!18731 merge openpose

Merge pull request !18731 from Maige/openpose

model_zoo/official/cv/openpose/README.md

@@ -56,12 +56,12 @@ In the currently provided training script, the coco2017 data set is used as an e
 
    ```python
    ├── dataset
-       ├── annotation
+       ├── annotations
            ├─person_keypoints_train2017.json
            └─person_keypoints_val2017.json
-       ├─ignore_mask_train
-       ├─ignore_mask_val
-       ├─tran2017
+       ├─ignore_mask_train2017
+       ├─ignore_mask_val2017
+       ├─train2017
        └─val2017
    ```
 
@@ -90,15 +90,15 @@ After installing MindSpore via the official website, you can start training and
 
   ```python
   # run training example
-  python train.py --train_dir train2017 --train_ann person_keypoints_train2017.json > train.log 2>&1 &
+  python train.py --imgpath_train ./train2017 --jsonpath_train ./person_keypoints_train2017.json --maskpath_train ./ignore_mask_train2017 > train.log 2>&1 &
 
   # run distributed training example
-  bash run_distribute_train.sh [RANK_TABLE_FILE]
+  bash run_distribute_train.sh [RANK_TABLE_FILE] [IMGPATH_TRAIN] [JSONPATH_TRAIN] [MASKPATH_TRAIN]
 
   # run evaluation example
   python eval.py --model_path path_to_eval_model.ckpt --imgpath_val ./dataset/val2017 --ann ./dataset/annotations/person_keypoints_val2017.json > eval.log 2>&1 &
   OR
-  bash scripts/run_eval_ascend.sh
+  bash scripts/run_eval_ascend.sh [MODEL_PATH] [IMGPATH_VAL] [ANN]
   ```
 
 [RANK_TABLE_FILE] is the path of the multi-card information configuration table in the environment. The configuration table can be automatically generated by the tool [hccl_tool](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools).
@@ -108,32 +108,38 @@ After installing MindSpore via the official website, you can start training and
 ## [Script and Sample Code](#contents)
 
 ```python
-├── ModelZoo_openpose_MS_MIT
+├── openpose
     ├── README.md                        // descriptions about openpose
     ├── scripts
     │   ├──run_standalone_train.sh       // shell script for distributed on Ascend
     │   ├──run_distribute_train.sh       // shell script for distributed on Ascend with 8p
     │   ├──run_eval_ascend.sh            // shell script for evaluation on Ascend
     ├── src
+    │   ├── model_utils
+    │       ├── config.py                           # Parameter config
+    │       ├── moxing_adapter.py                   # modelarts device configuration
+    │       └── device_adapter.py                   # Device Config
+    │       └── local_adapter.py                    # local device config
     │   ├──openposenet.py                // Openpose architecture
     │   ├──loss.py                       // Loss function
-    │   ├──config.py                     // parameter configuration
     │   ├──dataset.py                    // Data preprocessing
     │   ├──utils.py                      // Utils
     │   ├──gen_ignore_mask.py            // Generating mask data script
     ├── export.py                        // model conversion script
     ├── train.py                         // training script
-    ├── eval.py                          // evaluation script  
+    ├── eval.py                          // evaluation script
+    ├── mindspore_hub_config.py          // hub config file
+    ├── default_config.yaml              // config file
 ```
 
 ## [Script Parameters](#contents)
 
-Parameters for both training and evaluation can be set in config.py
+Parameters for both training and evaluation can be set in default_config.yaml
 
 - config for openpose
 
-  ```python
-  'data_dir': 'path to dataset'                    # absolute full path to the train and evaluation datasets
+  ```default_config.yaml
+  'imgpath_train': 'path to dataset'               # absolute full path to the train and evaluation datasets
   'vgg_path': 'path to vgg model'                  # absolute full path to vgg19 model
   'save_model_path': 'path of saving models'       # absolute full path to output models
   'load_pretrain': 'False'                         # whether training based on the pre-trained model
@@ -150,7 +156,7 @@ Parameters for both training and evaluation can be set in config.py
   'ckpt_interval': 5000                            # the interval of saving a output model
   ```
 
-For more configuration details, please refer the script `config.py`.
+For more configuration details, please refer the script `default_config.yaml`.
 
 ## [Training Process](#contents)
 
@@ -159,7 +165,7 @@ For more configuration details, please refer the script `config.py`.
 - running on Ascend
 
   ```python
-  python train.py --train_dir train2017 --train_ann person_keypoints_train2017.json > train.log 2>&1 &
+  python train.py --imgpath_train ./train2017 --jsonpath_train ./person_keypoints_train2017.json --maskpath_train ./ignore_mask_train2017 > train.log 2>&1 &
   ```
 
   The python command above will run in the background, you can view the results through the file `train.log`.
@@ -168,13 +174,70 @@ For more configuration details, please refer the script `config.py`.
 
   ```python
   # grep "epoch " train.log
-  epoch[0], iter[0], loss[0.29211228793809957], 0.13 imgs/sec, vgglr=0.0,baselr=2.499999936844688e-05,stagelr=9.999999747378752e-05
-  epoch[0], iter[100], loss[0.060355084178521694], 24.92 imgs/sec, vgglr=0.0,baselr=2.499999936844688e-05,stagelr=9.999999747378752e-05
-  epoch[0], iter[200], loss[0.026628130997662272], 26.20 imgs/sec, vgglr=0.0,baselr=2.499999936844688e-05,stagelr=9.999999747378752e-05
+  epoch[0], iter[23], mean loss is 0.292112287
+  epoch[0], iter[123], mean loss is 0.060355084
+  epoch[0], iter[223], mean loss is 0.026628130
   ...
   ```
 
-  The model checkpoint will be saved in the directory of config.py: 'save_model_path'.
+  The model checkpoint will be saved in the directory of default_config.yaml: 'save_model_path'.
+
+- running on ModelArts
+- If you want to train the model on modelarts, you can refer to the [official guidance document] of modelarts (https://support.huaweicloud.com/modelarts/)
+
+```python
+#  Example of using distributed training dpn on modelarts :
+#  Data set storage method
+#   ├── openpose_dataset
+#       ├── annotations
+#           ├─person_keypoints_train2017.json
+#           └─person_keypoints_val2017.json
+#       ├─ignore_mask_train2017
+#       ├─ignore_mask_val2017
+#       ├─train2017
+#       └─val2017
+#       └─checkpoint
+#       └─pre_trained
+#
+# (1) Choose either a (modify yaml file parameters) or b (modelArts create training job to modify parameters) 。
+#       a. set "enable_modelarts=True"
+#          set "vgg_path=/cache/data/pre_trained/vgg19-0-97_5004.ckpt"
+#          set "maskpath_train=/cache/data/ignore_mask_train2017"
+#          set "jsonpath_train=/cache/data/annotations/person_keypoints_train2017"
+#          set "save_model_path=/cache/train/checkpoint"
+#          set "imgpath_train=/cache/data/train2017"
+#
+#       b. add "enable_modelarts=True" Parameters are on the interface of modearts。
+#          Set the parameters required by method a on the modelarts interface
+#          Note: The path parameter does not need to be quoted
+
+# (2) Set the path of the network configuration file  "_config_path=/The path of config in default_config.yaml/"
+# (3) Set the code path on the modelarts interface "/path/openpose"。
+# (4) Set the model's startup file on the modelarts interface "train.py" 。
+# (5) Set the data path of the model on the modelarts interface ".../openpose_dataset"(choices openpose_dataset Folder path) ,
+# The output path of the model "Output file path" and the log path of the model "Job log path" 。
+# (6) start trainning the model。
+
+# Example of using model inference on modelarts
+# (1) Place the trained model to the corresponding position of the bucket。
+# (2) chocie a or b。
+#        a.set "enable_modelarts=True"
+#          set "ann=/cache/data/annotations/person_keypoints_val2017"
+#          set "output_img_path=/cache/data/output_imgs/"
+#          set "imgpath_val=/cache/data/val2017"
+#          set "model_path=/cache/data/checkpoint/0-80_663.ckpt"
+
+#       b. Add "enable_modelarts=True" parameter on the interface of modearts。
+#          Set the parameters required by method a on the modelarts interface
+#          Note: The path parameter does not need to be quoted
+
+# (3) Set the path of the network configuration file "_config_path=/The path of config in default_config.yaml/"
+# (4) Set the code path on the modelarts interface "/path/openpose"。
+# (5) Set the model's startup file on the modelarts interface "eval.py" 。
+# (6) Set the data path of the model on the modelarts interface ".../openpose_dataset"(openpose_dataset Folder path) ,
+# The output path of the model "Output file path" and the log path of the model "Job log path"  。
+# (7) Start model inference。
+```
 
 ## [Evaluation Process](#contents)
 
@@ -187,7 +250,7 @@ For more configuration details, please refer the script `config.py`.
   ```python
   python eval.py --model_path path_to_eval_model.ckpt --imgpath_val ./dataset/val2017 --ann ./dataset/annotations/person_keypoints_val2017.json > eval.log 2>&1 &
   OR
-  bash scripts/run_eval_ascend.sh
+  bash scripts/run_eval_ascend.sh [MODEL_PATH] [IMGPATH_VAL] [ANN]
   ```
 
   The above python command will run in the background. You can view the results through the file "eval.log". The accuracy of the test dataset will be as follows:
@@ -199,6 +262,27 @@ For more configuration details, please refer the script `config.py`.
 
   ```
 
