!13519 Adding densenet100 model to modelzoo

From: @huangbo77
Reviewed-by: 
Signed-off-by:

model_zoo/README.md

@@ -15,6 +15,7 @@ In order to facilitate developers to enjoy the benefits of MindSpore framework,
 - [Official](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official)
     - [Computer Vision](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv)
         - [Image Classification](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv)
+            - [DenseNet](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/densenet/README.md)
             - [GoogleNet](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/googlenet/README.md)
             - [ResNet50[benchmark]](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnet/README.md)
             - [ResNet50_Quant](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/resnet50_quant/README.md)

model_zoo/README_CN.md

@@ -15,6 +15,7 @@
 - [官方](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official)
     - [计算机视觉](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv)
         - [图像分类](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv)
+            - [DenseNet](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/densenet/README.md)
             - [GoogleNet](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/googlenet/README.md)
             - [ResNet-50[基准]](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnet/README.md)
             - [ResNet50_Quant](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/resnet50_quant/README.md)

model_zoo/official/cv/densenet/eval.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 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.
@@ -15,7 +15,7 @@
 
 """
 ##############test densenet example#################
-python eval.py --data_dir /PATH/TO/DATASET --pretrained /PATH/TO/CHECKPOINT
+python eval.py --net densenet121 --dataset imagenet --data_dir /PATH/TO/DATASET --pretrained /PATH/TO/CHECKPOINT
 """
 
 import os
@@ -34,10 +34,6 @@ from mindspore.ops import functional as F
 from mindspore.common import dtype as mstype
 
 from src.utils.logging import get_logger
-from src.datasets import classification_dataset
-from src.network import DenseNet121
-from src.config import config
-
 
 class ParameterReduce(nn.Cell):
     """
@@ -61,10 +57,13 @@ def parse_args(cloud_args=None):
     """
     parser = argparse.ArgumentParser('mindspore classification test')
 
+    # network and dataset choices
+    parser.add_argument('--net', type=str, default='', help='Densenet Model, densenet100 or densenet121')
+    parser.add_argument('--dataset', type=str, default='', help='Dataset, either cifar10 or imagenet')
+
     # dataset related
     parser.add_argument('--data_dir', type=str, default='', help='eval data dir')
-    parser.add_argument('--num_classes', type=int, default=1000, help='num of classes in dataset')
+
-    parser.add_argument('--image_size', type=str, default='224,224', help='image size of the dataset')
     # network related
     parser.add_argument('--backbone', default='resnet50', help='backbone')
     parser.add_argument('--pretrained', default='', type=str, help='fully path of pretrained model to load.'
@@ -80,12 +79,21 @@ def parse_args(cloud_args=None):
     parser.add_argument('--train_url', type=str, default="", help='train url')
 
     # platform
-    parser.add_argument('--device_target', type=str, default='Ascend', choices=('Ascend', 'GPU'), help='device target')
+    parser.add_argument('--device_target', type=str, default='Ascend', choices=('Ascend', 'GPU', 'CPU'),
+                        help='device target')
 
     args, _ = parser.parse_known_args()
     args = merge_args(args, cloud_args)
 
+    if args.net == "densenet100":
+        from src.config import config_100 as config
+    else:
+        from src.config import config_121 as config
+
     args.per_batch_size = config.per_batch_size
+    args.image_size = config.image_size
+    args.num_classes = config.num_classes
+
     args.image_size = list(map(int, args.image_size.split(',')))
 
     return args
@@ -151,7 +159,8 @@ def generate_results(model, rank, group_size, top1_correct, top5_correct, img_to
 
 def test(cloud_args=None):
     """
-    network eval function. Get top1 and top5 ACC from classification.
+    network eval function. Get top1 and top5 ACC from classification for imagenet,
+    and top1 ACC for cifar10.
     The result will be save at [./outputs] by default.
     """
     args = parse_args(cloud_args)
@@ -185,13 +194,23 @@ def test(cloud_args=None):
     else:
         args.models = [args.pretrained,]
 
