!1016 add resnet50_imagenet2012 to example

Merge pull request !1016 from gengdongjie/master

example/resnet50_cifar10/README.md

@@ -8,9 +8,9 @@ This is an example of training ResNet-50 with CIFAR-10 dataset in MindSpore.
 
 - Install [MindSpore](https://www.mindspore.cn/install/en).
 
-- Download the dataset [CIFAR-10](http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz).
+- Download the dataset CIFAR-10
 
-> Unzip the CIFAR-10 dataset to any path you want and the folder structure should be as follows:
+> Unzip the CIFAR-10 dataset to any path you want and the folder structure should include train and eval dataset as follows:
 > ```
 > .  
 > ├── cifar-10-batches-bin  # train dataset
@@ -26,9 +26,9 @@ This is an example of training ResNet-50 with CIFAR-10 dataset in MindSpore.
 ├── dataset.py                      # data preprocessing
 ├── eval.py                         # infer script
 ├── lr_generator.py                 # generate learning rate for each step
-├── run_distribute_train.sh         # launch distributed training
+├── run_distribute_train.sh         # launch distributed training(8 pcs)
 ├── run_infer.sh                    # launch infering
-├── run_standalone_train.sh         # launch standalone training 
+├── run_standalone_train.sh         # launch standalone training(1 pcs)
 └── train.py                        # train script
 ```
 
@@ -51,11 +51,11 @@ Parameters for both training and inference can be set in config.py.
 "save_checkpoint_steps": 195,     # the step interval between two checkpoints. By default, the last checkpoint will be saved after the last step
 "keep_checkpoint_max": 10,        # only keep the last keep_checkpoint_max checkpoint
 "save_checkpoint_path": "./",     # path to save checkpoint
+"warmup_epochs": 5,               # number of warmup epoch
+"lr_decay_mode": "poly"           # decay mode can be selected in steps, ploy and default
 "lr_init": 0.01,                  # initial learning rate
 "lr_end": 0.00001,                # final learning rate
 "lr_max": 0.1,                    # maximum learning rate
-"warmup_epochs": 5,               # number of warmup epoch
-"lr_decay_mode": "poly"           # decay mode can be selected in steps, ploy and default
 ```
 
 ## Running the example
@@ -65,7 +65,7 @@ Parameters for both training and inference can be set in config.py.
 #### Usage
 
 ```
-# distribute training
+# distributed training
 Usage: sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH]
 
 # standalone training
@@ -90,7 +90,7 @@ sh run_standalone_train.sh ~/cifar-10-batches-bin
 Training result will be stored in the example path, whose folder name begins with "train" or "train_parallel". Under this, you can find checkpoint file together with result like the followings in log.
 
 ```
-# distribute training result(8p)
+# distribute training result(8 pcs)
 epoch: 1 step: 195, loss is 1.9601055
 epoch: 2 step: 195, loss is 1.8555021
 epoch: 3 step: 195, loss is 1.6707983

example/resnet50_cifar10/config.py

@@ -31,9 +31,9 @@ config = ed({
     "save_checkpoint_steps": 195,
     "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,
+    "lr_max": 0.1
-    "warmup_epochs": 5,
-    "lr_decay_mode": "poly"
 })

example/resnet50_cifar10/dataset.py

@@ -40,39 +40,30 @@ def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32):
     rank_id = int(os.getenv("RANK_ID"))
 
     if device_num == 1:
-        ds = de.Cifar10Dataset(dataset_path, num_parallel_workers=4, shuffle=True)
+        ds = de.Cifar10Dataset(dataset_path, num_parallel_workers=8, shuffle=True)
     else:
-        ds = de.Cifar10Dataset(dataset_path, num_parallel_workers=4, shuffle=True,
+        ds = de.Cifar10Dataset(dataset_path, num_parallel_workers=8, shuffle=True,
                                num_shards=device_num, shard_id=rank_id)
 
