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e4b9a7b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 | # Copyright 2020 MONAI Consortium
# 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.
from monai.networks.blocks import Convolution, ResidualUnit
from tests.utils import TorchImageTestCase2D, TorchImageTestCase3D
class TestConvolution2D(TorchImageTestCase2D):
def test_conv1(self):
conv = Convolution(2, self.input_channels, self.output_channels)
out = conv(self.imt)
expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])
self.assertEqual(out.shape, expected_shape)
def test_conv1_no_acti(self):
conv = Convolution(2, self.input_channels, self.output_channels, act=None)
out = conv(self.imt)
expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])
self.assertEqual(out.shape, expected_shape)
def test_conv_only1(self):
conv = Convolution(2, self.input_channels, self.output_channels, conv_only=True)
out = conv(self.imt)
expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])
self.assertEqual(out.shape, expected_shape)
def test_stride1(self):
conv = Convolution(2, self.input_channels, self.output_channels, strides=2)
out = conv(self.imt)
expected_shape = (1, self.output_channels, self.im_shape[0] // 2, self.im_shape[1] // 2)
self.assertEqual(out.shape, expected_shape)
def test_dilation1(self):
conv = Convolution(2, self.input_channels, self.output_channels, dilation=3)
out = conv(self.imt)
expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])
self.assertEqual(out.shape, expected_shape)
def test_dropout1(self):
conv = Convolution(2, self.input_channels, self.output_channels, dropout=0.15)
out = conv(self.imt)
expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])
self.assertEqual(out.shape, expected_shape)
def test_transpose1(self):
conv = Convolution(2, self.input_channels, self.output_channels, is_transposed=True)
out = conv(self.imt)
expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])
self.assertEqual(out.shape, expected_shape)
def test_transpose2(self):
conv = Convolution(2, self.input_channels, self.output_channels, strides=2, is_transposed=True)
out = conv(self.imt)
expected_shape = (1, self.output_channels, self.im_shape[0] * 2, self.im_shape[1] * 2)
self.assertEqual(out.shape, expected_shape)
class TestConvolution3D(TorchImageTestCase3D):
def test_conv1(self):
conv = Convolution(3, self.input_channels, self.output_channels)
out = conv(self.imt)
expected_shape = (1, self.output_channels, self.im_shape[1], self.im_shape[0], self.im_shape[2])
self.assertEqual(out.shape, expected_shape)
def test_conv1_no_acti(self):
conv = Convolution(3, self.input_channels, self.output_channels, act=None)
out = conv(self.imt)
expected_shape = (1, self.output_channels, self.im_shape[1], self.im_shape[0], self.im_shape[2])
self.assertEqual(out.shape, expected_shape)
def test_conv_only1(self):
conv = Convolution(3, self.input_channels, self.output_channels, conv_only=True)
out = conv(self.imt)
expected_shape = (1, self.output_channels, self.im_shape[1], self.im_shape[0], self.im_shape[2])
self.assertEqual(out.shape, expected_shape)
def test_stride1(self):
conv = Convolution(3, self.input_channels, self.output_channels, strides=2)
out = conv(self.imt)
expected_shape = (1, self.output_channels, self.im_shape[1] // 2, self.im_shape[0] // 2, self.im_shape[2] // 2)
self.assertEqual(out.shape, expected_shape)
def test_dilation1(self):
conv = Convolution(3, self.input_channels, self.output_channels, dilation=3)
out = conv(self.imt)
expected_shape = (1, self.output_channels, self.im_shape[1], self.im_shape[0], self.im_shape[2])
self.assertEqual(out.shape, expected_shape)
def test_dropout1(self):
conv = Convolution(3, self.input_channels, self.output_channels, dropout=0.15)
out = conv(self.imt)
expected_shape = (1, self.output_channels, self.im_shape[1], self.im_shape[0], self.im_shape[2])
self.assertEqual(out.shape, expected_shape)
def test_transpose1(self):
conv = Convolution(3, self.input_channels, self.output_channels, is_transposed=True)
out = conv(self.imt)
expected_shape = (1, self.output_channels, self.im_shape[1], self.im_shape[0], self.im_shape[2])
self.assertEqual(out.shape, expected_shape)
def test_transpose2(self):
conv = Convolution(3, self.input_channels, self.output_channels, strides=2, is_transposed=True)
out = conv(self.imt)
expected_shape = (1, self.output_channels, self.im_shape[1] * 2, self.im_shape[0] * 2, self.im_shape[2] * 2)
self.assertEqual(out.shape, expected_shape)
class TestResidualUnit2D(TorchImageTestCase2D):
def test_conv_only1(self):
conv = ResidualUnit(2, 1, self.output_channels)
out = conv(self.imt)
expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])
self.assertEqual(out.shape, expected_shape)
def test_stride1(self):
conv = ResidualUnit(2, 1, self.output_channels, strides=2)
out = conv(self.imt)
expected_shape = (1, self.output_channels, self.im_shape[0] // 2, self.im_shape[1] // 2)
self.assertEqual(out.shape, expected_shape)
def test_dilation1(self):
conv = ResidualUnit(2, 1, self.output_channels, dilation=3)
out = conv(self.imt)
expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])
self.assertEqual(out.shape, expected_shape)
def test_dropout1(self):
conv = ResidualUnit(2, 1, self.output_channels, dropout=0.15)
out = conv(self.imt)
expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])
self.assertEqual(out.shape, expected_shape)
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