From f2a9ffef63a1c4cf9f6853a5df418a906b6a6826 Mon Sep 17 00:00:00 2001 From: huchunmei Date: Mon, 7 Jun 2021 17:22:56 +0800 Subject: [PATCH] clould --- model_zoo/official/cv/mobilenetv1/README.md | 28 ++-- .../cv/mobilenetv1/default_config.yaml | 94 +++++++++++++ .../mobilenetv1/default_config_imagenet.yaml | 96 +++++++++++++ model_zoo/official/cv/mobilenetv1/eval.py | 94 ++++++++++--- model_zoo/official/cv/mobilenetv1/export.py | 35 ++--- .../official/cv/mobilenetv1/postprocess.py | 22 +-- .../official/cv/mobilenetv1/preprocess.py | 30 ++--- .../scripts/run_distribute_train.sh | 19 ++- .../cv/mobilenetv1/scripts/run_eval.sh | 17 ++- .../cv/mobilenetv1/scripts/run_eval_cpu.sh | 17 ++- .../cv/mobilenetv1/scripts/run_infer_310.sh | 20 ++- .../scripts/run_standalone_train.sh | 19 ++- .../cv/mobilenetv1/scripts/run_train_cpu.sh | 19 ++- .../official/cv/mobilenetv1/src/config.py | 60 --------- .../mobilenetv1/src/model_utils/__init__.py | 0 .../cv/mobilenetv1/src/model_utils/config.py | 127 ++++++++++++++++++ .../src/model_utils/device_adapter.py | 27 ++++ .../src/model_utils/local_adapter.py | 36 +++++ .../src/model_utils/moxing_adapter.py | 122 +++++++++++++++++ model_zoo/official/cv/mobilenetv1/train.py | 107 +++++++++++---- model_zoo/official/cv/mobilenetv2/README.md | 10 +- .../official/cv/mobilenetv2/README_CN.md | 16 ++- .../cv/mobilenetv2/default_config.yaml | 102 ++++++++++++++ .../cv/mobilenetv2/default_config_cpu.yaml | 93 +++++++++++++ .../cv/mobilenetv2/default_config_gpu.yaml | 91 +++++++++++++ model_zoo/official/cv/mobilenetv2/eval.py | 89 ++++++++++-- model_zoo/official/cv/mobilenetv2/export.py | 41 +++--- .../official/cv/mobilenetv2/postprocess.py | 10 +- .../cv/mobilenetv2/scripts/run_eval.sh | 7 +- .../cv/mobilenetv2/scripts/run_infer_310.sh | 4 +- .../cv/mobilenetv2/scripts/run_train.sh | 10 ++ .../scripts/run_train_nfs_cache.sh | 10 +- model_zoo/official/cv/mobilenetv2/src/args.py | 61 --------- .../official/cv/mobilenetv2/src/config.py | 100 -------------- .../mobilenetv2/src/model_utils/__init__.py | 0 .../cv/mobilenetv2/src/model_utils/config.py | 127 ++++++++++++++++++ .../src/model_utils/device_adapter.py | 27 ++++ .../src/model_utils/local_adapter.py | 36 +++++ .../src/model_utils/moxing_adapter.py | 122 +++++++++++++++++ model_zoo/official/cv/mobilenetv2/train.py | 109 +++++++++++---- 40 files changed, 1634 insertions(+), 420 deletions(-) create mode 100644 model_zoo/official/cv/mobilenetv1/default_config.yaml create mode 100644 model_zoo/official/cv/mobilenetv1/default_config_imagenet.yaml delete mode 100755 model_zoo/official/cv/mobilenetv1/src/config.py create mode 100644 model_zoo/official/cv/mobilenetv1/src/model_utils/__init__.py create mode 100644 model_zoo/official/cv/mobilenetv1/src/model_utils/config.py create mode 100644 model_zoo/official/cv/mobilenetv1/src/model_utils/device_adapter.py create mode 100644 model_zoo/official/cv/mobilenetv1/src/model_utils/local_adapter.py create mode 100644 model_zoo/official/cv/mobilenetv1/src/model_utils/moxing_adapter.py create mode 100644 model_zoo/official/cv/mobilenetv2/default_config.yaml create mode 100644 model_zoo/official/cv/mobilenetv2/default_config_cpu.yaml create mode 100644 model_zoo/official/cv/mobilenetv2/default_config_gpu.yaml delete mode 100644 model_zoo/official/cv/mobilenetv2/src/args.py delete mode 100644 model_zoo/official/cv/mobilenetv2/src/config.py create mode 100644 model_zoo/official/cv/mobilenetv2/src/model_utils/__init__.py create mode 100644 model_zoo/official/cv/mobilenetv2/src/model_utils/config.py create mode 100644 model_zoo/official/cv/mobilenetv2/src/model_utils/device_adapter.py create mode 100644 model_zoo/official/cv/mobilenetv2/src/model_utils/local_adapter.py create mode 100644 model_zoo/official/cv/mobilenetv2/src/model_utils/moxing_adapter.py diff --git a/model_zoo/official/cv/mobilenetv1/README.md b/model_zoo/official/cv/mobilenetv1/README.md index 83228c9a7c9..614c1ca4509 100644 --- a/model_zoo/official/cv/mobilenetv1/README.md +++ b/model_zoo/official/cv/mobilenetv1/README.md @@ -1,4 +1,4 @@ -# Mobilenet_V1 +# Mobilenet_V1 - [Mobilenet_V1](#mobilenet_v1) - [MobileNetV1 Description](#mobilenetv1-description) @@ -79,17 +79,23 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil ├── MobileNetV1 ├── README.md # descriptions about MobileNetV1 ├── scripts - │ ├──run_distribute_train.sh # shell script for distribute train - │ ├──run_standalone_train.sh # shell script for standalone train - │ ├──run_eval.sh # shell script for evaluation + │ ├──run_distribute_train.sh # shell script for distribute train + │ ├──run_standalone_train.sh # shell script for standalone train + │ ├──run_eval.sh # shell script for evaluation ├── src - │ ├──config.py # parameter configuration - │ ├──dataset.py # creating dataset - │ ├──lr_generator.py # learning rate config - │ ├──mobilenet_v1_fpn.py # MobileNetV1 architecture - │ ├──CrossEntropySmooth.py # loss function - ├── train.py # training script - ├── eval.py # evaluation script + │ ├──dataset.py # creating dataset + │ ├──lr_generator.py # learning rate config + │ ├──mobilenet_v1_fpn.py # MobileNetV1 architecture + │ ├──CrossEntropySmooth.py # loss function + │ └──model_utils + │ ├──config.py # Processing configuration parameters + │ ├──device_adapter.py # Get cloud ID + │ ├──local_adapter.py # Get local ID + │ └──moxing_adapter.py # Parameter processing + ├── default_config.yaml # Training parameter profile(cifar10) + ├── default_config_imagenet.yaml # Training parameter profile(imagenet) + ├── train.py # training script + ├── eval.py # evaluation script ``` ## [Training process](#contents) diff --git a/model_zoo/official/cv/mobilenetv1/default_config.yaml b/model_zoo/official/cv/mobilenetv1/default_config.yaml new file mode 100644 index 00000000000..307aab4b75a --- /dev/null +++ b/model_zoo/official/cv/mobilenetv1/default_config.yaml @@ -0,0 +1,94 @@ +# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing) +enable_modelarts: False +data_url: "" +train_url: "" +checkpoint_url: "" +data_path: "/cache/data" +output_path: "/cache/train" +load_path: "/cache/checkpoint_path" +checkpoint_file: './checkpoint/mobilenetv1-90_625.ckpt' +device_target: Ascend +enable_profiling: False + +# ============================================================================== +modelarts_dataset_unzip_name: 'ImageNet_Original' +need_modelarts_dataset_unzip: True + +# config for mobilenet, cifar10 +class_num: 10 +batch_size: 32 +loss_scale: 1024 +momentum: 0.9 +weight_decay: 0.0001 # 1e-4 +epoch_size: 90 +pretrain_epoch_size: 0 +save_checkpoint: True +save_checkpoint_epochs: 5 +keep_checkpoint_max: 10 +save_checkpoint_path: "/cache/train" +warmup_epochs: 5 +lr_decay_mode: "poly" +lr_init: 0.01 +lr_end: 0.00001 +lr_max: 0.1 + +# Image classification - train +dataset: 'cifar10' +run_distribute: True +device_num: 1 +dataset_path: "/cache/data" +device_target: 'Ascend' +pre_trained: "./mobilenetv2-200_625.ckpt" # "./mobilenetv1-90_195.ckpt" +parameter_server: False + +# Image classification - eval +checkpoint_path: "./mobilenetv1-90_625.ckpt" + +# mobilenetv1 export +device_id: 0 +ckpt_file: "/cache/data/mobilenetv1-90_625.ckpt" +width: 224 +height: 224 +file_name: "mobilenetv1" +file_format: "AIR" + +# postprocess +result_dir: '' +label_dir: '' +dataset_name: 'cifar10' + +# preprocess +# data_path: '' help='eval data dir' +result_path: './preprocess_Result/' + +--- +# Config description for each option +enable_modelarts: 'Whether training on modelarts, default: False' +data_url: 'Dataset url for obs' +train_url: 'Training output url for obs' +data_path: 'Dataset path for local' +output_path: 'Training output path for local' +result_dir: "result files path." +label_dir: "image file path." + +file_name: "output file name." +dataset: "Dataset, either cifar10 or imagenet2012" +parameter_server: 'Run parameter server train' +width: 'input width' +height: 'input height' +device_target: 'Target device type' +enable_profiling: 'Whether enable profiling while training, default: False' +only_create_dataset: 'If set it true, only create Mindrecord, default is false.' +run_distribute: 'Run distribute, default is false.' +do_train: 'Do train or not, default is true.' +do_eval: 'Do eval or not, default is false.' +pre_trained: 'Pretrained checkpoint path' +device_id: 'Device id, default is 0.' +device_num: 'Use device nums, default is 1.' +rank_id: 'Rank id, default is 0.' +file_format: 'file format' + +--- +device_target: ['Ascend', 'GPU', 'CPU'] +file_format: ["AIR", "ONNX", "MINDIR"] +dataset_name: ["cifar10", "imagenet2012"] diff --git a/model_zoo/official/cv/mobilenetv1/default_config_imagenet.yaml b/model_zoo/official/cv/mobilenetv1/default_config_imagenet.yaml new file mode 100644 index 00000000000..699cd7740cf --- /dev/null +++ b/model_zoo/official/cv/mobilenetv1/default_config_imagenet.yaml @@ -0,0 +1,96 @@ +# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing) +enable_modelarts: False +data_url: "" +train_url: "" +checkpoint_url: "" +data_path: "/cache/data" +output_path: "/cache/train" +load_path: "/cache/checkpoint_path" +checkpoint_file: './checkpoint/mobilenetv1-90_625.ckpt' +device_target: Ascend +enable_profiling: False + +# ============================================================================== +modelarts_dataset_unzip_name: 'ImageNet_Original' +need_modelarts_dataset_unzip: True + +# config for mobilenet, imagenet2012 +class_num: 1001 +batch_size: 256 +loss_scale: 1024 +momentum: 0.9 +weight_decay: 0.0001 # 1e-4 +epoch_size: 90 +pretrain_epoch_size: 0 +save_checkpoint: True +save_checkpoint_epochs: 5 +keep_checkpoint_max: 10 +save_checkpoint_path: "./" +warmup_epochs: 0 +lr_decay_mode: "linear" +use_label_smooth: True +label_smooth_factor: 0.1 +lr_init: 0 +lr_max: 0.8 +lr_end: 0.0 + +# Image classification - train +dataset: 'imagenet2012' +run_distribute: True +device_num: 1 +dataset_path: "/cache/data" +device_target: 'Ascend' +pre_trained: "./mobilenetv2-200_625.ckpt" # "./mobilenetv1-90_625.ckpt" +parameter_server: False + +# Image classification - eval +checkpoint_path: "./mobilenetv1-90_625.ckpt" + +# mobilenetv1 export +device_id: 0 +ckpt_file: "/cache/data/mobilenetv1-90_625.ckpt" +width: 224 +height: 224 +file_name: "mobilenetv1" +file_format: "AIR" + +# postprocess +result_dir: '' +label_dir: '' +dataset_name: 'imagenet2012' + +# preprocess +# data_path: '' help='eval data dir' +result_path: './preprocess_Result/' + +--- +# Config description for each option +enable_modelarts: 'Whether training on modelarts, default: False' +data_url: 'Dataset url for obs' +train_url: 'Training output url for obs' +data_path: 'Dataset path for local' +output_path: 'Training output path for local' +result_dir: "result files path." +label_dir: "image file path." + +file_name: "output file name." +dataset: "Dataset, either cifar10 or imagenet2012" +parameter_server: 'Run parameter server train' +width: 'input width' +height: 'input height' +device_target: 'Target device type' +enable_profiling: 'Whether enable profiling while training, default: False' +only_create_dataset: 'If set it true, only create Mindrecord, default is false.' +run_distribute: 'Run distribute, default is false.' +do_train: 'Do train or not, default is true.' +do_eval: 'Do eval or not, default is false.' +pre_trained: 'Pretrained checkpoint path' +device_id: 'Device id, default is 0.' +device_num: 'Use device nums, default is 1.' +rank_id: 'Rank id, default is 0.' +file_format: 'file format' + +--- +device_target: ['Ascend', 'GPU', 'CPU'] +file_format: ["AIR", "ONNX", "MINDIR"] +dataset_name: ["cifar10", "imagenet2012"] diff --git a/model_zoo/official/cv/mobilenetv1/eval.py b/model_zoo/official/cv/mobilenetv1/eval.py index aa3348b54ce..9fc6d433a1e 100755 --- a/model_zoo/official/cv/mobilenetv1/eval.py +++ b/model_zoo/official/cv/mobilenetv1/eval.py @@ -14,7 +14,7 @@ # ============================================================================ """eval mobilenet_v1.""" import os -import argparse +import time from mindspore import context from mindspore.common import set_seed from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits @@ -22,26 +22,81 @@ from mindspore.train.model import Model from mindspore.train.serialization import load_checkpoint, load_param_into_net from src.CrossEntropySmooth import CrossEntropySmooth from src.mobilenet_v1 import mobilenet_v1 as mobilenet +from src.model_utils.config import config +from src.model_utils.moxing_adapter import moxing_wrapper +from src.model_utils.device_adapter import get_device_id, get_device_num -parser = argparse.ArgumentParser(description='Image classification') -parser.add_argument('--dataset', type=str, default=None, help='Dataset, either cifar10 or imagenet2012') - -parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path') -parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path') -parser.add_argument('--device_target', type=str, default='Ascend', help='Device target') -args_opt = parser.parse_args() set_seed(1) -if args_opt.dataset == 'cifar10': - from src.config import config1 as config +if config.dataset == 'cifar10': from src.dataset import create_dataset1 as create_dataset else: - from src.config import config2 as config from src.dataset import create_dataset2 as create_dataset -if __name__ == '__main__': - target = args_opt.device_target + +def modelarts_process(): + """ modelarts process """ + def unzip(zip_file, save_dir): + import zipfile + s_time = time.time() + if not os.path.exists(os.path.join(save_dir, config.modelarts_dataset_unzip_name)): + zip_isexist = zipfile.is_zipfile(zip_file) + if zip_isexist: + fz = zipfile.ZipFile(zip_file, 'r') + data_num = len(fz.namelist()) + print("Extract Start...") + print("unzip file num: {}".format(data_num)) + data_print = int(data_num / 100) if data_num > 100 else 1 + i = 0 + for file in fz.namelist(): + if i % data_print == 0: + print("unzip percent: {}%".format(int(i * 100 / data_num)), flush=True) + i += 1 + fz.extract(file, save_dir) + print("cost time: {}min:{}s.".format(int((time.time() - s_time) / 60),\ + int(int(time.time() - s_time) % 60))) + print("Extract Done") + else: + print("This is not zip.") + else: + print("Zip has been extracted.") + + if config.need_modelarts_dataset_unzip: + zip_file_1 = os.path.join(config.data_path, config.modelarts_dataset_unzip_name + ".zip") + save_dir_1 = os.path.join(config.data_path) + + sync_lock = "/tmp/unzip_sync.lock" + + # Each server contains 8 devices as most + if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock): + print("Zip file path: ", zip_file_1) + print("Unzip file save dir: ", save_dir_1) + unzip(zip_file_1, save_dir_1) + print("===Finish extract data synchronization===") + try: + os.mknod(sync_lock) + except IOError: + pass + + while True: + if os.path.exists(sync_lock): + break + time.sleep(1) + + print("Device: {}, Finish sync unzip data from {} to {}.".format(get_device_id(), zip_file_1, save_dir_1)) + print("#" * 200, os.listdir(save_dir_1)) + print("#" * 200, os.listdir(os.path.join(config.data_path, config.modelarts_dataset_unzip_name))) + + config.dataset_path = os.path.join(config.data_path, config.modelarts_dataset_unzip_name) + config.checkpoint_path = os.path.join(config.output_path, config.checkpoint_path) + + +@moxing_wrapper(pre_process=modelarts_process) +def eval_mobilenetv1(): + config.dataset_path = os.path.join(config.dataset_path, 'validation_preprocess') + print('\nconfig:\n', config) + target = config.device_target # init context context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False) @@ -50,20 +105,20 @@ if __name__ == '__main__': context.set_context(device_id=device_id) # create dataset - dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size, + dataset = create_dataset(dataset_path=config.dataset_path, do_train=False, batch_size=config.batch_size, target=target) - step_size = dataset.get_dataset_size() + # step_size = dataset.get_dataset_size() # define net net = mobilenet(class_num=config.class_num) # load checkpoint - param_dict = load_checkpoint(args_opt.checkpoint_path) + param_dict = load_checkpoint(config.checkpoint_path) load_param_into_net(net, param_dict) net.set_train(False) # define loss, model - if args_opt.dataset == "imagenet2012": + if config.dataset == "imagenet2012": if not config.use_label_smooth: config.label_smooth_factor = 0.0 loss = CrossEntropySmooth(sparse=True, reduction='mean', @@ -76,4 +131,7 @@ if __name__ == '__main__': # eval model res = model.eval(dataset) - print("result:", res, "ckpt=", args_opt.checkpoint_path) + print("result:", res, "ckpt=", config.checkpoint_path) + +if __name__ == '__main__': + eval_mobilenetv1() diff --git a/model_zoo/official/cv/mobilenetv1/export.py b/model_zoo/official/cv/mobilenetv1/export.py index 4b5264deec5..1778b677933 100644 --- a/model_zoo/official/cv/mobilenetv1/export.py +++ b/model_zoo/official/cv/mobilenetv1/export.py @@ -13,44 +13,25 @@ # limitations under the License. # ============================================================================ -import argparse import numpy as np from mindspore import context, Tensor from mindspore.train.serialization import export, load_checkpoint from src.mobilenet_v1 import mobilenet_v1 as mobilenet +from src.model_utils.config import config +from src.model_utils.device_adapter import get_device_id -parser = argparse.ArgumentParser(description="mobilenetv1 export") -parser.add_argument("--device_id", type=int, default=0, help="Device id") -parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.") -parser.add_argument("--dataset", type=str, default="imagenet2012", help="Dataset, either cifar10 or imagenet2012") -parser.add_argument('--width', type=int, default=224, help='input width') -parser.add_argument('--height', type=int, default=224, help='input height') -parser.add_argument("--file_name", type=str, default="mobilenetv1", 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") -args = parser.parse_args() -context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target) - -if args.dataset == "cifar10": - from src.config import config1 as config -else: - from src.config import config2 as config +context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target) if __name__ == "__main__": - target = args.device_target + target = config.device_target if target != "GPU": - context.set_context(device_id=args.device_id) + context.set_context(device_id=get_device_id()) network = mobilenet(class_num=config.class_num) - - param_dict = load_checkpoint(args.ckpt_file, net=network) - + param_dict = load_checkpoint(config.ckpt_file, net=network) network.set_train(False) - - input_data = Tensor(np.zeros([config.batch_size, 3, args.height, args.width]).astype(np.float32)) - - export(network, input_data, file_name=args.file_name, file_format=args.file_format) + input_data = Tensor(np.zeros([config.batch_size, 3, config.height, config.width]).astype(np.float32)) + export(network, input_data, file_name=config.file_name, file_format=config.file_format) diff --git a/model_zoo/official/cv/mobilenetv1/postprocess.py b/model_zoo/official/cv/mobilenetv1/postprocess.py index 10a4b97b749..ab1437fc2d2 100644 --- a/model_zoo/official/cv/mobilenetv1/postprocess.py +++ b/model_zoo/official/cv/mobilenetv1/postprocess.py @@ -15,41 +15,33 @@ """postprocess for 310 inference""" import os import json -import argparse import numpy as np from mindspore.nn import Top1CategoricalAccuracy, Top5CategoricalAccuracy +from src.model_utils.config import config -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.") -parser.add_argument('--dataset_name', type=str, choices=["cifar10", "imagenet2012"], default="imagenet2012") -args = parser.parse_args() - def calcul_acc(lab, preds): return sum(1 for x, y in zip(lab, preds) if x == y) / len(lab) if __name__ == '__main__': batch_size = 1 top1_acc = Top1CategoricalAccuracy() - rst_path = args.result_dir + rst_path = config.result_dir label_list = [] pred_list = [] - if args.dataset_name == "cifar10": - from src.config import config1 as cfg - labels = np.load(args.label_dir, allow_pickle=True) + if config.dataset_name == "cifar10": + labels = np.load(config.label_dir, allow_pickle=True) for idx, label in enumerate(labels): - f_name = os.path.join(rst_path, "mobilenetv1_data_bs" + str(cfg.batch_size) + "_" + str(idx) + "_0.bin") + f_name = os.path.join(rst_path, "mobilenetv1_data_bs" + str(config.batch_size) + "_" + str(idx) + "_0.bin") pred = np.fromfile(f_name, np.float32) - pred = pred.reshape(cfg.batch_size, int(pred.shape[0] / cfg.batch_size)) + pred = pred.reshape(config.batch_size, int(pred.shape[0] / config.batch_size)) top1_acc.update(pred, labels[idx]) print("acc: ", top1_acc.eval()) else: - from src.config import config2 as cfg top5_acc = Top5CategoricalAccuracy() file_list = os.listdir(rst_path) - with open(args.label_dir, "r") as label: + with open(config.label_dir, "r") as label: labels = json.load(label) for f in file_list: label = f.split("_0.bin")[0] + ".JPEG" diff --git a/model_zoo/official/cv/mobilenetv1/preprocess.py b/model_zoo/official/cv/mobilenetv1/preprocess.py index 41ea01f235a..d4fd4a96b7f 100644 --- a/model_zoo/official/cv/mobilenetv1/preprocess.py +++ b/model_zoo/official/cv/mobilenetv1/preprocess.py @@ -14,11 +14,13 @@ # ============================================================================ """preprocess""" import os -import argparse import json import numpy as np from src.dataset import create_dataset1 +from src.model_utils.config import config + + def create_label(result_path, dir_path): print("[WARNING] Create imagenet label. Currently only use for Imagenet2012!") dirs = os.listdir(dir_path) @@ -41,33 +43,23 @@ def create_label(result_path, dir_path): print("[INFO] Completed! Total {} data.".format(total)) -parser = argparse.ArgumentParser('preprocess') -parser.add_argument('--dataset', type=str, choices=["cifar10", "imagenet2012"], default="imagenet2012") -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') -args = parser.parse_args() -if args.dataset == "cifar10": - from src.config import config1 as cfg -else: - from src.config import config2 as cfg - -args.per_batch_size = cfg.batch_size -#args.image_size = list(map(int, cfg.image_size.split(','))) +config.per_batch_size = config.batch_size +#config.image_size = list(map(int, config.image_size.split(','))) if __name__ == "__main__": - if args.dataset == "cifar10": - dataset = create_dataset1(args.data_path, False, args.per_batch_size) - img_path = os.path.join(args.result_path, "00_data") + if config.dataset == "cifar10": + dataset = create_dataset1(config.data_path, False, config.per_batch_size) + img_path = os.path.join(config.result_path, "00_data") os.makedirs(img_path) label_list = [] for idx, data in enumerate(dataset.create_dict_iterator(output_numpy=True)): - file_name = "mobilenetv1_data_bs" + str(args.per_batch_size) + "_" + str(idx) + ".bin" + file_name = "mobilenetv1_data_bs" + str(config.per_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(config.result_path, "cifar10_label_ids.npy"), label_list) print("=" * 20, "export bin files finished", "=" * 20) else: - create_label(args.result_path, args.data_path) + create_label(config.result_path, config.data_path) diff --git a/model_zoo/official/cv/mobilenetv1/scripts/run_distribute_train.sh b/model_zoo/official/cv/mobilenetv1/scripts/run_distribute_train.sh index 32e2a5c1256..048f165e241 100755 --- a/model_zoo/official/cv/mobilenetv1/scripts/run_distribute_train.sh +++ b/model_zoo/official/cv/mobilenetv1/scripts/run_distribute_train.sh @@ -68,6 +68,20 @@ export RANK_TABLE_FILE=$PATH1 export SERVER_ID=0 rank_start=$((DEVICE_NUM * SERVER_ID)) +BASE_PATH=$(cd ./"`dirname $0`" || exit; pwd) +if [ $# -ge 1 ]; then + if [ $1 == 'cifar10' ]; then + CONFIG_FILE="${BASE_PATH}/../default_config.yaml" + elif [ $1 == 'imagenet2012' ]; then + CONFIG_FILE="${BASE_PATH}/../default_config_imagenet.yaml" + else + echo "Unrecognized parameter" + exit 1 + fi +else + CONFIG_FILE="${BASE_PATH}/../default_config.yaml" +fi + for((i=0; i<${DEVICE_NUM}; i++)) do export DEVICE_ID=${i} @@ -75,6 +89,7 @@ do rm -rf ./train_parallel$i mkdir ./train_parallel$i cp ../*.py ./train_parallel$i + cp ../*.yaml ./train_parallel$i cp *.sh ./train_parallel$i cp -r ../src ./train_parallel$i cd ./train_parallel$i || exit @@ -82,12 +97,12 @@ do env > env.log if [ $# == 3 ] then - python train.py --dataset=$1 --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 &> log & + python train.py --config_path=$CONFIG_FILE --dataset=$1 --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 &> log.txt & fi if [ $# == 4 ] then - python train.py --dataset=$1 --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 --pre_trained=$PATH3 &> log & + python train.py --config_path=$CONFIG_FILE --dataset=$1 --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 --pre_trained=$PATH3 &> log.txt & fi cd .. diff --git a/model_zoo/official/cv/mobilenetv1/scripts/run_eval.sh b/model_zoo/official/cv/mobilenetv1/scripts/run_eval.sh index 387bd0382df..8452973722c 100755 --- a/model_zoo/official/cv/mobilenetv1/scripts/run_eval.sh +++ b/model_zoo/official/cv/mobilenetv1/scripts/run_eval.sh @@ -56,16 +56,31 @@ export DEVICE_ID=0 export RANK_SIZE=$DEVICE_NUM export RANK_ID=0 +BASE_PATH=$(cd ./"`dirname $0`" || exit; pwd) +if [ $# -ge 1 ]; then + if [ $1 == 'cifar10' ]; then + CONFIG_FILE="${BASE_PATH}/../default_config.yaml" + elif [ $1 == 'imagenet2012' ]; then + CONFIG_FILE="${BASE_PATH}/../default_config_imagenet.yaml" + else + echo "Unrecognized parameter" + exit 1 + fi +else + CONFIG_FILE="${BASE_PATH}/../default_config.yaml" +fi + if [ -d "eval" ]; then rm -rf ./eval fi mkdir ./eval cp ../*.py ./eval +cp ../*.yaml ./eval cp *.sh ./eval cp -r ../src ./eval cd ./eval || exit env > env.log echo "start evaluation for device $DEVICE_ID" -python eval.py --dataset=$1 --dataset_path=$PATH1 --checkpoint_path=$PATH2 &> log & +python eval.py --config_path=$CONFIG_FILE --dataset=$1 --dataset_path=$PATH1 --checkpoint_path=$PATH2 &> log_eval.txt & cd .. diff --git a/model_zoo/official/cv/mobilenetv1/scripts/run_eval_cpu.sh b/model_zoo/official/cv/mobilenetv1/scripts/run_eval_cpu.sh index 75e1ed72a66..93cf4d91d25 100755 --- a/model_zoo/official/cv/mobilenetv1/scripts/run_eval_cpu.sh +++ b/model_zoo/official/cv/mobilenetv1/scripts/run_eval_cpu.sh @@ -50,15 +50,30 @@ then exit 1 fi +BASE_PATH=$(cd ./"`dirname $0`" || exit; pwd) +if [ $# -ge 1 ]; then + if [ $1 == 'cifar10' ]; then + CONFIG_FILE="${BASE_PATH}/../default_config.yaml" + elif [ $1 == 'imagenet2012' ]; then + CONFIG_FILE="${BASE_PATH}/../default_config_imagenet.yaml" + else + echo "Unrecognized parameter" + exit 1 + fi +else + CONFIG_FILE="${BASE_PATH}/../default_config.yaml" +fi + if [ -d "eval" ]; then rm -rf ./eval fi mkdir ./eval cp ../*.py ./eval +cp ../*.yaml ./eval cp *.sh ./eval cp -r ../src ./eval cd ./eval || exit env > env.log -python eval.py --dataset=$1 --dataset_path=$PATH1 --checkpoint_path=$PATH2 --device_target=CPU &> log & +python eval.py --config_path=$CONFIG_FILE --dataset=$1 --dataset_path=$PATH1 --checkpoint_path=$PATH2 --device_target=CPU &> log_eval_cpu.txt & cd .. diff --git a/model_zoo/official/cv/mobilenetv1/scripts/run_infer_310.sh b/model_zoo/official/cv/mobilenetv1/scripts/run_infer_310.sh index 46ce380f5e7..439d6744405 100644 --- a/model_zoo/official/cv/mobilenetv1/scripts/run_infer_310.sh +++ b/model_zoo/official/cv/mobilenetv1/scripts/run_infer_310.sh @@ -33,6 +33,21 @@ dataset_path=$(get_real_path $2) dataset_name="imagenet2012" DVPP="CPU" +BASE_PATH=$(cd ./"`dirname $0`" || exit; pwd) +if [ $# -ge 1 ]; then + if [ $dataset_name == 'cifar10' ]; then + CONFIG_FILE="${BASE_PATH}/../default_config.yaml" + elif [ $dataset_name == 'imagenet2012' ]; then + CONFIG_FILE="${BASE_PATH}/../default_config_imagenet.yaml" + else + echo "Unrecognized parameter" + exit 1 + fi +else + CONFIG_FILE="${BASE_PATH}/../default_config.yaml" +fi + + device_id=0 if [ $# == 3 ]; then device_id=$3 @@ -60,7 +75,6 @@ export SLOG_PRINT_to_STDOUT=0 export GLOG_v=2 export DUMP_GE_GRAPH=2 - export ASCEND_HOME=/usr/local/Ascend export PATH=$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/toolkit/bin:$PATH @@ -81,7 +95,7 @@ function preprocess_data() rm -rf ./preprocess_Result fi mkdir preprocess_Result - python3.7 ../preprocess.py --dataset=$dataset_name --data_path=$dataset_path --result_path=./preprocess_Result/ + python3.7 ../preprocess.py --config_path=$CONFIG_FILE --dataset=$dataset_name --data_path=$dataset_path --result_path=./preprocess_Result/ } function compile_app() @@ -112,7 +126,7 @@ function infer() function cal_acc() { - python3.7 ../postprocess.py --result_dir=./result_Files --label_dir=./preprocess_Result/imagenet_label.json &> acc.log + python3.7 ../postprocess.py --config_path=$CONFIG_FILE --result_dir=./result_Files --label_dir=./preprocess_Result/imagenet_label.json &> acc.log } diff --git a/model_zoo/official/cv/mobilenetv1/scripts/run_standalone_train.sh b/model_zoo/official/cv/mobilenetv1/scripts/run_standalone_train.sh index f733090af99..0512190797d 100755 --- a/model_zoo/official/cv/mobilenetv1/scripts/run_standalone_train.sh +++ b/model_zoo/official/cv/mobilenetv1/scripts/run_standalone_train.sh @@ -59,12 +59,27 @@ export DEVICE_ID=0 export RANK_ID=0 export RANK_SIZE=1 +BASE_PATH=$(cd ./"`dirname $0`" || exit; pwd) +if [ $# -ge 1 ]; then + if [ $1 == 'cifar10' ]; then + CONFIG_FILE="${BASE_PATH}/../default_config.yaml" + elif [ $1 == 'imagenet2012' ]; then + CONFIG_FILE="${BASE_PATH}/../default_config_imagenet.yaml" + else + echo "Unrecognized parameter" + exit 1 + fi +else + CONFIG_FILE="${BASE_PATH}/../default_config.yaml" +fi + if [ -d "train" ]; then rm -rf ./train fi mkdir ./train cp ../*.py ./train +cp ../*.yaml ./train cp *.sh ./train cp -r ../src ./train cd ./train || exit @@ -72,11 +87,11 @@ echo "start training for device $DEVICE_ID" env > env.log if [ $# == 2 ] then - python train.py --dataset=$1 --dataset_path=$PATH1 &> log & + python train.py --config_path=$CONFIG_FILE --dataset=$1 --dataset_path=$PATH1 &> log.txt & fi if [ $# == 3 ] then - python train.py --dataset=$1 --dataset_path=$PATH1 --pre_trained=$PATH2 &> log & + python train.py --config_path=$CONFIG_FILE --dataset=$1 --dataset_path=$PATH1 --pre_trained=$PATH2 &> log.txt & fi cd .. diff --git a/model_zoo/official/cv/mobilenetv1/scripts/run_train_cpu.sh b/model_zoo/official/cv/mobilenetv1/scripts/run_train_cpu.sh index ec010a47e66..94e519a29f1 100755 --- a/model_zoo/official/cv/mobilenetv1/scripts/run_train_cpu.sh +++ b/model_zoo/official/cv/mobilenetv1/scripts/run_train_cpu.sh @@ -53,23 +53,38 @@ then exit 1 fi +BASE_PATH=$(cd ./"`dirname $0`" || exit; pwd) +if [ $# -ge 1 ]; then + if [ $1 == 'cifar10' ]; then + CONFIG_FILE="${BASE_PATH}/../default_config.yaml" + elif [ $1 == 'imagenet2012' ]; then + CONFIG_FILE="${BASE_PATH}/../default_config_imagenet.yaml" + else + echo "Unrecognized parameter" + exit 1 + fi +else + CONFIG_FILE="${BASE_PATH}/../default_config.yaml" +fi + if [ -d "train" ]; then rm -rf ./train fi mkdir ./train cp ../*.py ./train +cp ../*.yaml ./train cp *.sh ./train cp -r ../src ./train cd ./train || exit env > env.log if [ $# == 2 ] then - python train.py --dataset=$1 --dataset_path=$PATH1 --device_target=CPU &> log & + python train.py --config_path=$CONFIG_FILE --dataset=$1 --dataset_path=$PATH1 --device_target=CPU &> log.txt & fi if [ $# == 3 ] then - python train.py --dataset=$1 --dataset_path=$PATH1 --pre_trained=$PATH2 --device_target=CPU &> log & + python train.py --config_path=$CONFIG_FILE --dataset=$1 --dataset_path=$PATH1 --pre_trained=$PATH2 --device_target=CPU &> log.txt & fi cd .. diff --git a/model_zoo/official/cv/mobilenetv1/src/config.py b/model_zoo/official/cv/mobilenetv1/src/config.py deleted file mode 100755 index ccdb37bc930..00000000000 --- a/model_zoo/official/cv/mobilenetv1/src/config.py +++ /dev/null @@ -1,60 +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. -# ============================================================================ -""" -network config setting, will be used in train.py and eval.py -""" -from easydict import EasyDict as ed - -# config for mobilenet, cifar10 -config1 = ed({ - "class_num": 10, - "batch_size": 32, - "loss_scale": 1024, - "momentum": 0.9, - "weight_decay": 1e-4, - "epoch_size": 90, - "pretrain_epoch_size": 0, - "save_checkpoint": True, - "save_checkpoint_epochs": 5, - "keep_checkpoint_max": 10, - "save_checkpoint_path": "./", - "warmup_epochs": 5, - "lr_decay_mode": "poly", - "lr_init": 0.01, - "lr_end": 0.00001, - "lr_max": 0.1 -}) - -# config for mobilenet, imagenet2012 -config2 = ed({ - "class_num": 1001, - "batch_size": 256, - "loss_scale": 1024, - "momentum": 0.9, - "weight_decay": 1e-4, - "epoch_size": 90, - "pretrain_epoch_size": 0, - "save_checkpoint": True, - "save_checkpoint_epochs": 5, - "keep_checkpoint_max": 10, - "save_checkpoint_path": "./", - "warmup_epochs": 0, - "lr_decay_mode": "linear", - "use_label_smooth": True, - "label_smooth_factor": 0.1, - "lr_init": 0, - "lr_max": 0.8, - "lr_end": 0.0 -}) diff --git a/model_zoo/official/cv/mobilenetv1/src/model_utils/__init__.py b/model_zoo/official/cv/mobilenetv1/src/model_utils/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/model_zoo/official/cv/mobilenetv1/src/model_utils/config.py b/model_zoo/official/cv/mobilenetv1/src/model_utils/config.py new file mode 100644 index 00000000000..7f1ff6e2b8d --- /dev/null +++ b/model_zoo/official/cv/mobilenetv1/src/model_utils/config.py @@ -0,0 +1,127 @@ +# 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. +# ============================================================================ + +"""Parse arguments""" + +import os +import ast +import argparse +from pprint import pprint, pformat +import yaml + +class Config: + """ + Configuration namespace. Convert dictionary to members. + """ + def __init__(self, cfg_dict): + for k, v in cfg_dict.items(): + if isinstance(v, (list, tuple)): + setattr(self, k, [Config(x) if isinstance(x, dict) else x for x in v]) + else: + setattr(self, k, Config(v) if isinstance(v, dict) else v) + + def __str__(self): + return pformat(self.__dict__) + + def __repr__(self): + return self.