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PaddleDetection
提交 db157eda 编写于 作者: Y Yang Yang(Tony) 提交者： GitHub
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New Op Test framework. (#4962) 

Pass all forward op test
上级 bc151174
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Showing with 157 addition and 638 deletion
paddle/operators/crop_op.cc
浏览文件 @ db157eda
......@@ -59,7 +59,8 @@ class CropOpMaker : public framework::OpProtoAndCheckerMaker {
"The input should be a k-D tensor(k > 0 and k < 7)");
AddInput("Y",
"The input used as reference for cropping"
" with the same dimension as X. ");
" with the same dimension as X. ")
.AsDispensable();
AddOutput("Out",
"The output of crop op "
"with the same dimension as X.");
......
paddle/operators/fc_op.cc 已删除 100644 → 0
浏览文件 @ bc151174
/* Copyright (c) 2016 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. */
#include "paddle/framework/op_registry.h"
#include "paddle/operators/net_op.h"
namespace paddle {
namespace operators {
class FCOp : public NetOp {
public:
FCOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: NetOp(type, inputs, outputs, attrs) {
PADDLE_ENFORCE(!Inputs("X").empty(),
"Inputs(X) of FCOp should not be null.");
PADDLE_ENFORCE(!Inputs("W").empty(),
"Inputs(W) of FCOp should not be null.");
PADDLE_ENFORCE(!Outputs("MulOut").empty(),
"Outputs(MulOut) of FCOp should not be null.");
PADDLE_ENFORCE_NE(Output("Out"), framework::kEmptyVarName,
"Output(Out) of FCOp should not be null.");
auto x = Inputs("X");
auto w = Inputs("W");
auto mul_out = Outputs("MulOut");
PADDLE_ENFORCE_EQ(
x.size(), w.size(),
"The size of inputs X(%d) should be the same as that of weights W(%d).",
x.size(), w.size());
PADDLE_ENFORCE_EQ(mul_out.size(), x.size(),
"The size of intermediate mul_out(%d) should be the same "
"as that of inputs X(%d).",
mul_out.size(), x.size());
size_t n = x.size();
PADDLE_ENFORCE_GE(n, static_cast<size_t>(1),
"The size of inputs X(%d) should be no less than 1.", n);
auto x_num_col_dims = Attr<std::vector<int>>("xNumColDims");
// Set all values or set no values (use the default value)
if (!x_num_col_dims.empty()) {
PADDLE_ENFORCE_EQ(x_num_col_dims.size(), n,
"The size of attribute xNumColDims(%d) should be the "
"same as that of inputs X(%d).",
x_num_col_dims.size(), n);
} else {
x_num_col_dims.resize(n);
for (size_t i = 0; i < n; i++) {
x_num_col_dims[i] = 1;
}
}
// mul_out[i] = X[i] * W[i]
for (size_t i = 0; i < n; i++) {
framework::AttributeMap mul_attr;
mul_attr["x_num_col_dims"] = static_cast<int>(x_num_col_dims[i]);
mul_attr["y_num_col_dims"] = static_cast<int>(1);
AppendOp(
framework::OpRegistry::CreateOp("mul", {{"X", {x[i]}}, {"Y", {w[i]}}},
{{"Out", {mul_out[i]}}}, mul_attr));
}
// sum_out = X[0] * W[0] + ... + X[n-1] * W[n-1]
auto sum_out = mul_out[0];
if (n > 1) {
PADDLE_ENFORCE_NE(Output("SumOut"), framework::kEmptyVarName,
"Output(SumOut) of FCOp should not be null when the "
"size of Inputs(X) > 1.");
sum_out = Output("SumOut");
AppendOp(framework::OpRegistry::CreateOp("sum", {{"X", {mul_out}}},
{{"Out", {sum_out}}}, {}));
} else {
if (Output("SumOut") != framework::kEmptyVarName) {
this->Rename(Output("SumOut"), framework::kEmptyVarName);
}
}
// add_out = sum_out + b
auto b = Input("B");
auto add_out = sum_out;
if (b != framework::kEmptyVarName) {
PADDLE_ENFORCE_NE(
Output("AddOut"), framework::kEmptyVarName,
"Output(AddOut) of FCOp should not be null when Input(B) is set.");
add_out = Output("AddOut");
AppendOp(framework::OpRegistry::CreateOp(
"elementwise_add", {{"X", {sum_out}}, {"Y", {Input("B")}}},
{{"Out", {add_out}}}, {}));
} else {
if (Output("AddOut") != framework::kEmptyVarName) {
this->Rename(Output("AddOut"), framework::kEmptyVarName);
}
}
auto activation = Attr<std::string>("activation");
AppendOp(framework::OpRegistry::CreateOp(activation, {{"X", {add_out}}},
{{"Y", {Output("Out")}}}, {}));
CompleteAddOp(false);
}
};
class FCOpMaker : public framework::OpProtoAndCheckerMaker {
public:
FCOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(A vector of Tensors) each input Tensor can be of arbitrary "
"dimension, and will be reshaped to a 2-D matrix of size "
"(minibatch, number_of_input_features) according to attribute "
"xNumColDims.")
