Merge pull request #1228 from npcmaci/dev-postgresql
create the application folder for the healthcare model zoo
diff --git a/examples/healthcare/data/malaria.py b/examples/healthcare/data/malaria.py
new file mode 100644
index 0000000..46422b7
--- /dev/null
+++ b/examples/healthcare/data/malaria.py
@@ -0,0 +1,122 @@
+#
+# 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.
+#
+
+try:
+ import pickle
+except ImportError:
+ import cPickle as pickle
+
+import numpy as np
+import os
+import sys
+from PIL import Image
+
+
+# need to save to specific local directories
+def load_train_data(dir_path="/tmp/malaria", resize_size=(128, 128)):
+ dir_path = check_dataset_exist(dirpath=dir_path)
+ path_train_label_1 = os.path.join(dir_path, "training_set/Parasitized")
+ path_train_label_0 = os.path.join(dir_path, "training_set/Uninfected")
+ train_label_1 = load_image_path(os.listdir(path_train_label_1))
+ train_label_0 = load_image_path(os.listdir(path_train_label_0))
+ labels = []
+ Images = np.empty((len(train_label_1) + len(train_label_0),
+ 3, resize_size[0], resize_size[1]), dtype=np.uint8)
+ for i in range(len(train_label_0)):
+ image_path = os.path.join(path_train_label_0, train_label_0[i])
+ temp_image = np.array(Image.open(image_path).resize(
+ resize_size).convert("RGB")).transpose(2, 0, 1)
+ Images[i] = temp_image
+ labels.append(0)
+ for i in range(len(train_label_1)):
+ image_path = os.path.join(path_train_label_1, train_label_1[i])
+ temp_image = np.array(Image.open(image_path).resize(
+ resize_size).convert("RGB")).transpose(2, 0, 1)
+ Images[i + len(train_label_0)] = temp_image
+ labels.append(1)
+
+ Images = np.array(Images, dtype=np.float32)
+ labels = np.array(labels, dtype=np.int32)
+ return Images, labels
+
+
+# need to save to specific local directories
+def load_test_data(dir_path='/tmp/malaria', resize_size=(128, 128)):
+ dir_path = check_dataset_exist(dirpath=dir_path)
+ path_test_label_1 = os.path.join(dir_path, "testing_set/Parasitized")
+ path_test_label_0 = os.path.join(dir_path, "testing_set/Uninfected")
+ test_label_1 = load_image_path(os.listdir(path_test_label_1))
+ test_label_0 = load_image_path(os.listdir(path_test_label_0))
+ labels = []
+ Images = np.empty((len(test_label_1) + len(test_label_0),
+ 3, resize_size[0], resize_size[1]), dtype=np.uint8)
+ for i in range(len(test_label_0)):
+ image_path = os.path.join(path_test_label_0, test_label_0[i])
+ temp_image = np.array(Image.open(image_path).resize(
+ resize_size).convert("RGB")).transpose(2, 0, 1)
+ Images[i] = temp_image
+ labels.append(0)
+ for i in range(len(test_label_1)):
+ image_path = os.path.join(path_test_label_1, test_label_1[i])
+ temp_image = np.array(Image.open(image_path).resize(
+ resize_size).convert("RGB")).transpose(2, 0, 1)
+ Images[i + len(test_label_0)] = temp_image
+ labels.append(1)
+
+ Images = np.array(Images, dtype=np.float32)
+ labels = np.array(labels, dtype=np.int32)
+ return Images, labels
+
+
+def load_image_path(list):
+ new_list = []
+ for image_path in list:
+ if (image_path.endswith(".png") or image_path.endswith(".jpg")):
+ new_list.append(image_path)
+ return new_list
+
+
+def check_dataset_exist(dirpath):
+ if not os.path.exists(dirpath):
+ print(
+ 'Please download the malaria dataset first'
+ )
+ sys.exit(0)
+ return dirpath
+
+
+def normalize(train_x, val_x):
+ mean = [0.5339, 0.4180, 0.4460] # mean for malaria dataset
+ std = [0.3329, 0.2637, 0.2761] # std for malaria dataset
+ train_x /= 255
+ val_x /= 255
+ for ch in range(0, 2):
+ train_x[:, ch, :, :] -= mean[ch]
+ train_x[:, ch, :, :] /= std[ch]
+ val_x[:, ch, :, :] -= mean[ch]
+ val_x[:, ch, :, :] /= std[ch]
+ return train_x, val_x
+
+
+def load(dir_path):
+ train_x, train_y = load_train_data(dir_path=dir_path)
+ val_x, val_y = load_test_data(dir_path=dir_path)
+ train_x, val_x = normalize(train_x, val_x)
+ train_y = train_y.flatten()
+ val_y = val_y.flatten()
+ return train_x, train_y, val_x, val_y
diff --git a/examples/healthcare/models/malaria_net.py b/examples/healthcare/models/malaria_net.py
new file mode 100644
index 0000000..2a10a70
--- /dev/null
+++ b/examples/healthcare/models/malaria_net.py
@@ -0,0 +1,146 @@
+#
+# 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 CNN(model.Model):
+
+ def __init__(self, num_classes=10, num_channels=1):
+ super(CNN, self).__init__()
+ self.num_classes = num_classes
+ self.input_size = 128
+ self.dimension = 4
+ self.conv1 = layer.Conv2d(num_channels, 32, 3, padding=0, activation="RELU")
+ self.conv2 = layer.Conv2d(32, 64, 3, padding=0, activation="RELU")
+ self.conv3 = layer.Conv2d(64, 64, 3, padding=0, activation="RELU")
+ self.linear1 = layer.Linear(128)
+ self.linear2 = layer.Linear(num_classes)
+ self.pooling1 = layer.MaxPool2d(2, 2, padding=0)
+ self.pooling2 = layer.MaxPool2d(2, 2, padding=0)
+ self.pooling3 = layer.MaxPool2d(2, 2, padding=0)
+ self.relu = layer.ReLU()
+ self.flatten = layer.Flatten()
+ self.softmax_cross_entropy = layer.SoftMaxCrossEntropy()
+ self.sigmoid = layer
+
+ def forward(self, x):
+ y = self.conv1(x)
+ y = self.pooling1(y)
+ y = self.conv2(y)
+ y = self.pooling2(y)
+ y = self.conv3(y)
+ y = self.pooling3(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
+
+
+class MLP(model.Model):
+
+ def __init__(self, perceptron_size=100, num_classes=10):
+ super(MLP, self).__init__()
+ self.num_classes = num_classes
+ self.dimension = 2
+
+ self.relu = layer.ReLU()
+ self.linear1 = layer.Linear(perceptron_size)
+ self.linear2 = layer.Linear(num_classes)
+ self.softmax_cross_entropy = layer.SoftMaxCrossEntropy()
+
+ def forward(self, inputs):
+ y = self.linear1(inputs)
+ 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_model(model_option='cnn', **kwargs):
+ """Constructs a CNN model.
+
+ Args:
+ pretrained (bool): If True, returns a pre-trained model.
+
+ Returns:
+ The created CNN model.
+ """
+ model = CNN(**kwargs)
+ if model_option=='mlp':
+ model = MLP(**kwargs)
+
+ return model
+
+
+__all__ = ['CNN', 'MLP', 'create_model']
\ No newline at end of file
