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- # Copyright 2021 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.
- # ============================================================================
- """Eval"""
-
- import argparse
- import ast
- import os
- import time
-
- import mindspore.dataset as ds
- from mindspore import context
- from mindspore.nn.metrics import Accuracy
- from mindspore.train import Model
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
-
- from src.dataset import DatasetGenerator
- from src.pointnet2 import PointNet2, NLLLoss
-
-
- def parse_args():
- """PARAMETERS"""
- parser = argparse.ArgumentParser('MindSpore PointNet++ Eval Configurations.')
- parser.add_argument('--batch_size', type=int, default=24, help='batch size in training')
- parser.add_argument('--data_path', type=str, default='../data/modelnet40_normal_resampled/', help='data path')
- parser.add_argument('--pretrained_ckpt', type=str, default='')
- parser.add_argument('--num_category', default=40, type=int, choices=[10, 40], help='training on ModelNet10/40')
- parser.add_argument('--num_point', type=int, default=1024, help='Point Number')
- parser.add_argument('--use_normals', type=ast.literal_eval, default=False, help='use normals')
- parser.add_argument('--process_data', action='store_true', default=False, help='save data offline')
- parser.add_argument('--use_uniform_sample', action='store_true', default=False, help='use uniform sampiling')
-
- parser.add_argument('--platform', type=str, default='Ascend', help='run platform')
- parser.add_argument('--enable_modelarts', type=ast.literal_eval, default=False)
- parser.add_argument('--data_url', type=str)
- parser.add_argument('--train_url', type=str)
-
- return parser.parse_known_args()[0]
-
-
- def run_eval():
- """Run eval"""
- args = parse_args()
-
- # INIT
- device_id = int(os.getenv('DEVICE_ID', '0'))
-
- if args.enable_modelarts:
- import moxing as mox
-
- local_data_url = "/cache/data"
- mox.file.copy_parallel(args.data_url, local_data_url)
- pretrained_ckpt_path = "/cache/pretrained_ckpt/pretrained.ckpt"
- mox.file.copy_parallel(args.pretrained_ckpt, pretrained_ckpt_path)
- local_eval_url = "/cache/eval_out"
- mox.file.copy_parallel(args.train_url, local_eval_url)
- else:
- local_data_url = args.data_path
- pretrained_ckpt_path = args.pretrained_ckpt
-
- if args.platform == "Ascend":
- context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=device_id)
- context.set_context(max_call_depth=2048)
- else:
- raise ValueError("Unsupported platform.")
-
- print(args)
-
- # DATA LOADING
- print('Load dataset ...')
- data_path = local_data_url
-
- num_workers = 8
- test_ds_generator = DatasetGenerator(root=data_path, args=args, split='test', process_data=args.process_data)
- test_ds = ds.GeneratorDataset(test_ds_generator, ["data", "label"], num_parallel_workers=num_workers, shuffle=False)
- test_ds = test_ds.batch(batch_size=args.batch_size, drop_remainder=True, num_parallel_workers=num_workers)
-
- # MODEL LOADING
- net = PointNet2(args.num_category, args.use_normals)
-
- # load checkpoint
- print("Load checkpoint: ", args.pretrained_ckpt)
- param_dict = load_checkpoint(pretrained_ckpt_path)
- load_param_into_net(net, param_dict)
-
- net_loss = NLLLoss()
-
- model = Model(net, net_loss, metrics={"Accuracy": Accuracy()})
-
- # EVAL
- net.set_train(False)
- print('Starting eval ...')
- time_start = time.time()
- print('Time: ', time.strftime("%Y-%m-%d-%H_%M_%S", time.localtime()))
-
- result = model.eval(test_ds, dataset_sink_mode=True)
- print("result : {}".format(result))
-
- # END
- print('Total time cost: {} min'.format("%.2f" % ((time.time() - time_start) / 60)))
-
- if args.enable_modelarts:
- mox.file.copy_parallel(local_eval_url, args.train_url)
-
-
- if __name__ == '__main__':
- run_eval()
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