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

@@ -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},

tutorials/pp-series/HRNet-Keypoint/configs/lite_hrnet_30_256x192_coco_dist_pact.yml

+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

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: https://paddle-model-ecology.bj.bcebos.com/model/hrnet_pose/lite_hrnet_30_256x192_coco.pdparams
 slim: QAT
 QAT:
   quant_config: {

tutorials/pp-series/HRNet-Keypoint/deploy/infer.py

@@ -335,10 +335,10 @@ def predict_image(detector, image_list, batch_size=1):
         if FLAGS.run_benchmark:
             # warmup
             detector.predict(
-                image_list, FLAGS.threshold, repeats=3, add_timer=False)
+                [img_file], FLAGS.threshold, repeats=3, add_timer=False)
             # run benchmark
             detector.predict(
-                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,