Merge pull request #1243 from streamjoin/script-hematologic-disease-p…

…rediction

Adding the training script for the hematologic disease prediction

examples/healthcare/application/Hematologic_Disease/train_cnn.py

@@ -0,0 +1,211 @@
+#
+# 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.
+#
+
+import time
+from singa import singa_wrap as singa
+from singa import device
+from singa import tensor
+from singa import opt
+import numpy as np
+from tqdm import tqdm
+import argparse
+import sys
+sys.path.append("../../..")
+
+from healthcare.data import bloodmnist
+from healthcare.models import hematologic_net
+
+np_dtype = {"float16": np.float16, "float32": np.float32}
+singa_dtype = {"float16": tensor.float16, "float32": tensor.float32}
+
+
+def accuracy(pred, target):
+    """Compute recall accuracy.
+
+    Args:
+        pred (Numpy ndarray): Prediction array, should be in shape (B, C)
+        target (Numpy ndarray): Ground truth array, should be in shape (B, ) 
+
+    Return:
+        correct (Float): Recall accuracy
+    """
+    # y is network output to be compared with ground truth (int)
+    y = np.argmax(pred, axis=1)
+    a = (y[:,None]==target).sum()
+    correct = np.array(a, "int").sum()
+    return correct
+
+def run(dir_path,
+        max_epoch,
+        batch_size,
+        model,
+        data,
+        lr,
+        graph,
+        verbosity,
+        dist_option='plain',
+        spars=None,
+        precision='float32'):
+    # Start training
+    dev = device.create_cpu_device()
+    dev.SetRandSeed(0)
+    np.random.seed(0)
+    if data == 'bloodmnist':
+        train_dataset, val_dataset, num_class = bloodmnist.load(dir_path=dir_path)
+    else:
+        print(
+            'Wrong dataset!'
+        )
+        sys.exit(0)
+
+    if model == 'cnn':
+        model = hematologic_net.create_model(num_classes=num_class)
+    else:
+        print(
+            'Wrong model!'
+        )
+        sys.exit(0)
+
+    # Model configuration for CNN
+    # criterion = layer.SoftMaxCrossEntropy()
+    optimizer_ft = opt.Adam(lr)
+
+    tx = tensor.Tensor(
+        (batch_size, 3, model.input_size, model.input_size), dev,
+        singa_dtype[precision])
+    ty = tensor.Tensor((batch_size,), dev, tensor.int32)
+
+    num_train_batch = train_dataset.__len__() // batch_size
+    num_val_batch = val_dataset.__len__() // batch_size
+    idx = np.arange(train_dataset.__len__(), dtype=np.int32)
+
+    # Attach model to graph
+    model.set_optimizer(optimizer_ft)
+    model.compile([tx], is_train=True, use_graph=graph, sequential=False)
+    dev.SetVerbosity(verbosity)
+
+    # Training and evaluation loop
+    for epoch in range(max_epoch):
+        print(f'Epoch {epoch}:')
+
+        start_time = time.time()
+
+        train_correct = np.zeros(shape=[1], dtype=np.float32)
+        test_correct = np.zeros(shape=[1], dtype=np.float32)
+        train_loss = np.zeros(shape=[1], dtype=np.float32)
+
+        # Training part
+        model.train()
+        for b in tqdm(range(num_train_batch)):
+            # Extract batch from image list
+            x, y = train_dataset.batchgenerator(idx[b * batch_size:(b + 1) * batch_size],
+                batch_size=batch_size, data_size=(3, model.input_size, model.input_size))
+            x = x.astype(np_dtype[precision])
+
+            tx.copy_from_numpy(x)
+            ty.copy_from_numpy(y)
+
+            out, loss = model(tx, ty, dist_option, spars)
+            train_correct += accuracy(tensor.to_numpy(out), y)
+            train_loss += tensor.to_numpy(loss)[0]
+        print('Training loss = %f, training accuracy = %f' %
+                      (train_loss, train_correct /
+                       (num_train_batch * batch_size)))
+
+        # Validation part
+        model.eval()
+        for b in tqdm(range(num_val_batch)):
+            x, y = train_dataset.batchgenerator(idx[b * batch_size:(b + 1) * batch_size],
+                batch_size=batch_size, data_size=(3, model.input_size, model.input_size))
+            x = x.astype(np_dtype[precision])
+
+            tx.copy_from_numpy(x)
+            ty.copy_from_numpy(y)
+
+            out = model(tx)
+            test_correct += accuracy(tensor.to_numpy(out), y)
+
+        print('Evaluation accuracy = %f, Elapsed Time = %fs' %
+                      (test_correct / (num_val_batch * batch_size),
+                       time.time() - start_time))
+
+
+if __name__ == '__main__':
+    # Use argparse to get command config: max_epoch, model, data, etc., for single gpu training
+    parser = argparse.ArgumentParser(
+        description='Training using the autograd and graph.')
+    parser.add_argument(
+        'model',
+        choices=['cnn'],
+        default='cnn')
+    parser.add_argument('data',
+                        choices=['bloodmnist'],
+                        default='bloodmnist')
+    parser.add_argument('-p',
+                        choices=['float32', 'float16'],
+                        default='float32',
+                        dest='precision')
+    parser.add_argument('-dir',
+                        '--dir-path',
+                        default="/tmp/bloodmnist",
+                        type=str,
+                        help='the directory to store the bloodmnist dataset',
+                        dest='dir_path')
+    parser.add_argument('-m',
+                        '--max-epoch',
+                        default=100,
+                        type=int,
+                        help='maximum epochs',
+                        dest='max_epoch')
+    parser.add_argument('-b',
+                        '--batch-size',
+                        default=256,
+                        type=int,
+                        help='batch size',
+                        dest='batch_size')
+    parser.add_argument('-l',
+                        '--learning-rate',
+                        default=0.003,
+                        type=float,
+                        help='initial learning rate',
+                        dest='lr')
+    parser.add_argument('-g',
+                        '--disable-graph',
+                        default='True',
+                        action='store_false',
+                        help='disable graph',
+                        dest='graph')
+    parser.add_argument('-v',
+                        '--log-verbosity',
+                        default=0,
+                        type=int,
+                        help='logging verbosity',
+                        dest='verbosity')
+
+    args = parser.parse_args()
+
+    run(args.dir_path,
+        args.max_epoch,
+        args.batch_size,
+        args.model,
+        args.data,
+        args.lr,
+        args.graph,
+        args.verbosity,
+        precision=args.precision)