+    if args.net == "densenet100":
+        from src.network.densenet import DenseNet100 as DenseNet
+    else:
+        from src.network.densenet import DenseNet121 as DenseNet
+
+    if args.dataset == "cifar10":
+        from src.datasets import classification_dataset_cifar10 as classification_dataset
+    else:
+        from src.datasets import classification_dataset_imagenet as classification_dataset
+
     for model in args.models:
         de_dataset = classification_dataset(args.data_dir, image_size=args.image_size,
                                             per_batch_size=args.per_batch_size,
                                             max_epoch=1, rank=args.rank, group_size=args.group_size,
                                             mode='eval')
         eval_dataloader = de_dataset.create_tuple_iterator()
-        network = DenseNet121(args.num_classes)
+        network = DenseNet(args.num_classes)
 
         param_dict = load_checkpoint(model)
         param_dict_new = {}
@@ -240,15 +259,13 @@ def test(cloud_args=None):
         img_tot = results[2, 0]
         acc1 = 100.0 * top1_correct / img_tot
         acc5 = 100.0 * top5_correct / img_tot
-        args.logger.info('after allreduce eval: top1_correct={}, tot={}, acc={:.2f}%'.format(top1_correct,
+        args.logger.info('after allreduce eval: top1_correct={}, tot={}, acc={:.2f}%'.format(top1_correct, img_tot,
-                                                                                             img_tot,
                                                                                              acc1))
-        args.logger.info('after allreduce eval: top5_correct={}, tot={}, acc={:.2f}%'.format(top5_correct,
+        if args.dataset == 'imagenet':
-                                                                                             img_tot,
+            args.logger.info('after allreduce eval: top5_correct={}, tot={}, acc={:.2f}%'.format(top5_correct, img_tot,
-                                                                                             acc5))
+                                                                                                 acc5))
     if args.is_distributed:
         release()
 
-
 if __name__ == "__main__":
     test()

model_zoo/official/cv/densenet/export.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 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.
@@ -20,14 +20,13 @@ from mindspore.common import dtype as mstype
 from mindspore import context, Tensor
 from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
 
-from src.network import DenseNet121
+parser = argparse.ArgumentParser(description="densenet export")
-from src.config import config
 
-parser = argparse.ArgumentParser(description="densenet121 export")
+parser.add_argument("--net", type=str, default='', help="Densenet Model, densenet100 or densenet121")
 parser.add_argument("--device_id", type=int, default=0, help="Device id")
 parser.add_argument("--batch_size", type=int, default=32, help="batch size")
 parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
-parser.add_argument("--file_name", type=str, default="densenet121", help="output file name.")
+parser.add_argument("--file_name", type=str, default="densenet", help="output file name.")
 parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
 parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
                     help="device target")
@@ -37,8 +36,15 @@ context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
 if args.device_target == "Ascend":
     context.set_context(device_id=args.device_id)
 
+if args.net == "densenet100":
+    from src.config import config_100 as config
+    from src.network.densenet import DenseNet100 as DenseNet
+else:
+    from src.config import config_121 as config
+    from src.network.densenet import DenseNet121 as DenseNet
+
 if __name__ == "__main__":
-    network = DenseNet121(config.num_classes)
+    network = DenseNet(config.num_classes)
 
     param_dict = load_checkpoint(args.ckpt_file)
 

model_zoo/official/cv/densenet/mindspore_hub_conf.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 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.
@@ -13,9 +13,11 @@
 # limitations under the License.
 # ============================================================================
 """hub config."""
-from src.network import DenseNet121
+from src.network import DenseNet121, DenseNet100
 
 def create_network(name, *args, **kwargs):
     if name == 'densenet121':
         return DenseNet121(*args, **kwargs)
+    if name == 'densenet100':
+        return DenseNet100(*args, **kwargs)
     raise NotImplementedError(f"{name} is not implemented in the repo")

