Make k=stride=2 ('avg2') pooling default for coatnet/maxvit. Add weig…

…ht links. Rename 'combined' partition to 'parallel'.

timm/models/maxxvit.py

@@ -74,26 +74,26 @@ def _cfg(url='', **kwargs):
     # Fiddling with configs / defaults / still pretraining
     'coatnet_pico_rw_224': _cfg(url=''),
     'coatnet_nano_rw_224': _cfg(
-        url='',
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/coatnet_nano_rw_224_sw-f53093b4.pth',
         crop_pct=0.9),
     'coatnet_0_rw_224': _cfg(
-        url=''),
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/coatnet_0_rw_224_sw-a6439706.pth'),
     'coatnet_1_rw_224': _cfg(
-        url=''
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/coatnet_1_rw_224_sw-5cae1ea8.pth'
     ),
     'coatnet_2_rw_224': _cfg(url=''),
 
     # Highly experimental configs
     'coatnet_bn_0_rw_224': _cfg(
-        url='',
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/coatnet_bn_0_rw_224_sw-c228e218.pth',
         mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD,
         crop_pct=0.95),
     'coatnet_rmlp_nano_rw_224': _cfg(
-        url='',
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/coatnet_rmlp_nano_rw_224_sw-bd1d51b3.pth',
         crop_pct=0.9),
     'coatnet_rmlp_0_rw_224': _cfg(url=''),
     'coatnet_rmlp_1_rw_224': _cfg(
-        url=''),
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/coatnet_rmlp_1_rw_224_sw-9051e6c3.pth'),
     'coatnet_nano_cc_224': _cfg(url=''),
     'coatnext_nano_rw_224': _cfg(url=''),
 
@@ -107,10 +107,12 @@ def _cfg(url='', **kwargs):
 
     # Experimental configs
     'maxvit_pico_rw_256': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
-    'maxvit_nano_rw_256': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
+    'maxvit_nano_rw_256': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/maxvit_nano_rw_256_sw-3e790ce3.pth',
+        input_size=(3, 256, 256), pool_size=(8, 8)),
     'maxvit_tiny_rw_224': _cfg(url=''),
     'maxvit_tiny_rw_256': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
-    'maxvit_tiny_cm_256': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
+    'maxvit_tiny_pm_256': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
     'maxxvit_nano_rw_256': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
 
     # Trying to be like the MaxViT paper configs
@@ -131,7 +133,7 @@ class MaxxVitTransformerCfg:
     attn_bias: bool = True
     attn_drop: float = 0.
     proj_drop: float = 0.
-    pool_type: str = 'avg'
+    pool_type: str = 'avg2'
     rel_pos_type: str = 'bias'
     rel_pos_dim: int = 512  # for relative position types w/ MLP
     window_size: Tuple[int, int] = (7, 7)
@@ -153,7 +155,7 @@ class MaxxVitConvCfg:
     pre_norm_act: bool = False  # activation after pre-norm
     output_bias: bool = True  # bias for shortcut + final 1x1 projection conv
     stride_mode: str = 'dw'  # stride done via one of 'pool', '1x1', 'dw'
-    pool_type: str = 'avg'
+    pool_type: str = 'avg2'
     downsample_pool_type: str = 'avg2'
     attn_early: bool = False  # apply attn between conv2 and norm2, instead of after norm2
     attn_layer: str = 'se'
@@ -241,7 +243,7 @@ def _rw_coat_cfg(
 
