rm wget dependency, del commented code, add unitest (#613)

* rm wget dependency, del commented code

hub_module/modules/video/classification/videotag_tsn_lstm/README.md

@@ -51,7 +51,7 @@ print(results)
 
 ## 依赖
 
-paddlepaddle >= 1.6.2
+paddlepaddle >= 1.7.2
 
 paddlehub >= 1.6.0
 

hub_module/modules/video/classification/videotag_tsn_lstm/resource/metrics/metrics_util.py

@@ -17,12 +17,11 @@ from __future__ import unicode_literals
 from __future__ import print_function
 from __future__ import division
 
-import os
 import io
 import logging
 
 import numpy as np
-import json
+
 from videotag_tsn_lstm.resource.metrics.youtube8m import eval_util as youtube8m_metrics
 
 logger = logging.getLogger(__name__)

hub_module/modules/video/classification/videotag_tsn_lstm/resource/models/attention_lstm/attention_lstm.py

@@ -12,7 +12,7 @@
 #See the License for the specific language governing permissions and
 #limitations under the License.
 
-import numpy as np
+import logging
 
 import paddle.fluid as fluid
 from paddle.fluid import ParamAttr
@@ -20,10 +20,8 @@ from paddle.fluid import ParamAttr
 from ..model import ModelBase
 from .lstm_attention import LSTMAttentionModel
 
-import logging
-logger = logging.getLogger(__name__)
-
 __all__ = ["AttentionLSTM"]
+logger = logging.getLogger(__name__)
 
 
 class AttentionLSTM(ModelBase):
@@ -51,7 +49,6 @@ class AttentionLSTM(ModelBase):
             self.feature_input.append(
                 fluid.data(
                     shape=[None, dim], lod_level=1, dtype='float32', name=name))
-#        self.label_input = None
         if use_dataloader:
             assert self.mode != 'infer', \
                     'dataloader is not recommendated when infer, please set use_dataloader to be false.'
@@ -138,15 +135,6 @@ class AttentionLSTM(ModelBase):
         )
 
     def load_pretrain_params(self, exe, pretrain, prog, place):
-        #def is_parameter(var):
-        #    return isinstance(var, fluid.framework.Parameter)
-
-        #params_list = list(filter(is_parameter, prog.list_vars()))
-        #for param in params_list:
-        #    print(param.name)
-
-        #assert False, "stop here"
-
         logger.info(
             "Load pretrain weights from {}, exclude fc layer.".format(pretrain))
 
@@ -159,18 +147,3 @@ class AttentionLSTM(ModelBase):
                     'Delete {} from pretrained parameters. Do not load it'.
                     format(name))
         fluid.set_program_state(prog, state_dict)
-
-
-#    def load_test_weights(self, exe, weights, prog):
-#        def is_parameter(var):
-#            return isinstance(var, fluid.framework.Parameter)
-#        params_list = list(filter(is_parameter, prog.list_vars()))
-
-#        state_dict = np.load(weights)
-#        for p in params_list:
-#            if p.name in state_dict.keys():
-#                logger.info('########### load param {} from file'.format(p.name))
-#            else:
-#                logger.info('----------- param {} not in file'.format(p.name))
-#        fluid.set_program_state(prog, state_dict)
-#        fluid.save(prog, './weights/attention_lstm')

hub_module/modules/video/classification/videotag_tsn_lstm/resource/models/attention_lstm/lstm_attention.py

@@ -11,10 +11,8 @@
 #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 paddle.fluid as fluid
 from paddle.fluid import ParamAttr
-import numpy as np
 
 
 class LSTMAttentionModel(object):
@@ -39,15 +37,6 @@ class LSTMAttentionModel(object):
                 initializer=fluid.initializer.NormalInitializer(scale=0.0)),
             name='rgb_fc')
 
-        #lstm_forward_fc = fluid.layers.fc(
-        #    input=input_fc,
-        #    size=self.lstm_size * 4,
-        #    act=None,
-        #    bias_attr=ParamAttr(
-        #        regularizer=fluid.regularizer.L2Decay(0.0),
-        #        initializer=fluid.initializer.NormalInitializer(scale=0.0)),
-        #    name='rgb_fc_forward')
-
         lstm_forward_fc = fluid.layers.fc(
             input=input_fc,
             size=self.lstm_size * 4,
@@ -61,15 +50,6 @@ class LSTMAttentionModel(object):
             is_reverse=False,
             name='rgb_lstm_forward')
 
