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# 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
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from __future__ import division
import argparse, time, os
import logging
import mxnet as mx
from mxnet import gluon
from mxnet import profiler
from mxnet.gluon import nn
from mxnet.gluon.model_zoo import vision as models
from mxnet import autograd as ag
from mxnet.test_utils import get_mnist_iterator
from mxnet.metric import Accuracy, TopKAccuracy, CompositeEvalMetric
import numpy as np
from data import (get_cifar10_iterator, get_imagenet_iterator,
get_caltech101_iterator, dummy_iterator)
# logging
fh = logging.FileHandler('image-classification.log')
logger = logging.getLogger()
formatter = logging.Formatter('%(message)s')
logging.debug('\n%s', '-' * 100)
formatter = logging.Formatter('%(asctime)s %(levelname)s %(message)s')
parser = argparse.ArgumentParser(description='Train a model for image classification.')
parser.add_argument('--dataset', type=str, default='cifar10',
help='dataset to use. options are mnist, cifar10, caltech101, imagenet and dummy.')
parser.add_argument('--data-dir', type=str, default='',
help='training directory of imagenet images, contains train/val subdirs.')
parser.add_argument('--num-worker', '-j', dest='num_workers', default=4, type=int,
help='number of workers for dataloader')
parser.add_argument('--batch-size', type=int, default=32,
help='training batch size per device (CPU/GPU).')
parser.add_argument('--gpus', type=str, default='',
help='ordinates of gpus to use, can be "0,1,2" or empty for cpu only.')
parser.add_argument('--epochs', type=int, default=120,
help='number of training epochs.')
parser.add_argument('--lr', type=float, default=0.1,
help='learning rate. default is 0.1.')
parser.add_argument('--momentum', type=float, default=0.9,
help='momentum value for optimizer, default is 0.9.')
parser.add_argument('--wd', type=float, default=0.0001,
help='weight decay rate. default is 0.0001.')
parser.add_argument('--seed', type=int, default=123,
help='random seed to use. Default=123.')
parser.add_argument('--mode', type=str,
help='mode in which to train the model. options are symbolic, imperative, hybrid')
parser.add_argument('--model', type=str, required=True,
help='type of model to use. see vision_model for options.')
parser.add_argument('--use_thumbnail', action='store_true',
help='use thumbnail or not in resnet. default is false.')
parser.add_argument('--batch-norm', action='store_true',
help='enable batch normalization or not in vgg. default is false.')
parser.add_argument('--use-pretrained', action='store_true',
help='enable using pretrained model from gluon.')
parser.add_argument('--prefix', default='', type=str,
help='path to checkpoint prefix, default is current working dir')
parser.add_argument('--start-epoch', default=0, type=int,
help='starting epoch, 0 for fresh training, > 0 to resume')
parser.add_argument('--resume', type=str, default='',
help='path to saved weight where you want resume')
parser.add_argument('--lr-factor', default=0.1, type=float,
help='learning rate decay ratio')
parser.add_argument('--lr-steps', default='30,60,90', type=str,
help='list of learning rate decay epochs as in str')
parser.add_argument('--dtype', default='float32', type=str,
help='data type, float32 or float16 if applicable')
parser.add_argument('--save-frequency', default=10, type=int,
help='epoch frequence to save model, best model will always be saved')
parser.add_argument('--kvstore', type=str, default='device',
help='kvstore to use for trainer/module.')
parser.add_argument('--log-interval', type=int, default=50,
help='Number of batches to wait before logging.')
parser.add_argument('--profile', action='store_true',
help='Option to turn on memory profiling for front-end, '\
'and prints out the memory usage by python function at the end.')
parser.add_argument('--builtin-profiler', type=int, default=0, help='Enable built-in profiler (0=off, 1=on)')
opt = parser.parse_args()
# global variables'Starting new image-classification task:, %s',opt)
model_name = opt.model
dataset_classes = {'mnist': 10, 'cifar10': 10, 'caltech101':101, 'imagenet': 1000, 'dummy': 1000}
batch_size, dataset, classes = opt.batch_size, opt.dataset, dataset_classes[opt.dataset]
context = [mx.gpu(int(i)) for i in opt.gpus.split(',')] if opt.gpus.strip() else [mx.cpu()]
num_gpus = len(context)
batch_size *= max(1, num_gpus)
lr_steps = [int(x) for x in opt.lr_steps.split(',') if x.strip()]
metric = CompositeEvalMetric([Accuracy(), TopKAccuracy(5)])
kv = mx.kv.create(opt.kvstore)
def get_model(model, ctx, opt):
"""Model initialization."""
kwargs = {'ctx': ctx, 'pretrained': opt.use_pretrained, 'classes': classes}
if model.startswith('resnet'):
kwargs['thumbnail'] = opt.use_thumbnail
elif model.startswith('vgg'):
kwargs['batch_norm'] = opt.batch_norm
net = models.get_model(model, **kwargs)
if opt.resume:
elif not opt.use_pretrained:
if model in ['alexnet']:
return net
net = get_model(opt.model, context, opt)
def get_data_iters(dataset, batch_size, opt):
"""get dataset iterators"""
if dataset == 'mnist':
train_data, val_data = get_mnist_iterator(batch_size, (1, 28, 28),
num_parts=kv.num_workers, part_index=kv.rank)
elif dataset == 'cifar10':
train_data, val_data = get_cifar10_iterator(batch_size, (3, 32, 32),
num_parts=kv.num_workers, part_index=kv.rank)
elif dataset == 'imagenet':
shape_dim = 299 if model_name == 'inceptionv3' else 224
if not opt.data_dir:
raise ValueError('Dir containing raw images in train/val is required for imagenet.'