-    resize_height = config.image_height
-    resize_width = config.image_width
-    rescale = 1.0 / 255.0
-    shift = 0.0
-
     # define map operations
-    random_crop_op = C.RandomCrop((32, 32), (4, 4, 4, 4))
-    random_horizontal_flip_op = C.RandomHorizontalFlip(rank_id / (rank_id + 1))
-
-    resize_op = C.Resize((resize_height, resize_width))
-    rescale_op = C.Rescale(rescale, shift)
-    normalize_op = C.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010])
-
-    change_swap_op = C.HWC2CHW()
-
     trans = []
     if do_train:
-        trans += [random_crop_op, random_horizontal_flip_op]
+        trans += [
+            C.RandomCrop((32, 32), (4, 4, 4, 4)),
+            C.RandomHorizontalFlip(prob=0.5)
+        ]
 
-    trans += [resize_op, rescale_op, normalize_op, change_swap_op]
+    trans += [
+        C.Resize((config.image_height, config.image_width)),
+        C.Rescale(1.0 / 255.0, 0.0),
+        C.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
+        C.HWC2CHW()
+    ]
 
     type_cast_op = C2.TypeCast(mstype.int32)
 
-    ds = ds.map(input_columns="label", operations=type_cast_op)
+    ds = ds.map(input_columns="label", num_parallel_workers=8, operations=type_cast_op)
-    ds = ds.map(input_columns="image", operations=trans)
+    ds = ds.map(input_columns="image", num_parallel_workers=8, operations=trans)
-
-    # apply shuffle operations
-    ds = ds.shuffle(buffer_size=config.buffer_size)
 
     # apply batch operations
     ds = ds.batch(batch_size, drop_remainder=True)

example/resnet50_cifar10/eval.py

@@ -17,8 +17,6 @@ eval.
 """
 import os
 import argparse
-import random
-import numpy as np
 from dataset import create_dataset
 from config import config
 from mindspore import context
@@ -27,13 +25,8 @@ from mindspore.parallel._auto_parallel_context import auto_parallel_context
 from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
 from mindspore.train.model import Model, ParallelMode
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-import mindspore.dataset.engine as de
 from mindspore.communication.management import init
 
-random.seed(1)
-np.random.seed(1)
-de.config.set_seed(1)
-
 parser = argparse.ArgumentParser(description='Image classification')
 parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
 parser.add_argument('--device_num', type=int, default=1, help='Device num.')

example/resnet50_cifar10/train.py

@@ -15,8 +15,6 @@
 """train_imagenet."""
 import os
 import argparse
-import random
-import numpy as np
 from dataset import create_dataset
 from lr_generator import get_lr
 from config import config
@@ -31,13 +29,8 @@ from mindspore.train.model import Model, ParallelMode
 
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
 from mindspore.train.loss_scale_manager import FixedLossScaleManager
-import mindspore.dataset.engine as de
 from mindspore.communication.management import init
 
-random.seed(1)
-np.random.seed(1)
-de.config.set_seed(1)
-
 parser = argparse.ArgumentParser(description='Image classification')
 parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
 parser.add_argument('--device_num', type=int, default=1, help='Device num.')

