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First Version of Gender Classification demo

app.py

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+from custom_torch_module.deploy_utils import Onnx_deploy_model
+import gradio as gr
+import time
+from PIL import Image
+
+model_path = "deploying model/" + "vit_xsmall_patch16_clip_224(trainble_0.15) (Acc 98.44%, Loss 0.168152).onnx"
+input_size = [1, 3, 224, 224]
+img_size = input_size[-1]
+
+title = "Gender Vision mini"
+description = "An ViT(xsmall_clip) based model(fine tuned with Custom dataset : around 800 train images & 200 test iamges) Accuracy : around 98.4% with the custom test dataset. Optimized with ONNX(around 1.7 times faster than PyTorch version on cpu)"
+article = "Through bunch of fine tuning and experiments. !REMEMBER! This model can be wrong."
+
+def predict(img):
+    start_time = time.time()
+    output = onnx_model.run(img, return_prob=True)
+    end_time = time.time()
+    elapsed_time = end_time - start_time
+
+    pred_label_and_probs = {"Men" : output[0],"Women" : output[1]}
+
+    return pred_label_and_probs, elapsed_time
+
+onnx_model = Onnx_deploy_model(model_path=model_path, img_size=img_size)
+
+# Create the Gradio demo
+demo = gr.Interface(fn=predict, 
+                    inputs=gr.Image(type="pil"),
+                    outputs=[gr.Label(num_top_classes=2, label="Predictions"),
+                             gr.Number(label="Prediction time (s)")], 
+                    title=title,
+                    description=description,
+                    article=article)
+
+# Launch the demo
+demo.launch()

custom_torch_module/deploy_utils.py

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+import torch
+import timm
+import numpy as np
+import onnx
+import onnxruntime
+from PIL import Image
+
+def export_onnx(model, weight_path, export_path, input_size:list, device="cpu"):
+    """
+    Save model with weights as onnx file
+    """
+    torch.set_default_device(device)
+    
+    weights = torch.load(f=weight_path)
+    model.load_state_dict(weights)
+    model.eval()
+    
+    example_input = torch.empty(input_size)
+    
+    # 모델 변환
+    torch.onnx.export(model,
+                      example_input,
+                      export_path,
+                      export_params=True,
+                      do_constant_folding=True,
+                      input_names = ['input'],
+                      output_names = ['output'],
+                      dynamic_axes={'input' : {0 : 'batch_size'},
+                                    'output' : {0 : 'batch_size'}})
+    print("[info] The model has succesfull exported.")
+    print(f"[info] File Path : {export_path}")
+
+class Onnx_deploy_model():
+    def __init__(self, model_path, img_size):
+        onnx_model = onnx.load(model_path)
+        onnx.checker.check_model(onnx_model)
+        
+        self.ort_session = onnxruntime.InferenceSession(model_path)
+        self.transform = build_transform(img_size)
+        
+    def run(self, x, return_prob=True):
+        """
+        input : Image(PIL or Numpy)
+        output : prob or logits
+        """
+        # img = Image.open(x).convert("RGB")
+        x = self.transform(x).unsqueeze(dim=0)
+        ort_inputs = {self.ort_session.get_inputs()[0].name: to_numpy(x)}
+        ort_outputs = self.ort_session.run(None, ort_inputs)
+        
+        if return_prob:
+            ort_outputs = softmax(ort_outputs)
+        
+        return ort_outputs.squeeze()
+
+def to_numpy(tensor):
+    return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
+
+def softmax(x):
+    max_num = np.max(x)   
+    exp_a = np.exp(x - max_num) # to prevent OverFlow
+    sum_exp_a = np.sum(exp_a)
+    y = exp_a / sum_exp_a
+
+    return y
+
+def build_transform(input_size,interpolation="bicubic"):
+    return timm.data.create_transform(input_size=input_size, interpolation=interpolation, is_training=False)
+
+
+

deploying model/vit_xsmall_patch16_clip_224(trainble_0.15) (Acc 98.44%, Loss 0.168152).onnx

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