models_vit_get_attention.py

""" Vision Transformer (ViT) in PyTorch

A PyTorch implement of Vision Transformers as described in
'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale' - https://arxiv.org/abs/2010.11929

The official jax code is released and available at https://github.com/google-research/vision_transformer

Status/TODO:
* Models updated to be compatible with official impl. Args added to support backward compat for old PyTorch weights.
* Weights ported from official jax impl for 384x384 base and small models, 16x16 and 32x32 patches.
* Trained (supervised on ImageNet-1k) my custom 'small' patch model to 77.9, 'base' to 79.4 top-1 with this code.
* Hopefully find time and GPUs for SSL or unsupervised pretraining on OpenImages w/ ImageNet fine-tune in future.

Acknowledgments:
* The paper authors for releasing code and weights, thanks!
* I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch ... check it out
for some einops/einsum fun
* Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT
* Bert reference code checks against Huggingface Transformers and Tensorflow Bert

Hacked together by / Copyright 2020 Ross Wightman
"""

import os
import shutil
from functools import partial

import torch
import torch.nn as nn
from einops.einops import rearrange
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helpers import load_pretrained
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.registry import register_model
from timm.models.resnet import resnet26d, resnet50d


def _cfg(url='', **kwargs):
    return {
        'url': url,
        'num_classes': 1000,
        'input_size': (3, 224, 224),
        'pool_size': None,
        'crop_pct': .9,
        'interpolation': 'bicubic',
        'mean': IMAGENET_DEFAULT_MEAN,
        'std': IMAGENET_DEFAULT_STD,
        'first_conv': 'patch_embed.proj',
        'classifier': 'head',
        **kwargs
    }


default_cfgs = {
    # patch models
    'vit_small_patch16_224':
    _cfg(
        url=
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/vit_small_p16_224-15ec54c9.pth',
    ),
    'vit_base_patch16_224':
    _cfg(
        url=
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth',
        mean=(0.5, 0.5, 0.5),
        std=(0.5, 0.5, 0.5),
    ),
    'vit_base_patch16_384':
    _cfg(
        url=
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_384-83fb41ba.pth',
        input_size=(3, 384, 384),
        mean=(0.5, 0.5, 0.5),
        std=(0.5, 0.5, 0.5),
        crop_pct=1.0),
    'vit_base_patch32_384':
    _cfg(
        url=
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p32_384-830016f5.pth',
        input_size=(3, 384, 384),
        mean=(0.5, 0.5, 0.5),
        std=(0.5, 0.5, 0.5),
        crop_pct=1.0),
    'vit_large_patch16_224':
    _cfg(
        url=
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_224-4ee7a4dc.pth',
        mean=(0.5, 0.5, 0.5),
        std=(0.5, 0.5, 0.5)),
    'vit_large_patch16_384':
    _cfg(
        url=
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_384-b3be5167.pth',
        input_size=(3, 384, 384),
        mean=(0.5, 0.5, 0.5),
        std=(0.5, 0.5, 0.5),
        crop_pct=1.0),
    'vit_large_patch32_384':
    _cfg(
        url=
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p32_384-9b920ba8.pth',
        input_size=(3, 384, 384),
        mean=(0.5, 0.5, 0.5),
        std=(0.5, 0.5, 0.5),
        crop_pct=1.0),
    'vit_huge_patch16_224':
    _cfg(),
    'vit_huge_patch32_384':
    _cfg(input_size=(3, 384, 384)),
    # hybrid models
    'vit_small_resnet26d_224':
    _cfg(),
    'vit_small_resnet50d_s3_224':
    _cfg(),
    'vit_base_resnet26d_224':
    _cfg(),
    'vit_base_resnet50d_224':
    _cfg(),
}


class Mlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class Attention(nn.Module):
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., return_attn=False):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
        self.scale = qk_scale or head_dim**-0.5

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        self.return_attn = return_attn

    def forward(self, x):
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)

        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        attn_softmax = attn.detach().clone()
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        if self.return_attn:
            return x, attn_softmax
        return x


class Block(nn.Module):
    def __init__(self,
                 dim,
                 num_heads,
                 mlp_ratio=4.,
                 qkv_bias=False,
                 qk_scale=None,
                 drop=0.,
                 attn_drop=0.,
                 drop_path=0.,
                 act_layer=nn.GELU,
                 norm_layer=nn.LayerNorm,
                 return_attn=False):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(dim,
                              num_heads=num_heads,
                              qkv_bias=qkv_bias,
                              qk_scale=qk_scale,
                              attn_drop=attn_drop,
                              proj_drop=drop,
                              return_attn=return_attn)
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
        self.return_attn = return_attn

    def forward(self, x):
        if self.return_attn:
            res = x
            x, attn = self.attn(self.norm1(x))
            x = res + self.drop_path(x)
            x = x + self.drop_path(self.mlp(self.norm2(x)))
            return x, attn

        x = x + self.drop_path(self.attn(self.norm1(x)))
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x


class PatchEmbed(nn.Module):
    """ Image to Patch Embedding
    """
    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
        self.img_size = img_size
        self.patch_size = patch_size
        self.num_patches = num_patches

        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)

    def forward(self, x):
        B, C, H, W = x.shape
        # FIXME look at relaxing size constraints
        assert H == self.img_size[0] and W == self.img_size[1], \
            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
        x = self.proj(x).flatten(2).transpose(1, 2)
        return x


class HybridEmbed(nn.Module):
    """ CNN Feature Map Embedding
    Extract feature map from CNN, flatten, project to embedding dim.
    """
    def __init__(self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768):
        super().__init__()
        assert isinstance(backbone, nn.Module)
        img_size = to_2tuple(img_size)
        self.img_size = img_size
        self.backbone = backbone
        if feature_size is None:
            with torch.no_grad():
                # FIXME this is hacky, but most reliable way of determining the exact dim of the output feature
                # map for all networks, the feature metadata has reliable channel and stride info, but using
                # stride to calc feature dim requires info about padding of each stage that isn't captured.
                training = backbone.training
                if training:
                    backbone.eval()
                o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))[-1]
                feature_size = o.shape[-2:]
                feature_dim = o.shape[1]
                backbone.train(training)
        else:
            feature_size = to_2tuple(feature_size)
            feature_dim = self.backbone.feature_info.channels()[-1]
        self.num_patches = feature_size[0] * feature_size[1]
        self.proj = nn.Linear(feature_dim, embed_dim)

    def forward(self, x):
        x = self.backbone(x)[-1]
        x = x.flatten(2).transpose(1, 2)
        x = self.proj(x)
        return x


class VisionTransformer(nn.Module):
    """ Vision Transformer with support for global average pooling
    """
    def __init__(self,
                 global_pool=False,
                 mask_ratio=None,
                 mask_type='random',
                 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=False,
                 qk_scale=None,
                 drop_rate=0.,
                 attn_drop_rate=0.,
                 drop_path_rate=0.,
                 hybrid_backbone=None,
                 norm_layer=nn.LayerNorm):
        super().__init__()

        if os.path.exists('images'):
            shutil.rmtree('images')
        os.makedirs('images', exist_ok=True)

        self.num_classes = num_classes
        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models

        if hybrid_backbone is not None:
            self.patch_embed = HybridEmbed(hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim)
        else:
            self.patch_embed = PatchEmbed(img_size=img_size,
                                          patch_size=patch_size,
                                          in_chans=in_chans,
                                          embed_dim=embed_dim)
        num_patches = self.patch_embed.num_patches

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
        self.pos_drop = nn.Dropout(p=drop_rate)

        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
        self.blocks = nn.Sequential(*[
            Block(dim=embed_dim,
                  num_heads=num_heads,
                  mlp_ratio=mlp_ratio,
                  qkv_bias=qkv_bias,
                  qk_scale=qk_scale,
                  drop=drop_rate,
                  attn_drop=attn_drop_rate,
                  drop_path=dpr[i],
                  norm_layer=norm_layer,
                  return_attn=(i == depth - 1)) for i in range(depth)
        ])
        self.norm = norm_layer(embed_dim)

        # NOTE as per official impl, we could have a pre-logits representation dense layer + tanh here
        #self.repr = nn.Linear(embed_dim, representation_size)
        #self.repr_act = nn.Tanh()

