!9023 add maskrcnn_mobilenetv1 network

From: @wang_hua_2019
Reviewed-by: 
Signed-off-by:

model_zoo/official/cv/maskrcnn_mobilenetv1/eval.py

@@ -0,0 +1,134 @@
+# 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
+#
+# less 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.
+# ============================================================================
+
+"""Evaluation for MaskRcnn"""
+import os
+import argparse
+import time
+import numpy as np
+from pycocotools.coco import COCO
+from mindspore import context, Tensor
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.common import set_seed
+
+from src.maskrcnn_mobilenetv1.mask_rcnn_mobilenetv1 import Mask_Rcnn_Mobilenetv1
+from src.config import config
+from src.dataset import data_to_mindrecord_byte_image, create_maskrcnn_dataset
+from src.util import coco_eval, bbox2result_1image, results2json, get_seg_masks
+
+set_seed(1)
+
+parser = argparse.ArgumentParser(description="MaskRcnn evaluation")
+parser.add_argument("--dataset", type=str, default="coco", help="Dataset, default is coco.")
+parser.add_argument("--ann_file", type=str, default="val.json", help="Ann file, default is val.json.")
+parser.add_argument("--checkpoint_path", type=str, required=True, help="Checkpoint file path.")
+parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
+args_opt = parser.parse_args()
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
+
+def MaskRcnn_eval(dataset_path, ckpt_path, ann_file):
+    """MaskRcnn evaluation."""
+    ds = create_maskrcnn_dataset(dataset_path, batch_size=config.test_batch_size, is_training=False)
+
+    net = Mask_Rcnn_Mobilenetv1(config)
+    param_dict = load_checkpoint(ckpt_path)
+    load_param_into_net(net, param_dict)
+    net.set_train(False)
+
+    eval_iter = 0
+    total = ds.get_dataset_size()
+    outputs = []
+    dataset_coco = COCO(ann_file)
+
+    print("\n========================================\n")
+    print("total images num: ", total)
+    print("Processing, please wait a moment.")
+    max_num = 128
+
+    for data in ds.create_dict_iterator(output_numpy=True, num_epochs=1):
+        img_data = data['image']
+        img_metas = data['image_shape']
+        gt_bboxes = data['box']
+        gt_labels = data['label']
+        gt_num = data['valid_num']
+        gt_mask = data["mask"]
+
+        start = time.time()
+        # run net
+        output = net(Tensor(img_data), Tensor(img_metas), Tensor(gt_bboxes), Tensor(gt_labels), Tensor(gt_num),
+                     Tensor(gt_mask))
+        end = time.time()
+        print("Iter {} cost time {}".format(eval_iter, end - start))
+
+        # output
+        all_bbox = output[0]
+        all_label = output[1]
+        all_mask = output[2]
+        all_mask_fb = output[3]
+
+        for j in range(config.test_batch_size):
+            all_bbox_squee = np.squeeze(all_bbox.asnumpy()[j, :, :])
+            all_label_squee = np.squeeze(all_label.asnumpy()[j, :, :])
+            all_mask_squee = np.squeeze(all_mask.asnumpy()[j, :, :])
+            all_mask_fb_squee = np.squeeze(all_mask_fb.asnumpy()[j, :, :, :])
+
+            all_bboxes_tmp_mask = all_bbox_squee[all_mask_squee, :]
+            all_labels_tmp_mask = all_label_squee[all_mask_squee]
+            all_mask_fb_tmp_mask = all_mask_fb_squee[all_mask_squee, :, :]
+
+            if all_bboxes_tmp_mask.shape[0] > max_num:
+                inds = np.argsort(-all_bboxes_tmp_mask[:, -1])
+                inds = inds[:max_num]
+                all_bboxes_tmp_mask = all_bboxes_tmp_mask[inds]
+                all_labels_tmp_mask = all_labels_tmp_mask[inds]
+                all_mask_fb_tmp_mask = all_mask_fb_tmp_mask[inds]
+
+
+            bbox_results = bbox2result_1image(all_bboxes_tmp_mask, all_labels_tmp_mask, config.num_classes)
+            segm_results = get_seg_masks(all_mask_fb_tmp_mask, all_bboxes_tmp_mask, all_labels_tmp_mask, img_metas[j],
+                                         True, config.num_classes)
+            outputs.append((bbox_results, segm_results))
+
+        eval_iter = eval_iter + 1
+
+    eval_types = ["bbox", "segm"]
+    result_files = results2json(dataset_coco, outputs, "./results.pkl")
+    coco_eval(result_files, eval_types, dataset_coco, single_result=False)
+
+if __name__ == '__main__':
+    prefix = "MaskRcnn_eval.mindrecord"
+    mindrecord_dir = config.mindrecord_dir
+    mindrecord_file = os.path.join(mindrecord_dir, prefix)
+    if not os.path.exists(mindrecord_file):
+        if not os.path.isdir(mindrecord_dir):
+            os.makedirs(mindrecord_dir)
+        if args_opt.dataset == "coco":
+            if os.path.isdir(config.coco_root):
+                print("Create Mindrecord.")
+                data_to_mindrecord_byte_image("coco", False, prefix, file_num=1)
+                print("Create Mindrecord Done, at {}".format(mindrecord_dir))
+            else:
+                print("coco_root not exits.")
+        else:
+            if os.path.isdir(config.IMAGE_DIR) and os.path.exists(config.ANNO_PATH):
+                print("Create Mindrecord.")
+                data_to_mindrecord_byte_image("other", False, prefix, file_num=1)
+                print("Create Mindrecord Done, at {}".format(mindrecord_dir))
+            else:
+                print("IMAGE_DIR or ANNO_PATH not exits.")
+
+    print("Start Eval!")
+    MaskRcnn_eval(mindrecord_file, args_opt.checkpoint_path, args_opt.ann_file)

model_zoo/official/cv/maskrcnn_mobilenetv1/mindspore_hub_conf.py

@@ -0,0 +1,22 @@
+# 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.
+# ============================================================================
+"""hub config."""
+from src.maskrcnn_mobilenetv1.mask_rcnn_mobilenetv1 import Mask_Rcnn_Mobilenetv1
+from src.config import config
+
+def create_network(name, *args, **kwargs):
+    if name == "maskrcnn_mobilenetv1":
+        return Mask_Rcnn_Mobilenetv1(config=config)
+    raise NotImplementedError(f"{name} is not implemented in the repo")

model_zoo/official/cv/maskrcnn_mobilenetv1/scripts/run_distribute_train.sh

@@ -0,0 +1,73 @@
+#!/bin/bash
+# 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.
+# ============================================================================
+
+if [ $# != 2 ]
+then 
+    echo "Usage: sh run_train.sh [RANK_TABLE_FILE] [PRETRAINED_PATH]"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+PATH1=$(get_real_path $1)
+PATH2=$2
+
+echo $PATH1
+echo $PATH2
+
+if [ ! -f $PATH1 ]
+then 
+    echo "error: RANK_TABLE_FILE=$PATH1 is not a file"
+exit 1
+fi 
+
+ulimit -u unlimited
+export HCCL_CONNECT_TIMEOUT=600
+export DEVICE_NUM=8
+export RANK_SIZE=8
+export RANK_TABLE_FILE=$PATH1
+
+echo 3 > /proc/sys/vm/drop_caches
+
+cpus=`cat /proc/cpuinfo| grep "processor"| wc -l`
+avg=`expr $cpus \/ $RANK_SIZE`
+gap=`expr $avg \- 1`
+
+for((i=0; i<${DEVICE_NUM}; i++))
+do
+    start=`expr $i \* $avg`
+    end=`expr $start \+ $gap`
+    cmdopt=$start"-"$end
+
+    export DEVICE_ID=$i
+    export RANK_ID=$i
+    rm -rf ./train_parallel$i
+    mkdir ./train_parallel$i
+    cp ../*.py ./train_parallel$i
+    cp *.sh ./train_parallel$i
+    cp -r ../src ./train_parallel$i
+    cd ./train_parallel$i || exit
+    echo "start training for rank $RANK_ID, device $DEVICE_ID"
+    env > env.log
+    taskset -c $cmdopt python train.py --do_train=True  --device_id=$i --rank_id=$i --run_distribute=True --device_num=$DEVICE_NUM \
+                    --pre_trained=$PATH2 &> log &
+    cd ..
+done

model_zoo/official/cv/maskrcnn_mobilenetv1/scripts/run_eval.sh

@@ -0,0 +1,65 @@
+#!/bin/bash
+# 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.
+# ============================================================================
+
+if [ $# != 2 ]
+then 
+    echo "Usage: sh run_eval.sh [ANN_FILE] [CHECKPOINT_PATH]"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+PATH1=$(get_real_path $1)
+PATH2=$(get_real_path $2)
+echo $PATH1
+echo $PATH2
+
+if [ ! -f $PATH1 ]
+then 
+    echo "error: ANN_FILE=$PATH1 is not a file"
+exit 1
+fi 
+
+if [ ! -f $PATH2 ]
+then 
+    echo "error: CHECKPOINT_PATH=$PATH2 is not a file"
+exit 1
+fi 
+
+ulimit -u unlimited
+export DEVICE_NUM=1
+export RANK_SIZE=$DEVICE_NUM
+export DEVICE_ID=0
+export RANK_ID=0
+
+if [ -d "eval" ];
+then
+    rm -rf ./eval
+fi
+mkdir ./eval
+cp ../*.py ./eval
+cp *.sh ./eval
+cp -r ../src ./eval
+cd ./eval || exit
+env > env.log
+echo "start eval for device $DEVICE_ID"
+python eval.py --device_id=$DEVICE_ID --ann_file=$PATH1 --checkpoint_path=$PATH2 &> log &
+cd ..

model_zoo/official/cv/maskrcnn_mobilenetv1/scripts/run_standalone_train.sh

@@ -0,0 +1,57 @@
+#!/bin/bash
+# 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.
+# ============================================================================
+
+if [ $# != 1 ]
+then 
+    echo "Usage: sh run_standalone_train.sh [PRETRAINED_PATH]"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+PATH1=$(get_real_path $1)
+echo $PATH1
+
+if [ ! -f $PATH1 ]
+then 
+    echo "error: PRETRAINED_PATH=$PATH1 is not a file"
+exit 1
+fi
+
+ulimit -u unlimited
+export DEVICE_NUM=1
+export DEVICE_ID=0
+export RANK_ID=0
+export RANK_SIZE=1
+
+if [ -d "train" ];
+then
+    rm -rf ./train
+fi
+mkdir ./train
+cp ../*.py ./train
+cp *.sh ./train
+cp -r ../src ./train
+cd ./train || exit
+echo "start training for device $DEVICE_ID"
+env > env.log
+python train.py --do_train=True --device_id=$DEVICE_ID --pre_trained=$PATH1 &> log &
+cd ..