 def _rw_max_cfg(
         stride_mode='dw',
-        pool_type='avg',
+        pool_type='avg2',
         conv_output_bias=False,
         conv_attn_ratio=1 / 16,
         conv_norm_layer='',
@@ -325,7 +327,6 @@ def _next_cfg(
         depths=(2, 3, 5, 2),
         stem_width=(32, 64),
         **_rw_max_cfg(  # using newer max defaults here
-            pool_type='avg2',
             conv_output_bias=True,
             conv_attn_ratio=0.25,
         ),
@@ -336,7 +337,6 @@ def _next_cfg(
         stem_width=(32, 64),
         **_rw_max_cfg(  # using newer max defaults here
             stride_mode='pool',
-            pool_type='avg2',
             conv_output_bias=True,
             conv_attn_ratio=0.25,
         ),
@@ -384,7 +384,6 @@ def _next_cfg(
         depths=(3, 4, 6, 3),
         stem_width=(32, 64),
         **_rw_max_cfg(
-            pool_type='avg2',
             conv_output_bias=True,
             conv_attn_ratio=0.25,
             rel_pos_type='mlp',
@@ -487,10 +486,10 @@ def _next_cfg(
         stem_width=(32, 64),
         **_rw_max_cfg(window_size=8),
     ),
-    maxvit_tiny_cm_256=MaxxVitCfg(
+    maxvit_tiny_pm_256=MaxxVitCfg(
         embed_dim=(64, 128, 256, 512),
         depths=(2, 2, 5, 2),
-        block_type=('CM',) * 4,
+        block_type=('PM',) * 4,
         stem_width=(32, 64),
         **_rw_max_cfg(window_size=8),
     ),
@@ -663,13 +662,15 @@ def __init__(
             bias: bool = True,
     ):
         super().__init__()
-        assert pool_type in ('max', 'avg', 'avg2')
+        assert pool_type in ('max', 'max2', 'avg', 'avg2')
         if pool_type == 'max':
             self.pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        elif pool_type == 'max2':
+            self.pool = nn.MaxPool2d(2)  # kernel_size == stride == 2
         elif pool_type == 'avg':
             self.pool = nn.AvgPool2d(kernel_size=3, stride=2, padding=1, count_include_pad=False)
         else:
-            self.pool = nn.AvgPool2d(2)
+            self.pool = nn.AvgPool2d(2)  # kernel_size == stride == 2
 
         if dim != dim_out:
             self.expand = nn.Conv2d(dim, dim_out, 1, bias=bias)
@@ -1073,7 +1074,7 @@ def forward(self, x):
         return x
 
 
-class CombinedPartitionAttention(nn.Module):
+class ParallelPartitionAttention(nn.Module):
     """ Experimental. Grid and Block partition + single FFN
     NxC tensor layout.
     """
@@ -1286,7 +1287,7 @@ def forward(self, x):
         return x
 
 
-class CombinedMaxxVitBlock(nn.Module):
+class ParallelMaxxVitBlock(nn.Module):
     """
     """
 
@@ -1309,7 +1310,7 @@ def __init__(
             self.conv = nn.Sequential(*convs)
         else:
             self.conv = conv_cls(dim, dim_out, stride=stride, cfg=conv_cfg, drop_path=drop_path)
-        self.attn = CombinedPartitionAttention(dim=dim_out, cfg=transformer_cfg, drop_path=drop_path)
+        self.attn = ParallelPartitionAttention(dim=dim_out, cfg=transformer_cfg, drop_path=drop_path)
 
     def init_weights(self, scheme=''):
         named_apply(partial(_init_transformer, scheme=scheme), self.attn)
@@ -1343,7 +1344,7 @@ def __init__(
         blocks = []
         for i, t in enumerate(block_types):
             block_stride = stride if i == 0 else 1
-            assert t in ('C', 'T', 'M', 'CM')
+            assert t in ('C', 'T', 'M', 'PM')
             if t == 'C':
                 conv_cls = ConvNeXtBlock if conv_cfg.block_type == 'convnext' else MbConvBlock
                 blocks += [conv_cls(
@@ -1372,8 +1373,8 @@ def __init__(
                     transformer_cfg=transformer_cfg,
                     drop_path=drop_path[i],
                 )]
-            elif t == 'CM':
-                blocks += [CombinedMaxxVitBlock(
+            elif t == 'PM':
+                blocks += [ParallelMaxxVitBlock(
                     in_chs,
                     out_chs,
                     stride=block_stride,
@@ -1415,7 +1416,6 @@ def __init__(
         self.norm1 = norm_act_layer(out_chs[0])
         self.conv2 = create_conv2d(out_chs[0], out_chs[1], kernel_size, stride=1)
 
-    @torch.jit.ignore
     def init_weights(self, scheme=''):
         named_apply(partial(_init_conv, scheme=scheme), self)
 
@@ -1659,8 +1659,8 @@ def maxvit_tiny_rw_256(pretrained=False, **kwargs):
 
 
 @register_model
-def maxvit_tiny_cm_256(pretrained=False, **kwargs):
-    return _create_maxxvit('maxvit_tiny_cm_256', pretrained=pretrained, **kwargs)
+def maxvit_tiny_pm_256(pretrained=False, **kwargs):
+    return _create_maxxvit('maxvit_tiny_pm_256', pretrained=pretrained, **kwargs)
 
 
 @register_model