),
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'xcit_tiny_12_p16_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_tiny_12_p16_224_dist.pth'),
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'xcit_tiny_12_p16_384_dist': _cfg(
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url='https://dl.fbaipublicfiles.com/xcit/xcit_tiny_12_p16_384_dist.pth', input_size=(3, 384, 384)),
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'xcit_tiny_24_p16_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_tiny_24_p16_224.pth'),
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'xcit_tiny_24_p16_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_tiny_24_p16_224_dist.pth'),
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'xcit_tiny_24_p16_384_dist': _cfg(
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url='https://dl.fbaipublicfiles.com/xcit/xcit_tiny_24_p16_384_dist.pth', input_size=(3, 384, 384)),
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'xcit_small_12_p16_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_small_12_p16_224.pth'),
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'xcit_small_12_p16_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_small_12_p16_224_dist.pth'),
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'xcit_small_12_p16_384_dist': _cfg(
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url='https://dl.fbaipublicfiles.com/xcit/xcit_small_12_p16_384_dist.pth', input_size=(3, 384, 384)),
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'xcit_small_24_p16_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_small_24_p16_224.pth'),
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'xcit_small_24_p16_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_small_24_p16_224_dist.pth'),
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'xcit_small_24_p16_384_dist': _cfg(
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url='https://dl.fbaipublicfiles.com/xcit/xcit_small_24_p16_384_dist.pth', input_size=(3, 384, 384)),
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'xcit_medium_24_p16_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_medium_24_p16_224.pth'),
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'xcit_medium_24_p16_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_medium_24_p16_224_dist.pth'),
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'xcit_medium_24_p16_384_dist': _cfg(
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url='https://dl.fbaipublicfiles.com/xcit/xcit_medium_24_p16_384_dist.pth', input_size=(3, 384, 384)),
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'xcit_large_24_p16_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_large_24_p16_224.pth'),
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'xcit_large_24_p16_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_large_24_p16_224_dist.pth'),
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'xcit_large_24_p16_384_dist': _cfg(
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url='https://dl.fbaipublicfiles.com/xcit/xcit_large_24_p16_384_dist.pth', input_size=(3, 384, 384)),
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# Patch size 8
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'xcit_nano_12_p8_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_nano_12_p8_224.pth'),
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'xcit_nano_12_p8_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_nano_12_p8_224_dist.pth'),
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'xcit_nano_12_p8_384_dist': _cfg(
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url='https://dl.fbaipublicfiles.com/xcit/xcit_nano_12_p8_384_dist.pth', input_size=(3, 384, 384)),
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'xcit_tiny_12_p8_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_tiny_12_p8_224.pth'),
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'xcit_tiny_12_p8_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_tiny_12_p8_224_dist.pth'),
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'xcit_tiny_12_p8_384_dist': _cfg(
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url='https://dl.fbaipublicfiles.com/xcit/xcit_tiny_12_p8_384_dist.pth', input_size=(3, 384, 384)),
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'xcit_tiny_24_p8_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_tiny_24_p8_224.pth'),
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'xcit_tiny_24_p8_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_tiny_24_p8_224_dist.pth'),
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'xcit_tiny_24_p8_384_dist': _cfg(
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url='https://dl.fbaipublicfiles.com/xcit/xcit_tiny_24_p8_384_dist.pth', input_size=(3, 384, 384)),
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'xcit_small_12_p8_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_small_12_p8_224.pth'),
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'xcit_small_12_p8_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_small_12_p8_224_dist.pth'),
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'xcit_small_12_p8_384_dist': _cfg(
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url='https://dl.fbaipublicfiles.com/xcit/xcit_small_12_p8_384_dist.pth', input_size=(3, 384, 384)),
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'xcit_small_24_p8_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_small_24_p8_224.pth'),
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'xcit_small_24_p8_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_small_24_p8_224_dist.pth'),
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'xcit_small_24_p8_384_dist': _cfg(
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url='https://dl.fbaipublicfiles.com/xcit/xcit_small_24_p8_384_dist.pth', input_size=(3, 384, 384)),
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'xcit_medium_24_p8_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_medium_24_p8_224.pth'),
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'xcit_medium_24_p8_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_medium_24_p8_224_dist.pth'),
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'xcit_medium_24_p8_384_dist': _cfg(
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url='https://dl.fbaipublicfiles.com/xcit/xcit_medium_24_p8_384_dist.pth', input_size=(3, 384, 384)),
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'xcit_large_24_p8_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_large_24_p8_224.pth'),
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'xcit_large_24_p8_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_large_24_p8_224_dist.pth'),
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'xcit_large_24_p8_384_dist': _cfg(
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url='https://dl.fbaipublicfiles.com/xcit/xcit_large_24_p8_384_dist.pth', input_size=(3, 384, 384)),
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}
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class PositionalEncodingFourier(nn.Module):
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"""
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Positional encoding relying on a fourier kernel matching the one used in the "Attention is all of Need" paper.