model_zoo/official/cv/densenet/scripts/run_distribute_eval.sh

@@ -1,5 +1,5 @@
 #!/bin/bash
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 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.
@@ -16,8 +16,8 @@
 
 echo "=============================================================================================================="
 echo "Please run the script as: "
-echo "sh scripts/run_distribute_eval.sh DEVICE_NUM RANK_TABLE_FILE DATASET CKPT_PATH"
+echo "sh scripts/run_distribute_eval.sh DEVICE_NUM RANK_TABLE_FILE NET_NAME DATASET_NAME DATASET CKPT_PATH"
-echo "for example: sh scripts/run_distribute_train.sh 8 /data/hccl.json /path/to/dataset /path/to/ckpt"
+echo "for example: sh scripts/run_distribute_train.sh 8 /data/hccl.json densenet121 imagenet /path/to/dataset /path/to/ckpt"
 echo "It is better to use absolute path."
 echo "================================================================================================================="
 
@@ -25,8 +25,10 @@ echo "After running the script, the network runs in the background. The log will
 
 export RANK_SIZE=$1
 export RANK_TABLE_FILE=$2
-DATASET=$3
+NET_NAME=$3
-CKPT_PATH=$4
+DATASET_NAME=$4
+DATASET=$5
+CKPT_PATH=$6
 
 for((i=0;i<RANK_SIZE;i++))
 do
@@ -40,9 +42,10 @@ do
     echo "start inferring for rank $i, device $DEVICE_ID"
     env > env.log
     python eval.py  \
+    --net=$NET_NAME \
+    --dataset=$DATASET_NAME \
     --data_dir=$DATASET  \
     --pretrained=$CKPT_PATH > log.txt 2>&1 &
 
     cd ../
 done
-

model_zoo/official/cv/densenet/scripts/run_distribute_eval_gpu.sh

@@ -14,26 +14,26 @@
 # limitations under the License.
 # ============================================================================
 
-if [ $# -lt 4 ]
+if [ $# -lt 6 ]
 then
-    echo "Usage: sh run_distribute_eval_gpu.sh [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] [CHECKPOINT_PATH]"
+    echo "Usage: sh run_distribute_eval_gpu.sh [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [NET_NAME] [DATASET_NAME] [DATASET_PATH] [CHECKPOINT_PATH]"
-exit 1
+    exit 1
 fi
 
 if [ $1 -lt 1 ] && [ $1 -gt 8 ]
 then
     echo "error: DEVICE_NUM=$1 is not in (1-8)"
-exit 1
+    exit 1
 fi
 
 export DEVICE_NUM=$1
 export RANK_SIZE=$1
 
 # check checkpoint file
-if [ ! -f $4 ]
+if [ ! -f $6 ]
 then
-    echo "error: CHECKPOINT_PATH=$4 is not a file"    
+    echo "error: CHECKPOINT_PATH=$6 is not a file"
-exit 1
+    exit 1
 fi
 
 BASEPATH=$(cd "`dirname $0`" || exit; pwd)
@@ -51,13 +51,17 @@ export CUDA_VISIBLE_DEVICES="$2"
 if [ $1 -gt 1 ]
 then
     mpirun -n $1 --allow-run-as-root python3 ${BASEPATH}/../eval.py \
-                                             --data_dir=$3 \
+                                             --net=$3 \
+                                             --dataset=$4 \
+                                             --data_dir=$5 \
                                              --device_target='GPU' \
-                                             --pretrained=$4 > eval.log 2>&1 &
+                                             --pretrained=$6 > eval.log 2>&1 &
 else
 
     python3 ${BASEPATH}/../eval.py \
-            --data_dir=$3 \
+            --net=$3 \
+            --dataset=$4 \
+            --data_dir=$5 \
             --device_target='GPU' \
-            --pretrained=$4 > eval.log 2>&1 &
+            --pretrained=$6 > eval.log 2>&1 &
 fi

