You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
69 lines
3.0 KiB
69 lines
3.0 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ============================================================================
|
|
"""Warpctc evaluation"""
|
|
import os
|
|
import math as m
|
|
import argparse
|
|
from mindspore import context
|
|
from mindspore.common import set_seed
|
|
from mindspore.train.model import Model
|
|
from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
|
|
|
from src.loss import CTCLoss
|
|
from src.config import config as cf
|
|
from src.dataset import create_dataset
|
|
from src.warpctc import StackedRNN, StackedRNNForGPU
|
|
from src.metric import WarpCTCAccuracy
|
|
|
|
set_seed(1)
|
|
|
|
parser = argparse.ArgumentParser(description="Warpctc training")
|
|
parser.add_argument("--dataset_path", type=str, default=None, help="Dataset, default is None.")
|
|
parser.add_argument("--checkpoint_path", type=str, default=None, help="checkpoint file path, default is None")
|
|
parser.add_argument('--platform', type=str, default='Ascend', choices=['Ascend', 'GPU'],
|
|
help='Running platform, choose from Ascend, GPU, and default is Ascend.')
|
|
args_opt = parser.parse_args()
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.platform, save_graphs=False)
|
|
if args_opt.platform == 'Ascend':
|
|
device_id = int(os.getenv('DEVICE_ID'))
|
|
context.set_context(device_id=device_id)
|
|
|
|
if __name__ == '__main__':
|
|
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,
|
|
device_target=args_opt.platform)
|
|
step_size = dataset.get_dataset_size()
|
|
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)
|
|
|
|
# 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={'WarpCTCAccuracy': WarpCTCAccuracy(args_opt.platform)})
|
|
# start evaluation
|
|
res = model.eval(dataset, dataset_sink_mode=args_opt.platform == 'Ascend')
|
|
print("result:", res, flush=True)
|