Fix gamma parameter

docs/source/model_doc/convnext.mdx

@@ -30,7 +30,7 @@ and outperforming Swin Transformers on COCO detection and ADE20K segmentation, w
 
 Tips:
 
-- One can use the [`AutoFeatureExtractor`] API to prepare images for the model.
+- See the code examples below each model regarding usage.
 
 This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/facebookresearch/ConvNeXt).
 

src/transformers/models/convnext/configuration_convnext.py

@@ -1,5 +1,5 @@
 # coding=utf-8
-# Copyright 2022 Facebook AI and The HuggingFace Inc. team. All rights reserved.
+# Copyright 2022 Meta Platforms, Inc. and The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -40,7 +40,7 @@ class ConvNextConfig(PretrainedConfig):
         num_channels (`int`, *optional*, defaults to 3):
             The number of input channels.
         patch_size (`int`, optional, defaults to 4):
-            Patch size to use in the stem layer.
+            Patch size to use in the patch embedding layer.
         num_stages (`int`, optional, defaults to 4):
             The number of stages in the model.
         hidden_sizes (`List[int]`, *optional*, defaults to [96, 192, 384, 768]):

src/transformers/models/convnext/convert_convnext_to_pytorch.py

@@ -102,7 +102,7 @@ def rename_key(name):
     if "norm" in name:
         name = name.replace("norm", "layernorm")
     if "gamma" in name:
-        name = name.replace("gamma", "gamma_parameter")
+        name = name.replace("gamma", "layer_scale_parameter")
     if "head" in name:
         name = name.replace("head", "classifier")
 

src/transformers/models/convnext/modeling_convnext.py

@@ -1,5 +1,5 @@
 # coding=utf-8
-# Copyright 2022 Facebook AI and The HuggingFace Inc. team. All rights reserved.
+# Copyright 2022 Meta Platforms, Inc. and The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -164,7 +164,7 @@ def forward(self, x):
 
 
 class ConvNextEmbeddings(nn.Module):
-    """This class is comparable to (and inspired by) the SwinPatchEmbeddings class
+    """This class is comparable to (and inspired by) the SwinEmbeddings class
     found in src/transformers/models/swin/modeling_swin.py.
     """
 
@@ -202,7 +202,7 @@ def __init__(self, config, dim, drop_path=0):
         self.pwconv1 = nn.Linear(dim, 4 * dim)  # pointwise/1x1 convs, implemented with linear layers
         self.act = ACT2FN[config.hidden_act]
         self.pwconv2 = nn.Linear(4 * dim, dim)
-        self.gamma_parameter = (
+        self.layer_scale_parameter = (
             nn.Parameter(config.layer_scale_init_value * torch.ones((dim)), requires_grad=True)
             if config.layer_scale_init_value > 0
             else None
@@ -217,8 +217,8 @@ def forward(self, hidden_states):
         x = self.pwconv1(x)
         x = self.act(x)
         x = self.pwconv2(x)
-        if self.gamma_parameter is not None:
-            x = self.gamma_parameter * x
+        if self.layer_scale_parameter is not None:
+            x = self.layer_scale_parameter * x
         x = x.permute(0, 3, 1, 2)  # (N, H, W, C) -> (N, C, H, W)
 
         x = input + self.drop_path(x)

tests/test_modeling_convnext.py

@@ -335,9 +335,6 @@ def test_inference_image_classification_head(self):
         expected_shape = torch.Size((1, 1000))
         self.assertEqual(outputs.logits.shape, expected_shape)
 
-        print("Predicted class:", model.config.id2label[torch.argmax(outputs.logits, dim=-1).item()])
-        print("Logits:", outputs.logits[0, :3])
-
-        expected_slice = torch.tensor([-0.0750, 0.2478, 0.5982]).to(torch_device)
+        expected_slice = torch.tensor([-0.1210, -0.6605, 0.1918]).to(torch_device)
 
         self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))