Add the implementation for the hematologic disease model

examples/healthcare/models/hematologic_net.py

@@ -0,0 +1,124 @@
+#
+# 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
+from singa import tensor
+from singa import opt
+from singa import device
+
+import numpy as np
+
+
+np_dtype = {"float16": np.float16, "float32": np.float32}
+
+singa_dtype = {"float16": tensor.float16, "float32": tensor.float32}
+
+
+class CNNModel(model.Model):
+    def __init__(self, num_classes):
+        super(CNNModel, self).__init__()
+        self.input_size = 28
+        self.dimension = 4
+        self.num_classes = num_classes
+
+        self.layer1 = layer.Conv2d(16, kernel_size=3, activation="RELU")
+        self.bn1 = layer.BatchNorm2d()
+        self.layer2 = layer.Conv2d(16, kernel_size=3, activation="RELU")
+        self.bn2 = layer.BatchNorm2d()
+        self.pooling2 = layer.MaxPool2d(kernel_size=2, stride=2)
+        self.layer3 = layer.Conv2d(64, kernel_size=3, activation="RELU")
+        self.bn3 = layer.BatchNorm2d()
+        self.layer4 = layer.Conv2d(64, kernel_size=3, activation="RELU")
+        self.bn4 = layer.BatchNorm2d()
+        self.layer5 = layer.Conv2d(64, kernel_size=3, padding=1, activation="RELU")
+        self.bn5 = layer.BatchNorm2d()
+        self.pooling5 = layer.MaxPool2d(kernel_size=2, stride=2)
+
+        self.flatten = layer.Flatten()
+
+        self.linear1 = layer.Linear(128)
+        self.linear2 = layer.Linear(128)
+        self.linear3 = layer.Linear(self.num_classes)
+
+        self.relu = layer.ReLU()
+
+        self.softmax_cross_entropy = layer.SoftMaxCrossEntropy()
+        self.dropout = layer.Dropout(ratio=0.3)
+
+    def forward(self, x):
+        x = self.layer1(x)
+        x = self.bn1(x)
+        x = self.layer2(x)
+        x = self.bn2(x)
+        x = self.pooling2(x)
+
+        x = self.layer3(x)
+        x = self.bn3(x)
+        x = self.layer4(x)
+        x = self.bn4(x)
+        x = self.layer5(x)
+        x = self.bn5(x)
+        x = self.pooling5(x)
+        x = self.flatten(x)
+        x = self.linear1(x)
+        x = self.relu(x)
+        x = self.linear2(x)
+        x = self.relu(x)
+        x = self.linear3(x)
+        return x
+
+    def set_optimizer(self, optimizer):
+        self.optimizer = optimizer
+
+    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 create_model(**kwargs):
+    """Constructs a CNN model.
+
+    Args:
+        pretrained (bool): If True, returns a pre-trained model.
+
+    Returns:
+        The created CNN model.
+    """
+    model = CNNModel(**kwargs)
+
+    return model
+
+
+__all__ = ['CNNModel', 'create_model']