Modify ssd_ghostnet network.

model_zoo/official/cv/openpose/README.md

@@ -195,7 +195,7 @@ For more configuration details, please refer the script `config.py`.
   ```python
   # grep "AP" eval.log
 
-  {'AP': 0.39830956300341397, 'Ap .5': 0.6658941566481336, 'AP .75': 0.396047897339743, 'AP (M)': 0.3075356543635785, 'AP (L)': 0.533772768618845, 'AR': 0.4519836272040302, 'AR .5': 0.693639798488665, 'AR .75': 0.4570214105793451, 'AR (M)': 0.32155148866429945, 'AR (L)': 0.6330360460795242}
+  {'AP': 0.40250956300341397, 'Ap .5': 0.6658941566481336, 'AP .75': 0.396047897339743, 'AP (M)': 0.3075356543635785, 'AP (L)': 0.533772768618845, 'AR': 0.4519836272040302, 'AR .5': 0.693639798488665, 'AR .75': 0.4570214105793451, 'AR (M)': 0.32155148866429945, 'AR (L)': 0.6330360460795242}
 
   ```
 

model_zoo/research/cv/ssd_ghostnet/README.md

@@ -9,6 +9,7 @@ SSD discretizes the output space of bounding boxes into a set of default boxes o
 The SSD approach is based on a feed-forward convolutional network that produces a fixed-size collection of bounding boxes and scores for the presence of object class instances in those boxes, followed by a non-maximum suppression step to produce the final detections. The early network layers are based on a standard architecture used for high quality image classification, which is called the base network. Then add auxiliary structure to the network to produce detections.
 
 # [Dataset](#contents)
+
 Dataset used: [COCO2017](<http://images.cocodataset.org/>)
 
 - Dataset size：19G
@@ -37,15 +38,16 @@ Dataset used: [COCO2017](<http://images.cocodataset.org/>)
     1. If coco dataset is used. **Select dataset to coco when run script.**
         Install Cython and pycocotool, and you can also install mmcv to process data.
 
-        ```
+        ```bash
         pip install Cython
 
         pip install pycocotools
 
         ```
+
         And change the COCO_ROOT and other settings you need in `config.py`. The directory structure is as follows:
 
-        ```
+        ```python
         .
         └─cocodataset
           ├─annotations
@@ -59,7 +61,7 @@ Dataset used: [COCO2017](<http://images.cocodataset.org/>)
     2. If your own dataset is used. **Select dataset to other when run script.**
         Organize the dataset infomation into a TXT file, each row in the file is as follows:
 
-        ```
+        ```python
         train2017/0000001.jpg 0,259,401,459,7 35,28,324,201,2 0,30,59,80,2
 
         ```
@@ -70,7 +72,7 @@ Dataset used: [COCO2017](<http://images.cocodataset.org/>)
 
 After installing MindSpore via the official website, you can start training and evaluation on Ascend as follows:
 
-```
+```bash
 # single npu training on Ascend
 python train.py
 
@@ -85,7 +87,7 @@ python eval.py --device_id 0 --dataset coco --checkpoint_path LOG4/ssd-500_458.c
 
 ## [Script and Sample Code](#contents)
 
-```shell
+```python
 
   ├── ssd_ghostnet
     ├── README.md                 ## readme file of ssd_ghostnet
@@ -106,7 +108,7 @@ python eval.py --device_id 0 --dataset coco --checkpoint_path LOG4/ssd-500_458.c
 
 ## [Script Parameters](#contents)
 
-  ```
+  ```python
   Major parameters in train.py and config_ghostnet_13x.py as follows:
 
     "device_num": 1                            # Use device nums
@@ -129,39 +131,46 @@ python eval.py --device_id 0 --dataset coco --checkpoint_path LOG4/ssd-500_458.c
 