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

@@ -1,5 +1,5 @@
 #!/bin/bash
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 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.
@@ -16,8 +16,8 @@
 
 echo "=============================================================================================================="
 echo "Please run the script as: "
-echo "sh scripts/run_distribute_train.sh DEVICE_NUM RANK_TABLE_FILE DATASET CKPT_FILE"
+echo "sh scripts/run_distribute_train.sh DEVICE_NUM RANK_TABLE_FILE NET_NAME DATASET_NAME DATASET CKPT_FILE"
-echo "for example: sh scripts/run_distribute_train.sh 8 /data/hccl.json /path/to/dataset ckpt_file"
+echo "for example: sh scripts/run_distribute_train.sh 8 /data/hccl.json densenet121 imagenet /path/to/dataset ckpt_file"
 echo "It is better to use absolute path."
 echo "================================================================================================================="
 
@@ -25,8 +25,10 @@ echo "After running the script, the network runs in the background. The log will
 
 export RANK_SIZE=$1
 export RANK_TABLE_FILE=$2
-DATASET=$3
+NET_NAME=$3
-CKPT_FILE=$4
+DATASET_NAME=$4
+DATASET=$5
+CKPT_FILE=$6
 
 for((i=0;i<RANK_SIZE;i++))
 do
@@ -41,9 +43,9 @@ do
     env > env.log
     if [ -f $CKPT_FILE ]
     then
-      python train.py --data_dir=$DATASET --pretrained=$CKPT_FILE > log.txt 2>&1 &
+      python train.py --net=$NET_NAME --dataset=$DATASET_NAME --data_dir=$DATASET --pretrained=$CKPT_FILE > log.txt 2>&1 &
     else
-      python train.py --data_dir=$DATASET > log.txt 2>&1 &
+      python train.py --net=$NET_NAME --dataset=$DATASET_NAME --data_dir=$DATASET > log.txt 2>&1 &
     fi
 
     cd ../

model_zoo/official/cv/densenet/scripts/run_distribute_train_gpu.sh

@@ -14,16 +14,16 @@
 # limitations under the License.
 # ============================================================================
 
-if [ $# -lt 3 ]
+if [ $# -lt 5 ]
 then
-    echo "Usage: sh run_distribute_train_gpu.sh [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] [PRE_TRAINED](optional)"
+    echo "Usage: sh run_distribute_train_gpu.sh [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [NET_NAME] [DATASET_NAME] [DATASET_PATH] [PRE_TRAINED](optional)"
-exit 1
+    exit 1
 fi
 
 if [ $1 -lt 1 ] && [ $1 -gt 8 ]
 then
     echo "error: DEVICE_NUM=$1 is not in (1-8)"
-exit 1
+    exit 1
 fi
 
 export DEVICE_NUM=$1
@@ -40,30 +40,38 @@ cd ../train || exit
 
 export CUDA_VISIBLE_DEVICES="$2"
 
-if [ -f $4 ]  # pretrained ckpt
+if [ -f $6 ]  # pretrained ckpt
-then 
+then
     if [ $1 -gt 1 ]
     then
         mpirun -n $1 --allow-run-as-root python3 ${BASEPATH}/../train.py \
-                                                 --data_dir=$3 \
+                                                 --net=$3 \
+                                                 --dataset=$4 \
+                                                 --data_dir=$5 \
                                                  --device_target='GPU' \
-                                                 --pretrained=$4 > train.log 2>&1 &
+                                                 --pretrained=$6 > train.log 2>&1 &
     else
         python3 ${BASEPATH}/../train.py \
-                --data_dir=$3 \
+                --net=$3 \
+                --dataset=$4 \
+                --data_dir=$5 \
                 --is_distributed=0 \
                 --device_target='GPU' \
-                --pretrained=$4 > train.log 2>&1 &
+                --pretrained=$6 > train.log 2>&1 &
     fi
 else
     if [ $1 -gt 1 ]
     then
         mpirun -n $1 --allow-run-as-root python3 ${BASEPATH}/../train.py \
-                                                 --data_dir=$3 \
+                                                 --net=$3 \
+                                                 --dataset=$4 \
+                                                 --data_dir=$5 \
                                                  --device_target='GPU' > train.log 2>&1 &
     else
         python3 ${BASEPATH}/../train.py \
-                --data_dir=$3 \
+                --net=$3 \
+                --dataset=$4 \
+                --data_dir=$5 \
                 --is_distributed=0 \
                 --device_target='GPU' > train.log 2>&1 &
     fi

