Update huggingface model

Update huggingface model


Update README.md


Update README.md


Update README.md


Update huggingface model


Update huggingface model

classification/huggingface/test.py

+import torch
+from PIL import Image
+from transformers import (AutoConfig, AutoModel,
+                          AutoModelForImageClassification, CLIPImageProcessor)
+
+
+def test_model(model_name):
+    print('model_name:', model_name)
+    image_path = 'img_1.png'
+    image_processor = CLIPImageProcessor.from_pretrained(model_name)
+    image = Image.open(image_path)
+    image = image_processor(images=image, return_tensors='pt').pixel_values
+    print('image shape:', image.shape)
+
+    model = AutoModel.from_pretrained(model_name, trust_remote_code=True)
+    output = model(image)
+    for k, v in output.items():
+        if type(v) == list:
+            for idx, item in enumerate(v):
+                print(f'{k}_{idx} shape: {item.shape}')
+        elif v is None:
+            print(f'{k} is None')
+        else:
+            print(f'{k} shape: {v.shape}')
+
+    print('------------------------')
+
+    model = AutoModelForImageClassification.from_pretrained(model_name, trust_remote_code=True)
+    output = model(image)
+    for k, v in output.items():
+        if type(v) == list:
+            for idx, item in enumerate(v):
+                print(f'{k}_{idx} shape: {item.shape}')
+        elif v is None:
+            print(f'{k} is None')
+        else:
+            print(f'{k} shape: {v.shape}')
+    logits = output['logits']
+    argmax = int(torch.argmax(logits, dim=1))
+    print(argmax)
+
+
+test_model('./22k_model/internimage_l_22k_384')
+test_model('./22k_model/internimage_xl_22k_384')
+test_model('./22k_model/internimage_h_jointto22k_384')
+test_model('./22k_model/internimage_g_jointto22k_384')
+test_model('./in1k_model/internimage_t_1k_224')
+test_model('./in1k_model/internimage_s_1k_224')
+test_model('./in1k_model/internimage_b_1k_224')
+test_model('./in1k_model/internimage_l_22kto1k_384')
+test_model('./in1k_model/internimage_xl_22kto1k_384')
+test_model('./in1k_model/internimage_h_22kto1k_640')
+test_model('./in1k_model/internimage_g_22kto1k_512')

classification/models/intern_image.py

 # --------------------------------------------------------
 # InternImage
-# Copyright (c) 2022 OpenGVLab
+# Copyright (c) 2025 OpenGVLab
 # Licensed under The MIT License [see LICENSE for details]
 # --------------------------------------------------------
 
@@ -231,7 +231,7 @@ class AttentionPoolingBlock(AttentiveBlock):
 
 
 class StemLayer(nn.Module):
-    r""" Stem layer of InternImage
+    r"""Stem layer of InternImage
     Args:
         in_chans (int): number of input channels
         out_chans (int): number of output channels
@@ -271,7 +271,7 @@ class StemLayer(nn.Module):
 
 
 class DownsampleLayer(nn.Module):
-    r""" Downsample layer of InternImage
+    r"""Downsample layer of InternImage
     Args:
         channels (int): number of input channels
         norm_layer (str): normalization layer
@@ -295,7 +295,7 @@ class DownsampleLayer(nn.Module):
 
 
 class MLPLayer(nn.Module):
-    r""" MLP layer of InternImage
+    r"""MLP layer of InternImage
     Args:
         in_features (int): number of input features
         hidden_features (int): number of hidden features
@@ -328,7 +328,7 @@ class MLPLayer(nn.Module):
 
 
 class InternImageLayer(nn.Module):
-    r""" Basic layer of InternImage
+    r"""Basic layer of InternImage
     Args:
         core_op (nn.Module): core operation of InternImage
         channels (int): number of input channels
@@ -432,7 +432,7 @@ class InternImageLayer(nn.Module):
 
 
 class InternImageBlock(nn.Module):
-    r""" Block of InternImage
+    r"""Block of InternImage
     Args:
         core_op (nn.Module): core operation of InternImage
         channels (int): number of input channels
@@ -526,7 +526,7 @@ class InternImageBlock(nn.Module):
 
 
 class InternImage(nn.Module):
-    r""" InternImage
+    r"""InternImage
         A PyTorch impl of : `InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions`  -
           https://arxiv.org/pdf/2103.14030
     Args: