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[Re-Identification] End-to-End Inference Using Triton
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,19 @@ | ||
| name: "re_identification_tao" | ||
| platform: "tensorrt_plan" | ||
| max_batch_size: 16 | ||
| input [ | ||
| { | ||
| name: "input" | ||
| data_type: TYPE_FP32 | ||
| format: FORMAT_NCHW | ||
| dims: [ 3, 256, 128 ] | ||
| } | ||
| ] | ||
| output [ | ||
| { | ||
| name: "fc_pred" | ||
| data_type: TYPE_FP32 | ||
| dims: [ 256 ] | ||
| } | ||
| ] | ||
| dynamic_batching { } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,14 @@ | ||
| #!/bin/bash | ||
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| # Generate a re_identification model. | ||
| echo "Converting the re_identification model" | ||
| mkdir -p /model_repository/re_identification_tao/1 | ||
| tao-converter /tao_models/re_id_model/resnet50_market1501.etlt \ | ||
| -k nvidia_tao \ | ||
| -d 3,256,128 \ | ||
| -p input,1x3x256x128,4x3x256x128,16x3x256x128 \ | ||
| -o fc_pred \ | ||
| -t fp16 \ | ||
| -m 16 \ | ||
| -e /model_repository/re_identification_tao/1/model.plan | ||
| /opt/tritonserver/bin/tritonserver --model-store /model_repository |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,61 @@ | ||
| import torch | ||
| import re | ||
| import sys | ||
| import os | ||
| import json | ||
| import numpy as np | ||
| import matplotlib.pyplot as plt | ||
| from re_ranking import R1_mAP_reranking | ||
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| def main(): | ||
| if sys.argv[2]: | ||
| json_metadata_path = sys.argv[1] | ||
| output_dir = sys.argv[2] | ||
| f = open(json_metadata_path) | ||
| pattern = re.compile(r'([-\d]+)_c(\d)') | ||
| data = json.load(f) | ||
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| pids = [] | ||
| camids = [] | ||
| img_paths = [] | ||
| embeddings = [] | ||
| num_query = 0 | ||
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| for row in data: | ||
| img_path = row["img_path"] | ||
| if "query" in img_path: | ||
| num_query += 1 | ||
| embedding = row["embedding"] | ||
| pid, camid = map(int, pattern.search(img_path).groups()) | ||
| if pid == -1: continue # junk images are ignored | ||
| camid -= 1 # index starts from 0 | ||
| embeddings.append(embedding) | ||
| pids.append(pid) | ||
| camids.append(camid) | ||
| img_paths.append(img_path) | ||
| metrics = R1_mAP_reranking(num_query, output_dir, feat_norm=True) | ||
| metrics.reset() | ||
| metrics.update(torch.tensor(embeddings), pids, camids, img_paths) | ||
| cmc, _ = metrics.compute() | ||
| f.close() | ||
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| plt.figure() | ||
| cmc_percentages = [value * 100 for value in cmc] | ||
| plt.xticks(np.arange(len(cmc_percentages)), np.arange(1, len(cmc_percentages)+1)) | ||
| plt.plot(cmc_percentages, marker="*") | ||
| plt.title('Cumulative Matching Characteristics (CMC) curve') | ||
| plt.grid() | ||
| plt.ylabel('Matching Rate[%]') | ||
| plt.xlabel('Rank') | ||
| output_cmc_curve_plot_path = os.path.join(output_dir, 'cmc_curve.png') | ||
| plt.savefig(output_cmc_curve_plot_path) | ||
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| print("Output CMC curve plot saved at %s" % output_cmc_curve_plot_path) | ||
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| else: | ||
| print("Usage: %s json_metadata_path output_dir" % __file__) | ||
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| if __name__ == '__main__': | ||
| main() |
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