+- Export MindIR on Modelarts
+
+```Modelarts
+Export MindIR example on ModelArts
+Data storage method is the same as training
+# (1) Choose either a (modify yaml file parameters) or b (modelArts create training job to modify parameters)。
+#       a. set "enable_modelarts=True"
+#          set "file_name=/cache/train/openpose"
+#          set "file_format=MINDIR"
+#          set "ckpt_file=/cache/data/checkpoint file name"
+
+#       b. Add "enable_modelarts=True" parameter on the interface of modearts。
+#          Set the parameters required by method a on the modelarts interface
+#          Note: The path parameter does not need to be quoted
+# (2)Set the path of the network configuration file "_config_path=/The path of config in default_config.yaml/"
+# (3) Set the code path on the modelarts interface "/path/openpose"。
+# (4) Set the model's startup file on the modelarts interface "export.py" 。
+# (5) Set the data path of the model on the modelarts interface ".../openpose_dataset/checkpoint"(choices openpose_dataset/checkpoint Folder path) ,
+# The output path of the model "Output file path" and the log path of the model "Job log path"  。
+```
+
 # [Model Description](#contents)
 
 ## [Performance](#contents)

model_zoo/official/cv/openpose/default_config.yaml

@@ -0,0 +1,152 @@
+# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unlesee you know exactly what you are doing)
+enable_modelarts: False
+# url for modelarts
+data_url: ""
+train_url: ""
+checkpoint_url: ""
+# path for local
+data_path: "/cache/data"
+output_path: "/cache/train"
+load_path: "/cache/checkpoint_path"
+device_target: "Ascend"
+enable_profiling: False
+checkpoint_path: "./checkpoint/"
+checkpoint_file: "./checkpoint/.ckpt"
+
+# ======================================================================================
+# Training options
+imgpath_train: ""
+jsonpath_train: ""
+maskpath_train: ""
+save_model_path: "./checkpoint/"
+load_pretrain: False
+pretrained_model_path: ""
+
+# train type
+train_type: "fix_loss_scale"
+train_type_NP: "clip_grad"
+
+# vgg bn
+vgg_with_bn: False
+vgg_path: ""
+
+#if clip_grad
+GRADIENT_CLIP_TYPE: 1
+GRADIENT_CLIP_VALUE: 10.0
+
+# optimizer and lr
+optimizer: "Adam"
+optimizer_NP: "Momentum"
+group_params: True
+group_params_NP: False
+lr: 1e-4
+lr_type: "default"   # chose in [default, cosine]
+lr_gamma: 0.1
+lr_steps: "100000,200000,250000"
+lr_steps_NP: "250000,300000"
+warmup_epoch: 5
+max_epoch_train: 60
+max_epoch_train_NP: 80
+loss_scale: 16384
+
+
+# default param
+batch_size: 10
+min_keypoints: 5
+min_area: 1024
+insize: 368
+downscale: 8
+paf_sigma: 8
+heatmap_sigma: 7
+keep_checkpoint_max: 5
+log_interval: 100
+ckpt_interval: 5304
+min_box_size: 64
+max_box_size: 512
+min_scale: 0.5
+max_scale: 2.0
+max_rotate_degree: 40
+center_perterb_max: 40
+
+
+# ======================================================================================
+# Eval options
+is_distributed: 0
+eva_num: 100
+model_path: ""
+imgpath_val: ""
+ann: ""
+output_img_path: "./output_imgs/"
+
+
+# inference params
+inference_img_size: 368
+inference_scales: [0.5, 1, 1.5, 2]
+heatmap_size: 320
+gaussian_sigma: 2.5
+ksize: 17
+n_integ_points: 10
+n_integ_points_thresh: 8
+heatmap_peak_thresh: 0.05
+inner_product_thresh: 0.05
+limb_length_ratio: 1.0
+length_penalty_value: 1
+n_subset_limbs_thresh: 3
+subset_score_thresh: 0.2
+
+# face params
+face_inference_img_size: 368
+face_heatmap_peak_thresh: 0.1
+face_crop_scale: 1.5
+face_line_indices: [
+        [0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [7, 8], [8, 9], [9, 10], [10, 11], [11, 12], [12, 13], [13, 14], [14, 15], [15, 16], # 轮廓
+        [17, 18], [18, 19], [19, 20], [20, 21],
+        [22, 23], [23, 24], [24, 25], [25, 26],
+        [27, 28], [28, 29], [29, 30],
+        [31, 32], [32, 33], [33, 34], [34, 35],
+        [36, 37], [37, 38], [38, 39], [39, 40], [40, 41], [41, 36],
+        [42, 43], [43, 44], [44, 45], [45, 46], [46, 47], [47, 42],
+        [48, 49], [49, 50], [50, 51], [51, 52], [52, 53], [53, 54], [54, 55], [55, 56], [56, 57], [57, 58], [58, 59], [59, 48], # 唇外廓
+        [60, 61], [61, 62], [62, 63], [63, 64], [64, 65], [65, 66], [66, 67], [67, 60]
+]
+
+# hand params
+hand_inference_img_size: 368
+hand_heatmap_peak_thresh: 0.1
+fingers_indices: [
+        [[0, 1], [1, 2], [2, 3], [3, 4]],
+        [[0, 5], [5, 6], [6, 7], [7, 8]],
+        [[0, 9], [9, 10], [10, 11], [11, 12]],
+        [[0, 13], [13, 14], [14, 15], [15, 16]],
+        [[0, 17], [17, 18], [18, 19], [19, 20]],
+]
+
+# ======================================================================================
+#export options
+device_id: 0
+export_batch_size: 1
+ckpt_file: ""
+file_name: "openpose"
+file_format: "MINDIR"
+
+---
+# Help description for each configuration
+enable_modelarts: "Whether training on modelarts default: False"
+data_url: "Url for modelarts"
+train_url: "Url for modelarts"
+data_path: "The location of input data"
+output_pah: "The location of the output file"
+device_target: "device id of GPU or Ascend. (Default: None)"
+enable_profiling: "Whether enable profiling while training default: False"
+is_distributed: "Run distribute, default is false."
+device_id: "device id"
+export_batch_size: "batch size"
+file_name: "output file name"
+file_format: "file format choices[AIR, MINDIR, ONNX]"
+ckpt_file: "Checkpoint file path."
+train_dir: "train data dir"
+train_ann: "train annotations json"
+model_path: "path of testing model"
+imgpath_val: "path of testing imgs"
+ann: "path of annotations"
+output_img_path: "path of testing imgs"

model_zoo/official/cv/openpose/eval.py

@@ -12,9 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
+
 import json
 import os
-import argparse
 import warnings
 import sys
 import numpy as np
@@ -23,34 +23,23 @@ from scipy.ndimage.filters import gaussian_filter
 from tqdm import tqdm
 from pycocotools.coco import COCO as LoadAnn
 from pycocotools.cocoeval import COCOeval as MapEval
-
 from mindspore import context, Tensor
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from mindspore.communication.management import init, get_rank, get_group_size
+from mindspore.communication.management import init
 from mindspore.common import dtype as mstype
-
-from src.config import params, JointType
 from src.openposenet import OpenPoseNet
 from src.dataset import valdata
+from src.model_utils.config import config, JointType
+from src.model_utils.moxing_adapter import moxing_wrapper
+from src.model_utils.device_adapter import get_device_id, get_rank_id, get_device_num
 
 
 warnings.filterwarnings("ignore")
-devid = int(os.getenv('DEVICE_ID'))
+devid = get_device_id()
 context.set_context(mode=context.GRAPH_MODE,
-                    device_target="Ascend", save_graphs=False, device_id=devid)
+                    device_target=config.device_target, save_graphs=False, device_id=devid)
 show_gt = 0
 
