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91 lines
3.1 KiB
Python
91 lines
3.1 KiB
Python
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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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import paddle
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from paddlevideo.utils import get_logger, load
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from ..loader.builder import build_dataloader, build_dataset
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from ..metrics import build_metric
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from ..modeling.builder import build_model
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logger = get_logger("paddlevideo")
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@paddle.no_grad()
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def test_model(cfg, weights, parallel=True):
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"""Test model entry
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Args:
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cfg (dict): configuration.
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weights (str): weights path to load.
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parallel (bool): Whether to do multi-cards testing. Default: True.
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"""
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if cfg.get('use_npu', False):
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places = paddle.set_device('npu')
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elif cfg.get('use_xpu', False):
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places = paddle.set_device('xpu')
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else:
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places = paddle.set_device('gpu')
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# 1. Construct model.
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if cfg.MODEL.get('backbone') and cfg.MODEL.backbone.get('pretrained'):
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cfg.MODEL.backbone.pretrained = '' # disable pretrain model init
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model = build_model(cfg.MODEL)
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if parallel:
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model = paddle.DataParallel(model)
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# 2. Construct dataset and dataloader.
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cfg.DATASET.test.test_mode = True
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dataset = build_dataset((cfg.DATASET.test, cfg.PIPELINE.test))
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batch_size = cfg.DATASET.get("test_batch_size", 8)
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# default num worker: 0, which means no subprocess will be created
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num_workers = cfg.DATASET.get('num_workers', 0)
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num_workers = cfg.DATASET.get('test_num_workers', num_workers)
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dataloader_setting = dict(batch_size=batch_size,
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num_workers=num_workers,
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places=places,
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drop_last=False,
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shuffle=False)
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data_loader = build_dataloader(
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dataset, **dataloader_setting) if cfg.model_name not in ['CFBI'
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] else dataset
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model.eval()
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state_dicts = load(weights)
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model.set_state_dict(state_dicts)
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# add params to metrics
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cfg.METRIC.data_size = len(dataset)
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cfg.METRIC.batch_size = batch_size
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Metric = build_metric(cfg.METRIC)
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if cfg.MODEL.framework == "FastRCNN":
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Metric.set_dataset_info(dataset.info, len(dataset))
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for batch_id, data in enumerate(data_loader):
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if cfg.model_name in [
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'CFBI'
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]: # for VOS task, dataset for video and dataloader for frames in each video
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Metric.update(batch_id, data, model)
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else:
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outputs = model(data, mode='test')
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Metric.update(batch_id, data, outputs)
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Metric.accumulate()
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