        # Classifier head
        self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()

        trunc_normal_(self.pos_embed, std=.02)
        trunc_normal_(self.cls_token, std=.02)
        self.apply(self._init_weights)

        self.global_pool = global_pool
        self.mask_ratio = mask_ratio
        self.mask_type = mask_type
        print(f'mask_type: {mask_type}')
        self.num_patches = self.patch_embed.num_patches
        if self.global_pool:
            self.fc_norm = norm_layer(embed_dim)

            del self.norm  # remove the original norm

    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.embed_dim, num_classes) if num_classes > 0 else nn.Identity()

    def masking(self, x):
        """
        Perform per-sample random masking by per-sample shuffling.
        Per-sample shuffling is done by argsort random noise.
        x: [N, L, D], sequence
        """
        N, L, D = x.shape  # batch, length, dim
        len_keep = int(L * (1 - self.mask_ratio))

        noise = torch.rand(N, L, device=x.device)  # noise in [0, 1]
        if self.mask_type == 'uniform':
            assert self.mask_ratio == 0.75
            M = int(L**0.5)
            noise = rearrange(noise, 'n (h p1 w p2) -> (n h w) (p1 p2)', n=N, p1=2, p2=2, h=M // 2, w=M // 2)
            index = noise.min(-1)[1]
            noise[range(len(index)), index] = -1
            noise = rearrange(noise, '(n h w) (p1 p2)-> n (h p1 w p2) ', n=N, p1=2, p2=2, h=M // 2, w=M // 2)

        # sort noise for each sample
        ids_shuffle = torch.argsort(noise, dim=1)  # ascend: small is keep, large is remove
        ids_restore = torch.argsort(ids_shuffle, dim=1)

        # keep the first subset
        ids_keep = ids_shuffle[:, :len_keep]
        x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))

        # generate the binary mask: 0 is keep, 1 is remove
        mask = torch.ones([N, L], device=x.device)
        mask[:, :len_keep] = 0
        # unshuffle to get the binary mask
        mask = torch.gather(mask, dim=1, index=ids_restore)

        return x_masked, mask, ids_restore

    def forward_features(self, x):
        B, _, H, W = x.shape
        x = self.patch_embed(x)
        # add pos embed w/o cls token
        x = x + self.pos_embed[:, 1:, :]

        if self.mask_ratio is not None and self.training:
            # masking: length -> length * mask_ratio
            x, mask, ids_restore = self.masking(x)

        # append cls token
        cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
        cls_tokens = cls_tokens + self.pos_embed[:, :1, :]
        x = torch.cat((cls_tokens, x), dim=1)

        x = self.pos_drop(x)

        x, attn = self.blocks(x)

        if self.global_pool:
            x = x[:, 1:, :].mean(dim=1)  # global pool without cls token
            outcome = self.fc_norm(x)
        else:
            x = self.norm(x)
            outcome = x[:, 0]

        return outcome, attn

    def forward(self, x):
        x, attn = self.forward_features(x)
        x = self.head(x)
        return x, attn


def _conv_filter(state_dict, patch_size=16):
    """ convert patch embedding weight from manual patchify + linear proj to conv"""
    out_dict = {}
    for k, v in state_dict.items():
        if 'patch_embed.proj.weight' in k:
            v = v.reshape((v.shape[0], 3, patch_size, patch_size))
        out_dict[k] = v
    return out_dict


def vit_base_patch16(**kwargs):
    model = VisionTransformer(patch_size=16,
                              embed_dim=768,
                              depth=12,
                              num_heads=12,
                              mlp_ratio=4,
                              qkv_bias=True,
                              norm_layer=partial(nn.LayerNorm, eps=1e-6),
                              **kwargs)
    return model


def vit_large_patch16(**kwargs):
    model = VisionTransformer(patch_size=16,
                              embed_dim=1024,
                              depth=24,
                              num_heads=16,
                              mlp_ratio=4,
                              qkv_bias=True,
                              norm_layer=partial(nn.LayerNorm, eps=1e-6),
                              **kwargs)
    return model


def vit_huge_patch14(**kwargs):
    model = VisionTransformer(patch_size=14,
                              embed_dim=1280,
                              depth=32,
                              num_heads=16,
                              mlp_ratio=4,
                              qkv_bias=True,
                              norm_layer=partial(nn.LayerNorm, eps=1e-6),
                              **kwargs)
    return model