Merge branch 'rwightman:master' into master

inference.py

@@ -114,13 +114,13 @@ def main():
                 _logger.info('Predict: [{0}/{1}] Time {batch_time.val:.3f} ({batch_time.avg:.3f})'.format(
                     batch_idx, len(loader), batch_time=batch_time))
 
-    topk_ids = np.concatenate(topk_ids, axis=0).squeeze()
+    topk_ids = np.concatenate(topk_ids, axis=0)
 
     with open(os.path.join(args.output_dir, './topk_ids.csv'), 'w') as out_file:
         filenames = loader.dataset.filenames(basename=True)
         for filename, label in zip(filenames, topk_ids):
-            out_file.write('{0},{1},{2},{3},{4},{5}\n'.format(
-                filename, label[0], label[1], label[2], label[3], label[4]))
+            out_file.write('{0},{1}\n'.format(
+                filename, ','.join([ str(v) for v in label])))
 
 
 if __name__ == '__main__':

tests/test_models.py

@@ -15,7 +15,7 @@ if hasattr(torch._C, '_jit_set_profiling_executor'):
     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_*']
+NON_STD_FILTERS = ['vit_*', 'tnt_*', 'pit_*', 'swin_*', 'coat_*', 'cait_*', '*mixer_*', 'gmlp_*', 'resmlp_*']
 NUM_NON_STD = len(NON_STD_FILTERS)
 
 # exclude models that cause specific test failures

timm/models/layers/__init__.py

@@ -20,7 +20,7 @@ from .helpers import to_ntuple, to_2tuple, to_3tuple, to_4tuple, make_divisible
 from .inplace_abn import InplaceAbn
 from .linear import Linear
 from .mixed_conv2d import MixedConv2d
-from .mlp import Mlp, GluMlp
+from .mlp import Mlp, GluMlp, GatedMlp
 from .norm import GroupNorm
 from .norm_act import BatchNormAct2d, GroupNormAct
 from .padding import get_padding, get_same_padding, pad_same

timm/models/layers/mlp.py

@@ -34,9 +34,10 @@ class GluMlp(nn.Module):
         super().__init__()
         out_features = out_features or in_features
         hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features * 2)
+        assert hidden_features % 2 == 0
+        self.fc1 = nn.Linear(in_features, hidden_features)
         self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.fc2 = nn.Linear(hidden_features // 2, out_features)
         self.drop = nn.Dropout(drop)
 
     def forward(self, x):
@@ -47,3 +48,32 @@ class GluMlp(nn.Module):
         x = self.fc2(x)
         x = self.drop(x)
         return x
+
+
+class GatedMlp(nn.Module):
+    """ MLP as used in gMLP
+    """
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU,
+                 gate_layer=None, 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()
+        if gate_layer is not None:
+            assert hidden_features % 2 == 0
+            self.gate = gate_layer(hidden_features)
+            hidden_features = hidden_features // 2  # FIXME base reduction on gate property?
+        else:
+            self.gate = nn.Identity()
+        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.gate(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x

timm/models/mlp_mixer.py

@@ -1,4 +1,6 @@
-""" MLP-Mixer in PyTorch
+""" MLP-Mixer, ResMLP, and gMLP in PyTorch
+
+This impl originally based on MLP-Mixer paper.
 
 Official JAX impl: https://github.com/google-research/vision_transformer/blob/linen/vit_jax/models_mixer.py
 
@@ -12,6 +14,25 @@ Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2
   year={2021}
 }
 
+Also supporting preliminary (not verified) implementations of ResMlp, gMLP, and possibly more...
+
+Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
+@misc{touvron2021resmlp,
+      title={ResMLP: Feedforward networks for image classification with data-efficient training},
+      author={Hugo Touvron and Piotr Bojanowski and Mathilde Caron and Matthieu Cord and Alaaeldin El-Nouby and
+        Edouard Grave and Armand Joulin and Gabriel Synnaeve and Jakob Verbeek and Hervé Jégou},
+      year={2021},
+      eprint={2105.03404},
+}
+
+Paper: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050
+@misc{liu2021pay,
+      title={Pay Attention to MLPs},
+      author={Hanxiao Liu and Zihang Dai and David R. So and Quoc V. Le},
+      year={2021},
+      eprint={2105.08050},
+}
+
 A thank you to paper authors for releasing code and weights.
 
