add googlenet scripts

example/googlenet_cifar10/README.md

+# Googlenet Example
+
+## Description
+
+This example is for Googlenet model training and evaluation.
+
+## Requirements
+
+- Install [MindSpore](https://www.mindspore.cn/install/en).
+
+- Download the dataset [CIFAR-10](http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz).
+
+> Unzip the CIFAR-10 dataset to any path you want and the folder structure should be as follows:
+> ```
+> .
+> ├── cifar-10-batches-bin  # train dataset
+> └── cifar-10-verify-bin   # infer dataset
+> ```
+
+## Running the Example
+
+### Training
+
+```
+python train.py --data_path=your_data_path --device_id=6 > out.train.log 2>&1 & 
+```
+The python command above will run in the background, you can view the results through the file `out.train.log`.
+
+After training, you'll get some checkpoint files under the script folder by default.
+
+You will get the loss value as following:
+```
+# grep "loss is " out.train.log
+epoch: 1 step: 390, loss is 1.4842823
+epcoh: 2 step: 390, loss is 1.0897788
+...
+```
+
+### Evaluation
+
+```
+python eval.py --data_path=your_data_path --device_id=6 --checkpoint_path=./train_googlenet_cifar10-125-390.ckpt > out.eval.log 2>&1 & 
+```
+The above python command will run in the background, you can view the results through the file `out.eval.log`.
+
+You will get the accuracy as following:
+```
+# grep "result: " out.eval.log
+result: {'acc': 0.934}
+```
+
+### Distribute Training
+```
+sh run_distribute_train.sh rank_table.json your_data_path
+```
+The above shell script will run distribute training in the background, you can view the results through the file `train_parallel[X]/log`.
+
+You will get the loss value as following:
+```
+# grep "result: " train_parallel*/log
+train_parallel0/log:epoch: 1 step: 48, loss is 1.4302931
+train_parallel0/log:epcoh: 2 step: 48, loss is 1.4023874
+...
+train_parallel1/log:epoch: 1 step: 48, loss is 1.3458025
+train_parallel1/log:epcoh: 2 step: 48, loss is 1.3729336
+...
+...
+```
+> About rank_table.json, you can refer to the [distributed training tutorial](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html).
+
+## Usage:
+
+### Training
+```
+usage: train.py [--device_target TARGET][--data_path DATA_PATH]
+                [--device_id DEVICE_ID]
+
+parameters/options:
+  --device_target       the training backend type, default is Ascend.
+  --data_path           the storage path of dataset
+  --device_id           the device which used to train model.
+
+```
+
+### Evaluation
+
+```
+usage: eval.py [--device_target TARGET][--data_path DATA_PATH]
+                [--device_id DEVICE_ID][--checkpoint_path CKPT_PATH]
+
+parameters/options:
+  --device_target       the evaluation backend type, default is Ascend.
+  --data_path           the storage path of datasetd 
+  --device_id           the device which used to evaluate model.
+  --checkpoint_path     the checkpoint file path used to evaluate model.
+```
+
+### Distribute Training
+
+```
+Usage: sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATA_PATH]
+
+parameters/options:
+  MINDSPORE_HCCL_CONFIG_PATH   HCCL configuration file path.
+  DATA_PATH                    the storage path of dataset.
+```

example/googlenet_cifar10/config.py

+# 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 main.py
+"""
+from easydict import EasyDict as edict
+
+cifar_cfg = edict({
+    'num_classes': 10,
+    'lr_init': 0.1,
+    'batch_size': 128,
+    'epoch_size': 125,
+    'momentum': 0.9,
+    'weight_decay': 5e-4,
+    'buffer_size': 10,
+    'image_height': 224,
+    'image_width': 224,
+    'keep_checkpoint_max': 10
+})

