parser = argparse.ArgumentParser(description='Image classification')
parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
args_opt = parser.parse_args()
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=device_id, save_graphs=False)
context.set_context(enable_task_sink=True)
context.set_context(enable_loop_sink=True)
context.set_context(enable_mem_reuse=True)
if __name__ == '__main__':
loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')
net = mobilenet_v2(num_classes=config.num_classes)
net.to_float(mstype.float16)
for _, cell in net.cells_and_names():
if isinstance(cell, nn.Dense):
cell.add_flags_recursive(fp32=True)
dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size)
step_size = dataset.get_dataset_size()
if args_opt.checkpoint_path:
param_dict = load_checkpoint(args_opt.checkpoint_path)
load_param_into_net(net, param_dict)
net.set_train(False)
model = Model(net, loss_fn=loss, metrics={'acc'})
res = model.eval(dataset)
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE,
device_target="Ascend",
device_id=device_id,
save_graphs=False)
context.set_context(enable_task_sink=True)
context.set_context(enable_loop_sink=True)
context.set_context(enable_mem_reuse=True)
if __name__ == '__main__':
loss = SoftmaxCrossEntropyWithLogits(is_grad=False,
sparse=True,
reduction='mean')
net = mobilenet_v2()
dataset = create_dataset(dataset_path=args_opt.dataset_path,
do_train=False,
batch_size=config.batch_size)
step_size = dataset.get_dataset_size()
if args_opt.checkpoint_path:
param_dict = load_checkpoint(args_opt.checkpoint_path)
load_param_into_net(net, param_dict)
net.set_train(False)
model = Model(net, loss_fn=loss, metrics={'acc'})
res = model.eval(dataset)
print("result:", res, "ckpt=", args_opt.checkpoint_path)