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From GitBox <...@apache.org>
Subject [GitHub] [singa] nudles commented on a change in pull request #651: Add new example APIs
Date Mon, 06 Apr 2020 09:30:41 GMT
nudles commented on a change in pull request #651: Add new example APIs
URL: https://github.com/apache/singa/pull/651#discussion_r403952306
 
 

 ##########
 File path: examples/autograd/train.py
 ##########
 @@ -0,0 +1,261 @@
+#
+# 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 singa_wrap as singa
+from singa import opt
+from singa import device
+from singa import tensor
+import numpy as np
+import time
+import argparse
+from PIL import Image
+
+
+# Data Augmentation
+def augmentation(x, batch_size):
+    xpad = np.pad(x, [[0, 0], [0, 0], [4, 4], [4, 4]], 'symmetric')
+    for data_num in range(0, batch_size):
+        offset = np.random.randint(8, size=2)
+        x[data_num, :, :, :] = xpad[data_num, :,
+                                    offset[0]:offset[0] + x.shape[2],
+                                    offset[1]:offset[1] + x.shape[2]]
+        if_flip = np.random.randint(2)
+        if (if_flip):
+            x[data_num, :, :, :] = x[data_num, :, :, ::-1]
+    return x
+
+
+# Calculate Accuracy
+def accuracy(pred, target):
+    # y is binary array for ground truth
+    y = np.argmax(pred, axis=1)
+    # t is integer for ground truth, the dim of t is (batch, 1)
+    t = np.max(target, axis=1)
+    a = y == t
+    correct = np.array(a, "int").sum()
+    # print(correct)
+    return correct
+
+
+# Data partition according to the rank
+def partition(global_rank, world_size, train_x, train_y, val_x, val_y):
+    # Partition training data
+    data_per_rank = train_x.shape[0] // world_size
+    idx_start = global_rank * data_per_rank
+    idx_end = (global_rank + 1) * data_per_rank
+    train_x = train_x[idx_start:idx_end]
+    train_y = train_y[idx_start:idx_end]
+    # Partition evaluation data
+    data_per_rank = val_x.shape[0] // world_size
+    idx_start = global_rank * data_per_rank
+    idx_end = (global_rank + 1) * data_per_rank
+    val_x = val_x[idx_start:idx_end]
+    val_y = val_y[idx_start:idx_end]
+    return train_x, train_y, val_x, val_y
+
+
+# Function to all reduce NUMPY Accuracy and Loss from Multiple Devices
+def reduce_variable(variable, dist_opt, reducer):
+    reducer.copy_from_numpy(variable)
+    dist_opt.all_reduce(reducer.data)
+    dist_opt.wait()
+    output = tensor.to_numpy(reducer)
+    return output
+
+
+def resize_dataset(x, image_size):
+    num_data = x.shape[0]
+    dim = x.shape[1]
+    X = np.zeros(shape=(num_data, dim, image_size, image_size), dtype=np.float32)
+    for n in range(0, num_data):
+        for d in range(0, dim):
+            X[n, d, :, :] = np.array(Image.fromarray(x[n, d, :, :]).resize(
+                (image_size, image_size), Image.BILINEAR),
+                                     dtype=np.float32)
+    return X
+
+
+def run(global_rank, world_size, local_rank, max_epoch, batch_size, model, data,
+        sgd):
+    dev = device.create_cuda_gpu_on(local_rank)
+    dev.SetRandSeed(0)
+    np.random.seed(0)
+
+    if data == 'cifar10':
+        from data import cifar10
+        train_x, train_y, val_x, val_y = cifar10.load()
+    elif data == 'mnist':
+        from data import mnist
+        train_x, train_y, val_x, val_y = mnist.load()
+
+    num_channels = train_x.shape[1]
+    image_size = train_x.shape[2]
+
+    print(num_channels)
+
+    if model == 'resnet':
+        from model import resnet
+        model = resnet.resnet50(num_channels=num_channels)
+    elif model == 'xceptionnet':
+        from model import xceptionnet
+        model = xceptionnet.create_model(num_channels=num_channels)
+    elif model == 'cnn':
+        from model import cnn
+        model = cnn.create_model(num_channels=num_channels)
+
+    # For distributed training, sequential gives better performance
+    if hasattr(sgd, "communicator"):
+        DIST = True
+        sequential = True
+    else:
+        DIST = False
+        sequential = False
+
+    if DIST:
+        train_x, train_y, val_x, val_y = partition(global_rank, world_size,
+                                                   train_x, train_y, val_x,
+                                                   val_y)
+    '''
+    # check dataset shape correctness
