2:fix normalize bug 3:add ci test3:add ci test3:add ci testpull/1956/head
unknown
5 years ago
parent
09433cdd06
commit
c929dab3dd
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#!/bin/bash
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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echo "=============================================================================================================="
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echo "Please run the scipt as: "
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echo "for example: bash run_deeplabv3_ci.sh DEVICE_ID DATA_PATH PRETRAINED_CKPT_PATH"
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echo "=============================================================================================================="
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DEVICE_ID=$1
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DATA_DIR=$2
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PATH_CHECKPOINT=$3
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BASE_PATH=$(cd "$(dirname $0)"; pwd)
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unset SLOG_PRINT_TO_STDOUT
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CODE_DIR="./"
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if [ -d ${BASE_PATH}/../../../../model_zoo/deeplabv3 ]; then
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CODE_DIR=${BASE_PATH}/../../../../model_zoo/deeplabv3
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elif [ -d ${BASE_PATH}/../../model_zoo/deeplabv3 ]; then
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CODE_DIR=${BASE_PATH}/../../model_zoo/deeplabv3
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else
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echo "[ERROR] code dir is not found"
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fi
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echo $CODE_DIR
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rm -rf ${BASE_PATH}/deeplabv3
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cp -r ${CODE_DIR} ${BASE_PATH}/deeplabv3
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cp -f ${BASE_PATH}/train_one_epoch_with_loss.py ${BASE_PATH}/deeplabv3/train_one_epoch_with_loss.py
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cd ${BASE_PATH}/deeplabv3
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python train_one_epoch_with_loss.py --data_url=$DATA_DIR --checkpoint_url=$PATH_CHECKPOINT --device_id=$DEVICE_ID > train_deeplabv3_ci.log 2>&1 &
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process_pid=`echo $!`
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wait ${process_pid}
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status=`echo $?`
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if [ "${status}" != "0" ]; then
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echo "[ERROR] test deeplabv3 failed. status: ${status}"
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exit 1
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else
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echo "[INFO] test deeplabv3 success."
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fi
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""train."""
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import argparse
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import time
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from mindspore import context
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from mindspore.nn.optim.momentum import Momentum
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from mindspore import Model
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.train.callback import Callback
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from src.md_dataset import create_dataset
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from src.losses import OhemLoss
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from src.deeplabv3 import deeplabv3_resnet50
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from src.config import config
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parser = argparse.ArgumentParser(description="Deeplabv3 training")
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parser.add_argument('--data_url', required=True, default=None, help='Train data url')
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parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
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parser.add_argument('--checkpoint_url', default=None, help='Checkpoint path')
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args_opt = parser.parse_args()
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print(args_opt)
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
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class LossCallBack(Callback):
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"""
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Monitor the loss in training.
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Note:
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if per_print_times is 0 do not print loss.
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Args:
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per_print_times (int): Print loss every times. Default: 1.
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"""
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def __init__(self, data_size, per_print_times=1):
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super(LossCallBack, self).__init__()
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if not isinstance(per_print_times, int) or per_print_times < 0:
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raise ValueError("print_step must be int and >= 0")
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self.data_size = data_size
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self._per_print_times = per_print_times
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self.time = 1000
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self.loss = 0
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def epoch_begin(self, run_context):
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self.epoch_time = time.time()
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def step_end(self, run_context):
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cb_params = run_context.original_args()
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epoch_mseconds = (time.time() - self.epoch_time) * 1000
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self.time = epoch_mseconds / self.data_size
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self.loss += cb_params.net_outputs
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print("epoch: {}, step: {}, outputs are {}".format(cb_params.cur_epoch_num, cb_params.cur_step_num,
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str(cb_params.net_outputs)))
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def model_fine_tune(flags, train_net, fix_weight_layer):
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checkpoint_path = flags.checkpoint_url
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if checkpoint_path is None:
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return
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param_dict = load_checkpoint(checkpoint_path)
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load_param_into_net(train_net, param_dict)
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for para in train_net.trainable_params():
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if fix_weight_layer in para.name:
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para.requires_grad = False
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if __name__ == "__main__":
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start_time = time.time()
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epoch_size = 3
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args_opt.base_size = config.crop_size
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args_opt.crop_size = config.crop_size
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train_dataset = create_dataset(args_opt, args_opt.data_url, epoch_size, config.batch_size,
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usage="train", shuffle=False)
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dataset_size = train_dataset.get_dataset_size()
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callback = LossCallBack(dataset_size)
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net = deeplabv3_resnet50(config.seg_num_classes, [config.batch_size, 3, args_opt.crop_size, args_opt.crop_size],
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infer_scale_sizes=config.eval_scales, atrous_rates=config.atrous_rates,
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decoder_output_stride=config.decoder_output_stride, output_stride=config.output_stride,
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fine_tune_batch_norm=config.fine_tune_batch_norm, image_pyramid=config.image_pyramid)
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net.set_train()
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model_fine_tune(args_opt, net, 'layer')
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loss = OhemLoss(config.seg_num_classes, config.ignore_label)
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opt = Momentum(filter(lambda x: 'beta' not in x.name and 'gamma' not in x.name and 'depth' not in x.name and 'bias' not in x.name, net.trainable_params()), learning_rate=config.learning_rate, momentum=config.momentum, weight_decay=config.weight_decay)
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model = Model(net, loss, opt)
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model.train(epoch_size, train_dataset, callback)
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print(time.time() - start_time)
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print("expect loss: ", callback.loss / 3)
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print("expect time: ", callback.time)
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expect_loss = 0.5
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expect_time = 35
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assert callback.loss.asnumpy() / 3 <= expect_loss
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assert callback.time <= expect_time
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