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The following commit(s) were added to refs/heads/dev by this push:
new 8b52cd7 Large dataset CNN
new 980ea11 Merge pull request #827 from chrishkchris/large_dataset
8b52cd7 is described below
commit 8b52cd79f4de8f02d357231fc8d01677eb517c5c
Author: Chris Yeung <[email protected]>
AuthorDate: Thu Jan 14 15:43:28 2021 +0800
Large dataset CNN
---
examples/largedataset_cnn/README.md | 33 +++
examples/largedataset_cnn/process_data.py | 82 ++++++++
examples/largedataset_cnn/train_largedata.py | 301 +++++++++++++++++++++++++++
examples/largedataset_cnn/train_mpi.py | 83 ++++++++
4 files changed, 499 insertions(+)
diff --git a/examples/largedataset_cnn/README.md
b/examples/largedataset_cnn/README.md
new file mode 100644
index 0000000..7d71279
--- /dev/null
+++ b/examples/largedataset_cnn/README.md
@@ -0,0 +1,33 @@
+<!--
+ 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.
+-->
+
+# Image Classification using Convolutional Neural Networks with a dataset from
the filesysetm
+
+Examples inside this folder show how to train CNN models using SINGA for image
classification.
+
+It read the dataset from the filesystem defined by `process_data.py`. Hence,
users can modify `process_data.py`
+for their perference of dataset format.
+
+Before running the code, the `model` folder in `examples/cnn` should be copied
to this directory.
+
+* `train_largedata.py` is the training script, which controls the training
flow by
+ doing BackPropagation and SGD update.
+
+* `train_mpi.py` is the script for distributed training (among multiple nodes)
+ using MPI and NCCL for communication.
diff --git a/examples/largedataset_cnn/process_data.py
b/examples/largedataset_cnn/process_data.py
new file mode 100644
index 0000000..0dd3c03
--- /dev/null
+++ b/examples/largedataset_cnn/process_data.py
@@ -0,0 +1,82 @@
+import os
+import imghdr
+import pickle as pkl
+import numpy as np
+from PIL import Image
+
+def paths_to_images(paths, image_size):
+ num_images=len(paths)
+ im = np.zeros((num_images,3,image_size,image_size), dtype=np.float32)
+
+ for i in range(num_images):
+ temp =
np.array(Image.open(paths[i]).convert('RGB').resize((image_size, image_size),
Image.BILINEAR))
+ temp = np.moveaxis(temp,-1,0)
+ im[i] = temp
+
+ im /= 255
+
+ return im
+
+
+def process_data(dataset_root, classes):
+ # load class names
+ with open(classes, 'r', encoding='utf-8') as f:
+ classes = f.readlines()
+ classes = list(map(lambda x: x.strip(), classes))
+ num_classes = len(classes)
+
+ # make input_paths and labels
+ input_paths, labels = [], []
+ for class_name in os.listdir(dataset_root):
+ class_root = os.path.join(dataset_root, class_name)
+ class_id = classes.index(class_name)
+ for path in os.listdir(class_root):
+ path = os.path.join(class_root, path)
+ if imghdr.what(path) is None:
+ # this is not an image file
+ continue
+ input_paths.append(path)
+ labels.append(class_id)
+
+ # convert to numpy array
+ input_paths = np.array(input_paths)
+ labels = np.array(labels, dtype=np.int32)
+
+ # shuffle dataset
+ np.random.seed(0)
+ perm = np.random.permutation(len(input_paths))
+ input_paths = input_paths[perm]
+ labels = labels[perm]
+
+ # split dataset for training and validation
+ border = int(len(input_paths) * 0.8)
+ train_labels = labels[:border]
+ val_labels = labels[border:]
+ train_input_paths = input_paths[:border]
+ val_input_paths = input_paths[border:]
+
+
+ print("Training on %d images and labels" % (len(train_input_paths)))
+ print("Validation on %d images and labels" % (len(val_input_paths)))
+
+ return train_input_paths, train_labels, val_input_paths, val_labels
+
+def loaddata():
+ dataset_root = '/Dataset/Data/'
+ classes = '/Dataset/classes.txt'
+ return process_data(dataset_root, classes)
+
+if __name__ == '__main__':
+
+ # test script in main
+ train_input_paths, train_labels, val_input_paths, val_labels = loaddata()
+
+ print(train_input_paths.shape)
+ print(train_labels.shape)
+ print(val_input_paths.shape)
+ print(val_labels.shape)
+
+ a=paths_to_images(paths=train_input_paths[0:5], image_size=299)
+ print(a)
+ print(a.shape)
+
\ No newline at end of file
diff --git a/examples/largedataset_cnn/train_largedata.py
b/examples/largedataset_cnn/train_largedata.py
new file mode 100644
index 0000000..dad4670
--- /dev/null
+++ b/examples/largedataset_cnn/train_largedata.py
@@ -0,0 +1,301 @@
+#
+# 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 device
+from singa import tensor
+from singa import opt
+import numpy as np
+import time
+import argparse
