def test_Softmax_CE(input, target):
f = Softmax_CE()
output = f.forward(input, target)
grad_output = f.backward()
softmax = Softmax()
CE = CrossEntropy()
pred = softmax.forward(input)
div_output = CE.forward(pred, target)
ce_grad = CE.backward()
div_grad = softmax.backward(ce_grad)
return output, grad_output, div_output, div_grad
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE,
device_target="Ascend",
save_graphs=False)
context.set_context(enable_task_sink=True, device_id=device_id)
context.set_context(enable_loop_sink=True)
context.set_context(enable_mem_reuse=True)
if __name__ == '__main__':
net = resnet50(class_num=config.class_num)
if not config.use_label_smooth:
config.label_smooth_factor = 0.0
loss = CrossEntropy(smooth_factor=config.label_smooth_factor,
num_classes=config.class_num)
if args_opt.do_eval:
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)