fast-reid/tools/deploy/README.md

Deployment

This directory contains:

1. A script that converts a fastreid model to Caffe format.

2. An exmpale that loads a R50 baseline model in Caffe and run inference.

Tutorial

This is a tiny example steps for convert baseline meta_arch to Caffe model, if you want to convert more complext architecture, you need to customize more things.

1. Change preprocess_image in fastreid/modeling/meta_arch/baseline.py as below

   def preprocess_image(self, batched_inputs):
       """
       Normalize and batch the input images.
       """
       # images = [x["images"] for x in batched_inputs]
       # images = batched_inputs["images"]
       images = batched_inputs
       images.sub_(self.pixel_mean).div_(self.pixel_std)
       return images
   

2. Run caffe_export.py to get the converted Caffe model,

   python caffe_export.py --config-file "/export/home/lxy/fast-reid/logs/market1501/bagtricks_R50/config.yaml" --name "baseline_R50" --output "logs/caffe_model" --opts MODEL.WEIGHTS "/export/home/lxy/fast-reid/logs/market1501/bagtricks_R50/model_final.pth"
   

   then you can check the Caffe model and prototxt in logs/caffe_model.

3. Change prototxt following next three steps:

   1. Edit max_pooling in baseline_R50.prototxt like this

      layer {
          name: "max_pool1"
          type: "Pooling"
          bottom: "relu_blob1"
          top: "max_pool_blob1"
          pooling_param {
              pool: MAX
              kernel_size: 3
              stride: 2
              pad: 0 # 1
              # ceil_mode: false
          }
      }
      

   2. Add avg_pooling right place in baseline_R50.prototxt

      layer {
          name: "avgpool1"
          type: "Pooling"
          bottom: "relu_blob49"
          top: "avgpool_blob1"
          pooling_param {
              pool: AVE
              global_pooling: true
          }
      }
      

   3. Change the last layer top name to output

      layer {
          name: "bn_scale54"
          type: "Scale"
          bottom: "batch_norm_blob54"
          top: "output" # bn_norm_blob54
          scale_param {
              bias_term: true
          }
      }
      

4. (optional) You can open Netscope, then enter you network prototxt to visualize the network.

5. Run caffe_inference.py to save Caffe model features with input images

    python caffe_inference.py --model-def "logs/caffe_model/baseline_R50.prototxt" \
    --model-weights "logs/caffe_model/baseline_R50.caffemodel" \
    --input \
    '/export/home/DATA/Market-1501-v15.09.15/bounding_box_test/1182_c5s3_015240_04.jpg' \
    '/export/home/DATA/Market-1501-v15.09.15/bounding_box_test/1182_c6s3_038217_01.jpg' \
    '/export/home/DATA/Market-1501-v15.09.15/bounding_box_test/1183_c5s3_006943_05.jpg' \
    --output "caffe_R34_output"
   

6. Run demo/demo.py to get fastreid model features with the same input images, then compute the cosine similarity of difference model features to verify if you convert Caffe model successfully.

Acknowledgements

Thank to CPFLAME, , YuxiangJohn and at JDAI Model Acceleration Group for help in PyTorch to Caffe model converting.