fix?

config.json

@@ -1,7 +1,7 @@
 {
-  "_name_or_path": "hf-internal-testing/tiny-random-resnet",
+  "_name_or_path": "/home/fxmarty/hf_internship/tiny-testing-remote-code",
   "architectures": [
-    "ResNetForImageClassification"
+    "ResNetCustomForImageClassification"
   ],
   "depths": [
     3,
@@ -2024,6 +2024,14 @@
   "layer_type": "bottleneck",
   "model_type": "resnet",
   "num_channels": 3,
+  "out_features": null,
+  "stage_names": [
+    "stem",
+    "stage1",
+    "stage2",
+    "stage3",
+    "stage4"
+  ],
   "torch_dtype": "float32",
-  "transformers_version": "4.22.0.dev0"
+  "transformers_version": "4.26.0.dev0"
 }

create_model.py

@@ -0,0 +1,8 @@
+from transformers import AutoConfig
+
+from modeling import ResNetCustomForImageClassification
+
+cfg = AutoConfig.from_pretrained("/home/fxmarty/hf_internship/tiny-testing-remote-code")
+model = ResNetCustomForImageClassification(cfg)
+
+model.save_pretrained("/home/fxmarty/hf_internship/tiny-testing-remote-code")

inference.py

@@ -0,0 +1,19 @@
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+import torch
+from datasets import load_dataset
+
+model = AutoModelForImageClassification.from_pretrained(".", trust_remote_code=True)
+
+dataset = load_dataset("huggingface/cats-image")
+image = dataset["test"]["image"][0]
+
+feature_extractor = AutoFeatureExtractor.from_pretrained(".")
+
+inputs = feature_extractor(image, return_tensors="pt")
+
+with torch.no_grad():
+    logits = model(**inputs).logits
+
+# model predicts one of the 1000 ImageNet classes
+predicted_label = logits.argmax(-1).item()
+print(model.config.id2label[predicted_label])

modeling.py → modeling_resnet.py

@@ -21,22 +21,22 @@ import torch.utils.checkpoint
 from torch import Tensor, nn
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
-from ...activations import ACT2FN
-from ...modeling_outputs import (
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (
     BackboneOutput,
     BaseModelOutputWithNoAttention,
     BaseModelOutputWithPoolingAndNoAttention,
     ImageClassifierOutputWithNoAttention,
 )
-from ...modeling_utils import BackboneMixin, PreTrainedModel
-from ...utils import (
+from transformers.modeling_utils import BackboneMixin, PreTrainedModel
+from transformers.utils import (
     add_code_sample_docstrings,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
     logging,
     replace_return_docstrings,
 )
-from .configuration_resnet import ResNetConfig
+from transformers import ResNetConfig
 
 
 logger = logging.get_logger(__name__)
@@ -356,7 +356,7 @@ class ResNetModel(ResNetPreTrainedModel):
     """,
     RESNET_START_DOCSTRING,
 )
-class ResNetForImageClassification(ResNetPreTrainedModel):
+class ResNetCustomForImageClassification(ResNetPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
         self.num_labels = config.num_labels

pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7e126c7921476a0e02a4aa614166508c9a4dcc15f4c6b3a7503ec8f324dd2e7c
-size 399041
+oid sha256:b41ec5a4bea6eee004ec1213ed11685f6d61f457f8e4ce91190dabb9b8edf680
+size 401037