 Hacked together by / Copyright 2021 Ross Wightman
@@ -25,7 +46,7 @@ import torch.nn as nn
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from .helpers import build_model_with_cfg, overlay_external_default_cfg
-from .layers import PatchEmbed, Mlp, GluMlp, DropPath, lecun_normal_
+from .layers import PatchEmbed, Mlp, GluMlp, GatedMlp, DropPath, lecun_normal_, to_2tuple
 from .registry import register_model
 
 
@@ -43,7 +64,6 @@ def _cfg(url='', **kwargs):
 default_cfgs = dict(
     mixer_s32_224=_cfg(),
     mixer_s16_224=_cfg(),
-    mixer_s16_glu_224=_cfg(),
     mixer_b32_224=_cfg(),
     mixer_b16_224=_cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_mixer_b16_224-76587d61.pth',
@@ -60,15 +80,29 @@ default_cfgs = dict(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_mixer_l16_224_in21k-846aa33c.pth',
         num_classes=21843
     ),
+
+    gmixer_12_224=_cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
+    gmixer_24_224=_cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
+
+    resmlp_12_224=_cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
+    resmlp_24_224=_cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
+    resmlp_36_224=_cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
+
+    gmlp_ti16_224=_cfg(),
+    gmlp_s16_224=_cfg(),
+    gmlp_b16_224=_cfg(),
 )
 
 
 class MixerBlock(nn.Module):
-
+    """ Residual Block w/ token mixing and channel MLPs
+    Based on: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
+    """
     def __init__(
-            self, dim, seq_len, tokens_dim, channels_dim,
-            mlp_layer=Mlp, norm_layer=partial(nn.LayerNorm, eps=1e-6), act_layer=nn.GELU, drop=0., drop_path=0.):
+            self, dim, seq_len, mlp_ratio=(0.5, 4.0), mlp_layer=Mlp,
+            norm_layer=partial(nn.LayerNorm, eps=1e-6), act_layer=nn.GELU, drop=0., drop_path=0.):
         super().__init__()
+        tokens_dim, channels_dim = [int(x * dim) for x in to_2tuple(mlp_ratio)]
         self.norm1 = norm_layer(dim)
         self.mlp_tokens = mlp_layer(seq_len, tokens_dim, act_layer=act_layer, drop=drop)
         self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
@@ -81,6 +115,78 @@ class MixerBlock(nn.Module):
         return x
 
 
+class Affine(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.alpha = nn.Parameter(torch.ones((1, 1, dim)))
+        self.beta = nn.Parameter(torch.zeros((1, 1, dim)))
+
+    def forward(self, x):
+        return torch.addcmul(self.beta, self.alpha, x)
+
+
+class ResBlock(nn.Module):
+    """ Residual MLP block w/ LayerScale and Affine 'norm'
+
+    Based on: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
+    """
+    def __init__(
+            self, dim, seq_len, mlp_ratio=4, mlp_layer=Mlp, norm_layer=Affine,
+            act_layer=nn.GELU, init_values=1e-4, drop=0., drop_path=0.):
+        super().__init__()
+        channel_dim = int(dim * mlp_ratio)
+        self.norm1 = norm_layer(dim)
+        self.linear_tokens = nn.Linear(seq_len, seq_len)
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        self.mlp_channels = mlp_layer(dim, channel_dim, act_layer=act_layer, drop=drop)
+        self.ls1 = nn.Parameter(init_values * torch.ones(dim))
+        self.ls2 = nn.Parameter(init_values * torch.ones(dim))
+
+    def forward(self, x):
+        x = x + self.drop_path(self.ls1 * self.linear_tokens(self.norm1(x).transpose(1, 2)).transpose(1, 2))
+        x = x + self.drop_path(self.ls2 * self.mlp_channels(self.norm2(x)))
+        return x
+
+
+class SpatialGatingUnit(nn.Module):
+    """ Spatial Gating Unit
+
+    Based on: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050
+    """
+    def __init__(self, dim, seq_len, norm_layer=nn.LayerNorm):
+        super().__init__()
+        gate_dim = dim // 2
+        self.norm = norm_layer(gate_dim)
+        self.proj = nn.Linear(seq_len, seq_len)
+
+    def forward(self, x):
+        u, v = x.chunk(2, dim=-1)
+        v = self.norm(v)
+        v = self.proj(v.transpose(-1, -2))
+        return u * v.transpose(-1, -2)
+
+
+class SpatialGatingBlock(nn.Module):
+    """ Residual Block w/ Spatial Gating
+
+    Based on: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050
+    """
+    def __init__(
+            self, dim, seq_len, mlp_ratio=4, mlp_layer=GatedMlp,
+            norm_layer=partial(nn.LayerNorm, eps=1e-6), act_layer=nn.GELU, drop=0., drop_path=0.):
+        super().__init__()
+        channel_dim = int(dim * mlp_ratio)
+        self.norm = norm_layer(dim)
+        sgu = partial(SpatialGatingUnit, seq_len=seq_len)
+        self.mlp_channels = mlp_layer(dim, channel_dim, act_layer=act_layer, gate_layer=sgu, drop=drop)
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+
+    def forward(self, x):
+        x = x + self.drop_path(self.mlp_channels(self.norm(x)))
+        return x
+
+
 class MlpMixer(nn.Module):
 
     def __init__(
@@ -91,24 +197,27 @@ class MlpMixer(nn.Module):
             patch_size=16,
             num_blocks=8,
             hidden_dim=512,
-            tokens_dim=256,
-            channels_dim=2048,
+            mlp_ratio=(0.5, 4.0),
+            block_layer=MixerBlock,
             mlp_layer=Mlp,
             norm_layer=partial(nn.LayerNorm, eps=1e-6),
             act_layer=nn.GELU,
             drop_rate=0.,
             drop_path_rate=0.,
             nlhb=False,
+            stem_norm=False,
     ):
         super().__init__()
         self.num_classes = num_classes
 
-        self.stem = PatchEmbed(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=hidden_dim)
-        # FIXME drop_path (stochastic depth scaling rule?)
+        self.stem = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=hidden_dim,
+            norm_layer=norm_layer if stem_norm else None)
+        # FIXME drop_path (stochastic depth scaling rule or all the same?)
         self.blocks = nn.Sequential(*[
-            MixerBlock(
-                hidden_dim, self.stem.num_patches, tokens_dim, channels_dim,
-                mlp_layer=mlp_layer, norm_layer=norm_layer, act_layer=act_layer, drop=drop_rate, drop_path=drop_path_rate)
+            block_layer(
+                hidden_dim, self.stem.num_patches, mlp_ratio, mlp_layer=mlp_layer, norm_layer=norm_layer,
+                act_layer=act_layer, drop=drop_rate, drop_path=drop_path_rate)
             for _ in range(num_blocks)])
         self.norm = norm_layer(hidden_dim)
         self.head = nn.Linear(hidden_dim, self.num_classes)  # zero init
@@ -136,6 +245,9 @@ def _init_weights(m, n: str, head_bias: float = 0.):
         if n.startswith('head'):
             nn.init.zeros_(m.weight)
             nn.init.constant_(m.bias, head_bias)
+        elif n.endswith('gate.proj'):
+            nn.init.normal_(m.weight, std=1e-4)
+            nn.init.ones_(m.bias)
         else:
             nn.init.xavier_uniform_(m.weight)
             if m.bias is not None:
@@ -177,8 +289,9 @@ def _create_mixer(variant, pretrained=False, default_cfg=None, **kwargs):
 @register_model
 def mixer_s32_224(pretrained=False, **kwargs):
     """ Mixer-S/32 224x224
+    Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
     """
-    model_args = dict(patch_size=32, num_blocks=8, hidden_dim=512, tokens_dim=256, channels_dim=2048, **kwargs)
+    model_args = dict(patch_size=32, num_blocks=8, hidden_dim=512, **kwargs)
     model = _create_mixer('mixer_s32_224', pretrained=pretrained, **model_args)
     return model
 