-        #lsmt_backward_fc = fluid.layers.fc(
-        #    input=input_fc,
-        #    size=self.lstm_size * 4,
-        #    act=None,
-        #    bias_attr=ParamAttr(
-        #        regularizer=fluid.regularizer.L2Decay(0.0),
-        #        initializer=fluid.initializer.NormalInitializer(scale=0.0)),
-        #    name='rgb_fc_backward')
-
         lsmt_backward_fc = fluid.layers.fc(
             input=input_fc,
             size=self.lstm_size * 4,
@@ -91,15 +71,6 @@ class LSTMAttentionModel(object):
             dropout_prob=self.drop_rate,
             is_test=(not is_training))
 
-        #lstm_weight = fluid.layers.fc(
-        #    input=lstm_dropout,
-        #    size=1,
-        #    act='sequence_softmax',
-        #    bias_attr=ParamAttr(
-        #        regularizer=fluid.regularizer.L2Decay(0.0),
-        #        initializer=fluid.initializer.NormalInitializer(scale=0.0)),
-        #    name='rgb_weight')
-
         lstm_weight = fluid.layers.fc(
             input=lstm_dropout,
             size=1,

hub_module/modules/video/classification/videotag_tsn_lstm/resource/models/model.py

@@ -13,7 +13,6 @@
 #limitations under the License.
 
 import os
-import wget
 import logging
 try:
     from configparser import ConfigParser
@@ -21,7 +20,6 @@ except:
     from ConfigParser import ConfigParser
 
 import paddle.fluid as fluid
-from .utils import download, AttrDict
 
 WEIGHT_DIR = os.path.join(os.path.expanduser('~'), '.paddle', 'weights')
 
@@ -103,21 +101,6 @@ class ModelBase(object):
         "get model weight default path and download url"
         raise NotImplementError(self, self.weights_info)
 
-    def get_weights(self):
-        "get model weight file path, download weight from Paddle if not exist"
-        path, url = self.weights_info()
-        path = os.path.join(WEIGHT_DIR, path)
-        if not os.path.isdir(WEIGHT_DIR):
-            logger.info('{} not exists, will be created automatically.'.format(
-                WEIGHT_DIR))
-            os.makedirs(WEIGHT_DIR)
-        if os.path.exists(path):
-            return path
-
-        logger.info("Download weights of {} from {}".format(self.name, url))
-        wget.download(url, path)
-        return path
-
     def dataloader(self):
         return self.dataloader
 
@@ -129,25 +112,6 @@ class ModelBase(object):
         "get pretrain base model directory"
         return (None, None)
 
-    def get_pretrain_weights(self):
-        "get model weight file path, download weight from Paddle if not exist"
-        path, url = self.pretrain_info()
-        if not path:
-            return None
-
-        path = os.path.join(WEIGHT_DIR, path)
-        if not os.path.isdir(WEIGHT_DIR):
-            logger.info('{} not exists, will be created automatically.'.format(
-                WEIGHT_DIR))
-            os.makedirs(WEIGHT_DIR)
-        if os.path.exists(path):
-            return path
-
-        logger.info("Download pretrain weights of {} from {}".format(
-            self.name, url))
-        download(url, path)
-        return path
-
     def load_pretrain_params(self, exe, pretrain, prog, place):
         logger.info("Load pretrain weights from {}".format(pretrain))
         state_dict = fluid.load_program_state(pretrain)

hub_module/modules/video/classification/videotag_tsn_lstm/resource/models/utils.py