-parser = argparse.ArgumentParser('mindspore openpose_net test')
-parser.add_argument('--model_path', type=str, default='./0-33_170000.ckpt', help='path of testing model')
-parser.add_argument('--imgpath_val', type=str, default='./dataset/coco/val2017', help='path of testing imgs')
-parser.add_argument('--ann', type=str, default='./dataset/coco/annotations/person_keypoints_val2017.json',
-                    help='path of annotations')
-parser.add_argument('--output_path', type=str, default='./output_img', help='path of testing imgs')
-# distributed related
-parser.add_argument('--is_distributed', type=int, default=0, help='if multi device')
-parser.add_argument('--rank', type=int, default=0, help='local rank of distributed')
-parser.add_argument('--group_size', type=int, default=1, help='world size of distributed')
-args, _ = parser.parse_known_args()
 
 def evaluate_mAP(res_file, ann_file, ann_type='keypoints', silence=True):
     class NullWriter():
@@ -94,6 +83,7 @@ def load_model(test_net, model_path):
 
     load_param_into_net(test_net, param_dict_new)
 
+
 def preprocess(img):
     x_data = img.astype('f')
     x_data /= 255
@@ -101,6 +91,7 @@ def preprocess(img):
     x_data = x_data.transpose(2, 0, 1)[None]
     return x_data
 
+
 def getImgsPath(img_dir_path):
     filepaths = []
     dirpaths = []
@@ -115,6 +106,7 @@ def getImgsPath(img_dir_path):
             dirpaths.append(dir_path)
     return filepaths
 
+
 def compute_optimal_size(orig_img, img_size, stride=8):
     orig_img_h, orig_img_w, _ = orig_img.shape
     aspect = orig_img_h / orig_img_w
@@ -132,6 +124,7 @@ def compute_optimal_size(orig_img, img_size, stride=8):
             img_h += stride - surplus
     return (img_w, img_h)
 
+
 def compute_peaks_from_heatmaps(heatmaps):
 
     heatmaps = heatmaps[:-1]
@@ -139,7 +132,7 @@ def compute_peaks_from_heatmaps(heatmaps):
     all_peaks = []
     peak_counter = 0
     for i, heatmap in enumerate(heatmaps):
-        heatmap = gaussian_filter(heatmap, sigma=params['gaussian_sigma'])
+        heatmap = gaussian_filter(heatmap, sigma=config.gaussian_sigma)
 
         map_left = np.zeros(heatmap.shape)
         map_right = np.zeros(heatmap.shape)
@@ -152,7 +145,7 @@ def compute_peaks_from_heatmaps(heatmaps):
         map_bottom[:, :-1] = heatmap[:, 1:]
 
         peaks_binary = np.logical_and.reduce((
-            heatmap > params['heatmap_peak_thresh'],
+            heatmap > config.heatmap_peak_thresh,
             heatmap > map_left,
             heatmap > map_right,
             heatmap > map_top,
@@ -172,6 +165,7 @@ def compute_peaks_from_heatmaps(heatmaps):
 
     return all_peaks
 
+
 def compute_candidate_connections(paf, cand_a, cand_b, img_len, params_):
     candidate_connections = []
     for joint_a in cand_a:
@@ -180,28 +174,29 @@ def compute_candidate_connections(paf, cand_a, cand_b, img_len, params_):
             norm = np.linalg.norm(vector)
             if norm == 0:
                 continue
-            ys = np.linspace(joint_a[1], joint_b[1], num=params_['n_integ_points'])
-            xs = np.linspace(joint_a[0], joint_b[0], num=params_['n_integ_points'])
+            ys = np.linspace(joint_a[1], joint_b[1], num=params_.n_integ_points)
+            xs = np.linspace(joint_a[0], joint_b[0], num=params_.n_integ_points)
             integ_points = np.stack([ys, xs]).T.round().astype('i')
 
             paf_in_edge = np.hstack([paf[0][np.hsplit(integ_points, 2)], paf[1][np.hsplit(integ_points, 2)]])
             unit_vector = vector / norm
             inner_products = np.dot(paf_in_edge, unit_vector)
             integ_value = inner_products.sum() / len(inner_products)
-            integ_value_with_dist_prior = integ_value + min(params_['limb_length_ratio'] * img_len / norm -
-                                                            params_['length_penalty_value'], 0)
-            n_valid_points = sum(inner_products > params_['inner_product_thresh'])
-            if n_valid_points > params_['n_integ_points_thresh'] and integ_value_with_dist_prior > 0:
+            integ_value_with_dist_prior = integ_value + min(params_.limb_length_ratio * img_len / norm -
+                                                            params_.length_penalty_value, 0)
+            n_valid_points = sum(inner_products > params_.inner_product_thresh)
+            if n_valid_points > params_.n_integ_points_thresh and integ_value_with_dist_prior > 0:
                 candidate_connections.append([int(joint_a[3]), int(joint_b[3]), integ_value_with_dist_prior])
     candidate_connections = sorted(candidate_connections, key=lambda x: x[2], reverse=True)
     return candidate_connections
 
+
 def compute_connections(pafs, all_peaks, img_len, params_):
     all_connections = []
-    for i in range(len(params_['limbs_point'])):
+    for i in range(len(params_.limbs_point)):
         paf_index = [i * 2, i * 2 + 1]
         paf = pafs[paf_index]  # shape: (2, 320, 320)
-        limb_point = params_['limbs_point'][i]  # example: [<JointType.Neck: 1>, <JointType.RightWaist: 8>]
+        limb_point = params_.limbs_point[i]  # example: [<JointType.Neck: 1>, <JointType.RightWaist: 8>]
         cand_a = all_peaks[all_peaks[:, 0] == limb_point[0]][:, 1:]
         cand_b = all_peaks[all_peaks[:, 0] == limb_point[1]][:, 1:]
 
@@ -224,7 +219,7 @@ def grouping_key_points(all_connections, candidate_peaks, params_):
     subsets = -1 * np.ones((0, 20))
 
     for l, connections in enumerate(all_connections):
-        joint_a, joint_b = params_['limbs_point'][l]
+        joint_a, joint_b = params_.limbs_point[l]
         for ind_a, ind_b, score in connections[:, :3]:
             ind_a, ind_b = int(ind_a), int(ind_b)
             joint_found_cnt = 0
@@ -284,11 +279,12 @@ def grouping_key_points(all_connections, candidate_peaks, params_):
                 pass
 
     # delete low score subsets
-    keep = np.logical_and(subsets[:, -1] >= params_['n_subset_limbs_thresh'],
-                          subsets[:, -2] / subsets[:, -1] >= params_['subset_score_thresh'])
+    keep = np.logical_and(subsets[:, -1] >= params_.n_subset_limbs_thresh,
+                          subsets[:, -2] / subsets[:, -1] >= params_.subset_score_thresh)
     subsets = subsets[keep]
     return subsets
 
+
 def subsets_to_pose_array(subsets, all_peaks):
     person_pose_array = []
     for subset in subsets:
@@ -308,8 +304,8 @@ def detect(img, network):
     orig_img = img.copy()
     orig_img_h, orig_img_w, _ = orig_img.shape
 
-    input_w, input_h = compute_optimal_size(orig_img, params['inference_img_size']) # 368
-    map_w, map_h = compute_optimal_size(orig_img, params['inference_img_size'])
+    input_w, input_h = compute_optimal_size(orig_img, config.inference_img_size) # 368
+    map_w, map_h = compute_optimal_size(orig_img, config.inference_img_size)
 
     resized_image = cv2.resize(orig_img, (input_w, input_h))
     x_data = preprocess(resized_image)
@@ -338,8 +334,8 @@ def detect(img, network):
     all_peaks = compute_peaks_from_heatmaps(heatmaps)
     if all_peaks.shape[0] == 0:
         return np.empty((0, len(JointType), 3)), np.empty(0)
-    all_connections = compute_connections(pafs, all_peaks, map_w, params)
-    subsets = grouping_key_points(all_connections, all_peaks, params)
+    all_connections = compute_connections(pafs, all_peaks, map_w, config)
+    subsets = grouping_key_points(all_connections, all_peaks, config)
     all_peaks[:, 1] *= orig_img_w / map_w
     all_peaks[:, 2] *= orig_img_h / map_h
     poses = subsets_to_pose_array(subsets, all_peaks)
@@ -369,7 +365,7 @@ def draw_person_pose(orig_img, poses):
 
     # limbs
     for pose in poses.round().astype('i'):
-        for i, (limb, color) in enumerate(zip(params['limbs_point'], limb_colors)):
+        for i, (limb, color) in enumerate(zip(config.limbs_point, limb_colors)):
             if i not in (9, 13):  # don't show ear-shoulder connection
                 limb_ind = np.array(limb)
                 if np.all(pose[limb_ind][:, 2] != 0):
@@ -383,6 +379,7 @@ def draw_person_pose(orig_img, poses):
                 cv2.circle(canvas, (x, y), 3, color, -1)
     return canvas
 
