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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 | # 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.nets import densenet121, densenet169, densenet201, densenet264
TEST_CASE_1 = [ # 4-channel 3D, batch 16
{"spatial_dims": 3, "in_channels": 2, "out_channels": 3},
torch.randn(16, 2, 32, 64, 48),
(16, 3),
]
TEST_CASE_2 = [ # 4-channel 2D, batch 16
{"spatial_dims": 2, "in_channels": 2, "out_channels": 3},
torch.randn(16, 2, 32, 64),
(16, 3),
]
TEST_CASE_3 = [ # 4-channel 1D, batch 16
{"spatial_dims": 1, "in_channels": 2, "out_channels": 3},
torch.randn(16, 2, 32),
(16, 3),
]
class TestDENSENET(unittest.TestCase):
@parameterized.expand([TEST_CASE_1])
def test_121_4d_shape(self, input_param, input_data, expected_shape):
net = densenet121(**input_param)
net.eval()
with torch.no_grad():
result = net.forward(input_data)
self.assertEqual(result.shape, expected_shape)
@parameterized.expand([TEST_CASE_1])
def test_169_4d_shape(self, input_param, input_data, expected_shape):
net = densenet169(**input_param)
net.eval()
with torch.no_grad():
result = net.forward(input_data)
self.assertEqual(result.shape, expected_shape)
@parameterized.expand([TEST_CASE_1])
def test_201_4d_shape(self, input_param, input_data, expected_shape):
net = densenet201(**input_param)
net.eval()
with torch.no_grad():
result = net.forward(input_data)
self.assertEqual(result.shape, expected_shape)
@parameterized.expand([TEST_CASE_1])
def test_264_4d_shape(self, input_param, input_data, expected_shape):
net = densenet264(**input_param)
net.eval()
with torch.no_grad():
result = net.forward(input_data)
self.assertEqual(result.shape, expected_shape)
@parameterized.expand([TEST_CASE_2])
def test_121_3d_shape(self, input_param, input_data, expected_shape):
net = densenet121(**input_param)
net.eval()
with torch.no_grad():
result = net.forward(input_data)
self.assertEqual(result.shape, expected_shape)
@parameterized.expand([TEST_CASE_2])
def test_169_3d_shape(self, input_param, input_data, expected_shape):
net = densenet169(**input_param)
net.eval()
with torch.no_grad():
result = net.forward(input_data)
self.assertEqual(result.shape, expected_shape)
@parameterized.expand([TEST_CASE_2])
def test_201_3d_shape(self, input_param, input_data, expected_shape):
net = densenet201(**input_param)
net.eval()
with torch.no_grad():
result = net.forward(input_data)
self.assertEqual(result.shape, expected_shape)
@parameterized.expand([TEST_CASE_2])
def test_264_3d_shape(self, input_param, input_data, expected_shape):
net = densenet264(**input_param)
net.eval()
with torch.no_grad():
result = net.forward(input_data)
self.assertEqual(result.shape, expected_shape)
@parameterized.expand([TEST_CASE_3])
def test_121_2d_shape(self, input_param, input_data, expected_shape):
net = densenet121(**input_param)
net.eval()
with torch.no_grad():
result = net.forward(input_data)
self.assertEqual(result.shape, expected_shape)
@parameterized.expand([TEST_CASE_3])
def test_169_2d_shape(self, input_param, input_data, expected_shape):
net = densenet169(**input_param)
net.eval()
with torch.no_grad():
result = net.forward(input_data)
self.assertEqual(result.shape, expected_shape)
@parameterized.expand([TEST_CASE_3])
def test_201_2d_shape(self, input_param, input_data, expected_shape):
net = densenet201(**input_param)
net.eval()
with torch.no_grad():
result = net.forward(input_data)
self.assertEqual(result.shape, expected_shape)
@parameterized.expand([TEST_CASE_3])
def test_264_2d_shape(self, input_param, input_data, expected_shape):
net = densenet264(**input_param)
net.eval()
with torch.no_grad():
result = net.forward(input_data)
self.assertEqual(result.shape, expected_shape)
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