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
@@ -26,10 +28,15 @@ 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)
     opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, cfg.momentum,
@@ -37,7 +44,11 @@ if __name__ == '__main__':
     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

+#!/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

+#!/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/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 = []
@@ -65,18 +64,31 @@ 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)
-    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)
+    device_target = cfg.device_target
 
+    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()
-        context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
-                                          mirror_mean=True)
-        init()
+
+    if device_target == "Ascend":
+        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)
+
+        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()
@@ -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")