example/googlenet_cifar10/dataset.py

+# 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.
+# ============================================================================
+"""
+Data operations, will be used in train.py and eval.py
+"""
+import os
+
+import mindspore.common.dtype as mstype
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms.vision.c_transforms as vision
+from config import cifar_cfg as cfg
+
+
+def create_dataset(data_home, repeat_num=1, training=True):
+    """Data operations."""
+    ds.config.set_seed(1)
+    data_dir = os.path.join(data_home, "cifar-10-batches-bin")
+    if not training:
+        data_dir = os.path.join(data_home, "cifar-10-verify-bin")
+
+    rank_size = int(os.environ.get("RANK_SIZE")) if os.environ.get("RANK_SIZE") else None
+    rank_id = int(os.environ.get("RANK_ID")) if os.environ.get("RANK_ID") else None
+    data_set = ds.Cifar10Dataset(data_dir, num_shards=rank_size, shard_id=rank_id)
+
+    resize_height = cfg.image_height
+    resize_width = cfg.image_width
+
+    # define map operations
+    random_crop_op = vision.RandomCrop((32, 32), (4, 4, 4, 4))  # padding_mode default CONSTANT
+    random_horizontal_op = vision.RandomHorizontalFlip()
+    resize_op = vision.Resize((resize_height, resize_width))  # interpolation default BILINEAR
+    normalize_op = vision.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
+    changeswap_op = vision.HWC2CHW()
+    type_cast_op = C.TypeCast(mstype.int32)
+
+    c_trans = []
+    if training:
+        c_trans = [random_crop_op, random_horizontal_op]
+    c_trans += [resize_op, normalize_op, changeswap_op]
+
+    # apply map operations on images
+    data_set = data_set.map(input_columns="label", operations=type_cast_op)
+    data_set = data_set.map(input_columns="image", operations=c_trans)
+
+    # apply repeat operations
+    data_set = data_set.repeat(repeat_num)
+
+    # apply shuffle operations
+    data_set = data_set.shuffle(buffer_size=10)
+
+    # apply batch operations
+    data_set = data_set.batch(batch_size=cfg.batch_size, drop_remainder=True)
+
+    return data_set

example/googlenet_cifar10/eval.py

+# 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.
+# ============================================================================
+"""
+##############test googlenet example on cifar10#################
+python eval.py --data_path=$DATA_HOME --device_id=$DEVICE_ID
+"""
+import argparse
+
+import mindspore.nn as nn
+from mindspore import context
+from mindspore.model_zoo.googlenet import GooGLeNet
+from mindspore.nn.optim.momentum import Momentum
+from mindspore.train.model import Model
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+
+import dataset
+from config import cifar_cfg as cfg
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Cifar10 classification')
+    parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'],
+                        help='device where the code will be implemented. (Default: Ascend)')
+    parser.add_argument('--data_path', type=str, default='./cifar', help='path where the dataset is saved')
+    parser.add_argument('--checkpoint_path', type=str, default=None, help='checkpoint file path.')
+    parser.add_argument('--device_id', type=int, default=None, help='device id of GPU or Ascend. (Default: None)')
+    args_opt = parser.parse_args()
+
+    context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
+    context.set_context(device_id=args_opt.device_id)
+    context.set_context(enable_mem_reuse=True)
+
+    net = GooGLeNet(num_classes=cfg.num_classes)
+    opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, cfg.momentum,
+                   weight_decay=cfg.weight_decay)
+    loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
+    model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
+
+    param_dict = load_checkpoint(args_opt.checkpoint_path)
+    load_param_into_net(net, param_dict)
+    net.set_train(False)
+    dataset = dataset.create_dataset(args_opt.data_path, 1, False)
+    res = model.eval(dataset)
+    print("result: ", res)

example/googlenet_cifar10/run_distribute_train.sh

+#!/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] [DATA_PATH]"
+exit 1
+fi
+
+if [ ! -f $1 ]
+then 
+    echo "error: MINDSPORE_HCCL_CONFIG_PATH=$1 is not a file"
+exit 1
+fi 
+
+if [ ! -d $2 ]
+then 
+    echo "error: DATA_PATH=$2 is not a directory"
+exit 1
+fi 
+
+ulimit -u unlimited
+export DEVICE_NUM=8
+export RANK_SIZE=8
+export MINDSPORE_HCCL_CONFIG_PATH=$1
+
+for((i=0; i<${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 --data_path=$2 --device_id=$i &> log &
+    cd ..
+done

