1:modify shell for deeplabv3

2:fix normalize bug
3:add ci test3:add ci test3:add ci test

model_zoo/deeplabv3/README.md

@@ -16,17 +16,17 @@ This is an example of training DeepLabv3 with PASCAL VOC 2012 dataset in MindSpo
 - Set options in config.py.
 - Run `run_standalone_train.sh` for non-distributed training.
 	``` bash 
-	sh scripts/run_standalone_train.sh DEVICE_ID EPOCH_SIZE DATA_DIR
+	sh scripts/run_standalone_train.sh DEVICE_ID DATA_PATH
 	```
 - Run `run_distribute_train.sh` for distributed training.
 	``` bash
-	sh scripts/run_distribute_train.sh DEVICE_NUM EPOCH_SIZE DATA_DIR MINDSPORE_HCCL_CONFIG_PATH
+	sh scripts/run_distribute_train.sh MINDSPORE_HCCL_CONFIG_PATH DATA_PATH
 	```
 ### Evaluation
 Set options in evaluation_config.py. Make sure the 'data_file' and 'finetune_ckpt' are set to your own path.
 - Run run_eval.sh for evaluation.
 	``` bash
-	sh scripts/run_eval.sh DEVICE_ID DATA_DIR
+	sh scripts/run_eval.sh DEVICE_ID DATA_PATH PRETRAINED_CKPT_PATH
 	```
 
 ## Options and Parameters
@@ -49,6 +49,11 @@ config.py:
 	decoder_output_stride			The ratio of input to output spatial resolution when employing decoder
 									to refine segmentation results, default is None.
 	image_pyramid					Input scales for multi-scale feature extraction, default is None.
+	epoch_size						Epoch size, default is 6.
+    batch_size                      batch size of input dataset: N, default is 2.
+	enable_save_ckpt				Enable save checkpoint, default is true.
+	save_checkpoint_steps			Save checkpoint steps, default is 1000.
+	save_checkpoint_num				Save checkpoint numbers, default is 1.
 ```	
 
 
@@ -56,11 +61,6 @@ config.py:
 ```
 Parameters for dataset and network:
     distribute						Run distribute, default is false.
-	epoch_size						Epoch size, default is 6.
-    batch_size                      batch size of input dataset: N, default is 2.
 	data_url						Train/Evaluation data url, required.
 	checkpoint_url					Checkpoint path, default is None.
-	enable_save_ckpt				Enable save checkpoint, default is true.
-	save_checkpoint_steps			Save checkpoint steps, default is 1000.
-	save_checkpoint_num				Save checkpoint numbers, default is 1.
 ```

model_zoo/deeplabv3/eval.py

@@ -25,9 +25,7 @@ from src.config import config
 
 
 parser = argparse.ArgumentParser(description="Deeplabv3 evaluation")
-parser.add_argument('--epoch_size', type=int, default=2, help='Epoch size.')
 parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
-parser.add_argument('--batch_size', type=int, default=2, help='Batch size.')
 parser.add_argument('--data_url', required=True, default=None, help='Evaluation data url')
 parser.add_argument('--checkpoint_url', default=None, help='Checkpoint path')
 
@@ -39,8 +37,8 @@ print(args_opt)
 if __name__ == "__main__":
     args_opt.crop_size = config.crop_size
     args_opt.base_size = config.crop_size
-    eval_dataset = create_dataset(args_opt, args_opt.data_url, args_opt.epoch_size, args_opt.batch_size, usage="eval")
+    eval_dataset = create_dataset(args_opt, args_opt.data_url, config.epoch_size, config.batch_size, usage="eval")
-    net = deeplabv3_resnet50(config.seg_num_classes, [args_opt.batch_size, 3, args_opt.crop_size, args_opt.crop_size],
+    net = deeplabv3_resnet50(config.seg_num_classes, [config.batch_size, 3, args_opt.crop_size, args_opt.crop_size],
                              infer_scale_sizes=config.eval_scales, atrous_rates=config.atrous_rates,
                              decoder_output_stride=config.decoder_output_stride, output_stride=config.output_stride,
                              fine_tune_batch_norm=config.fine_tune_batch_norm, image_pyramid=config.image_pyramid)

model_zoo/deeplabv3/scripts/run_distribute_train.sh

@@ -16,17 +16,21 @@
  
 echo "=============================================================================================================="
 echo "Please run the scipt as: "
-echo "bash run_distribute_train.sh DEVICE_NUM EPOCH_SIZE DATA_DIR MINDSPORE_HCCL_CONFIG_PATH"
+echo "bash run_distribute_train.sh MINDSPORE_HCCL_CONFIG_PATH DATA_PATH"
-echo "for example: bash run_distribute_train.sh 8 40 /path/zh-wiki/ /path/hccl.json"
+echo "for example: bash run_distribute_train.sh  MINDSPORE_HCCL_CONFIG_PATH DATA_PATH [PRETRAINED_CKPT_PATH](option)"
 echo "It is better to use absolute path."