   ```
 
-
 ## [Training Process](#contents)
 
 ### Training on Ascend
 
 To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/convert_dataset.html) files by `coco_root`(coco dataset) or `iamge_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.**
 
-
 - Distribute mode
 
-```
+```bash
     sh run_distribute_train_ghostnet.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABLE_FILE] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)
 ```
+
 We need five or seven parameters for this scripts.
+
 - `DEVICE_NUM`: the device number for distributed train.
+
 - `EPOCH_NUM`: epoch num for distributed train.
+
 - `LR`: learning rate init value for distributed train.
+
 - `DATASET`：the dataset mode for distributed train.
+
 - `RANK_TABLE_FILE :` the path of [rank_table.json](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools), it is better to use absolute path.
+
 - `PRE_TRAINED :` the path of pretrained checkpoint file, it is better to use absolute path.
+
 - `PRE_TRAINED_EPOCH_SIZE :` the epoch num of pretrained.
 
-    Training result will be stored in the current path, whose folder name begins with "LOG".  Under this, you can find checkpoint file together with result like the followings in LOG4/log.txt.
+Training result will be stored in the current path, whose folder name begins with "LOG".  Under this, you can find checkpoint file together with result like the followings in LOG4/log.txt.
 
 ## [Evaluation Process](#contents)
 
 ### Evaluation on Ascend
 
-```
+```bash
 python eval.py --device_id 0 --dataset coco --checkpoint_path LOG4/ssd-500_458.ckpt
 ```
 
 # [Model Description](#contents)
+
 ## [Performance](#contents)
 
 ### Evaluation Performance
@@ -177,7 +186,6 @@ python eval.py --device_id 0 --dataset coco --checkpoint_path LOG4/ssd-500_458.c
 | Loss Function              | Sigmoid Cross Entropy,SmoothL1Loss                           |
 | Total time                 | 8pcs: 12hours                                                |
 
-
 ### Inference Performance
 
 | Parameters          | Ascend                      |

model_zoo/research/cv/ssd_ghostnet/eval.py

@@ -19,7 +19,7 @@ import os
 import argparse
 import time
 import numpy as np
-from mindspore import context, Tensor
+from mindspore import context
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from src.ssd_ghostnet import SSD300, ssd_ghostnet
 from src.dataset import create_ssd_dataset, data_to_mindrecord_byte_image, voc_data_to_mindrecord
@@ -47,11 +47,11 @@ def ssd_eval(dataset_path, ckpt_path):
     print("total images num: ", total)
     print("Processing, please wait a moment.")
     for data in ds.create_dict_iterator():
-        img_id = data['img_id']
+        img_id = data['img_id'].asnumpy()
         img_np = data['image']
-        image_shape = data['image_shape']
+        image_shape = data['image_shape'].asnumpy()
 
-        output = net(Tensor(img_np))
+        output = net(img_np)
         for batch_idx in range(img_np.shape[0]):
             pred_data.append({"boxes": output[0].asnumpy()[batch_idx],
                               "box_scores": output[1].asnumpy()[batch_idx],

model_zoo/research/cv/ssd_ghostnet/src/dataset.py

@@ -24,7 +24,7 @@ import numpy as np
 import cv2
 
 import mindspore.dataset as de
-import mindspore.dataset.transforms.vision.c_transforms as C
+import mindspore.dataset.vision.c_transforms as C2
 from mindspore.mindrecord import FileWriter
 from .config_ghostnet_13x import config
 from .box_utils import jaccard_numpy, ssd_bboxes_encode
@@ -397,12 +397,12 @@ def create_ssd_dataset(mindrecord_file, batch_size=32, repeat_num=10, device_num
     """Create SSD dataset with MindDataset."""
     ds = de.MindDataset(mindrecord_file, columns_list=["img_id", "image", "annotation"], num_shards=device_num,
                         shard_id=rank, num_parallel_workers=num_parallel_workers, shuffle=is_training)
-    decode = C.Decode()
+    decode = C2.Decode()
     ds = ds.map(input_columns=["image"], operations=decode)
-    change_swap_op = C.HWC2CHW()
-    normalize_op = C.Normalize(
+    change_swap_op = C2.HWC2CHW()
+    normalize_op = C2.Normalize(
         mean=[0.485*255, 0.456*255, 0.406*255], std=[0.229*255, 0.224*255, 0.225*255])
-    color_adjust_op = C.RandomColorAdjust(