example/googlenet_cifar10/train.py

+# 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 googlent example on cifar10########################
+python train.py --data_path=$DATA_HOME --device_id=$DEVICE_ID
+"""
+import argparse
+import os
+import random
+
+import numpy as np
+
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore import context
+from mindspore.communication.management import init
+from mindspore.model_zoo.googlenet import GooGLeNet
+from mindspore.nn.optim.momentum import Momentum
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
+from mindspore.train.model import Model, ParallelMode
+
+
+from dataset import create_dataset
+from config import cifar_cfg as cfg
+
+random.seed(1)
+np.random.seed(1)
+
+
+def lr_steps(global_step, lr_max=None, total_epochs=None, steps_per_epoch=None):
+    """Set learning rate."""
+    lr_each_step = []
+    total_steps = steps_per_epoch * total_epochs
+    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_each_step.append(lr_max)
+        elif i < decay_epoch_index[1]:
+            lr_each_step.append(lr_max * 0.1)
+        elif i < decay_epoch_index[2]:
+            lr_each_step.append(lr_max * 0.01)
+        else:
+            lr_each_step.append(lr_max * 0.001)
+    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
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Cifar10 classification')
+    parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'],
+                        help='device where the code will be implemented. (Default: Ascend)')
+    parser.add_argument('--data_path', type=str, default='./cifar', help='path where the dataset is saved')
+    parser.add_argument('--device_id', type=int, default=None, help='device id of GPU or Ascend. (Default: None)')
+    args_opt = parser.parse_args()
+
+    context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
+    context.set_context(device_id=args_opt.device_id)
+    context.set_context(enable_task_sink=True)
+    context.set_context(enable_loop_sink=True)
+    context.set_context(enable_mem_reuse=True)
+
+    device_num = int(os.environ.get("DEVICE_NUM", 1))
+    if device_num > 1:
+        context.reset_auto_parallel_context()
+        context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
+                                          mirror_mean=True)
+        init()
+
+    dataset = create_dataset(args_opt.data_path, cfg.epoch_size)
+    batch_num = dataset.get_dataset_size()
+
+    net = GooGLeNet(num_classes=cfg.num_classes)
+    lr = lr_steps(0, lr_max=cfg.lr_init, total_epochs=cfg.epoch_size, steps_per_epoch=batch_num)
+    opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), Tensor(lr), cfg.momentum,
+                   weight_decay=cfg.weight_decay)
+    loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
+    model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'},
+                  amp_level="O2", keep_batchnorm_fp32=False, loss_scale_manager=None)
+
+    config_ck = CheckpointConfig(save_checkpoint_steps=batch_num * 5, keep_checkpoint_max=cfg.keep_checkpoint_max)
+    time_cb = TimeMonitor(data_size=batch_num)
+    ckpoint_cb = ModelCheckpoint(prefix="train_googlenet_cifar10", directory="./", config=config_ck)
+    loss_cb = LossMonitor()
+    model.train(cfg.epoch_size, dataset, callbacks=[time_cb, ckpoint_cb, loss_cb])
+    print("train success")

