Add GCViT

timm/models/__init__.py

@@ -15,6 +15,7 @@ from .dpn import *
 from .edgenext import *
 from .efficientformer import *
 from .efficientnet import *
+from .gcvit import *
 from .ghostnet import *
 from .gluon_resnet import *
 from .gluon_xception import *

timm/models/gcvit.py

@@ -0,0 +1,575 @@
+""" Global Context ViT
+
+From scratch implementation of GCViT in the style of timm swin_transformer_v2_cr.py
+
+Global Context Vision Transformers -https://arxiv.org/abs/2206.09959
+
+@article{hatamizadeh2022global,
+  title={Global Context Vision Transformers},
+  author={Hatamizadeh, Ali and Yin, Hongxu and Kautz, Jan and Molchanov, Pavlo},
+  journal={arXiv preprint arXiv:2206.09959},
+  year={2022}
+}
+
+Free of any code related to NVIDIA GCVit impl at https://github.com/NVlabs/GCVit.
+The license for this code release is Apache 2.0 with no commercial restrictions.
+
+However, weight files adapted from NVIDIA GCVit impl ARE under a non-commercial share-alike license
+(https://creativecommons.org/licenses/by-nc-sa/4.0/) until I have a chance to train new ones...
+
+Hacked together by / Copyright 2022, Ross Wightman
+"""
+import math
+from functools import partial
+from typing import Callable, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint as checkpoint
+
+from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from .fx_features import register_notrace_function
+from .helpers import build_model_with_cfg, named_apply
+from .layers import trunc_normal_tf_, DropPath, to_2tuple, Mlp, get_attn, get_act_layer, get_norm_layer, \
+    ClassifierHead, LayerNorm2d, _assert
+from .registry import register_model
+from .vision_transformer_relpos import RelPosMlp, RelPosBias  # FIXME move to common location
+
+__all__ = ['GlobalContextVit']
+
+
+def _cfg(url='', **kwargs):
+    return {
+        'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
+        'crop_pct': 0.875, 'interpolation': 'bicubic',
+        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
+        'first_conv': 'stem.conv', 'classifier': 'head.fc',
+        'fixed_input_size': True,
+        **kwargs
+    }
+
+
+default_cfgs = {
+    'gcvit_xxtiny': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-morevit/gcvit_xxtiny_224_nvidia-d1d86009.pth'),
+    'gcvit_xtiny': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-morevit/gcvit_xtiny_224_nvidia-274b92b7.pth'),
+    'gcvit_tiny': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-morevit/gcvit_tiny_224_nvidia-ac783954.pth'),
+    'gcvit_small': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-morevit/gcvit_small_224_nvidia-4e98afa2.pth'),
+    'gcvit_base': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-morevit/gcvit_base_224_nvidia-f009139b.pth'),
+}
+
+
+class MbConvBlock(nn.Module):
+    """ A depthwise separable / fused mbconv style residual block with SE, `no norm.
