!7887 opt maskrcnn dataset performance

Merge pull request !7887 from gengdongjie/master

model_zoo/official/cv/maskrcnn/README.md

@@ -35,6 +35,9 @@ MaskRCNN is a two-stage target detection network. It extends FasterRCNN by addin
 [Paper](http://cn.arxiv.org/pdf/1703.06870v3): Kaiming He, Georgia Gkioxari, Piotr Dollar and Ross Girshick. "MaskRCNN"
 
 # [Dataset](#contents)
+
+Note that you can run the scripts based on the dataset mentioned in original paper or widely used in relevant domain/network architecture. In the following sections, we will introduce how to run the scripts using the related dataset below.
+
 - [COCO2017](https://cocodataset.org/) is a popular dataset with bounding-box and pixel-level stuff annotations. These annotations can be used for scene understanding tasks like semantic segmentation, object detection and image captioning. There are 118K/5K images for train/val. 
 
 - Dataset size: 19G
@@ -398,12 +401,12 @@ Accumulating evaluation results...
 
 ### Evaluation Performance
 
-| Parameters                 | MaskRCNN                                                  |
+| Parameters                 | Ascend                                                      |
 | -------------------------- | ----------------------------------------------------------- |
 | Model Version              | V1                                                          |
-| Resource                   | Ascend 910; CPU 2.60GHz, 192cores; Memory, 755G              |
+| Resource                   | Ascend 910; CPU 2.60GHz, 192cores; Memory, 755G             |
 | uploaded Date              | 08/01/2020 (month/day/year)                                 |
-| MindSpore Version          | 0.6.0-alpha                                                 |
+| MindSpore Version          | 1.0.0                                                       |
 | Dataset                    | COCO2017                                                    |
 | Training Parameters        | epoch=12,  batch_size = 2                                   |
 | Optimizer                  | SGD                                                         |
@@ -414,18 +417,18 @@ Accumulating evaluation results...
 | Total time                 | 1pc: 46 hours;  8pcs: 5.38 hours                            |
 | Parameters (M)             | 84.8                                                        |
 | Checkpoint for Fine tuning | 85M(.ckpt file)                                             |
-| Model for inference        | 571M(.air file)                                                            |
+| Model for inference        | 571M(.air file)                                             |
 | Scripts                    | [maskrcnn script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/maskrcnn) |
 
 
 ### Inference Performance
 
-| Parameters          | MaskRCNN                    |
+| Parameters          | Ascend                      |
 | ------------------- | --------------------------- |
 | Model Version       | V1                          |
 | Resource            | Ascend 910                  |
 | Uploaded Date       | 08/01/2020 (month/day/year) |
-| MindSpore Version   | 0.6.0-alpha                 |
+| MindSpore Version   | 1.0.0                       |
 | Dataset             | COCO2017                    |
 | batch_size          | 2                           |
 | outputs             | mAP                         |

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

@@ -301,10 +301,8 @@ def pad_to_max(img, img_shape, gt_bboxes, gt_label, gt_num, gt_mask, instance_co
     gt_label_new = np.pad(gt_label, ((0, pad_max_number - instance_count)), mode="constant", constant_values=-1)
     gt_iscrowd_new = np.pad(gt_num, ((0, pad_max_number - instance_count)), mode="constant", constant_values=1)
     gt_iscrowd_new_revert = ~(gt_iscrowd_new.astype(np.bool))
-    gt_mask_new = np.pad(gt_mask, ((0, pad_max_number - instance_count), (0, 0), (0, 0)), mode="constant",
-                         constant_values=0)
 
-    return img, img_shape, gt_box_new, gt_label_new, gt_iscrowd_new_revert, gt_mask_new
+    return img, img_shape, gt_box_new, gt_label_new, gt_iscrowd_new_revert, gt_mask
 
 def preprocess_fn(image, box, mask, mask_shape, is_training):
     """Preprocess function for dataset."""
@@ -518,7 +516,7 @@ def create_maskrcnn_dataset(mindrecord_file, batch_size=2, device_num=1, rank_id
                     column_order=["image", "image_shape", "box", "label", "valid_num", "mask"],
                     python_multiprocessing=False,
                     num_parallel_workers=num_parallel_workers)
-        ds = ds.batch(batch_size, drop_remainder=True)
+        ds = ds.batch(batch_size, drop_remainder=True, pad_info={"mask": ([config.max_instance_count, None, None], 0)})
 
     else:
         ds = ds.map(operations=compose_map_func,