+
 def depreprocess(img):
     x_data = img[0]
     x_data += 0.5
@@ -391,19 +388,24 @@ def depreprocess(img):
     x_data = x_data.transpose(1, 2, 0)
     return x_data
 
+
+@moxing_wrapper(pre_process=None)
 def val():
-    if args.is_distributed:
+    config.rank = get_rank_id()
+    config.group_size = get_device_num()
+
+    if config.is_distributed:
         init()
-        args.rank = get_rank()
-        args.group_size = get_group_size()
-    if not os.path.exists(args.output_path):
-        os.mkdir(args.output_path)
-    network = OpenPoseNet(vgg_with_bn=params['vgg_with_bn'])
+        config.rank = get_rank_id()
+        config.group_size = get_device_num()
+    if not os.path.exists(config.output_img_path):
+        os.mkdir(config.output_img_path)
+    network = OpenPoseNet(vgg_with_bn=config.vgg_with_bn)
     network.set_train(False)
-    load_model(network, args.model_path)
+    load_model(network, config.model_path)
 
     print("load models right")
-    dataset = valdata(args.ann, args.imgpath_val, args.rank, args.group_size, mode='val')
+    dataset = valdata(config.ann, config.imgpath_val, config.rank, config.group_size, mode='val')
     dataset_size = dataset.get_dataset_size()
     de_dataset = dataset.create_tuple_iterator()
 
@@ -431,14 +433,15 @@ def val():
             print("Predict poses size is zero.", flush=True)
         img = draw_person_pose(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), poses)
 
-        save_path = os.path.join(args.output_path, str(img_id)+".png")
+        save_path = os.path.join(config.output_img_path, str(img_id)+".png")
         cv2.imwrite(save_path, img)
 
     result_json = 'eval_result.json'
-    with open(os.path.join(args.output_path, result_json), 'w') as fid:
+    with open(os.path.join(config.output_img_path, result_json), 'w') as fid:
         json.dump(kpt_json, fid)
-    res = evaluate_mAP(os.path.join(args.output_path, result_json), ann_file=args.ann)
+    res = evaluate_mAP(os.path.join(config.output_img_path, result_json), ann_file=config.ann)
     print('result: ', res)
 
+
 if __name__ == "__main__":
     val()

model_zoo/official/cv/openpose/export.py

@@ -14,34 +14,34 @@
 # ============================================================================
 """export"""
 
-import argparse
 import numpy as np
 from mindspore import Tensor
 from mindspore import context
 from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
-
 from src.openposenet import OpenPoseNet
-from src.config import params
+from src.model_utils.config import config
+from src.model_utils.moxing_adapter import moxing_wrapper
 
-parser = argparse.ArgumentParser(description="openpose export")
-parser.add_argument("--device_id", type=int, default=0, help="Device id")
-parser.add_argument("--batch_size", type=int, default=1, help="batch size")
-parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
-parser.add_argument("--file_name", type=str, default="openpose", help="output file name.")
-parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
-parser.add_argument("--device_target", type=str, default="Ascend",
-                    choices=["Ascend", "GPU", "CPU"], help="device target (default: Ascend)")
-args = parser.parse_args()
 
-context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=args.device_id)
+context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target, device_id=config.device_id)
 
-if __name__ == "__main__":
+
+def modelarts_pre_process():
+    pass
+
+
+@moxing_wrapper(pre_process=None)
+def model_export():
     context.set_context(mode=context.GRAPH_MODE, save_graphs=False)
     # define net
     net = OpenPoseNet()
 
     # load checkpoint
-    param_dict = load_checkpoint(args.ckpt_file)
+    param_dict = load_checkpoint(config.ckpt_file)
     load_param_into_net(net, param_dict)
-    inputs = np.ones([args.batch_size, 3, params["insize"], params["insize"]]).astype(np.float32)
-    export(net, Tensor(inputs), file_name=args.file_name, file_format=args.file_format)
+    inputs = np.ones([config.batch_size, 3, config.insize, config.insize]).astype(np.float32)
+    export(net, Tensor(inputs), file_name=config.file_name, file_format=config.file_format)
+
+
+if __name__ == '__main__':
+    model_export()

model_zoo/official/cv/openpose/scripts/run_distribute_train.sh

@@ -13,10 +13,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-
-if [ $# != 1 ]
+if [ $# != 4 ]
 then
-    echo "Usage: sh run_distribute_train.sh [RANK_TABLE_FILE]"
+    echo "Usage: sh scripts/run_distribute_train.sh [RANK_TABLE_FILE] [IAMGEPATH_TRAIN] [JSONPATH_TRAIN] [MASKPATH_TRAIN]"
 exit 1
 fi
 
@@ -47,15 +46,16 @@ do
     export RANK_ID=$i
     rm -rf ./train_parallel$i
     mkdir ./train_parallel$i
-    cp ../*.py ./train_parallel$i
-    cp -r ../src ./train_parallel$i
+    cp ./*.py ./train_parallel$i
+    cp -r ./src ./train_parallel$i
+    cp -r ./scripts ./train_parallel$i
+    cp ./*yaml ./train_parallel$i
     cd ./train_parallel$i || exit
     echo "start training for rank $RANK_ID, device $DEVICE_ID"
     env > env.log
     python train.py \
-        --train_dir train2017 \
-        --group_size 8 \
-        --train_ann person_keypoints_train2017.json > log.txt 2>&1 &
+        --imgpath_train=$2 \
+        --jsonpath_train=$3 \
+        --maskpath_train=$4 > log.txt 2>&1 &
     cd ..
 done
-

model_zoo/official/cv/openpose/scripts/run_eval_ascend.sh

@@ -14,9 +14,17 @@
 # limitations under the License.
 # ============================================================================
 
+if [ $# != 3 ]
+then
+    echo "Usage: sh scripts/run_eval_ascend.sh [MODEL_PATH] [IMPATH_VAL] [ANN]"
+exit 1
+fi
+
 export DEVICE_ID=0
+export DEVICE_NUM=1
+export RANK_ID=0
 python eval.py \
-  --model_path ./scripts/train_parallel0/checkpoints/ckpt_0/0-80_663.ckpt \
-  --imgpath_val ./dataset/val2017 \
-  --ann ./dataset/annotations/person_keypoints_val2017.json \
+  --model_path=$1 \
+  --imgpath_val=$2 \
+  --ann=$3 \
   > eval.log 2>&1 &

model_zoo/official/cv/openpose/scripts/run_standalone_train.sh

@@ -14,6 +14,20 @@
 # limitations under the License.
 # ============================================================================
 
+if [ $# != 3 ]
+then
+    echo "Usage: sh scripts/run_standalone_train.sh [IAMGEPATH_TRAIN] [JSONPATH_TRAIN] [MASKPATH_TRAIN]"
+exit 1
+fi
+
 export DEVICE_ID=0
-cd ..
-python train.py --train_dir train2017 --train_ann person_keypoints_train2017.json > scripts/train.log 2>&1 &
+export DEVICE_NUM=1
+export RANK_ID=0
+rm -rf train
+mkdir train
+cp -r ./src ./train
+cp -r ./scripts ./train
+cp ./*.py ./train
+cp ./*yaml ./train
+cd ./train
+python train.py --imgpath_train=$1 --jsonpath_train=$2 --maskpath_train=$3 > train.log 2>&1 &