.AsDuplicable();
AddInput("W",
"(A vector of Tensors) the weights of FC operator, a "
"vector of 2-D matrix of size "
"(number_of_input_features, number_of_neurons).")
.AsDuplicable();
AddInput("B",
"(Tensor) the bias of FC operator, a 1-D vector of size "
"number_of_neurons.");
AddOutput("Out",
"(Tensor) the activated output matrix of FC operator, a 2-D "
"matrix of size (minibatch, number_of_neurons).");
AddOutput("MulOut",
"(A vector of Tensors) the intermediate outputs of FC operator, "
"each Tensor saving the product of X_i * W_i.")
.AsIntermediate()
.AsDuplicable();
AddOutput(
"SumOut",
"(Tensor) the intermediate output of FC operator, "
"saving the sum of the products of X and W, that is sum{X_i * W_i}.")
.AsIntermediate();
AddOutput("AddOut",
"(Tensor) the non-actived output of FC operator, "
"saving sum{X_i * W_i} + B.")
.AsIntermediate();
AddAttr<std::string>(
"activation",
"(string, default identity) the activation type of FC operator.")
.SetDefault("identity")
.InEnum({"identity", "sigmoid", "softmax"});
AddAttr<std::vector<int>>(
"xNumColDims",
"(std::vector<int>) The inputs Tensors of FC operator can be of "
"more than 2 dimensions. In that case, each input Tensor `X_i` will be "
"reshaped to a 2-D matrix. The matrix's first dimension "
"(the length of column) will be the product of `X_i`'s last "
"`xNumColDims_i` dimensions, that is "
"`X_i.dims[0] x ... x X_i.dims[xNumColDims_i - 1]`. "
"The matrix's second dimension (the length of row) will be the product "
"of `X_i`'s first `rank - xNumColDims_i` dimensions, that is "
"`X_i.dims[xNumColDims_i] x ... x X_i.dims[rank - 1]`)")
.SetDefault(std::vector<int>{});
AddComment(R"DOC(
Fully Connected Operator, known as Fully Connected Layer or Inner Product Layer
in Convolutional Neural Networks. Neurons in a fully connected layer have
full connections to all activations in the previous layer.
It computes an inner product of a set of
learned weights with a matrix multiplication followed by a bias offset
(optionally).
Equation:
Out = Act(sum_n{X_i * W_i} + B)
where X_i is Tensor that will be reshaped to a 2-D matrix of size (M x K),
usually M is the minibatch size and K is the number of input features.
W_i is a 2-D matrix of size (K x N), where N means the number of neurons
in the fully connected layer. B is a 1-D vector of size N.
Thus, the output Out is a 2-D matrix of size (M x N).
Activation type can be set to `identity` (default), `sigmoid` or `softmax`.
All the inputs can carry the LoD (Level of Details) information,
or not. But the output only shares the LoD with first input (`X[0]`).
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(fc, ops::FCOp, ops::FCOpMaker);
paddle/operators/gru_unit_op.cc
浏览文件 @ db157eda
......@@ -54,8 +54,7 @@ class GRUUnitOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(
weight_width, frame_size * 3,
"The shape of Weight matrix must be [frame_size, frame_size * 3].");
auto bias = Input("Bias");
if (bias != framework::kEmptyVarName) {
if (ctx->HasInput("Bias")) {
auto bias_dims = ctx->GetInputDim("Bias");
int bias_height = bias_dims[0];
int bias_width = bias_dims[1];
......@@ -89,7 +88,8 @@ class GRUUnitOpMaker : public framework::OpProtoAndCheckerMaker {
"weights of output candidate with shape [frame_size, frame_size]");
AddInput("Bias",
"(Tensor) Bias vector with shape [1, frame_size * 3] concating "
"bias of the update gate, reset gate and output candidate.");
"bias of the update gate, reset gate and output candidate.")
