googlenet-gpu

model_zoo/official/cv/googlenet/eval.py

@@ -16,6 +16,8 @@
 ##############test googlenet example on cifar10#################
 python eval.py
 """
+import argparse
+
 import mindspore.nn as nn
 from mindspore import context
 from mindspore.nn.optim.momentum import Momentum
@@ -26,18 +28,27 @@ from src.config import cifar_cfg as cfg
 from src.dataset import create_dataset
 from src.googlenet import GoogleNet
 
+parser = argparse.ArgumentParser(description='googlenet')
+parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
+args_opt = parser.parse_args()
 
 if __name__ == '__main__':
+    device_target = cfg.device_target
     context.set_context(mode=context.GRAPH_MODE, device_target=cfg.device_target)
-    context.set_context(device_id=cfg.device_id)
+    if device_target == "Ascend":
+        context.set_context(device_id=cfg.device_id)
 
-    net = GoogleNet(num_classes=cfg.num_classes)
+    net = GoogleNet(num_classes=cfg.num_classes, platform=device_target)
     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(cfg.checkpoint_path)
+    if device_target == "Ascend":
+        param_dict = load_checkpoint(cfg.checkpoint_path)
+    else: # GPU
+        param_dict = load_checkpoint(args_opt.checkpoint_path)
+
     load_param_into_net(net, param_dict)
     net.set_train(False)
     dataset = create_dataset(cfg.data_path, 1, False)

model_zoo/official/cv/googlenet/scripts/run_eval_gpu.sh

@@ -0,0 +1,43 @@
+#!/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.
+# ============================================================================
+
+ulimit -u unlimited
+
+if [ $# != 1 ]
+then
+    echo "GPU: sh run_eval_gpu.sh [CHECKPOINT_PATH]"
+exit 1
+fi
+
+# check checkpoint file
+if [ ! -f $1 ]
+then
+    echo "error: CHECKPOINT_PATH=$1 is not a file"    
+exit 1
+fi
+
+BASEPATH=$(cd "`dirname $0`" || exit; pwd)
+export PYTHONPATH=${BASEPATH}:$PYTHONPATH
+export DEVICE_ID=0
+
+if [ -d "../eval" ];
+then
+    rm -rf ../eval
+fi
+mkdir ../eval
+cd ../eval || exit
+
+python3 ${BASEPATH}/../eval.py --checkpoint_path=$1 > ./eval.log 2>&1 &

model_zoo/official/cv/googlenet/scripts/run_train_gpu.sh

@@ -0,0 +1,45 @@
+#!/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 [ $# -lt 2 ]
+then
+    echo "Usage:\n \
+          sh run_train.sh [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)]\n \
+          "
+exit 1
+fi
+
+if [ $1 -lt 1 ] && [ $1 -gt 8 ]
+then
+    echo "error: DEVICE_NUM=$1 is not in (1-8)"
+exit 1
+fi
+
+export DEVICE_NUM=$1
+export RANK_SIZE=$1
+
+BASEPATH=$(cd "`dirname $0`" || exit; pwd)
+export PYTHONPATH=${BASEPATH}:$PYTHONPATH
+if [ -d "../train" ];
+then
+    rm -rf ../train
+fi
+mkdir ../train
+cd ../train || exit
+
+export CUDA_VISIBLE_DEVICES="$2"
+mpirun -n $1 --allow-run-as-root \
+python3 ${BASEPATH}/../train.py > train.log 2>&1 &

model_zoo/official/cv/googlenet/src/googlenet.py

@@ -56,24 +56,35 @@ class Inception(nn.Cell):
     Inception Block
     """
 
-    def __init__(self, in_channels, n1x1, n3x3red, n3x3, n5x5red, n5x5, pool_planes):
+    def __init__(self, in_channels, n1x1, n3x3red, n3x3, n5x5red, n5x5, pool_planes, platform="Ascend"):
         super(Inception, self).__init__()
+        self.platform = platform
         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")
+        if self.platform == "Ascend":
+            self.maxpool = P.MaxPoolWithArgmax(ksize=3, strides=1, padding="same")
+        else:  # GPU
+            self.maxpool = P.MaxPool(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):
+        '''
+        construct inception model
+        '''
         branch1 = self.b1(x)
         branch2 = self.b2(x)
         branch3 = self.b3(x)
-        cell, argmax = self.maxpool(x)
+        if self.platform == "Ascend":
-        branch4 = self.b4(cell)
+            cell, argmax = self.maxpool(x)
-        _ = argmax
+            branch4 = self.b4(cell)
+            _ = argmax
+        else: # GPU
+            cell = self.maxpool(x)
+            branch4 = self.b4(cell)
         return self.concat((branch1, branch2, branch3, branch4))
 
 
@@ -82,61 +93,82 @@ class GoogleNet(nn.Cell):
     Googlenet architecture
     """
 
-    def __init__(self, num_classes):
+    def __init__(self, num_classes, platform="Ascend"):
         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.platform = platform
-
         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, platform=self.platform)
-
+        self.block3b = Inception(256, 128, 128, 192, 32, 96, 64, platform=self.platform)
-        self.block3a = Inception(192, 64, 96, 128, 16, 32, 32)
+        self.block4a = Inception(480, 192, 96, 208, 16, 48, 64, platform=self.platform)
-        self.block3b = Inception(256, 128, 128, 192, 32, 96, 64)
+        self.block4b = Inception(512, 160, 112, 224, 24, 64, 64, platform=self.platform)
-        self.maxpool3 = P.MaxPoolWithArgmax(ksize=3, strides=2, padding="same")
+        self.block4c = Inception(512, 128, 128, 256, 24, 64, 64, platform=self.platform)
-
+        self.block4d = Inception(512, 112, 144, 288, 32, 64, 64, platform=self.platform)
-        self.block4a = Inception(480, 192, 96, 208, 16, 48, 64)
+        self.block4e = Inception(528, 256, 160, 320, 32, 128, 128, platform=self.platform)
-        self.block4b = Inception(512, 160, 112, 224, 24, 64, 64)
+        if self.platform == "Ascend":
-        self.block4c = Inception(512, 128, 128, 256, 24, 64, 64)
+            self.maxpool1 = P.MaxPoolWithArgmax(ksize=3, strides=2, padding="same")
-        self.block4d = Inception(512, 112, 144, 288, 32, 64, 64)
+            self.maxpool2 = P.MaxPoolWithArgmax(ksize=3, strides=2, padding="same")
-        self.block4e = Inception(528, 256, 160, 320, 32, 128, 128)
+            self.maxpool3 = P.MaxPoolWithArgmax(ksize=3, strides=2, padding="same")
-        self.maxpool4 = P.MaxPoolWithArgmax(ksize=2, strides=2, padding="same")
+            self.maxpool4 = P.MaxPoolWithArgmax(ksize=2, strides=2, padding="same")
-
+        else: # GPU
-        self.block5a = Inception(832, 256, 160, 320, 32, 128, 128)
+            self.maxpool1 = P.MaxPool(ksize=3, strides=2, padding="same")
-        self.block5b = Inception(832, 384, 192, 384, 48, 128, 128)
+            self.maxpool2 = P.MaxPool(ksize=3, strides=2, padding="same")
-
+            self.maxpool3 = P.MaxPool(ksize=3, strides=2, padding="same")
+            self.maxpool4 = P.MaxPool(ksize=2, strides=2, padding="same")
+        self.block5a = Inception(832, 256, 160, 320, 32, 128, 128, platform=self.platform)
+        self.block5b = Inception(832, 384, 192, 384, 48, 128, 128, platform=self.platform)
         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):
+        '''
+        construct googlenet model
+        '''
         x = self.conv1(x)
-        x, argmax = self.maxpool1(x)
+        if self.platform == "Ascend":
+            x, argmax = self.maxpool1(x)
+        else: # GPU
+            x = self.maxpool1(x)
 