model_zoo/official/cv/openpose/src/config.py

@@ -1,191 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-
-# http://www.apache.org/licenses/LICENSE-2.0
-
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-from enum import IntEnum
-
-class JointType(IntEnum):
-    Nose = 0
-
-    Neck = 1
-
-    RightShoulder = 2
-
-    RightElbow = 3
-
-    RightHand = 4
-
-    LeftShoulder = 5
-
-    LeftElbow = 6
-
-    LeftHand = 7
-
-    RightWaist = 8
-
-    RightKnee = 9
-
-    RightFoot = 10
-
-    LeftWaist = 11
-
-    LeftKnee = 12
-
-    LeftFoot = 13
-
-    RightEye = 14
-
-    LeftEye = 15
-
-    RightEar = 16
-
-    LeftEar = 17
-
-params = {
-    # paths
-    'data_dir': './dataset',
-    'save_model_path': './checkpoints/',
-    'load_pretrain': False,
-    'pretrained_model_path': "",
-
-    # train type
-    'train_type': 'fix_loss_scale', # chose in ['clip_grad', 'fix_loss_scale']
-    'train_type_NP': 'clip_grad',
-
-    # vgg bn
-    'vgg_with_bn': False,
-    'vgg_path': './vgg_model/vgg19-0-97_5004.ckpt',
-
-    # if clip_grad
-    'GRADIENT_CLIP_TYPE': 1,
-    'GRADIENT_CLIP_VALUE': 10.0,
-
-    # optimizer and lr
-    'optimizer': "Adam", # chose in ['Momentum', 'Adam']
-    'optimizer_NP': "Momentum",
-    'group_params': True,
-    'group_params_NP': False,
-    'lr': 1e-4,
-    'lr_type': 'default', # chose in ["default", "cosine"]
-    'lr_gamma': 0.1,                    # if default
-    'lr_steps': '100000,200000,250000', # if default
-    'lr_steps_NP': '250000,300000',     # if default
-    'warmup_epoch': 5,                  # if cosine
-    'max_epoch_train': 60,
-    'max_epoch_train_NP': 80,
-
-    'loss_scale': 16384,
-
-    # default param
-    'batch_size': 10,
-    'min_keypoints': 5,
-    'min_area': 32 * 32,
-    'insize': 368,
-    'downscale': 8,
-    'paf_sigma': 8,
-    'heatmap_sigma': 7,
-    'eva_num': 100,
-    'keep_checkpoint_max': 1,
-    'log_interval': 100,
-    'ckpt_interval': 5304,
-
-    'min_box_size': 64,
-    'max_box_size': 512,
-    'min_scale': 0.5,
-    'max_scale': 2.0,
-    'max_rotate_degree': 40,
-    'center_perterb_max': 40,
-
-    # inference params
-    'inference_img_size': 368,
-    'inference_scales': [0.5, 1, 1.5, 2],
-    # 'inference_scales': [1.0],
-    'heatmap_size': 320,
-    'gaussian_sigma': 2.5,
-    'ksize': 17,
-    'n_integ_points': 10,
-    'n_integ_points_thresh': 8,
-    'heatmap_peak_thresh': 0.05,
-    'inner_product_thresh': 0.05,
-    'limb_length_ratio': 1.0,
-    'length_penalty_value': 1,
-    'n_subset_limbs_thresh': 3,
-    'subset_score_thresh': 0.2,
-    'limbs_point': [
-        [JointType.Neck, JointType.RightWaist],
-        [JointType.RightWaist, JointType.RightKnee],
-        [JointType.RightKnee, JointType.RightFoot],
-        [JointType.Neck, JointType.LeftWaist],
-        [JointType.LeftWaist, JointType.LeftKnee],
-        [JointType.LeftKnee, JointType.LeftFoot],
-        [JointType.Neck, JointType.RightShoulder],
-        [JointType.RightShoulder, JointType.RightElbow],
-        [JointType.RightElbow, JointType.RightHand],
-        [JointType.RightShoulder, JointType.RightEar],
-        [JointType.Neck, JointType.LeftShoulder],
-        [JointType.LeftShoulder, JointType.LeftElbow],
-        [JointType.LeftElbow, JointType.LeftHand],
-        [JointType.LeftShoulder, JointType.LeftEar],
-        [JointType.Neck, JointType.Nose],
-        [JointType.Nose, JointType.RightEye],
-        [JointType.Nose, JointType.LeftEye],
-        [JointType.RightEye, JointType.RightEar],
-        [JointType.LeftEye, JointType.LeftEar]
-    ],
-    'joint_indices': [
-        JointType.Nose,
-        JointType.LeftEye,
-        JointType.RightEye,
-        JointType.LeftEar,
-        JointType.RightEar,
-        JointType.LeftShoulder,
-        JointType.RightShoulder,
-        JointType.LeftElbow,
-        JointType.RightElbow,
-        JointType.LeftHand,
-        JointType.RightHand,
-        JointType.LeftWaist,
-        JointType.RightWaist,
-        JointType.LeftKnee,
-        JointType.RightKnee,
-        JointType.LeftFoot,
-        JointType.RightFoot
-    ],
-
-    # face params
-    'face_inference_img_size': 368,
-    'face_heatmap_peak_thresh': 0.1,
-    'face_crop_scale': 1.5,
-    'face_line_indices': [
-        [0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [7, 8], [8, 9], [9, 10], [10, 11], [11, 12], [12, 13], [13, 14], [14, 15], [15, 16], # 轮廓
-        [17, 18], [18, 19], [19, 20], [20, 21],
-        [22, 23], [23, 24], [24, 25], [25, 26],
-        [27, 28], [28, 29], [29, 30],
-        [31, 32], [32, 33], [33, 34], [34, 35],
-        [36, 37], [37, 38], [38, 39], [39, 40], [40, 41], [41, 36],
-        [42, 43], [43, 44], [44, 45], [45, 46], [46, 47], [47, 42],
-        [48, 49], [49, 50], [50, 51], [51, 52], [52, 53], [53, 54], [54, 55], [55, 56], [56, 57], [57, 58], [58, 59], [59, 48], # 唇外廓
-        [60, 61], [61, 62], [62, 63], [63, 64], [64, 65], [65, 66], [66, 67], [67, 60]
-    ],
-
-    # hand params
-    'hand_inference_img_size': 368,
-    'hand_heatmap_peak_thresh': 0.1,
-    'fingers_indices': [
-        [[0, 1], [1, 2], [2, 3], [3, 4]],
-        [[0, 5], [5, 6], [6, 7], [7, 8]],
-        [[0, 9], [9, 10], [10, 11], [11, 12]],
-        [[0, 13], [13, 14], [14, 15], [15, 16]],
-        [[0, 17], [17, 18], [18, 19], [19, 20]],
-    ],
-}

model_zoo/official/cv/openpose/src/dataset.py

@@ -18,10 +18,10 @@ import random
 import numpy as np
 import cv2
 from pycocotools.coco import COCO as ReadJson
-
 import mindspore.dataset as de
+from src.model_utils.config import config, JointType
+
 
-from src.config import JointType, params
 
 cv2.setNumThreads(0)
 
@@ -60,8 +60,8 @@ class txtdataset():
                 valid_annotations_for_img = []
                 for annotation in annotations_for_img:
                     # if too few keypoints or too small
-                    if annotation['num_keypoints'] >= params['min_keypoints'] and \
-                            annotation['area'] > params['min_area']:
+                    if annotation['num_keypoints'] >= config.min_keypoints and \
+                            annotation['area'] > config.min_area:
                         person_cnt += 1
                         valid_annotations_for_img.append(annotation)
 
@@ -129,11 +129,11 @@ class txtdataset():
         joint_bboxes = self.get_pose_bboxes(poses)
         bbox_sizes = ((joint_bboxes[:, 2:] - joint_bboxes[:, :2] + 1) ** 2).sum(axis=1) ** 0.5
 
-        min_scale = params['min_box_size'] / bbox_sizes.min()
-        max_scale = params['max_box_size'] / bbox_sizes.max()
+        min_scale = config.min_box_size / bbox_sizes.min()
+        max_scale = config.max_box_size / bbox_sizes.max()
 
-        min_scale = min(max(min_scale, params['min_scale']), 1)
-        max_scale = min(max(max_scale, 1), params['max_scale'])
+        min_scale = min(max(min_scale, config.min_scale), 1)
+        max_scale = min(max(max_scale, 1), config.max_scale)
 
         scale = float((max_scale - min_scale) * random.random() + min_scale)
         shape = (round(w * scale), round(h * scale))
@@ -143,7 +143,7 @@ class txtdataset():
 
     def random_rotate_img(self, img, mask, poses):
         h, w, _ = img.shape
-        degree = np.random.randn() / 3 * params['max_rotate_degree']
+        degree = np.random.randn() / 3 * config.max_rotate_degree
         rad = degree * math.pi / 180
         center = (w / 2, h / 2)
         R = cv2.getRotationMatrix2D(center, degree, 1)
@@ -169,7 +169,7 @@ class txtdataset():
         bbox_center = bbox[:2] + (bbox[2:] - bbox[:2]) / 2
 
         r_xy = np.random.rand(2)
-        perturb = ((r_xy - 0.5) * 2 * params['center_perterb_max'])
+        perturb = ((r_xy - 0.5) * 2 * config.center_perterb_max)
         center = (bbox_center + perturb + 0.5).astype('i')
 
         crop_img = np.zeros((insize, insize, 3), 'uint8') + 127.5
@@ -329,7 +329,7 @@ class txtdataset():
     def generate_pafs(self, img, poses, paf_sigma):
         pafs = np.zeros((0,) + img.shape[:-1])
 
-        for limb in params['limbs_point']:
+        for limb in config.limbs_point:
             paf = np.zeros((2,) + img.shape[:-1])
             paf_flags = np.zeros(paf.shape) # for constant paf
 
@@ -376,7 +376,7 @@ class txtdataset():
         resize_shape = (img.shape[0]//8, img.shape[1]//8, 3)
         pafs = np.zeros((0,) + resize_shape[:-1])
 
-        for limb in params['limbs_point']:
+        for limb in config.limbs_point:
             paf = np.zeros((2,) + resize_shape[:-1])
             paf_flags = np.zeros(paf.shape) # for constant paf
 
