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488 lines
15 KiB
488 lines
15 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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import paddle.nn.functional as F
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from paddle import fluid
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import paddle.fluid.dygraph as dg
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import paddle.fluid.initializer as I
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import numpy as np
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import unittest
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from unittest import TestCase
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class TestFunctionalConv3DTranspose(TestCase):
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batch_size = 4
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spatial_shape = (8, 8, 8)
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dtype = "float32"
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output_size = None
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def setUp(self):
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self.in_channels = 3
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self.out_channels = 5
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self.filter_shape = 3
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self.padding = 0
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self.stride = 1
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self.dilation = 1
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self.groups = 1
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self.no_bias = False
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self.act = "sigmoid"
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self.data_format = "NDHWC"
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def prepare(self):
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if isinstance(self.filter_shape, int):
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filter_shape = (self.filter_shape, ) * 3
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else:
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filter_shape = tuple(self.filter_shape)
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self.weight = np.random.uniform(
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-1, 1, (self.in_channels, self.out_channels // self.groups
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) + filter_shape).astype(self.dtype)
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if not self.no_bias:
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self.bias = np.random.uniform(-1, 1, (
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self.out_channels, )).astype(self.dtype)
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self.channel_last = (self.data_format == "NDHWC")
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if self.channel_last:
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self.input_shape = (self.batch_size, ) + self.spatial_shape + (
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self.in_channels, )
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else:
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self.input_shape = (self.batch_size, self.in_channels
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) + self.spatial_shape
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self.input = np.random.uniform(-1, 1,
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self.input_shape).astype(self.dtype)
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def static_graph_case_1(self):
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main = fluid.Program()
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start = fluid.Program()
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with fluid.unique_name.guard():
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with fluid.program_guard(main, start):
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if self.channel_last:
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x = fluid.data(
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"input", (-1, -1, -1, -1, self.in_channels),
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dtype=self.dtype)
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else:
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x = fluid.data(
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"input", (-1, self.in_channels, -1, -1, -1),
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dtype=self.dtype)
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y = fluid.layers.conv3d_transpose(
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x,
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self.out_channels,
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output_size=self.output_size,
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filter_size=self.filter_shape,
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stride=self.stride,
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padding=self.padding,
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dilation=self.dilation,
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groups=self.groups,
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param_attr=I.NumpyArrayInitializer(self.weight),
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bias_attr=False
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if self.no_bias else I.NumpyArrayInitializer(self.bias),
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act=self.act,
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data_format=self.data_format)
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exe = fluid.Executor(self.place)
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exe.run(start)
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out, = exe.run(main, feed={"input": self.input}, fetch_list=[y])
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return out
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def static_graph_case_2(self):
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main = fluid.Program()
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start = fluid.Program()
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with fluid.unique_name.guard():
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with fluid.program_guard(main, start):
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if self.channel_last:
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x = x = fluid.data(
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"input", (-1, -1, -1, -1, self.in_channels),
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dtype=self.dtype)
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else:
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x = fluid.data(
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"input", (-1, self.in_channels, -1, -1, -1),
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dtype=self.dtype)
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weight = fluid.data(
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"weight", self.weight.shape, dtype=self.dtype)
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if not self.no_bias:
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bias = fluid.data("bias", self.bias.shape, dtype=self.dtype)
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y = F.conv_transpose3d(
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x,
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weight,
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None if self.no_bias else bias,
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output_size=self.output_size,
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padding=self.padding,
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stride=self.stride,
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dilation=self.dilation,
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groups=self.groups,
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data_format=self.data_format)
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if self.act == 'sigmoid':
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y = F.sigmoid(y)
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exe = fluid.Executor(self.place)
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exe.run(start)
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feed_dict = {"input": self.input, "weight": self.weight}
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if not self.no_bias:
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feed_dict["bias"] = self.bias
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out, = exe.run(main, feed=feed_dict, fetch_list=[y])
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return out
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def dygraph_case(self):
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with dg.guard(self.place):
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x = dg.to_variable(self.input)
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weight = dg.to_variable(self.weight)
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bias = None if self.no_bias else dg.to_variable(self.bias)
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y = F.conv_transpose3d(
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x,
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weight,
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bias,
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output_size=self.output_size,
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padding=self.padding,
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stride=self.stride,
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dilation=self.dilation,
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groups=self.groups,
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data_format=self.data_format)
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if self.act == 'sigmoid':
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y = F.sigmoid(y)
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out = y.numpy()
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return out
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def _test_identity(self):
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self.prepare()
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out1 = self.static_graph_case_1()
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out2 = self.static_graph_case_2()
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out3 = self.dygraph_case()
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np.testing.assert_array_almost_equal(out1, out2)
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np.testing.assert_array_almost_equal(out2, out3)