.AsDispensable();
AddOutput("Gate",
"(Tensor) Matrix with shape [batch_size, frame_size * 3] for the "
"output of update gate, reset gate and output candidate")
......
paddle/operators/identity_op.cc 已删除 100644 → 0
浏览文件 @ bc151174
/* Copyright (c) 2016 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. */
#include "paddle/operators/net_op.h"
#include "paddle/operators/scale_op.h"
namespace paddle {
namespace operators {
// The identity operator is an alias of the scale operator. This is also an
// example for creating an alias for an existing operator.
template <typename AttrType>
class IdentityOpMaker : public framework::OpProtoAndCheckerMaker {
public:
IdentityOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input tensor of identity operator.");
AddOutput("Y", "The output tensor of identity operator.");
AddComment(R"DOC(
The identity operator is an alias of the scale operator
with the attribute scale fixed to 1.0.
)DOC");
}
};
template <typename AttrType>
class IdentityOp : public NetOp {
public:
IdentityOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: NetOp(type, inputs, outputs, attrs) {
PADDLE_ENFORCE_NE(Input("X"), framework::kEmptyVarName,
"Input(X) of IdentityOp should not be null.");
PADDLE_ENFORCE_NE(Output("Y"), framework::kEmptyVarName,
"Output(Y) of IdentityOp should not be null.");
AppendOp(framework::OpRegistry::CreateOp(
"scale", {{"X", {Input("X")}}}, {{"Out", {Output("Y")}}},
{{"scale", static_cast<AttrType>(1)}}));
CompleteAddOp(false);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(identity, ops::IdentityOp<float>,
ops::IdentityOpMaker<float>);
paddle/operators/interp_op.cc 已删除 100644 → 0
浏览文件 @ bc151174
/* Copyright (c) 2016 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. */
#include "paddle/framework/op_registry.h"
#include "paddle/operators/net_op.h"
namespace paddle {
namespace operators {
class InterpOp : public NetOp {
public:
InterpOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: NetOp(type, inputs, outputs, attrs) {
PADDLE_ENFORCE_NE(Input("X"), framework::kEmptyVarName,
"Input(X) of InterpOp should not be null.");
PADDLE_ENFORCE_NE(Input("Y"), framework::kEmptyVarName,
"Input(Y) of InterpOp should not be null.");
PADDLE_ENFORCE_NE(Input("W"), framework::kEmptyVarName,
"Input(W) of InterpOp should not be null.");
PADDLE_ENFORCE_NE(Output("SubOut"), framework::kEmptyVarName,
"Output(SubOut) of InterpOp should not be null.");
PADDLE_ENFORCE_NE(Output("MulOut"), framework::kEmptyVarName,
"Output(MulOut) of InterpOp should not be null.");
PADDLE_ENFORCE_NE(Output("Out"), framework::kEmptyVarName,
"Output(Out) of InterpOp should not be null.");
// SubOut = X - Y
auto x = Input("X");
auto y = Input("Y");
auto sub_out = Output("SubOut");
AppendOp(framework::OpRegistry::CreateOp(
"elementwise_sub", {{"X", {x}}, {"Y", {y}}}, {{"Out", {sub_out}}}, {}));
// MulOut = SubOut * W = (X - Y) * W
auto w = Input("W");
auto mul_out = Output("MulOut");
AppendOp(framework::OpRegistry::CreateOp(
"elementwise_mul", {{"X", {sub_out}}, {"Y", {w}}}, {{"Out", {mul_out}}},
{{"axis", 0}}));
// Out = MulOut + Y = (X - Y) * W + Y = X * W + Y * (1 - W)
AppendOp(framework::OpRegistry::CreateOp("elementwise_add",
{{"X", {mul_out}}, {"Y", {y}}},
{{"Out", {Output("Out")}}}, {}));
CompleteAddOp(false);
}
};
class InterpOpMaker : public framework::OpProtoAndCheckerMaker {
public:
InterpOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(Tensor), 2-D Matrix of shape [batch_size, data_dim]"
"containing data samples, the first input of interp_op");
AddInput("Y",
"(Tensor), 2-D Matrix of shape `[batch_size, data_dim]`"
"containing data samples, the second input of interp_op");
AddInput("W",
"(Tensor), 1-D Vector of shape [batch_size],"
"the interpolated values in the half-open interval [0.0, 1.0)");
AddOutput("SubOut",
"(Tensor), the intermediate subtraction outputs, saving X - Y.")