@@ -410,7 +410,7 @@ class txtdataset():
             valid_annotations_for_img = []
             for annotation in annotations_for_img:
                 # if too few keypoints or too small
-                if annotation['num_keypoints'] >= params['min_keypoints'] and annotation['area'] > params['min_area']:
+                if annotation['num_keypoints'] >= config.min_keypoints and annotation['area'] > config.min_area:
                     person_cnt += 1
                     valid_annotations_for_img.append(annotation)
 
@@ -440,7 +440,7 @@ class txtdataset():
             pose = np.zeros((1, len(JointType), 3), dtype=np.int32)
 
             # convert poses position
-            for i, joint_index in enumerate(params['joint_indices']):
+            for i, joint_index in enumerate(config.joint_indices):
                 pose[0][joint_index] = ann_pose[i]
 
             # compute neck position
@@ -470,9 +470,9 @@ class txtdataset():
         resized_img, ignore_mask, resized_poses = self.resize_data(img, ignore_mask, poses,
                                                                    shape=(self.insize, self.insize))
 
-        resized_heatmaps = self.generate_heatmaps_fast(resized_img, resized_poses, params['heatmap_sigma'])
+        resized_heatmaps = self.generate_heatmaps_fast(resized_img, resized_poses, config.heatmap_sigma)
 
-        resized_pafs = self.generate_pafs_fast(resized_img, resized_poses, params['paf_sigma'])
+        resized_pafs = self.generate_pafs_fast(resized_img, resized_poses, config.paf_sigma)
 
         ignore_mask = cv2.morphologyEx(ignore_mask.astype('uint8'), cv2.MORPH_DILATE, np.ones((16, 16))).astype('bool')
         resized_ignore_mask = self.resize_output(ignore_mask)
@@ -540,10 +540,11 @@ class DistributedSampler():
     def __len__(self):
         return self.num_samplers
 
+
 def valdata(jsonpath, imgpath, rank, group_size, mode='val', maskpath=''):
     #cv2.setNumThreads(0)
     val = ReadJson(jsonpath)
-    dataset = txtdataset(val, imgpath, maskpath, params['insize'], mode=mode)
+    dataset = txtdataset(val, imgpath, maskpath, config.insize, mode=mode)
     sampler = DistributedSampler(dataset, rank, group_size)
     ds = de.GeneratorDataset(dataset, ['img', 'img_id'], num_parallel_workers=8, sampler=sampler)
     ds = ds.repeat(1)
@@ -554,7 +555,7 @@ def create_dataset(jsonpath, imgpath, maskpath, batch_size, rank, group_size, mo
                    multiprocessing=True, num_worker=20):
 
     train = ReadJson(jsonpath)
-    dataset = txtdataset(train, imgpath, maskpath, params['insize'], mode=mode)
+    dataset = txtdataset(train, imgpath, maskpath, config.insize, mode=mode)
     if group_size == 1:
         de_dataset = de.GeneratorDataset(dataset, ["image", "pafs", "heatmaps", "ignore_mask"],
                                          shuffle=shuffle,

model_zoo/official/cv/openpose/src/loss.py

@@ -22,8 +22,7 @@ from mindspore.context import ParallelMode, get_auto_parallel_context
 from mindspore.communication.management import get_group_size
 from mindspore import context
 from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
-
-from src.config import params
+from src.model_utils.config import config
 
 context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
 time_stamp_init = False
@@ -32,8 +31,8 @@ grad_scale = C.MultitypeFuncGraph("grad_scale")
 _grad_overflow = C.MultitypeFuncGraph("_grad_overflow")
 reciprocal = P.Reciprocal()
 
-GRADIENT_CLIP_TYPE = params['GRADIENT_CLIP_TYPE']
-GRADIENT_CLIP_VALUE = params['GRADIENT_CLIP_VALUE']
+GRADIENT_CLIP_TYPE = config.GRADIENT_CLIP_TYPE
+GRADIENT_CLIP_VALUE = config.GRADIENT_CLIP_VALUE
 
 clip_grad = C.MultitypeFuncGraph("clip_grad")
 

model_zoo/official/cv/openpose/src/model_utils/config.py

@@ -0,0 +1,219 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License Version 2.0(the "License");
+# you may not use this file except in compliance with the License.
+# you may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0#
+#
+# Unless required by applicable law or agreed to in writing software
+# distributed under the License is distributed on an "AS IS" BASIS
+# WITHOUT WARRANT IES OR CONITTONS OF ANY KIND， either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ====================================================================================
+
+"""Parse arguments"""
+import os
+import ast
+import argparse
+from pprint import pprint, pformat
+from enum import IntEnum
+import yaml
+
+
+global_yaml = '../../default_config.yaml'
+
+
+class JointType(IntEnum):
+    Nose = 0
+
+    Neck = 1
+
+    RightShoulder = 2
+
+    RightElbow = 3
+
+    RightHand = 4
+
+    LeftShoulder = 5
+
+    LeftElbow = 6
+
+    LeftHand = 7
+
+    RightWaist = 8
+
+    RightKnee = 9
+
+    RightFoot = 10
+
+    LeftWaist = 11
+
+    LeftKnee = 12
+
+    LeftFoot = 13
+
+    RightEye = 14
+
+    LeftEye = 15
+
+    RightEar = 16
+
+    LeftEar = 17
+
+
+limbs_point = [
+    [JointType.Neck, JointType.RightWaist],
+    [JointType.RightWaist, JointType.RightKnee],
+    [JointType.RightKnee, JointType.RightFoot],
+    [JointType.Neck, JointType.LeftWaist],
+    [JointType.LeftWaist, JointType.LeftKnee],
+    [JointType.LeftKnee, JointType.LeftFoot],
+    [JointType.Neck, JointType.RightShoulder],
+    [JointType.RightShoulder, JointType.RightElbow],
+    [JointType.RightElbow, JointType.RightHand],
+    [JointType.RightShoulder, JointType.RightEar],
+    [JointType.Neck, JointType.LeftShoulder],
+    [JointType.LeftShoulder, JointType.LeftElbow],
+    [JointType.LeftElbow, JointType.LeftHand],
+    [JointType.LeftShoulder, JointType.LeftEar],
+    [JointType.Neck, JointType.Nose],
+    [JointType.Nose, JointType.RightEye],
+    [JointType.Nose, JointType.LeftEye],
+    [JointType.RightEye, JointType.RightEar],
+    [JointType.LeftEye, JointType.LeftEar]
+]
+
+
+joint_indices = [
+    JointType.Nose,
+    JointType.LeftEye,
+    JointType.RightEye,
+    JointType.LeftEar,
+    JointType.RightEar,
+    JointType.LeftShoulder,
+    JointType.RightShoulder,
+    JointType.LeftElbow,
+    JointType.RightElbow,
+    JointType.LeftHand,
+    JointType.RightHand,
+    JointType.LeftWaist,
+    JointType.RightWaist,
+    JointType.LeftKnee,
+    JointType.RightKnee,
+    JointType.LeftFoot,
+    JointType.RightFoot
+]
+
+
+class Config:
+    """
+    Configuration namespace. Convert dictionary to members
+    """
+    def __init__(self, cfg_dict):
+        for k, v in cfg_dict.items():
+            if isinstance(v, (list, tuple)):
+                setattr(self, k, [Config(x) if isinstance(x, dict) else x for x in v])
+            else:
+                setattr(self, k, Config(v) if isinstance(v, dict) else v)
+
+    def __str__(self):
+        return pformat(self.__dict__)
+
+    def __repr__(self):
+        return self.__str__()
+
+
+def parse_cli_to_yaml(parser, cfg, helper=None, choices=None, cfg_path='default_config.yaml'):
+    """
+    Parse command line arguments to the configuration according to the default yaml
+
+    Args:
+        parser: Parent parser
+        cfg: Base configuration
+        helper: Helper description
+        cfg_path: Path to the default yaml config
+    """
+    parser = argparse.ArgumentParser(description='[REPLACE THIS at config.py]',
+                                     parents=[parser])
+    helper = {} if helper is None else helper
+    choices = {} if choices is None else choices
+    for item in cfg:
+        if not isinstance(cfg[item], list) and not isinstance(cfg[item], dict):
+            help_description = helper[item] if item in helper else 'Please reference to {}'.format(cfg_path)
+            choice = choices[item] if item in choices else None
+            if isinstance(cfg[item], bool):
+                parser.add_argument('--' + item, type=ast.literal_eval, default=cfg[item], choices=choice,
+                                    help=help_description)
+            else:
+                parser.add_argument('--' + item, type=type(cfg[item]), default=cfg[item], choices=choice,
+                                    help=help_description)
+    args = parser.parse_args()
+    return args
+
+
+def parse_yaml(yaml_path):
+    """
+    Parse the yaml config file
+
+    Args:
+        yaml_path: Path to the yaml config
+    """
+    with open(yaml_path, 'r', encoding='utf-8') as fin:
+        try:
+            cfgs = yaml.load_all(fin.read(), Loader=yaml.FullLoader)
+            cfgs = [x for x in cfgs]
+            if len(cfgs) == 1:
+                cfg_helper = {}
+                cfg = cfgs[0]
+                cfg_choices = {}
+            elif len(cfgs) == 2:
+                cfg, cfg_helper = cfgs
+                cfg_choices = {}
+            elif len(cfgs) == 3:
+                cfg, cfg_helper, cfg_choices = cfgs
+            else:
+                raise ValueError('At most 3 docs (config description for help, choices) are supported in config yaml')
+            print(cfg_helper)
+        except:
+            raise ValueError('Failed to parse yaml')
+    return cfg, cfg_helper, cfg_choices
+
+
+def merge(args, cfg):
+    """
+    Merge the base config from yaml file and command line arguments
+
+    Args:
+        args: command line arguments
+        cfg: Base configuration
+    """
+    args_var = vars(args)
+    for item in args_var:
+        cfg[item] = args_var[item]
+    return cfg
+
+
+def get_config():
+    """
+    Get Config according to the yaml file and cli arguments
+    """
+    parser = argparse.ArgumentParser(description='default name', add_help=False)
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    parser.add_argument('--config_path', type=str, default=os.path.join(current_dir, global_yaml),
+                        help='Config file path')
+    path_args, _ = parser.parse_known_args()
+    default, helper, choices = parse_yaml(path_args.config_path)
+    args = parse_cli_to_yaml(parser=parser, cfg=default, helper=helper, choices=choices, cfg_path=path_args.config_path)
+    final_config = merge(args, default)
+
+    configs = Config(final_config)
+    configs.limbs_point = limbs_point
+    configs.joint_indices = joint_indices
+    pprint(configs)
+
+    return configs
+
+
+config = get_config()