model_zoo/official/cv/maskrcnn_mobilenetv1/src/config.py

@@ -0,0 +1,159 @@
+# 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.
+#" :===========================================================================
+"""
+network config setting, will be used in train.py and eval.py
+"""
+from easydict import EasyDict as ed
+
+config = ed({
+    "img_width": 1280,
+    "img_height": 768,
+    "keep_ratio": True,
+    "flip_ratio": 0.5,
+    "photo_ratio": 0.5,
+    "expand_ratio": 1.0,
+
+    "max_instance_count": 128,
+    "mask_shape": (28, 28),
+
+    # anchor
+    "feature_shapes": [(192, 320), (96, 160), (48, 80), (24, 40), (12, 20)],
+    "anchor_scales": [8],
+    "anchor_ratios": [0.5, 1.0, 2.0],
+    "anchor_strides": [4, 8, 16, 32, 64],
+    "num_anchors": 3,
+
+    # fpn
+    "fpn_in_channels": [128, 256, 512, 1024],
+    "fpn_out_channels": 256,
+    "fpn_num_outs": 5,
+
+    # rpn
+    "rpn_in_channels": 256,
+    "rpn_feat_channels": 256,
+    "rpn_loss_cls_weight": 1.0,
+    "rpn_loss_reg_weight": 1.0,
+    "rpn_cls_out_channels": 1,
+    "rpn_target_means": [0., 0., 0., 0.],
+    "rpn_target_stds": [1.0, 1.0, 1.0, 1.0],
+
+    # bbox_assign_sampler
+    "neg_iou_thr": 0.3,
+    "pos_iou_thr": 0.7,
+    "min_pos_iou": 0.3,
+    "num_bboxes": 245520,
+    "num_gts": 128,
+    "num_expected_neg": 256,
+    "num_expected_pos": 128,
+
+    # proposal
+    "activate_num_classes": 2,
+    "use_sigmoid_cls": True,
+
+    # roi_align
+    "roi_layer": dict(type='RoIAlign', out_size=7, mask_out_size=14, sample_num=2),
+    "roi_align_out_channels": 256,
+    "roi_align_featmap_strides": [4, 8, 16, 32],
+    "roi_align_finest_scale": 56,
+    "roi_sample_num": 640,
+
+    # bbox_assign_sampler_stage2
+    "neg_iou_thr_stage2": 0.5,
+    "pos_iou_thr_stage2": 0.5,
+    "min_pos_iou_stage2": 0.5,
+    "num_bboxes_stage2": 2000,
+    "num_expected_pos_stage2": 128,
+    "num_expected_neg_stage2": 512,
+    "num_expected_total_stage2": 512,
+
+    # rcnn
+    "rcnn_num_layers": 2,
+    "rcnn_in_channels": 256,
+    "rcnn_fc_out_channels": 1024,
+    "rcnn_mask_out_channels": 256,
+    "rcnn_loss_cls_weight": 1,
+    "rcnn_loss_reg_weight": 1,
+    "rcnn_loss_mask_fb_weight": 1,
+    "rcnn_target_means": [0., 0., 0., 0.],
+    "rcnn_target_stds": [0.1, 0.1, 0.2, 0.2],
+
+    # train proposal
+    "rpn_proposal_nms_across_levels": False,
+    "rpn_proposal_nms_pre": 2000,
+    "rpn_proposal_nms_post": 2000,
+    "rpn_proposal_max_num": 2000,
+    "rpn_proposal_nms_thr": 0.7,
+    "rpn_proposal_min_bbox_size": 0,
+
+    # test proposal
+    "rpn_nms_across_levels": False,
+    "rpn_nms_pre": 1000,
+    "rpn_nms_post": 1000,
+    "rpn_max_num": 1000,
+    "rpn_nms_thr": 0.7,
+    "rpn_min_bbox_min_size": 0,
+    "test_score_thr": 0.05,
+    "test_iou_thr": 0.5,
+    "test_max_per_img": 100,
+    "test_batch_size": 2,
+
+    "rpn_head_loss_type": "CrossEntropyLoss",
+    "rpn_head_use_sigmoid": True,
+    "rpn_head_weight": 1.0,
+    "mask_thr_binary": 0.5,
+
+    # LR
+    "base_lr": 0.04,
+    "base_step": 58633,
+    "total_epoch": 13,
+    "warmup_step": 500,
+    "warmup_mode": "linear",
+    "warmup_ratio": 1/3.0,
+    "sgd_momentum": 0.9,
+
+    # train
+    "batch_size": 2,
+    "loss_scale": 1,
+    "momentum": 0.91,
+    "weight_decay": 1e-4,
+    "pretrain_epoch_size": 0,
+    "epoch_size": 12,
+    "save_checkpoint": True,
+    "save_checkpoint_epochs": 1,
+    "keep_checkpoint_max": 12,
+    "save_checkpoint_path": "./",
+
+    "mindrecord_dir": "/home/mask_rcnn/MindRecord_COCO2017_Train",
+    "coco_root": "/home/mask_rcnn/coco2017/",
+    "train_data_type": "train2017",
+    "val_data_type": "val2017",
+    "instance_set": "annotations/instances_{}.json",
+    "coco_classes": ('background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
+                     'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
+                     'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
+                     'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
+                     'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
+                     'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
+                     'kite', 'baseball bat', 'baseball glove', 'skateboard',
+                     'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
+                     'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
+                     'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
+                     'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
+                     'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
+                     'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
+                     'refrigerator', 'book', 'clock', 'vase', 'scissors',
+                     'teddy bear', 'hair drier', 'toothbrush'),
+    "num_classes": 81
+})

model_zoo/official/cv/maskrcnn_mobilenetv1/src/lr_schedule.py

@@ -0,0 +1,42 @@
+# 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.
+# ============================================================================
+"""lr generator for maskrcnn_mobilenetv1"""
+import math
+
+def linear_warmup_learning_rate(current_step, warmup_steps, base_lr, init_lr):
+    lr_inc = (float(base_lr) - float(init_lr)) / float(warmup_steps)
+    learning_rate = float(init_lr) + lr_inc * current_step
+    return learning_rate
+
+def a_cosine_learning_rate(current_step, base_lr, warmup_steps, decay_steps):
+    base = float(current_step - warmup_steps) / float(decay_steps)
+    learning_rate = (1 + math.cos(base * math.pi)) / 2 * base_lr
+    return learning_rate
+
+def dynamic_lr(config, rank_size=1, start_steps=0):
+    """dynamic learning rate generator"""
+    base_lr = config.base_lr
+
+    base_step = (config.base_step // rank_size) + rank_size
+    total_steps = int(base_step * config.total_epoch)
+    warmup_steps = int(config.warmup_step)
+    lr = []
+    for i in range(total_steps):
+        if i < warmup_steps:
+            lr.append(linear_warmup_learning_rate(i, warmup_steps, base_lr, base_lr * config.warmup_ratio))
+        else:
+            lr.append(a_cosine_learning_rate(i, base_lr, warmup_steps, total_steps))
+    learning_rate = lr[start_steps:]
+    return learning_rate

model_zoo/official/cv/maskrcnn_mobilenetv1/src/maskrcnn_mobilenetv1/__init__.py

@@ -0,0 +1,32 @@
+# 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.
+# ============================================================================
+"""MaskRcnn Init."""
+
+from .mobilenetv1 import MobileNetV1, MobileNetV1_FeatureSelector
+from .bbox_assign_sample import BboxAssignSample
+from .bbox_assign_sample_stage2 import BboxAssignSampleForRcnn
+from .fpn_neck import FeatPyramidNeck
+from .proposal_generator import Proposal
+from .rcnn_cls import RcnnCls
+from .rcnn_mask import RcnnMask
+from .rpn import RPN
+from .roi_align import SingleRoIExtractor
+from .anchor_generator import AnchorGenerator
+
+__all__ = [
+    "MobileNetV1", "MobileNetV1_FeatureSelector", "BboxAssignSample", "BboxAssignSampleForRcnn",
+    "FeatPyramidNeck", "Proposal", "RcnnCls", "RcnnMask",
+    "RPN", "SingleRoIExtractor", "AnchorGenerator"
+]

model_zoo/official/cv/maskrcnn_mobilenetv1/src/maskrcnn_mobilenetv1/anchor_generator.py

@@ -0,0 +1,84 @@
+# 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.
+# ============================================================================
+"""MaskRcnn anchor generator."""
+
+import numpy as np
+
+class AnchorGenerator():
+    """Anchor generator for MasKRcnn."""
+    def __init__(self, base_size, scales, ratios, scale_major=True, ctr=None):
+        """Anchor generator init method."""
+        self.base_size = base_size
+        self.scales = np.array(scales)
+        self.ratios = np.array(ratios)
+        self.scale_major = scale_major
+        self.ctr = ctr
+        self.base_anchors = self.gen_base_anchors()
+
+    def gen_base_anchors(self):
+        """Generate a single anchor."""
+        w = self.base_size
+        h = self.base_size
+        if self.ctr is None:
+            x_ctr = 0.5 * (w - 1)
+            y_ctr = 0.5 * (h - 1)
+        else:
+            x_ctr, y_ctr = self.ctr
+
+        h_ratios = np.sqrt(self.ratios)
+        w_ratios = 1 / h_ratios
+        if self.scale_major:
+            ws = (w * w_ratios[:, None] * self.scales[None, :]).reshape(-1)
+            hs = (h * h_ratios[:, None] * self.scales[None, :]).reshape(-1)
+        else:
+            ws = (w * self.scales[:, None] * w_ratios[None, :]).reshape(-1)
+            hs = (h * self.scales[:, None] * h_ratios[None, :]).reshape(-1)
+
+        base_anchors = np.stack(
+            [
+                x_ctr - 0.5 * (ws - 1), y_ctr - 0.5 * (hs - 1),
+                x_ctr + 0.5 * (ws - 1), y_ctr + 0.5 * (hs - 1)
+            ],
+            axis=-1).round()
+
+        return base_anchors
+
+    def _meshgrid(self, x, y, row_major=True):
+        """Generate grid."""
+        xx = np.repeat(x.reshape(1, len(x)), len(y), axis=0).reshape(-1)
+        yy = np.repeat(y, len(x))
+        if row_major:
+            return xx, yy
+
+        return yy, xx
+
+    def grid_anchors(self, featmap_size, stride=16):
+        """Generate anchor list."""
+        base_anchors = self.base_anchors
+
+        feat_h, feat_w = featmap_size
+        shift_x = np.arange(0, feat_w) * stride
+        shift_y = np.arange(0, feat_h) * stride
+        shift_xx, shift_yy = self._meshgrid(shift_x, shift_y)
+        shifts = np.stack([shift_xx, shift_yy, shift_xx, shift_yy], axis=-1)
+        shifts = shifts.astype(base_anchors.dtype)
+        # first feat_w elements correspond to the first row of shifts
+        # add A anchors (1, A, 4) to K shifts (K, 1, 4) to get
+        # shifted anchors (K, A, 4), reshape to (K*A, 4)
+
+        all_anchors = base_anchors[None, :, :] + shifts[:, None, :]
+        all_anchors = all_anchors.reshape(-1, 4)
+
+        return all_anchors

model_zoo/official/cv/maskrcnn_mobilenetv1/src/maskrcnn_mobilenetv1/bbox_assign_sample.py