example/resnet50_imagenet2012/README.md

@@ -0,0 +1,127 @@
+# ResNet-50 Example
+
+## Description
+
+This is an example of training ResNet-50 with ImageNet2012 dataset in MindSpore.
+
+## Requirements
+
+- Install [MindSpore](https://www.mindspore.cn/install/en).
+
+- Download the dataset ImageNet2012 
+
+> Unzip the ImageNet2012 dataset to any path you want and the folder structure should include train and eval dataset as follows:
+> ```
+> .  
+> ├── ilsvrc                  # train dataset
+> └── ilsvrc_eval             # infer dataset
+> ```
+
+
+## Example structure
+
+```shell
+.
+├── crossentropy.py                 # CrossEntropy loss function
+├── config.py                       # parameter configuration
+├── dataset.py                      # data preprocessing
+├── eval.py                         # infer script
+├── lr_generator.py                 # generate learning rate for each step
+├── run_distribute_train.sh         # launch distributed training(8 pcs)
+├── run_infer.sh                    # launch infering
+├── run_standalone_train.sh         # launch standalone training(1 pcs)
+└── train.py                        # train script
+```
+
+
+## Parameter configuration
+
+Parameters for both training and inference can be set in config.py.
+
+```
+"class_num": 1001,                # dataset class number
+"batch_size": 32,                 # batch size of input tensor
+"loss_scale": 1024,               # loss scale
+"momentum": 0.9,                  # momentum optimizer
+"weight_decay": 1e-4,             # weight decay 
+"epoch_size": 90,                 # only valid for taining, which is always 1 for inference 
+"buffer_size": 1000,              # number of queue size in data preprocessing
+"image_height": 224,              # image height
+"image_width": 224,               # image width
+"save_checkpoint": True,          # whether save checkpoint or not
+"save_checkpoint_epochs": 1,      # the epoch interval between two checkpoints. By default, the last checkpoint will be saved after the last epoch
+"keep_checkpoint_max": 10,        # only keep the last keep_checkpoint_max checkpoint
+"save_checkpoint_path": "./",     # path to save checkpoint relative to the executed path
+"warmup_epochs": 0,               # number of warmup epoch
+"lr_decay_mode": "cosine",        # decay mode for generating learning rate
+"label_smooth": True,             # label smooth
+"label_smooth_factor": 0.1,       # label smooth factor
+"lr_init": 0,                     # initial learning rate
+"lr_max": 0.1,                    # maximum learning rate
+```
+
+## Running the example
+
+### Train
+
+#### Usage
+
+```
+# distributed training
+Usage: sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH]
+
+# standalone training
+Usage: sh run_standalone_train.sh [DATASET_PATH]
+```
+
+
+#### Launch
+
+```bash
+# distributed training example(8 pcs)
+sh run_distribute_train.sh rank_table_8p.json dataset/ilsvrc
+
+# standalone training example(1 pcs)
+sh run_standalone_train.sh dataset/ilsvrc
+```
+
+> About rank_table.json, you can refer to the [distributed training tutorial](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html).
+
+#### Result
+
+Training result will be stored in the example path, whose folder name begins with "train" or "train_parallel". Under this, you can find checkpoint file together with result like the followings in log.
+
+```
+# distribute training result(8 pcs)
+epoch: 1 step: 5004, loss is 4.8995576
+epoch: 2 step: 5004, loss is 3.9235563
+epoch: 3 step: 5004, loss is 3.833077
+epoch: 4 step: 5004, loss is 3.2795618
+epoch: 5 step: 5004, loss is 3.1978393
+```
+
+### Infer
+
+#### Usage
+
+```
+# infer
+Usage: sh run_infer.sh [DATASET_PATH] [CHECKPOINT_PATH]
+```
+
+#### Launch
+
+```bash
+# infer with checkpoint
+sh run_infer.sh dataset/ilsvrc_eval train_parallel0/resnet-90_5004.ckpt
+```
+
+> checkpoint can be produced in training process.
+
+#### Result
+
+Inference result will be stored in the example path, whose folder name is "infer". Under this, you can find result like the followings in log.
+
+```
+result: {'acc': 0.7671054737516005} ckpt=train_parallel0/resnet-90_5004.ckpt
+```