@@ -186,28 +299,19 @@ def mixer_s32_224(pretrained=False, **kwargs):
 @register_model
 def mixer_s16_224(pretrained=False, **kwargs):
     """ Mixer-S/16 224x224
+    Paper:  'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
     """
-    model_args = dict(patch_size=16, num_blocks=8, hidden_dim=512, tokens_dim=256, channels_dim=2048, **kwargs)
+    model_args = dict(patch_size=16, num_blocks=8, hidden_dim=512, **kwargs)
     model = _create_mixer('mixer_s16_224', pretrained=pretrained, **model_args)
     return model
 
 
-@register_model
-def mixer_s16_glu_224(pretrained=False, **kwargs):
-    """ Mixer-S/16 224x224
-    """
-    model_args = dict(
-        patch_size=16, num_blocks=8, hidden_dim=512, tokens_dim=256, channels_dim=1536,
-        mlp_layer=GluMlp, act_layer=nn.SiLU, **kwargs)
-    model = _create_mixer('mixer_s16_glu_224', pretrained=pretrained, **model_args)
-    return model
-
-
 @register_model
 def mixer_b32_224(pretrained=False, **kwargs):
     """ Mixer-B/32 224x224
+    Paper:  'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
     """
-    model_args = dict(patch_size=32, num_blocks=12, hidden_dim=768, tokens_dim=384, channels_dim=3072, **kwargs)
+    model_args = dict(patch_size=32, num_blocks=12, hidden_dim=768, **kwargs)
     model = _create_mixer('mixer_b32_224', pretrained=pretrained, **model_args)
     return model
 
@@ -215,8 +319,9 @@ def mixer_b32_224(pretrained=False, **kwargs):
 @register_model
 def mixer_b16_224(pretrained=False, **kwargs):
     """ Mixer-B/16 224x224. ImageNet-1k pretrained weights.
+    Paper:  'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
     """
-    model_args = dict(patch_size=16, num_blocks=12, hidden_dim=768, tokens_dim=384, channels_dim=3072, **kwargs)
+    model_args = dict(patch_size=16, num_blocks=12, hidden_dim=768, **kwargs)
     model = _create_mixer('mixer_b16_224', pretrained=pretrained, **model_args)
     return model
 
@@ -224,8 +329,9 @@ def mixer_b16_224(pretrained=False, **kwargs):
 @register_model
 def mixer_b16_224_in21k(pretrained=False, **kwargs):
     """ Mixer-B/16 224x224. ImageNet-21k pretrained weights.
+    Paper:  'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
     """
-    model_args = dict(patch_size=16, num_blocks=12, hidden_dim=768, tokens_dim=384, channels_dim=3072, **kwargs)
+    model_args = dict(patch_size=16, num_blocks=12, hidden_dim=768, **kwargs)
     model = _create_mixer('mixer_b16_224_in21k', pretrained=pretrained, **model_args)
     return model
 
@@ -233,8 +339,9 @@ def mixer_b16_224_in21k(pretrained=False, **kwargs):
 @register_model
 def mixer_l32_224(pretrained=False, **kwargs):
     """ Mixer-L/32 224x224.
+    Paper:  'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
     """
-    model_args = dict(patch_size=32, num_blocks=24, hidden_dim=1024, tokens_dim=512, channels_dim=4096, **kwargs)
+    model_args = dict(patch_size=32, num_blocks=24, hidden_dim=1024, **kwargs)
     model = _create_mixer('mixer_l32_224', pretrained=pretrained, **model_args)
     return model
 
@@ -242,8 +349,9 @@ def mixer_l32_224(pretrained=False, **kwargs):
 @register_model
 def mixer_l16_224(pretrained=False, **kwargs):
     """ Mixer-L/16 224x224. ImageNet-1k pretrained weights.
+    Paper:  'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
     """
-    model_args = dict(patch_size=16, num_blocks=24, hidden_dim=1024, tokens_dim=512, channels_dim=4096, **kwargs)
+    model_args = dict(patch_size=16, num_blocks=24, hidden_dim=1024, **kwargs)
     model = _create_mixer('mixer_l16_224', pretrained=pretrained, **model_args)
     return model
 