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Based on the official XCiT code
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- https://github.com/facebookresearch/xcit/blob/master/xcit.py
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"""
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def __init__(self, hidden_dim=32, dim=768, temperature=10000):
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super().__init__()
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self.token_projection = nn.Conv2d(hidden_dim * 2, dim, kernel_size=1)
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self.scale = 2 * math.pi
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self.temperature = temperature
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self.hidden_dim = hidden_dim
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self.dim = dim
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self.eps = 1e-6
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def forward(self, B: int, H: int, W: int):
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device = self.token_projection.weight.device
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y_embed = torch.arange(1, H+1, dtype=torch.float32, device=device).unsqueeze(1).repeat(1, 1, W)
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x_embed = torch.arange(1, W+1, dtype=torch.float32, device=device).repeat(1, H, 1)
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y_embed = y_embed / (y_embed[:, -1:, :] + self.eps) * self.scale
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x_embed = x_embed / (x_embed[:, :, -1:] + self.eps) * self.scale
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dim_t = torch.arange(self.hidden_dim, dtype=torch.float32, device=device)
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dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode='floor') / self.hidden_dim)
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pos_x = x_embed[:, :, :, None] / dim_t
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pos_y = y_embed[:, :, :, None] / dim_t
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pos_x = torch.stack([pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()], dim=4).flatten(3)
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pos_y = torch.stack([pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()], dim=4).flatten(3)
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pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
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pos = self.token_projection(pos)
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return pos.repeat(B, 1, 1, 1) # (B, C, H, W)
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def conv3x3(in_planes, out_planes, stride=1):
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"""3x3 convolution + batch norm"""
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return torch.nn.Sequential(
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nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False),
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nn.BatchNorm2d(out_planes)
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)
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class ConvPatchEmbed(nn.Module):
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"""Image to Patch Embedding using multiple convolutional layers"""
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def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
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super().__init__()
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img_size = to_2tuple(img_size)
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num_patches = (img_size[1] // patch_size) * (img_size[0] // 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.num_patches = num_patches
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if patch_size == 16:
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self.proj = torch.nn.Sequential(
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conv3x3(in_chans, embed_dim // 8, 2),
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nn.GELU(),
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conv3x3(embed_dim // 8, embed_dim // 4, 2),
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nn.GELU(),
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conv3x3(embed_dim // 4, embed_dim // 2, 2),
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nn.GELU(),
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conv3x3(embed_dim // 2, embed_dim, 2),
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)
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elif patch_size == 8:
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self.proj = torch.nn.Sequential(
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conv3x3(3, embed_dim // 4, 2),
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nn.GELU(),
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conv3x3(embed_dim // 4, embed_dim // 2, 2),
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nn.GELU(),
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conv3x3(embed_dim // 2, embed_dim, 2),
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)
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else:
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raise('For convolutional projection, patch size has to be in [8, 16]')
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def forward(self, x):
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x = self.proj(x)
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Hp, Wp = x.shape[2], x.shape[3]
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x = x.flatten(2).transpose(1, 2) # (B, N, C)
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return x, (Hp, Wp)
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class LPI(nn.Module):
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"""
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Local Patch Interaction module that allows explicit communication between tokens in 3x3 windows to augment the
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implicit communication performed by the block diagonal scatter attention. Implemented using 2 layers of separable