@@ -0,0 +1,164 @@
+# 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.
+# ============================================================================
+"""MaskRcnn positive and negative sample screening for RPN."""
+
+import numpy as np
+import mindspore.nn as nn
+from mindspore.ops import operations as P
+from mindspore.common.tensor import Tensor
+import mindspore.common.dtype as mstype
+
+
+class BboxAssignSample(nn.Cell):
+    """
+    Bbox assigner and sampler defination.
+
+    Args:
+        config (dict): Config.
+        batch_size (int): Batchsize.
+        num_bboxes (int): The anchor nums.
+        add_gt_as_proposals (bool): add gt bboxes as proposals flag.
+
+    Returns:
+        Tensor, output tensor.
+        bbox_targets: bbox location, (batch_size, num_bboxes, 4)
+        bbox_weights: bbox weights, (batch_size, num_bboxes, 1)
+        labels: label for every bboxes, (batch_size, num_bboxes, 1)
+        label_weights: label weight for every bboxes, (batch_size, num_bboxes, 1)
+
+    Examples:
+        BboxAssignSample(config, 2, 1024, True)
+    """
+
+    def __init__(self, config, batch_size, num_bboxes, add_gt_as_proposals):
+        super(BboxAssignSample, self).__init__()
+        cfg = config
+        self.batch_size = batch_size
+
+        self.neg_iou_thr = Tensor(cfg.neg_iou_thr, mstype.float16)
+        self.pos_iou_thr = Tensor(cfg.pos_iou_thr, mstype.float16)
+        self.min_pos_iou = Tensor(cfg.min_pos_iou, mstype.float16)
+        self.zero_thr = Tensor(0.0, mstype.float16)
+
+        self.num_bboxes = num_bboxes
+        self.num_gts = cfg.num_gts
+        self.num_expected_pos = cfg.num_expected_pos
+        self.num_expected_neg = cfg.num_expected_neg
+        self.add_gt_as_proposals = add_gt_as_proposals
+
+        if self.add_gt_as_proposals:
+            self.label_inds = Tensor(np.arange(1, self.num_gts + 1))
+
+        self.concat = P.Concat(axis=0)
+        self.max_gt = P.ArgMaxWithValue(axis=0)
+        self.max_anchor = P.ArgMaxWithValue(axis=1)
+        self.sum_inds = P.ReduceSum()
+        self.iou = P.IOU()
+        self.greaterequal = P.GreaterEqual()
+        self.greater = P.Greater()
+        self.select = P.Select()
+        self.gatherND = P.GatherNd()
+        self.squeeze = P.Squeeze()
+        self.cast = P.Cast()
+        self.logicaland = P.LogicalAnd()
+        self.less = P.Less()
+        self.random_choice_with_mask_pos = P.RandomChoiceWithMask(self.num_expected_pos)
+        self.random_choice_with_mask_neg = P.RandomChoiceWithMask(self.num_expected_neg)
+        self.reshape = P.Reshape()
+        self.equal = P.Equal()
+        self.bounding_box_encode = P.BoundingBoxEncode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0))
+        self.scatterNdUpdate = P.ScatterNdUpdate()
+        self.scatterNd = P.ScatterNd()
+        self.logicalnot = P.LogicalNot()
+        self.tile = P.Tile()
+        self.zeros_like = P.ZerosLike()
+
+        self.assigned_gt_inds = Tensor(np.array(-1 * np.ones(num_bboxes), dtype=np.int32))
+        self.assigned_gt_zeros = Tensor(np.array(np.zeros(num_bboxes), dtype=np.int32))
+        self.assigned_gt_ones = Tensor(np.array(np.ones(num_bboxes), dtype=np.int32))
+        self.assigned_gt_ignores = Tensor(np.array(-1 * np.ones(num_bboxes), dtype=np.int32))
+        self.assigned_pos_ones = Tensor(np.array(np.ones(self.num_expected_pos), dtype=np.int32))
+
+        self.check_neg_mask = Tensor(np.array(np.ones(self.num_expected_neg - self.num_expected_pos), dtype=np.bool))
+        self.range_pos_size = Tensor(np.arange(self.num_expected_pos).astype(np.float16))
+        self.check_gt_one = Tensor(np.array(-1 * np.ones((self.num_gts, 4)), dtype=np.float16))
+        self.check_anchor_two = Tensor(np.array(-2 * np.ones((self.num_bboxes, 4)), dtype=np.float16))
+
+
+    def construct(self, gt_bboxes_i, gt_labels_i, valid_mask, bboxes, gt_valids):
+        gt_bboxes_i = self.select(self.cast(self.tile(self.reshape(self.cast(gt_valids, mstype.int32), \
+                                  (self.num_gts, 1)), (1, 4)), mstype.bool_), gt_bboxes_i, self.check_gt_one)
+        bboxes = self.select(self.cast(self.tile(self.reshape(self.cast(valid_mask, mstype.int32), \
+                             (self.num_bboxes, 1)), (1, 4)), mstype.bool_), bboxes, self.check_anchor_two)
+
+        overlaps = self.iou(bboxes, gt_bboxes_i)
+
+        max_overlaps_w_gt_index, max_overlaps_w_gt = self.max_gt(overlaps)
+        _, max_overlaps_w_ac = self.max_anchor(overlaps)
+
+        neg_sample_iou_mask = self.logicaland(self.greaterequal(max_overlaps_w_gt, self.zero_thr), \
+                                              self.less(max_overlaps_w_gt, self.neg_iou_thr))
+        assigned_gt_inds2 = self.select(neg_sample_iou_mask, self.assigned_gt_zeros, self.assigned_gt_inds)
+
+        pos_sample_iou_mask = self.greaterequal(max_overlaps_w_gt, self.pos_iou_thr)
+        assigned_gt_inds3 = self.select(pos_sample_iou_mask, \
+                                        max_overlaps_w_gt_index + self.assigned_gt_ones, assigned_gt_inds2)
+        assigned_gt_inds4 = assigned_gt_inds3
+        for j in range(self.num_gts):
+            max_overlaps_w_ac_j = max_overlaps_w_ac[j:j+1:1]
+            overlaps_w_gt_j = self.squeeze(overlaps[j:j+1:1, ::])
+
+            pos_mask_j = self.logicaland(self.greaterequal(max_overlaps_w_ac_j, self.min_pos_iou), \
+                                         self.equal(overlaps_w_gt_j, max_overlaps_w_ac_j))
+
+            assigned_gt_inds4 = self.select(pos_mask_j, self.assigned_gt_ones + j, assigned_gt_inds4)
+
+        assigned_gt_inds5 = self.select(valid_mask, assigned_gt_inds4, self.assigned_gt_ignores)
+
+        pos_index, valid_pos_index = self.random_choice_with_mask_pos(self.greater(assigned_gt_inds5, 0))
+
+        pos_check_valid = self.cast(self.greater(assigned_gt_inds5, 0), mstype.float16)
+        pos_check_valid = self.sum_inds(pos_check_valid, -1)
+        valid_pos_index = self.less(self.range_pos_size, pos_check_valid)
+        pos_index = pos_index * self.reshape(self.cast(valid_pos_index, mstype.int32), (self.num_expected_pos, 1))
+
+        pos_assigned_gt_index = self.gatherND(assigned_gt_inds5, pos_index) - self.assigned_pos_ones
+        pos_assigned_gt_index = pos_assigned_gt_index * self.cast(valid_pos_index, mstype.int32)
+        pos_assigned_gt_index = self.reshape(pos_assigned_gt_index, (self.num_expected_pos, 1))
+
+        neg_index, valid_neg_index = self.random_choice_with_mask_neg(self.equal(assigned_gt_inds5, 0))
+
+        num_pos = self.cast(self.logicalnot(valid_pos_index), mstype.float16)
+        num_pos = self.sum_inds(num_pos, -1)
+        unvalid_pos_index = self.less(self.range_pos_size, num_pos)
+        valid_neg_index = self.logicaland(self.concat((self.check_neg_mask, unvalid_pos_index)), valid_neg_index)
+
+        pos_bboxes_ = self.gatherND(bboxes, pos_index)
+        pos_gt_bboxes_ = self.gatherND(gt_bboxes_i, pos_assigned_gt_index)
+        pos_gt_labels = self.gatherND(gt_labels_i, pos_assigned_gt_index)
+
+        pos_bbox_targets_ = self.bounding_box_encode(pos_bboxes_, pos_gt_bboxes_)
+
+        valid_pos_index = self.cast(valid_pos_index, mstype.int32)
+        valid_neg_index = self.cast(valid_neg_index, mstype.int32)
+        bbox_targets_total = self.scatterNd(pos_index, pos_bbox_targets_, (self.num_bboxes, 4))
+        bbox_weights_total = self.scatterNd(pos_index, valid_pos_index, (self.num_bboxes,))
+        labels_total = self.scatterNd(pos_index, pos_gt_labels, (self.num_bboxes,))
+        total_index = self.concat((pos_index, neg_index))
+        total_valid_index = self.concat((valid_pos_index, valid_neg_index))
+        label_weights_total = self.scatterNd(total_index, total_valid_index, (self.num_bboxes,))
+
+        return bbox_targets_total, self.cast(bbox_weights_total, mstype.bool_), \
+               labels_total, self.cast(label_weights_total, mstype.bool_)

model_zoo/official/cv/maskrcnn_mobilenetv1/src/maskrcnn_mobilenetv1/bbox_assign_sample_stage2.py

@@ -0,0 +1,221 @@
+# 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.
+# ============================================================================
+"""MaskRcnn tpositive and negative sample screening for Rcnn."""
+
+import numpy as np
+import mindspore.nn as nn
+import mindspore.common.dtype as mstype
+from mindspore.ops import operations as P
+from mindspore.common.tensor import Tensor
+
+class BboxAssignSampleForRcnn(nn.Cell):
+    """
+    Bbox assigner and sampler defination.
+
+    Args:
+        config (dict): Config.
+        batch_size (int): Batchsize.
+        num_bboxes (int): The anchor nums.
+        add_gt_as_proposals (bool): add gt bboxes as proposals flag.
+
+    Returns:
+        Tensor, multiple output tensors.
+
+    Examples:
+        BboxAssignSampleForRcnn(config, 2, 1024, True)
+    """
+
+    def __init__(self, config, batch_size, num_bboxes, add_gt_as_proposals):
+        super(BboxAssignSampleForRcnn, self).__init__()
+        cfg = config
+        self.batch_size = batch_size
+        self.neg_iou_thr = cfg.neg_iou_thr_stage2
+        self.pos_iou_thr = cfg.pos_iou_thr_stage2
+        self.min_pos_iou = cfg.min_pos_iou_stage2
+        self.num_gts = cfg.num_gts
+        self.num_bboxes = num_bboxes
+        self.num_expected_pos = cfg.num_expected_pos_stage2
+        self.num_expected_neg = cfg.num_expected_neg_stage2
+        self.num_expected_total = cfg.num_expected_total_stage2
+
+        self.add_gt_as_proposals = add_gt_as_proposals
+        self.label_inds = Tensor(np.arange(1, self.num_gts + 1).astype(np.int32))
+        self.add_gt_as_proposals_valid = Tensor(np.array(self.add_gt_as_proposals * np.ones(self.num_gts),
+                                                         dtype=np.int32))
+
+        self.concat = P.Concat(axis=0)
+        self.max_gt = P.ArgMaxWithValue(axis=0)
+        self.max_anchor = P.ArgMaxWithValue(axis=1)
+        self.sum_inds = P.ReduceSum()
+        self.iou = P.IOU()
+        self.greaterequal = P.GreaterEqual()
+        self.greater = P.Greater()
+        self.select = P.Select()
+        self.gatherND = P.GatherNd()
+        self.squeeze = P.Squeeze()
+        self.cast = P.Cast()
+        self.logicaland = P.LogicalAnd()
+        self.less = P.Less()
+        self.random_choice_with_mask_pos = P.RandomChoiceWithMask(self.num_expected_pos)
+        self.random_choice_with_mask_neg = P.RandomChoiceWithMask(self.num_expected_neg)
+        self.reshape = P.Reshape()
+        self.equal = P.Equal()
+        self.bounding_box_encode = P.BoundingBoxEncode(means=(0.0, 0.0, 0.0, 0.0), stds=(0.1, 0.1, 0.2, 0.2))
+        self.concat_axis1 = P.Concat(axis=1)
+        self.logicalnot = P.LogicalNot()
+        self.tile = P.Tile()
+
+        # Check
+        self.check_gt_one = Tensor(np.array(-1 * np.ones((self.num_gts, 4)), dtype=np.float16))
+        self.check_anchor_two = Tensor(np.array(-2 * np.ones((self.num_bboxes, 4)), dtype=np.float16))
+
+        # Init tensor
+        self.assigned_gt_inds = Tensor(np.array(-1 * np.ones(num_bboxes), dtype=np.int32))
+        self.assigned_gt_zeros = Tensor(np.array(np.zeros(num_bboxes), dtype=np.int32))
+        self.assigned_gt_ones = Tensor(np.array(np.ones(num_bboxes), dtype=np.int32))
+        self.assigned_gt_ignores = Tensor(np.array(-1 * np.ones(num_bboxes), dtype=np.int32))
+        self.assigned_pos_ones = Tensor(np.array(np.ones(self.num_expected_pos), dtype=np.int32))
+
+        self.gt_ignores = Tensor(np.array(-1 * np.ones(self.num_gts), dtype=np.int32))
+        self.range_pos_size = Tensor(np.arange(self.num_expected_pos).astype(np.float16))
+        self.check_neg_mask = Tensor(np.array(np.ones(self.num_expected_neg - self.num_expected_pos), dtype=np.bool))
+        self.bboxs_neg_mask = Tensor(np.zeros((self.num_expected_neg, 4), dtype=np.float16))
+        self.labels_neg_mask = Tensor(np.array(np.zeros(self.num_expected_neg), dtype=np.uint8))
+
+        self.reshape_shape_pos = (self.num_expected_pos, 1)
+        self.reshape_shape_neg = (self.num_expected_neg, 1)
+
+        self.scalar_zero = Tensor(0.0, dtype=mstype.float16)
+        self.scalar_neg_iou_thr = Tensor(self.neg_iou_thr, dtype=mstype.float16)
+        self.scalar_pos_iou_thr = Tensor(self.pos_iou_thr, dtype=mstype.float16)
+        self.scalar_min_pos_iou = Tensor(self.min_pos_iou, dtype=mstype.float16)
+
+        self.expand_dims = P.ExpandDims()
+        self.split = P.Split(axis=1, output_num=4)
+        self.concat_last_axis = P.Concat(axis=-1)
+        self.round = P.Round()
+        self.image_h_w = Tensor([cfg.img_height, cfg.img_width, cfg.img_height, cfg.img_width], dtype=mstype.float16)
+        self.range = nn.Range(start=0, limit=cfg.num_expected_pos_stage2)
+        self.crop_and_resize = P.CropAndResize(method="bilinear_v2")
+        self.mask_shape = (cfg.mask_shape[0], cfg.mask_shape[1])
+        self.squeeze_mask_last = P.Squeeze(axis=-1)
+    def construct(self, gt_bboxes_i, gt_labels_i, valid_mask, bboxes, gt_valids, gt_masks_i):
+        gt_bboxes_i = self.select(self.cast(self.tile(self.reshape(self.cast(gt_valids, mstype.int32), \
+                                  (self.num_gts, 1)), (1, 4)), mstype.bool_), \
+                                  gt_bboxes_i, self.check_gt_one)
+        bboxes = self.select(self.cast(self.tile(self.reshape(self.cast(valid_mask, mstype.int32), \
+                             (self.num_bboxes, 1)), (1, 4)), mstype.bool_), \
+                             bboxes, self.check_anchor_two)
+
+        overlaps = self.iou(bboxes, gt_bboxes_i)
+
+        max_overlaps_w_gt_index, max_overlaps_w_gt = self.max_gt(overlaps)
+        _, max_overlaps_w_ac = self.max_anchor(overlaps)
+
+        neg_sample_iou_mask = self.logicaland(self.greaterequal(max_overlaps_w_gt,
+                                                                self.scalar_zero),
+                                              self.less(max_overlaps_w_gt,
+                                                        self.scalar_neg_iou_thr))
+
+        assigned_gt_inds2 = self.select(neg_sample_iou_mask, self.assigned_gt_zeros, self.assigned_gt_inds)
+
+        pos_sample_iou_mask = self.greaterequal(max_overlaps_w_gt, self.scalar_pos_iou_thr)
+        assigned_gt_inds3 = self.select(pos_sample_iou_mask, \
+                                        max_overlaps_w_gt_index + self.assigned_gt_ones, assigned_gt_inds2)
+
+        for j in range(self.num_gts):
+            max_overlaps_w_ac_j = max_overlaps_w_ac[j:j+1:1]
+            overlaps_w_ac_j = overlaps[j:j+1:1, ::]
+            temp1 = self.greaterequal(max_overlaps_w_ac_j, self.scalar_min_pos_iou)
+            temp2 = self.squeeze(self.equal(overlaps_w_ac_j, max_overlaps_w_ac_j))
+            pos_mask_j = self.logicaland(temp1, temp2)
+            assigned_gt_inds3 = self.select(pos_mask_j, (j+1)*self.assigned_gt_ones, assigned_gt_inds3)
+
+        assigned_gt_inds5 = self.select(valid_mask, assigned_gt_inds3, self.assigned_gt_ignores)
+
+        bboxes = self.concat((gt_bboxes_i, bboxes))
+        label_inds_valid = self.select(gt_valids, self.label_inds, self.gt_ignores)
+        label_inds_valid = label_inds_valid * self.add_gt_as_proposals_valid
+        assigned_gt_inds5 = self.concat((label_inds_valid, assigned_gt_inds5))
+
+        # Get pos index
+        pos_index, valid_pos_index = self.random_choice_with_mask_pos(self.greater(assigned_gt_inds5, 0))
+
+        pos_check_valid = self.cast(self.greater(assigned_gt_inds5, 0), mstype.float16)
+        pos_check_valid = self.sum_inds(pos_check_valid, -1)
+        valid_pos_index = self.less(self.range_pos_size, pos_check_valid)
+        pos_index = pos_index * self.reshape(self.cast(valid_pos_index, mstype.int32), (self.num_expected_pos, 1))
+
+        num_pos = self.sum_inds(self.cast(self.logicalnot(valid_pos_index), mstype.float16), -1)
+        valid_pos_index = self.cast(valid_pos_index, mstype.int32)
+        pos_index = self.reshape(pos_index, self.reshape_shape_pos)
+        valid_pos_index = self.reshape(valid_pos_index, self.reshape_shape_pos)
+        pos_index = pos_index * valid_pos_index
+
+        pos_assigned_gt_index = self.gatherND(assigned_gt_inds5, pos_index) - self.assigned_pos_ones
+        pos_assigned_gt_index = self.reshape(pos_assigned_gt_index, self.reshape_shape_pos)
+        pos_assigned_gt_index = pos_assigned_gt_index * valid_pos_index
+
+        pos_gt_labels = self.gatherND(gt_labels_i, pos_assigned_gt_index)
+
+        # Get neg index
+        neg_index, valid_neg_index = self.random_choice_with_mask_neg(self.equal(assigned_gt_inds5, 0))
+
+        unvalid_pos_index = self.less(self.range_pos_size, num_pos)
+        valid_neg_index = self.logicaland(self.concat((self.check_neg_mask, unvalid_pos_index)), valid_neg_index)
+        neg_index = self.reshape(neg_index, self.reshape_shape_neg)
+
+        valid_neg_index = self.cast(valid_neg_index, mstype.int32)
+        valid_neg_index = self.reshape(valid_neg_index, self.reshape_shape_neg)
+        neg_index = neg_index * valid_neg_index
+
+        pos_bboxes_ = self.gatherND(bboxes, pos_index)
+
+        neg_bboxes_ = self.gatherND(bboxes, neg_index)
+        pos_assigned_gt_index = self.reshape(pos_assigned_gt_index, self.reshape_shape_pos)
+        pos_gt_bboxes_ = self.gatherND(gt_bboxes_i, pos_assigned_gt_index)
+        pos_bbox_targets_ = self.bounding_box_encode(pos_bboxes_, pos_gt_bboxes_)
+
+        # assign positive ROIs to gt masks
+        # Pick the right front and background mask for each ROI
+        roi_pos_masks_fb = self.gatherND(gt_masks_i, pos_assigned_gt_index)
+        pos_masks_fb = self.cast(roi_pos_masks_fb, mstype.float32)
+        # compute mask targets
+        x1, y1, x2, y2 = self.split(pos_bboxes_)
+        boxes = self.concat_last_axis((y1, x1, y2, x2))
+        # normalized box coordinate
+        boxes = boxes / self.image_h_w
+        box_ids = self.range()
+        pos_masks_fb = self.expand_dims(pos_masks_fb, -1)
+        boxes = self.cast(boxes, mstype.float32)
+        pos_masks_fb = self.crop_and_resize(pos_masks_fb, boxes, box_ids, self.mask_shape)
+
+        # Remove the extra dimension from masks.
+        pos_masks_fb = self.squeeze_mask_last(pos_masks_fb)
+
+        # convert gt masks targets be 0 or 1 to use with binary cross entropy loss.
+        pos_masks_fb = self.round(pos_masks_fb)
+
+        pos_masks_fb = self.cast(pos_masks_fb, mstype.float16)
+        total_bboxes = self.concat((pos_bboxes_, neg_bboxes_))
+        total_deltas = self.concat((pos_bbox_targets_, self.bboxs_neg_mask))
+        total_labels = self.concat((pos_gt_labels, self.labels_neg_mask))
+
+        valid_pos_index = self.reshape(valid_pos_index, self.reshape_shape_pos)
+        valid_neg_index = self.reshape(valid_neg_index, self.reshape_shape_neg)
+        total_mask = self.concat((valid_pos_index, valid_neg_index))
+
+        return total_bboxes, total_deltas, total_labels, total_mask, pos_bboxes_, pos_masks_fb, \
+               pos_gt_labels, valid_pos_index

model_zoo/official/cv/maskrcnn_mobilenetv1/src/maskrcnn_mobilenetv1/fpn_neck.py

@@ -0,0 +1,111 @@
+# 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.
+# ============================================================================
+"""MaskRcnn feature pyramid network."""
+
+import numpy as np
+import mindspore.nn as nn
+from mindspore.ops import operations as P
+from mindspore.common.tensor import Tensor
+from mindspore.common import dtype as mstype
+from mindspore.common.initializer import initializer
+
+
+def bias_init_zeros(shape):
+    """Bias init method."""
+    return Tensor(np.array(np.zeros(shape).astype(np.float32)).astype(np.float16))
+
+def _conv(in_channels, out_channels, kernel_size=3, stride=1, padding=0, pad_mode='pad'):
+    """Conv2D wrapper."""
+    shape = (out_channels, in_channels, kernel_size, kernel_size)
+    weights = initializer("XavierUniform", shape=shape, dtype=mstype.float16).to_tensor()
+    shape_bias = (out_channels,)
+    biass = bias_init_zeros(shape_bias)
+    return nn.Conv2d(in_channels, out_channels,
+                     kernel_size=kernel_size, stride=stride, padding=padding,
+                     pad_mode=pad_mode, weight_init=weights, has_bias=True, bias_init=biass)
+
+class FeatPyramidNeck(nn.Cell):
+    """
+    Feature pyramid network cell, usually uses as network neck.
+
+    Applies the convolution on multiple, input feature maps
+    and output feature map with same channel size. if required num of
+    output larger then num of inputs, add extra maxpooling for further
+    downsampling;
+
+    Args:
+        in_channels (tuple) - Channel size of input feature maps.
+        out_channels (int) - Channel size output.
+        num_outs (int) - Num of output features.
+
+    Returns:
+        Tuple, with tensors of same channel size.
+
+    Examples:
+        neck = FeatPyramidNeck([100,200,300], 50, 4)
+        input_data = (normal(0,0.1,(1,c,1280//(4*2**i), 768//(4*2**i)),
+                      dtype=np.float32) \
+                      for i, c in enumerate(config.fpn_in_channels))
+        x = neck(input_data)
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 num_outs):
+        super(FeatPyramidNeck, self).__init__()
+        self.num_outs = num_outs
+        self.in_channels = in_channels
+        self.fpn_layer = len(self.in_channels)
+
+        assert not self.num_outs < len(in_channels)
+
+        self.lateral_convs_list_ = []
+        self.fpn_convs_ = []
+
+        for _, channel in enumerate(in_channels):
+            l_conv = _conv(channel, out_channels, kernel_size=1, stride=1, padding=0, pad_mode='valid')
+            fpn_conv = _conv(out_channels, out_channels, kernel_size=3, stride=1, padding=0, pad_mode='same')
+            self.lateral_convs_list_.append(l_conv)
+            self.fpn_convs_.append(fpn_conv)
+        self.lateral_convs_list = nn.layer.CellList(self.lateral_convs_list_)
+        self.fpn_convs_list = nn.layer.CellList(self.fpn_convs_)
+        self.interpolate1 = P.ResizeBilinear((48, 80))
+        self.interpolate2 = P.ResizeBilinear((96, 160))
+        self.interpolate3 = P.ResizeBilinear((192, 320))
+        self.cast = P.Cast()
+        self.maxpool = P.MaxPool(ksize=1, strides=2, padding="same")
+
+    def construct(self, inputs):
+        x = ()
+        for i in range(self.fpn_layer):
+            x += (self.lateral_convs_list[i](inputs[i]),)
+
+        y = (x[3],)
+        y = y + (x[2] + self.cast(self.interpolate1(y[self.fpn_layer - 4]), mstype.float16),)
+        y = y + (x[1] + self.cast(self.interpolate2(y[self.fpn_layer - 3]), mstype.float16),)
+        y = y + (x[0] + self.cast(self.interpolate3(y[self.fpn_layer - 2]), mstype.float16),)
+
+        z = ()
+        for i in range(self.fpn_layer - 1, -1, -1):
+            z = z + (y[i],)
+
+        outs = ()
+        for i in range(self.fpn_layer):
+            outs = outs + (self.fpn_convs_list[i](z[i]),)
+
+        for i in range(self.num_outs - self.fpn_layer):
+            outs = outs + (self.maxpool(outs[3]),)
+        return outs

model_zoo/official/cv/maskrcnn_mobilenetv1/src/maskrcnn_mobilenetv1/mobilenetv1.py