+    """
+    def __init__(
+            self,
+            in_chs,
+            out_chs=None,
+            expand_ratio=1.0,
+            attn_layer='se',
+            bias=False,
+            act_layer=nn.GELU,
+    ):
+        super().__init__()
+        attn_kwargs = dict(act_layer=act_layer)
+        if isinstance(attn_layer, str) and attn_layer == 'se' or attn_layer == 'eca':
+            attn_kwargs['rd_ratio'] = 0.25
+            attn_kwargs['bias'] = False
+        attn_layer = get_attn(attn_layer)
+        out_chs = out_chs or in_chs
+        mid_chs = int(expand_ratio * in_chs)
+
+        self.conv_dw = nn.Conv2d(in_chs, mid_chs, 3, 1, 1, groups=in_chs, bias=bias)
+        self.act = act_layer()
+        self.se = attn_layer(mid_chs, **attn_kwargs)
+        self.conv_pw = nn.Conv2d(mid_chs, out_chs, 1, 1, 0, bias=bias)
+
+    def forward(self, x):
+        shortcut = x
+        x = self.conv_dw(x)
+        x = self.act(x)
+        x = self.se(x)
+        x = self.conv_pw(x)
+        x = x + shortcut
+        return x
+
+
+class Downsample2d(nn.Module):
+    def __init__(
+            self,
+            dim,
+            dim_out=None,
+            reduction='conv',
+            act_layer=nn.GELU,
+            norm_layer=LayerNorm2d,
+    ):
+        super().__init__()
+        dim_out = dim_out or dim
+
+        self.norm1 = norm_layer(dim) if norm_layer is not None else nn.Identity()
+        self.conv_block = MbConvBlock(dim, act_layer=act_layer)
+        assert reduction in ('conv', 'max', 'avg')
+        if reduction == 'conv':
+            self.reduction = nn.Conv2d(dim, dim_out, 3, 2, 1, bias=False)
+        elif reduction == 'max':
+            assert dim == dim_out
+            self.reduction = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        else:
+            assert dim == dim_out
+            self.reduction = nn.AvgPool2d(kernel_size=2)
+        self.norm2 = norm_layer(dim_out) if norm_layer is not None else nn.Identity()
+
+    def forward(self, x):
+        x = self.norm1(x)
+        x = self.conv_block(x)
+        x = self.reduction(x)
+        x = self.norm2(x)
+        return x
+
+
+class FeatureBlock(nn.Module):
+    def __init__(
+            self,
+            dim,
+            levels=0,
+            reduction='max',
+            act_layer=nn.GELU,
+    ):
+        super().__init__()
+        reductions = levels
+        levels = max(1, levels)
+        if reduction == 'avg':
+            pool_fn = partial(nn.AvgPool2d, kernel_size=2)
+        else:
+            pool_fn = partial(nn.MaxPool2d, kernel_size=3, stride=2, padding=1)
+        self.blocks = nn.Sequential()
+        for i in range(levels):
+            self.blocks.add_module(f'conv{i+1}', MbConvBlock(dim, act_layer=act_layer))
+            if reductions:
+                self.blocks.add_module(f'pool{i+1}', pool_fn())
+                reductions -= 1
+
+    def forward(self, x):
+        return self.blocks(x)
+
+
+class Stem(nn.Module):
+    def __init__(
+            self,
+            in_chs: int = 3,
+            out_chs: int = 96,
+            act_layer: str = 'gelu',
+            norm_layer: str = 'layernorm2d',  # NOTE norm for NCHW
+    ):
+        super().__init__()
+        act_layer = get_act_layer(act_layer)
+        norm_layer = get_norm_layer(norm_layer)
+        self.conv1 = nn.Conv2d(in_chs, out_chs, kernel_size=3, stride=2, padding=1)
+        self.down = Downsample2d(out_chs, act_layer=act_layer, norm_layer=norm_layer)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.down(x)
+        return x
+
+
+class WindowAttentionGlobal(nn.Module):
+
+    def __init__(
+            self,
+            dim: int,
+            num_heads: int,
+            window_size: Tuple[int, int],
+            use_global: bool = True,
+            qkv_bias: bool = True,
+            attn_drop: float = 0.,
+            proj_drop: float = 0.,
+    ):
+        super().__init__()
+        window_size = to_2tuple(window_size)
+        self.window_size = window_size
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.scale = self.head_dim ** -0.5
+        self.use_global = use_global
+
+        self.rel_pos = RelPosBias(window_size=window_size, num_heads=num_heads)
+        if self.use_global:
+            self.qkv = nn.Linear(dim, dim * 2, bias=qkv_bias)
+        else:
+            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 forward(self, x, q_global: Optional[torch.Tensor] = None):
+        B, N, C = x.shape
+        if self.use_global:
+            _assert(q_global is not None, 'q_global must be passed in global mode')
+            _assert(x.shape[-1] == q_global.shape[-1], 'x and q_global seq lengths should be equal')
+
+            kv = self.qkv(x)
+            kv = kv.reshape(B, N, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
+            k, v = kv.unbind(0)
+
+            q = q_global.repeat(B // q_global.shape[0], 1, 1, 1)
+            q = q.reshape(B, N, self.num_heads, self.head_dim).permute(0, 2, 1, 3)
+        else:
+            qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
+            q, k, v = qkv.unbind(0)
+        q = q * self.scale
+
+        attn = (q @ k.transpose(-2, -1))
+        attn = self.rel_pos(attn)
+        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
+
+
+def window_partition(x, window_size: Tuple[int, int]):
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size[0], window_size[0], W // window_size[1], window_size[1], C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size[0], window_size[1], C)
+    return windows
+
+
+@register_notrace_function  # reason: int argument is a Proxy
+def window_reverse(windows, window_size: Tuple[int, int], img_size: Tuple[int, int]):
+    H, W = img_size
+    B = int(windows.shape[0] / (H * W / window_size[0] / window_size[1]))
+    x = windows.view(B, H // window_size[0], W // window_size[1], window_size[0], window_size[1], -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+
+class LayerScale(nn.Module):
+    def __init__(self, dim, init_values=1e-5, inplace=False):
+        super().__init__()
+        self.inplace = inplace
+        self.gamma = nn.Parameter(init_values * torch.ones(dim))
+
+    def forward(self, x):
+        return x.mul_(self.gamma) if self.inplace else x * self.gamma
+
+
+class GlobalContextVitBlock(nn.Module):
+    def __init__(
+            self,
+            dim: int,
+            feat_size: Tuple[int, int],
+            num_heads: int,
+            window_size: int = 7,
+            mlp_ratio: float = 4.,
+            use_global: bool = True,
+            qkv_bias: bool = True,
+            layer_scale: Optional[float] = None,
+            proj_drop: float = 0.,
+            attn_drop: float = 0.,
+            drop_path: float = 0.,
+            attn_layer: Callable = WindowAttentionGlobal,
+            act_layer: Callable = nn.GELU,
+            norm_layer: Callable = nn.LayerNorm,
+    ):
+        super().__init__()
+        feat_size = to_2tuple(feat_size)
+        window_size = to_2tuple(window_size)
+        self.window_size = window_size
+        self.num_windows = int((feat_size[0] // window_size[0]) * (feat_size[1] // window_size[1]))
+
+        self.norm1 = norm_layer(dim)
+        self.attn = attn_layer(
+            dim,
+            num_heads=num_heads,
+            window_size=window_size,
+            use_global=use_global,
+            qkv_bias=qkv_bias,
+            attn_drop=attn_drop,
+            proj_drop=proj_drop,
+        )
+        self.ls1 = LayerScale(dim, layer_scale) if layer_scale is not None else nn.Identity()
+        self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+
+        self.norm2 = norm_layer(dim)
+        self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=proj_drop)
+        self.ls2 = LayerScale(dim, layer_scale) if layer_scale is not None else nn.Identity()
+        self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+
+    def _window_attn(self, x, q_global: Optional[torch.Tensor] = None):
+        B, H, W, C = x.shape
+        x_win = window_partition(x, self.window_size)
+        x_win = x_win.view(-1, self.window_size[0] * self.window_size[1], C)
+        attn_win = self.attn(x_win, q_global)
+        x = window_reverse(attn_win, self.window_size, (H, W))
+        return x
+
+    def forward(self, x, q_global: Optional[torch.Tensor] = None):
+        x = x + self.drop_path1(self.ls1(self._window_attn(self.norm1(x), q_global)))
+        x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
+        return x
+
+
+class GlobalContextVitStage(nn.Module):
+    def __init__(
+            self,
+            dim,
+            depth: int,
+            num_heads: int,
+            feat_size: Tuple[int, int],
