From 165f055d049537c72c63a2c1a57792d882969abb Mon Sep 17 00:00:00 2001 From: littletomatodonkey Date: Thu, 21 Apr 2022 10:51:44 +0800 Subject: [PATCH] add inference time info and more models link (#5504) * add inference time info and more models link * rm unused images * fix infer for image dir * fix readme --- tutorials/pp-series/HRNet-Keypoint/README.md | 38 +++-- .../lite_hrnet_30_256x192_coco_dist_pact.yml | 150 ++++++++++++++++++ .../lite_hrnet_30_256x192_coco_pact.yml | 4 +- .../dataset/test_image/000000000785.jpg | Bin 0 -> 133674 bytes .../dataset/test_image/000000397133.jpg | Bin 200576 -> 0 bytes .../pp-series/HRNet-Keypoint/deploy/infer.py | 6 +- 6 files changed, 183 insertions(+), 15 deletions(-) create mode 100644 tutorials/pp-series/HRNet-Keypoint/configs/lite_hrnet_30_256x192_coco_dist_pact.yml create mode 100644 tutorials/pp-series/HRNet-Keypoint/dataset/test_image/000000000785.jpg delete mode 100755 tutorials/pp-series/HRNet-Keypoint/dataset/test_image/000000397133.jpg diff --git a/tutorials/pp-series/HRNet-Keypoint/README.md b/tutorials/pp-series/HRNet-Keypoint/README.md index 0d733d20..9f1b86c1 100644 --- a/tutorials/pp-series/HRNet-Keypoint/README.md +++ b/tutorials/pp-series/HRNet-Keypoint/README.md @@ -129,6 +129,10 @@ python tools/train.py -c configs/lite_hrnet_30_256x192_coco_pact.yml export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m paddle.distributed.launch --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/lite_hrnet_30_256x192_coco_pact.yml +# training with PACT quantization on multi-GPU using distilled trained model +export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +python -m paddle.distributed.launch --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/lite_hrnet_30_256x192_coco_dist_pact.yml + # GPU evaluation with PACT quantization export CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/lite_hrnet_30_256x192_coco_pact.yml -o weights=https://paddle-model-ecology.bj.bcebos.com/model/hrnet_pose/lite_hrnet_30_256x192_coco_pact.pdparams @@ -163,23 +167,37 @@ python deploy/infer.py --model_dir=output_inference/lite_hrnet_30_256x192_coco_ ``` ## 3 Result -COCO Dataset -| Model | Input Size | AP(%, coco val) | Model Download | Config File | Inference model size | -| :----------: | -------- | :----------: | :------------: | :---: | :---: | -| HRNet-w32 | 256x192 | 76.9 | [hrnet_w32_256x192.pdparams](https://paddle-model-ecology.bj.bcebos.com/model/hrnet_pose/hrnet_w32_256x192.pdparams) | [config](./configs/hrnet_w32_256x192.yml) | 118M | -| LiteHRNet-30 | 256x192 | 69.4 | [lite_hrnet_30_256x192_coco.pdparams](https://paddle-model-ecology.bj.bcebos.com/model/hrnet_pose/lite_hrnet_30_256x192_coco.pdparams) | [config](./configs/lite_hrnet_30_256x192_coco.yml) | 26M | -| LiteHRNet-30-distillation | 256x192 | 69.9 |[lite_hrnet_30_256x192_coco_dist.pdparams](https://paddle-model-ecology.bj.bcebos.com/model/hrnet_pose/lite_hrnet_30_256x192_coco_dist.pdparams) | [config](./configs/lite_hrnet_30_256x192_coco.yml) | 26M | -| LiteHRNet-30-PACT | 256x192 | 68.9 | [lite_hrnet_30_256x192_coco_pact.pdparams](https://paddle-model-ecology.bj.bcebos.com/model/hrnet_pose/lite_hrnet_30_256x192_coco_pact.pdparams) | [config](./configs/lite_hrnet_30_256x192_coco_pact.yml) | 8.0M | +* COCO Dataset benchmark + +| Model | Input Size | AP(%, coco val) | Model Download | Config File | Inference model size(M) | Inference time (ms/image) | +| :----------: | -------- | :----------: | :------------: | :---: | :---: | :---: | +| HRNet-w32 | 256x192 | 76.9 | [hrnet_w32_256x192.pdparams](https://paddle-model-ecology.bj.bcebos.com/model/hrnet_pose/hrnet_w32_256x192.pdparams) | [config](./configs/hrnet_w32_256x192.yml) | 118 | 357.1 | +| LiteHRNet-30 | 256x192 | 69.4 | [lite_hrnet_30_256x192_coco.pdparams](https://paddle-model-ecology.bj.bcebos.com/model/hrnet_pose/lite_hrnet_30_256x192_coco.pdparams) | [config](./configs/lite_hrnet_30_256x192_coco.yml) | 26 | 160.6 +| LiteHRNet-30-distillation | 256x192 | 69.9 |[lite_hrnet_30_256x192_coco_dist.pdparams](https://paddle-model-ecology.bj.bcebos.com/model/hrnet_pose/lite_hrnet_30_256x192_coco_dist.pdparams) | [config](./configs/lite_hrnet_30_256x192_coco.yml) | 26 | 160.6 +| LiteHRNet-30-PACT | 256x192 | 68.9 | [lite_hrnet_30_256x192_coco_pact.pdparams](https://paddle-model-ecology.bj.bcebos.com/model/hrnet_pose/lite_hrnet_30_256x192_coco_pact.pdparams) | [config](./configs/lite_hrnet_30_256x192_coco_pact.yml) | 8 | 156.7 +| LiteHRNet-30-distillation-PACT | 256x192 | 70.2 | [lite_hrnet_30_256x192_coco_dist_pact.pdparams](https://paddle-model-ecology.bj.bcebos.com/model/hrnet_pose/lite_hrnet_30_256x192_coco_dist_pact.pdparams) | [config](./configs/lite_hrnet_30_256x192_coco_dist_pact.yml) | 8 | 156.7 + + +**NOTE:** +* Inference model size is obtained by summing `pdiparams` and `pdmodel` file size. +* The inference time is tested on CPU(`Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz`) without MKLDNN using paddlepaddle-develop. -* note: Inference model size is obtained by summing `pdiparams` and `pdmodel` file size. +**Visualization** + +Input: + +![](./dataset/test_image/hrnet_demo.jpg) + +Output: + +![](./deploy/output/hrnet_demo_vis.jpg) -![](/dataset/test_image/hrnet_demo.jpg) -![](/deploy/output/hrnet_demo_vis.jpg) ## Citation + ```` @inproceedings{cheng2020bottom, title={Deep High-Resolution Representation Learning for Human Pose Estimation}, diff --git a/tutorials/pp-series/HRNet-Keypoint/configs/lite_hrnet_30_256x192_coco_dist_pact.yml b/tutorials/pp-series/HRNet-Keypoint/configs/lite_hrnet_30_256x192_coco_dist_pact.yml new file mode 100644 index 00000000..d0d7458a --- /dev/null +++ b/tutorials/pp-series/HRNet-Keypoint/configs/lite_hrnet_30_256x192_coco_dist_pact.yml @@ -0,0 +1,150 @@ +use_gpu: true +log_iter: 5 +save_dir: output +snapshot_epoch: 10 +weights: output/lite_hrnet_30_256x192_coco_dist_pact/model_final +epoch: 50 +num_joints: &num_joints 17 +pixel_std: &pixel_std 200 +metric: KeyPointTopDownCOCOEval +num_classes: 1 +train_height: &train_height 256 +train_width: &train_width 192 +trainsize: &trainsize [*train_width, *train_height] +hmsize: &hmsize [48, 64] +flip_perm: &flip_perm [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16]] + + +pretrain_weights: https://paddle-model-ecology.bj.bcebos.com/model/hrnet_pose/lite_hrnet_30_256x192_coco_dist.pdparams +slim: QAT +QAT: + quant_config: { + 'activation_preprocess_type': 'PACT', + 'weight_quantize_type': 'channel_wise_abs_max', 'activation_quantize_type': 'moving_average_abs_max', + 'weight_bits': 8, 'activation_bits': 8, 'dtype': 'int8', 'window_size': 10000, 'moving_rate': 0.9, + 'quantizable_layer_type': ['Conv2D', 'Linear']} + print_model: True + + +architecture: TopDownHRNet + +TopDownHRNet: + backbone: LiteHRNet + post_process: HRNetPostProcess + flip_perm: *flip_perm + num_joints: *num_joints + width: &width 40 + loss: KeyPointMSELoss + use_dark: false + +LiteHRNet: + network_type: lite_30 + freeze_at: -1 + freeze_norm: false + return_idx: [0] + +KeyPointMSELoss: + use_target_weight: true + loss_scale: 1.0 + +# optimizer +LearningRate: + base_lr: 0.001 + schedulers: + - !PiecewiseDecay + milestones: [40, 45] + gamma: 0.1 + - !LinearWarmup + start_factor: 0.001 + steps: 500 + +OptimizerBuilder: + optimizer: + type: Adam + regularizer: + factor: 0.0 + type: L2 + + +#####data +TrainDataset: + !KeypointTopDownCocoDataset + image_dir: train2017 + anno_path: annotations/person_keypoints_train2017.json + dataset_dir: dataset/coco + num_joints: *num_joints + trainsize: *trainsize + pixel_std: *pixel_std + use_gt_bbox: True + + +EvalDataset: + !KeypointTopDownCocoDataset + image_dir: val2017 + anno_path: annotations/person_keypoints_val2017.json + dataset_dir: dataset/coco + num_joints: *num_joints + trainsize: *trainsize + pixel_std: *pixel_std + use_gt_bbox: True + image_thre: 0.0 + + +TestDataset: + !ImageFolder + anno_path: dataset/coco/keypoint_imagelist.txt + +worker_num: 4 +global_mean: &global_mean [0.485, 0.456, 0.406] +global_std: &global_std [0.229, 0.224, 0.225] +TrainReader: + sample_transforms: + - RandomFlipHalfBodyTransform: + scale: 0.25 + rot: 30 + num_joints_half_body: 8 + prob_half_body: 0.3 + pixel_std: *pixel_std + trainsize: *trainsize + upper_body_ids: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + flip_pairs: *flip_perm + - TopDownAffine: + trainsize: *trainsize + - ToHeatmapsTopDown: + hmsize: *hmsize + sigma: 2 + batch_transforms: + - NormalizeImage: + mean: *global_mean + std: *global_std + is_scale: true + - Permute: {} + batch_size: 64 + shuffle: true + drop_last: false + +EvalReader: + sample_transforms: + - TopDownAffine: + trainsize: *trainsize + batch_transforms: + - NormalizeImage: + mean: *global_mean + std: *global_std + is_scale: true + - Permute: {} + batch_size: 16 + +TestReader: + inputs_def: + image_shape: [3, *train_height, *train_width] + sample_transforms: + - Decode: {} + - TopDownEvalAffine: + trainsize: *trainsize + - NormalizeImage: + mean: *global_mean + std: *global_std + is_scale: true + - Permute: {} + batch_size: 1 diff --git a/tutorials/pp-series/HRNet-Keypoint/configs/lite_hrnet_30_256x192_coco_pact.yml b/tutorials/pp-series/HRNet-Keypoint/configs/lite_hrnet_30_256x192_coco_pact.yml index 26004392..72d1eb11 100644 --- a/tutorials/pp-series/HRNet-Keypoint/configs/lite_hrnet_30_256x192_coco_pact.yml +++ b/tutorials/pp-series/HRNet-Keypoint/configs/lite_hrnet_30_256x192_coco_pact.yml @@ -2,7 +2,7 @@ use_gpu: true log_iter: 5 save_dir: output snapshot_epoch: 10 -weights: output/lite_hrnet_30_256x192_coco/model_final +weights: output/lite_hrnet_30_256x192_coco_pact/model_final epoch: 50 num_joints: &num_joints 17 pixel_std: &pixel_std 200 @@ -15,7 +15,7 @@ hmsize: &hmsize [48, 64] flip_perm: &flip_perm [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16]] -pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/keypoint/lite_hrnet_30_256x192_coco.pdparams +pretrain_weights: 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image_list, FLAGS.threshold, repeats=3, add_timer=True) + [img_file], FLAGS.threshold, repeats=3, add_timer=True) cm, gm, gu = get_current_memory_mb() detector.cpu_mem += cm @@ -346,7 +346,7 @@ def predict_image(detector, image_list, batch_size=1): detector.gpu_util += gu print('Test iter {}'.format(i)) else: - results = detector.predict(image_list, FLAGS.threshold) + results = detector.predict([img_file], FLAGS.threshold) draw_pose( img_file, results, -- GitLab