if grid.ndim == 3 and grid.shape[2] == 1:
grid = grid.squeeze()
return grid
# %%
# Fix the seed
# mx.random.seed(42)
# %%
# -- Get some data to train on
mnist_train = gluon.data.vision.datasets.MNIST(train=True)
mnist_valid = gluon.data.vision.datasets.MNIST(train=False)
transformer = transforms.Compose([transforms.ToTensor()])
# %%
# -- Create a data iterator that feeds batches
train_data = gluon.data.DataLoader(mnist_train.transform_first(transformer),
batch_size=BATCH_SIZE,
shuffle=True,
last_batch="rollover",
num_workers=N_WORKERS)
eval_data = gluon.data.DataLoader(mnist_valid.transform_first(transformer),
batch_size=BATCH_SIZE,
shuffle=False,
last_batch="rollover",
num_workers=N_WORKERS)
def forward(self, x):
x = mx.image.copyMakeBorder(x, self.padding, self.padding,
self.padding, self.padding)
# print(x.shape)
x, _ = mx.image.random_crop(x, (self.size, self.size))
return x
normalize = T.Normalize(mean=[0.491, 0.482, 0.447], std=[0.247, 0.243, 0.262])
train_transfrom = T.Compose([
RandomCrop(32, padding=4),
# T.RandomResizedCrop(32),
T.RandomFlipLeftRight(),
T.ToTensor(),
normalize
])
val_transform = T.Compose([T.ToTensor(), normalize])
trainset = datasets.CIFAR10('./data',
train=True).transform_first(train_transfrom)
trainloader = gluon.data.DataLoader(trainset,
batch_size=args.batch_size,
shuffle=True,
num_workers=2)
testset = datasets.CIFAR10('./data',
train=False).transform_first(val_transform)
testloader = gluon.data.DataLoader(testset,
Read with GluonCV
-----------------
The prepared dataset can be loaded with utility class :py:class:`gluoncv.data.ImageNet`
directly. Here is an example that randomly reads 128 images each time and
performs randomized resizing and cropping.
"""
from gluoncv.data import ImageNet
from mxnet.gluon.data import DataLoader
from mxnet.gluon.data.vision import transforms
train_trans = transforms.Compose(
[transforms.RandomResizedCrop(224),
transforms.ToTensor()])
# You need to specify ``root`` for ImageNet if you extracted the images into
# a different folder
train_data = DataLoader(ImageNet(train=True).transform_first(train_trans),
batch_size=128,
shuffle=True)
#########################################################################
for x, y in train_data:
print(x.shape, y.shape)
break
#########################################################################
# Plot some validation images
from gluoncv.utils import viz
def run_mnist(
hook=None,
set_modes=False,
num_steps_train=None,
num_steps_eval=None,
epochs=2,
save_interval=None,
save_path="./saveParams",
):
batch_size = 4
normalize_mean = 0.13
mnist_train = datasets.FashionMNIST(train=True)
X, y = mnist_train[0]
("X shape: ", X.shape, "X dtype", X.dtype, "y:", y)
text_labels = [
"t-shirt",
"trouser",
"pullover",
"dress",
"coat",
"sandal",
"shirt",
"sneaker",
"bag",
"ankle boot",
]
transformer = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize(normalize_mean, 0.31)])
mnist_train = mnist_train.transform_first(transformer)
mnist_valid = gluon.data.vision.FashionMNIST(train=False)
train_data = gluon.data.DataLoader(mnist_train,
batch_size=batch_size,
shuffle=True,
num_workers=4)
valid_data = gluon.data.DataLoader(
mnist_valid.transform_first(transformer),
batch_size=batch_size,
num_workers=4)
# Create Model in Gluon
net = nn.HybridSequential()
net.add(
nn.Conv2D(channels=6, kernel_size=5, activation="relu"),
nn.MaxPool2D(pool_size=2, strides=2),
nn.Conv2D(channels=16, kernel_size=3, activation="relu"),
nn.MaxPool2D(pool_size=2, strides=2),
nn.Flatten(),
nn.Dense(120, activation="relu"),
nn.Dense(84, activation="relu"),
nn.Dense(10),
)
net.initialize(init=init.Xavier(), ctx=mx.cpu())
if hook is not None:
# Register the forward Hook
hook.register_hook(net)
softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), "sgd",
{"learning_rate": 0.1})
hook.register_hook(softmax_cross_entropy)
