mindspore/model_zoo/official/cv/warpctc/eval.py

# 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 random
import argparse
import numpy as np
from mindspore import context
from mindspore import dataset as de
from mindspore.train.model import Model
from mindspore.train.serialization import load_checkpoint, load_param_into_net

from src.loss import CTCLoss, CTCLossV2
from src.config import config as cf
from src.dataset import create_dataset
from src.warpctc import StackedRNN, StackedRNNForGPU
from src.metric import WarpCTCAccuracy

random.seed(1)
np.random.seed(1)
de.config.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()
    if args_opt.platform == 'Ascend':
        loss = CTCLoss(max_sequence_length=cf.captcha_width,
                       max_label_length=max_captcha_digits,
                       batch_size=cf.batch_size)
        net = StackedRNN(input_size=input_size, batch_size=cf.batch_size, hidden_size=cf.hidden_size)
    else:
        loss = CTCLossV2(max_sequence_length=cf.captcha_width, batch_size=cf.batch_size)
        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)