Expand scope of testing for non-std vision transformer / mlp models. Some related cleanup and create fn cleanup for all vision transformer and mlp models. More CoaT weights.
Ross Wightman
4 years ago
parent
18bf520ad1
commit
bfc72f75d3
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# Copyright (c) 2015-present, Facebook, Inc.
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# All rights reserved.
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# Modified from
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# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
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# Copyright 2020 Ross Wightman, Apache-2.0 License
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import itertools
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import torch
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from timm.data import IMAGENET_DEFAULT_STD, IMAGENET_DEFAULT_MEAN
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from .vision_transformer import trunc_normal_
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from .registry import register_model
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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': .9, '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', 'classifier': 'head',
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**kwargs
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}
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specification = {
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'levit_c_128s': {
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'C': '128_256_384', 'D': 16, 'N': '4_6_8', 'X': '2_3_4', 'drop_path': 0,
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'weights': 'https://dl.fbaipublicfiles.com/LeViT/LeViT-128S-96703c44.pth'},
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'levit_c_128': {
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'C': '128_256_384', 'D': 16, 'N': '4_8_12', 'X': '4_4_4', 'drop_path': 0,
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'weights': 'https://dl.fbaipublicfiles.com/LeViT/LeViT-128-b88c2750.pth'},
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'levit_c_192': {
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'C': '192_288_384', 'D': 32, 'N': '3_5_6', 'X': '4_4_4', 'drop_path': 0,
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'weights': 'https://dl.fbaipublicfiles.com/LeViT/LeViT-192-92712e41.pth'},
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'levit_c_256': {
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'C': '256_384_512', 'D': 32, 'N': '4_6_8', 'X': '4_4_4', 'drop_path': 0,
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'weights': 'https://dl.fbaipublicfiles.com/LeViT/LeViT-256-13b5763e.pth'},
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'levit_c_384': {
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'C': '384_512_768', 'D': 32, 'N': '6_9_12', 'X': '4_4_4', 'drop_path': 0.1,
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'weights': 'https://dl.fbaipublicfiles.com/LeViT/LeViT-384-9bdaf2e2.pth'},
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}
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__all__ = ['Levit']
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@register_model
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def levit_c_128s(num_classes=1000, distillation=True, pretrained=False, fuse=False, **kwargs):
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return model_factory(**specification['levit_c_128s'], num_classes=num_classes,
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distillation=distillation, pretrained=pretrained, fuse=fuse)
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@register_model
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def levit_c_128(num_classes=1000, distillation=True, pretrained=False, fuse=False, **kwargs):
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return model_factory(**specification['levit_c_128'], num_classes=num_classes,
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distillation=distillation, pretrained=pretrained, fuse=fuse)
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@register_model
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def levit_c_192(num_classes=1000, distillation=True, pretrained=False, fuse=False, **kwargs):
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return model_factory(**specification['levit_c_192'], num_classes=num_classes,
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distillation=distillation, pretrained=pretrained, fuse=fuse)
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@register_model
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def levit_c_256(num_classes=1000, distillation=True, pretrained=False, fuse=False, **kwargs):
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return model_factory(**specification['levit_c_256'], num_classes=num_classes,
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distillation=distillation, pretrained=pretrained, fuse=fuse)
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@register_model
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def levit_c_384(num_classes=1000, distillation=True, pretrained=False, fuse=False, **kwargs):
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return model_factory(**specification['levit_c_384'], num_classes=num_classes,
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distillation=distillation, pretrained=pretrained, fuse=fuse)
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class ConvNorm(torch.nn.Sequential):
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def __init__(
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self, a, b, ks=1, stride=1, pad=0, dilation=1, groups=1, bn_weight_init=1, resolution=-10000):
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super().__init__()
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self.add_module('c', torch.nn.Conv2d(a, b, ks, stride, pad, dilation, groups, bias=False))
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bn = torch.nn.BatchNorm2d(b)
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torch.nn.init.constant_(bn.weight, bn_weight_init)
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torch.nn.init.constant_(bn.bias, 0)
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self.add_module('bn', bn)
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@torch.no_grad()
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def fuse(self):
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c, bn = self._modules.values()
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w = bn.weight / (bn.running_var + bn.eps) ** 0.5
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w = c.weight * w[:, None, None, None]
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b = bn.bias - bn.running_mean * bn.weight / \
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(bn.running_var + bn.eps) ** 0.5
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m = torch.nn.Conv2d(
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w.size(1), w.size(0), w.shape[2:], stride=self.c.stride,
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padding=self.c.padding, dilation=self.c.dilation, groups=self.c.groups)
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m.weight.data.copy_(w)
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m.bias.data.copy_(b)
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return m
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class NormLinear(torch.nn.Sequential):