+    color_adjust_op = C2.RandomColorAdjust(
         brightness=0.4, contrast=0.4, saturation=0.4)
     compose_map_func = (lambda img_id, image, annotation: preprocess_fn(
         img_id, image, annotation, is_training))
@@ -413,7 +413,7 @@ def create_ssd_dataset(mindrecord_file, batch_size=32, repeat_num=10, device_num
         output_columns = ["img_id", "image", "image_shape"]
         trans = [normalize_op, change_swap_op]
     ds = ds.map(input_columns=["img_id", "image", "annotation"],
-                output_columns=output_columns, columns_order=output_columns,
+                output_columns=output_columns, column_order=output_columns,
                 operations=compose_map_func, python_multiprocessing=is_training,
                 num_parallel_workers=num_parallel_workers)
     ds = ds.map(input_columns=["image"], operations=trans, python_multiprocessing=is_training,

model_zoo/research/cv/ssd_ghostnet/src/init_params.py

@@ -25,16 +25,16 @@ def init_net_param(network, initialize_mode='TruncatedNormal'):
         if 'beta' not in p.name and 'gamma' not in p.name and 'bias' not in p.name:
             np.random.seed(seed=1)
             if initialize_mode == 'TruncatedNormal':
-                p.set_parameter_data(initializer(
+                p.set_data(initializer(
                     TruncatedNormal(), p.data.shape, p.data.dtype))
             else:
-                p.set_parameter_data(
-                    initialize_mode, p.data.shape, p.data.dtype)
+                p.set_data(
+                    initialize_mode, p.data.shape)
 
 
 def load_backbone_params(network, param_dict):
     """Init the parameters from pre-train model, default is mobilenetv2."""
-    for _, param in net.parameters_and_names():
+    for _, param in network.parameters_and_names():
         param_name = param.name.replace('network.backbone.', '')
         name_split = param_name.split('.')
         if 'features_1' in param_name:

model_zoo/research/cv/ssd_ghostnet/src/ssd_ghostnet.py

@@ -118,13 +118,11 @@ class DepthwiseConv(nn.Cell):
                                                       stride=stride, pad_mode=pad_mode, pad=pad)
         self.bias_add = P.BiasAdd()
         weight_shape = [channel_multiplier, in_planes, *self.kernel_size]
-        self.weight = Parameter(initializer(
-            'ones', weight_shape))
+        self.weight = Parameter(initializer('ones', weight_shape), name="weight")
 
         if has_bias:
             bias_shape = [channel_multiplier * in_planes]
-            self.bias = Parameter(initializer(
-                'zeros', bias_shape))
+            self.bias = Parameter(initializer('zeros', bias_shape), name="bias")
         else:
             self.bias = None
 
@@ -624,15 +622,15 @@ class TrainingWrapper(nn.Cell):
         self.network = network
         self.weights = ms.ParameterTuple(network.trainable_params())
         self.optimizer = optimizer
-        self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
+        self.grad = C.GradOperation(get_by_list=True, sens_param=True)
         self.sens = sens
         self.reducer_flag = False
         self.grad_reducer = None
         self.parallel_mode = context.get_auto_parallel_context("parallel_mode")
-        if self.parallel_mode in [ms.ParallelMode.DATA_PARALLEL, ms.ParallelMode.HYBRID_PARALLEL]:
+        if self.parallel_mode in [context.ParallelMode.DATA_PARALLEL, context.ParallelMode.HYBRID_PARALLEL]:
             self.reducer_flag = True
         if self.reducer_flag:
-            mean = context.get_auto_parallel_context("mirror_mean")
+            mean = context.get_auto_parallel_context("gradients_mean")
             if auto_parallel_context().get_device_num_is_set():
                 degree = context.get_auto_parallel_context("device_num")
             else:

model_zoo/research/cv/ssd_ghostnet/train.py

@@ -70,7 +70,7 @@ def main():
     if args_opt.distribute:
         device_num = args_opt.device_num
         context.reset_auto_parallel_context()
-        context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True,
+        context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True,
                                           device_num=device_num)
         init()
         rank = args_opt.device_id % device_num