Merge pull request #1919 from ChengpengChen/main

Add RepGhost models and weights

tests/test_models.py

@@ -175,7 +175,7 @@ def test_model_default_cfgs(model_name, batch_size):
         outputs = model.forward_features(input_tensor)
         assert outputs.shape[spatial_axis[0]] == pool_size[0], 'unpooled feature shape != config'
         assert outputs.shape[spatial_axis[1]] == pool_size[1], 'unpooled feature shape != config'
-        if not isinstance(model, (timm.models.MobileNetV3, timm.models.GhostNet, timm.models.VGG)):
+        if not isinstance(model, (timm.models.MobileNetV3, timm.models.GhostNet, timm.models.RepGhostNet, timm.models.VGG)):
             assert outputs.shape[feat_axis] == model.num_features
 
         # test forward after deleting the classifier, output should be poooled, size(-1) == model.num_features
@@ -188,16 +188,16 @@ def test_model_default_cfgs(model_name, batch_size):
         model.reset_classifier(0, '')  # reset classifier and set global pooling to pass-through
         outputs = model.forward(input_tensor)
         assert len(outputs.shape) == 4
-        if not isinstance(model, (timm.models.MobileNetV3, timm.models.GhostNet, timm.models.VGG)):
-            # mobilenetv3/ghostnet/vgg forward_features vs removed pooling differ due to location or lack of GAP
+        if not isinstance(model, (timm.models.MobileNetV3, timm.models.GhostNet, timm.models.RepGhostNet, timm.models.VGG)):
+            # mobilenetv3/ghostnet/repghostnet/vgg forward_features vs removed pooling differ due to location or lack of GAP
             assert outputs.shape[spatial_axis[0]] == pool_size[0] and outputs.shape[spatial_axis[1]] == pool_size[1]
 
         if 'pruned' not in model_name:  # FIXME better pruned model handling
             # test classifier + global pool deletion via __init__
             model = create_model(model_name, pretrained=False, num_classes=0, global_pool='').eval()
             outputs = model.forward(input_tensor)
             assert len(outputs.shape) == 4
-            if not isinstance(model, (timm.models.MobileNetV3, timm.models.GhostNet, timm.models.VGG)):
+            if not isinstance(model, (timm.models.MobileNetV3, timm.models.GhostNet, timm.models.RepGhostNet, timm.models.VGG)):
                 assert outputs.shape[spatial_axis[0]] == pool_size[0] and outputs.shape[spatial_axis[1]] == pool_size[1]
 
     # check classifier name matches default_cfg

timm/models/__init__.py

@@ -42,6 +42,7 @@
 from .pnasnet import *
 from .pvt_v2 import *
 from .regnet import *
+from .repghost import *
 from .repvit import *
 from .res2net import *
 from .resnest import *