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3x3 convolutions with GeLU and BatchNorm2d
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"""
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def __init__(self, in_features, out_features=None, act_layer=nn.GELU, kernel_size=3):
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super().__init__()
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out_features = out_features or in_features
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padding = kernel_size // 2
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self.conv1 = torch.nn.Conv2d(
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in_features, in_features, kernel_size=kernel_size, padding=padding, groups=in_features)
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self.act = act_layer()
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self.bn = nn.BatchNorm2d(in_features)
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self.conv2 = torch.nn.Conv2d(
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in_features, out_features, kernel_size=kernel_size, padding=padding, groups=out_features)
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def forward(self, x, H: int, W: int):
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B, N, C = x.shape
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x = x.permute(0, 2, 1).reshape(B, C, H, W)
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x = self.conv1(x)
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x = self.act(x)
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x = self.bn(x)
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x = self.conv2(x)
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x = x.reshape(B, C, N).permute(0, 2, 1)
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return x
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class ClassAttentionBlock(nn.Module):
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"""Class Attention Layer as in CaiT https://arxiv.org/abs/2103.17239"""
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def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., drop_path=0.,
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act_layer=nn.GELU, norm_layer=nn.LayerNorm, eta=1., tokens_norm=False):
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.attn = ClassAttn(
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dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.norm2 = norm_layer(dim)
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self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
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if eta is not None: # LayerScale Initialization (no layerscale when None)
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self.gamma1 = nn.Parameter(eta * torch.ones(dim), requires_grad=True)
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self.gamma2 = nn.Parameter(eta * torch.ones(dim), requires_grad=True)
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else:
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self.gamma1, self.gamma2 = 1.0, 1.0
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# See https://github.com/rwightman/pytorch-image-models/pull/747#issuecomment-877795721
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self.tokens_norm = tokens_norm
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def forward(self, x):
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x_norm1 = self.norm1(x)
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x_attn = torch.cat([self.attn(x_norm1), x_norm1[:, 1:]], dim=1)
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x = x + self.drop_path(self.gamma1 * x_attn)
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if self.tokens_norm:
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x = self.norm2(x)
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else:
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x = torch.cat([self.norm2(x[:, 0:1]), x[:, 1:]], dim=1)
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x_res = x
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cls_token = x[:, 0:1]
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cls_token = self.gamma2 * self.mlp(cls_token)
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x = torch.cat([cls_token, x[:, 1:]], dim=1)
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x = x_res + self.drop_path(x)
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return x
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class XCA(nn.Module):
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""" Cross-Covariance Attention (XCA)
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Operation where the channels are updated using a weighted sum. The weights are obtained from the (softmax
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normalized) Cross-covariance matrix (Q^T \\cdot K \\in d_h \\times d_h)
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"""
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def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
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super().__init__()
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self.num_heads = num_heads
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self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1))
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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def forward(self, x):
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B, N, C = x.shape
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# Result of next line is (qkv, B, num (H)eads, (C')hannels per head, N)
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qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 4, 1)
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q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
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# Paper section 3.2 l2-Normalization and temperature scaling
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q = torch.nn.functional.normalize(q, dim=-1)
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k = torch.nn.functional.normalize(k, dim=-1)
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attn = (q @ k.transpose(-2, -1)) * self.temperature