 echo "=============================================================================================================="
  
-EPOCH_SIZE=$2
+DATA_DIR=$2
-DATA_DIR=$3
  
-export MINDSPORE_HCCL_CONFIG_PATH=$4
+export MINDSPORE_HCCL_CONFIG_PATH=$1
-export RANK_TABLE_FILE=$4
+export RANK_TABLE_FILE=$1
-export RANK_SIZE=$1
+export RANK_SIZE=8
+PATH_CHECKPOINT=""
+if [ $# == 3 ]
+then
+	PATH_CHECKPOINT=$3
+fi
 cores=`cat /proc/cpuinfo|grep "processor" |wc -l`
 echo "the number of logical core" $cores
 avg_core_per_rank=`expr $cores \/ $RANK_SIZE`
@@ -55,12 +59,8 @@ do
     env > env.log
     taskset -c $cmdopt python ../train.py  \
     --distribute="true" \
-    --epoch_size=$EPOCH_SIZE \
     --device_id=$DEVICE_ID \
-    --enable_save_ckpt="true" \
+    --checkpoint_url=$PATH_CHECKPOINT \
-    --checkpoint_url="" \
-    --save_checkpoint_steps=10000 \
-    --save_checkpoint_num=1 \
     --data_url=$DATA_DIR > log.txt 2>&1 &
     cd ../
 done

model_zoo/deeplabv3/scripts/run_eval.sh

@@ -15,18 +15,20 @@
 # ============================================================================
 echo "=============================================================================================================="
 echo "Please run the scipt as: "
-echo "bash run_eval.sh DEVICE_ID DATA_DIR"
+echo "bash run_eval.sh DEVICE_ID DATA_PATH PRETRAINED_CKPT_PATH"
-echo "for example: bash run_eval.sh /path/zh-wiki/ "
+echo "for example: bash run_eval.sh DEVICE_ID DATA_PATH PRETRAINED_CKPT_PATH"
 echo "=============================================================================================================="
  
 DEVICE_ID=$1
 DATA_DIR=$2
+PATH_CHECKPOINT=$3
+
  
 mkdir -p ms_log 
 CUR_DIR=`pwd`
 export GLOG_log_dir=${CUR_DIR}/ms_log
 export GLOG_logtostderr=0
-python evaluation.py  \
+python eval.py  \
     --device_id=$DEVICE_ID \
-    --checkpoint_url="" \
+    --checkpoint_url=$PATH_CHECKPOINT \
     --data_url=$DATA_DIR > log.txt 2>&1 &

model_zoo/deeplabv3/scripts/run_standalone_train.sh

@@ -15,13 +15,17 @@
 # ============================================================================
 echo "=============================================================================================================="
 echo "Please run the scipt as: "
-echo "bash run_standalone_pretrain.sh DEVICE_ID EPOCH_SIZE DATA_DIR"
+echo "bash run_standalone_pretrain.sh DEVICE_ID DATA_PATH"
-echo "for example: bash run_standalone_train.sh 0 40 /path/zh-wiki/ "
+echo "for example: bash run_standalone_train.sh DEVICE_ID DATA_PATH [PRETRAINED_CKPT_PATH](option)"
 echo "=============================================================================================================="
  
 DEVICE_ID=$1
-EPOCH_SIZE=$2
+DATA_DIR=$2
-DATA_DIR=$3
+PATH_CHECKPOINT=""
+if [ $# == 3 ]
+then
+	PATH_CHECKPOINT=$3
+fi
  
 mkdir -p ms_log 
 CUR_DIR=`pwd`
@@ -29,10 +33,6 @@ export GLOG_log_dir=${CUR_DIR}/ms_log
 export GLOG_logtostderr=0
 python train.py  \
     --distribute="false" \
-    --epoch_size=$EPOCH_SIZE \
     --device_id=$DEVICE_ID \
-    --enable_save_ckpt="true" \
+    --checkpoint_url=$PATH_CHECKPOINT \
-    --checkpoint_url="" \
-    --save_checkpoint_steps=10000 \
-    --save_checkpoint_num=1 \