-#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
-#
-#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 wget
-import tarfile
-
-__all__ = ['decompress', 'download', 'AttrDict']
-
-
-def decompress(path):
-    t = tarfile.open(path)
-    t.extractall(path=os.path.split(path)[0])
-    t.close()
-    os.remove(path)
-
-
-def download(url, path):
-    weight_dir = os.path.split(path)[0]
-    if not os.path.exists(weight_dir):
-        os.makedirs(weight_dir)
-
-    path = path + ".tar.gz"
-    wget.download(url, path)
-    decompress(path)
-
-
-class AttrDict(dict):
-    def __getattr__(self, key):
-        return self[key]
-
-    def __setattr__(self, key, value):
-        if key in self.__dict__:
-            self.__dict__[key] = value
-        else:
-            self[key] = value

hub_module/modules/video/classification/videotag_tsn_lstm/resource/reader/kinetics_reader.py

@@ -11,23 +11,22 @@
 #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 cv2
-import math
+
 import random
 import functools
+import logging
 try:
     import cPickle as pickle
     from cStringIO import StringIO
 except ImportError:
     import pickle
     from io import BytesIO
-import numpy as np
+
 import paddle
-from PIL import Image, ImageEnhance
-import logging
+import cv2
+import numpy as np
+from PIL import Image
 
 from .reader_utils import DataReader
 

hub_module/modules/video/classification/videotag_tsn_lstm/resource/reader/reader_utils.py

@@ -12,11 +12,6 @@
 #See the License for the specific language governing permissions and
 #limitations under the License.
 
-import pickle
-import cv2
-import numpy as np
-import random
-
 
 class ReaderNotFoundError(Exception):
     "Error: reader not found"

hub_module/modules/video/classification/videotag_tsn_lstm/resource/utils/config_utils.py

@@ -12,9 +12,10 @@
 #See the License for the specific language governing permissions and
 #limitations under the License.
 
-import yaml
-from .utility import AttrDict
 import logging
+
+from .utility import AttrDict
+
 logger = logging.getLogger(__name__)
 
 CONFIG_SECS = [

hub_module/modules/video/classification/videotag_tsn_lstm/resource/utils/train_utils.py

@@ -14,13 +14,12 @@
 
 import os
 import sys
+import logging
+
 import time
 import numpy as np
-import paddle
 import paddle.fluid as fluid
 from paddle.fluid import profiler
-import logging
-import shutil
 
 logger = logging.getLogger(__name__)
 

hub_module/modules/video/classification/videotag_tsn_lstm/resource/utils/utility.py

@@ -16,7 +16,7 @@ import os
 import sys
 import signal
 import logging
-import paddle
+
 import paddle.fluid as fluid
 
 __all__ = ['AttrDict']

hub_module/tests/unittests/test_videotag_tsn_lstm.py

+# coding=utf-8
+import unittest
+import paddlehub as hub
+
+
+class TestVideoTag(unittest.TestCase):
+    def setUp(self):
+        "Call setUp() to prepare environment\n"
+        self.module = hub.Module(name='videotag_tsn_lstm')
+        self.test_video = [
+            "../video_dataset/classification/1.mp4",
+            "../video_dataset/classification/2.mp4"
+        ]
+
+    def test_classification(self):
+        default_expect1 = {
+            '训练': 0.9771281480789185,
+            '蹲': 0.9389840960502625,
+            '杠铃': 0.8554490804672241,
+            '健身房': 0.8479971885681152
+        }
+        default_expect2 = {'舞蹈': 0.8504238724708557}
+        for use_gpu in [True, False]:
+            for threshold in [0.5, 0.9]:
+                for top_k in [10, 1]:
+                    expect1 = {}
+                    expect2 = {}
+                    for key, value in default_expect1.items():
+                        if value >= threshold:
+                            expect1[key] = value
+                        if len(expect1.keys()) >= top_k:
+                            break
+                    for key, value in default_expect2.items():
+                        if value >= threshold:
+                            expect2[key] = value
+                        if len(expect2.keys()) >= top_k:
+                            break
+                    results = self.module.classify(
+                        paths=self.test_video,
+                        use_gpu=use_gpu,
+                        threshold=threshold,
+                        top_k=top_k)
+                    for result in results:
+                        if '1.mp4' in result['path']:
+                            self.assertEqual(result['prediction'], expect1)
+                        else:
+                            self.assertEqual(result['prediction'], expect2)
+
+
+if __name__ == "__main__":
+    unittest.main()