+    if global_rank == 0:
+        print("Check the shape of dataset:")
+        print(train_x.shape)
+        print(train_y.shape)
+    '''
+
+    tx = tensor.Tensor(
+        (batch_size, num_channels, model.input_size, model.input_size), dev,
+        tensor.float32)
+    ty = tensor.Tensor((batch_size,), dev, tensor.int32)
+    num_train_batch = train_x.shape[0] // batch_size
+    num_val_batch = val_x.shape[0] // batch_size
+    idx = np.arange(train_x.shape[0], dtype=np.int32)
+
+    # attached model to graph
+    model.on_device(dev)
+    model.set_optimizer(sgd)
+    model.graph(True, sequential)
+
+    # Training and Evaluation Loop
+    for epoch in range(max_epoch):
+        start_time = time.time()
+        np.random.shuffle(idx)
+
+        if global_rank == 0:
+            print('Starting Epoch %d:' % (epoch))
+
+        # Training Phase
+        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)
+
+        model.train()
+        for b in range(num_train_batch):
+            # Generate the patch data in this iteration
+            x = train_x[idx[b * batch_size:(b + 1) * batch_size]]
+            x = augmentation(x, batch_size)
+            if (image_size != model.input_size):
+                x = resize_dataset(x, model.input_size)
+            y = train_y[idx[b * batch_size:(b + 1) * batch_size]]
+
+            # Copy the patch data into input tensors
+            tx.copy_from_numpy(x)
+            ty.copy_from_numpy(y)
+
+            # Train the model
+            out = model(tx)
+            loss = model.loss(out, ty)
+            model.optim(loss)
+            train_correct += accuracy(tensor.to_numpy(out),y)
+            train_loss += tensor.to_numpy(loss)[0]
+
+        if DIST:
+            # Reduce the Evaluation Accuracy and Loss from Multiple Devices
+            reducer = tensor.Tensor((1,), dev, tensor.float32)
+            train_correct = reduce_variable(train_correct, sgd, reducer)
+            train_loss = reduce_variable(train_loss, sgd, reducer)
+
+        if global_rank == 0:
+            print('Training loss = %f, training accuracy = %f' %
+                  (train_loss, train_correct /
+                   (num_train_batch * batch_size * world_size)),
+                  flush=True)
+
+        # Evaluation Phase
+        model.eval()
+        for b in range(num_val_batch):
+            x = val_x[b * batch_size:(b + 1) * batch_size]
+            if (image_size != model.input_size):
+                x = resize_dataset(x, model.input_size)
+            y = val_y[b * batch_size:(b + 1) * batch_size]
+            tx.copy_from_numpy(x)
+            ty.copy_from_numpy(y)
+            out_test = model(tx)
+            test_correct += accuracy(tensor.to_numpy(out_test),y)
+
+        if DIST:
+            # Reduce the Evaulation Accuracy from Multiple Devices
+            test_correct = reduce_variable(test_correct, sgd, reducer)
+
+        # Output the Evaluation Accuracy
+        if global_rank == 0:
+            print('Evaluation accuracy = %f, Elapsed Time = %fs' %
+                  (test_correct / (num_val_batch * batch_size * world_size),
+                   time.time() - start_time),
+                  flush=True)
+
+
+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=['resnet', 'xceptionnet', 'cnn'],
+                        default='cnn')
+    parser.add_argument('data', choices=['cifar10', 'mnist'], default='mnist')
+    parser.add_argument('--epoch',
+                        '--max-epoch',
+                        default=10,
+                        type=int,
+                        help='maximum epochs',
+                        dest='max_epoch')
+    parser.add_argument('--bs',
+                        '--batch-size',
+                        default=64,
+                        type=int,
+                        help='batch size',
+                        dest='batch_size')
+    parser.add_argument('--lr',
+                        '--learning-rate',
+                        default=0.005,
+                        type=float,
+                        help='initial learning rate',
+                        dest='lr')
+    # determine which gpu to use in gpu
+    parser.add_argument('--id',
 
 Review comment:
   the same for train_mpi and train_multiprocess.py

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