!17701 revert alexnet datset.py config.py&modify 310 infer

From: @zeyangao
Reviewed-by: @c_34,@wuxuejian
Signed-off-by: @c_34

model_zoo/official/cv/alexnet/postprocess.py

@@ -14,24 +14,23 @@
 # ============================================================================
 """postprocess for 310 inference"""
 import os
-import json
 import argparse
 import numpy as np
-from mindspore.nn import Top1CategoricalAccuracy, Top5CategoricalAccuracy
-
+from mindspore.nn import Top1CategoricalAccuracy
+from src.model_utils.config import config as cfg
 
 batch_size = 1
 parser = argparse.ArgumentParser(description="postprocess")
-parser.add_argument("--result_dir", type=str, required=True, help="result files path.")
-parser.add_argument("--label_dir", type=str, required=True, help="image file path.")
+label_path = "./preprocess_Result/cifar10_label_ids.npy"
+parser.add_argument("--result_dir", type=str, default="./result_Files", help="result files path.")
+parser.add_argument("--label_dir", type=str, default=label_path, help="image file path.")
+parser.add_argument("--config_path", type=str, default="../default_config.yaml", help="config file path.")
 parser.add_argument('--dataset_name', type=str, choices=["cifar10", "imagenet2012"], default="cifar10")
 args = parser.parse_args()
-
+cfg.config_path = args.config_path
 if __name__ == '__main__':
     top1_acc = Top1CategoricalAccuracy()
     rst_path = args.result_dir
-    if args.dataset_name == "cifar10":
-        from src.config import alexnet_cifar10_cfg as cfg
     labels = np.load(args.label_dir, allow_pickle=True)
     for idx, label in enumerate(labels):
         f_name = os.path.join(rst_path, "alexnet_data_bs" + str(cfg.batch_size) + "_" + str(idx) + "_0.bin")
@@ -39,17 +38,3 @@ if __name__ == '__main__':
         pred = pred.reshape(cfg.batch_size, int(pred.shape[0] / cfg.batch_size))
         top1_acc.update(pred, labels[idx])
     print("acc: ", top1_acc.eval())
-    else:
-        from src.config import alexnet_imagenet_cfg as cfg
-        top5_acc = Top5CategoricalAccuracy()
-        file_list = os.listdir(rst_path)
-        with open(args.label_dir, "r") as label:
-            labels = json.load(label)
-        for f in file_list:
-            label = f.split("_0.bin")[0] + ".JPEG"
-            pred = np.fromfile(os.path.join(rst_path, f), np.float32)
-            pred = pred.reshape(cfg.batch_size, int(pred.shape[0] / cfg.batch_size))
-            top1_acc.update(pred, [labels[label],])
-            top5_acc.update(pred, [labels[label],])
-        print("Top1 acc: ", top1_acc.eval())
-        print("Top5 acc: ", top5_acc.eval())

