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test_fc_op.py
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test_fc_op.py
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import unittest
import numpy as np
from op_test import OpTest
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
def fc_refer(matrix, with_bias, with_relu=False):
in_n, in_c, in_h, in_w = matrix.input.shape
w_i, w_o = matrix.weights.shape
x_data = np.reshape(matrix.input, [in_n, in_c * in_h * in_w])
w_data = np.reshape(matrix.weights, [w_i, w_o])
b_data = np.reshape(matrix.bias, [1, w_o])
result = None
if with_bias:
result = np.dot(x_data, w_data) + b_data
else:
result = np.dot(x_data, w_data)
if with_relu:
return np.maximum(result, 0)
else:
return result
class MatrixGenerate:
def __init__(self, mb, ic, oc, h, w, bias_dims=2):
self.input = np.random.random((mb, ic, h, w)).astype("float32")
self.weights = np.random.random((ic * h * w, oc)).astype("float32")
if bias_dims == 2:
self.bias = np.random.random((1, oc)).astype("float32")
else:
self.bias = np.random.random((oc)).astype("float32")
class TestFCOp(OpTest):
def config(self):
self.with_bias = True
self.with_relu = True
self.matrix = MatrixGenerate(1, 10, 15, 3, 3, 2)
def setUp(self):
self.op_type = "fc"
self.config()
if self.with_bias:
self.inputs = {
'Input': self.matrix.input,
'W': self.matrix.weights,
'Bias': self.matrix.bias
}
else:
self.inputs = {'Input': self.matrix.input, 'W': self.matrix.weights}
if self.with_relu:
activation_type = "relu"
else:
activation_type = ""
self.attrs = {'use_mkldnn': False, 'activation_type': activation_type}
self.outputs = {
'Out': fc_refer(self.matrix, self.with_bias, self.with_relu)
}
def test_check_output(self):
self.check_output()
class TestFCOpNoBias1(TestFCOp):
def config(self):
self.with_bias = False
self.with_relu = False
self.matrix = MatrixGenerate(2, 8, 10, 1, 1, 2)
class TestFCOpNoBias2(TestFCOp):
def config(self):
self.with_bias = False
self.with_relu = False
self.matrix = MatrixGenerate(4, 5, 6, 2, 2, 1)
class TestFCOpNoBias4(TestFCOp):
def config(self):
self.with_bias = False
self.with_relu = False
self.matrix = MatrixGenerate(1, 32, 64, 3, 3, 1)
class TestFCOpWithBias1(TestFCOp):
def config(self):
self.with_bias = True
self.with_relu = False
self.matrix = MatrixGenerate(3, 8, 10, 2, 1, 2)
class TestFCOpWithBias2(TestFCOp):
def config(self):
self.with_bias = True
self.with_relu = True
self.matrix = MatrixGenerate(4, 5, 6, 2, 2, 1)
class TestFCOpWithBias3(TestFCOp):
def config(self):
self.with_bias = True
self.with_relu = True
self.matrix = MatrixGenerate(1, 64, 32, 3, 3, 1)
class TestFCOpWithPadding(TestFCOp):
def config(self):
self.with_bias = True
self.with_relu = True
self.matrix = MatrixGenerate(1, 4, 3, 128, 128, 2)
class TestFCOpError(OpTest):
def test_errors(self):
with program_guard(Program(), Program()):
input_data = np.random.random((2, 4)).astype("float32")
def test_Variable():
# the input type must be Variable
fluid.layers.fc(input=input_data, size=1)
self.assertRaises(TypeError, test_Variable)
def test_input_list():
# each of input(list) must be Variable
fluid.layers.fc(input=[input_data], size=1)
self.assertRaises(TypeError, test_input_list)
def test_type():
# dtype must be float32 or float64
x2 = fluid.layers.data(name='x2', shape=[4], dtype='int32')
fluid.layers.fc(input=x2, size=1)
self.assertRaises(TypeError, test_type)
# The input dtype of fc can be float16 in GPU, test for warning
x3 = fluid.layers.data(name='x3', shape=[4], dtype='float16')
fluid.layers.fc(input=x3, size=1)
if __name__ == "__main__":
unittest.main()