example/resnet50_imagenet2012/config.py

@@ -0,0 +1,41 @@
+# 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 = ed({
+    "class_num": 1001,
+    "batch_size": 32,
+    "loss_scale": 1024,
+    "momentum": 0.9,
+    "weight_decay": 1e-4,
+    "epoch_size": 90,
+    "buffer_size": 1000,
+    "image_height": 224,
+    "image_width": 224,
+    "save_checkpoint": True,
+    "save_checkpoint_epochs": 1,
+    "keep_checkpoint_max": 10,
+    "save_checkpoint_path": "./",
+    "warmup_epochs": 0,
+    "lr_decay_mode": "cosine",
+    "use_label_smooth": True,
+    "label_smooth_factor": 0.1,
+    "lr_init": 0,
+    "lr_max": 0.1
+
+})

example/resnet50_imagenet2012/crossentropy.py

@@ -0,0 +1,39 @@
+# 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.
+# ============================================================================
+"""define loss function for network"""
+from mindspore.nn.loss.loss import _Loss
+from mindspore.ops import operations as P
+from mindspore.ops import functional as F
+from mindspore import Tensor
+from mindspore.common import dtype as mstype
+import mindspore.nn as nn
+
+
+class CrossEntropy(_Loss):
+    """the redefined loss function with SoftmaxCrossEntropyWithLogits"""
+
+    def __init__(self, smooth_factor=0, num_classes=1001):
+        super(CrossEntropy, self).__init__()
+        self.onehot = P.OneHot()
+        self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
+        self.off_value = Tensor(1.0 * smooth_factor / (num_classes - 1), mstype.float32)
+        self.ce = nn.SoftmaxCrossEntropyWithLogits()
+        self.mean = P.ReduceMean(False)
+
+    def construct(self, logit, label):
+        one_hot_label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value)
+        loss = self.ce(logit, one_hot_label)
+        loss = self.mean(loss, 0)
+        return loss

example/resnet50_imagenet2012/dataset.py

@@ -0,0 +1,79 @@
+# 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.
+# ============================================================================
+"""
+create train or eval dataset.
+"""
+import os
+import mindspore.common.dtype as mstype
+import mindspore.dataset.engine as de
+import mindspore.dataset.transforms.vision.c_transforms as C
+import mindspore.dataset.transforms.c_transforms as C2
+
+
+def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32):
+    """
+    create a train or eval dataset
+
+    Args:
+        dataset_path(string): the path of dataset.
+        do_train(bool): whether dataset is used for train or eval.
+        repeat_num(int): the repeat times of dataset. Default: 1
+        batch_size(int): the batch size of dataset. Default: 32
+
+    Returns:
+        dataset
+    """
+    device_num = int(os.getenv("DEVICE_NUM"))
+    rank_id = int(os.getenv("RANK_ID"))
+
+    if device_num == 1:
+        ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True)
+    else:
+        ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True,
+                                     num_shards=device_num, shard_id=rank_id)
+
+    image_size = 224
+    mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
+    std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
+
+    # define map operations
+    if do_train:
+        trans = [
+            C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+            C.RandomHorizontalFlip(prob=0.5),
+            C.Normalize(mean=mean, std=std),
+            C.HWC2CHW()
+        ]
+    else:
+        trans = [
+            C.Decode(),
+            C.Resize((256, 256)),
+            C.CenterCrop(image_size),
+            C.Normalize(mean=mean, std=std),
+            C.HWC2CHW()
+        ]
+
+    type_cast_op = C2.TypeCast(mstype.int32)
+
+    ds = ds.map(input_columns="image", num_parallel_workers=8, operations=trans)
+    ds = ds.map(input_columns="label", num_parallel_workers=8, operations=type_cast_op)
+
+    # apply batch operations
+    ds = ds.batch(batch_size, drop_remainder=True)
+
+    # apply dataset repeat operation
+    ds = ds.repeat(repeat_num)
+
+    return ds