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def test_identity_cpu(self):
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self.place = fluid.CPUPlace()
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self._test_identity()
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@unittest.skipIf(not fluid.core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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def test_identity_gpu(self):
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self.place = fluid.CUDAPlace(0)
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self._test_identity()
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class TestFunctionalConv3DTransposeError(TestCase):
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batch_size = 4
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spatial_shape = (8, 8, 8)
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dtype = "float32"
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output_size = None
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def setUp(self):
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self.in_channels = 3
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self.out_channels = 5
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self.filter_shape = 3
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self.padding = "not_valid"
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self.stride = 1
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self.dilation = 1
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self.groups = 1
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self.no_bias = False
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self.act = "sigmoid"
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self.data_format = "NDHWC"
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def test_exception(self):
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self.prepare()
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with self.assertRaises(ValueError):
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self.static_graph_case()
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def prepare(self):
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if isinstance(self.filter_shape, int):
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filter_shape = (self.filter_shape, ) * 3
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else:
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filter_shape = tuple(self.filter_shape)
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self.weight_shape = (self.in_channels, self.out_channels // self.groups
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) + filter_shape
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self.bias_shape = (self.out_channels, )
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def static_graph_case(self):
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main = fluid.Program()
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start = fluid.Program()
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with fluid.unique_name.guard():
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with fluid.program_guard(main, start):
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self.channel_last = self.data_format == "NDHWC"
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if self.channel_last:
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x = x = fluid.data(
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"input", (-1, -1, -1, -1, self.in_channels),
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dtype=self.dtype)
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else:
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x = fluid.data(
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"input", (-1, self.in_channels, -1, -1, -1),
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dtype=self.dtype)
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weight = fluid.data(
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"weight", self.weight_shape, dtype=self.dtype)
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if not self.no_bias:
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bias = fluid.data("bias", self.bias_shape, dtype=self.dtype)
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y = F.conv_transpose3d(
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x,
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weight,
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None if self.no_bias else bias,
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output_size=self.output_size,
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padding=self.padding,
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stride=self.stride,
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dilation=self.dilation,
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groups=self.groups,
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data_format=self.data_format)
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if self.act == 'sigmoid':
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y = F.sigmoid(y)
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class TestFunctionalConv3DTransposeCase2(TestFunctionalConv3DTranspose):
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def setUp(self):
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self.in_channels = 3
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self.out_channels = 5
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self.filter_shape = 3
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self.padding = 0
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self.stride = 1
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self.dilation = 1
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self.groups = 1
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self.no_bias = False
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self.act = "sigmoid"
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self.data_format = "NCDHW"
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class TestFunctionalConv3DTransposeCase3(TestFunctionalConv3DTranspose):
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def setUp(self):
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self.in_channels = 4
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self.out_channels = 6
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self.filter_shape = 3
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self.padding = 0
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self.stride = 1
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self.dilation = 1
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self.groups = 2
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self.no_bias = False
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self.act = "sigmoid"
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self.data_format = "NDHWC"
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class TestFunctionalConv3DTransposeCase4(TestFunctionalConv3DTranspose):
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def setUp(self):
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self.in_channels = 4
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self.out_channels = 6
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self.filter_shape = 3
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self.padding = "same"
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self.stride = 1
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self.dilation = 1
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self.groups = 2
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self.no_bias = True
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self.act = "sigmoid"
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self.data_format = "NDHWC"
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class TestFunctionalConv3DTransposeCase5(TestFunctionalConv3DTranspose):
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def setUp(self):
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self.in_channels = 4
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self.out_channels = 6
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self.filter_shape = 3
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self.padding = "valid"
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self.stride = (1, 2, 1)
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self.dilation = (2, 1, 1)
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self.groups = 2
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self.no_bias = False
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self.act = "sigmoid"
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self.data_format = "NDHWC"
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class TestFunctionalConv3DTransposeCase6(TestFunctionalConv3DTranspose):
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def setUp(self):
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self.in_channels = 4
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self.out_channels = 4
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self.filter_shape = 3
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self.padding = "valid"
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self.stride = (1, 2, 1)
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self.dilation = 1
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self.groups = 4
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self.no_bias = False
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self.act = "sigmoid"
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self.data_format = "NDHWC"
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class TestFunctionalConv3DTransposeCase7(TestFunctionalConv3DTranspose):
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def setUp(self):
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self.in_channels = 4
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self.out_channels = 4
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self.filter_shape = 3
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self.padding = "valid"
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self.output_size = (10, 17, 10)
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self.stride = (1, 2, 1)
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self.dilation = 1
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self.groups = 1
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self.no_bias = False
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self.act = "sigmoid"
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self.data_format = "NCDHW"
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class TestFunctionalConv3DTransposeCase8(TestFunctionalConv3DTranspose):