.AsIntermediate();
AddOutput("MulOut",
"(Tensor), the intermediate multiplication outputs,"
"saving the elementwise multiplication of (X - Y) and W.")
.AsIntermediate();
AddOutput("Out",
"(Tensor), the output of interp_op, same shape with X,"
"returns the first-dimensional piecewise linear interpolant "
"between X and Y");
AddComment(R"DOC(
Linear Interpolation with two inputs, used in NEURAL TURING MACHINE.
Equation:
Out.row[i] = X.row[i] * W[i] + Y.row[i] * (1 - W[i])
= (X.row[i] - Y.row[i]) * W[i] + Y.row[i]
Example:
X = [[1,2],[3,4]],
Y = [[2,1],[4,3]],
W = [0.3, 0.4]
Then, Out = [[1.7,1.3],[3.6,3.4]]
where 1.7 = 1*0.3+2*(1-0.3),
1.3 = 2*0.3+1*(1-0.3),
3.6 = 3*0.4+4*(1-0.4),
3.4 = 4*0.4+3*(1-0.4)
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(interp, ops::InterpOp, ops::InterpOpMaker);
paddle/operators/reduce_op.cc
浏览文件 @ db157eda
......@@ -160,66 +160,6 @@ class ReduceMinOpMaker : public ReduceOpMaker {
}
};
class NormOp : public NetOp {
public:
NormOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: NetOp(type, inputs, outputs, attrs) {
PADDLE_ENFORCE_NE(Input("X"), framework::kEmptyVarName,
"Input(X) of NormOp should not be null.");
PADDLE_ENFORCE_NE(Output("AbsOut"), framework::kEmptyVarName,
"Output(AbsOut) of NormOp should not be null.");
PADDLE_ENFORCE_NE(Output("PowOut"), framework::kEmptyVarName,
"Output(PowOut) of NormOp should not be null.");
PADDLE_ENFORCE_NE(Output("SumOut"), framework::kEmptyVarName,
"Output(SumOut) of NormOp should not be null.");
PADDLE_ENFORCE_NE(Output("Out"), framework::kEmptyVarName,
"Output(Out) of NormOp should not be null.");
auto dim = Attr<int>("dim");
auto keep_dim = Attr<bool>("keep_dim");
auto p = Attr<float>("p");
PADDLE_ENFORCE_GT(p, 0, "Order of the norm should be positive.");
AppendOp(framework::OpRegistry::CreateOp("abs", {{"X", {Input("X")}}},
{{"Y", {Output("AbsOut")}}}, {}));
AppendOp(framework::OpRegistry::CreateOp("pow", {{"X", {Output("AbsOut")}}},
{{"Y", {Output("PowOut")}}},
{{"factor", p}}));
framework::AttributeMap sum_attr;
sum_attr["dim"] = dim;
sum_attr["keep_dim"] = keep_dim;
AppendOp(framework::OpRegistry::CreateOp(
"reduce_sum", {{"X", {Output("PowOut")}}},
{{"Out", {Output("SumOut")}}}, sum_attr));
AppendOp(framework::OpRegistry::CreateOp(
"pow", {{"X", {Output("SumOut")}}}, {{"Y", {Output("Out")}}},
{{"factor", static_cast<float>(1. / p)}}));
CompleteAddOp(false);
}
};
class NormOpMaker : public ReduceOpMaker {
public:
NormOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: ReduceOpMaker(proto, op_checker) {
AddOutput("AbsOut",
"(Tensor) The intermediate output of Norm operator, "
"saving the absolute value of the input tensor X.")