model_zoo/official/cv/densenet/scripts/run_eval_cpu.sh

@@ -0,0 +1,46 @@
+#!/bin/bash
+# Copyright 2021 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 [ $# -lt 4 ]
+then
+    echo "Usage: sh run_eval_cpu.sh [NET_NAME] [DATASET_NAME] [DATASET_PATH] [CHECKPOINT_PATH]"
+    exit 1
+fi
+
+# check checkpoint file
+if [ ! -f $4 ]
+then
+    echo "error: CHECKPOINT_PATH=$4 is not a file"
+    exit 1
+fi
+
+BASEPATH=$(cd "`dirname $0`" || exit; pwd)
+export PYTHONPATH=${BASEPATH}:$PYTHONPATH
+
+if [ -d "../eval" ];
+then
+    rm -rf ../eval
+fi
+mkdir ../eval
+cd ../eval || exit
+
+python ${BASEPATH}/../eval.py \
+            --net=$1 \
+            --dataset=$2 \
+            --data_dir=$3 \
+            --device_target='CPU' \
+            --is_distributed=0 \
+            --pretrained=$4 > eval.log 2>&1 &

model_zoo/official/cv/densenet/scripts/run_train_cpu.sh

@@ -0,0 +1,49 @@
+#!/bin/bash
+# Copyright 2021 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 [ $# -lt 3 ]
+then
+    echo "Usage: sh run_train_cpu.sh [NET_NAME] [DATASET_NAME] [DATASET_PATH] [PRE_TRAINED](optional)"
+    exit 1
+fi
+
+BASEPATH=$(cd "`dirname $0`" || exit; pwd)
+export PYTHONPATH=${BASEPATH}:$PYTHONPATH
+if [ -d "../train" ];
+then
+    rm -rf ../train
+fi
+mkdir ../train
+cd ../train || exit
+
+
+if [ -f $4 ]  # pretrained ckpt
+then
+        python ${BASEPATH}/../train.py \
+                --net=$1 \
+                --dataset=$2 \
+                --data_dir=$3 \
+                --is_distributed=0 \
+                --device_target='CPU' \
+                --pretrained=$4 > train.log 2>&1 &
+else
+        python ${BASEPATH}/../train.py \
+                --net=$1 \
+                --dataset=$2 \
+                --data_dir=$3 \
+                --is_distributed=0 \
+                --device_target='CPU' > train.log 2>&1 &
+fi

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

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 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.
@@ -15,7 +15,39 @@
 """config"""
 from easydict import EasyDict as ed
 
-config = ed({
+#config for densenet100 and cifar10
+config_100 = ed({
+    "image_size": '32, 32',
+    "num_classes": 10,
+
+    "lr": 0.1,
+    "lr_scheduler": 'exponential',
+    "lr_epochs": '150, 225, 300',
+    "lr_gamma": 0.1,
+    "eta_min": 0,
+    "T_max": 120,
+    "max_epoch": 300,
+    "per_batch_size": 64,
+    "warmup_epochs": 0,
+
+    "weight_decay": 0.0001,
+    "momentum": 0.9,
+    "is_dynamic_loss_scale": 0,
+    "loss_scale": 1024,
+    "label_smooth": 0,
+    "label_smooth_factor": 0.1,
+
+    "log_interval": 100,
+    "ckpt_interval": 3124,
+    "ckpt_path": 'outputs_cifar10/',
+    "is_save_on_master": 1,
+
+    "rank": 0,
+    "group_size": 1
+})
+
+# config for densenet121 and imagenet
+config_121 = ed({
     "image_size": '224,224',
     "num_classes": 1000,
 
