rank = 0
max_captcha_digits = cf.max_captcha_digits
input_size = m.ceil(cf.captcha_height / 64) * 64 * 3
# create dataset
dataset = create_dataset(dataset_path=args_opt.dataset_path,
batch_size=cf.batch_size,
num_shards=device_num,
shard_id=rank,
device_target=args_opt.platform)
step_size = dataset.get_dataset_size()
# define lr
lr_init = cf.learning_rate if not args_opt.run_distribute else cf.learning_rate * device_num * lr_scale
lr = get_lr(cf.epoch_size, step_size, lr_init)
loss = CTCLoss(max_sequence_length=cf.captcha_width,
max_label_length=max_captcha_digits,
batch_size=cf.batch_size)
if args_opt.platform == 'Ascend':
net = StackedRNN(input_size=input_size,
batch_size=cf.batch_size,
hidden_size=cf.hidden_size)
else:
net = StackedRNNForGPU(input_size=input_size,
batch_size=cf.batch_size,
hidden_size=cf.hidden_size)
opt = nn.SGD(params=net.trainable_params(),
learning_rate=lr,
momentum=cf.momentum)
net = WithLossCell(net, loss)
net = TrainOneStepCellWithGradClip(net, opt).set_train()
if args_opt.platform == 'Ascend':
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(device_id=device_id)
if __name__ == '__main__':
config.batch_size = 1
max_text_length = config.max_text_length
input_size = config.input_size
# create dataset
dataset = create_dataset(name=args_opt.dataset,
dataset_path=args_opt.dataset_path,
batch_size=config.batch_size,
is_training=False,
config=config)
step_size = dataset.get_dataset_size()
loss = CTCLoss(max_sequence_length=config.num_step,
max_label_length=max_text_length,
batch_size=config.batch_size)
net = CRNN(config)
# load checkpoint
param_dict = load_checkpoint(args_opt.checkpoint_path)
load_param_into_net(net, param_dict)
net.set_train(False)
# define model
model = Model(net,
loss_fn=loss,
metrics={'CRNNAccuracy': CRNNAccuracy(config)})
# start evaluation
res = model.eval(dataset, dataset_sink_mode=args_opt.platform == 'Ascend')
print("result:", res, flush=True)