+from PIL import Image
+import process_data
+
+# 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 network output to be compared with ground truth (int)
+ y = np.argmax(pred, axis=1)
+ a = y == target
+ 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,
+ graph,
+ verbosity,
+ dist_option='fp32',
+ spars=None):
+ dev = device.create_cuda_gpu_on(local_rank)
+ dev.SetRandSeed(0)
+ np.random.seed(0)
+
+
+ train_x, train_y, val_x, val_y = process_data.loadfood172()
+
+ num_channels = 3
+ num_classes = (np.max(train_y) + 1).item()
+ print(num_classes)
+
+ if model == 'resnet':
+ from model import resnet
+ model = resnet.resnet50(num_channels=num_channels,
+ num_classes=num_classes)
+ elif model == 'xceptionnet':
+ from model import xceptionnet
+ model = xceptionnet.create_model(num_channels=num_channels,
+ num_classes=num_classes)
+ elif model == 'cnn':
+ from model import cnn
+ model = cnn.create_model(num_channels=num_channels,
+ num_classes=num_classes)
+ elif model == 'alexnet':
+ from model import alexnet
+ model = alexnet.create_model(num_channels=num_channels,
+ num_classes=num_classes)
+
+ # 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)
+ '''
+
+ if model.dimension == 4:
+ tx = tensor.Tensor(
+ (batch_size, num_channels, model.input_size, model.input_size),
dev,
+ tensor.float32)
+ elif model.dimension == 2:
+ tx = tensor.Tensor((batch_size, data_size), dev, tensor.float32)
+ np.reshape(train_x, (train_x.shape[0], -1))
+ np.reshape(val_x, (val_x.shape[0], -1))
+
+ 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.compile([tx], is_train=True, use_graph=graph, sequential=sequential)
+ dev.SetVerbosity(verbosity)
+
+ checkpointpath="checkpoint.zip"
+
+ import os
+ if os.path.exists(checkpointpath):
+ model.load_states(fpath=checkpointpath)
+
+ # 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 = process_data.paths_to_images(x,model.input_size)
+ if model.dimension == 4:
+ x = augmentation(x, batch_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, loss = model(tx, ty, dist_option, spars)
+ 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]
+ x = process_data.paths_to_images(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)
+
+ dev.PrintTimeProfiling()
+
+ if global_rank == 0:
+ if os.path.exists(checkpointpath):
+ os.remove(checkpointpath)
+ model.save_states(checkpointpath)
+
+
+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', 'alexnet'],
+ default='cnn')
+ 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
+ parser.add_argument('--id',
+ '--device-id',
+ default=0,
+ type=int,
+ help='which GPU to use',
+ dest='device_id')
+ parser.add_argument('--no-graph',
+ '--disable-graph',
+ default='True',
+ action='store_false',
+ help='disable graph',
+ dest='graph')
+ parser.add_argument('--verbosity',
+ '--log-verbosity',
+ default=0,
+ type=int,
+ help='logging verbosity',
+ dest='verbosity')
+
+ args = parser.parse_args()
+
+ sgd = opt.SGD(lr=args.lr, momentum=0.9, weight_decay=1e-5)
+ run(0, 1, args.device_id, args.max_epoch, args.batch_size, args.model,
+ "no", sgd, args.graph, args.verbosity)
diff --git a/examples/largedataset_cnn/train_mpi.py
b/examples/largedataset_cnn/train_mpi.py
new file mode 100644
index 0000000..28d260b
--- /dev/null
+++ b/examples/largedataset_cnn/train_mpi.py
@@ -0,0 +1,83 @@
+#
+# 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
+import argparse
+import train_largedata
+
+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', 'mlp'],
+ default='cnn')
+ 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')
+ parser.add_argument('--op',
+ '--option',
+ default='fp32',
+
choices=['fp32','fp16','partialUpdate','sparseTopK','sparseThreshold'],
+ help='distibuted training options',
+ dest='dist_option') # currently partialUpdate support
graph=False only
+ parser.add_argument('--spars',
+ '--sparsification',
+ default='0.05',
+ type=float,
+ help='the sparsity parameter used for sparsification,
between 0 to 1',
+ dest='spars')
+ parser.add_argument('--no-graph',
+ '--disable-graph',
+ default='True',
+ action='store_false',
+ help='disable graph',
+ dest='graph')
+ parser.add_argument('--verbosity',
+ '--log-verbosity',
+ default=0,
+ type=int,
+ help='logging verbosity',
+ dest='verbosity')
+
+ args = parser.parse_args()
+
+ sgd = opt.SGD(lr=args.lr, momentum=0.9, weight_decay=1e-5)
+ sgd = opt.DistOpt(sgd)
+
+ train_largedata.run(sgd.global_rank, sgd.world_size, sgd.local_rank,
args.max_epoch,
+ args.batch_size, args.model, "no", sgd, args.graph,
+ args.verbosity, args.dist_option, args.spars)
