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Posted to commits@singa.apache.org by zh...@apache.org on 2023/09/22 12:07:47 UTC
[singa] branch dev-postgresql updated: Add the training script for CNN models
This is an automated email from the ASF dual-hosted git repository.
zhaojing pushed a commit to branch dev-postgresql
in repository https://gitbox.apache.org/repos/asf/singa.git
The following commit(s) were added to refs/heads/dev-postgresql by this push:
new 41a37727 Add the training script for CNN models
new 931a3129 Merge pull request #1101 from daoducanhc/chris-sep-22th
41a37727 is described below
commit 41a37727dd4bfb4d099f3d7d7ea5bcf2d36b9953
Author: daoducanhc <da...@gmail.com>
AuthorDate: Fri Sep 22 13:11:15 2023 +0800
Add the training script for CNN models
---
examples/model_selection_psql/ms_mlp/train_cnn.py | 329 ++++++++++++++++++++++
1 file changed, 329 insertions(+)
diff --git a/examples/model_selection_psql/ms_mlp/train_cnn.py b/examples/model_selection_psql/ms_mlp/train_cnn.py
new file mode 100644
index 00000000..6eab096b
--- /dev/null
+++ b/examples/model_selection_psql/ms_mlp/train_cnn.py
@@ -0,0 +1,329 @@
+#
+# 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
+
+np_dtype = {"float16": np.float16, "float32": np.float32}
+
+singa_dtype = {"float16": tensor.float16, "float32": tensor.float32}
+
+
+# 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()
+ 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='plain',
+ spars=None,
+ precision='float32'):
+ dev = device.create_cuda_gpu_on(local_rank) # need to change to CPU device for CPU-only machines
+ 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 == 'cifar100':
+ from data import cifar100
+ train_x, train_y, val_x, val_y = cifar100.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]
+ data_size = np.prod(train_x.shape[1:train_x.ndim]).item()
+ num_classes = (np.max(train_y) + 1).item()
+
+ 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)
+ elif model == 'mlp':
+ import os, sys, inspect
+ current = os.path.dirname(
+ os.path.abspath(inspect.getfile(inspect.currentframe())))
+ parent = os.path.dirname(current)
+ sys.path.insert(0, parent)
+ from mlp import model
+ model = model.create_model(data_size=data_size,
+ num_classes=num_classes)
+
+ # For distributed training, sequential has 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)
+
+ if model.dimension == 4:
+ tx = tensor.Tensor(
+ (batch_size, num_channels, model.input_size, model.input_size), dev,
+ singa_dtype[precision])
+ elif model.dimension == 2:
+ tx = tensor.Tensor((batch_size, data_size), dev, singa_dtype[precision])
+ 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)
+
+ # Attach model to graph
+ model.set_optimizer(sgd)
+ model.compile([tx], is_train=True, use_graph=graph, sequential=sequential)
+ dev.SetVerbosity(verbosity)
+
+ # 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):
+ # if b % 100 == 0:
+ # print ("b: \n", b)
+ # Generate the patch data in this iteration
+ x = train_x[idx[b * batch_size:(b + 1) * batch_size]]
+ if model.dimension == 4:
+ x = augmentation(x, batch_size)
+ if (image_size != model.input_size):
+ x = resize_dataset(x, model.input_size)
+ x = x.astype(np_dtype[precision])
+ 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]
+ if model.dimension == 4:
+ if (image_size != model.input_size):
+ x = resize_dataset(x, model.input_size)
+ x = x.astype(np_dtype[precision])
+ 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 __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', 'resnet', 'xceptionnet', 'mlp', 'alexnet'],
+ default='cnn')
+ parser.add_argument('data',
+ choices=['mnist', 'cifar10', 'cifar100'],
+ default='mnist')
+ parser.add_argument('-p',
+ choices=['float32', 'float16'],
+ default='float32',
+ dest='precision')
+ parser.add_argument('-m',
+ '--max-epoch',
+ default=100,
+ type=int,
+ help='maximum epochs',
+ dest='max_epoch')
+ parser.add_argument('-b',
+ '--batch-size',
+ default=64,
+ type=int,
+ help='batch size',
+ dest='batch_size')
+ parser.add_argument('-l',
+ '--learning-rate',
+ default=0.005,
+ type=float,
+ help='initial learning rate',
+ dest='lr')
+ # Determine which gpu to use
+ parser.add_argument('-i',
+ '--device-id',
+ default=0,
+ type=int,
+ help='which GPU to use',
+ dest='device_id')
+ 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()
+
+ sgd = opt.SGD(lr=args.lr, momentum=0.9, weight_decay=1e-5, dtype=singa_dtype[args.precision])
+ run(0,
+ 1,
+ args.device_id,
+ args.max_epoch,
+ args.batch_size,
+ args.model,
+ args.data,
+ sgd,
+ args.graph,
+ args.verbosity,
+ precision=args.precision)