@@ -38,7 +70,7 @@ config = ed({
 
     "log_interval": 100,
     "ckpt_interval": 50000,
-    "ckpt_path": 'outputs/',
+    "ckpt_path": 'outputs_imagenet/',
     "is_save_on_master": 1,
 
     "rank": 0,

model_zoo/official/cv/densenet/src/datasets/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 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.
@@ -17,6 +17,6 @@
 read dataset for classification
 """
 
-from .classification import classification_dataset
+from .classification import classification_dataset_cifar10, classification_dataset_imagenet
 
-__all__ = ["classification_dataset"]
+__all__ = ["classification_dataset_cifar10", "classification_dataset_imagenet"]

model_zoo/official/cv/densenet/src/datasets/classification.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 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.
@@ -50,17 +50,10 @@ class TxtDataset():
         return len(self.imgs)
 
 
-def classification_dataset(data_dir, image_size, per_batch_size, max_epoch, rank, group_size,
+def classification_dataset_imagenet(data_dir, image_size, per_batch_size, max_epoch, rank, group_size, mode='train',
-                           mode='train',
+                                    input_mode='folder', root='', num_parallel_workers=None, shuffle=None,
-                           input_mode='folder',
+                                    sampler=None, class_indexing=None, drop_remainder=True, transform=None,
-                           root='',
+                                    target_transform=None):
-                           num_parallel_workers=None,
-                           shuffle=None,
-                           sampler=None,
-                           class_indexing=None,
-                           drop_remainder=True,
-                           transform=None,
-                           target_transform=None):
     """
     A function that returns a dataset for classification. The mode of input dataset could be "folder" or "txt".
     If it is "folder", all images within one folder have the same label. If it is "txt", all paths of images
@@ -88,7 +81,7 @@ def classification_dataset(data_dir, image_size, per_batch_size, max_epoch, rank
             unique index starting from 0).
 
     Examples:
-        >>> from src.datasets.classification import classification_dataset
+        >>> from src.datasets.classification import classification_dataset_imagenet
         >>> # path to imagefolder directory. This directory needs to contain sub-directories which contain the images
         >>> data_dir = "/path/to/imagefolder_directory"
         >>> de_dataset = classification_dataset(data_dir, image_size=[224, 244],
@@ -152,3 +145,77 @@ def classification_dataset(data_dir, image_size, per_batch_size, max_epoch, rank
     de_dataset = de_dataset.repeat(1)
 