example/resnet50_imagenet2012/eval.py

@@ -0,0 +1,62 @@
+# 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.
+# ============================================================================
+"""
+eval.
+"""
+import os
+import argparse
+from dataset import create_dataset
+from config import config
+from mindspore import context
+from mindspore.model_zoo.resnet import resnet50
+from mindspore.train.model import Model
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from crossentropy import CrossEntropy
+
+parser = argparse.ArgumentParser(description='Image classification')
+parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
+parser.add_argument('--device_num', type=int, default=1, help='Device num.')
+parser.add_argument('--do_train', type=bool, default=False, help='Do train or not.')
+parser.add_argument('--do_eval', type=bool, default=True, help='Do eval or not.')
+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')
+args_opt = parser.parse_args()
+
+device_id = int(os.getenv('DEVICE_ID'))
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
+context.set_context(enable_task_sink=True, device_id=device_id)
+context.set_context(enable_loop_sink=True)
+context.set_context(enable_mem_reuse=True)
+
+if __name__ == '__main__':
+
+    net = resnet50(class_num=config.class_num)
+    if not config.use_label_smooth:
+        config.label_smooth_factor = 0.0
+    loss = CrossEntropy(smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
+
+    if args_opt.do_eval:
+        dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size)
+        step_size = dataset.get_dataset_size()
+
+        if args_opt.checkpoint_path:
+            param_dict = load_checkpoint(args_opt.checkpoint_path)
+            load_param_into_net(net, param_dict)
+        net.set_train(False)
+
+        model = Model(net, loss_fn=loss, metrics={'acc'})
+        res = model.eval(dataset)
+        print("result:", res, "ckpt=", args_opt.checkpoint_path)

example/resnet50_imagenet2012/lr_generator.py

@@ -0,0 +1,90 @@
+# 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.
+# ============================================================================
+"""learning rate generator"""
+import math
+import numpy as np
+
+
+def get_lr(global_step, lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch, lr_decay_mode):
+    """
+    generate learning rate array
+
+    Args:
+       global_step(int): total steps of the training
+       lr_init(float): init learning rate
+       lr_end(float): end learning rate
+       lr_max(float): max learning rate
+       warmup_epochs(int): number of warmup epochs
+       total_epochs(int): total epoch of training
+       steps_per_epoch(int): steps of one epoch
+       lr_decay_mode(string): learning rate decay mode, including steps, poly, cosine or default
+
+    Returns:
+       np.array, learning rate array
+    """
+    lr_each_step = []
+    total_steps = steps_per_epoch * total_epochs
+    warmup_steps = steps_per_epoch * warmup_epochs
+    if lr_decay_mode == 'steps':
+        decay_epoch_index = [0.3 * total_steps, 0.6 * total_steps, 0.8 * total_steps]
+        for i in range(total_steps):
+            if i < decay_epoch_index[0]:
+                lr = lr_max
+            elif i < decay_epoch_index[1]:
+                lr = lr_max * 0.1
+            elif i < decay_epoch_index[2]:
+                lr = lr_max * 0.01
+            else:
+                lr = lr_max * 0.001
+            lr_each_step.append(lr)
+    elif lr_decay_mode == 'poly':
+        if warmup_steps != 0:
+            inc_each_step = (float(lr_max) - float(lr_init)) / float(warmup_steps)
+        else:
+            inc_each_step = 0
+        for i in range(total_steps):
+            if i < warmup_steps:
+                lr = float(lr_init) + inc_each_step * float(i)
+            else:
+                base = (1.0 - (float(i) - float(warmup_steps)) / (float(total_steps) - float(warmup_steps)))
+                lr = float(lr_max) * base * base
+                if lr < 0.0:
+                    lr = 0.0
+            lr_each_step.append(lr)
+    elif lr_decay_mode == 'cosine':
+        decay_steps = total_steps - warmup_steps
+        for i in range(total_steps):
+            if i < warmup_steps:
+                lr_inc = (float(lr_max) - float(lr_init)) / float(warmup_steps)
+                lr = float(lr_init) + lr_inc * (i + 1)
+            else:
+                linear_decay = (total_steps - i) / decay_steps
+                cosine_decay = 0.5 * (1 + math.cos(math.pi * 2 * 0.47 * i / decay_steps))
+                decayed = linear_decay * cosine_decay + 0.00001
+                lr = lr_max * decayed
+            lr_each_step.append(lr)
+    else:
+        for i in range(total_steps):
+            if i < warmup_steps:
+                lr = lr_init + (lr_max - lr_init) * i / warmup_steps
+            else:
+                lr = lr_max - (lr_max - lr_end) * (i - warmup_steps) / (total_steps - warmup_steps)
+            lr_each_step.append(lr)
+
+    current_step = global_step
+    lr_each_step = np.array(lr_each_step).astype(np.float32)
+    learning_rate = lr_each_step[current_step:]
+
+    return learning_rate

