Hugging Face's logo

Spaces:
Darknsu
/
SAT
Sleeping

App Files Community
Darknsu commited on
Commit
3974320
·
verified ·
1 Parent(s): 7f32c66

Update dataset.py

Browse files
Files changed (1) hide show
  1. dataset.py +12 -12
dataset.py CHANGED
@@ -97,7 +97,7 @@ class VideoDataSet(data.Dataset):
97
  self.feature_rgb_file = {}
98
  self.feature_flow_file = {}
99
  for file in self.video_list:
100
- feature_path = opt["video_feature_all_train"] + file + '.npz'
101
  if not os.path.exists(feature_path):
102
  raise ValueError(f"Feature file {feature_path} not found")
103
  feature_All[file] = np.load(feature_path)['feats']
@@ -110,7 +110,7 @@ class VideoDataSet(data.Dataset):
110
  self.feature_rgb_file = {}
111
  self.feature_flow_file = {}
112
  for file in self.video_list:
113
- feature_path = opt["video_feature_all_train"] + file + '.npz'
114
  if not os.path.exists(feature_path):
115
  raise ValueError(f"Feature file {feature_path} not found")
116
  feature_All[file] = np.load(feature_path)
@@ -123,7 +123,7 @@ class VideoDataSet(data.Dataset):
123
  self.feature_rgb_file = {}
124
  self.feature_flow_file = {}
125
  for file in self.video_list:
126
- feature_path = opt["video_feature_all_train"] + file + '.pt'
127
  if not os.path.exists(feature_path):
128
  raise ValueError(f"Feature file {feature_path} not found")
129
  feature_All[file] = torch.load(feature_path)
@@ -164,7 +164,7 @@ class VideoDataSet(data.Dataset):
164
  self.feature_rgb_file = {}
165
  self.feature_flow_file = {}
166
  for file in self.video_list:
167
- feature_path = opt["video_feature_all_test"] + file + '.npz'
168
  if not os.path.exists(feature_path):
169
  raise ValueError(f"Feature file {feature_path} not found")
170
  feature_All[file] = np.load(feature_path)['feats']
@@ -177,7 +177,7 @@ class VideoDataSet(data.Dataset):
177
  self.feature_rgb_file = {}
178
  self.feature_flow_file = {}
179
  for file in self.video_list:
180
- feature_path = opt["video_feature_all_test"] + file + '.npz'
181
  if not os.path.exists(feature_path):
182
  raise ValueError(f"Feature file {feature_path} not found")
183
  feature_All[file] = np.load(feature_path)
@@ -190,7 +190,7 @@ class VideoDataSet(data.Dataset):
190
  self.feature_rgb_file = {}
191
  self.feature_flow_file = {}
192
  for file in self.video_list:
193
- feature_path = opt["video_feature_all_test"] + file + '.pt'
194
  if not os.path.exists(feature_path):
195
  raise ValueError(f"Feature file {feature_path} not found")
196
  feature_All[file] = torch.load(feature_path)
@@ -213,15 +213,15 @@ class VideoDataSet(data.Dataset):
213
  elif opt['data_format'] == "npz":
214
  feature_file = {}
215
  for file in self.video_list:
216
- feature_file[file] = np.load(opt["video_feature_all_train"] + file + '.npz')['feats']
217
  elif opt['data_format'] == "npz_i3d":
218
  feature_file = {}
219
  for file in self.video_list:
220
- feature_file[file] = np.load(opt["video_feature_all_train"] + file + '.npz')
221
  elif opt['data_format'] == "pt":
222
  feature_file = {}
223
  for file in self.video_list:
224
- feature_file[file] = torch.load(opt["video_feature_all_train"] + file + '.pt')
225
  else:
226
  if opt['data_format'] == "h5":
227
  feature_file = h5py.File(opt["video_feature_rgb_test"], 'r')
@@ -230,15 +230,15 @@ class VideoDataSet(data.Dataset):
230
  elif opt['data_format'] == "npz":
231
  feature_file = {}
232
  for file in self.video_list:
233
- feature_file[file] = np.load(opt["video_feature_all_test"] + file + '.npz')['feats']
234
  elif opt['data_format'] == "npz_i3d":
235
  feature_file = {}
236
  for file in self.video_list:
237
- feature_file[file] = np.load(opt["video_feature_all_test"] + file + '.npz')
238
  elif opt['data_format'] == "pt":
239
  feature_file = {}
240
  for file in self.video_list:
241
- feature_file[file] = torch.load(opt["video_feature_all_test"] + file + '.pt')
242
 
