Merge branch 'amaarora-convit'

rwightman · rwightman · commit d4c00d61500e · 2021-05-21T17:04:56.000-07:00
diff --git a/tests/test_models.py b/tests/test_models.py
@@ -15,7 +15,8 @@
     torch._C._jit_set_profiling_mode(False)
 
 # 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_*']
+NON_STD_FILTERS = [
+    'vit_*', 'tnt_*', 'pit_*', 'swin_*', 'coat_*', 'cait_*', '*mixer_*', 'gmlp_*', 'resmlp_*', 'twins_*', 'convit_*']
 NUM_NON_STD = len(NON_STD_FILTERS)
 
 # exclude models that cause specific test failures
diff --git a/timm/models/__init__.py b/timm/models/__init__.py
@@ -2,6 +2,7 @@
 from .byobnet import *
 from .cait import *
 from .coat import *
+from .convit import *
 from .cspnet import *
 from .densenet import *
 from .dla import *
diff --git a/timm/models/convit.py b/timm/models/convit.py
@@ -0,0 +1,350 @@
+""" ConViT Model
+
+@article{d2021convit,
+  title={ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases},
+  author={d'Ascoli, St{\'e}phane and Touvron, Hugo and Leavitt, Matthew and Morcos, Ari and Biroli, Giulio and Sagun, Levent},
+  journal={arXiv preprint arXiv:2103.10697},
+  year={2021}
+}
+
+Paper link: https://arxiv.org/abs/2103.10697
+Original code: https://github.com/facebookresearch/convit, original copyright below
+"""
+# Copyright (c) 2015-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the CC-by-NC license found in the
+# LICENSE file in the root directory of this source tree.
+#
+'''These modules are adapted from those of timm, see
+https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
+'''
+
+import torch
+import torch.nn as nn
+from functools import partial
+import torch.nn.functional as F
+
+from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from .helpers import build_model_with_cfg
+from .layers import DropPath, to_2tuple, trunc_normal_, PatchEmbed, Mlp
+from .registry import register_model
+from .vision_transformer_hybrid import HybridEmbed
+
+import torch
+import torch.nn as nn
+
+
+def _cfg(url='', **kwargs):
+    return {
+        'url': url,
+        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
+        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
+        'first_conv': 'patch_embed.proj', 'classifier': 'head',
+        **kwargs
+    }
+
+
+default_cfgs = {
+    # ConViT
+    'convit_tiny': _cfg(
+        url="https://dl.fbaipublicfiles.com/convit/convit_tiny.pth"),
+    'convit_small': _cfg(
+        url="https://dl.fbaipublicfiles.com/convit/convit_small.pth"),
+    'convit_base': _cfg(
+        url="https://dl.fbaipublicfiles.com/convit/convit_base.pth")
+}
+
+
+class GPSA(nn.Module):
+    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.,
+                 locality_strength=1.):
+        super().__init__()
+        self.num_heads = num_heads
+        self.dim = dim
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+        self.locality_strength = locality_strength
+
+        self.qk = nn.Linear(dim, dim * 2, bias=qkv_bias)
+        self.v = nn.Linear(dim, dim, bias=qkv_bias)
+
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.pos_proj = nn.Linear(3, num_heads)
+        self.proj_drop = nn.Dropout(proj_drop)
+        self.locality_strength = locality_strength
+        self.gating_param = nn.Parameter(torch.ones(self.num_heads))
+        self.rel_indices: torch.Tensor = torch.zeros(1, 1, 1, 3)  # silly torchscript hack, won't work with None
+
+    def forward(self, x):
+        B, N, C = x.shape
+        if self.rel_indices is None or self.rel_indices.shape[1] != N:
+            self.rel_indices = self.get_rel_indices(N)
+        attn = self.get_attention(x)
+        v = self.v(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    def get_attention(self, x):
+        B, N, C = x.shape
+        qk = self.qk(x).reshape(B, N, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k = qk[0], qk[1]
+        pos_score = self.rel_indices.expand(B, -1, -1, -1)
+        pos_score = self.pos_proj(pos_score).permute(0, 3, 1, 2)
+        patch_score = (q @ k.transpose(-2, -1)) * self.scale
+        patch_score = patch_score.softmax(dim=-1)
+        pos_score = pos_score.softmax(dim=-1)
+
+        gating = self.gating_param.view(1, -1, 1, 1)
+        attn = (1. - torch.sigmoid(gating)) * patch_score + torch.sigmoid(gating) * pos_score
+        attn /= attn.sum(dim=-1).unsqueeze(-1)
+        attn = self.attn_drop(attn)
+        return attn
+
+    def get_attention_map(self, x, return_map=False):
+        attn_map = self.get_attention(x).mean(0)  # average over batch
+        distances = self.rel_indices.squeeze()[:, :, -1] ** .5
+        dist = torch.einsum('nm,hnm->h', (distances, attn_map)) / distances.size(0)
+        if return_map:
+            return dist, attn_map
+        else:
+            return dist
+
+    def local_init(self):
+        self.v.weight.data.copy_(torch.eye(self.dim))
+        locality_distance = 1  # max(1,1/locality_strength**.5)
+
+        kernel_size = int(self.num_heads ** .5)
+        center = (kernel_size - 1) / 2 if kernel_size % 2 == 0 else kernel_size // 2
+        for h1 in range(kernel_size):
+            for h2 in range(kernel_size):
+                position = h1 + kernel_size * h2
+                self.pos_proj.weight.data[position, 2] = -1
+                self.pos_proj.weight.data[position, 1] = 2 * (h1 - center) * locality_distance
+                self.pos_proj.weight.data[position, 0] = 2 * (h2 - center) * locality_distance
+        self.pos_proj.weight.data *= self.locality_strength
+
+    def get_rel_indices(self, num_patches: int) -> torch.Tensor:
+        img_size = int(num_patches ** .5)
+        rel_indices = torch.zeros(1, num_patches, num_patches, 3)
+        ind = torch.arange(img_size).view(1, -1) - torch.arange(img_size).view(-1, 1)
+        indx = ind.repeat(img_size, img_size)
