|
1
|
|
|
#!/usr/bin/env python |
|
2
|
|
|
# -*- coding: utf-8 -*- |
|
3
|
|
|
|
|
4
|
|
|
from theano.tensor.var import TensorVariable |
|
5
|
|
|
from deepy.utils.map_dict import MapDict |
|
6
|
|
|
|
|
7
|
|
|
def convert_to_theano_var(obj): |
|
8
|
|
|
""" |
|
9
|
|
|
Convert neural vars to theano vars. |
|
10
|
|
|
:param obj: NeuralVariable or list or dict or tuple |
|
11
|
|
|
:return: theano var, test var, tensor found, neural var found |
|
12
|
|
|
""" |
|
13
|
|
|
from deepy.core.neural_var import NeuralVariable |
|
14
|
|
|
if type(obj) == tuple: |
|
15
|
|
|
return tuple(convert_to_theano_var(list(obj))) |
|
16
|
|
|
if type(obj) == list: |
|
17
|
|
|
unpacked_list = map(convert_to_theano_var, obj) |
|
18
|
|
|
normal_list = [] |
|
19
|
|
|
test_list = [] |
|
20
|
|
|
theano_var_found = False |
|
21
|
|
|
neural_var_found = False |
|
22
|
|
|
for normal_var, tensor_found, neural_found in unpacked_list: |
|
23
|
|
|
normal_list.append(normal_var) |
|
24
|
|
|
if tensor_found: theano_var_found = True |
|
25
|
|
|
if neural_found: neural_var_found = True |
|
26
|
|
|
return normal_list, theano_var_found, neural_var_found |
|
27
|
|
|
elif type(obj) == dict: |
|
28
|
|
|
normal_map = {} |
|
29
|
|
|
theano_var_found = False |
|
30
|
|
|
neural_var_found = False |
|
31
|
|
|
for key in obj: |
|
32
|
|
|
normal_var, tensor_found, neural_found = convert_to_theano_var(obj[key]) |
|
33
|
|
|
normal_map[key] = normal_var |
|
34
|
|
|
if tensor_found: theano_var_found = True |
|
35
|
|
|
if neural_found: neural_var_found = True |
|
36
|
|
|
return normal_map, theano_var_found, neural_var_found |
|
37
|
|
|
elif type(obj) == MapDict: |
|
38
|
|
|
normal_map = {} |
|
39
|
|
|
theano_var_found = False |
|
40
|
|
|
neural_var_found = False |
|
41
|
|
|
for key in obj: |
|
42
|
|
|
normal_var, tensor_found, neural_found = convert_to_theano_var(obj[key]) |
|
43
|
|
|
normal_map[key] = normal_var |
|
44
|
|
|
if tensor_found: theano_var_found = True |
|
45
|
|
|
if neural_found: neural_var_found = True |
|
46
|
|
|
return MapDict(normal_map), theano_var_found, neural_var_found |
|
47
|
|
|
elif type(obj) == NeuralVariable: |
|
48
|
|
|
theano_tensor = obj.tensor |
|
49
|
|
|
theano_tensor.tag.last_dim = obj.dim() |
|
50
|
|
|
return theano_tensor, False, True |
|
51
|
|
|
elif type(obj) == TensorVariable: |
|
52
|
|
|
return obj, True, False |
|
53
|
|
|
elif type(obj) == slice: |
|
54
|
|
|
normal_args = [] |
|
55
|
|
|
theano_var_found = False |
|
56
|
|
|
neural_var_found = False |
|
57
|
|
|
for arg in [obj.start, obj.stop, obj.step]: |
|
58
|
|
|
normal_var, tensor_found, neural_found = convert_to_theano_var(arg) |
|
59
|
|
|
normal_args.append(normal_var) |
|
60
|
|
|
if tensor_found: theano_var_found = True |
|
61
|
|
|
if neural_found: neural_var_found = True |
|
62
|
|
|
return slice(*normal_args), theano_var_found, neural_var_found |
|
63
|
|
|
else: |
|
64
|
|
|
return obj, False, False |
|
65
|
|
|
|
|
66
|
|
|
def convert_to_neural_var(obj): |
|
67
|
|
|
""" |
|
68
|
|
|
Convert object and a test object into neural var. |
