Repository: Project-MONAI/MONAI

Issue:
Port resnext
a subtask of #78, port resnext for 2d/3d classification


Discussion:

Resolving patch:
diff --git a/monai/networks/nets/resnext3d.py b/monai/networks/nets/resnext3d.py
new file mode 100644
--- /dev/null
+++ b/monai/networks/nets/resnext3d.py
@@ -0,0 +1,233 @@
+# 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 torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+from functools import partial
+
+
+def conv3x3x3(in_planes, out_planes, stride=1):
+    # 3x3x3 convolution with padding
+    return nn.Conv3d(
+        in_planes,
+        out_planes,
+        kernel_size=3,
+        stride=stride,
+        padding=1,
+        bias=False)
+
+
+def downsample_basic_block(x, planes, stride):
+    out = F.avg_pool3d(x, kernel_size=1, stride=stride)
+    zero_pads = torch.Tensor(
+        out.size(0), planes - out.size(1), out.size(2), out.size(3),
+        out.size(4)).zero_()
+    if isinstance(out.data, torch.cuda.FloatTensor):
+        zero_pads = zero_pads.cuda()
+
+    out = Variable(torch.cat([out.data, zero_pads], dim=1))
+
+    return out
+
+
+class ResNeXtBottleneck(nn.Module):
+    expansion = 2
+
+    def __init__(self, inplanes, planes, cardinality, stride=1,
+                 downsample=None):
+        super(ResNeXtBottleneck, self).__init__()
+        mid_planes = cardinality * int(planes / 32)
+        self.conv1 = nn.Conv3d(inplanes, mid_planes, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm3d(mid_planes)
+        self.conv2 = nn.Conv3d(
+            mid_planes,
+            mid_planes,
+            kernel_size=3,
+            stride=stride,
+            padding=1,
+            groups=cardinality,
+            bias=False)
+        self.bn2 = nn.BatchNorm3d(mid_planes)
+        self.conv3 = nn.Conv3d(
+            mid_planes, planes * self.expansion, kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm3d(planes * self.expansion)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class ResNeXt(nn.Module):
+    """ResNeXt model class.
+    Based on: "Aggregated Residual Transformation for Deep Neural Networks" https://arxiv.org/pdf/1611.05431.pdf
+    Adapted from PyTorch Hub 2D version:
+    https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
+    Args:
+        block (nn.Module): bottleneck type.
+        layers (list): number of layers in every block.
+        num_input_channel (Int): number of the input image channel.
+        num_classes (Int): number of classification classes.
+        shortcut_type (String): type of shortcut, A or B, default is B.
+        cardinality (Int): group number in every block.
+    """
+
+    def __init__(self,
+                 block,
+                 layers,
+                 num_input_channel=3,
+                 num_classes=1000,
+                 shortcut_type='B',
+                 cardinality=32):
+        self.inplanes = 64
+        super(ResNeXt, self).__init__()
+        self.conv1 = nn.Conv3d(
+            num_input_channel,
+            64,
+            kernel_size=7,
+            stride=2,
+            padding=3,
+            bias=False)
+        self.bn1 = nn.BatchNorm3d(64)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool3d(kernel_size=(3, 3, 3), stride=2, padding=1)
+        self.layer1 = self._make_layer(block, 128, layers[0], shortcut_type,
+                                       cardinality)
+        self.layer2 = self._make_layer(
+            block, 256, layers[1], shortcut_type, cardinality, stride=2)
+        self.layer3 = self._make_layer(
+            block, 512, layers[2], shortcut_type, cardinality, stride=2)
+        self.layer4 = self._make_layer(
+            block, 1024, layers[3], shortcut_type, cardinality, stride=2)
+
+        self.fc = nn.Linear(cardinality * 32 * block.expansion, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv3d):
+                nn.init.kaiming_normal_(m.weight)
+            elif isinstance(m, nn.BatchNorm3d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.constant_(m.bias, 0)
+
+    def _make_layer(self,
+                    block,
+                    planes,
+                    blocks,
+                    shortcut_type,
+                    cardinality,
+                    stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            if shortcut_type == 'A':
+                downsample = partial(
+                    downsample_basic_block,
+                    planes=planes * block.expansion,
+                    stride=stride)
+            else:
+                downsample = nn.Sequential(
+                    nn.Conv3d(
+                        self.inplanes,
+                        planes * block.expansion,
+                        kernel_size=1,
+                        stride=stride,
+                        bias=False), nn.BatchNorm3d(planes * block.expansion))
+
+        layers = []
+        layers.append(
+            block(self.inplanes, planes, cardinality, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes, cardinality))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = F.adaptive_avg_pool3d(x, (1, 1, 1))
+        x = torch.flatten(x, 1)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        return x
+
+
+def get_fine_tuning_parameters(model, ft_begin_index):
+    if ft_begin_index == 0:
+        return model.parameters()
+
+    ft_module_names = []
+    for i in range(ft_begin_index, 5):
+        ft_module_names.append('layer{}'.format(i))
+    ft_module_names.append('fc')
+
+    parameters = []
+    for k, v in model.named_parameters():
+        for ft_module in ft_module_names:
+            if ft_module in k:
+                parameters.append({'params': v})
+                break
+        else:
+            parameters.append({'params': v, 'lr': 0.0})
+
+    return parameters
+
+
+def resnext50(**kwargs):
+    """Constructs a ResNet-50 model.
+    """
+    model = ResNeXt(ResNeXtBottleneck, [3, 4, 6, 3], **kwargs)
+    return model
+
+
+def resnext101(**kwargs):
+    """Constructs a ResNet-101 model.
+    """
+    model = ResNeXt(ResNeXtBottleneck, [3, 4, 23, 3], **kwargs)
+    return model
+
+
+def resnext152(**kwargs):
+    """Constructs a ResNet-152 model.
+    """
+    model = ResNeXt(ResNeXtBottleneck, [3, 8, 36, 3], **kwargs)
+    return model


Test patch:
diff --git a/tests/test_resnext3d.py b/tests/test_resnext3d.py
new file mode 100644
--- /dev/null
+++ b/tests/test_resnext3d.py
@@ -0,0 +1,54 @@
+# 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.resnext3d import resnext50, resnext101, resnext152
+
+
+TEST_CASE_1 = [  # 4-channel 3D, batch 16
+    {
+        'num_input_channel': 2,
+        'num_classes': 3
+    },
+    torch.randn(16, 2, 32, 64, 48),
+    (16, 3)
+]
+
+
+class TestRESNEXT3D(unittest.TestCase):
+
+    @parameterized.expand([TEST_CASE_1])
+    def test_50_shape(self, input_param, input_data, expected_shape):
+        net = resnext50(**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_101_shape(self, input_param, input_data, expected_shape):
+        net = resnext101(**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_152_shape(self, input_param, input_data, expected_shape):
+        net = resnext152(**input_param)
+        net.eval()
+        with torch.no_grad():
+            result = net.forward(input_data)
+            self.assertEqual(result.shape, expected_shape)