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attn = attn.softmax(dim=-1)
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attn = self.attn_drop(attn)
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# (B, H, C', N), permute -> (B, N, H, C')
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x = (attn @ v).permute(0, 3, 1, 2).reshape(B, N, C)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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@torch.jit.ignore
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def no_weight_decay(self):
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return {'temperature'}
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class XCABlock(nn.Module):
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def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
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drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, eta=1.):
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.attn = XCA(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.norm3 = norm_layer(dim)
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self.local_mp = LPI(in_features=dim, act_layer=act_layer)
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self.norm2 = norm_layer(dim)
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self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
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self.gamma1 = nn.Parameter(eta * torch.ones(dim), requires_grad=True)
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self.gamma3 = nn.Parameter(eta * torch.ones(dim), requires_grad=True)
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self.gamma2 = nn.Parameter(eta * torch.ones(dim), requires_grad=True)
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def forward(self, x, H: int, W: int):
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x = x + self.drop_path(self.gamma1 * self.attn(self.norm1(x)))
|
||||
# NOTE official code has 3 then 2, so keeping it the same to be consistent with loaded weights
|
||||
# See https://github.com/rwightman/pytorch-image-models/pull/747#issuecomment-877795721
|
||||
x = x + self.drop_path(self.gamma3 * self.local_mp(self.norm3(x), H, W))
|
||||
x = x + self.drop_path(self.gamma2 * self.mlp(self.norm2(x)))
|
||||
return x
|
||||
|
||||
|
||||
class XCiT(nn.Module):
|
||||
"""
|
||||
Based on timm and DeiT code bases
|
||||
https://github.com/rwightman/pytorch-image-models/tree/master/timm
|
||||
https://github.com/facebookresearch/deit/
|
||||
"""
|
||||
|
||||
def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
|
||||
num_heads=12, mlp_ratio=4., qkv_bias=True, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.,
|
||||
norm_layer=None, cls_attn_layers=2, use_pos_embed=True, eta=1., tokens_norm=False):
|
||||
"""
|
||||
Args:
|
||||
img_size (int, tuple): input image size
|
||||
patch_size (int): patch size
|
||||
in_chans (int): number of input channels
|
||||
num_classes (int): number of classes for classification head
|
||||
embed_dim (int): embedding dimension
|
||||
depth (int): depth of transformer
|
||||
num_heads (int): number of attention heads
|
||||
mlp_ratio (int): ratio of mlp hidden dim to embedding dim
|
||||
qkv_bias (bool): enable bias for qkv if True
|
||||
drop_rate (float): dropout rate after positional embedding, and in XCA/CA projection + MLP
|
||||
attn_drop_rate (float): attention dropout rate
|
||||
drop_path_rate (float): stochastic depth rate (constant across all layers)
|
||||
norm_layer: (nn.Module): normalization layer
|
||||
cls_attn_layers: (int) Depth of Class attention layers
|
||||
use_pos_embed: (bool) whether to use positional encoding
|
||||
eta: (float) layerscale initialization value
|
||||
tokens_norm: (bool) Whether to normalize all tokens or just the cls_token in the CA
|
||||
|
||||
Notes:
|
||||
- Although `layer_norm` is user specifiable, there are hard-coded `BatchNorm2d`s in the local patch
|
||||
interaction (class LPI) and the patch embedding (class ConvPatchEmbed)
|
||||
"""
|
||||
super().__init__()
|
||||
img_size = to_2tuple(img_size)
|
||||
assert (img_size[0] % patch_size == 0) and (img_size[0] % patch_size == 0), \
|
||||
'`patch_size` should divide image dimensions evenly'
|
||||
|
||||
self.num_classes = num_classes
|
||||
self.num_features = self.embed_dim = embed_dim
|
||||
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
|
||||
|
||||
self.patch_embed = ConvPatchEmbed(
|
||||
img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
|
||||
|
||||
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
|
||||
self.use_pos_embed = use_pos_embed
|
||||
if use_pos_embed:
|
||||
self.pos_embed = PositionalEncodingFourier(dim=embed_dim)
|
||||
self.pos_drop = nn.Dropout(p=drop_rate)
|
||||
|
||||
self.blocks = nn.ModuleList([
|
||||
XCABlock(
|
||||
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate,
|
||||
attn_drop=attn_drop_rate, drop_path=drop_path_rate, norm_layer=norm_layer, eta=eta)
|
||||
for _ in range(depth)])
|
||||
|
||||
self.cls_attn_blocks = nn.ModuleList([
|
||||
ClassAttentionBlock(
|
||||
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate,
|
||||
attn_drop=attn_drop_rate, norm_layer=norm_layer, eta=eta, tokens_norm=tokens_norm)
|
||||
for _ in range(cls_attn_layers)])
|
||||
|
||||
# Classifier head
|
||||
self.norm = norm_layer(embed_dim)
|
||||
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
|
||||
|
||||
# Init weights
|
||||
trunc_normal_(self.cls_token, std=.02)
|
||||
self.apply(self._init_weights)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
nn.init.constant_(m.bias, 0)
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
|
||||
@torch.jit.ignore
|
||||
def no_weight_decay(self):
|
||||
return {'pos_embed', 'cls_token'}
|
||||
|
||||
def get_classifier(self):
|
||||
return self.head
|
||||
|
||||
def reset_classifier(self, num_classes, global_pool=''):
|
||||
self.num_classes = num_classes
|
||||
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
|
||||
|
||||
def forward_features(self, x):
|
||||
B = x.shape[0]
|
||||
# x is (B, N, C). (Hp, Hw) is (height in units of patches, width in units of patches)
|
||||
x, (Hp, Wp) = self.patch_embed(x)
|
||||
|
||||
if self.use_pos_embed:
|
||||
# `pos_embed` (B, C, Hp, Wp), reshape -> (B, C, N), permute -> (B, N, C)
|
||||