|
69
|
|
|
:param obj: tensor or list or dict or tuple |
|
70
|
|
|
:param test_obj: NeuralVar or list or dict or tuple |
|
71
|
|
|
:return: |
|
72
|
|
|
""" |
|
73
|
|
|
from theano.tensor.var import TensorVariable |
|
74
|
|
|
from deepy.core.neural_var import NeuralVariable |
|
75
|
|
|
if type(obj) == list: |
|
76
|
|
|
return [convert_to_neural_var(item) for item in obj] |
|
77
|
|
|
elif type(obj) == tuple: |
|
78
|
|
|
return tuple(convert_to_neural_var(list(obj))) |
|
79
|
|
|
elif type(obj) == dict: |
|
80
|
|
|
merged_map = {} |
|
81
|
|
|
for key in obj: |
|
82
|
|
|
merged_map[key] = convert_to_neural_var(obj[key]) |
|
83
|
|
|
return merged_map |
|
84
|
|
|
elif type(obj) == MapDict: |
|
85
|
|
|
merged_map = {} |
|
86
|
|
|
for key in obj: |
|
87
|
|
|
merged_map[key] = convert_to_neural_var(obj[key]) |
|
88
|
|
|
return MapDict(merged_map) |
|
89
|
|
|
elif type(obj) == TensorVariable: |
|
90
|
|
|
deepy_var = NeuralVariable(obj) |
|
91
|
|
|
if hasattr(obj, 'tag') and hasattr(obj.tag, 'last_dim'): |
|
92
|
|
|
deepy_var.output_dim = obj.tag.last_dim |
|
93
|
|
|
return deepy_var |
|
94
|
|
|
else: |
|
95
|
|
|
return obj |
|
96
|
|
|
|
|
97
|
|
|
def neural_computation(original_func, prefer_tensor=False): |
|
98
|
|
|
""" |
|
99
|
|
|
An annotation to enable theano-based fucntions to be called with NeuralVar. |
|
100
|
|
|
:param original_func: |
|
101
|
|
|
:param prefer_tensor: a switch to return tensors when no inputs |
|
102
|
|
|
:return: |
|
103
|
|
|
""" |
|
104
|
|
|
|
|
105
|
|
|
def wrapper(*args, **kwargs): |
|
106
|
|
|
normal_args, tensor_found_in_args, neural_found_in_args = convert_to_theano_var(args) |
|
107
|
|
|
normal_kwargs, tensor_found_in_kwargs, neural_found_in_kwargs = convert_to_theano_var(kwargs) |
|
108
|
|
|
|
|
109
|
|
|
tensor_found = tensor_found_in_args or tensor_found_in_kwargs |
|
110
|
|
|
neural_found = neural_found_in_args or neural_found_in_kwargs |
|
111
|
|
|
|
|
112
|
|
|
if tensor_found and neural_found: |
|
113
|
|
|
raise Exception("Theano tensor variables can not be used together with neural variables.") |
|
114
|
|
|
|
|
115
|
|
|
normal_result = original_func(*normal_args, **normal_kwargs) |
|
116
|
|
|
|
|
117
|
|
|
if tensor_found or (not neural_found and prefer_tensor): |
|
118
|
|
|
# No neural variables are inputted, so output tensors |
|
119
|
|
|
return normal_result |
|
120
|
|
|
else: |
|
121
|
|
|
# Output neural variables, auto set output_dim |
|
122
|
|
|
result_var = convert_to_neural_var(normal_result) |
|
123
|
|
|
if (isinstance(normal_result, TensorVariable) and |
|
124
|
|
|
hasattr(normal_result.tag, "test_value") and |
|
125
|
|
|
hasattr(normal_result.tag.test_value, "shape") and |
|
126
|
|
|
normal_result.tag.test_value.shape): |
|
127
|
|
|
result_var.output_dim = normal_result.tag.test_value.shape[-1] |
|
128
|
|
|
return result_var |
|
129
|
|
|
return wrapper |
|
130
|
|
|
|
|
131
|
|
|
def neural_computation_prefer_tensor(original_func): |
|
132
|
|
|
return neural_computation(original_func, prefer_tensor=True) |
zomux /
deepy