model_zoo/official/cv/openpose/src/model_utils/device_adapter.py

@@ -0,0 +1,26 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License Version 2.0(the "License");
+# you may not use this file except in compliance with the License.
+# you may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0#
+#
+# Unless required by applicable law or agreed to in writing software
+# distributed under the License is distributed on an "AS IS" BASIS
+# WITHOUT WARRANT IES OR CONITTONS OF ANY KIND， either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ====================================================================================
+
+"""Device adapter for ModelArts"""
+
+from .config import config
+if config.enable_modelarts:
+    from .moxing_adapter import get_device_id, get_device_num, get_rank_id, get_job_id
+else:
+    from .local_adapter import get_device_id, get_device_num, get_rank_id, get_job_id
+
+__all__ = [
+    'get_device_id', 'get_device_num', 'get_job_id', 'get_rank_id'
+]

model_zoo/official/cv/openpose/src/model_utils/local_adapter.py

@@ -0,0 +1,36 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License Version 2.0(the "License");
+# you may not use this file except in compliance with the License.
+# you may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0#
+#
+# Unless required by applicable law or agreed to in writing software
+# distributed under the License is distributed on an "AS IS" BASIS
+# WITHOUT WARRANT IES OR CONITTONS OF ANY KIND， either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ====================================================================================
+
+"""Local adapter"""
+
+import os
+
+def get_device_id():
+    device_id = os.getenv('DEVICE_ID', '0')
+    return int(device_id)
+
+
+def get_device_num():
+    device_num = os.getenv('RANK_SIZE', '1')
+    return int(device_num)
+
+
+def get_rank_id():
+    global_rank_id = os.getenv('RANK_ID', '0')
+    return int(global_rank_id)
+
+
+def get_job_id():
+    return 'Local Job'

model_zoo/official/cv/openpose/src/model_utils/moxing_adapter.py

@@ -0,0 +1,124 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License Version 2.0(the "License");
+# you may not use this file except in compliance with the License.
+# you may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0#
+#
+# Unless required by applicable law or agreed to in writing software
+# distributed under the License is distributed on an "AS IS" BASIS
+# WITHOUT WARRANT IES OR CONITTONS OF ANY KIND， either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ====================================================================================
+
+"""Moxing adapter for ModelArts"""
+
+import os
+import functools
+from mindspore import context
+from .config import config
+
+
+_global_syn_count = 0
+
+
+def get_device_id():
+    device_id = os.getenv('DEVICE_ID', '0')
+    return int(device_id)
+
+
+def get_device_num():
+    device_num = os.getenv('RANK_SIZE', '1')
+    return int(device_num)
+
+
+def get_rank_id():
+    global_rank_id = os.getenv('RANK_ID', '0')
+    return int(global_rank_id)
+
+
+def get_job_id():
+    job_id = os.getenv('JOB_ID')
+    job_id = job_id if job_id != "" else "default"
+    return job_id
+
+
+def sync_data(from_path, to_path):
+    """
+    Download data from remote obs to local directory if the first url is remote url and the second one is local
+    Uploca data from local directory to remote obs in contrast
+    """
+    import moxing as mox
+    import time
+    global _global_syn_count
+    sync_lock = '/tmp/copy_sync.lock' + str(_global_syn_count)
+    _global_syn_count += 1
+
+    # Each server contains 8 devices as most
+    if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock):
+        print('from path: ', from_path)
+        print('to path: ', to_path)
+        mox.file.copy_parallel(from_path, to_path)
+        print('===finished data synchronization===')
+        try:
+            os.mknod(sync_lock)
+        except IOError:
+            pass
+        print('===save flag===')
+
+    while True:
+        if os.path.exists(sync_lock):
+            break
+        time.sleep(1)
+    print('Finish sync data from {} to {}'.format(from_path, to_path))
+
+
+def moxing_wrapper(pre_process=None, post_process=None):
+    """
+    Moxing wrapper to download dataset and upload outputs
+    """
+    def wrapper(run_func):
+        @functools.wraps(run_func)
+        def wrapped_func(*args, **kwargs):
+            # Download data from data_url
+            if config.enable_modelarts:
+                if config.data_url:
+                    sync_data(config.data_url, config.data_path)
+                    print('Dataset downloaded: ', os.listdir(config.data_path))
+                if config.checkpoint_url:
+                    if not os.path.exists(config.load_path):
+                        # os.makedirs(config.load_path)
+                        print('=' * 20 + 'makedirs')
+                        if os.path.isdir(config.load_path):
+                            print('=' * 20 + 'makedirs success')
+                        else:
+                            print('=' * 20 + 'makedirs fail')
+                    sync_data(config.checkpoint_url, config.load_path)
+                    print('Preload downloaded: ', os.listdir(config.load_path))
+                if config.train_url:
+                    sync_data(config.train_url, config.output_path)
+                    print('Workspace downloaded: ', os.listdir(config.output_path))
+
+                context.set_context(save_graphs_path=os.path.join(config.output_path, str(get_rank_id())))
+                config.device_num = get_device_num()
+                config.device_id = get_device_id()
+                if not os.path.exists(config.output_path):
+                    os.makedirs(config.output_path)
+
+                if pre_process:
+                    pre_process()
+
+            run_func(*args, **kwargs)
+
+            # Upload data to train_url
+            if config.enable_modelarts:
+                if post_process:
+                    post_process()
+
+                if config.train_url:
+                    print('Start to copy output directory')
+                    sync_data(config.output_path, config.train_url)
+        return wrapped_func
+    return wrapper

model_zoo/official/cv/openpose/train.py

@@ -13,86 +13,84 @@
 # limitations under the License.
 # ============================================================================
 import os
-import argparse
-
+from ast import literal_eval as liter
 import mindspore
 from mindspore import context
 from mindspore.context import ParallelMode
-from mindspore.communication.management import init, get_rank, get_group_size
+from mindspore.communication.management import init
 from mindspore.train import Model
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor
 from mindspore.nn.optim import Adam, Momentum
 from mindspore.train.loss_scale_manager import FixedLossScaleManager
-
 from src.dataset import create_dataset
 from src.openposenet import OpenPoseNet
 from src.loss import openpose_loss, BuildTrainNetwork, TrainOneStepWithClipGradientCell
-from src.config import params
 from src.utils import get_lr, load_model, MyLossMonitor
+from src.model_utils.moxing_adapter import moxing_wrapper
+from src.model_utils.config import config
+from src.model_utils.device_adapter import get_rank_id, get_device_num
 
+
+mindspore.common.seed.set_seed(1)
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
 
