def test_noop_sched():
os.environ['MS_ROLE'] = 'MS_SCHED'
context.set_ps_context(enable_ps=True)
data1 = ds.VOCDataset(DATA_DIR, task="Segmentation", usage="train", shuffle=False, decode=True)
num = 0
for _ in data1.create_dict_iterator(num_epochs=1):
num += 1
assert num == 0
del os.environ['MS_ROLE']
context.set_ps_context(enable_ps=False)
def no_ps_impl(self, dataset):
context.set_ps_context(enable_ps=False)
net = Menet(self.in_channels, self.out_channels, self.kernel_size, self.vocab_size,
self.embedding_size, self.output_channels, self.target, self.sparse)
net.conv.conv2d.add_prim_attr('primitive_target', 'CPU')
net.conv.bias_add.add_prim_attr('primitive_target', 'CPU')
net.set_train()
loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
opt = Adam(params=filter(lambda x: x.requires_grad, net.get_parameters()))
opt.target = 'CPU'
model = Model(net, loss, opt)
model.train(self.epoch_size, dataset, dataset_sink_mode=False)
input_me = Tensor(self.input_np)
out_me = model.predict(input_me)
context.set_ps_context(enable_ps=True)
return out_me.asnumpy()
else:
from src.resnet import se_resnet50 as resnet
from src.config import config4 as config
from src.dataset import create_dataset4 as create_dataset
if __name__ == '__main__':
target = args.device_target
config.batch_size = args.batch_size
ckpt_save_dir = config.save_checkpoint_path
# init context
context.set_context(mode=context.GRAPH_MODE,
device_target=target,
save_graphs=False)
if args.parameter_server:
context.set_ps_context(enable_ps=True)
if args.run_distribute:
if target == "Ascend":
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(device_id=device_id,
enable_auto_mixed_precision=True)
context.set_auto_parallel_context(
device_num=args.device_num,
parallel_mode=ParallelMode.DATA_PARALLEL,
gradients_mean=True)
if args.net == "resnet50" or args.net == "se-resnet50":
context.set_auto_parallel_context(
all_reduce_fusion_config=[85, 160])
else:
context.set_auto_parallel_context(
all_reduce_fusion_config=[180, 313])