243
  keys = self.video_list
244
  if opt['data_format'] == "h5":
 
97
  self.feature_rgb_file = {}
98
  self.feature_flow_file = {}
99
  for file in self.video_list:
100
+ feature_path = os.path.join(opt["video_feature_all_train"], file + '.npz')
101
  if not os.path.exists(feature_path):
102
  raise ValueError(f"Feature file {feature_path} not found")
103
  feature_All[file] = np.load(feature_path)['feats']
 
110
  self.feature_rgb_file = {}
111
  self.feature_flow_file = {}
112
  for file in self.video_list:
113
+ feature_path = os.path.join(opt["video_feature_all_train"], file + '.npz')
114
  if not os.path.exists(feature_path):
115
  raise ValueError(f"Feature file {feature_path} not found")
116
  feature_All[file] = np.load(feature_path)
 
123
  self.feature_rgb_file = {}
124
  self.feature_flow_file = {}
125
  for file in self.video_list:
126
+ feature_path = os.path.join(opt["video_feature_all_train"], file + '.pt')
127
  if not os.path.exists(feature_path):
128
  raise ValueError(f"Feature file {feature_path} not found")
129
  feature_All[file] = torch.load(feature_path)
 
164
  self.feature_rgb_file = {}
165
  self.feature_flow_file = {}
166
  for file in self.video_list:
167
+ feature_path = os.path.join(opt["video_feature_all_test"], file + '.npz')
168
  if not os.path.exists(feature_path):
169
  raise ValueError(f"Feature file {feature_path} not found")
170
  feature_All[file] = np.load(feature_path)['feats']
 
177
  self.feature_rgb_file = {}
178
  self.feature_flow_file = {}
179
  for file in self.video_list:
180
+ feature_path = os.path.join(opt["video_feature_all_test"], file + '.npz')
181
  if not os.path.exists(feature_path):
182
  raise ValueError(f"Feature file {feature_path} not found")
183
  feature_All[file] = np.load(feature_path)
 
190
  self.feature_rgb_file = {}
191
  self.feature_flow_file = {}
192
  for file in self.video_list:
193
+ feature_path = os.path.join(opt["video_feature_all_test"], file + '.pt')
194
  if not os.path.exists(feature_path):
195
  raise ValueError(f"Feature file {feature_path} not found")
196
  feature_All[file] = torch.load(feature_path)
 
213
  elif opt['data_format'] == "npz":
214
  feature_file = {}
215
  for file in self.video_list:
216
+ feature_file[file] = np.load(os.path.join(opt["video_feature_all_train"], file + '.npz'))['feats']
217
  elif opt['data_format'] == "npz_i3d":
218
  feature_file = {}
219
  for file in self.video_list:
220
+ feature_file[file] = np.load(os.path.join(opt["video_feature_all_train"], file + '.npz'))
221
  elif opt['data_format'] == "pt":
222
  feature_file = {}
223
  for file in self.video_list:
224
+ feature_file[file] = torch.load(os.path.join(opt["video_feature_all_train"], file + '.pt'))
225
  else:
226
  if opt['data_format'] == "h5":
227
  feature_file = h5py.File(opt["video_feature_rgb_test"], 'r')
 
230
  elif opt['data_format'] == "npz":
231
  feature_file = {}
232
  for file in self.video_list:
233
+ feature_file[file] = np.load(os.path.join(opt["video_feature_all_test"], file + '.npz'))['feats']
234
  elif opt['data_format'] == "npz_i3d":
235
  feature_file = {}
236
  for file in self.video_list:
237
+ feature_file[file] = np.load(os.path.join(opt["video_feature_all_test"], file + '.npz'))
238
  elif opt['data_format'] == "pt":
239
  feature_file = {}
240
  for file in self.video_list:
241
+ feature_file[file] = torch.load(os.path.join(opt["video_feature_all_test"], file + '.pt'))
242
 
243
  keys = self.video_list
244
  if opt['data_format'] == "h5":