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def setUp(self):
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self.in_channels = 4
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self.out_channels = 6
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self.filter_shape = 3
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self.padding = [[0, 0], [1, 2], [1, 2], [2, 1], [0, 0]]
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self.stride = 1
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self.dilation = 1
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self.groups = 2
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self.no_bias = False
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self.act = "sigmoid"
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self.data_format = "NDHWC"
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class TestFunctionalConv3DTransposeCase9(TestFunctionalConv3DTranspose):
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def setUp(self):
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self.in_channels = 4
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self.out_channels = 6
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self.filter_shape = 3
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self.padding = [[0, 0], [0, 0], [1, 1], [1, 1], [2, 2]]
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self.stride = 1
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self.dilation = 1
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self.groups = 2
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self.no_bias = False
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self.act = "sigmoid"
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self.data_format = "NCDHW"
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class TestFunctionalConv3DTransposeCase10(TestFunctionalConv3DTranspose):
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def setUp(self):
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self.in_channels = 4
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self.out_channels = 6
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self.filter_shape = 3
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self.padding = [1, 1, 2, 2, 1, 1]
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self.stride = 1
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self.dilation = 1
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self.groups = 2
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self.no_bias = False
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self.act = "sigmoid"
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self.data_format = "NCDHW"
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class TestFunctionalConv3DTransposeCase11(TestFunctionalConv3DTranspose):
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def setUp(self):
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self.in_channels = 4
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self.out_channels = 6
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self.filter_shape = 3
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self.padding = [1, 2, 1]
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self.stride = 1
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self.dilation = 1
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self.groups = 2
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self.no_bias = False
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self.act = "sigmoid"
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self.data_format = "NCDHW"
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class TestFunctionalConv3DTransposeErrorCase2(
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TestFunctionalConv3DTransposeError):
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def setUp(self):
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self.in_channels = 3
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self.out_channels = 5
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self.filter_shape = 3
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self.padding = [1, 2, 2, 1, 3]
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self.stride = 1
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self.dilation = 1
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self.groups = 1
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self.no_bias = False
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self.act = "sigmoid"
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self.data_format = "NDHWC"
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class TestFunctionalConv3DTransposeErrorCase3(
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TestFunctionalConv3DTransposeError):
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def setUp(self):
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self.in_channels = 3
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self.out_channels = 5
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self.filter_shape = 3
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self.padding = [[0, 0], [0, 0], [1, 1], [1, 2], [2, 1]]
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self.stride = 1
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self.dilation = 1
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self.groups = 1
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self.no_bias = False
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self.act = "sigmoid"
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self.data_format = "NDHWC"
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class TestFunctionalConv3DTransposeErrorCase4(
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TestFunctionalConv3DTransposeError):
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def setUp(self):
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self.in_channels = 3
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self.out_channels = 5
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self.filter_shape = 3
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self.padding = [[0, 0], [1, 2], [1, 1], [0, 0], [2, 1]]
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self.stride = 1
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self.dilation = 1
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self.groups = 1
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self.no_bias = False
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self.act = "sigmoid"
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self.data_format = "NCDHW"
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class TestFunctionalConv3DTransposeErrorCase5(
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TestFunctionalConv3DTransposeError):
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def setUp(self):
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self.in_channels = -2
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self.out_channels = 5
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self.filter_shape = 3
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self.padding = 0
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self.stride = 1
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self.dilation = 1
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self.groups = 1
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self.no_bias = False
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self.act = "sigmoid"
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self.data_format = "NCDHW"
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class TestFunctionalConv3DTransposeErrorCase7(
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TestFunctionalConv3DTransposeError):
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def setUp(self):
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self.in_channels = 4
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self.out_channels = 5
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self.filter_shape = 3
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self.padding = 0
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self.output_size = "not_valid"
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self.stride = 1
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self.dilation = 1
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self.groups = 1
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self.no_bias = False
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self.act = "sigmoid"
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self.data_format = "NCDHW"
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class TestFunctionalConv3DTransposeErrorCase8(
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TestFunctionalConv3DTransposeError):
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def setUp(self):
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self.in_channels = 4
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self.out_channels = 5
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self.filter_shape = 3
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self.padding = 0
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self.stride = 1
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self.dilation = 1
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self.groups = 1
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self.no_bias = False
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self.act = "sigmoid"
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self.data_format = "not_valid"
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class TestFunctionalConv3DTransposeErrorCase9(
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TestFunctionalConv3DTransposeError):
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def setUp(self):
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self.in_channels = 3
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self.out_channels = 4
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self.filter_shape = 3
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self.padding = 0
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self.stride = 1
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self.dilation = 1
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self.groups = 2
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self.no_bias = False
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self.act = "sigmoid"
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self.data_format = "NCDHW"
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if __name__ == "__main__":
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unittest.main()
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