     --data_url=$DATA_DIR > log.txt 2>&1 &

model_zoo/deeplabv3/src/config.py

@@ -29,5 +29,10 @@ config = ed({
     "fine_tune_batch_norm": False,
     "ignore_label": 255,
     "decoder_output_stride": None,
-    "seg_num_classes": 21
+    "seg_num_classes": 21,
+    "epoch_size": 6,
+    "batch_size": 2,
+    "enable_save_ckpt": True,
+    "save_checkpoint_steps": 10000,
+    "save_checkpoint_num": 1
 })

model_zoo/deeplabv3/src/md_dataset.py

@@ -16,6 +16,7 @@
 from PIL import Image
 import mindspore.dataset as de
 import mindspore.dataset.transforms.vision.c_transforms as C
+import numpy as np
 
 from .ei_dataset import HwVocRawDataset
 from .utils import custom_transforms as tr
@@ -52,8 +53,8 @@ class DataTransform:
         rhf_tr = tr.RandomHorizontalFlip()
         image, label = rhf_tr(image, label)
 
-        nor_tr = tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
+        image = np.array(image).astype(np.float32)
-        image, label = nor_tr(image, label)
+        label = np.array(label).astype(np.float32)
 
         return image, label
 
@@ -71,13 +72,13 @@ class DataTransform:
         fsc_tr = tr.FixScaleCrop(crop_size=self.args.crop_size)
         image, label = fsc_tr(image, label)
 
-        nor_tr = tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
+        image = np.array(image).astype(np.float32)
-        image, label = nor_tr(image, label)
+        label = np.array(label).astype(np.float32)
 
         return image, label
 
 
-def create_dataset(args, data_url, epoch_num=1, batch_size=1, usage="train"):
+def create_dataset(args, data_url, epoch_num=1, batch_size=1, usage="train", shuffle=True):
     """
     Create Dataset for DeepLabV3.
 
@@ -106,7 +107,7 @@ def create_dataset(args, data_url, epoch_num=1, batch_size=1, usage="train"):
     # 1464 samples / batch_size 8 = 183 batches
     # epoch_num is num of steps
     # 3658 steps / 183 = 20 epochs
-    if usage == "train":
+    if usage == "train" and shuffle:
         dataset = dataset.shuffle(1464)
     dataset = dataset.batch(batch_size, drop_remainder=(usage == "train"))
     dataset = dataset.repeat(count=epoch_num)

model_zoo/deeplabv3/src/utils/custom_transforms.py

@@ -33,6 +33,7 @@ class Normalize:
     def __call__(self, img, mask):
         img = np.array(img).astype(np.float32)
         mask = np.array(mask).astype(np.float32)
+        img = ((img - self.mean) / self.std).astype(np.float32)
 
         return img, mask
 

model_zoo/deeplabv3/train.py

@@ -27,14 +27,10 @@ from src.config import config
 
 parser = argparse.ArgumentParser(description="Deeplabv3 training")
 parser.add_argument("--distribute", type=str, default="false", help="Run distribute, default is false.")
-parser.add_argument('--epoch_size', type=int, default=6, help='Epoch size.')
-parser.add_argument('--batch_size', type=int, default=2, help='Batch size.')
 parser.add_argument('--data_url', required=True, default=None, help='Train data url')
 parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
 parser.add_argument('--checkpoint_url', default=None, help='Checkpoint path')
-parser.add_argument("--enable_save_ckpt", type=str, default="true", help="Enable save checkpoint, default is true.")