example/resnet50_imagenet2012/run_distribute_train.sh

@@ -0,0 +1,65 @@
+#!/bin/bash
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+if [ $# != 2 ]
+then 
+    echo "Usage: sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH]"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+PATH2=$(get_real_path $2)
+
+if [ ! -f "$PATH1" ]
+then 
+    echo "error: MINDSPORE_HCCL_CONFIG_PATH=$PATH1 is not a file"
+exit 1
+fi 
+
+if [ ! -d "$PATH2" ]
+then 
+    echo "error: DATASET_PATH=$PATH2 is not a directory"
+exit 1
+fi 
+
+ulimit -u unlimited
+export DEVICE_NUM=8
+export RANK_SIZE=8
+export MINDSPORE_HCCL_CONFIG_PATH=$PATH1
+export RANK_TABLE_FILE=$PATH1
+
+for((i=0; i<${DEVICE_NUM}; i++))
+do
+    export DEVICE_ID=$i
+    export RANK_ID=$i
+    rm -rf ./train_parallel$i
+    mkdir ./train_parallel$i
+    cp *.py ./train_parallel$i
+    cp *.sh ./train_parallel$i
+    cd ./train_parallel$i || exit
+    echo "start training for rank $RANK_ID, device $DEVICE_ID"
+    env > env.log
+    python train.py --do_train=True --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 &> log &
+    cd ..
+done

example/resnet50_imagenet2012/run_infer.sh

@@ -0,0 +1,64 @@
+#!/bin/bash
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+if [ $# != 2 ]
+then 
+    echo "Usage: sh run_infer.sh [DATASET_PATH] [CHECKPOINT_PATH]"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+PATH2=$(get_real_path $2)
+
+
+if [ ! -d $PATH1 ]
+then 
+    echo "error: DATASET_PATH=$1 is not a directory"
+exit 1
+fi 
+
+if [ ! -f $PATH2 ]
+then 
+    echo "error: CHECKPOINT_PATH=$2 is not a file"
+exit 1
+fi 
+
+ulimit -u unlimited
+export DEVICE_NUM=1
+export DEVICE_ID=0
+export RANK_SIZE=$DEVICE_NUM
+export RANK_ID=0
+
+if [ -d "infer" ];
+then
+    rm -rf ./infer
+fi
+mkdir ./infer
+cp *.py ./infer
+cp *.sh ./infer
+cd ./infer || exit
+env > env.log
+echo "start infering for device $DEVICE_ID"
+python eval.py --do_eval=True --dataset_path=$PATH1 --checkpoint_path=$PATH2 &> log &
+cd ..

example/resnet50_imagenet2012/run_standalone_train.sh

@@ -0,0 +1,55 @@
+#!/bin/bash
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+if [ $# != 1 ]
+then 
+    echo "Usage: sh run_standalone_train.sh [DATASET_PATH]"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+
+if [ ! -d "$PATH1" ]
+then 
+    echo "error: DATASET_PATH=$PATH1 is not a directory"
+exit 1
+fi 
+
+ulimit -u unlimited
+export DEVICE_NUM=1
+export DEVICE_ID=0
+export RANK_ID=0
+
+if [ -d "train" ];
+then
+    rm -rf ./train
+fi
+mkdir ./train
+cp *.py ./train
+cp *.sh ./train
+cd ./train || exit
+echo "start training for device $DEVICE_ID"
+env > env.log
+python train.py --do_train=True --dataset_path=$PATH1 &> log &
+cd ..