pos_encoding = self.pos_embed(B, Hp, Wp).reshape(B, -1, x.shape[1]).permute(0, 2, 1)
|
||||
x = x + pos_encoding
|
||||
|
||||
x = self.pos_drop(x)
|
||||
|
||||
for blk in self.blocks:
|
||||
x = blk(x, Hp, Wp)
|
||||
|
||||
cls_tokens = self.cls_token.expand(B, -1, -1)
|
||||
x = torch.cat((cls_tokens, x), dim=1)
|
||||
|
||||
for blk in self.cls_attn_blocks:
|
||||
x = blk(x)
|
||||
|
||||
x = self.norm(x)[:, 0]
|
||||
return x
|
||||
|
||||
def forward(self, x):
|
||||
x = self.forward_features(x)
|
||||
x = self.head(x)
|
||||
return x
|
||||
|
||||
|
||||
def checkpoint_filter_fn(state_dict, model):
|
||||
if 'model' in state_dict:
|
||||
state_dict = state_dict['model']
|
||||
# For consistency with timm's transformer models while being compatible with official weights source we rename
|
||||
# pos_embeder to pos_embed. Also account for use_pos_embed == False
|
||||
use_pos_embed = getattr(model, 'pos_embed', None) is not None
|
||||
pos_embed_keys = [k for k in state_dict if k.startswith('pos_embed')]
|
||||
for k in pos_embed_keys:
|
||||
if use_pos_embed:
|
||||
state_dict[k.replace('pos_embeder.', 'pos_embed.')] = state_dict.pop(k)
|
||||
else:
|
||||
del state_dict[k]
|
||||
# timm's implementation of class attention in CaiT is slightly more efficient as it does not compute query vectors
|
||||
# for all tokens, just the class token. To use official weights source we must split qkv into q, k, v
|
||||
if 'cls_attn_blocks.0.attn.qkv.weight' in state_dict and 'cls_attn_blocks.0.attn.q.weight' in model.state_dict():
|
||||
num_ca_blocks = len(model.cls_attn_blocks)
|
||||
for i in range(num_ca_blocks):
|
||||
qkv_weight = state_dict.pop(f'cls_attn_blocks.{i}.attn.qkv.weight')
|
||||
qkv_weight = qkv_weight.reshape(3, -1, qkv_weight.shape[-1])
|
||||
for j, subscript in enumerate('qkv'):
|
||||
state_dict[f'cls_attn_blocks.{i}.attn.{subscript}.weight'] = qkv_weight[j]
|
||||
qkv_bias = state_dict.pop(f'cls_attn_blocks.{i}.attn.qkv.bias', None)
|
||||
if qkv_bias is not None:
|
||||
qkv_bias = qkv_bias.reshape(3, -1)
|
||||
for j, subscript in enumerate('qkv'):
|
||||
state_dict[f'cls_attn_blocks.{i}.attn.{subscript}.bias'] = qkv_bias[j]
|
||||
return state_dict
|
||||
|
||||
|
||||
def _create_xcit(variant, pretrained=False, default_cfg=None, **kwargs):
|
||||
default_cfg = default_cfg or default_cfgs[variant]
|
||||
model = build_model_with_cfg(
|
||||
XCiT, variant, pretrained, default_cfg=default_cfg, pretrained_filter_fn=checkpoint_filter_fn, **kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_nano_12_p16_224(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=128, depth=12, num_heads=4, eta=1.0, tokens_norm=False, **kwargs)
|
||||
model = _create_xcit('xcit_nano_12_p16_224', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_nano_12_p16_224_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=128, depth=12, num_heads=4, eta=1.0, tokens_norm=False, **kwargs)
|
||||
model = _create_xcit('xcit_nano_12_p16_224_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_nano_12_p16_384_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=128, depth=12, num_heads=4, eta=1.0, tokens_norm=False, img_size=384, **kwargs)
|
||||
model = _create_xcit('xcit_nano_12_p16_384_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_tiny_12_p16_224(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=192, depth=12, num_heads=4, eta=1.0, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_tiny_12_p16_224', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_tiny_12_p16_224_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=192, depth=12, num_heads=4, eta=1.0, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_tiny_12_p16_224_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_tiny_12_p16_384_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=192, depth=12, num_heads=4, eta=1.0, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_tiny_12_p16_384_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_small_12_p16_224(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=384, depth=12, num_heads=8, eta=1.0, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_small_12_p16_224', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_small_12_p16_224_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=384, depth=12, num_heads=8, eta=1.0, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_small_12_p16_224_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_small_12_p16_384_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=384, depth=12, num_heads=8, eta=1.0, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_small_12_p16_384_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_tiny_24_p16_224(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=192, depth=24, num_heads=4, eta=1e-5, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_tiny_24_p16_224', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_tiny_24_p16_224_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=192, depth=24, num_heads=4, eta=1e-5, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_tiny_24_p16_224_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_tiny_24_p16_384_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=192, depth=24, num_heads=4, eta=1e-5, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_tiny_24_p16_384_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_small_24_p16_224(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=384, depth=24, num_heads=8, eta=1e-5, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_small_24_p16_224', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_small_24_p16_224_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=384, depth=24, num_heads=8, eta=1e-5, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_small_24_p16_224_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_small_24_p16_384_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=384, depth=24, num_heads=8, eta=1e-5, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_small_24_p16_384_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_medium_24_p16_224(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=512, depth=24, num_heads=8, eta=1e-5, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_medium_24_p16_224', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_medium_24_p16_224_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