+
-parser.add_argument("--save_checkpoint_steps", type=int, default=1000, help="Save checkpoint steps, default is 1000.")
-parser.add_argument("--save_checkpoint_num", type=int, default=1, help="Save checkpoint numbers, default is 1.")
 args_opt = parser.parse_args()
 print(args_opt)
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
@@ -70,16 +66,16 @@ if __name__ == "__main__":
         init()
     args_opt.base_size = config.crop_size
     args_opt.crop_size = config.crop_size
-    train_dataset = create_dataset(args_opt, args_opt.data_url, args_opt.epoch_size, args_opt.batch_size, usage="train")
+    train_dataset = create_dataset(args_opt, args_opt.data_url, config.epoch_size, config.batch_size, usage="train")
     dataset_size = train_dataset.get_dataset_size()
     time_cb = TimeMonitor(data_size=dataset_size)
     callback = [time_cb, LossCallBack()]
-    if args_opt.enable_save_ckpt == "true":
+    if config.enable_save_ckpt:
-        config_ck = CheckpointConfig(save_checkpoint_steps=args_opt.save_checkpoint_steps,
+        config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_steps,
-                                     keep_checkpoint_max=args_opt.save_checkpoint_num)
+                                     keep_checkpoint_max=config.save_checkpoint_num)
         ckpoint_cb = ModelCheckpoint(prefix='checkpoint_deeplabv3', config=config_ck)
         callback.append(ckpoint_cb)
-    net = deeplabv3_resnet50(config.seg_num_classes, [args_opt.batch_size, 3, args_opt.crop_size, args_opt.crop_size],
+    net = deeplabv3_resnet50(config.seg_num_classes, [config.batch_size, 3, args_opt.crop_size, args_opt.crop_size],
                              infer_scale_sizes=config.eval_scales, atrous_rates=config.atrous_rates,
                              decoder_output_stride=config.decoder_output_stride, output_stride=config.output_stride,
                              fine_tune_batch_norm=config.fine_tune_batch_norm, image_pyramid=config.image_pyramid)
@@ -88,5 +84,5 @@ if __name__ == "__main__":
     loss = OhemLoss(config.seg_num_classes, config.ignore_label)
     opt = Momentum(filter(lambda x: 'beta' not in x.name and 'gamma' not in x.name and 'depth' not in x.name and 'bias' not in x.name, net.trainable_params()), learning_rate=config.learning_rate, momentum=config.momentum, weight_decay=config.weight_decay)
     model = Model(net, loss, opt)
-    model.train(args_opt.epoch_size, train_dataset, callback)
+    model.train(config.epoch_size, train_dataset, callback)
     

tests/st/model_zoo_tests/deeplabv3/run_deeplabv3_ci.sh

@@ -0,0 +1,47 @@
+#!/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.
+# ============================================================================
+echo "=============================================================================================================="
+echo "Please run the scipt as: "
+echo "for example: bash run_deeplabv3_ci.sh DEVICE_ID DATA_PATH PRETRAINED_CKPT_PATH"
+echo "=============================================================================================================="
+DEVICE_ID=$1
+DATA_DIR=$2
+PATH_CHECKPOINT=$3
+BASE_PATH=$(cd "$(dirname $0)"; pwd)
+unset SLOG_PRINT_TO_STDOUT
+CODE_DIR="./"
+if [ -d ${BASE_PATH}/../../../../model_zoo/deeplabv3 ]; then
+    CODE_DIR=${BASE_PATH}/../../../../model_zoo/deeplabv3
+elif [ -d ${BASE_PATH}/../../model_zoo/deeplabv3 ]; then
+    CODE_DIR=${BASE_PATH}/../../model_zoo/deeplabv3
+else
+     echo "[ERROR] code dir is not found"
+fi
+echo $CODE_DIR
+rm -rf ${BASE_PATH}/deeplabv3
+cp -r ${CODE_DIR}  ${BASE_PATH}/deeplabv3
+cp -f ${BASE_PATH}/train_one_epoch_with_loss.py ${BASE_PATH}/deeplabv3/train_one_epoch_with_loss.py
+cd ${BASE_PATH}/deeplabv3
+python train_one_epoch_with_loss.py --data_url=$DATA_DIR --checkpoint_url=$PATH_CHECKPOINT --device_id=$DEVICE_ID > train_deeplabv3_ci.log 2>&1 &
+process_pid=`echo $!`
+wait ${process_pid}
+status=`echo $?`
+if [ "${status}" != "0" ]; then
+        echo "[ERROR] test deeplabv3 failed. status: ${status}"
+    exit 1
+else
+    echo "[INFO] test deeplabv3 success."