.AsIntermediate();
AddOutput("PowOut",
"(Tensor) The intermediate output of Norm operator, "
"saving the p-th power of the output tensor AbsOut.")
.AsIntermediate();
AddOutput("SumOut",
"(Tensor) the intermediate output of Norm operator, "
"saving the sum of PowOut reduced on the given dimension.")
.AsIntermediate();
AddAttr<float>("p", "(float, default 2) The order of Norm.").SetDefault(2);
SetComment("Norm", "vector p-norm");
AddComment(comment_);
}
};
} // namespace operators
} // namespace paddle
......@@ -237,8 +177,6 @@ REGISTER_OP(reduce_max, ops::ReduceOp, ops::ReduceMaxOpMaker, reduce_max_grad,
REGISTER_OP(reduce_min, ops::ReduceOp, ops::ReduceMinOpMaker, reduce_min_grad,
ops::ReduceGradOp);
REGISTER_OP_WITHOUT_GRADIENT(norm, ops::NormOp, ops::NormOpMaker);
#define REGISTER_REDUCE_CPU_KERNEL(reduce_type, functor, grad_functor) \
REGISTER_OP_CPU_KERNEL( \
reduce_type, \
......
paddle/operators/smooth_l1_loss_op.cc
浏览文件 @ db157eda
......@@ -62,11 +62,13 @@ class SmoothL1LossOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("InsideWeight",
"Optional input tensor of smooth l1 loss op with the same shape "
"as X. If provided, the result of (X - Y) will be multiplied "
"by this tensor element by element.");
"by this tensor element by element.")
.AsDispensable();
AddInput("OutsideWeight",
"Optinal input of smooth l1 loss op with the same shape as X."
"If provided, the output smooth l1 loss will be multiplied by "
"this tensor element by element.");
"this tensor element by element.")
.AsDispensable();
AddOutput("Diff", "Intermediate variable to cache InsideWeight*(X-Y).")
.AsIntermediate();
AddOutput("Out", "Smooth l1 loss.");
......
python/paddle/v2/framework/framework.py
浏览文件 @ db157eda
......@@ -191,32 +191,33 @@ class Operator(object):
"`type` to initilized an Operator can not be None.")
self.desc.set_type(type)
proto = OpProtoHolder.instance().get_op_proto(type)
if inputs is not None:
given = set()
need = set()
for n in inputs:
given.add(n)
for m in proto.inputs:
need.add(m.name)
if not given == need:
raise ValueError(
"Incorrect setting for input(s) of operator \"%s\". Need: [%s] Given: [%s]"
% (type, ", ".join(str(e) for e in need), ", ".join(
str(e) for e in given)))
def find_name(var_list, name):
for var_name in var_list:
if var_name == name:
return True
return False
if inputs is not None:
for in_proto in proto.inputs:
found = find_name(inputs, in_proto.name)
assert found or in_proto.dispensable, "Input {} not found".format(
in_proto.name)
if found:
in_argus = inputs[in_proto.name]
if not isinstance(in_argus, list):
in_argus = [in_argus]
if not in_proto.duplicable and len(in_argus) > 1:
raise ValueError(
"Input %s expects only one input, but %d are given." %
(in_proto.name, len(in_argus)))
"Input %s expects only one input, but %d are given."
% (in_proto.name, len(in_argus)))
in_argu_names = []
for argu in in_argus:
in_argu_names.append(argu.name)
self.desc.set_input(in_proto.name, in_argu_names)
else:
self.desc.set_input(in_proto.name, [])
if outputs is not None:
given = set()
......@@ -250,10 +251,10 @@ class Operator(object):
attr_name = attr.name
if (not attr_name in attrs) or (attrs[attr_name] is None):
continue
if not isinstance(attrs[attr_name], Block):
self.desc.set_attr(attr_name, attrs[attr_name])
else:
if isinstance(attrs[attr_name], Block):
self.desc.set_block_attr(attr_name, attrs[attr_name].desc)
else:
self.desc.set_attr(attr_name, attrs[attr_name])
self.desc.check_attrs()
if type not in {'feed', 'fetch'}:
......
