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config.json

@@ -1,12 +1,10 @@
 {
-  "_name_or_path": "./efficientnet/temp",
   "architectures": [
-    "EfficientNetModelForImageClassification"
+    "EfficientNetModel"
   ],
   "auto_map": {
     "AutoConfig": "configuration_efficientnet.EfficientNetConfig",
-    "AutoModel": "modeling_efficientnet.EfficientNetModel",
-    "AutoModelForImageClassification": "modeling_efficientnet.EfficientNetModelForImageClassification"
+    "AutoModel": "modeling_efficientnet.EfficientNetModel"
   },
   "model_name": "efficientnet_b0",
   "model_type": "efficientnet",

configuration_efficientnet.py

@@ -34,19 +34,78 @@ class EfficientNetConfig(PretrainedConfig):
         
         self.model_name = model_name
         self.pretrained = pretrained
+
+        # add attributes
+        # "batch_norm_eps": 0.001,
+        # "batch_norm_momentum": 0.99,
+        # "depth_coefficient": 3.1,
+        # "depth_divisor": 8,
+        # "depthwise_padding": [],
+        # "drop_connect_rate": 0.2,
+        # "dropout_rate": 0.5,
+        # "expand_ratios": [
+        # 1,
+        # 6,
+        # 6
+        # ],
+        # "hidden_act": "gelu",
+        # "hidden_dim": 2560,
+        # "id2label": { IMAGE NET DATASET
+        # "0": "LABEL_0",
+        # "1": "LABEL_1",
+        # "2": "LABEL_2",
+        # ...
+        # },
+        # "image_size": 600,
+        # "in_channels": [
+        # 32,
+        # 16,
+        # 24
+        # ],
+        # "initializer_range": 0.02,
+        # "kernel_sizes": [
+        # 3,
+        # 3,
+        # 5
+        # ],
+        # "label2id": {
+        # "LABEL_0": 0,
+        # "LABEL_1": 1,
+        # "LABEL_2": 2,
+        # ...
+        # },
+        # "model_type": "efficientnet",
+        # "num_block_repeats": [
+        # 1,
+        # 1,
+        # 2
+        # ],
+        # "num_channels": 3,
+        # "num_hidden_layers": 16,
+        # "out_channels": [
+        # 16,
+        # 24,
+        # 40
+        # ],
+        # "pooling_type": "mean",
+        # "squeeze_expansion_ratio": 0.25,
+        # "strides": [
+        # 1,
+        # 1,
+        # 2
+        # ],
+        # "width_coefficient": 2.0
+        # }
        
         super().__init__(**kwargs)
 
 class EfficientNetOnnxConfig(ViTOnnxConfig):
-
     @property
     def outputs(self) -> Dict[str, Dict[int, str]]:
         common_outputs = super().outputs 
 
         if self.task == "image-classification":
             common_outputs["logits"] = {0: "batch_size", 1: "num_classes"}
-        elif self.task == "feature-extraction":
-            common_outputs["last_hidden_state"] = {0: "batch_size", 1: "num_features", 2: "height", 3: "width"}
         
         return common_outputs
     

modeling_efficientnet.py

@@ -13,6 +13,7 @@ class EfficientNetModel(PreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
 
+        self.config = config
         self.model = create_model(config.model_name, pretrained = config.pretrained)
     
     def forward(self, pixel_values):
@@ -27,6 +28,7 @@ class EfficientNetModelForImageClassification(PreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
 
+        self.config = config
         self.model = create_model(config.model_name, pretrained = config.pretrained)
     
     def forward(self, pixel_values, labels=None):