@@ -251,7 +359,101 @@ def mixer_l16_224(pretrained=False, **kwargs):
 @register_model
 def mixer_l16_224_in21k(pretrained=False, **kwargs):
     """ Mixer-L/16 224x224. ImageNet-21k pretrained weights.
+    Paper:  'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
     """
-    model_args = dict(patch_size=16, num_blocks=24, hidden_dim=1024, tokens_dim=512, channels_dim=4096, **kwargs)
+    model_args = dict(patch_size=16, num_blocks=24, hidden_dim=1024, **kwargs)
     model = _create_mixer('mixer_l16_224_in21k', pretrained=pretrained, **model_args)
     return model
+
+
+@register_model
+def gmixer_12_224(pretrained=False, **kwargs):
+    """ Glu-Mixer-12 224x224 (short & fat)
+    Experiment by Ross Wightman, adding (Si)GLU to MLP-Mixer
+    """
+    model_args = dict(
+        patch_size=20, num_blocks=12, hidden_dim=512, mlp_ratio=(1.0, 6.0),
+        mlp_layer=GluMlp, act_layer=nn.SiLU, **kwargs)
+    model = _create_mixer('gmixer_12_224', pretrained=pretrained, **model_args)
+    return model
+
+
+@register_model
+def gmixer_24_224(pretrained=False, **kwargs):
+    """ Glu-Mixer-24 224x224 (tall & slim)
+    Experiment by Ross Wightman, adding (Si)GLU to MLP-Mixer
+    """
+    model_args = dict(
+        patch_size=20, num_blocks=24, hidden_dim=384, mlp_ratio=(1.0, 6.0),
+        mlp_layer=GluMlp, act_layer=nn.SiLU, **kwargs)
+    model = _create_mixer('gmixer_24_224', pretrained=pretrained, **model_args)
+    return model
+
+
+@register_model
+def resmlp_12_224(pretrained=False, **kwargs):
+    """ ResMLP-12
+    Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
+    """
+    model_args = dict(
+        patch_size=16, num_blocks=12, hidden_dim=384, mlp_ratio=4, block_layer=ResBlock, norm_layer=Affine, **kwargs)
+    model = _create_mixer('resmlp_12_224', pretrained=pretrained, **model_args)
+    return model
+
+
+@register_model
+def resmlp_24_224(pretrained=False, **kwargs):
+    """ ResMLP-24
+    Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
+    """
+    model_args = dict(
+        patch_size=16, num_blocks=24, hidden_dim=384, mlp_ratio=4, block_layer=ResBlock, norm_layer=Affine, **kwargs)
+    model = _create_mixer('resmlp_24_224', pretrained=pretrained, **model_args)
+    return model
+
+
+@register_model
+def resmlp_36_224(pretrained=False, **kwargs):
+    """ ResMLP-36
+    Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
+    """
+    model_args = dict(
+        patch_size=16, num_blocks=36, hidden_dim=384, mlp_ratio=4, block_layer=ResBlock, norm_layer=Affine, **kwargs)
+    model = _create_mixer('resmlp_36_224', pretrained=pretrained, **model_args)
+    return model
+
+
+@register_model
+def gmlp_ti16_224(pretrained=False, **kwargs):
+    """ gMLP-Tiny
+    Paper: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050
+    """
+    model_args = dict(
+        patch_size=16, num_blocks=30, hidden_dim=128, mlp_ratio=6, block_layer=SpatialGatingBlock,
+        mlp_layer=GatedMlp, **kwargs)
+    model = _create_mixer('gmlp_ti16_224', pretrained=pretrained, **model_args)
+    return model
+
+
+@register_model
+def gmlp_s16_224(pretrained=False, **kwargs):
+    """ gMLP-Small
+    Paper: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050
+    """
+    model_args = dict(
+        patch_size=16, num_blocks=30, hidden_dim=256, mlp_ratio=6, block_layer=SpatialGatingBlock,
+        mlp_layer=GatedMlp, **kwargs)
+    model = _create_mixer('gmlp_s16_224', pretrained=pretrained, **model_args)
+    return model
+
+
+@register_model
+def gmlp_b16_224(pretrained=False, **kwargs):
+    """ gMLP-Base
+    Paper: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050
+    """
+    model_args = dict(
+        patch_size=16, num_blocks=30, hidden_dim=512, mlp_ratio=6, block_layer=SpatialGatingBlock,
+        mlp_layer=GatedMlp, **kwargs)
+    model = _create_mixer('gmlp_b16_224', pretrained=pretrained, **model_args)
+    return model