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def __init__(self, a, b, bias=True, std=0.02):
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super().__init__()
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self.add_module('bn', torch.nn.BatchNorm1d(a))
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l = torch.nn.Linear(a, b, bias=bias)
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trunc_normal_(l.weight, std=std)
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if bias:
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torch.nn.init.constant_(l.bias, 0)
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self.add_module('l', l)
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@torch.no_grad()
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def fuse(self):
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bn, l = self._modules.values()
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w = bn.weight / (bn.running_var + bn.eps) ** 0.5
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b = bn.bias - self.bn.running_mean * \
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self.bn.weight / (bn.running_var + bn.eps) ** 0.5
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w = l.weight * w[None, :]
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if l.bias is None:
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b = b @ self.l.weight.T
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else:
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b = (l.weight @ b[:, None]).view(-1) + self.l.bias
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m = torch.nn.Linear(w.size(1), w.size(0))
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m.weight.data.copy_(w)
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m.bias.data.copy_(b)
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return m
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def b16(n, activation, resolution=224):
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return torch.nn.Sequential(
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ConvNorm(3, n // 8, 3, 2, 1, resolution=resolution),
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activation(),
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ConvNorm(n // 8, n // 4, 3, 2, 1, resolution=resolution // 2),
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activation(),
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ConvNorm(n // 4, n // 2, 3, 2, 1, resolution=resolution // 4),
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activation(),
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ConvNorm(n // 2, n, 3, 2, 1, resolution=resolution // 8))
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class Residual(torch.nn.Module):
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def __init__(self, m, drop):
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super().__init__()
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self.m = m
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self.drop = drop
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def forward(self, x):
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if self.training and self.drop > 0:
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return x + self.m(x) * torch.rand(
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x.size(0), 1, 1, device=x.device).ge_(self.drop).div(1 - self.drop).detach()
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else:
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return x + self.m(x)
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class Attention(torch.nn.Module):
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def __init__(self, dim, key_dim, num_heads=8,
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attn_ratio=4, act_layer=None, resolution=14):
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super().__init__()
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self.num_heads = num_heads
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self.scale = key_dim ** -0.5
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self.key_dim = key_dim
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self.nh_kd = nh_kd = key_dim * num_heads
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self.d = int(attn_ratio * key_dim)
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self.dh = int(attn_ratio * key_dim) * num_heads
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self.attn_ratio = attn_ratio
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h = self.dh + nh_kd * 2
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self.qkv = ConvNorm(dim, h, resolution=resolution)
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self.proj = torch.nn.Sequential(
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act_layer(),
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ConvNorm(self.dh, dim, bn_weight_init=0, resolution=resolution))
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points = list(itertools.product(range(resolution), range(resolution)))
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N = len(points)
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attention_offsets = {}
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idxs = []
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for p1 in points:
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for p2 in points:
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offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1]))
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if offset not in attention_offsets:
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attention_offsets[offset] = len(attention_offsets)
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idxs.append(attention_offsets[offset])
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self.attention_biases = torch.nn.Parameter(torch.zeros(num_heads, len(attention_offsets)))
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self.register_buffer('attention_bias_idxs', torch.LongTensor(idxs).view(N, N))
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self.ab = None
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@torch.no_grad()
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def train(self, mode=True):
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super().train(mode)
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if mode and self.ab is not None:
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self.ab = None
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else:
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self.ab = self.attention_biases[:, self.attention_bias_idxs]
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def forward(self, x): # x (B,C,H,W)
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B, C, H, W = x.shape
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q, k, v = self.qkv(x).view(B, self.num_heads, -1, H * W).split([self.key_dim, self.key_dim, self.d], dim=2)
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ab = self.attention_biases[:, self.attention_bias_idxs] if self.training else self.ab
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attn = (q.transpose(-2, -1) @ k) * self.scale + ab
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attn = attn.softmax(dim=-1)
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x = (v @ attn.transpose(-2, -1)).view(B, -1, H, W)
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x = self.proj(x)