+fi

tests/st/model_zoo_tests/deeplabv3/train_one_epoch_with_loss.py

@@ -0,0 +1,96 @@
+# 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.
+# ============================================================================
+"""train."""
+import argparse
+import time
+from mindspore import context
+from mindspore.nn.optim.momentum import Momentum
+from mindspore import Model
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.train.callback import Callback
+from src.md_dataset import create_dataset
+from src.losses import OhemLoss
+from src.deeplabv3 import deeplabv3_resnet50
+from src.config import config
+parser = argparse.ArgumentParser(description="Deeplabv3 training")
+parser.add_argument('--data_url', required=True, default=None, help='Train data url')
+parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
+parser.add_argument('--checkpoint_url', default=None, help='Checkpoint path')
+args_opt = parser.parse_args()
+print(args_opt)
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
+
+class LossCallBack(Callback):
+    """
+    Monitor the loss in 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, data_size, per_print_times=1):
+        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.data_size = data_size
+        self._per_print_times = per_print_times
+        self.time = 1000
+        self.loss = 0
+    def epoch_begin(self, run_context):
+        self.epoch_time = time.time()
+    def step_end(self, run_context):
+        cb_params = run_context.original_args()
+        epoch_mseconds = (time.time() - self.epoch_time) * 1000
+        self.time = epoch_mseconds / self.data_size
+        self.loss += cb_params.net_outputs
+        print("epoch: {}, step: {}, outputs are {}".format(cb_params.cur_epoch_num, cb_params.cur_step_num,
+                                                           str(cb_params.net_outputs)))
+
+def model_fine_tune(flags, train_net, fix_weight_layer):
+    checkpoint_path = flags.checkpoint_url
+    if checkpoint_path is None:
+        return
+    param_dict = load_checkpoint(checkpoint_path)
+    load_param_into_net(train_net, param_dict)
+    for para in train_net.trainable_params():
+        if fix_weight_layer in para.name:
+            para.requires_grad = False
+
+if __name__ == "__main__":
+    start_time = time.time()
+    epoch_size = 3
+    args_opt.base_size = config.crop_size
+    args_opt.crop_size = config.crop_size
+    train_dataset = create_dataset(args_opt, args_opt.data_url, epoch_size, config.batch_size,
+                                   usage="train", shuffle=False)
+    dataset_size = train_dataset.get_dataset_size()
+    callback = LossCallBack(dataset_size)
+    net = deeplabv3_resnet50(config.seg_num_classes, [config.batch_size, 3, args_opt.crop_size, args_opt.crop_size],
+                             infer_scale_sizes=config.eval_scales, atrous_rates=config.atrous_rates,
+                             decoder_output_stride=config.decoder_output_stride, output_stride=config.output_stride,
+                             fine_tune_batch_norm=config.fine_tune_batch_norm, image_pyramid=config.image_pyramid)
+    net.set_train()
+    model_fine_tune(args_opt, net, 'layer')
+    loss = OhemLoss(config.seg_num_classes, config.ignore_label)
+    opt = Momentum(filter(lambda x: 'beta' not in x.name and 'gamma' not in x.name and 'depth' not in x.name and 'bias' not in x.name, net.trainable_params()), learning_rate=config.learning_rate, momentum=config.momentum, weight_decay=config.weight_decay)
+    model = Model(net, loss, opt)
+    model.train(epoch_size, train_dataset, callback)
+    print(time.time() - start_time)
+    print("expect loss: ", callback.loss / 3)
+    print("expect time: ", callback.time)
+    expect_loss = 0.5
+    expect_time = 35
+    assert callback.loss.asnumpy() / 3 <= expect_loss
+    assert callback.time <= expect_time