'Please specify "--data-dir"')
train_data, val_data = get_imagenet_iterator(opt.data_dir, batch_size,
opt.num_workers, shape_dim, opt.dtype)
elif dataset == 'caltech101':
train_data, val_data = get_caltech101_iterator(batch_size, opt.num_workers, opt.dtype)
elif dataset == 'dummy':
shape_dim = 299 if model_name == 'inceptionv3' else 224
train_data, val_data = dummy_iterator(batch_size, (3, shape_dim, shape_dim))
return train_data, val_data
def test(ctx, val_data):
for batch in val_data:
data = gluon.utils.split_and_load([0].astype(opt.dtype, copy=False),
ctx_list=ctx, batch_axis=0)
label = gluon.utils.split_and_load(batch.label[0].astype(opt.dtype, copy=False),
ctx_list=ctx, batch_axis=0)
outputs = [net(X) for X in data]
metric.update(label, outputs)
return metric.get()
def update_learning_rate(lr, trainer, epoch, ratio, steps):
"""Set the learning rate to the initial value decayed by ratio every N epochs."""
new_lr = lr * (ratio ** int(np.sum(np.array(steps) < epoch)))
return trainer
def save_checkpoint(epoch, top1, best_acc):
if opt.save_frequency and (epoch + 1) % opt.save_frequency == 0:
fname = os.path.join(opt.prefix, '%s_%d_acc_%.4f.params' % (opt.model, epoch, top1))
net.save_parameters(fname)'[Epoch %d] Saving checkpoint to %s with Accuracy: %.4f', epoch, fname, top1)
if top1 > best_acc[0]:
best_acc[0] = top1
fname = os.path.join(opt.prefix, '%s_best.params' % (opt.model))
net.save_parameters(fname)'[Epoch %d] Saving checkpoint to %s with Accuracy: %.4f', epoch, fname, top1)
def train(opt, ctx):
if isinstance(ctx, mx.Context):
ctx = [ctx]
train_data, val_data = get_data_iters(dataset, batch_size, opt)
trainer = gluon.Trainer(net.collect_params(), 'sgd',
'wd': opt.wd,
'momentum': opt.momentum,
'multi_precision': True},
loss = gluon.loss.SoftmaxCrossEntropyLoss()
total_time = 0
num_epochs = 0
best_acc = [0]
for epoch in range(opt.start_epoch, opt.epochs):
trainer = update_learning_rate(, trainer, epoch, opt.lr_factor, lr_steps)
tic = time.time()
btic = time.time()
for i, batch in enumerate(train_data):
data = gluon.utils.split_and_load([0].astype(opt.dtype), ctx_list=ctx, batch_axis=0)
label = gluon.utils.split_and_load(batch.label[0].astype(opt.dtype), ctx_list=ctx, batch_axis=0)
outputs = []
Ls = []
with ag.record():
for x, y in zip(data, label):
z = net(x)
L = loss(z, y)
# store the loss and do backward after we have done forward
# on all GPUs for better speed on multiple GPUs.
metric.update(label, outputs)
if opt.log_interval and not (i+1)%opt.log_interval:
name, acc = metric.get()'Epoch[%d] Batch [%d]\tSpeed: %f samples/sec\t%s=%f, %s=%f'%(
epoch, i, batch_size/(time.time()-btic), name[0], acc[0], name[1], acc[1]))
btic = time.time()
epoch_time = time.time()-tic
# First epoch will usually be much slower than the subsequent epics,
# so don't factor into the average
if num_epochs > 0:
total_time = total_time + epoch_time
num_epochs = num_epochs + 1
name, acc = metric.get()'[Epoch %d] training: %s=%f, %s=%f'%(epoch, name[0], acc[0], name[1], acc[1]))'[Epoch %d] time cost: %f'%(epoch, epoch_time))
name, val_acc = test(ctx, val_data)'[Epoch %d] validation: %s=%f, %s=%f'%(epoch, name[0], val_acc[0], name[1], val_acc[1]))
# save model if meet requirements
save_checkpoint(epoch, val_acc[0], best_acc)
if num_epochs > 1:
print('Average epoch time: {}'.format(float(total_time)/(num_epochs - 1)))
def main():
if opt.builtin_profiler > 0:
profiler.set_config(profile_all=True, aggregate_stats=True)
if opt.mode == 'symbolic':
data = mx.sym.var('data')
if opt.dtype == 'float16':
data = mx.sym.Cast(data=data, dtype=np.float16)
out = net(data)
if opt.dtype == 'float16':
out = mx.sym.Cast(data=out, dtype=np.float32)
softmax = mx.sym.SoftmaxOutput(out, name='softmax')
mod = mx.mod.Module(softmax, context=context)
train_data, val_data = get_data_iters(dataset, batch_size, opt),
batch_end_callback = mx.callback.Speedometer(batch_size, max(1, opt.log_interval)),
epoch_end_callback = mx.callback.do_checkpoint('image-classifier-%s'% opt.model),
optimizer = 'sgd',
optimizer_params = {'learning_rate':, 'wd': opt.wd, 'momentum': opt.momentum, 'multi_precision': True},
initializer = mx.init.Xavier(magnitude=2))
mod.save_parameters('image-classifier-%s-%d-final.params'%(opt.model, opt.epochs))
if opt.mode == 'hybrid':
train(opt, context)
if opt.builtin_profiler > 0:
if __name__ == '__main__':
if opt.profile:
import hotshot, hotshot.stats
prof = hotshot.Profile(''%(opt.model, opt.mode))
stats = hotshot.stats.load(''%(opt.model, opt.mode))
stats.sort_stats('cumtime', 'calls')
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