Skip to content

Latest commit

 

History

History
54 lines (39 loc) · 2.58 KB

File metadata and controls

54 lines (39 loc) · 2.58 KB

English | 简体中文

ResNet Ready-to-deploy Model

Export the ONNX Model

Import Torchvision, load the pre-trained model, and conduct model transformation as the following steps.

  import torch
  import torchvision.models as models

  model = models.resnet50(pretrained=True)
  batch_size = 1  #Batch size
  input_shape = (3, 224, 224)   #Input data, and change to personal input shape
  # #set the model to inference mode
  model.eval()
  x = torch.randn(batch_size, *input_shape)	# Generate tensor
  export_onnx_file = "ResNet50.onnx"			# Purpose ONNX file name
  torch.onnx.export(model,
                      x,
                      export_onnx_file,
                      opset_version=12,
                      input_names=["input"],	# Input name
                      output_names=["output"],	# Output name
                      dynamic_axes={"input":{0:"batch_size"},  # Batch variables
                                      "output":{0:"batch_size"}})

Download Pre-trained ONNX Model

For developers' testing, models exported by ResNet are provided below. Developers can download them directly. (The model accuracy in the following table is derived from the source official repository)

Model Size Accuracy
ResNet-18 45MB
ResNet-34 84MB
ResNet-50 98MB
ResNet-101 170MB

Detailed Deployment Documents

Release Note