python/paddle/v2/framework/tests/op_test.py
浏览文件 @ db157eda
......@@ -4,6 +4,8 @@ import random
import itertools
import paddle.v2.framework.core as core
from paddle.v2.framework.op import Operator
from paddle.v2.framework.executor import Executor
from paddle.v2.framework.framework import Program, OpProtoHolder
def grad_var_name(var_name):
......@@ -197,6 +199,48 @@ def get_gradient(scope, op, inputs, outputs, grad_name, place,
return out
def append_input_output(block, op_proto, np_list, is_input):
'''Insert VarDesc and generate Python variable instance'''
proto_list = op_proto.inputs if is_input else op_proto.outputs
def create_var(block, name, np_list, var_proto):
if name not in np_list:
assert var_proto.intermediate, "{} not found".format(name)
shape = None
lod_level = None
else:
np_value = np_list[name]
if isinstance(np_value, tuple):
shape = list(np_value[0].shape)
lod_level = len(np_value[1])
else:
shape = list(np_value.shape)
lod_level = 0
return block.create_var(
dtype="float32", shape=shape, lod_level=lod_level, name=name)
var_dict = {}
for var_proto in proto_list:
var_name = str(var_proto.name)
if is_input:
if (var_name not in np_list) and var_proto.dispensable:
continue
assert (var_name in np_list) or (var_proto.dispensable), \
"Missing {} as input".format(var_name)
if var_proto.duplicable:
assert isinstance(np_list[var_name], list), \
"Duplicable {} should be set as list".format(var_name)
var_list = []
for (name, np_value) in np_list[var_name]:
var_list.append(
create_var(block, name, {name: np_value}, var_proto))
var_dict[var_name] = var_list
else:
var_dict[var_name] = create_var(block, var_name, np_list, var_proto)
return var_dict
class OpTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
......@@ -213,40 +257,85 @@ class OpTest(unittest.TestCase):
np.random.set_state(cls._np_rand_state)
random.setstate(cls._py_rand_state)
def feed_var(self, input_vars, place):
feed_map = {}
for var_name in input_vars:
if isinstance(input_vars[var_name], list):
for name, np_value in self.inputs[var_name]:
tensor = core.LoDTensor()
tensor.set(np_value, place)
feed_map[name] = tensor
else:
tensor = core.LoDTensor()
if isinstance(self.inputs[var_name], tuple):
tensor.set(self.inputs[var_name][0], place)
tensor.set_lod(self.inputs[var_name][1])
else:
tensor.set(self.inputs[var_name], place)
feed_map[var_name] = tensor
return feed_map
def check_output_with_place(self, place, atol):
self.scope = core.Scope()
op_inputs = self.inputs if hasattr(self, "inputs") else dict()
op_outputs = self.outputs if hasattr(self, "outputs") else dict()
op_attrs = self.attrs if hasattr(self, "attrs") else dict()
self.op = create_op(self.scope, self.op_type, op_inputs, op_outputs,
op_attrs)
if isinstance(place, core.GPUPlace) and not self.op.support_gpu():
return
set_input(self.scope, self.op, self.inputs, place)
ctx = core.DeviceContext.create(place)
self.op.run(self.scope, ctx)
op_proto = OpProtoHolder.instance().get_op_proto(self.op_type)
program = Program()
block = program.global_block()
inputs = append_input_output(block, op_proto, self.inputs, True)
outputs = append_input_output(block, op_proto, self.outputs, False)
op = block.append_op(
type=self.op_type,
inputs=inputs,
outputs=outputs,
attrs=self.attrs if hasattr(self, "attrs") else dict())
fetch_list = []
for var_name, var in outputs.iteritems():
if var_name in self.outputs:
if isinstance(var, list):
for v in var:
fetch_list.append(v)
else:
fetch_list.append(var)
feed_map = self.feed_var(inputs, place)
for out_name, out_dup in Operator.get_op_outputs(self.op.type()):
exe = Executor(place)
outs = exe.run(program, feed=feed_map, fetch_list=fetch_list)
for out_name, out_dup in Operator.get_op_outputs(self.op_type):
if out_name not in self.outputs:
continue
def find_actual(target_name, fetch_list):
found = [
i for i, var in enumerate(fetch_list)
if var.name == target_name
]
self.assertTrue(
len(found) == 1, "Found {} {}".format(
len(found), target_name))
return found[0]
if out_dup:
sub_out = self.outputs[out_name]
if not isinstance(sub_out, list):
raise AssertionError("sub_out type %s is not list",
type(sub_out))
for sub_out_name, expect in sub_out:
actual = np.array(
self.scope.find_var(sub_out_name).get_tensor())
idx = find_actual(sub_out_name, fetch_list)
actual = outs[idx]
self.assertTrue(
np.allclose(
actual, expect, atol=atol),
"Output (" + out_name + ") has diff at " + str(place))
"Output (" + sub_out_name + ") has diff at " +
str(place))
else:
actual = np.array(self.scope.find_var(out_name).get_tensor())
idx = find_actual(out_name, fetch_list)
actual = outs[idx]
expect = self.outputs[out_name]
self.assertTrue(
np.allclose(
actual, expect, atol=atol),
......@@ -254,7 +343,7 @@ class OpTest(unittest.TestCase):
def check_output(self, atol=1e-5):
places = [core.CPUPlace()]
if core.is_compile_gpu():
if core.is_compile_gpu() and core.op_support_gpu(self.op_type):
places.append(core.GPUPlace(0))
for place in places:
self.check_output_with_place(place, atol)
......
