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
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
|
#include "caffe2/operators/cast_op.h"
namespace caffe2 {
template <typename DstType, typename SrcType>
struct CastHelper {
static DstType call(SrcType data) {
return static_cast<DstType>(data);
}
};
template <typename SrcType>
struct CastHelper<std::string, SrcType> {
static std::string call(SrcType data) {
return caffe2::to_string(data);
}
};
template <>
template <typename DstType, typename SrcType>
bool CastOp<CPUContext>::DoRunWithType() {
auto& input = Input(0);
auto* output = Output(0, input.sizes(), at::dtype<DstType>());
const auto* data = input.template data<SrcType>();
auto* out = output->template mutable_data<DstType>();
auto N = input.numel();
for (int64_t i = 0; i < N; ++i) {
out[i] = CastHelper<DstType, SrcType>::call(data[i]);
}
return true;
}
template <>
void CastOp<CPUContext>::SetBody(TensorProto_DataType to) {
switch (to) {
case TensorProto_DataType_FLOAT:
// body_ = &CastOp::DoRunIncFp16WithDstType<float>;
body_ = &CastOp<CPUContext>::DoRunWithDstType<float>;
break;
case TensorProto_DataType_INT32:
body_ = &CastOp<CPUContext>::DoRunWithDstType<int>;
break;
case TensorProto_DataType_BYTE:
LOG(FATAL) << "BYTE is deprecated";
break;
case TensorProto_DataType_STRING:
body_ = &CastOp<CPUContext>::DoRunWithDstType<std::string>;
break;
case TensorProto_DataType_BOOL:
body_ = &CastOp<CPUContext>::DoRunWithDstType<bool>;
break;
case TensorProto_DataType_UINT8:
body_ = &CastOp<CPUContext>::DoRunWithDstType<uint8_t>;
break;
case TensorProto_DataType_INT8:
body_ = &CastOp<CPUContext>::DoRunWithDstType<int8_t>;
break;
case TensorProto_DataType_UINT16:
body_ = &CastOp<CPUContext>::DoRunWithDstType<uint16_t>;
break;
case TensorProto_DataType_INT16:
body_ = &CastOp<CPUContext>::DoRunWithDstType<int16_t>;
break;
case TensorProto_DataType_INT64:
body_ = &CastOp<CPUContext>::DoRunWithDstType<int64_t>;
break;
case TensorProto_DataType_FLOAT16:
CAFFE_THROW("Casting to and from at::Half on CPU is not supported yet");
// break;
case TensorProto_DataType_DOUBLE:
// body_ = &CastOp::DoRunIncFp16WithDstType<double>;
body_ = &CastOp<CPUContext>::DoRunWithDstType<double>;
break;
case TensorProto_DataType_UNDEFINED:
CAFFE_THROW("Cast op must have 'to' argument of type DataType");
// break;
default:
CAFFE_THROW("Unexpected 'to' argument value: ", to);
}
}
template <>
template <typename DstType>
bool CastOp<CPUContext>::DoRunWithDstType() {
return DispatchHelper<
TensorTypes<
float,
int32_t,
bool,
uint8_t,
int8_t,
uint16_t,
int16_t,
int64_t,
double>,
DstType>::call(this, Input(0));
}
REGISTER_CPU_OPERATOR(Cast, CastOp<CPUContext>);
OPERATOR_SCHEMA(Cast)
.NumInputs(1)
.NumOutputs(1)
.TensorInferenceFunction([](const OperatorDef& def,
const vector<TensorShape>& in) {
ArgumentHelper helper(def);
vector<TensorShape> out;
out.push_back(in[0]);
out[0].set_data_type(cast::GetCastDataType(helper, "to"));
return out;
})
.SetDoc(R"DOC(
Casts the elements of a given input tensor to a data type specified by the `to`
argument and returns an output tensor of the same size in the converted type.
The `to` argument must be one of the data types specified in the *DataType*
enum field in the TensorProto message (see below). If the `to` argument is not
provided or is not one of the enumerated types in *DataType*, Caffe2 throws an
Enforce error.
NOTE: Casting from strings is not supported, and casting to strings is only
supported on CPU.
TensorProto *DataType* field:
```
message TensorProto {
...
enum DataType {
UNDEFINED = 0;
FLOAT = 1; // float
INT32 = 2; // int
BYTE = 3; // BYTE, when deserialized, is going to be restored as uint8.
STRING = 4; // string
BOOL = 5; // bool
UINT8 = 6; // uint8_t
INT8 = 7; // int8_t
UINT16 = 8; // uint16_t
INT16 = 9; // int16_t
INT64 = 10; // int64_t
FLOAT16 = 12; // at::Half
DOUBLE = 13; // double
}
```
Github Links:
- https://github.com/pytorch/pytorch/blob/master/caffe2/operators/cast_op.cc
<details>
<summary> <b>Example</b> </summary>
**Code**
```
workspace.ResetWorkspace()
op = core.CreateOperator(
"Cast",
["X"],
["Y"],
to=2
)
workspace.FeedBlob("X", (np.random.rand(3,3)).astype(np.float32)*10)
print("X:", workspace.FetchBlob("X"))
workspace.RunOperatorOnce(op)
print("Y:", workspace.FetchBlob("Y"))
```
**Result**
```
X: [[9.436466 5.8529844 0.54932857]
[1.1583444 2.9936118 0.22950427]
[3.9143739 3.4040766 8.905341 ]]
Y: [[9 5 0]
[1 2 0]
[3 3 8]]
```
</details>
)DOC")
.Arg(
"to",
"*(type: int)* Data type to which the elements of the input tensor are "
"cast. Strictly must be one of the types from *DataType* enum in "
"TensorProto.")
.Input(0, "X", "*(type: Tensor)* Input tensor to be cast.")
.Output(
0,
"Y",
"*(type: Tensor`<'to' type>`)* Output tensor with the same shape as "
"input with type specified by the `to` argument.")
.InheritOnnxSchema();
// Some Casts are compatible with gradients, but for now we don't support it
// GRADIENT_NOT_IMPLEMENTED_YET(Cast);
class GetCastGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
vector<OperatorDef> defs = SingleGradientDef("Cast", "", vector<string>{GO(0)}, vector<string>{GI(0)});
// now modify the arguments in defs[0]
ArgumentHelper argsHelper(def_);
auto to_name = cast::GetCastDataType(argsHelper, "to");
CAFFE_ENFORCE(
argsHelper.HasSingleArgumentOfType<string>("from_type") ||
argsHelper.HasSingleArgumentOfType<int>("from_type"),
"Argument 'from_type' of type int or string"
" is required to get the gradient of CastOp");
auto from_name = cast::GetCastDataType(argsHelper, "from_type");
Argument *to = defs[0].add_arg();
to->set_name("to");
to->set_i(from_name);
Argument *from = defs[0].add_arg();
from->set_name("from_type");
from->set_i(to_name);
return defs;
}
bool CopyArguments() const override {
return false;
}
};
REGISTER_GRADIENT(Cast, GetCastGradient);
} // namespace caffe2
|