=512, depth=24, num_heads=8, eta=1e-5, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_medium_24_p16_224_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_medium_24_p16_384_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=512, depth=24, num_heads=8, eta=1e-5, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_medium_24_p16_384_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_large_24_p16_224(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=768, depth=24, num_heads=16, eta=1e-5, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_large_24_p16_224', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_large_24_p16_224_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=768, depth=24, num_heads=16, eta=1e-5, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_large_24_p16_224_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_large_24_p16_384_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=768, depth=24, num_heads=16, eta=1e-5, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_large_24_p16_384_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
# Patch size 8x8 models
|
||||
@register_model
|
||||
def xcit_nano_12_p8_224(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=8, embed_dim=128, depth=12, num_heads=4, eta=1.0, tokens_norm=False, **kwargs)
|
||||
model = _create_xcit('xcit_nano_12_p8_224', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_nano_12_p8_224_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=8, embed_dim=128, depth=12, num_heads=4, eta=1.0, tokens_norm=False, **kwargs)
|
||||
model = _create_xcit('xcit_nano_12_p8_224_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_nano_12_p8_384_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=8, embed_dim=128, depth=12, num_heads=4, eta=1.0, tokens_norm=False, **kwargs)
|
||||
model = _create_xcit('xcit_nano_12_p8_384_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_tiny_12_p8_224(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=8, embed_dim=192, depth=12, num_heads=4, eta=1.0, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_tiny_12_p8_224', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_tiny_12_p8_224_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=8, embed_dim=192, depth=12, num_heads=4, eta=1.0, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_tiny_12_p8_224_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_tiny_12_p8_384_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=8, embed_dim=192, depth=12, num_heads=4, eta=1.0, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_tiny_12_p8_384_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_small_12_p8_224(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=8, embed_dim=384, depth=12, num_heads=8, eta=1.0, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_small_12_p8_224', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_small_12_p8_224_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=8, embed_dim=384, depth=12, num_heads=8, eta=1.0, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_small_12_p8_224_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_small_12_p8_384_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=8, embed_dim=384, depth=12, num_heads=8, eta=1.0, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_small_12_p8_384_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_tiny_24_p8_224(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=8, embed_dim=192, depth=24, num_heads=4, eta=1e-5, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_tiny_24_p8_224', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_tiny_24_p8_224_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=8, embed_dim=192, depth=24, num_heads=4, eta=1e-5, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_tiny_24_p8_224_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_tiny_24_p8_384_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=8, embed_dim=192, depth=24, num_heads=4, eta=1e-5, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_tiny_24_p8_384_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_small_24_p8_224(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=8, embed_dim=384, depth=24, num_heads=8, eta=1e-5, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_small_24_p8_224', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_small_24_p8_224_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=8, embed_dim=384, depth=24, num_heads=8, eta=1e-5, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_small_24_p8_224_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_small_24_p8_384_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=8, embed_dim=384, depth=24, num_heads=8, eta=1e-5, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_small_24_p8_384_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_medium_24_p8_224(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=8, embed_dim=512, depth=24, num_heads=8, eta=1e-5, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_medium_24_p8_224', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_medium_24_p8_224_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=8, embed_dim=512, depth=24, num_heads=8, eta=1e-5, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_medium_24_p8_224_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_medium_24_p8_384_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=8, embed_dim=512, depth=24, num_heads=8, eta=1e-5, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_medium_24_p8_384_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_large_24_p8_224(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=8, embed_dim=768, depth=24, num_heads=16, eta=1e-5, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_large_24_p8_224', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_large_24_p8_224_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=8, embed_dim=768, depth=24, num_heads=16, eta=1e-5, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_large_24_p8_224_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def xcit_large_24_p8_384_dist(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=8, embed_dim=768, depth=24, num_heads=16, eta=1e-5, tokens_norm=True, **kwargs)
|
||||
model = _create_xcit('xcit_large_24_p8_384_dist', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
Loading…
Reference in new issue