__str__() + + +def parse_cli_to_yaml(parser, cfg, helper=None, choices=None, cfg_path="default_config.yaml"): + """ + Parse command line arguments to the configuration according to the default yaml. + + Args: + parser: Parent parser. + cfg: Base configuration. + helper: Helper description. + cfg_path: Path to the default yaml config. + """ + parser = argparse.ArgumentParser(description="[REPLACE THIS at config.py]", + parents=[parser]) + helper = {} if helper is None else helper + choices = {} if choices is None else choices + for item in cfg: + if not isinstance(cfg[item], list) and not isinstance(cfg[item], dict): + help_description = helper[item] if item in helper else "Please reference to {}".format(cfg_path) + choice = choices[item] if item in choices else None + if isinstance(cfg[item], bool): + parser.add_argument("--" + item, type=ast.literal_eval, default=cfg[item], choices=choice, + help=help_description) + else: + parser.add_argument("--" + item, type=type(cfg[item]), default=cfg[item], choices=choice, + help=help_description) + args = parser.parse_args() + return args + + +def parse_yaml(yaml_path): + """ + Parse the yaml config file. + + Args: + yaml_path: Path to the yaml config. + """ + with open(yaml_path, 'r') as fin: + try: + cfgs = yaml.load_all(fin.read(), Loader=yaml.FullLoader) + cfgs = [x for x in cfgs] + if len(cfgs) == 1: + cfg_helper = {} + cfg = cfgs[0] + cfg_choices = {} + elif len(cfgs) == 2: + cfg, cfg_helper = cfgs + cfg_choices = {} + elif len(cfgs) == 3: + cfg, cfg_helper, cfg_choices = cfgs + else: + raise ValueError("At most 3 docs (config, description for help, choices) are supported in config yaml") + print(cfg_helper) + except: + raise ValueError("Failed to parse yaml") + return cfg, cfg_helper, cfg_choices + + +def merge(args, cfg): + """ + Merge the base config from yaml file and command line arguments. + + Args: + args: Command line arguments. + cfg: Base configuration. + """ + args_var = vars(args) + for item in args_var: + cfg[item] = args_var[item] + return cfg + + +def get_config(): + """ + Get Config according to the yaml file and cli arguments. + """ + parser = argparse.ArgumentParser(description="default name", add_help=False) + current_dir = os.path.dirname(os.path.abspath(__file__)) + parser.add_argument("--config_path", type=str, default=os.path.join(current_dir, "../../default_config.yaml"), + help="Config file path") + path_args, _ = parser.parse_known_args() + default, helper, choices = parse_yaml(path_args.config_path) + pprint(default) + args = parse_cli_to_yaml(parser=parser, cfg=default, helper=helper, choices=choices, cfg_path=path_args.config_path) + final_config = merge(args, default) + return Config(final_config) + +config = get_config() diff --git a/model_zoo/official/cv/mobilenetv1/src/model_utils/device_adapter.py b/model_zoo/official/cv/mobilenetv1/src/model_utils/device_adapter.py new file mode 100644 index 00000000000..7c5d7f837dd --- /dev/null +++ b/model_zoo/official/cv/mobilenetv1/src/model_utils/device_adapter.py @@ -0,0 +1,27 @@ +# 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. +# ============================================================================ + +"""Device adapter for ModelArts""" + +from .config import config + +if config.enable_modelarts: + from .moxing_adapter import get_device_id, get_device_num, get_rank_id, get_job_id +else: + from .local_adapter import get_device_id, get_device_num, get_rank_id, get_job_id + +__all__ = [ + "get_device_id", "get_device_num", "get_rank_id", "get_job_id" +] diff --git a/model_zoo/official/cv/mobilenetv1/src/model_utils/local_adapter.py b/model_zoo/official/cv/mobilenetv1/src/model_utils/local_adapter.py new file mode 100644 index 00000000000..769fa6dc78e --- /dev/null +++ b/model_zoo/official/cv/mobilenetv1/src/model_utils/local_adapter.py @@ -0,0 +1,36 @@ +# 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. +# ============================================================================ + +"""Local adapter""" + +import os + +def get_device_id(): + device_id = os.getenv('DEVICE_ID', '0') + return int(device_id) + + +def get_device_num(): + device_num = os.getenv('RANK_SIZE', '1') + return int(device_num) + + +def get_rank_id(): + global_rank_id = os.getenv('RANK_ID', '0') + return int(global_rank_id) + + +def get_job_id(): + return "Local Job" diff --git a/model_zoo/official/cv/mobilenetv1/src/model_utils/moxing_adapter.py b/model_zoo/official/cv/mobilenetv1/src/model_utils/moxing_adapter.py new file mode 100644 index 00000000000..830d19a6fc9 --- /dev/null +++ b/model_zoo/official/cv/mobilenetv1/src/model_utils/moxing_adapter.py @@ -0,0 +1,122 @@ +# 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. +# ============================================================================ + +"""Moxing adapter for ModelArts""" + +import os +import functools +from mindspore import context +from mindspore.profiler import Profiler +from .config import config + +_global_sync_count = 0 + +def get_device_id(): + device_id = os.getenv('DEVICE_ID', '0') + return int(device_id) + + +def get_device_num(): + device_num = os.getenv('RANK_SIZE', '1') + return int(device_num) + + +def get_rank_id(): + global_rank_id = os.getenv('RANK_ID', '0') + return int(global_rank_id) + + +def get_job_id(): + job_id = os.getenv('JOB_ID') + job_id = job_id if job_id != "" else "default" + return job_id + +def sync_data(from_path, to_path): + """ + Download data from remote obs to local directory if the first url is remote url and the second one is local path + Upload data from local directory to remote obs in contrast. + """ + import moxing as mox + import time + global _global_sync_count + sync_lock = "/tmp/copy_sync.lock" + str(_global_sync_count) + _global_sync_count += 1 + + # Each server contains 8 devices as most. + if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock): + print("from path: ", from_path) + print("to path: ", to_path) + mox.file.copy_parallel(from_path, to_path) + print("===finish data synchronization===") + try: + os.mknod(sync_lock) + except IOError: + pass + print("===save flag===") + + while True: + if os.path.exists(sync_lock): + break + time.sleep(1) + + print("Finish sync data from {} to {}.".format(from_path, to_path)) + + +def moxing_wrapper(pre_process=None, post_process=None): + """ + Moxing wrapper to download dataset and upload outputs. + """ + def wrapper(run_func): + @functools.wraps(run_func) + def wrapped_func(*args, **kwargs): + # Download data from data_url + if config.enable_modelarts: + if config.data_url: + sync_data(config.data_url, config.data_path) + print("Dataset downloaded: ", os.listdir(config.data_path)) + if config.checkpoint_url: + sync_data(config.checkpoint_url, config.load_path) + print("Preload downloaded: ", os.listdir(config.load_path)) + if config.train_url: + sync_data(config.train_url, config.output_path) + print("Workspace downloaded: ", os.listdir(config.output_path)) + + context.set_context(save_graphs_path=os.path.join(config.output_path, str(get_rank_id()))) + config.device_num = get_device_num() + config.device_id = get_device_id() + if not os.path.exists(config.output_path): + os.makedirs(config.output_path) + + if pre_process: + pre_process() + + if config.enable_profiling: + profiler = Profiler() + + run_func(*args, **kwargs) + + if config.enable_profiling: + profiler.analyse() + + # Upload data to train_url + if config.enable_modelarts: + if post_process: + post_process() + + if config.train_url: + print("Start to copy output directory") + sync_data(config.output_path, config.train_url) + return wrapped_func + return wrapper diff --git a/model_zoo/official/cv/mobilenetv1/train.py b/model_zoo/official/cv/mobilenetv1/train.py index 5427dbb2ee6..562109a9787 100755 --- a/model_zoo/official/cv/mobilenetv1/train.py +++ b/model_zoo/official/cv/mobilenetv1/train.py @@ -14,8 +14,7 @@ # ============================================================================ """train mobilenet_v1.""" import os -import argparse -import ast +import time from mindspore import context from mindspore import Tensor from mindspore.nn.optim.momentum import Momentum @@ -32,40 +31,91 @@ import mindspore.common.initializer as weight_init from src.lr_generator import get_lr from src.CrossEntropySmooth import CrossEntropySmooth from src.mobilenet_v1 import mobilenet_v1 as mobilenet +from src.model_utils.config import config +from src.model_utils.moxing_adapter import moxing_wrapper +from src.model_utils.device_adapter import get_device_id, get_device_num -parser = argparse.ArgumentParser(description='Image classification') -parser.add_argument('--dataset', type=str, default=None, help='Dataset, either cifar10 or imagenet2012') -parser.add_argument('--run_distribute', type=ast.literal_eval, default=False, help='Run distribute') -parser.add_argument('--device_num', type=int, default=1, help='Device num.') - -parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path') -parser.add_argument('--device_target', type=str, default='Ascend', help='Device target') -parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path') -parser.add_argument('--parameter_server', type=ast.literal_eval, default=False, help='Run parameter server train') -args_opt = parser.parse_args() set_seed(1) -if args_opt.dataset == 'cifar10': - from src.config import config1 as config +if config.dataset == 'cifar10': from src.dataset import create_dataset1 as create_dataset else: - from src.config import config2 as config from src.dataset import create_dataset2 as create_dataset -if __name__ == '__main__': - target = args_opt.device_target + +def modelarts_pre_process(): + def unzip(zip_file, save_dir): + import zipfile + s_time = time.time() + if not os.path.exists(os.path.join(save_dir, config.modelarts_dataset_unzip_name)): + zip_isexist = zipfile.is_zipfile(zip_file) + if zip_isexist: + fz = zipfile.ZipFile(zip_file, 'r') + data_num = len(fz.namelist()) + print("Extract Start...") + print("unzip file num: {}".format(data_num)) + data_print = int(data_num / 100) if data_num > 100 else 1 + i = 0 + for file in fz.namelist(): + if i % data_print == 0: + print("unzip percent: {}%".format(int(i * 100 / data_num)), flush=True) + i += 1 + fz.extract(file, save_dir) + print("cost time: {}min:{}s.".format(int((time.time() - s_time) / 60),\ + int(int(time.time() - s_time) % 60))) + print("Extract Done") + else: + print("This is not zip.") + else: + print("Zip has been extracted.") + + if config.need_modelarts_dataset_unzip: + zip_file_1 = os.path.join(config.data_path, config.modelarts_dataset_unzip_name + ".zip") + save_dir_1 = os.path.join(config.data_path) + + sync_lock = "/tmp/unzip_sync.lock" + + # Each server contains 8 devices as most + if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock): + print("Zip file path: ", zip_file_1) + print("Unzip file save dir: ", save_dir_1) + unzip(zip_file_1, save_dir_1) + print("===Finish extract data synchronization===") + try: + os.mknod(sync_lock) + except IOError: + pass + + while True: + if os.path.exists(sync_lock): + break + time.sleep(1) + + print("Device: {}, Finish sync unzip data from {} to {}.".format(get_device_id(), zip_file_1, save_dir_1)) + print("#" * 200, os.listdir(save_dir_1)) + print("#" * 200, os.listdir(os.path.join(config.data_path, config.modelarts_dataset_unzip_name))) + + config.dataset_path = os.path.join(config.data_path, config.modelarts_dataset_unzip_name) + config.ckpt_path = config.output_path + config.pre_trained = os.path.join(config.dataset_path, config.pre_trained) + + +@moxing_wrapper(pre_process=modelarts_pre_process) +def train_mobilenetv1(): + config.dataset_path = os.path.join(config.dataset_path, 'train') + target = config.device_target ckpt_save_dir = config.save_checkpoint_path # init context context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False) - if args_opt.parameter_server: + if config.parameter_server: context.set_ps_context(enable_ps=True) device_id = int(os.getenv('DEVICE_ID', '0')) - if args_opt.run_distribute: + if config.run_distribute: if target == "Ascend": context.set_context(device_id=device_id, enable_auto_mixed_precision=True) - context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL, + context.set_auto_parallel_context(device_num=get_device_num(), parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True) init() context.set_auto_parallel_context(all_reduce_fusion_config=[75]) @@ -77,18 +127,18 @@ if __name__ == '__main__': ckpt_save_dir = config.save_checkpoint_path + "ckpt_" + str(get_rank()) + "/" # create dataset - dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True, repeat_num=1, + dataset = create_dataset(dataset_path=config.dataset_path, do_train=True, repeat_num=1, batch_size=config.batch_size, target=target) step_size = dataset.get_dataset_size() # define net net = mobilenet(class_num=config.class_num) - if args_opt.parameter_server: + if config.parameter_server: net.set_param_ps() # init weight - if args_opt.pre_trained: - param_dict = load_checkpoint(args_opt.pre_trained) + if config.pre_trained: + param_dict = load_checkpoint(config.pre_trained) load_param_into_net(net, param_dict) else: for _, cell in net.cells_and_names(): @@ -125,7 +175,7 @@ if __name__ == '__main__': opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum, config.weight_decay, config.loss_scale) # define loss, model - if args_opt.dataset == "imagenet2012": + if config.dataset == "imagenet2012": if not config.use_label_smooth: config.label_smooth_factor = 0.0 loss = CrossEntropySmooth(sparse=True, reduction="mean", @@ -143,7 +193,7 @@ if __name__ == '__main__': time_cb = TimeMonitor(data_size=step_size) loss_cb = LossMonitor() cb = [time_cb, loss_cb] - if config.save_checkpoint and device_id % min(8, args_opt.device_num) == 0: + if config.save_checkpoint and device_id % min(8, get_device_num()) == 0: config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * step_size, keep_checkpoint_max=config.keep_checkpoint_max) ckpt_cb = ModelCheckpoint(prefix="mobilenetv1", directory=ckpt_save_dir, config=config_ck) @@ -151,4 +201,7 @@ if __name__ == '__main__': # train model model.train(config.epoch_size - config.pretrain_epoch_size, dataset, callbacks=cb, - sink_size=dataset.get_dataset_size(), dataset_sink_mode=(not args_opt.parameter_server)) + sink_size=dataset.get_dataset_size(), dataset_sink_mode=(not config.parameter_server)) + +if __name__ == '__main__': + train_mobilenetv1() diff --git a/model_zoo/official/cv/mobilenetv2/README.md b/model_zoo/official/cv/mobilenetv2/README.md index f5b117c3dad..80a24c413d7 100644 --- a/model_zoo/official/cv/mobilenetv2/README.md +++ b/model_zoo/official/cv/mobilenetv2/README.md @@ -77,13 +77,19 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil │ ├──run_train_nfs_cache.sh # shell script for train with NFS dataset and leverage caching service for better performance ├── src │ ├──aipp.cfg # aipp config - │ ├──args.py # parse args - │ ├──config.py # parameter configuration │ ├──dataset.py # creating dataset │ ├──lr_generator.py # learning rate config │ ├──mobilenetV2.py # MobileNetV2 architecture │ ├──models.py # contain define_net and Loss, Monitor │ ├──utils.py # utils to load ckpt_file for fine tune or incremental learn + │ └──model_utils + │ ├──config.py # Processing configuration parameters + │ ├──device_adapter.py # Get cloud ID + │ ├──local_adapter.py # Get local ID + │ └──moxing_adapter.py # Parameter processing + ├── default_config.yaml # Training parameter profile(ascend) + ├── default_config_cpu.yaml # Training parameter profile(cpu) + ├── default_config_gpu.yaml # Training parameter profile(gpu) ├── train.py # training script ├── eval.py # evaluation script ├── export.py # export mindir script diff --git a/model_zoo/official/cv/mobilenetv2/README_CN.md b/model_zoo/official/cv/mobilenetv2/README_CN.md index a0eba476f8f..1d7cee845a3 100644 --- a/model_zoo/official/cv/mobilenetv2/README_CN.md +++ b/model_zoo/official/cv/mobilenetv2/README_CN.md @@ -73,24 +73,30 @@ MobileNetV2总体网络架构如下： ```python ├── MobileNetV2 - ├── README.md # MobileNetV2相关描述 - ├── ascend310_infer # 用于310推理 + ├── README.md # MobileNetV2相关描述 + ├── ascend310_infer # 用于310推理 ├── scripts │ ├──run_train.sh # 使用CPU、GPU或Ascend进行训练、微调或增量学习的shell脚本 │ ├──run_eval.sh # 使用CPU、GPU或Ascend进行评估的shell脚本 │ ├──cache_util.sh # 包含一些使用cache的帮助函数 │ ├──run_train_nfs_cache.sh # 使用NFS的数据集进行训练并利用缓存服务进行加速的shell脚本 - │ ├──run_infer_310.sh # 使用Dvpp 或CPU算子进行推理的shell脚本 + │ ├──run_infer_310.sh # 使用Dvpp 或CPU算子进行推理的shell脚本 ├── src │ ├──aipp.cfg # aipp配置 - │ ├──args.py # 参数解析 - │ ├──config.py # 参数配置 │ ├──dataset.py # 创建数据集 │ ├──launch.py # 启动python脚本 │ ├──lr_generator.py # 配置学习率 │ ├──mobilenetV2.py # MobileNetV2架构 │ ├──models.py # 加载define_net、Loss、及Monitor │ ├──utils.py # 加载ckpt_file进行微调或增量学习 + │ └──model_utils + │ ├──config.py # 获取.yaml配置参数 + │ ├──device_adapter.py # 获取云上id + │ ├──local_adapter.py # 获取本地id + │ └──moxing_adapter.py # 云上数据准备 + ├── default_config.yaml # 训练配置参数(ascend) + ├── default_config_cpu.yaml # 训练配置参数(cpu) + ├── default_config_gpu.yaml # 训练配置参数(gpu) ├── train.py # 训练脚本 ├── eval.py # 评估脚本 ├── export.py # 模型导出脚本 diff --git a/model_zoo/official/cv/mobilenetv2/default_config.yaml b/model_zoo/official/cv/mobilenetv2/default_config.yaml new file mode 100644 index 00000000000..66f8f383851 --- /dev/null +++ b/model_zoo/official/cv/mobilenetv2/default_config.yaml @@ -0,0 +1,102 @@ +# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing) +enable_modelarts: False +data_url: "" +train_url: "" +checkpoint_url: "" +data_path: "/cache/data" +output_path: "/cache/train" +load_path: "/cache/checkpoint_path" +checkpoint_path: './checkpoint/' +device_target: Ascend +enable_profiling: False + +# ============================================================================== +modelarts_dataset_unzip_name: 'ImageNet_Original' +need_modelarts_dataset_unzip: True + +num_classes: 1000 +image_height: 224 +image_width: 224 +batch_size: 256 +epoch_size: 200 +warmup_epochs: 4 +lr_init: 0.00 +lr_end: 0.00 +lr_max: 0.4 +momentum: 0.9 +weight_decay: 0.00001 # 4e-5 +label_smooth: 0.1 +loss_scale: 1024 +save_checkpoint: True +save_checkpoint_epochs: 1 +keep_checkpoint_max: 200 +save_checkpoint_path: "./" +platform: 'Ascend' +device_id: int(os.getenv('DEVICE_ID', '0')) +rank_id: int(os.getenv('RANK_ID', '0')) +rank_size: int(os.getenv('RANK_SIZE', '1')) +run_distribute: int(os.getenv('RANK_SIZE', '1')) > 1. +activation: "Softmax" + +# Image classification trian. train_parse_args():return train_args +dataset_path: "/cache/data" +pretrain_ckpt: "./mobilenetv2-200_625.ckpt" +freeze_layer: "" +filter_head: False +enable_cache: False +cache_session_id: "" +is_training: True + +# mobilenetv2 eval +is_training_eval: False +run_distribute_eval: False + +# mobilenetv2 export +device_id_export: 0 +batch_size_export: 1 +ckpt_file: "/cache/train/mobilenetv2-200_625.ckpt" +file_name: "mobilenetv2" +file_format: "MINDIR" +is_training_export: False +run_distribute_export: False + +# postprocess.py / mobilenetv2 acc calculation +batch_size_postprocess: 1 +result_path: '' # "result files path." +label_path: '' # "label path." + +--- +# Config description for each option +enable_modelarts: 'Whether training on modelarts, default: False' +data_url: 'Dataset url for obs' +train_url: 'Training output url for obs' +data_path: 'Dataset path for local' +output_path: 'Training output path for local' +ann_file: 'Ann file, default is val.json.' + +pretrain_ckpt: 'Pretrained checkpoint path for fine tune or incremental learning' +platform: 'Target device type' +freeze_layer: 'freeze the weights of network from start to which layers' +filter_head: 'Filter head weight parameters when load checkpoint, default is False.' +enable_cache: 'Caching the dataset in memory to speedup dataset processing, default is False.' +cache_session_id: 'The session id for cache service.' +file_name: "output file name." + +enable_profiling: 'Whether enable profiling while training, default: False' +only_create_dataset: 'If set it true, only create Mindrecord, default is false.' +run_distribute: 'Run distribute, default is false.' +do_train: 'Do train or not, default is true.' +do_eval: 'Do eval or not, default is false.' +dataset: 'Dataset, default is coco.' +pre_trained: 'Pretrain file path.' +device_id: 'Device id, default is 0.' +device_num: 'Use device nums, default is 1.' +rank_id: 'Rank id, default is 0.' +file_format: 'file format' +img_path: "image file path." +result_path: "result file path." + +--- +platform: ['Ascend', 'GPU', 'CPU'] +file_format: ["AIR", "ONNX", "MINDIR"] +freeze_layer: ["", "none", "backbone"] \ No newline at end of file diff --git a/model_zoo/official/cv/mobilenetv2/default_config_cpu.yaml b/model_zoo/official/cv/mobilenetv2/default_config_cpu.yaml new file mode 100644 index 00000000000..360539e6feb --- /dev/null +++ b/model_zoo/official/cv/mobilenetv2/default_config_cpu.yaml @@ -0,0 +1,93 @@ +# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing) +enable_modelarts: False +data_url: "" +train_url: "" +checkpoint_url: "" +data_path: "/cache/data" +output_path: "/cache/train" +load_path: "/cache/checkpoint_path" +checkpoint_path: './checkpoint/' +device_target: Ascend +enable_profiling: False + +# ============================================================================== +modelarts_dataset_unzip_name: 'ImageNet_Original' +need_modelarts_dataset_unzip: True + +num_classes: 26 +image_height: 224 +image_width: 224 +batch_size: 150 +epoch_size: 15 +warmup_epochs: 0 +lr_init: .0 +lr_end: 0.03 +lr_max: 0.03 +momentum: 0.9 +weight_decay: 0.00001 # 4e-5 +label_smooth: 0.1 +loss_scale: 1024 +save_checkpoint: True +save_checkpoint_epochs: 1 +keep_checkpoint_max: 20 +save_checkpoint_path: "./" +platform: 'CPU' +run_distribute: False +activation: "Softmax" +run_distribute: False + +# Image classification trian. train_parse_args():return train_args +dataset_path: "/cache/data" +pretrain_ckpt: "./mobilenetv2-200_625.ckpt" +freeze_layer: "" +filter_head: False +enable_cache: False +cache_session_id: "" +is_training: True + +# mobilenetv2 eval +is_training_eval: False +run_distribute_eval: False + +# mobilenetv2 export +device_id_export: 0 +batch_size_export: 1 +ckpt_file: "/cache/train/mobilenetv2-200_625.ckpt" +file_name: "mobilenetv2" +file_format: "MINDIR" +is_training_export: False +run_distribute_export: False + +# postprocess.py / mobilenetv2 acc calculation +batch_size_postprocess: 1 +result_path: '' # "result files path." +label_path: '' # "label path." + + +--- +# Config description for each option +enable_modelarts: 'Whether training on modelarts, default: False' +data_url: 'Dataset url for obs' +train_url: 'Training output url for obs' +data_path: 'Dataset path for local' +output_path: 'Training output path for local' +ann_file: 'Ann file, default is val.json.' + +device_target: 'Target device type' +enable_profiling: 'Whether enable profiling while training, default: False' +only_create_dataset: 'If set it true, only create Mindrecord, default is false.' +run_distribute: 'Run distribute, default is false.' +do_train: 'Do train or not, default is true.' +do_eval: 'Do eval or not, default is false.' +dataset: 'Dataset, default is coco.' +pre_trained: 'Pretrain file path.' +device_id: 'Device id, default is 0.' +device_num: 'Use device nums, default is 1.' +rank_id: 'Rank id, default is 0.' +file_format: 'file format' +img_path: "image file path." +result_path: "result file path." + +--- +device_target: ['Ascend', 'GPU', 'CPU'] +file_format: ["AIR", "ONNX", "MINDIR"] diff --git a/model_zoo/official/cv/mobilenetv2/default_config_gpu.yaml b/model_zoo/official/cv/mobilenetv2/default_config_gpu.yaml new file mode 100644 index 00000000000..50d7a4e3e6c --- /dev/null +++ b/model_zoo/official/cv/mobilenetv2/default_config_gpu.yaml @@ -0,0 +1,91 @@ +# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing) +enable_modelarts: False +data_url: "" +train_url: "" +checkpoint_url: "" +data_path: "/cache/data" +output_path: "/cache/train" +load_path: "/cache/checkpoint_path" +checkpoint_path: './checkpoint/' +device_target: Ascend +enable_profiling: False + +# ============================================================================== +modelarts_dataset_unzip_name: 'ImageNet_Original' +need_modelarts_dataset_unzip: True + +num_classes: 1000 +image_height: 224 +image_width: 224 +batch_size: 150 +epoch_size: 200 +warmup_epochs: 0 +lr_init: .0 +lr_end: .0 +lr_max: 0.8 +momentum: 0.9 +weight_decay: 0.00001 # 4e-5 +label_smooth: 0.1 +loss_scale: 1024 +save_checkpoint: True +save_checkpoint_epochs: 1 +keep_checkpoint_max: 200 +save_checkpoint_path: "./" +platform: 'GPU' +run_distribute: False +activation: "Softmax" + +# Image classification trian. train_parse_args():return train_args +dataset_path: "/cache/data" +pretrain_ckpt: "./mobilenetv2-200_625.ckpt" +freeze_layer: "" +filter_head: False +enable_cache: False +cache_session_id: "" +is_training: True + +# mobilenetv2 eval +is_training_eval: False +run_distribute_eval: False + +# mobilenetv2 export +device_id_export: 0 +batch_size_export: 1 +ckpt_file: "/cache/train/mobilenetv2-200_625.ckpt" +file_name: "mobilenetv2" +file_format: "MINDIR" +is_training_export: False +run_distribute_export: False + +# postprocess.py / mobilenetv2 acc calculation +batch_size_postprocess: 1 +result_path: '' # "result files path." +label_path: '' # "label path." + +--- +# Config description for each option +enable_modelarts: 'Whether training on modelarts, default: False' +data_url: 'Dataset url for obs' +train_url: 'Training output url for obs' +data_path: 'Dataset path for local' +output_path: 'Training output path for local' +ann_file: 'Ann file, default is val.json.' + +device_target: 'Target device type' +enable_profiling: 'Whether enable profiling while training, default: False' +only_create_dataset: 'If set it true, only create Mindrecord, default is false.' +run_distribute: 'Run distribute, default is false.' +do_train: 'Do train or not, default is true.' +do_eval: 'Do eval or not, default is false.' +dataset: 'Dataset, default is coco.' +pre_trained: 'Pretrain file path.' +device_id: 'Device id, default is 0.' +device_num: 'Use device nums, default is 1.' +rank_id: 'Rank id, default is 0.' +file_format: 'file format' +img_path: "image file path." +result_path: "result file path." + +--- +device_target: ['Ascend', 'GPU', 'CPU'] +file_format: ["AIR", "ONNX", "MINDIR"] diff --git a/model_zoo/official/cv/mobilenetv2/eval.py b/model_zoo/official/cv/mobilenetv2/eval.py index 8960abaa74c..5f0c175b5a0 100644 --- a/model_zoo/official/cv/mobilenetv2/eval.py +++ b/model_zoo/official/cv/mobilenetv2/eval.py @@ -15,27 +15,95 @@ """ eval. """ +import time +import os from mindspore import nn from mindspore.train.model import Model from mindspore.common import dtype as mstype from src.dataset import create_dataset -from src.config import set_config -from src.args import eval_parse_args from src.models import define_net, load_ckpt from src.utils import switch_precision, set_context +from src.model_utils.config import config +from src.model_utils.moxing_adapter import moxing_wrapper +from src.model_utils.device_adapter import get_device_id, get_device_num, get_rank_id -if __name__ == '__main__': - args_opt = eval_parse_args() - config = set_config(args_opt) + +config.is_training = config.is_training_eval +config.device_id = get_device_id() +config.rank_id = get_rank_id() +config.rank_size = get_device_num() +config.run_distribute = config.rank_size > 1. + +def modelarts_process(): + """ modelarts process """ + def unzip(zip_file, save_dir): + import zipfile + s_time = time.time() + if not os.path.exists(os.path.join(save_dir, config.modelarts_dataset_unzip_name)): + zip_isexist = zipfile.is_zipfile(zip_file) + if zip_isexist: + fz = zipfile.ZipFile(zip_file, 'r') + data_num = len(fz.namelist()) + print("Extract Start...") + print("unzip file num: {}".format(data_num)) + data_print = int(data_num / 100) if data_num > 100 else 1 + i = 0 + for file in fz.namelist(): + if i % data_print == 0: + print("unzip percent: {}%".format(int(i * 100 / data_num)), flush=True) + i += 1 + fz.extract(file, save_dir) + print("cost time: {}min:{}s.".format(int((time.time() - s_time) / 60),\ + int(int(time.time() - s_time) % 60))) + print("Extract Done") + else: + print("This is not zip.") + else: + print("Zip has been extracted.") + + if config.need_modelarts_dataset_unzip: + zip_file_1 = os.path.join(config.data_path, config.modelarts_dataset_unzip_name + ".zip") + save_dir_1 = os.path.join(config.data_path) + + sync_lock = "/tmp/unzip_sync.lock" + + # Each server contains 8 devices as most + if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock): + print("Zip file path: ", zip_file_1) + print("Unzip file save dir: ", save_dir_1) + unzip(zip_file_1, save_dir_1) + print("===Finish extract data synchronization===") + try: + os.mknod(sync_lock) + except IOError: + pass + + while True: + if os.path.exists(sync_lock): + break + time.sleep(1) + + print("Device: {}, Finish sync unzip data from {} to {}.".format(get_device_id(), zip_file_1, save_dir_1)) + print("#" * 200, os.listdir(save_dir_1)) + print("#" * 200, os.listdir(os.path.join(config.data_path, config.modelarts_dataset_unzip_name))) + + config.dataset_path = os.path.join(config.data_path, config.modelarts_dataset_unzip_name) + config.pretrain_ckpt = os.path.join(config.output_path, config.pretrain_ckpt) + + +@moxing_wrapper(pre_process=modelarts_process) +def eval_mobilenetv2(): + config.dataset_path = os.path.join(config.dataset_path, 'validation_preprocess') + print('\nconfig: \n', config) set_context(config) - backbone_net, head_net, net = define_net(config, args_opt.is_training) + _, _, net = define_net(config, config.is_training) - load_ckpt(net, args_opt.pretrain_ckpt) + load_ckpt(net, config.pretrain_ckpt) switch_precision(net, mstype.float16, config) - dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, config=config) + dataset = create_dataset(dataset_path=config.dataset_path, do_train=False, config=config) step_size = dataset.get_dataset_size() if step_size == 0: raise ValueError("The step_size of dataset is zero. Check if the images count of eval dataset is more \ @@ -47,4 +115,7 @@ if __name__ == '__main__': model = Model(net, loss_fn=loss, metrics={'acc'}) res = model.eval(dataset) - print(f"result:{res}\npretrain_ckpt={args_opt.pretrain_ckpt}") + print(f"result:{res}\npretrain_ckpt={config.pretrain_ckpt}") + +if __name__ == '__main__': + eval_mobilenetv2() diff --git a/model_zoo/official/cv/mobilenetv2/export.py b/model_zoo/official/cv/mobilenetv2/export.py index 75fbd705f69..e292a37434c 100644 --- a/model_zoo/official/cv/mobilenetv2/export.py +++ b/model_zoo/official/cv/mobilenetv2/export.py @@ -15,35 +15,32 @@ """ mobilenetv2 export file. """ -import argparse import numpy as np from mindspore import Tensor, export, context -from src.config import set_config from src.models import define_net, load_ckpt from src.utils import set_context +from src.model_utils.config import config +from src.model_utils.device_adapter import get_device_id, get_device_num, get_rank_id -parser = argparse.ArgumentParser(description="mobilenetv2 export") -parser.add_argument("--device_id", type=int, default=0, help="Device id") -parser.add_argument("--batch_size", type=int, default=1, help="batch size") -parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.") -parser.add_argument("--file_name", type=str, default="mobilenetv2", help="output file name.") -parser.add_argument("--file_format", type=str, choices=["AIR", "MINDIR"], default="AIR", help="file format") -parser.add_argument('--platform', type=str, default="Ascend", choices=("Ascend", "GPU", "CPU"), - help='run platform, only support GPU, CPU and Ascend') -args = parser.parse_args() -args.is_training = False -args.run_distribute = False -context.set_context(mode=context.GRAPH_MODE, device_target=args.platform) -if args.platform == "Ascend": - context.set_context(device_id=args.device_id) +config.device_id = get_device_id() +config.rank_id = get_rank_id() +config.rank_size = get_device_num() +config.run_distribute = config.rank_size > 1. + +config.batch_size = config.batch_size_export +config.is_training = config.is_training_export + +context.set_context(mode=context.GRAPH_MODE, device_target=config.platform) +if config.platform == "Ascend": + context.set_context(device_id=get_device_id()) if __name__ == '__main__': - cfg = set_config(args) - set_context(cfg) - _, _, net = define_net(cfg, args.is_training) + print('\nconfig: \n', config) + set_context(config) + _, _, net = define_net(config, config.is_training) - load_ckpt(net, args.ckpt_file) - input_shp = [args.batch_size, 3, cfg.image_height, cfg.image_width] + load_ckpt(net, config.ckpt_file) + input_shp = [config.batch_size, 3, config.image_height, config.image_width] input_array = Tensor(np.random.uniform(-1.0, 1.0, size=input_shp).astype(np.float32)) - export(net, input_array, file_name=args.file_name, file_format=args.file_format) + export(net, input_array, file_name=config.file_name, file_format=config.file_format) diff --git a/model_zoo/official/cv/mobilenetv2/postprocess.py b/model_zoo/official/cv/mobilenetv2/postprocess.py index 4f31c3ba510..d1e9b1865b2 100644 --- a/model_zoo/official/cv/mobilenetv2/postprocess.py +++ b/model_zoo/official/cv/mobilenetv2/postprocess.py @@ -14,15 +14,11 @@ # ============================================================================ """post process for 310 inference""" import os -import argparse import numpy as np +from src.model_utils.config import config -batch_size = 1 -parser = argparse.ArgumentParser(description="mobilenetv2 acc calculation") -parser.add_argument("--result_path", type=str, required=True, help="result files path.") -parser.add_argument("--label_path", type=str, required=True, help="label path.") -args = parser.parse_args() +config.batch_size = config.batch_size_postprocess def calcul_acc(labels, preds): return sum(1 for x, y in zip(labels, preds) if x == y) / len(labels) @@ -55,4 +51,4 @@ def get_result(result_path, label_path): if __name__ == '__main__': - get_result(args.result_path, args.label_path) + get_result(config.result_path, config.label_path) diff --git a/model_zoo/official/cv/mobilenetv2/scripts/run_eval.sh b/model_zoo/official/cv/mobilenetv2/scripts/run_eval.sh index f4623b68719..c4f66d1cec9 100644 --- a/model_zoo/official/cv/mobilenetv2/scripts/run_eval.sh +++ b/model_zoo/official/cv/mobilenetv2/scripts/run_eval.sh @@ -15,7 +15,6 @@ # ============================================================================ - run_ascend() { # check pretrain_ckpt file @@ -27,6 +26,7 @@ run_ascend() # set environment BASEPATH=$(cd "`dirname $0`" || exit; pwd) + CONFIG_FILE="${BASEPATH}/../default_config.yaml" export PYTHONPATH=${BASEPATH}:$PYTHONPATH export DEVICE_ID=0 export RANK_ID=0 @@ -40,6 +40,7 @@ run_ascend() # launch python ${BASEPATH}/../eval.py \ + --config_path=$CONFIG_FILE \ --platform=$1 \ --dataset_path=$2 \ --pretrain_ckpt=$3 \ @@ -56,6 +57,7 @@ run_gpu() fi BASEPATH=$(cd "`dirname $0`" || exit; pwd) + CONFIG_FILE="${BASEPATH}/../default_config_gpu.yaml" export PYTHONPATH=${BASEPATH}:$PYTHONPATH if [ -d "../eval" ]; then @@ -65,6 +67,7 @@ run_gpu() cd ../eval || exit python ${BASEPATH}/../eval.py \ + --config_path=$CONFIG_FILE \ --platform=$1 \ --dataset_path=$2 \ --pretrain_ckpt=$3 \ @@ -81,6 +84,7 @@ run_cpu() fi BASEPATH=$(cd "`dirname $0`" || exit; pwd) + CONFIG_FILE="${BASEPATH}/../default_config_cpu.yaml" export PYTHONPATH=${BASEPATH}:$PYTHONPATH if [ -d "../eval" ]; then @@ -90,6 +94,7 @@ run_cpu() cd ../eval || exit python ${BASEPATH}/../eval.py \ + --config_path=$CONFIG_FILE \ --platform=$1 \ --dataset_path=$2 \ --pretrain_ckpt=$3 \ diff --git a/model_zoo/official/cv/mobilenetv2/scripts/run_infer_310.sh b/model_zoo/official/cv/mobilenetv2/scripts/run_infer_310.sh index f491b18dcb0..1987256805d 100644 --- a/model_zoo/official/cv/mobilenetv2/scripts/run_infer_310.sh +++ b/model_zoo/official/cv/mobilenetv2/scripts/run_infer_310.sh @@ -87,7 +87,9 @@ function infer() function cal_acc() { - python3.7 ../postprocess.py --result_path=./result_Files --label_path=$label_path &> acc.log & + BASEPATH=$(cd "`dirname $0`" || exit; pwd) + CONFIG_FILE="${BASEPATH}/../default_config.yaml" + python3.7 ../postprocess.py --config_path=$CONFIG_FILE --result_path=./result_Files --label_path=$label_path &> acc.log & } compile_app diff --git a/model_zoo/official/cv/mobilenetv2/scripts/run_train.sh b/model_zoo/official/cv/mobilenetv2/scripts/run_train.sh index 1e2dc2acf06..882cb439370 100644 --- a/model_zoo/official/cv/mobilenetv2/scripts/run_train.sh +++ b/model_zoo/official/cv/mobilenetv2/scripts/run_train.sh @@ -48,6 +48,8 @@ run_ascend() fi BASEPATH=$(cd "`dirname $0`" || exit; pwd) + CONFIG_FILE="${BASEPATH}/../default_config.yaml" + VISIABLE_DEVICES=$3 IFS="," read -r -a CANDIDATE_DEVICE <<< "$VISIABLE_DEVICES" if [ ${#CANDIDATE_DEVICE[@]} -ne $2 ] @@ -71,11 +73,13 @@ run_ascend() rm -rf ./rank$i mkdir ./rank$i cp ../*.py ./rank$i + cp ../*.yaml ./rank$i cp -r ../src ./rank$i cd ./rank$i || exit echo "start training for rank $RANK_ID, device $DEVICE_ID" env > env.log python train.py \ + --config_path=$CONFIG_FILE \ --platform=$1 \ --dataset_path=$5 \ --pretrain_ckpt=$PRETRAINED_CKPT \ @@ -119,6 +123,8 @@ run_gpu() fi BASEPATH=$(cd "`dirname $0`" || exit; pwd) + CONFIG_FILE="${BASEPATH}/../default_config_gpu.yaml" + export PYTHONPATH=${BASEPATH}:$PYTHONPATH if [ -d "../train" ]; then @@ -130,6 +136,7 @@ run_gpu() export CUDA_VISIBLE_DEVICES="$3" mpirun -n $2 --allow-run-as-root --output-filename log_output --merge-stderr-to-stdout \ python ${BASEPATH}/../train.py \ + --config_path=$CONFIG_FILE \ --platform=$1 \ --dataset_path=$4 \ --pretrain_ckpt=$PRETRAINED_CKPT \ @@ -165,6 +172,8 @@ run_cpu() fi BASEPATH=$(cd "`dirname $0`" || exit; pwd) + CONFIG_FILE="${BASEPATH}/../default_config_cpu.yaml" + export PYTHONPATH=${BASEPATH}:$PYTHONPATH if [ -d "../train" ]; then @@ -174,6 +183,7 @@ run_cpu() cd ../train || exit python ${BASEPATH}/../train.py \ + --config_path=$CONFIG_FILE \ --platform=$1 \ --dataset_path=$2 \ --pretrain_ckpt=$PRETRAINED_CKPT \ diff --git a/model_zoo/official/cv/mobilenetv2/scripts/run_train_nfs_cache.sh b/model_zoo/official/cv/mobilenetv2/scripts/run_train_nfs_cache.sh index 7912341cfde..6c6b872bcd6 100644 --- a/model_zoo/official/cv/mobilenetv2/scripts/run_train_nfs_cache.sh +++ b/model_zoo/official/cv/mobilenetv2/scripts/run_train_nfs_cache.sh @@ -54,6 +54,8 @@ run_ascend() CACHE_SESSION_ID=$(generate_cache_session) BASEPATH=$(cd "`dirname $0`" || exit; pwd) + CONFIG_FILE="${BASEPATH}/../default_config.yaml" + VISIABLE_DEVICES=$3 IFS="," read -r -a CANDIDATE_DEVICE <<< "$VISIABLE_DEVICES" if [ ${#CANDIDATE_DEVICE[@]} -ne $2 ] @@ -77,11 +79,13 @@ run_ascend() rm -rf ./rank$i mkdir ./rank$i cp ../*.py ./rank$i + cp ../*.yaml ./rank$i cp -r ../src ./rank$i cd ./rank$i || exit echo "start training for rank $RANK_ID, device $DEVICE_ID" env > env.log python train.py \ + --config_path=$CONFIG_FILE \ --platform=$1 \ --dataset_path=$5 \ --pretrain_ckpt=$PRETRAINED_CKPT \ @@ -131,6 +135,7 @@ run_gpu() CACHE_SESSION_ID=$(generate_cache_session) BASEPATH=$(cd "`dirname $0`" || exit; pwd) + CONFIG_FILE="${BASEPATH}/../default_config_gpu.yaml" export PYTHONPATH=${BASEPATH}:$PYTHONPATH if [ -d "../train" ]; then @@ -142,6 +147,7 @@ run_gpu() export CUDA_VISIBLE_DEVICES="$3" mpirun -n $2 --allow-run-as-root --output-filename log_output --merge-stderr-to-stdout \ python ${BASEPATH}/../train.py \ + --config_path=$CONFIG_FILE \ --platform=$1 \ --dataset_path=$4 \ --pretrain_ckpt=$PRETRAINED_CKPT \ @@ -183,6 +189,7 @@ run_cpu() CACHE_SESSION_ID=$(generate_cache_session) BASEPATH=$(cd "`dirname $0`" || exit; pwd) + CONFIG_FILE="${BASEPATH}/../default_config_cpu.yaml" export PYTHONPATH=${BASEPATH}:$PYTHONPATH if [ -d "../train" ]; then @@ -192,6 +199,7 @@ run_cpu() cd ../train || exit python ${BASEPATH}/../train.py \ + --config_path=$CONFIG_FILE \ --platform=$1 \ --dataset_path=$2 \ --pretrain_ckpt=$PRETRAINED_CKPT \ @@ -211,4 +219,4 @@ elif [ $1 = "CPU" ] ; then run_cpu "$@" else echo "Unsupported platform." -fi; \ No newline at end of file +fi; diff --git a/model_zoo/official/cv/mobilenetv2/src/args.py b/model_zoo/official/cv/mobilenetv2/src/args.py deleted file mode 100644 index 9317a08b112..00000000000 --- a/model_zoo/official/cv/mobilenetv2/src/args.py +++ /dev/null @@ -1,61 +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. -# ============================================================================ - -import argparse -import ast - -def train_parse_args(): - train_parser = argparse.ArgumentParser(description='Image classification trian') - train_parser.add_argument('--platform', type=str, default="Ascend", choices=("CPU", "GPU", "Ascend"), \ - help='run platform, only support CPU, GPU and Ascend') - train_parser.add_argument('--dataset_path', type=str, required=True, help='Dataset path') - train_parser.add_argument('--pretrain_ckpt', type=str, default="", help='Pretrained checkpoint path \ - for fine tune or incremental learning') - train_parser.add_argument('--freeze_layer', type=str, default="", choices=["", "none", "backbone"], \ - help="freeze the weights of network from start to which layers") - train_parser.add_argument('--run_distribute', type=ast.literal_eval, default=True, help='Run distribute') - train_parser.add_argument('--filter_head', type=ast.literal_eval, default=False,\ - help='Filter head weight parameters when load checkpoint, default is False.') - train_parser.add_argument('--enable_cache', type=ast.literal_eval, default=False, \ - help='Caching the dataset in memory to speedup dataset processing, default is False.') - train_parser.add_argument('--cache_session_id', type=str, default="", help='The session id for cache service.') - train_args = train_parser.parse_args() - train_args.is_training = True - if train_args.platform == "CPU": - train_args.run_distribute = False - return train_args - -def eval_parse_args(): - eval_parser = argparse.ArgumentParser(description='Image classification eval') - eval_parser.add_argument('--platform', type=str, default="Ascend", choices=("Ascend", "GPU", "CPU"), \ - help='run platform, only support GPU, CPU and Ascend') - eval_parser.add_argument('--dataset_path', type=str, required=True, help='Dataset path') - eval_parser.add_argument('--pretrain_ckpt', type=str, required=True, help='Pretrained checkpoint path \ - for fine tune or incremental learning') - eval_parser.add_argument('--run_distribute', type=ast.literal_eval, default=False, help='If run distribute in GPU.') - eval_args = eval_parser.parse_args() - eval_args.is_training = False - return eval_args - -def export_parse_args(): - export_parser = argparse.ArgumentParser(description='Image classification export') - export_parser.add_argument('--platform', type=str, default="Ascend", choices=("Ascend", "GPU", "CPU"), \ - help='run platform, only support GPU, CPU and Ascend') - export_parser.add_argument('--pretrain_ckpt', type=str, required=True, help='Pretrained checkpoint path \ - for fine tune or incremental learning') - export_args = export_parser.parse_args() - export_args.is_training = False - export_args.run_distribute = False - return export_args diff --git a/model_zoo/official/cv/mobilenetv2/src/config.py b/model_zoo/official/cv/mobilenetv2/src/config.py deleted file mode 100644 index 64bfa805185..00000000000 --- a/model_zoo/official/cv/mobilenetv2/src/config.py +++ /dev/null @@ -1,100 +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. -# ============================================================================ -""" -network config setting, will be used in train.py and eval.py -""" -import os -from easydict import EasyDict as ed - -def set_config(args): - if not args.run_distribute: - args.run_distribute = False - config_cpu = ed({ - "num_classes": 26, - "image_height": 224, - "image_width": 224, - "batch_size": 150, - "epoch_size": 15, - "warmup_epochs": 0, - "lr_init": .0, - "lr_end": 0.03, - "lr_max": 0.03, - "momentum": 0.9, - "weight_decay": 4e-5, - "label_smooth": 0.1, - "loss_scale": 1024, - "save_checkpoint": True, - "save_checkpoint_epochs": 1, - "keep_checkpoint_max": 20, - "save_checkpoint_path": "./", - "platform": args.platform, - "run_distribute": args.run_distribute, - "activation": "Softmax" - }) - config_gpu = ed({ - "num_classes": 1000, - "image_height": 224, - "image_width": 224, - "batch_size": 150, - "epoch_size": 200, - "warmup_epochs": 0, - "lr_init": .0, - "lr_end": .0, - "lr_max": 0.8, - "momentum": 0.9, - "weight_decay": 4e-5, - "label_smooth": 0.1, - "loss_scale": 1024, - "save_checkpoint": True, - "save_checkpoint_epochs": 1, - "keep_checkpoint_max": 200, - "save_checkpoint_path": "./", - "platform": args.platform, - "run_distribute": args.run_distribute, - "activation": "Softmax" - }) - config_ascend = ed({ - "num_classes": 1000, - "image_height": 224, - "image_width": 224, - "batch_size": 256, - "epoch_size": 200, - "warmup_epochs": 4, - "lr_init": 0.00, - "lr_end": 0.00, - "lr_max": 0.4, - "momentum": 0.9, - "weight_decay": 4e-5, - "label_smooth": 0.1, - "loss_scale": 1024, - "save_checkpoint": True, - "save_checkpoint_epochs": 1, - "keep_checkpoint_max": 200, - "save_checkpoint_path": "./", - "platform": args.platform, - "device_id": int(os.getenv('DEVICE_ID', '0')), - "rank_id": int(os.getenv('RANK_ID', '0')), - "rank_size": int(os.getenv('RANK_SIZE', '1')), - "run_distribute": int(os.getenv('RANK_SIZE', '1')) > 1., - "activation": "Softmax" - }) - config = ed({"CPU": config_cpu, - "GPU": config_gpu, - "Ascend": config_ascend}) - - if args.platform not in config.keys(): - raise ValueError("Unsupported platform.") - - return config[args.platform] diff --git a/model_zoo/official/cv/mobilenetv2/src/model_utils/__init__.py b/model_zoo/official/cv/mobilenetv2/src/model_utils/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/model_zoo/official/cv/mobilenetv2/src/model_utils/config.py b/model_zoo/official/cv/mobilenetv2/src/model_utils/config.py new file mode 100644 index 00000000000..7f1ff6e2b8d --- /dev/null +++ b/model_zoo/official/cv/mobilenetv2/src/model_utils/config.py @@ -0,0 +1,127 @@ +# 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. +# ============================================================================ + +"""Parse arguments""" + +import os +import ast +import argparse +from pprint import pprint, pformat +import yaml + +class Config: + """ + Configuration namespace. Convert dictionary to members. + """ + def __init__(self, cfg_dict): + for k, v in cfg_dict.items(): + if isinstance(v, (list, tuple)): + setattr(self, k, [Config(x) if isinstance(x, dict) else x for x in v]) + else: + setattr(self, k, Config(v) if isinstance(v, dict) else v) + + def __str__(self): + return pformat(self.__dict__) + + def __repr__(self): + return self.