@@ -0,0 +1,117 @@
+# 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.
+# ============================================================================
+"""MobilenetV1 backbone."""
+
+import mindspore.nn as nn
+from mindspore.ops import operations as P
+
+def conv_bn_relu(in_channel, out_channel, kernel_size, stride, depthwise, activation='relu6'):
+    output = []
+    output.append(nn.Conv2d(in_channel, out_channel, kernel_size, stride, pad_mode='same',
+                            group=1 if not depthwise else in_channel))
+    output.append(nn.BatchNorm2d(out_channel))
+    if activation:
+        output.append(nn.get_activation(activation))
+    return nn.SequentialCell(output)
+
+class MobileNetV1(nn.Cell):
+    """
+    MobileNet V1 backbone
+    """
+    def __init__(self, class_num=1001, features_only=False):
+        super(MobileNetV1, self).__init__()
+        self.features_only = features_only
+        cnn = [
+            conv_bn_relu(3, 32, 3, 2, False),
+
+            conv_bn_relu(32, 32, 3, 1, True),
+            conv_bn_relu(32, 64, 1, 1, False),
+
+            conv_bn_relu(64, 64, 3, 2, True),
+            conv_bn_relu(64, 128, 1, 1, False),
+            conv_bn_relu(128, 128, 3, 1, True),
+            conv_bn_relu(128, 128, 1, 1, False),
+
+            conv_bn_relu(128, 128, 3, 2, True),
+            conv_bn_relu(128, 256, 1, 1, False),
+            conv_bn_relu(256, 256, 3, 1, True),
+            conv_bn_relu(256, 256, 1, 1, False),
+
+            conv_bn_relu(256, 256, 3, 2, True),
+            conv_bn_relu(256, 512, 1, 1, False),
+            conv_bn_relu(512, 512, 3, 1, True),
+            conv_bn_relu(512, 512, 1, 1, False),
+            conv_bn_relu(512, 512, 3, 1, True),
+            conv_bn_relu(512, 512, 1, 1, False),
+            conv_bn_relu(512, 512, 3, 1, True),
+            conv_bn_relu(512, 512, 1, 1, False),
+            conv_bn_relu(512, 512, 3, 1, True),
+            conv_bn_relu(512, 512, 1, 1, False),
+            conv_bn_relu(512, 512, 3, 1, True),
+            conv_bn_relu(512, 512, 1, 1, False),
+
+            conv_bn_relu(512, 512, 3, 2, True),
+            conv_bn_relu(512, 1024, 1, 1, False),
+            conv_bn_relu(1024, 1024, 3, 1, True),
+            conv_bn_relu(1024, 1024, 1, 1, False),
+        ]
+
+        if self.features_only:
+            self.network = nn.CellList(cnn)
+        else:
+            self.network = nn.SequentialCell(cnn)
+            self.fc = nn.Dense(1024, class_num)
+
+    def construct(self, x):
+        output = x
+        if self.features_only:
+            features = ()
+            for block in self.network:
+                output = block(output)
+                features = features + (output,)
+            return features
+
+        output = self.network(x)
+        output = P.ReduceMean()(output, (2, 3))
+        output = self.fc(output)
+        return output
+
+class FeatureSelector(nn.Cell):
+    """
+    Select specific layers from en entire feature list
+    """
+    def __init__(self, feature_idxes):
+        super(FeatureSelector, self).__init__()
+        self.feature_idxes = feature_idxes
+
+    def construct(self, feature_list):
+        selected = ()
+        for i in self.feature_idxes:
+            selected = selected + (feature_list[i],)
+        return selected
+
+class MobileNetV1_FeatureSelector(nn.Cell):
+    """
+    Mobilenet v1 feature selector
+    """
+    def __init__(self, class_num=1001, features_only=False):
+        super(MobileNetV1_FeatureSelector, self).__init__()
+        self.mobilenet_v1 = MobileNetV1(class_num=class_num, features_only=features_only)
+        self.selector = FeatureSelector([6, 10, 22, 26])
+
+    def construct(self, x):
+        features = self.mobilenet_v1(x)
+        features = self.selector(features)
+        return features

model_zoo/official/cv/maskrcnn_mobilenetv1/src/maskrcnn_mobilenetv1/proposal_generator.py

@@ -0,0 +1,195 @@
+# 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.
+# ============================================================================
+"""MaskRcnn proposal generator."""
+
+import numpy as np
+import mindspore.nn as nn
+import mindspore.common.dtype as mstype
+from mindspore.ops import operations as P
+from mindspore import Tensor
+
+
+class Proposal(nn.Cell):
+    """
+    Proposal subnet.
+
+    Args:
+        config (dict): Config.
+        batch_size (int): Batchsize.
+        num_classes (int) - Class number.
+        use_sigmoid_cls (bool) - Select sigmoid or softmax function.
+        target_means (tuple) - Means for encode function. Default: (.0, .0, .0, .0).
+        target_stds (tuple) - Stds for encode function. Default: (1.0, 1.0, 1.0, 1.0).
+
+    Returns:
+        Tuple, tuple of output tensor,(proposal, mask).
+
+    Examples:
+        Proposal(config = config, batch_size = 1, num_classes = 81, use_sigmoid_cls = True, \
+                 target_means=(.0, .0, .0, .0), target_stds=(1.0, 1.0, 1.0, 1.0))
+    """
+    def __init__(self,
+                 config,
+                 batch_size,
+                 num_classes,
+                 use_sigmoid_cls,
+                 target_means=(.0, .0, .0, .0),
+                 target_stds=(1.0, 1.0, 1.0, 1.0)
+                 ):
+        super(Proposal, self).__init__()
+        cfg = config
+        self.batch_size = batch_size
+        self.num_classes = num_classes
+        self.target_means = target_means
+        self.target_stds = target_stds
+        self.use_sigmoid_cls = use_sigmoid_cls
+
+        if self.use_sigmoid_cls:
+            self.cls_out_channels = num_classes - 1
+            self.activation = P.Sigmoid()
+            self.reshape_shape = (-1, 1)
+        else:
+            self.cls_out_channels = num_classes
+            self.activation = P.Softmax(axis=1)
+            self.reshape_shape = (-1, 2)
+
+        if self.cls_out_channels <= 0:
+            raise ValueError('num_classes={} is too small'.format(num_classes))
+
+        self.num_pre = cfg.rpn_proposal_nms_pre
+        self.min_box_size = cfg.rpn_proposal_min_bbox_size
+        self.nms_thr = cfg.rpn_proposal_nms_thr
+        self.nms_post = cfg.rpn_proposal_nms_post
+        self.nms_across_levels = cfg.rpn_proposal_nms_across_levels
+        self.max_num = cfg.rpn_proposal_max_num
+        self.num_levels = cfg.fpn_num_outs
+
+        # Op Define
+        self.squeeze = P.Squeeze()
+        self.reshape = P.Reshape()
+        self.cast = P.Cast()
+
+        self.feature_shapes = cfg.feature_shapes
+
+        self.transpose_shape = (1, 2, 0)
+
+        self.decode = P.BoundingBoxDecode(max_shape=(cfg.img_height, cfg.img_width), \
+                                          means=self.target_means, \
+                                          stds=self.target_stds)
+
+        self.nms = P.NMSWithMask(self.nms_thr)
+        self.concat_axis0 = P.Concat(axis=0)
+        self.concat_axis1 = P.Concat(axis=1)
+        self.split = P.Split(axis=1, output_num=5)
+        self.min = P.Minimum()
+        self.gatherND = P.GatherNd()
+        self.slice = P.Slice()
+        self.select = P.Select()
+        self.greater = P.Greater()
+        self.transpose = P.Transpose()
+        self.tile = P.Tile()
+        self.set_train_local(config, training=True)
+
+        self.multi_10 = Tensor(10.0, mstype.float16)
+
+    def set_train_local(self, config, training=True):
+        """Set training flag."""
+        self.training_local = training
+
+        cfg = config
+        self.topK_stage1 = ()
+        self.topK_shape = ()
+        total_max_topk_input = 0
+        if not self.training_local:
+            self.num_pre = cfg.rpn_nms_pre
+            self.min_box_size = cfg.rpn_min_bbox_min_size
+            self.nms_thr = cfg.rpn_nms_thr
+            self.nms_post = cfg.rpn_nms_post
+            self.nms_across_levels = cfg.rpn_nms_across_levels
+            self.max_num = cfg.rpn_max_num
+
+        for shp in self.feature_shapes:
+            k_num = min(self.num_pre, (shp[0] * shp[1] * 3))
+            total_max_topk_input += k_num
+            self.topK_stage1 += (k_num,)
+            self.topK_shape += ((k_num, 1),)
+
+        self.topKv2 = P.TopK(sorted=True)
+        self.topK_shape_stage2 = (self.max_num, 1)
+        self.min_float_num = -65536.0
+        self.topK_mask = Tensor(self.min_float_num * np.ones(total_max_topk_input, np.float16))
+
+    def construct(self, rpn_cls_score_total, rpn_bbox_pred_total, anchor_list):
+        proposals_tuple = ()
+        masks_tuple = ()
+        for img_id in range(self.batch_size):
+            cls_score_list = ()
+            bbox_pred_list = ()
+            for i in range(self.num_levels):
+                rpn_cls_score_i = self.squeeze(rpn_cls_score_total[i][img_id:img_id+1:1, ::, ::, ::])
+                rpn_bbox_pred_i = self.squeeze(rpn_bbox_pred_total[i][img_id:img_id+1:1, ::, ::, ::])
+
+                cls_score_list = cls_score_list + (rpn_cls_score_i,)
+                bbox_pred_list = bbox_pred_list + (rpn_bbox_pred_i,)
+
+            proposals, masks = self.get_bboxes_single(cls_score_list, bbox_pred_list, anchor_list)
+            proposals_tuple += (proposals,)
+            masks_tuple += (masks,)
+        return proposals_tuple, masks_tuple
+
+    def get_bboxes_single(self, cls_scores, bbox_preds, mlvl_anchors):
+        """Get proposal boundingbox."""
+        mlvl_proposals = ()
+        mlvl_mask = ()
+        for idx in range(self.num_levels):
+            rpn_cls_score = self.transpose(cls_scores[idx], self.transpose_shape)
+            rpn_bbox_pred = self.transpose(bbox_preds[idx], self.transpose_shape)
+            anchors = mlvl_anchors[idx]
+
+            rpn_cls_score = self.reshape(rpn_cls_score, self.reshape_shape)
+            rpn_cls_score = self.activation(rpn_cls_score)
+            rpn_cls_score_process = self.cast(self.squeeze(rpn_cls_score[::, 0::]), mstype.float16)
+
+            rpn_bbox_pred_process = self.cast(self.reshape(rpn_bbox_pred, (-1, 4)), mstype.float16)
+
+            scores_sorted, topk_inds = self.topKv2(rpn_cls_score_process, self.topK_stage1[idx])
+
+            topk_inds = self.reshape(topk_inds, self.topK_shape[idx])
+
+            bboxes_sorted = self.gatherND(rpn_bbox_pred_process, topk_inds)
+            anchors_sorted = self.cast(self.gatherND(anchors, topk_inds), mstype.float16)
+
+            proposals_decode = self.decode(anchors_sorted, bboxes_sorted)
+
+            proposals_decode = self.concat_axis1((proposals_decode, self.reshape(scores_sorted, self.topK_shape[idx])))
+            proposals, _, mask_valid = self.nms(proposals_decode)
+
+            mlvl_proposals = mlvl_proposals + (proposals,)
+            mlvl_mask = mlvl_mask + (mask_valid,)
+
+        proposals = self.concat_axis0(mlvl_proposals)
+        masks = self.concat_axis0(mlvl_mask)
+
+        _, _, _, _, scores = self.split(proposals)
+        scores = self.squeeze(scores)
+        topk_mask = self.cast(self.topK_mask, mstype.float16)
+        scores_using = self.select(masks, scores, topk_mask)
+
+        _, topk_inds = self.topKv2(scores_using, self.max_num)
+
+        topk_inds = self.reshape(topk_inds, self.topK_shape_stage2)
+        proposals = self.gatherND(proposals, topk_inds)
+        masks = self.gatherND(masks, topk_inds)
+        return proposals, masks

