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@ -10,27 +10,40 @@ def conv3d_forward_naive(input, filter, group, conv_param):
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assert np.mod(out_c, group) == 0
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sub_out_c = out_c / group
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stride, pad = conv_param['stride'], conv_param['pad']
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out_d = 1 + (in_d + 2 * pad[0] - f_h) / stride[0]
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out_h = 1 + (in_h + 2 * pad[1] - f_h) / stride[1]
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out_w = 1 + (in_w + 2 * pad[2] - f_w) / stride[2]
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stride, pad, dilation = conv_param['stride'], conv_param['pad'], conv_param[
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'dilations']
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out_d = 1 + (in_d + 2 * pad[0] - (dilation[0] * (f_d - 1) + 1)) / stride[0]
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out_h = 1 + (in_h + 2 * pad[1] - (dilation[1] * (f_h - 1) + 1)) / stride[1]
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out_w = 1 + (in_w + 2 * pad[2] - (dilation[2] * (f_w - 1) + 1)) / stride[2]
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out = np.zeros((in_n, out_c, out_d, out_h, out_w))
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d_bolck_d = (dilation[0] * (f_d - 1) + 1)
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d_bolck_h = (dilation[1] * (f_h - 1) + 1)
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d_bolck_w = (dilation[2] * (f_w - 1) + 1)
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input_pad = np.pad(input, ((0, ), (0, ), (pad[0], ), (pad[1], ),
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(pad[2], )),
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mode='constant',
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constant_values=0)
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filter_dilation = np.zeros((out_c, f_c, d_bolck_d, d_bolck_h, d_bolck_w))
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filter_dilation[:, :, 0:d_bolck_d:dilation[0], 0:d_bolck_h:dilation[1], 0:
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d_bolck_w:dilation[2]] = filter
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for d in range(out_d):
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for i in range(out_h):
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for j in range(out_w):
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for g in range(group):
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input_pad_masked = \
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input_pad[:, g * f_c:(g + 1) * f_c,
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d * stride[0]:d * stride[0] + f_d,
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i * stride[1]:i * stride[1] + f_h,
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j * stride[2]:j * stride[2] + f_w]
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f_sub = filter[g * sub_out_c:(g + 1) *
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sub_out_c, :, :, :, :]
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d * stride[0]:d * stride[0] + d_bolck_d,
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i * stride[1]:i * stride[1] + d_bolck_h,
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j * stride[2]:j * stride[2] + d_bolck_w]
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f_sub = filter_dilation[g * sub_out_c:(g + 1) *
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sub_out_c, :, :, :, :]
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for k in range(sub_out_c):
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out[:, g * sub_out_c + k, d, i, j] = \
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np.sum(input_pad_masked * f_sub[k, :, :, :, :],
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@ -43,9 +56,14 @@ class TestConv3dOp(OpTest):
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def setUp(self):
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self.init_group()
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self.init_op_type()
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self.init_dilation()
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self.init_test_case()
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conv3d_param = {'stride': self.stride, 'pad': self.pad}
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conv3d_param = {
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'stride': self.stride,
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'pad': self.pad,
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'dilations': self.dilations
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}
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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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output = conv3d_forward_naive(input, filter, self.groups,
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@ -55,7 +73,8 @@ class TestConv3dOp(OpTest):
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self.attrs = {
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'strides': self.stride,
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'paddings': self.pad,
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'groups': self.groups
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'groups': self.groups,
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'dilations': self.dilations
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}
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self.outputs = {'Output': output}
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@ -88,6 +107,9 @@ class TestConv3dOp(OpTest):
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f_c = self.input_size[1] / self.groups
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self.filter_size = [6, f_c, 3, 3, 3]
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def init_dilation(self):
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self.dilations = [1, 1, 1]
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def init_group(self):
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self.groups = 1
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@ -104,27 +126,47 @@ class TestCase1(TestConv3dOp):
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f_c = self.input_size[1] / self.groups
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self.filter_size = [6, f_c, 3, 3, 3]
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def init_group(self):
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self.groups = 1
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def init_op_type(self):
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self.op_type = "conv3d"
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class TestWithGroup1(TestConv3dOp):
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def init_group(self):
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self.groups = 3
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class TestWithGroup1(TestConv3dOp):
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class TestWithGroup2(TestCase1):
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def init_group(self):
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self.groups = 3
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def init_op_type(self):
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self.op_type = "conv3d"
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class TestWith1x1(TestConv3dOp):
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def init_test_case(self):
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self.pad = [0, 0, 0]
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self.stride = [1, 1, 1]
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self.input_size = [2, 3, 4, 4, 4] # NCHW
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assert np.mod(self.input_size[1], self.groups) == 0
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f_c = self.input_size[1] / self.groups
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self.filter_size = [6, f_c, 1, 1, 1]
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def init_dilation(self):
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self.dilations = [1, 1, 1]
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class TestWithGroup2(TestCase1):
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def init_group(self):
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self.groups = 3
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def init_op_type(self):
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self.op_type = "conv3d"
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class TestWithDilation(TestConv3dOp):
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def init_test_case(self):
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self.pad = [0, 0, 0]
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self.stride = [1, 1, 1]
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self.input_size = [2, 3, 6, 6, 6] # NCDHW
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assert np.mod(self.input_size[1], self.groups) == 0
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f_c = self.input_size[1] / self.groups
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self.filter_size = [6, f_c, 2, 2, 2]
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def init_dilation(self):
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self.dilations = [2, 2, 2]
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def init_group(self):
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self.groups = 3
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if __name__ == '__main__':
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