vit, tresnet and mobilenetV3 ImageNet-21K-P weights

timm/models/mobilenetv3.py

@@ -39,6 +39,12 @@ default_cfgs = {
     'mobilenetv3_large_100': _cfg(
         interpolation='bicubic',
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_large_100_ra-f55367f5.pth'),
+    'mobilenetv3_large_100_1k_miil_77_9': _cfg(
+        interpolation='bilinear', mean=(0, 0, 0), std=(1, 1, 1),
+        url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/mobilenetv3_large_100_1k_miil_77_9.pth'),
+    'mobilenetv3_large_100_21k_miil': _cfg(
+        interpolation='bilinear', mean=(0, 0, 0), std=(1, 1, 1),
+        url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/mobilenetv3_large_100_21k_miil.pth', num_classes=11221),
     'mobilenetv3_small_075': _cfg(url=''),
     'mobilenetv3_small_100': _cfg(url=''),
     'mobilenetv3_rw': _cfg(
@@ -367,6 +373,20 @@ def mobilenetv3_large_100(pretrained=False, **kwargs):
     return model
 
 
+@register_model
+def mobilenetv3_large_100_1k_miil(pretrained=False, **kwargs):
+    """ MobileNet V3 """
+    model = _gen_mobilenet_v3('mobilenetv3_large_100_1k_miil_77_9', 1.0, pretrained=pretrained, **kwargs)
+    return model
+
+
+@register_model
+def mobilenetv3_large_100_21k_miil(pretrained=False, **kwargs):
+    """ MobileNet V3 """
+    model = _gen_mobilenet_v3('mobilenetv3_large_100_21k_miil', 1.0, pretrained=pretrained, **kwargs)
+    return model
+
+
 @register_model
 def mobilenetv3_small_075(pretrained=False, **kwargs):
     """ MobileNet V3 """

timm/models/tresnet.py

@@ -32,7 +32,9 @@ def _cfg(url='', **kwargs):
 
 default_cfgs = {
     'tresnet_m': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_m_80_8-dbc13962.pth'),
+        url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/tresnet_m_1k_miil_83_1.pth'),
+    'tresnet_m_21k_miil': _cfg(
+        url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/tresnet_m_miil_21k.pth', num_classes=11221),
     'tresnet_l': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_l_81_5-235b486c.pth'),
     'tresnet_xl': _cfg(
@@ -264,6 +266,10 @@ def tresnet_m(pretrained=False, **kwargs):
     model_kwargs = dict(layers=[3, 4, 11, 3], **kwargs)
     return _create_tresnet('tresnet_m', pretrained=pretrained, **model_kwargs)
 
+@register_model
+def tresnet_m_21k_miil(pretrained=False, **kwargs):
+    model_kwargs = dict(layers=[3, 4, 11, 3], **kwargs)
+    return _create_tresnet('tresnet_m_21k_miil', pretrained=pretrained, **model_kwargs)
 
 @register_model
 def tresnet_l(pretrained=False, **kwargs):

timm/models/vision_transformer.py

@@ -118,6 +118,17 @@ default_cfgs = {
     'vit_deit_base_distilled_patch16_384': _cfg(
         url='https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_384-d0272ac0.pth',
         input_size=(3, 384, 384), crop_pct=1.0, classifier=('head', 'head_dist')),
+
+    # ViT ImageNet-21K-P pretraining
+    'vit_base_patch16_224_21k_miil': _cfg(
+        url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/vit_base_patch16_224_21k_miil.pth',
+        mean=(0, 0, 0), std=(1, 1, 1), crop_pct=0.875, interpolation='bilinear', num_classes=11221,
+    ),
+    'vit_base_patch16_224_1k_miil': _cfg(
+        url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm'
+            '/vit_base_patch16_224_1k_miil_84_4.pth',
+        mean=(0, 0, 0), std=(1, 1, 1), crop_pct=0.875, interpolation='bilinear',
+    ),
 }
 
 
@@ -155,7 +166,7 @@ class Attention(nn.Module):
     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]   # make torchscript happy (cannot use tensor as tuple)
+        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
 
         attn = (q @ k.transpose(-2, -1)) * self.scale
         attn = attn.softmax(dim=-1)
@@ -652,7 +663,7 @@ def vit_deit_tiny_distilled_patch16_224(pretrained=False, **kwargs):
     """
     model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
     model = _create_vision_transformer(
-        'vit_deit_tiny_distilled_patch16_224', pretrained=pretrained,  distilled=True, **model_kwargs)
+        'vit_deit_tiny_distilled_patch16_224', pretrained=pretrained, distilled=True, **model_kwargs)
     return model
 
 
@@ -663,7 +674,7 @@ def vit_deit_small_distilled_patch16_224(pretrained=False, **kwargs):
     """
     model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
     model = _create_vision_transformer(
-        'vit_deit_small_distilled_patch16_224', pretrained=pretrained,  distilled=True, **model_kwargs)
+        'vit_deit_small_distilled_patch16_224', pretrained=pretrained, distilled=True, **model_kwargs)
     return model
 
 
@@ -674,7 +685,7 @@ def vit_deit_base_distilled_patch16_224(pretrained=False, **kwargs):
     """
     model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
     model = _create_vision_transformer(
-        'vit_deit_base_distilled_patch16_224', pretrained=pretrained,  distilled=True, **model_kwargs)
+        'vit_deit_base_distilled_patch16_224', pretrained=pretrained, distilled=True, **model_kwargs)
     return model
 
 
@@ -687,3 +698,21 @@ def vit_deit_base_distilled_patch16_384(pretrained=False, **kwargs):
     model = _create_vision_transformer(
         'vit_deit_base_distilled_patch16_384', pretrained=pretrained, distilled=True, **model_kwargs)
     return model
+
+@register_model
+def vit_base_patch16_224_21k_miil(pretrained=False, **kwargs):
+    """ ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
+    Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K
+    """
+    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, **kwargs)
+    model = _create_vision_transformer('vit_base_patch16_224_21k_miil', pretrained=pretrained, **model_kwargs)
+    return model
+
+@register_model
+def vit_base_patch16_224_1k_miil(pretrained=False, **kwargs):
+    """ ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
+    Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K
+    """
+    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, **kwargs)
+    model = _create_vision_transformer('vit_base_patch16_224_1k_miil_84_4', pretrained=pretrained, **model_kwargs)
+    return model