model_zoo/official/cv/maskrcnn_mobilenetv1/src/maskrcnn_mobilenetv1/rcnn_cls.py

@@ -0,0 +1,178 @@
+# 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.
+# ============================================================================
+"""MaskRcnn Rcnn classification and box regression network."""
+
+import numpy as np
+import mindspore.common.dtype as mstype
+import mindspore.nn as nn
+from mindspore.ops import operations as P
+from mindspore.common.tensor import Tensor
+from mindspore.common.initializer import initializer
+from mindspore.common.parameter import Parameter
+
+class DenseNoTranpose(nn.Cell):
+    """Dense method"""
+    def __init__(self, input_channels, output_channels, weight_init):
+        super(DenseNoTranpose, self).__init__()
+        self.weight = Parameter(initializer(weight_init, [input_channels, output_channels], mstype.float16),
+                                name="weight")
+        self.bias = Parameter(initializer("zeros", [output_channels], mstype.float16).to_tensor(), name="bias")
+        self.matmul = P.MatMul(transpose_b=False)
+        self.bias_add = P.BiasAdd()
+
+    def construct(self, x):
+        output = self.bias_add(self.matmul(x, self.weight), self.bias)
+        return output
+
+class FpnCls(nn.Cell):
+    """dense layer of classification and box head"""
+    def __init__(self, input_channels, output_channels, num_classes, pool_size):
+        super(FpnCls, self).__init__()
+        representation_size = input_channels * pool_size * pool_size
+        shape_0 = (output_channels, representation_size)
+        weights_0 = initializer("XavierUniform", shape=shape_0[::-1], dtype=mstype.float16).to_tensor()
+        shape_1 = (output_channels, output_channels)
+        weights_1 = initializer("XavierUniform", shape=shape_1[::-1], dtype=mstype.float16).to_tensor()
+        self.shared_fc_0 = DenseNoTranpose(representation_size, output_channels, weights_0)
+        self.shared_fc_1 = DenseNoTranpose(output_channels, output_channels, weights_1)
+
+        cls_weight = initializer('Normal', shape=[num_classes, output_channels][::-1],
+                                 dtype=mstype.float16).to_tensor()
+        reg_weight = initializer('Normal', shape=[num_classes * 4, output_channels][::-1],
+                                 dtype=mstype.float16).to_tensor()
+        self.cls_scores = DenseNoTranpose(output_channels, num_classes, cls_weight)
+        self.reg_scores = DenseNoTranpose(output_channels, num_classes * 4, reg_weight)
+
+        self.relu = P.ReLU()
+        self.flatten = P.Flatten()
+
+    def construct(self, x):
+        # two share fc layer
+        x = self.flatten(x)
+
+        x = self.relu(self.shared_fc_0(x))
+        x = self.relu(self.shared_fc_1(x))
+
+        # classifier head
+        cls_scores = self.cls_scores(x)
+        # bbox head
+        reg_scores = self.reg_scores(x)
+
+        return cls_scores, reg_scores
+
+class RcnnCls(nn.Cell):
+    """
+    Rcnn for classification and box regression subnet.
+
+    Args:
+        config (dict) - Config.
+        batch_size (int) - Batchsize.
+        num_classes (int) - Class number.
+        target_means (list) - Means for encode function. Default: (.0, .0, .0, .0]).
+        target_stds (list) - Stds for encode function. Default: (0.1, 0.1, 0.2, 0.2).
+
+    Returns:
+        Tuple, tuple of output tensor.
+
+    Examples:
+        RcnnCls(config=config, representation_size = 1024, batch_size=2, num_classes = 81, \
+             target_means=(0., 0., 0., 0.), target_stds=(0.1, 0.1, 0.2, 0.2))
+    """
+    def __init__(self,
+                 config,
+                 batch_size,
+                 num_classes,
+                 target_means=(0., 0., 0., 0.),
+                 target_stds=(0.1, 0.1, 0.2, 0.2)
+                 ):
+        super(RcnnCls, self).__init__()
+        cfg = config
+        self.rcnn_loss_cls_weight = Tensor(np.array(cfg.rcnn_loss_cls_weight).astype(np.float16))
+        self.rcnn_loss_reg_weight = Tensor(np.array(cfg.rcnn_loss_reg_weight).astype(np.float16))
+        self.rcnn_fc_out_channels = cfg.rcnn_fc_out_channels
+        self.target_means = target_means
+        self.target_stds = target_stds
+        self.num_classes = num_classes
+        self.in_channels = cfg.rcnn_in_channels
+        self.train_batch_size = batch_size
+        self.test_batch_size = cfg.test_batch_size
+
+        self.fpn_cls = FpnCls(self.in_channels, self.rcnn_fc_out_channels, self.num_classes, cfg.roi_layer["out_size"])
+        self.relu = P.ReLU()
+        self.logicaland = P.LogicalAnd()
+        self.loss_cls = P.SoftmaxCrossEntropyWithLogits()
+        self.loss_bbox = P.SmoothL1Loss(beta=1.0)
+        self.loss_mask = P.SigmoidCrossEntropyWithLogits()
+        self.reshape = P.Reshape()
+        self.onehot = P.OneHot()
+        self.greater = P.Greater()
+        self.cast = P.Cast()
+        self.sum_loss = P.ReduceSum()
+        self.tile = P.Tile()
+        self.expandims = P.ExpandDims()
+
+        self.gather = P.GatherNd()
+        self.argmax = P.ArgMaxWithValue(axis=1)
+
+        self.on_value = Tensor(1.0, mstype.float32)
+        self.off_value = Tensor(0.0, mstype.float32)
+        self.value = Tensor(1.0, mstype.float16)
+
+        self.num_bboxes = (cfg.num_expected_pos_stage2 + cfg.num_expected_neg_stage2) * batch_size
+
+        rmv_first = np.ones((self.num_bboxes, self.num_classes))
+        rmv_first[:, 0] = np.zeros((self.num_bboxes,))
+        self.rmv_first_tensor = Tensor(rmv_first.astype(np.float16))
+
+        self.num_bboxes_test = cfg.rpn_max_num * cfg.test_batch_size
+
+    def construct(self, featuremap, bbox_targets, labels, mask):
+        x_cls, x_reg = self.fpn_cls(featuremap)
+
+        if self.training:
+            bbox_weights = self.cast(self.logicaland(self.greater(labels, 0), mask), mstype.int32) * labels
+            labels = self.cast(self.onehot(labels, self.num_classes, self.on_value, self.off_value), mstype.float16)
+            bbox_targets = self.tile(self.expandims(bbox_targets, 1), (1, self.num_classes, 1))
+
+            loss_cls, loss_reg = self.loss(x_cls, x_reg,
+                                           bbox_targets, bbox_weights,
+                                           labels,
+                                           mask)
+            out = (loss_cls, loss_reg)
+        else:
+            out = (x_cls, x_reg)
+
+        return out
+
+    def loss(self, cls_score, bbox_pred, bbox_targets, bbox_weights, labels, weights):
+        """Loss method."""
+        # loss_cls
+        loss_cls, _ = self.loss_cls(cls_score, labels)
+        weights = self.cast(weights, mstype.float16)
+        loss_cls = loss_cls * weights
+        loss_cls = self.sum_loss(loss_cls, (0,)) / self.sum_loss(weights, (0,))
+
+        # loss_reg
+        bbox_weights = self.cast(self.onehot(bbox_weights, self.num_classes, self.on_value, self.off_value),
+                                 mstype.float16)
+        bbox_weights = bbox_weights * self.rmv_first_tensor   #  * self.rmv_first_tensor  exclude background
+        pos_bbox_pred = self.reshape(bbox_pred, (self.num_bboxes, -1, 4))
+        loss_reg = self.loss_bbox(pos_bbox_pred, bbox_targets)
+        loss_reg = self.sum_loss(loss_reg, (2,))
+        loss_reg = loss_reg * bbox_weights
+        loss_reg = loss_reg / self.sum_loss(weights, (0,))
+        loss_reg = self.sum_loss(loss_reg, (0, 1))
+
+        return loss_cls, loss_reg

model_zoo/official/cv/maskrcnn_mobilenetv1/src/maskrcnn_mobilenetv1/rcnn_mask.py

@@ -0,0 +1,168 @@
+# 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.
+# ============================================================================
+"""MaskRcnn Rcnn for mask network."""
+
+import numpy as np
+import mindspore.common.dtype as mstype
+import mindspore.nn as nn
+from mindspore.ops import operations as P
+from mindspore.common.tensor import Tensor
+from mindspore.common.initializer import initializer
+
+def _conv(in_channels, out_channels, kernel_size=1, stride=1, padding=0, pad_mode='pad'):
+    """Conv2D wrapper."""
+    shape = (out_channels, in_channels, kernel_size, kernel_size)
+    weights = initializer("XavierUniform", shape=shape, dtype=mstype.float16).to_tensor()
+    shape_bias = (out_channels,)
+    bias = Tensor(np.array(np.zeros(shape_bias)).astype(np.float16))
+    return nn.Conv2d(in_channels, out_channels,
+                     kernel_size=kernel_size, stride=stride, padding=padding,
+                     pad_mode=pad_mode, weight_init=weights, has_bias=True, bias_init=bias)
+
+def _convTanspose(in_channels, out_channels, kernel_size=1, stride=1, padding=0, pad_mode='pad'):
+    """ConvTranspose wrapper."""
+    shape = (out_channels, in_channels, kernel_size, kernel_size)
+    weights = initializer("XavierUniform", shape=shape, dtype=mstype.float16).to_tensor()
+    shape_bias = (out_channels,)
+    bias = Tensor(np.array(np.zeros(shape_bias)).astype(np.float16))
+    return nn.Conv2dTranspose(in_channels, out_channels,
+                              kernel_size=kernel_size, stride=stride, padding=padding,
+                              pad_mode=pad_mode, weight_init=weights, has_bias=True, bias_init=bias)
+
+class FpnMask(nn.Cell):
+    """conv layers of mask head"""
+    def __init__(self, input_channels, output_channels, num_classes):
+        super(FpnMask, self).__init__()
+        self.mask_conv1 = _conv(input_channels, output_channels, kernel_size=3, pad_mode="same")
+        self.mask_relu1 = P.ReLU()
+
+        self.mask_conv2 = _conv(output_channels, output_channels, kernel_size=3, pad_mode="same")
+        self.mask_relu2 = P.ReLU()
+
+        self.mask_conv3 = _conv(output_channels, output_channels, kernel_size=3, pad_mode="same")
+        self.mask_relu3 = P.ReLU()
+
+        self.mask_conv4 = _conv(output_channels, output_channels, kernel_size=3, pad_mode="same")
+        self.mask_relu4 = P.ReLU()
+
+        self.mask_deconv5 = _convTanspose(output_channels, output_channels, kernel_size=2, stride=2, pad_mode="valid")
+        self.mask_relu5 = P.ReLU()
+        self.mask_conv6 = _conv(output_channels, num_classes, kernel_size=1, stride=1, pad_mode="valid")
+
+    def construct(self, x):
+        x = self.mask_conv1(x)
+        x = self.mask_relu1(x)
+
+        x = self.mask_conv2(x)
+        x = self.mask_relu2(x)
+
+        x = self.mask_conv3(x)
+        x = self.mask_relu3(x)
+
+        x = self.mask_conv4(x)
+        x = self.mask_relu4(x)
+
+        x = self.mask_deconv5(x)
+        x = self.mask_relu5(x)
+
+        x = self.mask_conv6(x)
+
+        return x
+
+class RcnnMask(nn.Cell):
+    """
+    Rcnn for mask subnet.
+
+    Args:
+        config (dict) - Config.
+        batch_size (int) - Batchsize.
+        num_classes (int) - Class number.
+        target_means (list) - Means for encode function. Default: (.0, .0, .0, .0]).
+        target_stds (list) - Stds for encode function. Default: (0.1, 0.1, 0.2, 0.2).
+
+    Returns:
+        Tuple, tuple of output tensor.
+
+    Examples:
+        RcnnMask(config=config, representation_size = 1024, batch_size=2, num_classes = 81, \
+             target_means=(0., 0., 0., 0.), target_stds=(0.1, 0.1, 0.2, 0.2))
+    """
+    def __init__(self,
+                 config,
+                 batch_size,
+                 num_classes,
+                 target_means=(0., 0., 0., 0.),
+                 target_stds=(0.1, 0.1, 0.2, 0.2)
+                 ):
+        super(RcnnMask, self).__init__()
+        cfg = config
+        self.rcnn_loss_mask_fb_weight = Tensor(np.array(cfg.rcnn_loss_mask_fb_weight).astype(np.float16))
+        self.rcnn_mask_out_channels = cfg.rcnn_mask_out_channels
+        self.target_means = target_means
+        self.target_stds = target_stds
+        self.num_classes = num_classes
+        self.in_channels = cfg.rcnn_in_channels
+
+        self.fpn_mask = FpnMask(self.in_channels, self.rcnn_mask_out_channels, self.num_classes)
+
+        self.logicaland = P.LogicalAnd()
+        self.loss_mask = P.SigmoidCrossEntropyWithLogits()
+        self.onehot = P.OneHot()
+        self.greater = P.Greater()
+        self.cast = P.Cast()
+        self.sum_loss = P.ReduceSum()
+        self.tile = P.Tile()
+        self.expandims = P.ExpandDims()
+
+        self.on_value = Tensor(1.0, mstype.float32)
+        self.off_value = Tensor(0.0, mstype.float32)
+
+        self.num_bboxes = cfg.num_expected_pos_stage2 * batch_size
+        rmv_first = np.ones((self.num_bboxes, self.num_classes))
+        rmv_first[:, 0] = np.zeros((self.num_bboxes,))
+        self.rmv_first_tensor = Tensor(rmv_first.astype(np.float16))
+        self.mean_loss = P.ReduceMean()
+
+    def construct(self, mask_featuremap, labels=None, mask=None, mask_fb_targets=None):
+        x_mask_fb = self.fpn_mask(mask_featuremap)
+
+        if self.training:
+            bbox_weights = self.cast(self.logicaland(self.greater(labels, 0), mask), mstype.int32) * labels
+            mask_fb_targets = self.tile(self.expandims(mask_fb_targets, 1), (1, self.num_classes, 1, 1))
+
+            loss_mask_fb = self.loss(x_mask_fb, bbox_weights, mask, mask_fb_targets)
+            out = loss_mask_fb
+        else:
+            out = x_mask_fb
+
+        return out
+
+
+    def loss(self, masks_fb_pred, bbox_weights, weights, masks_fb_targets):
+        """Loss method."""
+        weights = self.cast(weights, mstype.float16)
+        bbox_weights = self.cast(self.onehot(bbox_weights, self.num_classes, self.on_value, self.off_value),
+                                 mstype.float16)
+        bbox_weights = bbox_weights * self.rmv_first_tensor   #  * self.rmv_first_tensor  exclude background
+
+        # loss_mask_fb
+        masks_fb_targets = self.cast(masks_fb_targets, mstype.float16)
+        loss_mask_fb = self.loss_mask(masks_fb_pred, masks_fb_targets)
+        loss_mask_fb = self.mean_loss(loss_mask_fb, (2, 3))
+        loss_mask_fb = loss_mask_fb * bbox_weights
+        loss_mask_fb = loss_mask_fb / self.sum_loss(weights, (0,))
+        loss_mask_fb = self.sum_loss(loss_mask_fb, (0, 1))
+
+        return loss_mask_fb