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return x
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class AttentionSubsample(torch.nn.Module):
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def __init__(
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self, in_dim, out_dim, key_dim, num_heads=8, attn_ratio=2,
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act_layer=None, stride=2, resolution=14, resolution_=7):
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super().__init__()
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self.num_heads = num_heads
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self.scale = key_dim ** -0.5
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self.key_dim = key_dim
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self.nh_kd = nh_kd = key_dim * num_heads
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self.d = int(attn_ratio * key_dim)
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self.dh = int(attn_ratio * key_dim) * self.num_heads
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self.attn_ratio = attn_ratio
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self.resolution_ = resolution_
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self.resolution_2 = resolution_ ** 2
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h = self.dh + nh_kd
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self.kv = ConvNorm(in_dim, h, resolution=resolution)
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self.q = torch.nn.Sequential(
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torch.nn.AvgPool2d(1, stride, 0),
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ConvNorm(in_dim, nh_kd, resolution=resolution_))
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self.proj = torch.nn.Sequential(
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act_layer(),
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ConvNorm(self.d * num_heads, out_dim, resolution=resolution_))
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self.stride = stride
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self.resolution = resolution
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points = list(itertools.product(range(resolution), range(resolution)))
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points_ = list(itertools.product(range(resolution_), range(resolution_)))
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N = len(points)
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N_ = len(points_)
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attention_offsets = {}
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idxs = []
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for p1 in points_:
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for p2 in points:
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size = 1
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offset = (
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abs(p1[0] * stride - p2[0] + (size - 1) / 2),
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abs(p1[1] * stride - p2[1] + (size - 1) / 2))
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if offset not in attention_offsets:
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attention_offsets[offset] = len(attention_offsets)
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idxs.append(attention_offsets[offset])
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self.attention_biases = torch.nn.Parameter(torch.zeros(num_heads, len(attention_offsets)))
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self.register_buffer('attention_bias_idxs', torch.LongTensor(idxs).view(N_, N))
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self.ab = None
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@torch.no_grad()
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def train(self, mode=True):
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super().train(mode)
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if mode and self.ab is not None:
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self.ab = None
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else:
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self.ab = self.attention_biases[:, self.attention_bias_idxs]
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def forward(self, x):
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B, C, H, W = x.shape
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k, v = self.kv(x).view(B, self.num_heads, -1, H * W).split([self.key_dim, self.d], dim=2)
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q = self.q(x).view(B, self.num_heads, self.key_dim, self.resolution_2)
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ab = self.attention_biases[:, self.attention_bias_idxs] if self.training else self.ab
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attn = (q.transpose(-2, -1) @ k) * self.scale + ab
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attn = attn.softmax(dim=-1)
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x = (v @ attn.transpose(-2, -1)).reshape(B, -1, self.resolution_, self.resolution_)
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x = self.proj(x)
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return x
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class Levit(torch.nn.Module):
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""" Vision Transformer with support for patch or hybrid CNN input stage
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"""
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def __init__(
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self,
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img_size=224,
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patch_size=16,
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in_chans=3,
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num_classes=1000,
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embed_dim=[192],
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key_dim=[64],
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depth=[12],
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num_heads=[3],
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attn_ratio=[2],
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mlp_ratio=[2],
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hybrid_backbone=None,
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down_ops=[],
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attn_act_layer=torch.nn.Hardswish,
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mlp_act_layer=torch.nn.Hardswish,
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distillation=True,
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drop_path=0):
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super().__init__()
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self.num_classes = num_classes
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self.num_features = embed_dim[-1]
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self.embed_dim = embed_dim
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self.distillation = distillation
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self.patch_embed = hybrid_backbone
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self.blocks = []
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down_ops.append([''])
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resolution = img_size // patch_size
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for i, (ed, kd, dpth, nh, ar, mr, do) in enumerate(
