-
Notifications
You must be signed in to change notification settings - Fork 103
/
datasets_factory.py
83 lines (78 loc) · 4.11 KB
/
datasets_factory.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
from core.data_provider import kth_action, mnist, bair
datasets_map = {
'mnist': mnist,
'action': kth_action,
'bair': bair,
}
def data_provider(dataset_name, train_data_paths, valid_data_paths, batch_size,
img_width, seq_length, injection_action, is_training=True):
if dataset_name not in datasets_map:
raise ValueError('Name of dataset unknown %s' % dataset_name)
train_data_list = train_data_paths.split(',')
valid_data_list = valid_data_paths.split(',')
if dataset_name == 'mnist':
test_input_param = {'paths': valid_data_list,
'minibatch_size': batch_size,
'input_data_type': 'float32',
'is_output_sequence': True,
'name': dataset_name + 'test iterator'}
test_input_handle = datasets_map[dataset_name].InputHandle(test_input_param)
test_input_handle.begin(do_shuffle=False)
if is_training:
train_input_param = {'paths': train_data_list,
'minibatch_size': batch_size,
'input_data_type': 'float32',
'is_output_sequence': True,
'name': dataset_name + ' train iterator'}
train_input_handle = datasets_map[dataset_name].InputHandle(train_input_param)
train_input_handle.begin(do_shuffle=True)
return train_input_handle, test_input_handle
else:
return test_input_handle
if dataset_name == 'action':
input_param = {'paths': valid_data_list,
'image_width': img_width,
'minibatch_size': batch_size,
'seq_length': seq_length,
'input_data_type': 'float32',
'name': dataset_name + ' iterator'}
input_handle = datasets_map[dataset_name].DataProcess(input_param)
if is_training:
train_input_handle = input_handle.get_train_input_handle()
train_input_handle.begin(do_shuffle=True)
test_input_handle = input_handle.get_test_input_handle()
test_input_handle.begin(do_shuffle=False)
return train_input_handle, test_input_handle
else:
test_input_handle = input_handle.get_test_input_handle()
test_input_handle.begin(do_shuffle=False)
return test_input_handle
if dataset_name == 'bair':
test_input_param = {'valid_data_paths': valid_data_list,
'train_data_paths': train_data_list,
'batch_size': batch_size,
'image_width': img_width,
'image_height': img_width,
'seq_length': seq_length,
'injection_action': injection_action,
'input_data_type': 'float32',
'name': dataset_name + 'test iterator'}
input_handle_test = datasets_map[dataset_name].DataProcess(test_input_param)
test_input_handle = input_handle_test.get_test_input_handle()
test_input_handle.begin(do_shuffle=False)
if is_training:
train_input_param = {'valid_data_paths': valid_data_list,
'train_data_paths': train_data_list,
'image_width': img_width,
'image_height': img_width,
'batch_size': batch_size,
'seq_length': seq_length,
'injection_action': injection_action,
'input_data_type': 'float32',
'name': dataset_name + ' train iterator'}
input_handle_train = datasets_map[dataset_name].DataProcess(train_input_param)
train_input_handle = input_handle_train.get_train_input_handle()
train_input_handle.begin(do_shuffle=True)
return train_input_handle, test_input_handle
else:
return test_input_handle