delete launch file

model_zoo/official/cv/mobilenetv2/README.md

@@ -70,7 +70,6 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
   │   ├──args.py        # parse args
   │   ├──config.py      # parameter configuration
   │   ├──dataset.py     # creating dataset
-  │   ├──launch.py      # start python script
   │   ├──lr_generator.py     # learning rate config
   │   ├──mobilenetV2.py      # MobileNetV2 architecture
   │   ├──models.py      # contain define_net and Loss, Monitor

model_zoo/official/cv/mobilenetv2/scripts/run_train.sh

@@ -31,21 +31,32 @@ run_ascend()
     BASEPATH=$(cd "`dirname $0`" || exit; pwd)
     export PYTHONPATH=${BASEPATH}:$PYTHONPATH
     export RANK_TABLE_FILE=$4
+    DEVICE_NUM=$2
     if [ -d "../train" ];
     then
         rm -rf ../train
     fi
     mkdir ../train
     cd ../train || exit
-    python ${BASEPATH}/../src/launch.py \
+    for((i=0; i<${DEVICE_NUM}; i++))
+    do
+        export DEVICE_ID=$i
+        export RANK_ID=$i
+        rm -rf ./rank$i
+        mkdir ./rank$i
+        cp ../*.py ./rank$i
+        cp -r ../src ./rank$i
+        cd ./rank$i || exit
+        echo "start training for rank $RANK_ID, device $DEVICE_ID"
+        env > env.log
+        python train.py \
             --platform=$1 \
-            --nproc_per_node=$2 \
-            --visible_devices=$3 \
-            --training_script=${BASEPATH}/../train.py \
             --dataset_path=$5 \
             --pretrain_ckpt=$6 \
             --freeze_layer=$7 \
-            &> ../train.log &  # dataset train folder
+            &> log$i.log & 
+        cd ..
+    done
 }
 
 run_gpu()

model_zoo/official/cv/mobilenetv2/src/launch.py

@@ -1,64 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""launch train script"""
-import os
-import sys
-import subprocess
-import shutil
-from args import launch_parse_args
-
-def main():
-    print("start", __file__)
-    args = launch_parse_args()
-    print(args)
-    visible_devices = args.visible_devices.split(',')
-    assert os.path.isfile(args.training_script)
-    assert len(visible_devices) >= args.nproc_per_node
-    print('visible_devices:{}'.format(visible_devices))
-
-    # spawn the processes
-    processes = []
-    cmds = []
-    log_files = []
-    env = os.environ.copy()
-    env['RANK_SIZE'] = str(args.nproc_per_node)
-    cur_path = os.getcwd()
-    for rank_id in range(0, args.nproc_per_node):
-        os.chdir(cur_path)
-        device_id = visible_devices[rank_id]
-        rank_dir = os.path.join(cur_path, 'rank{}'.format(rank_id))
-        env['RANK_ID'] = str(rank_id)
-        env['DEVICE_ID'] = str(device_id)
-        if os.path.exists(rank_dir):
-            shutil.rmtree(rank_dir)
-        os.mkdir(rank_dir)
-        os.chdir(rank_dir)
-        cmd = [sys.executable, '-u']
-        cmd.append(args.training_script)
-        cmd.extend(args.training_script_args)
-        log_file = open(f'{rank_dir}/log{rank_id}.log', 'w')
-        process = subprocess.Popen(cmd, stdout=log_file, stderr=log_file, env=env)
-        processes.append(process)
-        cmds.append(cmd)
-        log_files.append(log_file)
-    for process, cmd, log_file in zip(processes, cmds, log_files):
-        process.wait()
-        if process.returncode != 0:
-            raise subprocess.CalledProcessError(returncode=process, cmd=cmd)
-        log_file.close()
-
-
-if __name__ == "__main__":
-    main()

model_zoo/official/cv/mobilenetv3/Readme.md

@@ -64,7 +64,6 @@ Dataset used: [imagenet](http://www.image-net.org/)
   ├── src                              
   │   ├──config.py      # parameter configuration               
   │   ├──dataset.py     # creating dataset
-  │   ├──launch.py      # start python script
   │   ├──lr_generator.py     # learning rate config                            
   │   ├──mobilenetV3.py      # MobileNetV3 architecture
   ├── train.py      # training script

