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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 | # 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.
import unittest
import torch
from parameterized import parameterized
from monai.networks.blocks import FCN, MCFCN
from monai.networks.nets import AHNet
TEST_CASE_FCN_1 = [{"out_channels": 3, "upsample_mode": "transpose"}, torch.randn(5, 3, 64, 64), (5, 3, 64, 64)]
TEST_CASE_FCN_2 = [{"out_channels": 2, "upsample_mode": "transpose"}, torch.randn(5, 3, 64, 64), (5, 2, 64, 64)]
TEST_CASE_FCN_3 = [{"out_channels": 1, "upsample_mode": "bilinear"}, torch.randn(5, 3, 64, 64), (5, 1, 64, 64)]
TEST_CASE_MCFCN_1 = [
{"out_channels": 3, "in_channels": 8, "upsample_mode": "transpose"},
torch.randn(5, 8, 64, 64),
(5, 3, 64, 64),
]
TEST_CASE_MCFCN_2 = [
{"out_channels": 2, "in_channels": 1, "upsample_mode": "transpose"},
torch.randn(5, 1, 64, 64),
(5, 2, 64, 64),
]
TEST_CASE_MCFCN_3 = [
{"out_channels": 1, "in_channels": 2, "upsample_mode": "bilinear"},
torch.randn(5, 2, 64, 64),
(5, 1, 64, 64),
]
TEST_CASE_AHNET_2D_1 = [
{"spatial_dims": 2, "upsample_mode": "bilinear"},
torch.randn(3, 1, 128, 128),
(3, 1, 128, 128),
]
TEST_CASE_AHNET_2D_2 = [
{"spatial_dims": 2, "upsample_mode": "transpose", "out_channels": 2},
torch.randn(2, 1, 128, 128),
(2, 2, 128, 128),
]
TEST_CASE_AHNET_3D_1 = [
{"spatial_dims": 3, "upsample_mode": "trilinear"},
torch.randn(3, 1, 128, 128, 64),
(3, 1, 128, 128, 64),
]
TEST_CASE_AHNET_3D_2 = [
{"spatial_dims": 3, "upsample_mode": "transpose", "out_channels": 2},
torch.randn(2, 1, 128, 128, 64),
(2, 2, 128, 128, 64),
]
TEST_CASE_AHNET_3D_WITH_PRETRAIN_1 = [
{"spatial_dims": 3, "upsample_mode": "trilinear"},
torch.randn(3, 1, 128, 128, 64),
(3, 1, 128, 128, 64),
{"out_channels": 1, "upsample_mode": "transpose"},
]
TEST_CASE_AHNET_3D_WITH_PRETRAIN_2 = [
{"spatial_dims": 3, "upsample_mode": "transpose", "out_channels": 2},
torch.randn(2, 1, 128, 128, 64),
(2, 2, 128, 128, 64),
{"out_channels": 1, "upsample_mode": "bilinear"},
]
class TestFCN(unittest.TestCase):
@parameterized.expand([TEST_CASE_FCN_1, TEST_CASE_FCN_2, TEST_CASE_FCN_3])
def test_fcn_shape(self, input_param, input_data, expected_shape):
net = FCN(**input_param)
net.eval()
with torch.no_grad():
result = net.forward(input_data)
self.assertEqual(result.shape, expected_shape)
class TestMCFCN(unittest.TestCase):
@parameterized.expand([TEST_CASE_MCFCN_1, TEST_CASE_MCFCN_2, TEST_CASE_MCFCN_3])
def test_mcfcn_shape(self, input_param, input_data, expected_shape):
net = MCFCN(**input_param)
net.eval()
with torch.no_grad():
result = net.forward(input_data)
self.assertEqual(result.shape, expected_shape)
class TestAHNET(unittest.TestCase):
@parameterized.expand([TEST_CASE_AHNET_2D_1, TEST_CASE_AHNET_2D_2, TEST_CASE_AHNET_3D_1, TEST_CASE_AHNET_3D_2])
def test_ahnet_shape(self, input_param, input_data, expected_shape):
net = AHNet(**input_param)
net.eval()
with torch.no_grad():
result = net.forward(input_data)
self.assertEqual(result.shape, expected_shape)
class TestAHNETWithPretrain(unittest.TestCase):
@parameterized.expand([TEST_CASE_AHNET_3D_WITH_PRETRAIN_1, TEST_CASE_AHNET_3D_WITH_PRETRAIN_2])
def test_ahnet_shape(self, input_param, input_data, expected_shape, fcn_input_param):
net = AHNet(**input_param)
net2d = FCN(**fcn_input_param)
net.copy_from(net2d)
net.eval()
with torch.no_grad():
result = net.forward(input_data)
self.assertEqual(result.shape, expected_shape)
if __name__ == "__main__":
unittest.main()
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