mindspore/model_zoo/googlenet.py

+# 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.
+# ============================================================================
+"""GoogleNet"""
+import mindspore.nn as nn
+from mindspore.common.initializer import TruncatedNormal
+from mindspore.ops import operations as P
+
+
+def weight_variable():
+    """Weight variable."""
+    return TruncatedNormal(0.02)
+
+
+class Conv2dBlock(nn.Cell):
+    """
+     Basic convolutional block
+     Args:
+         in_channles (int): Input channel.
+         out_channels (int): Output channel.
+         kernel_size (int): Input kernel size. Default: 1
+         stride (int): Stride size for the first convolutional layer. Default: 1.
+         padding (int): Implicit paddings on both sides of the input. Default: 0.
+         pad_mode (int): Padding mode. Optional values are "same", "valid", "pad". Default: "same".
+      Returns:
+          Tensor, output tensor.
+    """
+
+    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, pad_mode="same"):
+        super(Conv2dBlock, self).__init__()
+        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride,
+                              padding=padding, pad_mode=pad_mode, weight_init=weight_variable(),
+                              bias_init=False)
+        self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
+        self.relu = nn.ReLU()
+
+    def construct(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+class Inception(nn.Cell):
+    """
+    Inception Block
+    """
+
+    def __init__(self, in_channels, n1x1, n3x3red, n3x3, n5x5red, n5x5, pool_planes):
+        super(Inception, self).__init__()
+        self.b1 = Conv2dBlock(in_channels, n1x1, kernel_size=1)
+        self.b2 = nn.SequentialCell([Conv2dBlock(in_channels, n3x3red, kernel_size=1),
+                                     Conv2dBlock(n3x3red, n3x3, kernel_size=3, padding=0)])
+        self.b3 = nn.SequentialCell([Conv2dBlock(in_channels, n5x5red, kernel_size=1),
+                                     Conv2dBlock(n5x5red, n5x5, kernel_size=3, padding=0)])
+        self.maxpool = P.MaxPoolWithArgmax(ksize=3, strides=1, padding="same")
+        self.b4 = Conv2dBlock(in_channels, pool_planes, kernel_size=1)
+        self.concat = P.Concat(axis=1)
+
+    def construct(self, x):
+        branch1 = self.b1(x)
+        branch2 = self.b2(x)
+        branch3 = self.b3(x)
+        cell, argmax = self.maxpool(x)
+        branch4 = self.b4(cell)
+        _ = argmax
+        return self.concat((branch1, branch2, branch3, branch4))
+
+
+class GooGLeNet(nn.Cell):
+    """
+    Googlenet architecture
+    """
+
+    def __init__(self, num_classes):
+        super(GooGLeNet, self).__init__()
+        self.conv1 = Conv2dBlock(3, 64, kernel_size=7, stride=2, padding=0)
+        self.maxpool1 = P.MaxPoolWithArgmax(ksize=3, strides=2, padding="same")
+
+        self.conv2 = Conv2dBlock(64, 64, kernel_size=1)
+        self.conv3 = Conv2dBlock(64, 192, kernel_size=3, padding=0)
+        self.maxpool2 = P.MaxPoolWithArgmax(ksize=3, strides=2, padding="same")
+
+        self.block3a = Inception(192, 64, 96, 128, 16, 32, 32)
+        self.block3b = Inception(256, 128, 128, 192, 32, 96, 64)
+        self.maxpool3 = P.MaxPoolWithArgmax(ksize=3, strides=2, padding="same")
+
+        self.block4a = Inception(480, 192, 96, 208, 16, 48, 64)
+        self.block4b = Inception(512, 160, 112, 224, 24, 64, 64)
+        self.block4c = Inception(512, 128, 128, 256, 24, 64, 64)
+        self.block4d = Inception(512, 112, 144, 288, 32, 64, 64)
+        self.block4e = Inception(528, 256, 160, 320, 32, 128, 128)
+        self.maxpool4 = P.MaxPoolWithArgmax(ksize=2, strides=2, padding="same")
+
+        self.block5a = Inception(832, 256, 160, 320, 32, 128, 128)
+        self.block5b = Inception(832, 384, 192, 384, 48, 128, 128)
+
+        self.mean = P.ReduceMean(keep_dims=True)
+        self.dropout = nn.Dropout(keep_prob=0.8)
+        self.flatten = nn.Flatten()
+        self.classifier = nn.Dense(1024, num_classes, weight_init=weight_variable(),
+                                   bias_init=weight_variable())
+
+
+    def construct(self, x):
+        x = self.conv1(x)
+        x, argmax = self.maxpool1(x)
+
+        x = self.conv2(x)
+        x = self.conv3(x)
+        x, argmax = self.maxpool2(x)
+
+        x = self.block3a(x)
+        x = self.block3b(x)
+        x, argmax = self.maxpool3(x)
+
+        x = self.block4a(x)
+        x = self.block4b(x)
+        x = self.block4c(x)
+        x = self.block4d(x)
+        x = self.block4e(x)
+        x, argmax = self.maxpool4(x)
+
+        x = self.block5a(x)
+        x = self.block5b(x)
+
+        x = self.mean(x, (2, 3))
+        x = self.flatten(x)
+        x = self.classifier(x)
+
+        _ = argmax
+        return x