Add the implementation of model for the TED CT

examples/healthcare/models/tedct_net.py

@@ -0,0 +1,95 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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 singa import layer
+from singa import model
+import singa.tensor as tensor
+from singa import autograd
+from singa.tensor import Tensor
+
+
+class CNN(model.Model):
+
+    def __init__(self, num_classes=10, num_channels=1):
+        super(CNN, self).__init__()
+        self.num_classes = num_classes
+        self.input_size = 28
+        self.dimension = 4
+        self.conv1 = layer.Conv2d(num_channels, 20, 5, padding=0, activation="RELU")
+        self.conv2 = layer.Conv2d(20, 50, 5, padding=0, activation="RELU")
+        self.linear1 = layer.Linear(500)
+        self.linear2 = layer.Linear(num_classes)
+        self.pooling1 = layer.MaxPool2d(2, 2, padding=0)
+        self.pooling2 = layer.MaxPool2d(2, 2, padding=0)
+        self.relu = layer.ReLU()
+        self.flatten = layer.Flatten()
+        self.softmax_cross_entropy = layer.SoftMaxCrossEntropy()
+
+    def forward(self, x):
+        y = self.conv1(x)
+        y = self.pooling1(y)
+        y = self.conv2(y)
+        y = self.pooling2(y)
+        y = self.flatten(y)
+        y = self.linear1(y)
+        y = self.relu(y)
+        y = self.linear2(y)
+        return y
+
+    def train_one_batch(self, x, y, dist_option, spars):
+        out = self.forward(x)
+        loss = self.softmax_cross_entropy(out, y)
+
+        if dist_option == 'plain':
+            self.optimizer(loss)
+        elif dist_option == 'half':
+            self.optimizer.backward_and_update_half(loss)
+        elif dist_option == 'partialUpdate':
+            self.optimizer.backward_and_partial_update(loss)
+        elif dist_option == 'sparseTopK':
+            self.optimizer.backward_and_sparse_update(loss,
+                                                      topK=True,
+                                                      spars=spars)
+        elif dist_option == 'sparseThreshold':
+            self.optimizer.backward_and_sparse_update(loss,
+                                                      topK=False,
+                                                      spars=spars)
+        return out, loss
+
+    def set_optimizer(self, optimizer):
+        self.optimizer = optimizer
+
+def create_cnn_model(pretrained=False, **kwargs):
+    """Constructs a CNN model.
+
+    Args:
+        pretrained (bool): If True, returns a pre-trained model.
+
+    Returns:
+        The created CNN model.
+    """
+    model = CNN(**kwargs)
+
+    return model
+
+def create_model(backbone, prototype_count=2, lamb=0.5, temp=10.0):
+    model = CPL(backbone, prototype_count=prototype_count, lamb=lamb, temp=temp)
+    return model
+
+
+__all__ = ["CPL", "CNN", "create_cnn_model", "create_model"]