+            window_size: int,
+            downsample: bool = True,
+            global_norm: bool = False,
+            stage_norm: bool = False,
+            mlp_ratio: float = 4.,
+            qkv_bias: bool = True,
+            layer_scale: Optional[float] = None,
+            proj_drop: float = 0.,
+            attn_drop: float = 0.,
+            drop_path: Union[List[float], float] = 0.0,
+            act_layer: str = 'gelu',
+            norm_layer: str = 'layernorm2d',
+            norm_layer_cl: str = 'layernorm',
+    ):
+        super().__init__()
+        act_layer = get_act_layer(act_layer)
+        norm_layer = get_norm_layer(norm_layer)
+        norm_layer_cl = get_norm_layer(norm_layer_cl)
+
+        if downsample:
+            self.downsample = Downsample2d(
+                dim=dim,
+                dim_out=dim * 2,
+                norm_layer=norm_layer,
+            )
+            dim = dim * 2
+            feat_size = (feat_size[0] // 2, feat_size[1] // 2)
+        else:
+            self.downsample = nn.Identity()
+        self.feat_size = feat_size
+
+        feat_levels = int(math.log2(min(feat_size) / window_size))
+        self.global_block = FeatureBlock(dim, feat_levels)
+        self.global_norm = norm_layer_cl(dim) if global_norm else nn.Identity()
+
+        self.blocks = nn.ModuleList([
+            GlobalContextVitBlock(
+                dim=dim,
+                num_heads=num_heads,
+                feat_size=feat_size,
+                window_size=window_size,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                use_global=(i % 2 != 0),
+                layer_scale=layer_scale,
+                proj_drop=proj_drop,
+                attn_drop=attn_drop,
+                drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                act_layer=act_layer,
+                norm_layer=norm_layer_cl,
+            )
+            for i in range(depth)
+        ])
+        self.norm = norm_layer_cl(dim) if stage_norm else nn.Identity()
+        self.dim = dim
+        self.feat_size = feat_size
+        self.grad_checkpointing = False
+
+    def forward(self, x):
+        # input NCHW, downsample & global block are 2d conv + pooling
+        x = self.downsample(x)
+        global_query = self.global_block(x)
+
+        # reshape NCHW --> NHWC for transformer blocks
+        x = x.permute(0, 2, 3, 1)
+        global_query = self.global_norm(global_query.permute(0, 2, 3, 1))
+        for blk in self.blocks:
+            if self.grad_checkpointing and not torch.jit.is_scripting():
+                x = checkpoint.checkpoint(blk, x)
+            else:
+                x = blk(x, global_query)
+        x = self.norm(x)
+        x = x.permute(0, 3, 1, 2).contiguous()  # back to NCHW
+        return x
+
+
+class GlobalContextVit(nn.Module):
+    def __init__(
+            self,
+            in_chans: int = 3,
+            num_classes: int = 1000,
+            global_pool: str = 'avg',
+            img_size: Tuple[int, int] = 224,
+            window_size: Tuple[int, ...] = (7, 7, 14, 7),
+            embed_dim: int = 64,
+            depths: Tuple[int, ...] = (3, 4, 19, 5),
+            num_heads: Tuple[int, ...] = (2, 4, 8, 16),
+            mlp_ratio: float = 3.0,
+            qkv_bias: bool = True,
+            layer_scale: Optional[float] = None,
+            drop_rate: float = 0.,
+            proj_drop_rate: float = 0.,
+            attn_drop_rate: float = 0.,
+            drop_path_rate: float = 0.,
+            weight_init='vit',
+            act_layer: str = 'gelu',
+            norm_layer: str = 'layernorm2d',
+            norm_layer_cl: str = 'layernorm',
+    ):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        feat_size = tuple(d // 4 for d in img_size)  # stem reduction by 4
+        self.global_pool = global_pool
+        self.num_classes = num_classes
+        num_stages = len(depths)
+        self.num_features = int(embed_dim * 2 ** (num_stages - 1))
+
+        self.stem = Stem(
+            in_chs=in_chans, out_chs=embed_dim, act_layer=act_layer, norm_layer=norm_layer)
+
+        dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
+        stages = []
+        for i in range(num_stages):
+            last_stage = i == num_stages - 1
+            stage_scale = 2 ** max(i - 1, 0)
