add infer.py

tsm/README.md

@@ -119,6 +119,28 @@ python main.py --data=<path/to/dataset> --eval_only --weights=tsm_checkpoint/fin
 |:-:|:-:|
 |76%|98%|
 
+### 模型推断
+
+可通过如下两种方式进行模型推断。
+
+1. 自动下载Paddle发布的[TSM-ResNet50](https://paddlemodels.bj.bcebos.com/hapi/tsm_resnet50.pdparams)权重推断
+
+```bash
+python infer.py --data=<path/to/dataset> --label_list=<path/to/label_list> --infer_file=<path/to/pickle>
+```
+
+2. 加载checkpoint进行精度推断
+
+```bash
+python infer.py --data=<path/to/dataset> --label_list=<path/to/label_list> --infer_file=<path/to/pickle> --weights=tsm_checkpoint/final
+```
+
+模型推断结果会以如下日志形式输出
+
+```text
+2020-04-03 07:37:16,321-INFO: Sample ./kineteics/val_10/data_batch_10-042_6 predict label: 6, ground truth label: 6
+```
+
 ## 参考论文
 
 - [Temporal Shift Module for Efficient Video Understanding](https://arxiv.org/abs/1811.08383v1), Ji Lin, Chuang Gan, Song Han

tsm/infer.py

+# Copyright (c) 2020 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.
+
+from __future__ import division
+from __future__ import print_function
+
+import os
+import argparse
+import numpy as np
+
+from model import Input, set_device
+
+from check import check_gpu, check_version
+from modeling import tsm_resnet50
+from kinetics_dataset import KineticsDataset
+from transforms import *
+
+import logging
+logger = logging.getLogger(__name__)
+
+
+def main():
+    device = set_device(FLAGS.device)
+    fluid.enable_dygraph(device) if FLAGS.dynamic else None
+
+    transform = Compose([GroupScale(),
+                         GroupCenterCrop(),
+                         NormalizeImage()])
+    dataset = KineticsDataset(
+            pickle_file=FLAGS.infer_file,
+            label_list=FLAGS.label_list,
+            mode='test',
+            transform=transform)
+    labels = dataset.label_list
+
+    model = tsm_resnet50(num_classes=len(labels),
+                         pretrained=FLAGS.weights is None)
+
+    inputs = [Input([None, 8, 3, 224, 224], 'float32', name='image')]
+
+    model.prepare(inputs=inputs, device=FLAGS.device)
+
+    if FLAGS.weights is not None:
+        model.load(FLAGS.weights, reset_optimizer=True)
+
+    imgs, label = dataset[0]
+    pred = model.test([imgs[np.newaxis, :]])
+    pred = labels[np.argmax(pred)]
+    logger.info("Sample {} predict label: {}, ground truth label: {}" \
+                .format(FLAGS.infer_file, pred, labels[int(label)]))
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser("CNN training on TSM")
+    parser.add_argument(
+        "--data", type=str, default='dataset/kinetics',
+        help="path to dataset root directory")
+    parser.add_argument(
+        "--device", type=str, default='gpu',
+        help="device to use, gpu or cpu")
+    parser.add_argument(
+        "-d", "--dynamic", action='store_true',
+        help="enable dygraph mode")
+    parser.add_argument(
+        "--label_list", type=str, default=None,
+        help="path to category index label list file")
+    parser.add_argument(
+        "--infer_file", type=str, default=None,
+        help="path to pickle file for inference")
+    parser.add_argument(
+        "-w",
+        "--weights",
+        default=None,
+        type=str,
+        help="weights path for evaluation")
+    FLAGS = parser.parse_args()
+
+    check_gpu(str.lower(FLAGS.device) == 'gpu')
+    check_version()
+    main()

tsm/kinetics_dataset.py

@@ -56,21 +56,32 @@ class KineticsDataset(Dataset):
     """
 
     def __init__(self,
-                 file_list,
-                 pickle_dir,
+                 file_list=None,
+                 pickle_dir=None,
+                 pickle_file=None,
                  label_list=None,
                  mode='train',
                  seg_num=8,
                  seg_len=1,
                  transform=None):
-        assert os.path.isfile(file_list), \
-                "file_list {} not a file".format(file_list)
-        with open(file_list) as f:
-            self.pickle_paths = [l.strip() for l in f]
-
-        assert os.path.isdir(pickle_dir), \
-                "pickle_dir {} not a directory".format(pickle_dir)
-        self.pickle_dir = pickle_dir
+        assert str.lower(mode) in ['train', 'val', 'test'], \
+                "mode can only be 'train' 'val' or 'test'"
+        self.mode = str.lower(mode)
+
+        if self.mode in ['train', 'val']:
+            assert os.path.isfile(file_list), \
+                    "file_list {} not a file".format(file_list)
+            with open(file_list) as f:
+                self.pickle_paths = [l.strip() for l in f]
+
+            assert os.path.isdir(pickle_dir), \
+                    "pickle_dir {} not a directory".format(pickle_dir)
+            self.pickle_dir = pickle_dir
+        else:
+            assert os.path.isfile(pickle_file), \
+                    "pickle_file {} not a file".format(pickle_file)
+            self.pickle_dir = ''
+            self.pickle_paths = [pickle_file]
 
         self.label_list = label_list
         if self.label_list is not None:
@@ -79,10 +90,6 @@ class KineticsDataset(Dataset):
             with open(self.label_list) as f:
                 self.label_list = [int(l.strip()) for l in f]
 
-        assert mode in ['train', 'val'], \
-                "mode can only be 'train' or 'val'"
-        self.mode = mode
-
         self.seg_num = seg_num
         self.seg_len = seg_len
         self.transform = transform