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168 lines
4.9 KiB
168 lines
4.9 KiB
import unittest
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import numpy as np
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from op_test import OpTest
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def conv2dtranspose_forward_naive(input_, filter_, attrs):
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in_n, in_c, in_h, in_w = input_.shape
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f_c, out_c, f_h, f_w = filter_.shape
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assert in_c == f_c
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stride, pad, dilations = attrs['strides'], attrs['paddings'], attrs[
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'dilations']
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d_bolck_h = dilations[0] * (f_h - 1) + 1
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d_bolck_w = dilations[1] * (f_w - 1) + 1
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out_h = (in_h - 1) * stride[0] + d_bolck_h
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out_w = (in_w - 1) * stride[1] + d_bolck_w
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out = np.zeros((in_n, out_c, out_h, out_w))
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for n in range(in_n):
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for i in range(in_h):
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for j in range(in_w):
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input_masked = input_[n, :, i, j] # (c)
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input_masked = np.reshape(input_masked, (in_c, 1, 1))
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input_masked = np.tile(input_masked, (1, f_h, f_w))
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for k in range(out_c):
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tmp_out = np.sum(input_masked * filter_[:, k, :, :], axis=0)
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i1, i2 = i * stride[0], i * stride[0] + d_bolck_h
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j1, j2 = j * stride[0], j * stride[0] + d_bolck_h
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out[n, k, i1:i2:dilations[0], j1:j2:dilations[1]] += tmp_out
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out = out[:, :, pad[0]:out_h - pad[0], pad[1]:out_w - pad[1]]
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return out
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class TestConv2dTransposeOp(OpTest):
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def setUp(self):
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# init as conv transpose
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self.init_op_type()
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self.init_test_case()
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input_ = np.random.random(self.input_size).astype("float32")
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filter_ = np.random.random(self.filter_size).astype("float32")
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self.inputs = {'Input': input_, 'Filter': filter_}
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self.attrs = {
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'strides': self.stride,
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'paddings': self.pad,
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'dilations': self.dilations
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}
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output = conv2dtranspose_forward_naive(input_, filter_,
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self.attrs).astype('float32')
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self.outputs = {'Output': output}
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def test_check_output(self):
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self.check_output()
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def test_check_grad_no_input(self):
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self.check_grad(
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['Filter'],
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'Output',
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max_relative_error=0.02,
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no_grad_set=set(['Input']))
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def test_check_grad_no_filter(self):
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self.check_grad(
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['Input'],
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'Output',
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max_relative_error=0.02,
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no_grad_set=set(['Filter']))
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def test_check_grad(self):
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self.check_grad(
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set(['Input', 'Filter']), 'Output', max_relative_error=0.02)
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def init_test_case(self):
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self.pad = [0, 0]
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self.stride = [1, 1]
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self.dilations = [1, 1]
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self.input_size = [2, 3, 5, 5] # NCHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3]
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def init_op_type(self):
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self.op_type = "conv2d_transpose"
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class TestWithPad(TestConv2dTransposeOp):
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def init_test_case(self):
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self.pad = [1, 1]
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self.stride = [1, 1]
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self.dilations = [1, 1]
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self.input_size = [2, 3, 5, 5] # NCHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3]
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class TestWithStride(TestConv2dTransposeOp):
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def init_test_case(self):
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self.pad = [1, 1]
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self.stride = [2, 2]
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self.dilations = [1, 1]
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self.input_size = [2, 3, 5, 5] # NCHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3]
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class TestWithDilation(TestConv2dTransposeOp):
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def init_test_case(self):
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self.pad = [1, 1]
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self.stride = [1, 1]
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self.dilations = [2, 2]
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self.input_size = [2, 3, 5, 5] # NCHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3]
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# ------------ test_cudnn ------------
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class TestCudnn(TestConv2dTransposeOp):
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def init_op_type(self):
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self.op_type = "conv2d_transpose_cudnn"
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class TestCudnnWithPad(TestWithPad):
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def init_test_case(self):
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self.pad = [1, 1]
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self.stride = [1, 1]
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self.dilations = [1, 1]
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self.input_size = [2, 3, 5, 5] # NCHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3]
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def init_op_type(self):
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self.op_type = "conv2d_transpose_cudnn"
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class TestCudnnWithStride(TestWithStride):
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def init_test_case(self):
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self.pad = [1, 1]
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self.stride = [2, 2]
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self.dilations = [1, 1]
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self.input_size = [2, 3, 5, 5] # NCHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3]
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def init_op_type(self):
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self.op_type = "conv2d_transpose_cudnn"
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# #cudnn v5 does not support dilation conv.
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# class TestCudnnWithDilation(TestWithDilation):
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# def init_test_case(self):
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# self.pad = [1, 1]
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# self.stride = [2, 2]
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# self.dilations = [2, 2]
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# self.input_size = [2, 3, 5, 5] # NCHW
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# f_c = self.input_size[1]
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# self.filter_size = [f_c, 6, 3, 3]
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#
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# def init_op_type(self):
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# self.op_type = "conv2d_transpose_cudnn"
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if __name__ == '__main__':
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unittest.main()
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