python/paddle/v2/framework/tests/test_accuracy_op.py
浏览文件 @ db157eda
......@@ -16,7 +16,9 @@ class TestAccuracyOp(OpTest):
if ele == label[rowid]:
num_correct += 1
break
self.outputs = {'Accuracy': [num_correct / float(n)]}
self.outputs = {
'Accuracy': np.array([num_correct / float(n)]).astype("float32")
}
def test_check_output(self):
self.check_output()
......
python/paddle/v2/framework/tests/test_activation_op.py
浏览文件 @ db157eda
......@@ -172,8 +172,8 @@ class TestBRelu(OpTest):
def setUp(self):
self.op_type = "brelu"
x = np.random.uniform(-1, 1, [4, 4]).astype("float32")
t_min = 1
t_max = 4
t_min = 1.0
t_max = 4.0
# The same with TestAbs
x[np.abs(x - t_min) < 0.005] = t_min + 0.02
x[np.abs(x - t_max) < 0.005] = t_max + 0.02
......@@ -218,7 +218,7 @@ class TestSoftRelu(OpTest):
def setUp(self):
self.op_type = "soft_relu"
x = np.random.uniform(-3, 3, [4, 4]).astype("float32")
threshold = 2
threshold = 2.0
# The same reason with TestAbs
x[np.abs(x - threshold) < 0.005] = threshold + 0.02
x[np.abs(x + threshold) < 0.005] = -threshold + 0.02
......@@ -303,7 +303,7 @@ class TestPow(OpTest):
def setUp(self):
self.op_type = "pow"
self.inputs = {'X': np.random.uniform(1, 2, [11, 17]).astype("float32")}
self.attrs = {'factor': 3}
self.attrs = {'factor': 3.0}
self.outputs = {'Y': np.power(self.inputs['X'], 3)}
def test_check_output(self):
......
python/paddle/v2/framework/tests/test_clip_op.py
浏览文件 @ db157eda
......@@ -37,14 +37,14 @@ class TestCase1(TestClipOp):
def initTestCase(self):
self.shape = (8, 16, 8)
self.max = 0.7
self.min = 0
self.min = 0.0
class TestCase2(TestClipOp):
def initTestCase(self):
self.shape = (8, 16)
self.max = 1
self.min = 0
self.max = 1.0
self.min = 0.0
class TestCase3(TestClipOp):
......