model_zoo/official/cv/alexnet/preprocess.py

@@ -15,59 +15,28 @@
 """preprocess"""
 import os
 import argparse
-import json
 import numpy as np
+from src.model_utils.config import config
 from src.dataset import create_dataset_cifar10
-
-def create_label(result_path, dir_path):
-    print("[WARNING] Create imagenet label. Currently only use for Imagenet2012!")
-    dirs = os.listdir(dir_path)
-    file_list = []
-    for file in dirs:
-        file_list.append(file)
-    file_list = sorted(file_list)
-
-    total = 0
-    img_label = {}
-    for i, file_dir in enumerate(file_list):
-        files = os.listdir(os.path.join(dir_path, file_dir))
-        for f in files:
-            img_label[f] = i
-        total += len(files)
-
-    json_file = os.path.join(result_path, "imagenet_label.json")
-    with open(json_file, "w+") as label:
-        json.dump(img_label, label)
-
-    print("[INFO] Completed! Total {} data.".format(total))
-
 parser = argparse.ArgumentParser('preprocess')
-parser.add_argument('--dataset', type=str, choices=["cifar10", "imagenet2012"], default="cifar10")
+parser.add_argument('--dataset_name', type=str, choices=["cifar10", "imagenet2012"], default="cifar10")
 parser.add_argument('--data_path', type=str, default='', help='eval data dir')
-parser.add_argument('--result_path', type=str, default='./preprocess_Result/', help='result path')
+parser.add_argument("--config_path", type=str, default="../default_config.yaml", help="config file path.")
+result_path = './preprocess_Result/'
+#parser.add_argument('--result_path', type=str, default='./preprocess_Result/', help='result path')
 args = parser.parse_args()
-
-if args.dataset == "cifar10":
-    from src.config import alexnet_cifar10_cfg as cfg
-else:
-    from src.config import alexnet_imagenet_cfg as cfg
-
-args.per_batch_size = cfg.batch_size
-#args.image_size = cfg.image_size
-
-
+config.config_path = args.config_path
 if __name__ == "__main__":
-    if args.dataset == "cifar10":
-        dataset = create_dataset_cifar10(args.data_path, args.per_batch_size, training=False)
-        img_path = os.path.join(args.result_path, "00_data")
+    if args.dataset_name == "cifar10":
+        dataset = create_dataset_cifar10(config, args.data_path, batch_size=config.batch_size, status="eval")
+        img_path = os.path.join(result_path, "00_data")
         os.makedirs(img_path)
         label_list = []
         for idx, data in enumerate(dataset.create_dict_iterator(output_numpy=True)):
-            file_name = "alexnet_data_bs" + str(args.per_batch_size) + "_" + str(idx) + ".bin"
+            file_name = "alexnet_data_bs" + str(config.batch_size) + "_" + str(idx) + ".bin"
             file_path = os.path.join(img_path, file_name)
             data["image"].tofile(file_path)
             label_list.append(data["label"])
-        np.save(os.path.join(args.result_path, "cifar10_label_ids.npy"), label_list)
+        np.save(os.path.join(result_path, "cifar10_label_ids.npy"), label_list)
         print("=" * 20, "export bin files finished", "=" * 20)
-    else:
-        create_label(args.result_path, args.data_path)
+        

model_zoo/official/cv/alexnet/scripts/run_infer_310.sh

@@ -45,7 +45,10 @@ device_id=0
 if [ $# == 4 ]; then
     device_id=$4
 fi
-
+BASEPATH=$(dirname "$(pwd)") 
+config_path=$BASEPATH"/default_config.yaml"
+echo "base path :"$BASEPATH
+echo "config path :"$config_path 
 echo "mindir name: "$model
 echo "dataset name: "$dataset_name
 echo "dataset path: "$dataset_path
@@ -91,7 +94,7 @@ function preprocess_data()
         rm -rf ./preprocess_Result
     fi
     mkdir preprocess_Result
-    python3.7 ../preprocess.py --dataset=$dataset_name --data_path=$dataset_path --result_path=./preprocess_Result/
+    python3.7 ../preprocess.py --config_path=$config_path --dataset_name=$dataset_name --data_path=$dataset_path 
 }
 
 function compile_app()
@@ -112,20 +115,13 @@ function infer()
     mkdir result_Files
     mkdir time_Result
 
-    if [ "$dataset_name" == "cifar10" ]; then
     ../ascend310_infer/out/main --mindir_path=$model --dataset_name=$dataset_name --input0_path=./preprocess_Result/00_data --device_id=$device_id  &> infer.log
-    else
-        ../ascend310_infer/out/main --mindir_path=$model --dataset_name=$dataset_name --input0_path=$dataset_path --device_id=$device_id  &> infer.log
-    fi
+
 }
 
 function cal_acc()
 {
-    if [ "$dataset_name" == "cifar10" ]; then
-        python3.7 ../postprocess.py --result_dir=./result_Files --label_dir=./preprocess_Result/cifar10_label_ids.npy --dataset_name=$dataset_name  &> acc.log
-    else
-        python3.7 ../postprocess.py --result_dir=./result_Files --label_dir=./preprocess_Result/imagenet_label.json --dataset_name=$dataset_name  &> acc.log
-    fi
+    python3.7 ../postprocess.py  --dataset_name=$dataset_name  &> acc.log
 }
 