         x = self.conv2(x)
         x = self.conv3(x)
-        x, argmax = self.maxpool2(x)
+        if self.platform == "Ascend":
+            x, argmax = self.maxpool2(x)
+        else: # GPU
+            x = self.maxpool2(x)
 
         x = self.block3a(x)
         x = self.block3b(x)
-        x, argmax = self.maxpool3(x)
+        if self.platform == "Ascend":
+            x, argmax = self.maxpool3(x)
+        else: # GPU
+            x = 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)
+        if self.platform == "Ascend":
+            x, argmax = self.maxpool4(x)
+            x = self.block5a(x)
+            x = self.block5b(x)
 
-        x = self.block5a(x)
+            x = self.mean(x, (2, 3))
-        x = self.block5b(x)
+            x = self.flatten(x)
+            x = self.classifier(x)
+            _ = argmax
+        else: # GPU
+            x = self.maxpool4(x)
+            x = self.block5a(x)
+            x = self.block5b(x)
 
-        x = self.mean(x, (2, 3))
+            x = self.mean(x, (2, 3))
-        x = self.flatten(x)
+            x = self.flatten(x)
-        x = self.classifier(x)
+            x = self.classifier(x)
 
-        _ = argmax
         return x

model_zoo/official/cv/googlenet/train.py

@@ -25,7 +25,7 @@ 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.communication.management import init, get_rank
 from mindspore.nn.optim.momentum import Momentum
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
 from mindspore.train.model import Model, ParallelMode
@@ -38,7 +38,6 @@ from src.googlenet import GoogleNet
 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 = []
@@ -65,23 +64,36 @@ if __name__ == '__main__':
     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=cfg.device_target)
+    device_target = cfg.device_target
-    if args_opt.device_id is not None:
-        context.set_context(device_id=args_opt.device_id)
-    else:
-        context.set_context(device_id=cfg.device_id)
 
+    context.set_context(mode=context.GRAPH_MODE, device_target=cfg.device_target)
     device_num = int(os.environ.get("DEVICE_NUM", 1))
-    if device_num > 1:
+
-        context.reset_auto_parallel_context()
+    if device_target == "Ascend":
-        context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
+        if args_opt.device_id is not None:
-                                          mirror_mean=True)
+            context.set_context(device_id=args_opt.device_id)
-        init()
+        else:
+            context.set_context(device_id=cfg.device_id)
+
+        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()
+    elif device_target == "GPU":
+        init("nccl")
+
+        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)
+    else:
+        raise ValueError("Unsupport platform.")
 
     dataset = create_dataset(cfg.data_path, 1)
     batch_num = dataset.get_dataset_size()
 
-    net = GoogleNet(num_classes=cfg.num_classes)
+    net = GoogleNet(num_classes=cfg.num_classes, platform=device_target)
     # Continue training if set pre_trained to be True
     if cfg.pre_trained:
         param_dict = load_checkpoint(cfg.checkpoint_path)
@@ -90,12 +102,19 @@ if __name__ == '__main__':
     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)
+    if device_target == "Ascend":
+        model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'},
+                      amp_level="O2", keep_batchnorm_fp32=False, loss_scale_manager=None)
+        ckpt_save_dir = "./"
+    else: # GPU
+        model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'},
+                      amp_level="O2", keep_batchnorm_fp32=True, loss_scale_manager=None)
+        ckpt_save_dir = "./ckpt_" + str(get_rank()) + "/"
 
     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)
+    ckpoint_cb = ModelCheckpoint(prefix="train_googlenet_cifar10", directory=ckpt_save_dir, config=config_ck)
     loss_cb = LossMonitor()
     model.train(cfg.epoch_size, dataset, callbacks=[time_cb, ckpoint_cb, loss_cb])
     print("train success")