__str__() + + +def parse_cli_to_yaml(parser, cfg, helper=None, choices=None, cfg_path="default_config.yaml"): + """ + Parse command line arguments to the configuration according to the default yaml. + + Args: + parser: Parent parser. + cfg: Base configuration. + helper: Helper description. + cfg_path: Path to the default yaml config. + """ + parser = argparse.ArgumentParser(description="[REPLACE THIS at config.py]", + parents=[parser]) + helper = {} if helper is None else helper + choices = {} if choices is None else choices + for item in cfg: + if not isinstance(cfg[item], list) and not isinstance(cfg[item], dict): + help_description = helper[item] if item in helper else "Please reference to {}".format(cfg_path) + choice = choices[item] if item in choices else None + if isinstance(cfg[item], bool): + parser.add_argument("--" + item, type=ast.literal_eval, default=cfg[item], choices=choice, + help=help_description) + else: + parser.add_argument("--" + item, type=type(cfg[item]), default=cfg[item], choices=choice, + help=help_description) + args = parser.parse_args() + return args + + +def parse_yaml(yaml_path): + """ + Parse the yaml config file. + + Args: + yaml_path: Path to the yaml config. + """ + with open(yaml_path, 'r') as fin: + try: + cfgs = yaml.load_all(fin.read(), Loader=yaml.FullLoader) + cfgs = [x for x in cfgs] + if len(cfgs) == 1: + cfg_helper = {} + cfg = cfgs[0] + cfg_choices = {} + elif len(cfgs) == 2: + cfg, cfg_helper = cfgs + cfg_choices = {} + elif len(cfgs) == 3: + cfg, cfg_helper, cfg_choices = cfgs + else: + raise ValueError("At most 3 docs (config, description for help, choices) are supported in config yaml") + print(cfg_helper) + except: + raise ValueError("Failed to parse yaml") + return cfg, cfg_helper, cfg_choices + + +def merge(args, cfg): + """ + Merge the base config from yaml file and command line arguments. + + Args: + args: Command line arguments. + cfg: Base configuration. + """ + args_var = vars(args) + for item in args_var: + cfg[item] = args_var[item] + return cfg + + +def get_config(): + """ + Get Config according to the yaml file and cli arguments. + """ + parser = argparse.ArgumentParser(description="default name", add_help=False) + current_dir = os.path.dirname(os.path.abspath(__file__)) + parser.add_argument("--config_path", type=str, default=os.path.join(current_dir, "../../default_config.yaml"), + help="Config file path") + path_args, _ = parser.parse_known_args() + default, helper, choices = parse_yaml(path_args.config_path) + pprint(default) + args = parse_cli_to_yaml(parser=parser, cfg=default, helper=helper, choices=choices, cfg_path=path_args.config_path) + final_config = merge(args, default) + return Config(final_config) + +config = get_config() diff --git a/model_zoo/official/cv/mobilenetv2/src/model_utils/device_adapter.py b/model_zoo/official/cv/mobilenetv2/src/model_utils/device_adapter.py new file mode 100644 index 00000000000..7c5d7f837dd --- /dev/null +++ b/model_zoo/official/cv/mobilenetv2/src/model_utils/device_adapter.py @@ -0,0 +1,27 @@ +# 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. +# ============================================================================ + +"""Device adapter for ModelArts""" + +from .config import config + +if config.enable_modelarts: + from .moxing_adapter import get_device_id, get_device_num, get_rank_id, get_job_id +else: + from .local_adapter import get_device_id, get_device_num, get_rank_id, get_job_id + +__all__ = [ + "get_device_id", "get_device_num", "get_rank_id", "get_job_id" +] diff --git a/model_zoo/official/cv/mobilenetv2/src/model_utils/local_adapter.py b/model_zoo/official/cv/mobilenetv2/src/model_utils/local_adapter.py new file mode 100644 index 00000000000..769fa6dc78e --- /dev/null +++ b/model_zoo/official/cv/mobilenetv2/src/model_utils/local_adapter.py @@ -0,0 +1,36 @@ +# 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. +# ============================================================================ + +"""Local adapter""" + +import os + +def get_device_id(): + device_id = os.getenv('DEVICE_ID', '0') + return int(device_id) + + +def get_device_num(): + device_num = os.getenv('RANK_SIZE', '1') + return int(device_num) + + +def get_rank_id(): + global_rank_id = os.getenv('RANK_ID', '0') + return int(global_rank_id) + + +def get_job_id(): + return "Local Job" diff --git a/model_zoo/official/cv/mobilenetv2/src/model_utils/moxing_adapter.py b/model_zoo/official/cv/mobilenetv2/src/model_utils/moxing_adapter.py new file mode 100644 index 00000000000..830d19a6fc9 --- /dev/null +++ b/model_zoo/official/cv/mobilenetv2/src/model_utils/moxing_adapter.py @@ -0,0 +1,122 @@ +# 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. +# ============================================================================ + +"""Moxing adapter for ModelArts""" + +import os +import functools +from mindspore import context +from mindspore.profiler import Profiler +from .config import config + +_global_sync_count = 0 + +def get_device_id(): + device_id = os.getenv('DEVICE_ID', '0') + return int(device_id) + + +def get_device_num(): + device_num = os.getenv('RANK_SIZE', '1') + return int(device_num) + + +def get_rank_id(): + global_rank_id = os.getenv('RANK_ID', '0') + return int(global_rank_id) + + +def get_job_id(): + job_id = os.getenv('JOB_ID') + job_id = job_id if job_id != "" else "default" + return job_id + +def sync_data(from_path, to_path): + """ + Download data from remote obs to local directory if the first url is remote url and the second one is local path + Upload data from local directory to remote obs in contrast. + """ + import moxing as mox + import time + global _global_sync_count + sync_lock = "/tmp/copy_sync.lock" + str(_global_sync_count) + _global_sync_count += 1 + + # Each server contains 8 devices as most. + if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock): + print("from path: ", from_path) + print("to path: ", to_path) + mox.file.copy_parallel(from_path, to_path) + print("===finish data synchronization===") + try: + os.mknod(sync_lock) + except IOError: + pass + print("===save flag===") + + while True: + if os.path.exists(sync_lock): + break + time.sleep(1) + + print("Finish sync data from {} to {}.".format(from_path, to_path)) + + +def moxing_wrapper(pre_process=None, post_process=None): + """ + Moxing wrapper to download dataset and upload outputs. + """ + def wrapper(run_func): + @functools.wraps(run_func) + def wrapped_func(*args, **kwargs): + # Download data from data_url + if config.enable_modelarts: + if config.data_url: + sync_data(config.data_url, config.data_path) + print("Dataset downloaded: ", os.listdir(config.data_path)) + if config.checkpoint_url: + sync_data(config.checkpoint_url, config.load_path) + print("Preload downloaded: ", os.listdir(config.load_path)) + if config.train_url: + sync_data(config.train_url, config.output_path) + print("Workspace downloaded: ", os.listdir(config.output_path)) + + context.set_context(save_graphs_path=os.path.join(config.output_path, str(get_rank_id()))) + config.device_num = get_device_num() + config.device_id = get_device_id() + if not os.path.exists(config.output_path): + os.makedirs(config.output_path) + + if pre_process: + pre_process() + + if config.enable_profiling: + profiler = Profiler() + + run_func(*args, **kwargs) + + if config.enable_profiling: + profiler.analyse() + + # Upload data to train_url + if config.enable_modelarts: + if post_process: + post_process() + + if config.train_url: + print("Start to copy output directory") + sync_data(config.output_path, config.train_url) + return wrapped_func + return wrapper diff --git a/model_zoo/official/cv/mobilenetv2/train.py b/model_zoo/official/cv/mobilenetv2/train.py index ac8d13d3988..f62a748fdae 100644 --- a/model_zoo/official/cv/mobilenetv2/train.py +++ b/model_zoo/official/cv/mobilenetv2/train.py @@ -33,40 +33,99 @@ from mindspore.common import set_seed from src.dataset import create_dataset, extract_features from src.lr_generator import get_lr -from src.config import set_config - -from src.args import train_parse_args from src.utils import context_device_init, switch_precision, config_ckpoint from src.models import CrossEntropyWithLabelSmooth, define_net, load_ckpt +from src.model_utils.config import config +from src.model_utils.moxing_adapter import moxing_wrapper +from src.model_utils.device_adapter import get_device_id, get_device_num, get_rank_id + set_seed(1) +config.device_id = get_device_id() +config.rank_id = get_rank_id() +config.rank_size = get_device_num() +config.run_distribute = config.rank_size > 1. -if __name__ == '__main__': - args_opt = train_parse_args() - args_opt.dataset_path = os.path.abspath(args_opt.dataset_path) - config = set_config(args_opt) + +def modelarts_pre_process(): + def unzip(zip_file, save_dir): + import zipfile + s_time = time.time() + if not os.path.exists(os.path.join(save_dir, config.modelarts_dataset_unzip_name)): + zip_isexist = zipfile.is_zipfile(zip_file) + if zip_isexist: + fz = zipfile.ZipFile(zip_file, 'r') + data_num = len(fz.namelist()) + print("Extract Start...") + print("unzip file num: {}".format(data_num)) + data_print = int(data_num / 100) if data_num > 100 else 1 + i = 0 + for file in fz.namelist(): + if i % data_print == 0: + print("unzip percent: {}%".format(int(i * 100 / data_num)), flush=True) + i += 1 + fz.extract(file, save_dir) + print("cost time: {}min:{}s.".format(int((time.time() - s_time) / 60),\ + int(int(time.time() - s_time) % 60))) + print("Extract Done") + else: + print("This is not zip.") + else: + print("Zip has been extracted.") + + if config.need_modelarts_dataset_unzip: + zip_file_1 = os.path.join(config.data_path, config.modelarts_dataset_unzip_name + ".zip") + save_dir_1 = os.path.join(config.data_path) + + sync_lock = "/tmp/unzip_sync.lock" + + # Each server contains 8 devices as most + if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock): + print("Zip file path: ", zip_file_1) + print("Unzip file save dir: ", save_dir_1) + unzip(zip_file_1, save_dir_1) + print("===Finish extract data synchronization===") + try: + os.mknod(sync_lock) + except IOError: + pass + + while True: + if os.path.exists(sync_lock): + break + time.sleep(1) + + print("Device: {}, Finish sync unzip data from {} to {}.".format(get_device_id(), zip_file_1, save_dir_1)) + print("#" * 200, os.listdir(save_dir_1)) + print("#" * 200, os.listdir(os.path.join(config.data_path, config.modelarts_dataset_unzip_name))) + + config.dataset_path = os.path.join(config.data_path, config.modelarts_dataset_unzip_name) + config.pretrain_ckpt = os.path.join(config.output_path, config.pretrain_ckpt) + + +@moxing_wrapper(pre_process=modelarts_pre_process) +def train_mobilenetv2(): + config.dataset_path = os.path.join(config.dataset_path, 'train') + print('\nconfig: \n', config) start = time.time() - - print(f"train args: {args_opt}\ncfg: {config}") - # set context and device init context_device_init(config) # define network - backbone_net, head_net, net = define_net(config, args_opt.is_training) - dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True, config=config, - enable_cache=args_opt.enable_cache, cache_session_id=args_opt.cache_session_id) + backbone_net, head_net, net = define_net(config, config.is_training) + dataset = create_dataset(dataset_path=config.dataset_path, do_train=True, config=config, + enable_cache=config.enable_cache, cache_session_id=config.cache_session_id) step_size = dataset.get_dataset_size() if config.platform == "GPU": context.set_context(enable_graph_kernel=True) - if args_opt.pretrain_ckpt: - if args_opt.freeze_layer == "backbone": - load_ckpt(backbone_net, args_opt.pretrain_ckpt, trainable=False) - step_size = extract_features(backbone_net, args_opt.dataset_path, config) - elif args_opt.filter_head: - load_ckpt(backbone_net, args_opt.pretrain_ckpt) + if config.pretrain_ckpt: + if config.freeze_layer == "backbone": + load_ckpt(backbone_net, config.pretrain_ckpt, trainable=False) + step_size = extract_features(backbone_net, config.dataset_path, config) + elif config.filter_head: + load_ckpt(backbone_net, config.pretrain_ckpt) else: - load_ckpt(net, args_opt.pretrain_ckpt) + load_ckpt(net, config.pretrain_ckpt) if step_size == 0: raise ValueError("The step_size of dataset is zero. Check if the images' count of train dataset is more \ than batch_size in config.py") @@ -92,7 +151,7 @@ if __name__ == '__main__': total_epochs=epoch_size, steps_per_epoch=step_size)) - if args_opt.pretrain_ckpt == "" or args_opt.freeze_layer != "backbone": + if config.pretrain_ckpt == "" or config.freeze_layer != "backbone": loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False) opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum, config.weight_decay, config.loss_scale) @@ -111,7 +170,7 @@ if __name__ == '__main__': network = TrainOneStepCell(network, opt) network.set_train() - features_path = args_opt.dataset_path + '_features' + features_path = config.dataset_path + '_features' idx_list = list(range(step_size)) rank = 0 if config.run_distribute: @@ -136,5 +195,9 @@ if __name__ == '__main__': save_checkpoint(net, os.path.join(save_ckpt_path, f"mobilenetv2_{epoch+1}.ckpt")) print("total cost {:5.4f} s".format(time.time() - start)) - if args_opt.enable_cache: + if config.enable_cache: print("Remember to shut down the cache server via \"cache_admin --stop\"") + + +if __name__ == '__main__': + train_mobilenetv2()