 if [ $need_preprocess == "y" ]; then

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

@@ -1,54 +0,0 @@
-# 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.
-# ============================================================================
-
-"""
-network config setting, will be used in train.py
-"""
-from easydict import EasyDict as edict
-
-alexnet_cifar10_cfg = edict({
-    'num_classes': 10,
-    'learning_rate': 0.002,
-    'momentum': 0.9,
-    'epoch_size': 30,
-    'batch_size': 32,
-    'buffer_size': 1000,
-    'image_height': 227,
-    'image_width': 227,
-    'save_checkpoint_steps': 1562,
-    'keep_checkpoint_max': 10,
-    'air_name': "alexnet.air",
-})
-
-alexnet_imagenet_cfg = edict({
-    'num_classes': 1000,
-    'learning_rate': 0.13,
-    'momentum': 0.9,
-    'epoch_size': 150,
-    'batch_size': 256,
-    'buffer_size': None, # invalid parameter
-    'image_height': 224,
-    'image_width': 224,
-    'save_checkpoint_steps': 625,
-    'keep_checkpoint_max': 10,
-    'air_name': "alexnet.air",
-
-    # opt
-    'weight_decay': 0.0001,
-    'loss_scale': 1024,
-
-    # lr
-    'is_dynamic_loss_scale': 0,
-})

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

@@ -22,10 +22,9 @@ import mindspore.dataset.transforms.c_transforms as C
 import mindspore.dataset.vision.c_transforms as CV
 from mindspore.common import dtype as mstype
 from mindspore.communication.management import get_rank, get_group_size
-from .config import alexnet_cifar10_cfg, alexnet_imagenet_cfg
 
 
-def create_dataset_cifar10(data_path, cfg, batch_size=32, repeat_size=1, training=True, target="Ascend"):
+def create_dataset_cifar10(cfg, data_path, batch_size=32, repeat_size=1, status="train", target="Ascend"):
     """
     create dataset for train or test
     """
@@ -40,18 +39,18 @@ def create_dataset_cifar10(data_path, cfg, batch_size=32, repeat_size=1, trainin
                                      num_shards=device_num, shard_id=rank_id)
     rescale = 1.0 / 255.0
     shift = 0.0
-    cfg = alexnet_cifar10_cfg
+    # cfg = alexnet_cifar10_cfg
 
     resize_op = CV.Resize((cfg.image_height, cfg.image_width))
     rescale_op = CV.Rescale(rescale, shift)
     normalize_op = CV.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
-    if training:
+    if status == "train":
         random_crop_op = CV.RandomCrop([32, 32], [4, 4, 4, 4])
         random_horizontal_op = CV.RandomHorizontalFlip()
     channel_swap_op = CV.HWC2CHW()
     typecast_op = C.TypeCast(mstype.int32)
     cifar_ds = cifar_ds.map(input_columns="label", operations=typecast_op, num_parallel_workers=8)
-    if training:
+    if status == "train":
         cifar_ds = cifar_ds.map(input_columns="image", operations=random_crop_op, num_parallel_workers=8)
         cifar_ds = cifar_ds.map(input_columns="image", operations=random_horizontal_op, num_parallel_workers=8)
     cifar_ds = cifar_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=8)
@@ -65,7 +64,7 @@ def create_dataset_cifar10(data_path, cfg, batch_size=32, repeat_size=1, trainin
     return cifar_ds
 
 
-def create_dataset_imagenet(dataset_path, cfg, batch_size=32, repeat_num=1, training=True,
+def create_dataset_imagenet(cfg, dataset_path, batch_size=32, repeat_num=1, training=True,
                             num_parallel_workers=None, shuffle=None, sampler=None, class_indexing=None):
     """
     create a train or eval imagenet2012 dataset for resnet50
@@ -82,7 +81,7 @@ def create_dataset_imagenet(dataset_path, cfg, batch_size=32, repeat_num=1, trai
     """
 
     device_num, rank_id = _get_rank_info()
-    cfg = alexnet_imagenet_cfg
+    # cfg = alexnet_imagenet_cfg
 
     num_parallel_workers = 16
     if device_num == 1: