Add BeiT 'finetuned' 1k weights and pretrained 22k weights, pretraining specific (masked) model excluded for now

tests/test_models.py

@@ -17,7 +17,7 @@ if hasattr(torch._C, '_jit_set_profiling_executor'):
 # transformer models don't support many of the spatial / feature based model functionalities
 NON_STD_FILTERS = [
     'vit_*', 'tnt_*', 'pit_*', 'swin_*', 'coat_*', 'cait_*', '*mixer_*', 'gmlp_*', 'resmlp_*', 'twins_*',
-    'convit_*', 'levit*', 'visformer*', 'deit*', 'jx_nest_*', 'nest_*', 'xcit_*', 'crossvit_*']
+    'convit_*', 'levit*', 'visformer*', 'deit*', 'jx_nest_*', 'nest_*', 'xcit_*', 'crossvit_*', 'beit_*']
 NUM_NON_STD = len(NON_STD_FILTERS)
 
 # exclude models that cause specific test failures

timm/models/__init__.py

@@ -1,3 +1,4 @@
+from .beit import *
 from .byoanet import *
 from .byobnet import *
 from .cait import *

timm/models/beit.py

@@ -0,0 +1,420 @@
+""" BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
+
+Model from official source: https://github.com/microsoft/unilm/tree/master/beit
+
+At this point only the 1k fine-tuned classification weights and model configs have been added,
+see original source above for pre-training models and procedure.
+
+Modifications by / Copyright 2021 Ross Wightman, original copyrights below
+"""
+# --------------------------------------------------------
+# BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
+# Github source: https://github.com/microsoft/unilm/tree/master/beit
+# Copyright (c) 2021 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# By Hangbo Bao
+# Based on timm and DeiT code bases
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/facebookresearch/deit/
+# https://github.com/facebookresearch/dino
+# --------------------------------------------------------'
+import math
+from functools import partial
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .helpers import build_model_with_cfg
+from .layers import PatchEmbed, Mlp, DropPath, trunc_normal_
+from .registry import register_model
+from .vision_transformer import checkpoint_filter_fn
+
+
+def _cfg(url='', **kwargs):
+    return {
+        'url': url,
+        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
+        'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
+        'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5),
+        'first_conv': 'patch_embed.proj', 'classifier': 'head',
+        **kwargs
+    }
+
+
+default_cfgs = {
+    'beit_base_patch16_224': _cfg(
+        url='https://unilm.blob.core.windows.net/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth'),
+    'beit_base_patch16_384': _cfg(
+        url='https://unilm.blob.core.windows.net/beit/beit_base_patch16_384_pt22k_ft22kto1k.pth',
+        input_size=(3, 384, 384), crop_pct=1.0,
+    ),
+    'beit_base_patch16_224_in22k': _cfg(
+        url='https://unilm.blob.core.windows.net/beit/beit_base_patch16_224_pt22k_ft22k.pth',
+        num_classes=21841,
+    ),
+    'beit_large_patch16_224': _cfg(
+        url='https://unilm.blob.core.windows.net/beit/beit_large_patch16_224_pt22k_ft22kto1k.pth'),
+    'beit_large_patch16_384': _cfg(
+        url='https://unilm.blob.core.windows.net/beit/beit_large_patch16_384_pt22k_ft22kto1k.pth',
+        input_size=(3, 384, 384), crop_pct=1.0,
+    ),
+    'beit_large_patch16_512': _cfg(
+        url='https://unilm.blob.core.windows.net/beit/beit_large_patch16_512_pt22k_ft22kto1k.pth',
+        input_size=(3, 512, 512), crop_pct=1.0,
+    ),
+    'beit_large_patch16_224_in22k': _cfg(
+        url='https://unilm.blob.core.windows.net/beit/beit_large_patch16_224_pt22k_ft22k.pth',
+        num_classes=21841,
+    ),
+}
+
+
+class Attention(nn.Module):
+    def __init__(
+            self, dim, num_heads=8, qkv_bias=False, attn_drop=0.,
+            proj_drop=0., window_size=None, attn_head_dim=None):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        if attn_head_dim is not None:
+            head_dim = attn_head_dim
+        all_head_dim = head_dim * self.num_heads
+        self.scale = head_dim ** -0.5
+
+        self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
+        if qkv_bias:
+            self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
+            self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
+        else:
+            self.q_bias = None
+            self.v_bias = None
+
+        if window_size:
+            self.window_size = window_size
+            self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
+            self.relative_position_bias_table = nn.Parameter(
+                torch.zeros(self.num_relative_distance, num_heads))  # 2*Wh-1 * 2*Ww-1, nH
+            # cls to token & token 2 cls & cls to cls
+
+            # get pair-wise relative position index for each token inside the window
+            coords_h = torch.arange(window_size[0])
+            coords_w = torch.arange(window_size[1])
+            coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+            coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+            relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+            relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+            relative_coords[:, :, 0] += window_size[0] - 1  # shift to start from 0
+            relative_coords[:, :, 1] += window_size[1] - 1
+            relative_coords[:, :, 0] *= 2 * window_size[1] - 1
+            relative_position_index = \
+                torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype)
+            relative_position_index[1:, 1:] = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+            relative_position_index[0, 0:] = self.num_relative_distance - 3
+            relative_position_index[0:, 0] = self.num_relative_distance - 2
+            relative_position_index[0, 0] = self.num_relative_distance - 1
+
+            self.register_buffer("relative_position_index", relative_position_index)
+        else:
+            self.window_size = None
+            self.relative_position_bias_table = None
+            self.relative_position_index = None
+
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(all_head_dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x, rel_pos_bias: Optional[torch.Tensor] = None):
+        B, N, C = x.shape
+        qkv_bias = None
+        if self.q_bias is not None:
+            if torch.jit.is_scripting():
+                # FIXME requires_grad breaks w/ torchscript
+                qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias), self.v_bias))
+            else:
+                qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
+        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
+        qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
+
+        q = q * self.scale
+        attn = (q @ k.transpose(-2, -1))
+
+        if self.relative_position_bias_table is not None:
+            relative_position_bias = \
+                self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
+                    self.window_size[0] * self.window_size[1] + 1,
+                    self.window_size[0] * self.window_size[1] + 1, -1)  # Wh*Ww,Wh*Ww,nH
+            relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+            attn = attn + relative_position_bias.unsqueeze(0)
+
+        if rel_pos_bias is not None:
+            attn = attn + rel_pos_bias
+
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class Block(nn.Module):
+
+    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
+                 drop_path=0., init_values=None, act_layer=nn.GELU, norm_layer=nn.LayerNorm,
+                 window_size=None, attn_head_dim=None):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(
+            dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
+            window_size=window_size, attn_head_dim=attn_head_dim)
+        # 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)
+
+        if init_values:
+            self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
+            self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
+        else:
+            self.gamma_1, self.gamma_2 = None, None
+
+    def forward(self, x, rel_pos_bias: Optional[torch.Tensor] = None):
+        if self.gamma_1 is None:
+            x = x + self.drop_path(self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias))
+            x = x + self.drop_path(self.mlp(self.norm2(x)))
+        else:
+            x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias))
+            x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
+        return x
+
+
+class RelativePositionBias(nn.Module):
+
+    def __init__(self, window_size, num_heads):
+        super().__init__()
+        self.window_size = window_size
+        self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros(self.num_relative_distance, num_heads))  # 2*Wh-1 * 2*Ww-1, nH
+        # cls to token & token 2 cls & cls to cls
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(window_size[0])
+        coords_w = torch.arange(window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :, 0] += window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * window_size[1] - 1
+        relative_position_index = \
+            torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype)
+        relative_position_index[1:, 1:] = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        relative_position_index[0, 0:] = self.num_relative_distance - 3
+        relative_position_index[0:, 0] = self.num_relative_distance - 2
+        relative_position_index[0, 0] = self.num_relative_distance - 1
+
+        self.register_buffer("relative_position_index", relative_position_index)
+
+        # trunc_normal_(self.relative_position_bias_table, std=.02)
+
+    def forward(self):
+        relative_position_bias = \
+            self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
+                self.window_size[0] * self.window_size[1] + 1,
+                self.window_size[0] * self.window_size[1] + 1, -1)  # Wh*Ww,Wh*Ww,nH
+        return relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+
+
+class Beit(nn.Module):
+    """ Vision Transformer with support for patch or hybrid CNN input stage
+    """
+
+    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=partial(nn.LayerNorm, eps=1e-6), init_values=None,
+                 use_abs_pos_emb=True, use_rel_pos_bias=False, use_shared_rel_pos_bias=False,
+                 use_mean_pooling=True, init_scale=0.001):
+        super().__init__()
+        self.num_classes = num_classes
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+
+        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.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+        if use_abs_pos_emb:
+            self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
+        else:
+            self.pos_embed = None
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        if use_shared_rel_pos_bias:
+            self.rel_pos_bias = RelativePositionBias(window_size=self.patch_embed.grid_size, num_heads=num_heads)
+        else:
+            self.rel_pos_bias = None
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        self.use_rel_pos_bias = use_rel_pos_bias
+        self.blocks = nn.ModuleList([
+            Block(
+                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=dpr[i], norm_layer=norm_layer,
+                init_values=init_values, window_size=self.patch_embed.grid_size if use_rel_pos_bias else None)
+            for i in range(depth)])
+        self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim)
+        self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None
+        self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+
+        self.apply(self._init_weights)
+        if self.pos_embed is not None:
+            trunc_normal_(self.pos_embed, std=.02)
+        trunc_normal_(self.cls_token, std=.02)
+        # trunc_normal_(self.mask_token, std=.02)
+        self.fix_init_weight()
+        if isinstance(self.head, nn.Linear):
+            trunc_normal_(self.head.weight, std=.02)
+            self.head.weight.data.mul_(init_scale)
+            self.head.bias.data.mul_(init_scale)
+
+    def fix_init_weight(self):
+        def rescale(param, layer_id):
+            param.div_(math.sqrt(2.0 * layer_id))
+
+        for layer_id, layer in enumerate(self.blocks):
+            rescale(layer.attn.proj.weight.data, layer_id + 1)
+            rescale(layer.mlp.fc2.weight.data, layer_id + 1)
+
+    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)
+
+    def get_num_layers(self):
+        return len(self.blocks)
+
+    @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 forward_features(self, x):
+        x = self.patch_embed(x)
+        batch_size, seq_len, _ = x.size()
+
+        cls_tokens = self.cls_token.expand(batch_size, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
+        x = torch.cat((cls_tokens, x), dim=1)
+        if self.pos_embed is not None:
+            x = x + self.pos_embed
+        x = self.pos_drop(x)
+
+        rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None
+        for blk in self.blocks:
+            x = blk(x, rel_pos_bias=rel_pos_bias)
+
+        x = self.norm(x)
+        if self.fc_norm is not None:
+            t = x[:, 1:, :]
+            return self.fc_norm(t.mean(1))
+        else:
+            return x[:, 0]
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        x = self.head(x)
+        return x
+
+
+def _create_beit(variant, pretrained=False, default_cfg=None, **kwargs):
+    default_cfg = default_cfg or default_cfgs[variant]
+    if kwargs.get('features_only', None):
+        raise RuntimeError('features_only not implemented for Beit models.')
+
+    model = build_model_with_cfg(
+        Beit, variant, pretrained,
+        default_cfg=default_cfg,
+        # FIXME an updated filter fn needed to interpolate rel pos emb if fine tuning to diff model sizes
+        pretrained_filter_fn=checkpoint_filter_fn,
+        **kwargs)
+    return model
+
+
+@register_model
+def beit_base_patch16_224(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
+        use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=0.1, **kwargs)
+    model = _create_beit('beit_base_patch16_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def beit_base_patch16_384(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        img_size=384, patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
+        use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=0.1, **kwargs)
+    model = _create_beit('beit_base_patch16_384', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def beit_base_patch16_224_in22k(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
+        use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=0.1, **kwargs)
+    model = _create_beit('beit_base_patch16_224_in22k', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def beit_large_patch16_224(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
+        use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5,  **kwargs)
+    model = _create_beit('beit_large_patch16_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def beit_large_patch16_384(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        img_size=384, patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
+        use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5, **kwargs)
+    model = _create_beit('beit_large_patch16_384', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def beit_large_patch16_512(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        img_size=512, patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
+        use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5, **kwargs)
+    model = _create_beit('beit_large_patch16_512', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def beit_large_patch16_224_in22k(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
+        use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5,  **kwargs)
+    model = _create_beit('beit_large_patch16_224_in22k', pretrained=pretrained, **model_kwargs)
+    return model