+        indy = ind.repeat_interleave(img_size, dim=0).repeat_interleave(img_size, dim=1)
+        indd = indx ** 2 + indy ** 2
+        rel_indices[:, :, :, 2] = indd.unsqueeze(0)
+        rel_indices[:, :, :, 1] = indy.unsqueeze(0)
+        rel_indices[:, :, :, 0] = indx.unsqueeze(0)
+        device = self.qk.weight.device
+        return rel_indices.to(device)
+
+
+class MHSA(nn.Module):
+    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        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)
+
+    def get_attention_map(self, x, return_map=False):
+        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]
+        attn_map = (q @ k.transpose(-2, -1)) * self.scale
+        attn_map = attn_map.softmax(dim=-1).mean(0)
+
+        img_size = int(N ** .5)
+        ind = torch.arange(img_size).view(1, -1) - torch.arange(img_size).view(-1, 1)
+        indx = ind.repeat(img_size, img_size)
+        indy = ind.repeat_interleave(img_size, dim=0).repeat_interleave(img_size, dim=1)
+        indd = indx ** 2 + indy ** 2
+        distances = indd ** .5
+        distances = distances.to('cuda')
+
+        dist = torch.einsum('nm,hnm->h', (distances, attn_map)) / N
+        if return_map:
+            return dist, attn_map
+        else:
+            return dist
+
+    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]
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        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, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_gpsa=True, **kwargs):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.use_gpsa = use_gpsa
+        if self.use_gpsa:
+            self.attn = GPSA(
+                dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop,
+                proj_drop=drop, **kwargs)
+        else:
+            self.attn = MHSA(
+                dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop,
+                proj_drop=drop, **kwargs)
+        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)
+
+    def forward(self, x):
+        x = x + self.drop_path(self.attn(self.norm1(x)))
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        return x
+
+
+class ConViT(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=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
+                 drop_path_rate=0., hybrid_backbone=None, norm_layer=nn.LayerNorm, global_pool=None,
+                 local_up_to_layer=3, locality_strength=1., use_pos_embed=True):
+        super().__init__()
+        embed_dim *= num_heads
+        self.num_classes = num_classes
+        self.local_up_to_layer = local_up_to_layer
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+        self.locality_strength = locality_strength
+        self.use_pos_embed = use_pos_embed
+
+        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.num_patches = num_patches
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        if self.use_pos_embed:
+            self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
+            trunc_normal_(self.pos_embed, std=.02)
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        self.blocks = nn.ModuleList([
+            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,
+                use_gpsa=True,
+                locality_strength=locality_strength)
+            if i < local_up_to_layer else
+            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,
+                use_gpsa=False)
+            for i in range(depth)])
+        self.norm = norm_layer(embed_dim)
+
+        # Classifier head
+        self.feature_info = [dict(num_chs=embed_dim, reduction=0, module='head')]
+        self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+
+        trunc_normal_(self.cls_token, std=.02)
+        self.apply(self._init_weights)
+        for n, m in self.named_modules():
+            if hasattr(m, 'local_init'):
+                m.local_init()
+
+    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 forward_features(self, x):
+        B = x.shape[0]
+        x = self.patch_embed(x)
+
+        cls_tokens = self.cls_token.expand(B, -1, -1)
+
+        if self.use_pos_embed:
+            x = x + self.pos_embed
+        x = self.pos_drop(x)
+
+        for u, blk in enumerate(self.blocks):
+            if u == self.local_up_to_layer:
+                x = torch.cat((cls_tokens, x), dim=1)
+            x = blk(x)
+
+        x = self.norm(x)
+        return x[:, 0]
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        x = self.head(x)
+        return x
+
+
+def _create_convit(variant, pretrained=False, **kwargs):
+    return build_model_with_cfg(
+        ConViT, variant, pretrained,
+        default_cfg=default_cfgs[variant],
+        **kwargs)
+
+
+@register_model
+def convit_tiny(pretrained=False, **kwargs):
+    model_args = dict(
+        local_up_to_layer=10, locality_strength=1.0, embed_dim=48,
+        num_heads=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
+    model = _create_convit(variant='convit_tiny', pretrained=pretrained, **model_args)
+    return model
+
+
+@register_model
+def convit_small(pretrained=False, **kwargs):
+    model_args = dict(
+        local_up_to_layer=10, locality_strength=1.0, embed_dim=48,
+        num_heads=9, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
+    model = _create_convit(variant='convit_small', pretrained=pretrained, **model_args)
+    return model
+
+
+@register_model
+def convit_base(pretrained=False, **kwargs):
+    model_args = dict(
+        local_up_to_layer=10, locality_strength=1.0, embed_dim=48,
+        num_heads=16, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
+    model = _create_convit(variant='convit_base', pretrained=pretrained, **model_args)
+    return model
