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gpu update resnet in modelzoo

This commit is contained in:
VectorSL 2020-08-01 10:46:19 +08:00
parent 6c4ee3f3d1
commit 73806e0c4b
3 changed files with 18 additions and 8 deletions
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2
model_zoo/official/cv/resnet/README.md
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@ -241,7 +241,7 @@ result: {'top_5_accuracy': 0.9429417413572343, 'top_1_accuracy': 0.7853513124199
### Running on GPU
```
# distributed training example
mpirun -n 8 python train.py ---net=resnet50 --dataset=cifar10 -dataset_path=~/cifar-10-batches-bin --device_target="GPU" --run_distribute=True
mpirun -n 8 python train.py --net=resnet50 --dataset=cifar10 --dataset_path=~/cifar-10-batches-bin --device_target="GPU" --run_distribute=True
# standalone training example
python train.py --net=resnet50 --dataset=cifar10 --dataset_path=~/cifar-10-batches-bin --device_target="GPU"

6
model_zoo/official/cv/resnet/eval.py
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@ -54,8 +54,10 @@ if __name__ == '__main__':
target = args_opt.device_target
# init context
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False, device_id=device_id)
context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
if target != "GPU":
device_id = int(os.getenv('DEVICE_ID'))
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,

18
model_zoo/official/cv/resnet/train.py
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@ -143,13 +143,21 @@ if __name__ == '__main__':
amp_level="O2", keep_batchnorm_fp32=False)
else:
# GPU target
loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean", is_grad=False,
smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
if args_opt.dataset == "imagenet2012":
if not config.use_label_smooth:
config.label_smooth_factor = 0.0
loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean", is_grad=False,
smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
else:
loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean", is_grad=False,
num_classes=config.class_num)
## fp32 training
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum, config.weight_decay)
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
##Mixed precision
#model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'},
# amp_level="O2", keep_batchnorm_fp32=True)
# # Mixed precision
# 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)
# model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'}, amp_level="O2")
# define callbacks
time_cb = TimeMonitor(data_size=step_size)