+            stages.append(GlobalContextVitStage(
+                dim=embed_dim * stage_scale,
+                depth=depths[i],
+                num_heads=num_heads[i],
+                feat_size=(feat_size[0] // stage_scale, feat_size[1] // stage_scale),
+                window_size=window_size[i],
+                downsample=i != 0,
+                stage_norm=last_stage,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                layer_scale=layer_scale,
+                proj_drop=proj_drop_rate,
+                attn_drop=attn_drop_rate,
+                drop_path=dpr[i],
+                act_layer=act_layer,
+                norm_layer=norm_layer,
+                norm_layer_cl=norm_layer_cl,
+            ))
+        self.stages = nn.Sequential(*stages)
+
+        # Classifier head
+        self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=drop_rate)
+
+        if weight_init:
+            named_apply(partial(self._init_weights, scheme=weight_init), self)
+
+    def _init_weights(self, module, name, scheme='vit'):
+        # note Conv2d left as default init
+        if scheme == 'vit':
+            if isinstance(module, nn.Linear):
+                nn.init.xavier_uniform_(module.weight)
+                if module.bias is not None:
+                    if 'mlp' in name:
+                        nn.init.normal_(module.bias, std=1e-6)
+                    else:
+                        nn.init.zeros_(module.bias)
+        else:
+            if isinstance(module, nn.Linear):
+                trunc_normal_tf_(module.weight, std=.02)
+                if module.bias is not None:
+                    nn.init.zeros_(module.bias)
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {
+            k for k, _ in self.named_parameters()
+            if any(n in k for n in ["relative_position_bias_table", "rel_pos.mlp"])}
+
+
+    @torch.jit.ignore
+    def group_matcher(self, coarse=False):
+        matcher = dict(
+            stem=r'^stem',  # stem and embed
+            blocks=(r'^stages\.(\d+)', None)
+        )
+        return matcher
+
+    @torch.jit.ignore
+    def set_grad_checkpointing(self, enable=True):
+        for s in self.stages:
+            s.grad_checkpointing = enable
+
+    def forward_features(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.stem(x)
+        x = self.stages(x)
+        return x
+
+    def forward_head(self, x, pre_logits: bool = False):
+        return self.head(x, pre_logits=pre_logits)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.forward_features(x)
+        x = self.forward_head(x)
+        return x
+
+
+def _create_gcvit(variant, pretrained=False, **kwargs):
+    if kwargs.get('features_only', None):
+        raise RuntimeError('features_only not implemented for Vision Transformer models.')
+    model = build_model_with_cfg(GlobalContextVit, variant, pretrained, **kwargs)
+    return model
+
+
+@register_model
+def gcvit_xxtiny(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        depths=(2, 2, 6, 2),
+        num_heads=(2, 4, 8, 16),
+        **kwargs)
+    return _create_gcvit('gcvit_xxtiny', pretrained=pretrained, **model_kwargs)
+
+
+@register_model
+def gcvit_xtiny(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        depths=(3, 4, 6, 5),
+        num_heads=(2, 4, 8, 16),
+        **kwargs)
+    return _create_gcvit('gcvit_xtiny', pretrained=pretrained, **model_kwargs)
+
+
+@register_model
+def gcvit_tiny(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        depths=(3, 4, 19, 5),
+        num_heads=(2, 4, 8, 16),
+        **kwargs)
+    return _create_gcvit('gcvit_tiny', pretrained=pretrained, **model_kwargs)
+
+
+@register_model
+def gcvit_small(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        depths=(3, 4, 19, 5),
+        num_heads=(3, 6, 12, 24),
+        window_size=(7, 7, 14, 7),
+        embed_dim=96,
+        mlp_ratio=2,
+        layer_scale=1e-5,
+        **kwargs)
+    return _create_gcvit('gcvit_small', pretrained=pretrained, **model_kwargs)
+
+
+@register_model
+def gcvit_base(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        depths=(3, 4, 19, 5),
+        num_heads=(4, 8, 16, 32),
+        window_size=(7, 7, 14, 7),
+        embed_dim=128,
+        mlp_ratio=2,
+        layer_scale=1e-5,
+        **kwargs)
+    return _create_gcvit('gcvit_base', pretrained=pretrained, **model_kwargs)