model_zoo/official/cv/maskrcnn_mobilenetv1/src/maskrcnn_mobilenetv1/roi_align.py

@@ -0,0 +1,186 @@
+# 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.
+# ============================================================================
+"""MaskRcnn ROIAlign module."""
+
+import numpy as np
+import mindspore.nn as nn
+import mindspore.common.dtype as mstype
+from mindspore.ops import operations as P
+from mindspore.ops import composite as C
+from mindspore.nn import layer as L
+from mindspore.common.tensor import Tensor
+
+class ROIAlign(nn.Cell):
+    """
+    Extract RoI features from mulitple feature map.
+
+    Args:
+        out_size_h (int) - RoI height.
+        out_size_w (int) - RoI width.
+        spatial_scale (int) - RoI spatial scale.
+        sample_num (int) - RoI sample number.
+        roi_align_mode (int)- RoI align mode
+    """
+    def __init__(self,
+                 out_size_h,
+                 out_size_w,
+                 spatial_scale,
+                 sample_num=0,
+                 roi_align_mode=1):
+        super(ROIAlign, self).__init__()
+
+        self.out_size = (out_size_h, out_size_w)
+        self.spatial_scale = float(spatial_scale)
+        self.sample_num = int(sample_num)
+        self.align_op = P.ROIAlign(self.out_size[0], self.out_size[1],
+                                   self.spatial_scale, self.sample_num, roi_align_mode)
+
+    def construct(self, features, rois):
+        return self.align_op(features, rois)
+
+    def __repr__(self):
+        format_str = self.__class__.__name__
+        format_str += '(out_size={}, spatial_scale={}, sample_num={}'.format(
+            self.out_size, self.spatial_scale, self.sample_num)
+        return format_str
+
+
+class SingleRoIExtractor(nn.Cell):
+    """
+    Extract RoI features from a single level feature map.
+
+    If there are mulitple input feature levels, each RoI is mapped to a level
+    according to its scale.
+
+    Args:
+        config (dict): Config
+        roi_layer (dict): Specify RoI layer type and arguments.
+        out_channels (int): Output channels of RoI layers.
+        featmap_strides (int): Strides of input feature maps.
+        batch_size (int)： Batchsize.
+        finest_scale (int): Scale threshold of mapping to level 0.
+        mask (bool): Specify ROIAlign for cls or mask branch
+    """
+
+    def __init__(self,
+                 config,
+                 roi_layer,
+                 out_channels,
+                 featmap_strides,
+                 batch_size=1,
+                 finest_scale=56,
+                 mask=False):
+        super(SingleRoIExtractor, self).__init__()
+        cfg = config
+        self.train_batch_size = batch_size
+        self.out_channels = out_channels
+        self.featmap_strides = featmap_strides
+        self.num_levels = len(self.featmap_strides)
+        self.out_size = roi_layer['mask_out_size'] if mask else roi_layer['out_size']
+        self.mask = mask
+        self.sample_num = roi_layer['sample_num']
+        self.roi_layers = self.build_roi_layers(self.featmap_strides)
+        self.roi_layers = L.CellList(self.roi_layers)
+
+        self.sqrt = P.Sqrt()
+        self.log = P.Log()
+        self.finest_scale_ = finest_scale
+        self.clamp = C.clip_by_value
+
+        self.cast = P.Cast()
+        self.equal = P.Equal()
+        self.select = P.Select()
+
+        _mode_16 = False
+        self.dtype = np.float16 if _mode_16 else np.float32
+        self.ms_dtype = mstype.float16 if _mode_16 else mstype.float32
+        self.set_train_local(cfg, training=True)
+
+    def set_train_local(self, config, training=True):
+        """Set training flag."""
+        self.training_local = training
+
+        cfg = config
+        # Init tensor
+        roi_sample_num = cfg.num_expected_pos_stage2 if self.mask else cfg.roi_sample_num
+        self.batch_size = roi_sample_num if self.training_local else cfg.rpn_max_num
+        self.batch_size = self.train_batch_size*self.batch_size \
+            if self.training_local else cfg.test_batch_size*self.batch_size
+        self.ones = Tensor(np.array(np.ones((self.batch_size, 1)), dtype=self.dtype))
+        finest_scale = np.array(np.ones((self.batch_size, 1)), dtype=self.dtype) * self.finest_scale_
+        self.finest_scale = Tensor(finest_scale)
+        self.epslion = Tensor(np.array(np.ones((self.batch_size, 1)), dtype=self.dtype)*self.dtype(1e-6))
+        self.zeros = Tensor(np.array(np.zeros((self.batch_size, 1)), dtype=np.int32))
+        self.max_levels = Tensor(np.array(np.ones((self.batch_size, 1)), dtype=np.int32)*(self.num_levels-1))
+        self.twos = Tensor(np.array(np.ones((self.batch_size, 1)), dtype=self.dtype) * 2)
+        self.res_ = Tensor(np.array(np.zeros((self.batch_size, self.out_channels,
+                                              self.out_size, self.out_size)), dtype=self.dtype))
+    def num_inputs(self):
+        return len(self.featmap_strides)
+
+    def init_weights(self):
+        pass
+
+    def log2(self, value):
+        return self.log(value) / self.log(self.twos)
+
+    def build_roi_layers(self, featmap_strides):
+        roi_layers = []
+        for s in featmap_strides:
+            layer_cls = ROIAlign(self.out_size, self.out_size,
+                                 spatial_scale=1 / s,
+                                 sample_num=self.sample_num,
+                                 roi_align_mode=0)
+            roi_layers.append(layer_cls)
+        return roi_layers
+
+    def _c_map_roi_levels(self, rois):
+        """Map rois to corresponding feature levels by scales.
+
+        - scale < finest_scale * 2: level 0
+        - finest_scale * 2 <= scale < finest_scale * 4: level 1
+        - finest_scale * 4 <= scale < finest_scale * 8: level 2
+        - scale >= finest_scale * 8: level 3
+
+        Args:
+            rois (Tensor): Input RoIs, shape (k, 5).
+            num_levels (int): Total level number.
+
+        Returns:
+            Tensor: Level index (0-based) of each RoI, shape (k, )
+        """
+        scale = self.sqrt(rois[::, 3:4:1] - rois[::, 1:2:1] + self.ones) * \
+             self.sqrt(rois[::, 4:5:1] - rois[::, 2:3:1] + self.ones)
+
+        target_lvls = self.log2(scale / self.finest_scale + self.epslion)
+        target_lvls = P.Floor()(target_lvls)
+        target_lvls = self.cast(target_lvls, mstype.int32)
+        target_lvls = self.clamp(target_lvls, self.zeros, self.max_levels)
+
+        return target_lvls
+
+    def construct(self, rois, feat1, feat2, feat3, feat4):
+        feats = (feat1, feat2, feat3, feat4)
+        res = self.res_
+        target_lvls = self._c_map_roi_levels(rois)
+        for i in range(self.num_levels):
+            mask = self.equal(target_lvls, P.ScalarToArray()(i))
+            mask = P.Reshape()(mask, (-1, 1, 1, 1))
+            roi_feats_t = self.roi_layers[i](feats[i], rois)
+            mask = self.cast(P.Tile()(self.cast(mask, mstype.int32), (1, 256, self.out_size, self.out_size)),
+                             mstype.bool_)
+            res = self.select(mask, roi_feats_t, res)
+
+        return res