     return de_dataset
+
+
+def classification_dataset_cifar10(data_dir, image_size, per_batch_size, max_epoch, rank, group_size, mode='train',
+                                   num_parallel_workers=None, shuffle=None, sampler=None, drop_remainder=True,
+                                   transform=None, target_transform=None):
+    """
+    A function that returns cifar10 dataset for classification.
+
+    Args:
+        data_dir (str): Path to the root directory that contains the dataset's bin files.
+        image_size (Union(int, sequence)): Size of the input images.
+        per_batch_size (int): the batch size of evey step during training.
+        max_epoch (int): the number of epochs.
+        rank (int): The shard ID within num_shards (default=None).
+        group_size (int): Number of shards that the dataset should be divided
+            into (default=None).
+        mode (str): "train" or others. Default: " train".
+        input_mode (str): The form of the input dataset. "folder" or "txt". Default: "folder".
+        root (str): the images path for "input_mode="txt"". Default: " ".
+        num_parallel_workers (int): Number of workers to read the data. Default: None.
+        shuffle (bool): Whether or not to perform shuffle on the dataset
+            (default=None, performs shuffle).
+        sampler (Sampler): Object used to choose samples from the dataset. Default: None.
+
+    Examples:
+        >>> from src.datasets.classification import classification_dataset_cifar10
+        >>> # path to imagefolder directory. This directory needs to contain bin files of data.
+        >>> data_dir = "/path/to/datafolder_directory"
+        >>> de_dataset = classification_dataset_cifar10(data_dir, image_size=[32, 32],
+        >>>                               per_batch_size=64, max_epoch=100,
+        >>>                               rank=0, group_size=1)
+    """
+
+    mean = [0.5 * 255, 0.5 * 255, 0.5 * 255]
+    std = [0.5 * 255, 0.5 * 255, 0.5 * 255]
+
+    if transform is None:
+        if mode == 'train':
+            transform_img = [
+                vision_C.RandomCrop(image_size, padding=4),
+                vision_C.RandomHorizontalFlip(prob=0.5),
+                vision_C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4),
+                vision_C.Normalize(mean=mean, std=std),
+                vision_C.HWC2CHW()
+            ]
+        else:
+            transform_img = [
+                vision_C.Normalize(mean=mean, std=std),
+                vision_C.HWC2CHW()
+            ]
+    else:
+        transform_img = transform
+
+    if target_transform is None:
+        transform_label = [
+            normal_C.TypeCast(mstype.int32)
+        ]
+    else:
+        transform_label = target_transform
+
+    de_dataset = de.Cifar10Dataset(data_dir, num_parallel_workers=num_parallel_workers, shuffle=shuffle,
+                                                                      sampler=sampler, num_shards=group_size,
+                                                                      shard_id=rank)
+
+    de_dataset = de_dataset.map(input_columns="image", num_parallel_workers=8, operations=transform_img)
+    de_dataset = de_dataset.map(input_columns="label", num_parallel_workers=8, operations=transform_label)
+
+    columns_to_project = ["image", "label"]
+    de_dataset = de_dataset.project(columns=columns_to_project)
+
+    de_dataset = de_dataset.batch(per_batch_size, drop_remainder=drop_remainder)
+    de_dataset = de_dataset.repeat(1)
+
+    return de_dataset

model_zoo/official/cv/densenet/src/network/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 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.
@@ -15,4 +15,4 @@
 """
 densenet network
 """
-from .densenet import DenseNet121
+from .densenet import DenseNet121, DenseNet100

model_zoo/official/cv/densenet/src/network/densenet.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 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.
@@ -25,7 +25,7 @@ from mindspore.ops import operations as P
 from mindspore.common import initializer as init
 from src.utils.var_init import default_recurisive_init, KaimingNormal
 
-__all__ = ["DenseNet121"]
+__all__ = ["DenseNet121", "DenseNet100"]
 
 class GlobalAvgPooling(nn.Cell):
     """
@@ -123,13 +123,17 @@ class _Transition(nn.Cell):
     """
     the transition layer
     """
-    def __init__(self, num_input_features, num_output_features):
+    def __init__(self, num_input_features, num_output_features, avgpool=False):
         super(_Transition, self).__init__()
+        if avgpool:
+            poollayer = nn.AvgPool2d(kernel_size=2, stride=2)
+        else:
+            poollayer = nn.MaxPool2d(kernel_size=2, stride=2)
         self.features = nn.SequentialCell(OrderedDict([
             ('norm', nn.BatchNorm2d(num_input_features)),
             ('relu', nn.ReLU()),
             ('conv', conv1x1(num_input_features, num_output_features)),
-            ('pool', nn.MaxPool2d(kernel_size=2, stride=2))
+            ('pool', poollayer)
         ]))
 
     def construct(self, x):
@@ -142,17 +146,23 @@ class Densenet(nn.Cell):
     """
     __constants__ = ['features']
 