model_zoo/official/cv/mobilenetv3/src/launch.py

@@ -1,162 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""launch train script"""
-import os
-import sys
-import json
-import subprocess
-import shutil
-from argparse import ArgumentParser
-
-def parse_args():
-    """
-    parse args .
-
-    Args:
-
-    Returns:
-        args.
-
-    Examples:
-        >>> parse_args()
-    """
-    parser = ArgumentParser(description="mindspore distributed training launch "
-                                        "helper utilty that will spawn up "
-                                        "multiple distributed processes")
-    parser.add_argument("--nproc_per_node", type=int, default=1,
-                        help="The number of processes to launch on each node, "
-                             "for D training, this is recommended to be set "
-                             "to the number of D in your system so that "
-                             "each process can be bound to a single D.")
-    parser.add_argument("--visible_devices", type=str, default="0,1,2,3,4,5,6,7",
-                        help="will use the visible devices sequentially")
-    parser.add_argument("--server_id", type=str, default="",
-                        help="server ip")
-    parser.add_argument("--training_script", type=str,
-                        help="The full path to the single D training "
-                             "program/script to be launched in parallel, "
-                             "followed by all the arguments for the "
-                             "training script")
-    # rest from the training program
-    args, unknown = parser.parse_known_args()
-    args.training_script_args = unknown
-    return args
-
-
-def main():
-    print("start", __file__)
-    args = parse_args()
-    print(args)
-    visible_devices = args.visible_devices.split(',')
-    assert os.path.isfile(args.training_script)
-    assert len(visible_devices) >= args.nproc_per_node
-    print('visible_devices:{}'.format(visible_devices))
-    if not args.server_id:
-        print('pleaser input server ip!!!')
-        exit(0)
-    print('server_id:{}'.format(args.server_id))
-
-    # construct hccn_table
-    hccn_configs = open('/etc/hccn.conf', 'r').readlines()
-    device_ips = {}
-    for hccn_item in hccn_configs:
-        hccn_item = hccn_item.strip()
-        if hccn_item.startswith('address_'):
-            device_id, device_ip = hccn_item.split('=')
-            device_id = device_id.split('_')[1]
-            device_ips[device_id] = device_ip
-            print('device_id:{}, device_ip:{}'.format(device_id, device_ip))
-    hccn_table = {}
-    hccn_table['board_id'] = '0x0000'
-    hccn_table['chip_info'] = '910'
-    hccn_table['deploy_mode'] = 'lab'
-    hccn_table['group_count'] = '1'
-    hccn_table['group_list'] = []
-    instance_list = []
-    usable_dev = ''
-    for instance_id in range(args.nproc_per_node):
-        instance = {}
-        instance['devices'] = []
-        device_id = visible_devices[instance_id]
-        device_ip = device_ips[device_id]
-        usable_dev += str(device_id)
-        instance['devices'].append({
-            'device_id': device_id,
-            'device_ip': device_ip,
-        })
-        instance['rank_id'] = str(instance_id)
-        instance['server_id'] = args.server_id
-        instance_list.append(instance)
-    hccn_table['group_list'].append({
-        'device_num': str(args.nproc_per_node),
-        'server_num': '1',
-        'group_name': '',
-        'instance_count': str(args.nproc_per_node),
-        'instance_list': instance_list,
-    })
-    hccn_table['para_plane_nic_location'] = 'device'
-    hccn_table['para_plane_nic_name'] = []
-    for instance_id in range(args.nproc_per_node):
-        eth_id = visible_devices[instance_id]
-        hccn_table['para_plane_nic_name'].append('eth{}'.format(eth_id))
-    hccn_table['para_plane_nic_num'] = str(args.nproc_per_node)
-    hccn_table['status'] = 'completed'
-
-    # save hccn_table to file
-    table_path = os.getcwd()
-    if not os.path.exists(table_path):
-        os.mkdir(table_path)
-    table_fn = os.path.join(table_path,
-                            'rank_table_{}p_{}_{}.json'.format(args.nproc_per_node, usable_dev, args.server_id))
-    with open(table_fn, 'w') as table_fp:
-        json.dump(hccn_table, table_fp, indent=4)
-    sys.stdout.flush()
-
-    # spawn the processes
-    processes = []
-    cmds = []
-    log_files = []
-    env = os.environ.copy()
-    env['RANK_SIZE'] = str(args.nproc_per_node)
-    cur_path = os.getcwd()
-    for rank_id in range(0, args.nproc_per_node):
-        os.chdir(cur_path)
-        device_id = visible_devices[rank_id]
-        device_dir = os.path.join(cur_path, 'device{}'.format(rank_id))
-        env['RANK_ID'] = str(rank_id)
-        env['DEVICE_ID'] = str(device_id)
-        if args.nproc_per_node > 1:
-            env['RANK_TABLE_FILE'] = table_fn
-        if os.path.exists(device_dir):
-            shutil.rmtree(device_dir)
-        os.mkdir(device_dir)
-        os.chdir(device_dir)
-        cmd = [sys.executable, '-u']
-        cmd.append(args.training_script)
-        cmd.extend(args.training_script_args)
-        log_file = open('{dir}/log{id}.log'.format(dir=device_dir, id=rank_id), 'w')
-        process = subprocess.Popen(cmd, stdout=log_file, stderr=log_file, env=env)
-        processes.append(process)
-        cmds.append(cmd)
-        log_files.append(log_file)
-    for process, cmd, log_file in zip(processes, cmds, log_files):
-        process.wait()
-        if process.returncode != 0:
-            raise subprocess.CalledProcessError(returncode=process, cmd=cmd)
-        log_file.close()
-
-
-if __name__ == "__main__":
-    main()