model_zoo/official/cv/maskrcnn_mobilenetv1/src/network_define.py

@@ -0,0 +1,221 @@
+# 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.
+# ============================================================================
+"""MaskRcnn training network wrapper."""
+
+import time
+import numpy as np
+import mindspore.nn as nn
+from mindspore.common.tensor import Tensor
+from mindspore.ops import functional as F
+from mindspore.ops import composite as C
+from mindspore import ParameterTuple
+from mindspore.train.callback import Callback
+from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
+
+time_stamp_init = False
+time_stamp_first = 0
+
+GRADIENT_CLIP_TYPE = 1
+GRADIENT_CLIP_VALUE = 1.0
+
+clip_grad = C.MultitypeFuncGraph("clip_grad")
+
+
+@clip_grad.register("Number", "Number", "Tensor")
+def _clip_grad(clip_type, clip_value, grad):
+    """
+    Clip gradients.
+
+    Inputs:
+        clip_type (int): The way to clip, 0 for 'value', 1 for 'norm'.
+        clip_value (float): Specifies how much to clip.
+        grad (tuple[Tensor]): Gradients.
+
+    Outputs:
+        tuple[Tensor], clipped gradients.
+    """
+    if clip_type not in (0, 1):
+        return grad
+    dt = F.dtype(grad)
+    if clip_type == 0:
+        new_grad = C.clip_by_value(grad, F.cast(F.tuple_to_array((-clip_value,)), dt),
+                                   F.cast(F.tuple_to_array((clip_value,)), dt))
+    else:
+        new_grad = nn.ClipByNorm()(grad, F.cast(F.tuple_to_array((clip_value,)), dt))
+    return F.cast(new_grad, dt)
+
+class LossCallBack(Callback):
+    """
+    Monitor the loss in training.
+
+    If the loss is NAN or INF terminating training.
+
+    Note:
+        If per_print_times is 0 do not print loss.
+
+    Args:
+        per_print_times (int): Print loss every times. Default: 1.
+    """
+
+    def __init__(self, per_print_times=1, rank_id=0):
+        super(LossCallBack, self).__init__()
+        if not isinstance(per_print_times, int) or per_print_times < 0:
+            raise ValueError("print_step must be int and >= 0.")
+        self._per_print_times = per_print_times
+        self.count = 0
+        self.rpn_loss_sum = 0
+        self.rcnn_loss_sum = 0
+        self.rpn_cls_loss_sum = 0
+        self.rpn_reg_loss_sum = 0
+        self.rcnn_cls_loss_sum = 0
+        self.rcnn_reg_loss_sum = 0
+        self.rcnn_mask_loss_sum = 0
+        self.rank_id = rank_id
+
+        global time_stamp_init, time_stamp_first
+        if not time_stamp_init:
+            time_stamp_first = time.time()
+            time_stamp_init = True
+
+    def step_end(self, run_context):
+        cb_params = run_context.original_args()
+        rpn_loss = cb_params.net_outputs[0].asnumpy()
+        rcnn_loss = cb_params.net_outputs[1].asnumpy()
+        rpn_cls_loss = cb_params.net_outputs[2].asnumpy()
+
+        rpn_reg_loss = cb_params.net_outputs[3].asnumpy()
+        rcnn_cls_loss = cb_params.net_outputs[4].asnumpy()
+        rcnn_reg_loss = cb_params.net_outputs[5].asnumpy()
+        rcnn_mask_loss = cb_params.net_outputs[6].asnumpy()
+
+        self.count += 1
+        self.rpn_loss_sum += float(rpn_loss)
+        self.rcnn_loss_sum += float(rcnn_loss)
+        self.rpn_cls_loss_sum += float(rpn_cls_loss)
+        self.rpn_reg_loss_sum += float(rpn_reg_loss)
+        self.rcnn_cls_loss_sum += float(rcnn_cls_loss)
+        self.rcnn_reg_loss_sum += float(rcnn_reg_loss)
+        self.rcnn_mask_loss_sum += float(rcnn_mask_loss)
+
+        cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
+
+        if self.count >= 1:
+            global time_stamp_first
+            time_stamp_current = time.time()
+
+            rpn_loss = self.rpn_loss_sum/self.count
+            rcnn_loss = self.rcnn_loss_sum/self.count
+            rpn_cls_loss = self.rpn_cls_loss_sum/self.count
+
+            rpn_reg_loss = self.rpn_reg_loss_sum/self.count
+            rcnn_cls_loss = self.rcnn_cls_loss_sum/self.count
+            rcnn_reg_loss = self.rcnn_reg_loss_sum/self.count
+            rcnn_mask_loss = self.rcnn_mask_loss_sum/self.count
+
+            total_loss = rpn_loss + rcnn_loss
+
+            loss_file = open("./loss_{}.log".format(self.rank_id), "a+")
+            loss_file.write("%lu epoch: %s step: %s ,rpn_loss: %.5f, rcnn_loss: %.5f, rpn_cls_loss: %.5f, "
+                            "rpn_reg_loss: %.5f, rcnn_cls_loss: %.5f, rcnn_reg_loss: %.5f, rcnn_mask_loss: %.5f, "
+                            "total_loss: %.5f" %
+                            (time_stamp_current - time_stamp_first, cb_params.cur_epoch_num, cur_step_in_epoch,
+                             rpn_loss, rcnn_loss, rpn_cls_loss, rpn_reg_loss,
+                             rcnn_cls_loss, rcnn_reg_loss, rcnn_mask_loss, total_loss))
+            loss_file.write("\n")
+            loss_file.close()
+
+            self.count = 0
+            self.rpn_loss_sum = 0
+            self.rcnn_loss_sum = 0
+            self.rpn_cls_loss_sum = 0
+            self.rpn_reg_loss_sum = 0
+            self.rcnn_cls_loss_sum = 0
+            self.rcnn_reg_loss_sum = 0
+            self.rcnn_mask_loss_sum = 0
+
+class LossNet(nn.Cell):
+    """MaskRcnn loss method"""
+    def construct(self, x1, x2, x3, x4, x5, x6, x7):
+        return x1 + x2
+
+class WithLossCell(nn.Cell):
+    """
+    Wrap the network with loss function to compute loss.
+
+    Args:
+        backbone (Cell): The target network to wrap.
+        loss_fn (Cell): The loss function used to compute loss.
+    """
+    def __init__(self, backbone, loss_fn):
+        super(WithLossCell, self).__init__(auto_prefix=False)
+        self._backbone = backbone
+        self._loss_fn = loss_fn
+
+    def construct(self, x, img_shape, gt_bboxe, gt_label, gt_num, gt_mask):
+        loss1, loss2, loss3, loss4, loss5, loss6, loss7 = self._backbone(x, img_shape, gt_bboxe, gt_label,
+                                                                         gt_num, gt_mask)
+        return self._loss_fn(loss1, loss2, loss3, loss4, loss5, loss6, loss7)
+
+    @property
+    def backbone_network(self):
+        """
+        Get the backbone network.
+
+        Returns:
+            Cell, return backbone network.
+        """
+        return self._backbone
+
+
+class TrainOneStepCell(nn.Cell):
+    """
+    Network training package class.
+
+    Append an optimizer to the training network after that the construct function
+    can be called to create the backward graph.
+
+    Args:
+        network (Cell): The training network.
+        network_backbone (Cell): The forward network.
+        optimizer (Cell): Optimizer for updating the weights.
+        sens (Number): The adjust parameter. Default value is 1.0.
+        reduce_flag (bool): The reduce flag. Default value is False.
+        mean (bool): Allreduce method. Default value is False.
+        degree (int): Device number. Default value is None.
+    """
+    def __init__(self, network, network_backbone, optimizer, sens=1.0, reduce_flag=False, mean=True, degree=None):
+        super(TrainOneStepCell, self).__init__(auto_prefix=False)
+        self.network = network
+        self.network.set_grad()
+        self.backbone = network_backbone
+        self.weights = ParameterTuple(network.trainable_params())
+        self.optimizer = optimizer
+        self.grad = C.GradOperation(get_by_list=True,
+                                    sens_param=True)
+        self.sens = Tensor((np.ones((1,)) * sens).astype(np.float16))
+        self.reduce_flag = reduce_flag
+        self.hyper_map = C.HyperMap()
+        if reduce_flag:
+            self.grad_reducer = DistributedGradReducer(optimizer.parameters, mean, degree)
+
+    def construct(self, x, img_shape, gt_bboxe, gt_label, gt_num, gt_mask):
+        weights = self.weights
+        loss1, loss2, loss3, loss4, loss5, loss6, loss7 = self.backbone(x, img_shape, gt_bboxe, gt_label,
+                                                                        gt_num, gt_mask)
+        grads = self.grad(self.network, weights)(x, img_shape, gt_bboxe, gt_label, gt_num, gt_mask, self.sens)
+        if self.reduce_flag:
+            grads = self.grad_reducer(grads)
+        grads = self.hyper_map(F.partial(clip_grad, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE), grads)
+        return F.depend(loss1, self.optimizer(grads)), loss2, loss3, loss4, loss5, loss6, loss7

model_zoo/official/cv/maskrcnn_mobilenetv1/train.py

@@ -0,0 +1,142 @@
+# 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
+#
+# less 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.
+# ============================================================================
+
+"""train MaskRcnn and get checkpoint files."""
+
+import os
+import time
+import argparse
+import ast
+
+import mindspore.common.dtype as mstype
+from mindspore import context, Tensor
+from mindspore.communication.management import init
+from mindspore.train.callback import CheckpointConfig, ModelCheckpoint, TimeMonitor
+from mindspore.train import Model
+from mindspore.context import ParallelMode
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.nn import Momentum
+from mindspore.common import set_seed
+
+from src.maskrcnn_mobilenetv1.mask_rcnn_mobilenetv1 import Mask_Rcnn_Mobilenetv1
+from src.network_define import LossCallBack, WithLossCell, TrainOneStepCell, LossNet
+from src.config import config
+from src.dataset import data_to_mindrecord_byte_image, create_maskrcnn_dataset
+from src.lr_schedule import dynamic_lr
+
+set_seed(1)
+
+parser = argparse.ArgumentParser(description="MaskRcnn training")
+parser.add_argument("--only_create_dataset", type=ast.literal_eval, default=False, help="If set it true, only create "
+                    "Mindrecord, default is false.")
+parser.add_argument("--run_distribute", type=ast.literal_eval, default=False, help="Run distribute, default is false.")
+parser.add_argument("--do_train", type=ast.literal_eval, default=True, help="Do train or not, default is true.")
+parser.add_argument("--do_eval", type=ast.literal_eval, default=False, help="Do eval or not, default is false.")
+parser.add_argument("--dataset", type=str, default="coco", help="Dataset, default is coco.")
+parser.add_argument("--pre_trained", type=str, default="", help="Pretrain file path.")
+parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
+parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.")
+parser.add_argument("--rank_id", type=int, default=0, help="Rank id, default is 0.")
+args_opt = parser.parse_args()
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
+
+if __name__ == '__main__':
+    print("Start train for maskrcnn_mobilenetv1!")
+    if not args_opt.do_eval and args_opt.run_distribute:
+        rank = args_opt.rank_id
+        device_num = args_opt.device_num
+        context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
+                                          gradients_mean=True)
+        init()
+    else:
+        rank = 0
+        device_num = 1
+
+    print("Start create dataset!")
+
+    # It will generate mindrecord file in args_opt.mindrecord_dir,
+    # and the file name is MaskRcnn.mindrecord0, 1, ... file_num.
+    prefix = "MaskRcnn.mindrecord"
+    mindrecord_dir = config.mindrecord_dir
+    mindrecord_file = os.path.join(mindrecord_dir, prefix + "0")
+    if rank == 0 and not os.path.exists(mindrecord_file):
+        if not os.path.isdir(mindrecord_dir):
+            os.makedirs(mindrecord_dir)
+        if args_opt.dataset == "coco":
+            if os.path.isdir(config.coco_root):
+                print("Create Mindrecord.")
+                data_to_mindrecord_byte_image("coco", True, prefix)
+                print("Create Mindrecord Done, at {}".format(mindrecord_dir))
+            else:
+                raise Exception("coco_root not exits.")
+        else:
+            if os.path.isdir(config.IMAGE_DIR) and os.path.exists(config.ANNO_PATH):
+                print("Create Mindrecord.")
+                data_to_mindrecord_byte_image("other", True, prefix)
+                print("Create Mindrecord Done, at {}".format(mindrecord_dir))
+            else:
+                raise Exception("IMAGE_DIR or ANNO_PATH not exits.")
+    while not os.path.exists(mindrecord_file+".db"):
+        time.sleep(5)
+
+    if not args_opt.only_create_dataset:
+        loss_scale = float(config.loss_scale)
+
+        # When create MindDataset, using the fitst mindrecord file, such as MaskRcnn.mindrecord0.
+        dataset = create_maskrcnn_dataset(mindrecord_file, batch_size=config.batch_size,
+                                          device_num=device_num, rank_id=rank)
+
+        dataset_size = dataset.get_dataset_size()
+        print("total images num: ", dataset_size)
+        print("Create dataset done!")
+
+        net = Mask_Rcnn_Mobilenetv1(config=config)
+        net = net.set_train()
+
+        load_path = args_opt.pre_trained
+        if load_path != "":
+            param_dict = load_checkpoint(load_path)
+            if config.pretrain_epoch_size == 0:
+                for item in list(param_dict.keys()):
+                    if not (item.startswith('backbone') or item.startswith('rcnn_mask')):
+                        param_dict.pop(item)
+            load_param_into_net(net, param_dict)
+
+        loss = LossNet()
+        lr = Tensor(dynamic_lr(config, rank_size=device_num, start_steps=config.pretrain_epoch_size * dataset_size),
+                    mstype.float32)
+        opt = Momentum(params=net.trainable_params(), learning_rate=lr, momentum=config.momentum,
+                       weight_decay=config.weight_decay, loss_scale=config.loss_scale)
+
+        net_with_loss = WithLossCell(net, loss)
+        if args_opt.run_distribute:
+            net = TrainOneStepCell(net_with_loss, net, opt, sens=config.loss_scale, reduce_flag=True,
+                                   mean=True, degree=device_num)
+        else:
+            net = TrainOneStepCell(net_with_loss, net, opt, sens=config.loss_scale)
+
+        time_cb = TimeMonitor(data_size=dataset_size)
+        loss_cb = LossCallBack(rank_id=rank)
+        cb = [time_cb, loss_cb]
+        if config.save_checkpoint:
+            ckptconfig = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * dataset_size,
+                                          keep_checkpoint_max=config.keep_checkpoint_max)
+            save_checkpoint_path = os.path.join(config.save_checkpoint_path, 'ckpt_' + str(rank) + '/')
+            ckpoint_cb = ModelCheckpoint(prefix='mask_rcnn', directory=save_checkpoint_path, config=ckptconfig)
+            cb += [ckpoint_cb]
+
+        model = Model(net)
+        model.train(config.epoch_size, dataset, callbacks=cb)