python/paddle/v2/framework/tests/test_fc_op.py 已删除 100644 → 0
浏览文件 @ bc151174
import unittest
import numpy as np
from op_test import OpTest
class TestFCOp1(OpTest):
def setUp(self):
x0 = np.random.random((16, 32)).astype("float32")
w0 = np.random.random((32, 10)).astype("float32")
mul_out0 = np.dot(x0, w0)
identity_out = mul_out0
self.op_type = "fc"
self.inputs = {"X": [("X0", x0)], "W": [("W0", w0)]}
self.outputs = {"MulOut": [("MulOut0", mul_out0)], "Out": identity_out}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X0", "W0"], "Out", max_relative_error=0.01)
# FIXME: Disable TestFCOp2 since C++ fc will be removed
# class TestFCOp2(OpTest):
# def setUp(self):
# x0 = np.random.random((16, 4, 8)).astype("float32")
# x1 = np.random.random((4, 4, 32)).astype("float32")
# w0 = np.random.random((32, 10)).astype("float32")
# w1 = np.random.random((32, 10)).astype("float32")
# b = np.random.random(10).astype("float32")
#
# mul_out0 = np.dot(x0.reshape(16, 4 * 8), w0)
# mul_out1 = np.dot(x1.reshape(4 * 4, 32), w1)
# sum_out = mul_out0 + mul_out1
# add_out = np.add(sum_out, b)
# sigmoid_out = 1 / (1 + np.exp(-add_out))
#
# self.op_type = "fc"
# self.inputs = {
# "X": [("X0", x0), ("X1", x1)],
# "W": [("W0", w0), ("W1", w1)],
# "B": b
# }
# self.attrs = {"xNumColDims": [1, 2], "activation": "sigmoid"}
# self.outputs = {
# "MulOut": [("MulOut0", mul_out0), ("MulOut1", mul_out1)],
# "SumOut": sum_out,
# "AddOut": add_out,
# "Out": sigmoid_out
# }
#
# def test_check_output(self):
# self.check_output()
#
# def test_check_grad(self):
# self.check_grad(
# ["X0", "X1", "W0", "W1", "B"], "Out", max_relative_error=0.01)
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_identity_op.py 已删除 100644 → 0
浏览文件 @ bc151174
import unittest
import numpy as np
from op_test import OpTest
class TestIdentityOp(OpTest):
def setUp(self):
self.op_type = "identity"
self.inputs = {'X': np.random.random((10, 10)).astype("float32")}
self.outputs = {'Y': self.inputs['X']}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y')
if __name__ == "__main__":
unittest.main()
python/paddle/v2/framework/tests/test_interp_op.py 已删除 100644 → 0
浏览文件 @ bc151174
import unittest
import numpy as np
from op_test import OpTest
class TestInterpOp(OpTest):
def setUp(self):
self.op_type = "interp"
x = np.random.random((2, 3)).astype("float32")
y = np.random.random((2, 3)).astype("float32")
w = np.random.random(2).astype("float32")
sub_out = x - y
mul_out = sub_out * w.reshape(2, 1)
out = mul_out + y
self.inputs = {'X': x, 'Y': y, 'W': w}
self.outputs = {'Out': out, 'SubOut': sub_out, 'MulOut': mul_out}
def test_check_output(self):
self.check_output()
def test_check_grad_normal(self):
self.check_grad(['X', 'Y'], 'Out')
if __name__ == "__main__":
unittest.main()
python/paddle/v2/framework/tests/test_pad_op.py
浏览文件 @ db157eda
......@@ -27,7 +27,7 @@ class TestPadOp(OpTest):
def initTestCase(self):
self.shape = (16, 16)
self.paddings = [(0, 1), (2, 3)]
self.pad_value = 0
self.pad_value = 0.0
class TestCase1(TestPadOp):
......@@ -41,7 +41,7 @@ class TestCase2(TestPadOp):
def initTestCase(self):
self.shape = (2, 2, 2)
self.paddings = [(0, 0), (0, 0), (1, 2)]
self.pad_value = 1
self.pad_value = 1.0
class TestCase3(TestPadOp):
......
python/paddle/v2/framework/tests/test_reduce_op.py
浏览文件 @ db157eda
......@@ -85,33 +85,5 @@ class Test1DReduce(OpTest):
self.check_grad(['X'], 'Out')
class TestNorm(OpTest):
def setUp(self):
# use x away from 0 to avoid errors of numerical gradient when gradient near 0
x = np.random.random((5, 6, 10)).astype("float32") + 0.2
p = 2
dim = 1
keep_dim = False
abs_out = np.absolute(x)
pow_out = np.power(x, p)
sum_out = np.sum(pow_out, axis=dim, keepdims=keep_dim)
out = np.power(sum_out, 1. / p)
self.op_type = "norm"
self.inputs = {'X': x}
self.attrs = {"p": p, "dim": dim, "keep_dim": keep_dim}
self.outputs = {
"AbsOut": abs_out,
"PowOut": pow_out,
"SumOut": sum_out,
"Out": out
}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out', max_relative_error=0.01)
if __name__ == '__main__':
unittest.main()
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