model_zoo/official/cv/ssd/src/box_utils.py

@@ -66,7 +66,7 @@ default_boxes_ltrb = GeneratDefaultBoxes().default_boxes_ltrb
 default_boxes = GeneratDefaultBoxes().default_boxes
 y1, x1, y2, x2 = np.split(default_boxes_ltrb[:, :4], 4, axis=-1)
 vol_anchors = (x2 - x1) * (y2 - y1)
-matching_threshold = config.match_thershold
+matching_threshold = config.match_threshold
 
 
 def ssd_bboxes_encode(boxes):

model_zoo/official/cv/ssd/src/coco_eval.py

@@ -100,7 +100,7 @@ def metrics(pred_data):
             class_boxes = pred_boxes[score_mask] * [h, w, h, w]
 
             if score_mask.any():
-                nms_index = apply_nms(class_boxes, class_box_scores, config.nms_thershold, config.max_boxes)
+                nms_index = apply_nms(class_boxes, class_box_scores, config.nms_threshold, config.max_boxes)
                 class_boxes = class_boxes[nms_index]
                 class_box_scores = class_box_scores[nms_index]
 

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

@@ -21,8 +21,8 @@ config = ed({
     "img_shape": [300, 300],
     "num_ssd_boxes": 1917,
     "neg_pre_positive": 3,
-    "match_thershold": 0.5,
-    "nms_thershold": 0.6,
+    "match_threshold": 0.5,
+    "nms_threshold": 0.6,
     "min_score": 0.1,
     "max_boxes": 100,
 
@@ -38,7 +38,7 @@ config = ed({
     "num_default": [3, 6, 6, 6, 6, 6],
     "extras_in_channels": [256, 576, 1280, 512, 256, 256],
     "extras_out_channels": [576, 1280, 512, 256, 256, 128],
-    "extras_srides": [1, 1, 2, 2, 2, 2],
+    "extras_strides": [1, 1, 2, 2, 2, 2],
     "extras_ratio": [0.2, 0.2, 0.2, 0.25, 0.5, 0.25],
     "feature_size": [19, 10, 5, 3, 2, 1],
     "min_scale": 0.2,

model_zoo/official/cv/ssd/src/ssd.py

@@ -228,7 +228,7 @@ class SSD300(nn.Cell):
         in_channels = config.extras_in_channels
         out_channels = config.extras_out_channels
         ratios = config.extras_ratio
-        strides = config.extras_srides
+        strides = config.extras_strides
         residual_list = []
         for i in range(2, len(in_channels)):
             residual = InvertedResidual(in_channels[i], out_channels[i], stride=strides[i],