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train_cifar.py
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train_cifar.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @File : train_cifar.py
# @Author: Piston Yang
# @Date : 18-9-6
from model.residual_attention_network import ResidualAttentionModel_32input
import mxnet as mx
from mxnet import gluon, image, nd, autograd
from mxnet.gluon import loss as gloss
import numpy as np
import time
import os
from lib.piston_util import cutout
from gluoncv.utils.lr_scheduler import LRSequential, LRScheduler
import logging
os.environ['MXNET_GLUON_REPO'] = 'https://apache-mxnet.s3.cn-north-1.amazonaws.com.cn/'
batch_size = 128
train_samples = int(5e4)
dtype = 'float32'
assert dtype in ('float32', 'float16')
random_eraser = cutout()
def transformer(data, label):
im = data.asnumpy()
im = np.pad(im, pad_width=((4, 4), (4, 4), (0, 0)), mode='constant')
im = random_eraser(im)
im = nd.array(im) / 255.
auglist = image.CreateAugmenter(data_shape=(3, 32, 32), rand_crop=True, rand_mirror=True,
mean=mx.nd.array([0.4914, 0.4824, 0.4467]),
std=mx.nd.array([0.2471, 0.2435, 0.2616]))
for aug in auglist:
im = aug(im)
im = nd.transpose(im, (2, 0, 1))
return im, label
def trans_test(data, label):
im = data.astype(np.float32) / 255.
auglist = image.CreateAugmenter(data_shape=(3, 32, 32),
mean=mx.nd.array([0.4914, 0.4824, 0.4467]),
std=mx.nd.array([0.2471, 0.2435, 0.2616]))
for aug in auglist:
im = aug(im)
im = nd.transpose(im, (2, 0, 1))
return im, label
train_data = gluon.data.DataLoader(
gluon.data.vision.CIFAR10(train=True, transform=transformer),
batch_size=batch_size, shuffle=True, num_workers=12, last_batch='discard')
val_data = gluon.data.DataLoader(
gluon.data.vision.CIFAR10(train=False, transform=trans_test),
batch_size=batch_size, last_batch='keep')
def label_transform(label, classes):
ind = label.astype('int')
res = nd.zeros((ind.shape[0], classes), ctx=label.context)
res[nd.arange(ind.shape[0], ctx=label.context), ind] = 1
return res
def test(test_net, ctx, test_loader, iteration, logger):
Loss = gloss.SoftmaxCrossEntropyLoss()
metric = mx.metric.Accuracy()
test_loss = mx.metric.Loss()
for batch in test_loader:
data = gluon.utils.split_and_load(batch[0], ctx_list=ctx, batch_axis=0)
label = gluon.utils.split_and_load(batch[1], ctx_list=ctx, batch_axis=0)
outputs = [test_net(X.astype(dtype, copy=False)) for X in data]
losses = [Loss(yhat, y.astype(dtype, copy=False)) for yhat, y in zip(outputs, label)]
test_loss.update(0, losses)
metric.update(label, outputs)
_, test_acc = metric.get()
_, test_loss = test_loss.get()
test_net.save_parameters('cifar_param/test_epoch%d_%.5f.param' % (iteration, test_acc))
test_str = ("Test Loss: %f, Test acc %f." % (test_loss, test_acc))
logger.info(test_str)
def train(train_net, epochs, lr, wd, ctx, warmup_epochs, train_loader, test_loader, use_mixup, logger):
num_batches = train_samples // batch_size
lr_scheduler = LRSequential([
LRScheduler('linear', base_lr=0, target_lr=lr,
nepochs=warmup_epochs, iters_per_epoch=num_batches),
LRScheduler('cosine', base_lr=lr, target_lr=0,
nepochs=epochs - warmup_epochs,
iters_per_epoch=num_batches)
])
opt_params = {'learning_rate': lr, 'momentum': 0.9, 'wd': wd, 'lr_scheduler': lr_scheduler}
if dtype != 'float32':
opt_params['multi_precision'] = True
trainer = gluon.Trainer(train_net.collect_params(), 'nag', opt_params)
Loss = gluon.loss.SoftmaxCrossEntropyLoss(sparse_label=False)
metric = mx.metric.RMSE()
train_loss = mx.metric.Loss()
alpha = 1
classes = 10
print("Start training with mixup.")
for epoch in range(epochs):
metric.reset()
train_loss.reset()
st_time = time.time()
for i, batch in enumerate(train_loader):
lam = np.random.beta(alpha, alpha)
if epoch >= (epochs - 20) or not use_mixup:
lam = 1
data = gluon.utils.split_and_load(batch[0], ctx_list=ctx, batch_axis=0)
label = gluon.utils.split_and_load(batch[1], ctx_list=ctx, batch_axis=0)
trans = [lam * X + (1 - lam) * X[::-1] for X in data]
labels = []
for Y in label:
y1 = label_transform(Y, classes)
y2 = label_transform(Y[::-1], classes)
labels.append(lam * y1 + (1 - lam) * y2)
with autograd.record():
outputs = [train_net(X.astype(dtype, copy=False)) for X in trans]
losses = [Loss(yhat, y.astype(dtype, copy=False)) for yhat, y in zip(outputs, labels)]
for l in losses:
l.backward()
trainer.step(batch_size)
train_loss.update(0, losses)
metric.update(labels, outputs)
cur_time = time.time() - st_time
eps_samples = int(train_samples // cur_time)
_, train_acc = metric.get()
_, epoch_loss = train_loss.get()
epoch_str = ("Epoch %d. Loss: %.5f, Train RMSE %.5f. %d samples/s. lr %.5f"
% (epoch, epoch_loss, train_acc, eps_samples, trainer.learning_rate))
logger.info(epoch_str)
test(train_net, ctx, test_loader, epoch, logger)
if __name__ == '__main__':
from gluoncv.model_zoo.residual_attentionnet import cifar_residualattentionnet452
ctx = [mx.gpu(i) for i in range(4)]
assert batch_size // len(ctx) == 0, "Pre batch on each GPU should be same."
mix_up = True
no_wd = True
# net = ResidualAttentionModel_32input(additional_stage=True)
net = cifar_residualattentionnet452()
net.hybridize(static_alloc=True, static_shape=True)
net.initialize(init=mx.init.MSRAPrelu(), ctx=ctx)
if dtype != 'float32':
net.cast('float16')
if no_wd:
for k, v in net.collect_params('.*beta|.*gamma|.*bias').items():
v.wd_mult = 0.0
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
log_file_path = 'Attention92_cifar10_train.log'
fh = logging.FileHandler(log_file_path)
logger.addHandler(fh)
epochs = 220
lr = 0.1 * (batch_size // 64)
wd = 1e-4
train(train_net=net, epochs=epochs, lr=lr, wd=wd, ctx=ctx, warmup_epochs=5,
train_loader=train_data, test_loader=val_data, use_mixup=mix_up, logger=logger)