-    def __init__(self, growth_rate, block_config, num_init_features, bn_size=4, drop_rate=0):
+    def __init__(self, growth_rate, block_config, num_init_features=None, bn_size=4, drop_rate=0):
         super(Densenet, self).__init__()
 
         layers = OrderedDict()
-        layers['conv0'] = conv7x7(3, num_init_features, stride=2, padding=3)
+        if num_init_features:
-        layers['norm0'] = nn.BatchNorm2d(num_init_features)
+            layers['conv0'] = conv7x7(3, num_init_features, stride=2, padding=3)
-        layers['relu0'] = nn.ReLU()
+            layers['norm0'] = nn.BatchNorm2d(num_init_features)
-        layers['pool0'] = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
+            layers['relu0'] = nn.ReLU()
+            layers['pool0'] = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
+            num_features = num_init_features
+        else:
+            layers['conv0'] = conv3x3(3, growth_rate*2, stride=1, padding=1)
+            layers['norm0'] = nn.BatchNorm2d(growth_rate*2)
+            layers['relu0'] = nn.ReLU()
+            num_features = growth_rate * 2
 
         # Each denseblock
-        num_features = num_init_features
         for i, num_layers in enumerate(block_config):
             block = _DenseBlock(
                 num_layers=num_layers,
@@ -165,8 +175,12 @@ class Densenet(nn.Cell):
             num_features = num_features + num_layers*growth_rate
 
             if i != len(block_config)-1:
-                trans = _Transition(num_input_features=num_features,
+                if num_init_features:
-                                    num_output_features=num_features // 2)
+                    trans = _Transition(num_input_features=num_features, num_output_features=num_features // 2,
+                                        avgpool=False)
+                else:
+                    trans = _Transition(num_input_features=num_features, num_output_features=num_features // 2,
+                                        avgpool=True)
                 layers['transition%d'%(i+1)] = trans
                 num_features = num_features // 2
 
@@ -184,6 +198,11 @@ class Densenet(nn.Cell):
     def get_out_channels(self):
         return self.out_channels
 
+
+def _densenet100(**kwargs):
+    return Densenet(growth_rate=12, block_config=(16, 16, 16), **kwargs)
+
+
 def _densenet121(**kwargs):
     return Densenet(growth_rate=32, block_config=(6, 12, 24, 16), num_init_features=64, **kwargs)
 
@@ -200,6 +219,38 @@ def _densenet201(**kwargs):
     return Densenet(growth_rate=32, block_config=(6, 12, 48, 32), num_init_features=64, **kwargs)
 
 
+class DenseNet100(nn.Cell):
+    """
+    the densenet100 architecture
+    """
+    def __init__(self, num_classes, include_top=True):
+        super(DenseNet100, self).__init__()
+        self.backbone = _densenet100()
+        out_channels = self.backbone.get_out_channels()
+        self.include_top = include_top
+        if self.include_top:
+            self.head = CommonHead(num_classes, out_channels)
+
+        default_recurisive_init(self)
+        for _, cell in self.cells_and_names():
+            if isinstance(cell, nn.Conv2d):
+                cell.weight.set_data(init.initializer(KaimingNormal(a=math.sqrt(5), mode='fan_out',
+                                                                    nonlinearity='relu'),
+                                                      cell.weight.shape,
+                                                      cell.weight.dtype))
+            elif isinstance(cell, nn.BatchNorm2d):
+                cell.gamma.set_data(init.initializer('ones', cell.gamma.shape))
+                cell.beta.set_data(init.initializer('zeros', cell.beta.shape))
+            elif isinstance(cell, nn.Dense):
+                cell.bias.set_data(init.initializer('zeros', cell.bias.shape))
+
+    def construct(self, x):
+        x = self.backbone(x)
+        if not self.include_top:
+            return x
+        x = self.head(x)
+        return x
+
 
 class DenseNet121(nn.Cell):
     """