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 for converting fastreid-baseline in meta_arch to Caffe model, if you want to convert more complex 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, gcong18, YuxiangJohn and wiggin66 at JDAI Model Acceleration Group for help in PyTorch to Caffe model converting.