更换文档检测模型

paddle_detection/deploy/auto_compression/eval.py

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+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+import os
+import sys
+import numpy as np
+import argparse
+import paddle
+from ppdet.core.workspace import load_config, merge_config
+from ppdet.core.workspace import create
+from ppdet.metrics import COCOMetric, VOCMetric, KeyPointTopDownCOCOEval
+from paddleslim.auto_compression.config_helpers import load_config as load_slim_config
+from post_process import PPYOLOEPostProcess
+
+
+def argsparser():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        '--config_path',
+        type=str,
+        default=None,
+        help="path of compression strategy config.",
+        required=True)
+    parser.add_argument(
+        '--devices',
+        type=str,
+        default='gpu',
+        help="which device used to compress.")
+
+    return parser
+
+
+def reader_wrapper(reader, input_list):
+    def gen():
+        for data in reader:
+            in_dict = {}
+            if isinstance(input_list, list):
+                for input_name in input_list:
+                    in_dict[input_name] = data[input_name]
+            elif isinstance(input_list, dict):
+                for input_name in input_list.keys():
+                    in_dict[input_list[input_name]] = data[input_name]
+            yield in_dict
+
+    return gen
+
+
+def convert_numpy_data(data, metric):
+    data_all = {}
+    data_all = {k: np.array(v) for k, v in data.items()}
+    if isinstance(metric, VOCMetric):
+        for k, v in data_all.items():
+            if not isinstance(v[0], np.ndarray):
+                tmp_list = []
+                for t in v:
+                    tmp_list.append(np.array(t))
+                data_all[k] = np.array(tmp_list)
+    else:
+        data_all = {k: np.array(v) for k, v in data.items()}
+    return data_all
+
+
+def eval():
+
+    place = paddle.CUDAPlace(0) if FLAGS.devices == 'gpu' else paddle.CPUPlace()
+    exe = paddle.static.Executor(place)
+
+    val_program, feed_target_names, fetch_targets = paddle.static.load_inference_model(
+        global_config["model_dir"].rstrip('/'),
+        exe,
+        model_filename=global_config["model_filename"],
+        params_filename=global_config["params_filename"])
+    print('Loaded model from: {}'.format(global_config["model_dir"]))
+
+    metric = global_config['metric']
+    for batch_id, data in enumerate(val_loader):
+        data_all = convert_numpy_data(data, metric)
+        data_input = {}
+        for k, v in data.items():
+            if isinstance(global_config['input_list'], list):
+                if k in global_config['input_list']:
+                    data_input[k] = np.array(v)
+            elif isinstance(global_config['input_list'], dict):
+                if k in global_config['input_list'].keys():
+                    data_input[global_config['input_list'][k]] = np.array(v)
+
+        outs = exe.run(val_program,
+                       feed=data_input,
+                       fetch_list=fetch_targets,
+                       return_numpy=False)
+        res = {}
+        if 'arch' in global_config and global_config['arch'] == 'PPYOLOE':
+            postprocess = PPYOLOEPostProcess(
+                score_threshold=0.01, nms_threshold=0.6)
+            res = postprocess(np.array(outs[0]), data_all['scale_factor'])
+        else:
+            for out in outs:
+                v = np.array(out)
+                if len(v.shape) > 1:
+                    res['bbox'] = v
+                else:
+                    res['bbox_num'] = v
+        metric.update(data_all, res)
+        if batch_id % 100 == 0:
+            print('Eval iter:', batch_id)
+    metric.accumulate()
+    metric.log()
+    metric.reset()
+
+
+def main():
+    global global_config
+    all_config = load_slim_config(FLAGS.config_path)
+    assert "Global" in all_config, "Key 'Global' not found in config file."
+    global_config = all_config["Global"]
+    reader_cfg = load_config(global_config['reader_config'])
+
+    dataset = reader_cfg['EvalDataset']
+    global val_loader
+    val_loader = create('EvalReader')(reader_cfg['EvalDataset'],
+                                      reader_cfg['worker_num'],
+                                      return_list=True)
+    metric = None
+    if reader_cfg['metric'] == 'COCO':
+        clsid2catid = {v: k for k, v in dataset.catid2clsid.items()}
+        anno_file = dataset.get_anno()
+        metric = COCOMetric(
+            anno_file=anno_file, clsid2catid=clsid2catid, IouType='bbox')
+    elif reader_cfg['metric'] == 'VOC':
+        metric = VOCMetric(
+            label_list=dataset.get_label_list(),
+            class_num=reader_cfg['num_classes'],
+            map_type=reader_cfg['map_type'])
+    elif reader_cfg['metric'] == 'KeyPointTopDownCOCOEval':
+        anno_file = dataset.get_anno()
+        metric = KeyPointTopDownCOCOEval(anno_file,
+                                         len(dataset), 17, 'output_eval')
+    else:
+        raise ValueError("metric currently only supports COCO and VOC.")
+    global_config['metric'] = metric
+
+    eval()
+
+
+if __name__ == '__main__':
+    paddle.enable_static()
+    parser = argsparser()
+    FLAGS = parser.parse_args()
+    assert FLAGS.devices in ['cpu', 'gpu', 'xpu', 'npu']
+    paddle.set_device(FLAGS.devices)
+
+    main()