-parser = argparse.ArgumentParser('mindspore openpose training')
-parser.add_argument('--train_dir', type=str, default='train2017', help='train data dir')
-parser.add_argument('--train_ann', type=str, default='person_keypoints_train2017.json',
-                    help='train annotations json')
-parser.add_argument('--group_size', type=int, default=1, help='world size of distributed')
-args, _ = parser.parse_known_args()
-args.jsonpath_train = os.path.join(params['data_dir'], 'annotations/' + args.train_ann)
-args.imgpath_train = os.path.join(params['data_dir'], args.train_dir)
-args.maskpath_train = os.path.join(params['data_dir'], 'ignore_mask_train')
+
+def modelarts_pre_process():
+    pass
 
 
+@moxing_wrapper(pre_process=modelarts_pre_process)
 def train():
     """Train function."""
+    config.lr = liter(config.lr)
+    config.outputs_dir = config.save_model_path
+    device_num = get_device_num()
 
-    args.outputs_dir = params['save_model_path']
-
-    if args.group_size > 1:
+    if device_num > 1:
         init()
-        context.set_auto_parallel_context(device_num=get_group_size(), parallel_mode=ParallelMode.DATA_PARALLEL,
+        context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
                                           gradients_mean=True)
-        args.outputs_dir = os.path.join(args.outputs_dir, "ckpt_{}/".format(str(get_rank())))
-        args.rank = get_rank()
+        config.rank = get_rank_id()
+        config.outputs_dir = os.path.join(config.outputs_dir, "ckpt_{}/".format(config.rank))
     else:
-        args.outputs_dir = os.path.join(args.outputs_dir, "ckpt_0/")
-        args.rank = 0
+        config.outputs_dir = os.path.join(config.outputs_dir, "ckpt_0/")
+        config.rank = 0
 
-    if args.group_size > 1:
-        args.max_epoch = params["max_epoch_train_NP"]
-        args.loss_scale = params['loss_scale'] / 2
-        args.lr_steps = list(map(int, params["lr_steps_NP"].split(',')))
-        params['train_type'] = params['train_type_NP']
-        params['optimizer'] = params['optimizer_NP']
-        params['group_params'] = params['group_params_NP']
+    if device_num > 1:
+        config.max_epoch = config.max_epoch_train_NP
+        config.loss_scale = config.loss_scale / 2
+        config.lr_steps = list(map(int, config.lr_steps_NP.split(',')))
+        config.train_type = config.train_type_NP
+        config.optimizer = config.optimizer_NP
+        config.group_params = config.group_params_NP
     else:
-        args.max_epoch = params["max_epoch_train"]
-        args.loss_scale = params['loss_scale']
-        args.lr_steps = list(map(int, params["lr_steps"].split(',')))
+        config.max_epoch = config.max_epoch_train
+        config.loss_scale = config.loss_scale
+        config.lr_steps = list(map(int, config.lr_steps.split(',')))
 
     # create network
     print('start create network')
     criterion = openpose_loss()
     criterion.add_flags_recursive(fp32=True)
-    network = OpenPoseNet(vggpath=params['vgg_path'], vgg_with_bn=params['vgg_with_bn'])
-    if params["load_pretrain"]:
-        print("load pretrain model:", params["pretrained_model_path"])
-        load_model(network, params["pretrained_model_path"])
+    network = OpenPoseNet(vggpath=config.vgg_path, vgg_with_bn=config.vgg_with_bn)
+    if config.load_pretrain:
+        print("load pretrain model:", config.pretrained_model_path)
+        load_model(network, config.pretrained_model_path)
     train_net = BuildTrainNetwork(network, criterion)
 
     # create dataset
-    if os.path.exists(args.jsonpath_train) and os.path.exists(args.imgpath_train) \
-            and os.path.exists(args.maskpath_train):
+    if os.path.exists(config.jsonpath_train) and os.path.exists(config.imgpath_train) \
+            and os.path.exists(config.maskpath_train):
         print('start create dataset')
     else:
         print('Error: wrong data path')
         return 0
 
-    num_worker = 20 if args.group_size > 1 else 48
-    de_dataset_train = create_dataset(args.jsonpath_train, args.imgpath_train, args.maskpath_train,
-                                      batch_size=params['batch_size'],
-                                      rank=args.rank,
-                                      group_size=args.group_size,
+    num_worker = 20 if device_num > 1 else 48
+    de_dataset_train = create_dataset(config.jsonpath_train, config.imgpath_train, config.maskpath_train,
+                                      batch_size=config.batch_size,
+                                      rank=config.rank,
+                                      group_size=device_num,
                                       num_worker=num_worker,
                                       multiprocessing=True,
                                       shuffle=True,
@@ -101,17 +99,17 @@ def train():
     print("steps_per_epoch: ", steps_per_epoch)
 
     # lr scheduler
-    lr_stage, lr_base, lr_vgg = get_lr(params['lr'] * args.group_size,
-                                       params['lr_gamma'],
+    lr_stage, lr_base, lr_vgg = get_lr(config.lr * device_num,
+                                       config.lr_gamma,
                                        steps_per_epoch,
-                                       args.max_epoch,
-                                       args.lr_steps,
-                                       args.group_size,
-                                       lr_type=params['lr_type'],
-                                       warmup_epoch=params['warmup_epoch'])
+                                       config.max_epoch,
+                                       config.lr_steps,
+                                       device_num,
+                                       lr_type=config.lr_type,
+                                       warmup_epoch=config.warmup_epoch)
 
     # optimizer
-    if params['group_params']:
+    if config.group_params:
         vgg19_base_params = list(filter(lambda x: 'base.vgg_base' in x.name, train_net.trainable_params()))
         base_params = list(filter(lambda x: 'base.conv' in x.name, train_net.trainable_params()))
         stages_params = list(filter(lambda x: 'base' not in x.name, train_net.trainable_params()))
@@ -120,47 +118,47 @@ def train():
                         {'params': base_params, 'lr': lr_base},
                         {'params': stages_params, 'lr': lr_stage}]
 
-        if params['optimizer'] == "Momentum":
+        if config.optimizer == "Momentum":
             opt = Momentum(group_params, learning_rate=lr_stage, momentum=0.9)
-        elif params['optimizer'] == "Adam":
+        elif config.optimizer == "Adam":
             opt = Adam(group_params)
         else:
             raise ValueError("optimizer not support.")
     else:
-        if params['optimizer'] == "Momentum":
+        if config.optimizer == "Momentum":
             opt = Momentum(train_net.trainable_params(), learning_rate=lr_stage, momentum=0.9)
-        elif params['optimizer'] == "Adam":
+        elif config.optimizer == "Adam":
             opt = Adam(train_net.trainable_params(), learning_rate=lr_stage)
         else:
             raise ValueError("optimizer not support.")
 
     # callback
-    config_ck = CheckpointConfig(save_checkpoint_steps=params['ckpt_interval'],
-                                 keep_checkpoint_max=params["keep_checkpoint_max"])
-    ckpoint_cb = ModelCheckpoint(prefix='{}'.format(args.rank), directory=args.outputs_dir, config=config_ck)
+    config_ck = CheckpointConfig(save_checkpoint_steps=config.ckpt_interval,
+                                 keep_checkpoint_max=config.keep_checkpoint_max)
+    ckpoint_cb = ModelCheckpoint(prefix='{}'.format(config.rank), directory=config.outputs_dir, config=config_ck)
     time_cb = TimeMonitor(data_size=de_dataset_train.get_dataset_size())
-    if args.rank == 0:
+    if config.rank == 0:
         callback_list = [MyLossMonitor(), time_cb, ckpoint_cb]
     else:
         callback_list = [MyLossMonitor(), time_cb]
 
     # train
-    if params['train_type'] == 'clip_grad':
-        train_net = TrainOneStepWithClipGradientCell(train_net, opt, sens=args.loss_scale)
+    if config.train_type == 'clip_grad':
+        train_net = TrainOneStepWithClipGradientCell(train_net, opt, sens=config.loss_scale)
         train_net.set_train()
         model = Model(train_net)
-    elif params['train_type'] == 'fix_loss_scale':
-        loss_scale_manager = FixedLossScaleManager(args.loss_scale, drop_overflow_update=False)
+    elif config.train_type == 'fix_loss_scale':
+        loss_scale_manager = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
         train_net.set_train()
         model = Model(train_net, optimizer=opt, loss_scale_manager=loss_scale_manager)
     else:
-        raise ValueError("Type {} is not support.".format(params['train_type']))
+        raise ValueError("Type {} is not support.".format(config.train_type))
 
     print("============== Starting Training ==============")
-    model.train(args.max_epoch, de_dataset_train, callbacks=callback_list,
+    model.train(config.max_epoch, de_dataset_train, callbacks=callback_list,
                 dataset_sink_mode=False)
     return 0
 
+
 if __name__ == "__main__":
-    mindspore.common.seed.set_seed(1)
     train()