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zip(embed_dim, key_dim, depth, num_heads, attn_ratio, mlp_ratio, down_ops)):
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for _ in range(dpth):
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self.blocks.append(
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Residual(
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Attention(ed, kd, nh, attn_ratio=ar, act_layer=attn_act_layer, resolution=resolution),
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drop_path))
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if mr > 0:
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h = int(ed * mr)
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self.blocks.append(
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Residual(torch.nn.Sequential(
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ConvNorm(ed, h, resolution=resolution),
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mlp_act_layer(),
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ConvNorm(h, ed, bn_weight_init=0, resolution=resolution),
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), drop_path))
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if do[0] == 'Subsample':
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# ('Subsample',key_dim, num_heads, attn_ratio, mlp_ratio, stride)
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resolution_ = (resolution - 1) // do[5] + 1
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self.blocks.append(
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AttentionSubsample(
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*embed_dim[i:i + 2], key_dim=do[1], num_heads=do[2], attn_ratio=do[3],
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act_layer=attn_act_layer, stride=do[5],
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resolution=resolution, resolution_=resolution_))
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resolution = resolution_
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if do[4] > 0: # mlp_ratio
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h = int(embed_dim[i + 1] * do[4])
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self.blocks.append(
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Residual(torch.nn.Sequential(
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ConvNorm(embed_dim[i + 1], h, resolution=resolution),
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mlp_act_layer(),
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ConvNorm(h, embed_dim[i + 1], bn_weight_init=0, resolution=resolution),
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), drop_path))
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self.blocks = torch.nn.Sequential(*self.blocks)
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# Classifier head
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self.head = NormLinear(
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embed_dim[-1], num_classes) if num_classes > 0 else torch.nn.Identity()
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if distillation:
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self.head_dist = NormLinear(
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embed_dim[-1], num_classes) if num_classes > 0 else torch.nn.Identity()
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@torch.jit.ignore
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def no_weight_decay(self):
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return {x for x in self.state_dict().keys() if 'attention_biases' in x}
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def forward(self, x):
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x = self.patch_embed(x)
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x = self.blocks(x)
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x = torch.nn.functional.adaptive_avg_pool2d(x, 1).flatten(1)
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if self.distillation:
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x = self.head(x), self.head_dist(x)
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if not self.training:
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x = (x[0] + x[1]) / 2
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else:
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x = self.head(x)
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return x
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def model_factory(C, D, X, N, drop_path, weights, num_classes, distillation, pretrained, fuse):
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embed_dim = [int(x) for x in C.split('_')]
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num_heads = [int(x) for x in N.split('_')]
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depth = [int(x) for x in X.split('_')]
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act = torch.nn.Hardswish
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model = Levit(
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patch_size=16,
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embed_dim=embed_dim,
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num_heads=num_heads,
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key_dim=[D] * 3,
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depth=depth,
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attn_ratio=[2, 2, 2],
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mlp_ratio=[2, 2, 2],
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down_ops=[
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# ('Subsample',key_dim, num_heads, attn_ratio, mlp_ratio, stride)
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['Subsample', D, embed_dim[0] // D, 4, 2, 2],
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['Subsample', D, embed_dim[1] // D, 4, 2, 2],
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],
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attn_act_layer=act,
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mlp_act_layer=act,
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hybrid_backbone=b16(embed_dim[0], activation=act),
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num_classes=num_classes,
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drop_path=drop_path,
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distillation=distillation
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)
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model.default_cfg = _cfg()
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if pretrained:
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checkpoint = torch.hub.load_state_dict_from_url(
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weights, map_location='cpu')
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d = checkpoint['model']
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D = model.state_dict()
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for k in d.keys():
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if D[k].shape != d[k].shape:
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d[k] = d[k][:, :, None, None]
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model.load_state_dict(d)
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#if fuse:
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# utils.replace_batchnorm(model)
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return model
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