models_vit.py

# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# timm: https://github.com/rwightman/pytorch-image-models/tree/master/timm
# DeiT: https://github.com/facebookresearch/deit
# --------------------------------------------------------

from functools import partial

import torch
import torch.nn as nn

import timm.models.vision_transformer


class VisionTransformer(timm.models.vision_transformer.VisionTransformer):
    """ Vision Transformer with support for global average pooling
    """
    def __init__(self, global_pool=False, **kwargs):
        super(VisionTransformer, self).__init__(**kwargs)

        self.global_pool = global_pool
        if self.global_pool:
            norm_layer = kwargs['norm_layer']
            embed_dim = kwargs['embed_dim']
            self.fc_norm = norm_layer(embed_dim)

            del self.norm  # remove the original norm

    def forward_features(self, x):
        B = x.shape[0]
        x = self.patch_embed(x)

        cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
        x = torch.cat((cls_tokens, x), dim=1)
        x = x + self.pos_embed
        x = self.pos_drop(x)

        for blk in self.blocks:
            x = blk(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


def vit_small_patch16(**kwargs):
    model = VisionTransformer(
        patch_size=16, embed_dim=384, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,  # ViT-small config in MOCO_V3
        # patch_size=16, embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, qkv_bias=True,  # ViT-small config in timm
        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model


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


# class VisionTransformerWithProjector(VisionTransformer):
#     def __init__(self, vit_model, model_encoder, feat_cl_dim=128):
#         super(VisionTransformerWithProjector, self).__init__()
#         self.encoder = vit_model
#         embed_dim = {'vit_base_patch16': 768, 'vit_large_patch16': 1024, 'vit_huge_patch14': 1280}
#         self.projection_head = nn.Sequential(
#             nn.Linear(embed_dim[model_encoder], embed_dim[model_encoder]),
#             nn.ReLU(inplace=True),
#             nn.Linear(embed_dim[model_encoder], feat_cl_dim)
#         )
#
#     def forward(self, x):
#         x = self.encoder(x)
#         latent_cl = self.projection_head(x)  # [N, feat_cl_dim]
#         features = nn.functional.normalize(latent_cl, dim=-1)  # [N, feat_cl_dim]
#         return features