example/resnet50_imagenet2012/train.py

@@ -0,0 +1,99 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""train_imagenet."""
+import os
+import argparse
+from dataset import create_dataset
+from lr_generator import get_lr
+from config import config
+from mindspore import context
+from mindspore import Tensor
+from mindspore.model_zoo.resnet import resnet50
+from mindspore.parallel._auto_parallel_context import auto_parallel_context
+from mindspore.nn.optim.momentum import Momentum
+
+from mindspore.train.model import Model, ParallelMode
+
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
+from mindspore.train.loss_scale_manager import FixedLossScaleManager
+from mindspore.communication.management import init
+import mindspore.nn as nn
+import mindspore.common.initializer as weight_init
+from crossentropy import CrossEntropy
+
+parser = argparse.ArgumentParser(description='Image classification')
+parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
+parser.add_argument('--device_num', type=int, default=1, help='Device num.')
+parser.add_argument('--do_train', type=bool, default=True, help='Do train or not.')
+parser.add_argument('--do_eval', type=bool, default=False, help='Do eval or not.')
+parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
+args_opt = parser.parse_args()
+
+device_id = int(os.getenv('DEVICE_ID'))
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
+context.set_context(enable_task_sink=True, device_id=device_id)
+context.set_context(enable_loop_sink=True)
+context.set_context(enable_mem_reuse=True)
+
+if __name__ == '__main__':
+    if not args_opt.do_eval and args_opt.run_distribute:
+        context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
+                                          mirror_mean=True, parameter_broadcast=True)
+        auto_parallel_context().set_all_reduce_fusion_split_indices([107, 160])
+        init()
+
+    epoch_size = config.epoch_size
+    net = resnet50(class_num=config.class_num)
+
+    # weight init
+    for _, cell in net.cells_and_names():
+        if isinstance(cell, nn.Conv2d):
+            cell.weight.default_input = weight_init.initializer(weight_init.XavierUniform(),
+                                                                cell.weight.default_input.shape(),
+                                                                cell.weight.default_input.dtype())
+        if isinstance(cell, nn.Dense):
+            cell.weight.default_input = weight_init.initializer(weight_init.TruncatedNormal(),
+                                                                cell.weight.default_input.shape(),
+                                                                cell.weight.default_input.dtype())
+    if not config.use_label_smooth:
+        config.label_smooth_factor = 0.0
+
+    loss = CrossEntropy(smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
+
+    if args_opt.do_train:
+        dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True,
+                                 repeat_num=epoch_size, batch_size=config.batch_size)
+        step_size = dataset.get_dataset_size()
+
+        loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
+        lr = Tensor(get_lr(global_step=0, lr_init=config.lr_init, lr_end=0.0, lr_max=config.lr_max,
+                           warmup_epochs=config.warmup_epochs, total_epochs=epoch_size, steps_per_epoch=step_size,
+                           lr_decay_mode='cosine'))
+
+        opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum,
+                       config.weight_decay, config.loss_scale)
+
+        model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'})
+
+        time_cb = TimeMonitor(data_size=step_size)
+        loss_cb = LossMonitor()
+        cb = [time_cb, loss_cb]
+        if config.save_checkpoint:
+            config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs*step_size,
+                                         keep_checkpoint_max=config.keep_checkpoint_max)
+            ckpt_cb = ModelCheckpoint(prefix="resnet", directory=config.save_checkpoint_path, config=config_ck)
+            cb += [ckpt_cb]
+        model.train(epoch_size, dataset, callbacks=cb)