# Start the training.
for epoch in range(epochs):
train_loss, train_acc, valid_acc = 0.0, 0.0, 0.0
tic = time.time()
if set_modes:
hook.set_mode(modes.TRAIN)
i = 0
for data, label in train_data:
data = data.as_in_context(mx.cpu(0))
# forward + backward
with autograd.record():
output = net(data)
loss = softmax_cross_entropy(output, label)
loss.backward()
# update parameters
trainer.step(batch_size)
# calculate training metrics
train_loss += loss.mean().asscalar()
train_acc += acc(output, label)
i += 1
if num_steps_train is not None and i >= num_steps_train:
break
# calculate validation accuracy
if set_modes:
hook.set_mode(modes.EVAL)
i = 0
for data, label in valid_data:
data = data.as_in_context(mx.cpu(0))
val_output = net(data)
valid_acc += acc(val_output, label)
loss = softmax_cross_entropy(val_output, label)
i += 1
if num_steps_eval is not None and i >= num_steps_eval:
break
print(
"Epoch %d: loss %.3f, train acc %.3f, test acc %.3f, in %.1f sec" %
(
epoch,
train_loss / len(train_data),
train_acc / len(train_data),
valid_acc / len(valid_data),
time.time() - tic,
))
if save_interval is not None and (epoch % save_interval) == 0:
net.save_parameters("{0}/params_{1}.params".format(
save_path, epoch))
def train_cifar10(config):
args = config.pop("args")
vars(args).update(config)
np.random.seed(args.seed)
random.seed(args.seed)
mx.random.seed(args.seed)
# Set Hyper-params
batch_size = args.batch_size * max(args.num_gpus, 1)
ctx = [mx.gpu(i)
for i in range(args.num_gpus)] if args.num_gpus > 0 else [mx.cpu()]
# Define DataLoader
transform_train = transforms.Compose([
gcv_transforms.RandomCrop(32, pad=4),
transforms.RandomFlipLeftRight(),
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465],
[0.2023, 0.1994, 0.2010]),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465],
[0.2023, 0.1994, 0.2010]),
])
train_data = gluon.data.DataLoader(
gluon.data.vision.CIFAR10(train=True).transform_first(transform_train),
batch_size=batch_size,
shuffle=True,
last_batch="discard",
num_workers=args.num_workers,
)
test_data = gluon.data.DataLoader(
gluon.data.vision.CIFAR10(train=False).transform_first(transform_test),
batch_size=batch_size,
shuffle=False,
num_workers=args.num_workers,
)
# Load model architecture and Initialize the net with pretrained model
finetune_net = get_model(args.model, pretrained=True)
with finetune_net.name_scope():
finetune_net.fc = nn.Dense(args.classes)
finetune_net.fc.initialize(init.Xavier(), ctx=ctx)
finetune_net.collect_params().reset_ctx(ctx)
finetune_net.hybridize()
# Define trainer
trainer = gluon.Trainer(
finetune_net.collect_params(),
"sgd",
{
"learning_rate": args.lr,
"momentum": args.momentum,
"wd": args.wd
},
)
L = gluon.loss.SoftmaxCrossEntropyLoss()
metric = mx.metric.Accuracy()
def train(epoch):
for i, batch in enumerate(train_data):
data = gluon.utils.split_and_load(batch[0],
ctx_list=ctx,
batch_axis=0,
even_split=False)
label = gluon.utils.split_and_load(batch[1],
ctx_list=ctx,
batch_axis=0,
even_split=False)
with ag.record():
outputs = [finetune_net(X) for X in data]
loss = [L(yhat, y) for yhat, y in zip(outputs, label)]
for ls in loss:
ls.backward()
trainer.step(batch_size)
mx.nd.waitall()
def test():
test_loss = 0
for i, batch in enumerate(test_data):
data = gluon.utils.split_and_load(batch[0],
ctx_list=ctx,
batch_axis=0,
even_split=False)
label = gluon.utils.split_and_load(batch[1],
ctx_list=ctx,
batch_axis=0,
even_split=False)
outputs = [finetune_net(X) for X in data]
loss = [L(yhat, y) for yhat, y in zip(outputs, label)]
test_loss += sum(ls.mean().asscalar() for ls in loss) / len(loss)
metric.update(label, outputs)
_, test_acc = metric.get()
test_loss /= len(test_data)
return test_loss, test_acc
for epoch in range(1, args.epochs + 1):
train(epoch)
test_loss, test_acc = test()
tune.report(mean_loss=test_loss, mean_accuracy=test_acc)
def main():
opt = parse_args()
batch_size = opt.batch_size
classes = 10
num_gpus = opt.num_gpus
batch_size *= max(1, num_gpus)
context = [mx.gpu(i) for i in range(num_gpus)] if num_gpus > 0 else [mx.cpu()]
num_workers = opt.num_workers
lr_decay = opt.lr_decay
lr_decay_epoch = [int(i) for i in opt.lr_decay_epoch.split(',')] + [np.inf]
model_name = opt.model
if model_name.startswith('cifar_wideresnet'):
kwargs = {'classes': classes,
'drop_rate': opt.drop_rate}
else:
kwargs = {'classes': classes}
net = get_model(model_name, **kwargs)
if opt.resume_from:
net.load_parameters(opt.resume_from, ctx = context)
optimizer = 'nag'
save_period = opt.save_period
if opt.save_dir and save_period:
save_dir = opt.save_dir
makedirs(save_dir)
else:
save_dir = ''
save_period = 0
plot_path = opt.save_plot_dir
logging.basicConfig(level=logging.INFO)
logging.info(opt)
transform_train = transforms.Compose([
gcv_transforms.RandomCrop(32, pad=4),
transforms.RandomFlipLeftRight(),
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010])
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010])
])
def test(ctx, val_data):
metric = mx.metric.Accuracy()
for i, batch in enumerate(val_data):
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 = [net(X) for X in data]
metric.update(label, outputs)
return metric.get()
def train(epochs, ctx):
if isinstance(ctx, mx.Context):
ctx = [ctx]
net.initialize(mx.init.Xavier(), ctx=ctx)
train_data = gluon.data.DataLoader(
gluon.data.vision.CIFAR10(train=True).transform_first(transform_train),
batch_size=batch_size, shuffle=True, last_batch='discard', num_workers=num_workers)
val_data = gluon.data.DataLoader(
gluon.data.vision.CIFAR10(train=False).transform_first(transform_test),
batch_size=batch_size, shuffle=False, num_workers=num_workers)
trainer = gluon.Trainer(net.collect_params(), optimizer,
{'learning_rate': opt.lr, 'wd': opt.wd, 'momentum': opt.momentum})
metric = mx.metric.Accuracy()
train_metric = mx.metric.Accuracy()
loss_fn = gluon.loss.SoftmaxCrossEntropyLoss()
train_history = TrainingHistory(['training-error', 'validation-error'])
iteration = 0
lr_decay_count = 0
best_val_score = 0
for epoch in range(epochs):
tic = time.time()
train_metric.reset()
metric.reset()
train_loss = 0
num_batch = len(train_data)
alpha = 1
if epoch == lr_decay_epoch[lr_decay_count]:
trainer.set_learning_rate(trainer.learning_rate*lr_decay)
lr_decay_count += 1
for i, batch in enumerate(train_data):
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)
with ag.record():
output = [net(X) for X in data]
loss = [loss_fn(yhat, y) for yhat, y in zip(output, label)]
for l in loss:
l.backward()
trainer.step(batch_size)
train_loss += sum([l.sum().asscalar() for l in loss])
train_metric.update(label, output)
name, acc = train_metric.get()
iteration += 1
train_loss /= batch_size * num_batch
name, acc = train_metric.get()
name, val_acc = test(ctx, val_data)
train_history.update([1-acc, 1-val_acc])
train_history.plot(save_path='%s/%s_history.png'%(plot_path, model_name))
if val_acc > best_val_score:
best_val_score = val_acc
net.save_parameters('%s/%.4f-cifar-%s-%d-best.params'%(save_dir, best_val_score, model_name, epoch))
logging.info('[Epoch %d] train=%f val=%f loss=%f time: %f' %
(epoch, acc, val_acc, train_loss, time.time()-tic))
if save_period and save_dir and (epoch + 1) % save_period == 0:
net.save_parameters('%s/cifar10-%s-%d.params'%(save_dir, model_name, epoch))
if save_period and save_dir:
net.save_parameters('%s/cifar10-%s-%d.params'%(save_dir, model_name, epochs-1))
if opt.mode == 'hybrid':
net.hybridize()
train(opt.num_epochs, context)
net = get_model(model_name, **kwargs)
net.cast(opt.dtype)
if opt.params_file:
net.load_params(opt.params_file, ctx=ctx)
net.hybridize()
acc_top1 = mx.metric.Accuracy()
acc_top5 = mx.metric.TopKAccuracy(5)
normalize = transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
transform_test = transforms.Compose([
transforms.Resize(256, keep_ratio=True),
transforms.CenterCrop(224),
transforms.ToTensor(), normalize
])
def test(ctx, val_data, mode='image'):
acc_top1.reset()
acc_top5.reset()
if not opt.rec_dir:
num_batch = len(val_data)
for i, batch in enumerate(val_data):
if mode == 'image':
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,
txts.append(txt)
plt.savefig(output_path)
def inf_train_gen(loader):
"""
Using iterations train network.
:param loader: Dataloader
:return: batch of data
"""
while True:
for batch in loader:
yield batch
transform_test = transforms.Compose([transforms.ToTensor()])
_transform_train = transforms.Compose([
transforms.RandomBrightness(0.3),
transforms.RandomContrast(0.3),
transforms.RandomSaturation(0.3),
transforms.RandomFlipLeftRight(),
transforms.ToTensor()
])
def transform_train(data, label):
im = _transform_train(data)
return im, label
#tmp = nd.zeros(shape=(self.n_frame, self.crop_size, self.crop_size, 3), dtype='float32')
if current_length < self.n_frame:
#construct the last frame and concat
extra_data = nd.tile(nd_image_list[-1],
reps=(self.n_frame - current_length, 1, 1, 1))
extra_data = extra_data.transpose((1, 0, 2, 3))
cthw_data = nd.concat(cthw_data, extra_data, dim=1)
# begin to construct the label
label_nd = np.zeros(shape=(self.max_label), dtype=np.float32)
for tag_index in labels:
label_nd[tag_index - 1] = 1
return cthw_data, label_nd
train_transform = T.Compose([
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
def get_simple_meitu_dataloader(datadir,
batch_size=4,
n_frame=32,
crop_size=112,
scale_h=128,
scale_w=171,
num_workers=6):
"""construct the dataset and then set the datasetloader"""
train_dataset = SimpleMeitu(datadir,
n_frame,
crop_size,
def main():
opt = parse_args()
filehandler = logging.FileHandler(opt.logging_file, mode='a+')
# streamhandler = logging.StreamHandler()
logger = logging.getLogger('ImageNet')
logger.setLevel(level=logging.DEBUG)
logger.addHandler(filehandler)
# logger.addHandler(streamhandler)
logger.info(opt)
if opt.amp:
amp.init()
batch_size = opt.batch_size
classes = 1000
num_training_samples = 1281167
num_validating_samples = 50000
num_gpus = opt.num_gpus
batch_size *= max(1, num_gpus)
context = [mx.gpu(i)
for i in range(num_gpus)] if num_gpus > 0 else [mx.cpu()]
num_workers = opt.num_workers
accumulate = opt.accumulate
lr_decay = opt.lr_decay
lr_decay_period = opt.lr_decay_period
if opt.lr_decay_period > 0:
lr_decay_epoch = list(
range(lr_decay_period, opt.num_epochs, lr_decay_period))
else:
lr_decay_epoch = [int(i) for i in opt.lr_decay_epoch.split(',')]
lr_decay_epoch = [e - opt.warmup_epochs for e in lr_decay_epoch]
num_batches = num_training_samples // batch_size
lr_scheduler = LRSequential([
LRScheduler('linear',
base_lr=0,
target_lr=opt.lr,
nepochs=opt.warmup_epochs,
iters_per_epoch=num_batches),
LRScheduler(opt.lr_mode,
base_lr=opt.lr,
target_lr=0,
nepochs=opt.num_epochs - opt.warmup_epochs,
iters_per_epoch=num_batches,
step_epoch=lr_decay_epoch,
step_factor=lr_decay,
power=2)
])
model_name = opt.model
kwargs = {'ctx': context, 'pretrained': opt.use_pretrained}
if opt.use_gn:
kwargs['norm_layer'] = gcv.nn.GroupNorm
if model_name.startswith('vgg'):
kwargs['batch_norm'] = opt.batch_norm
elif model_name.startswith('resnext'):
kwargs['use_se'] = opt.use_se
if opt.last_gamma:
kwargs['last_gamma'] = True
optimizer = 'sgd'
optimizer_params = {
'wd': opt.wd,
'momentum': opt.momentum,
'lr_scheduler': lr_scheduler,
'begin_num_update': num_batches * opt.resume_epoch
}
# if opt.dtype != 'float32':
# optimizer_params['multi_precision'] = True
# net = get_model(model_name, **kwargs)
if opt.model_backend == 'gluoncv':
net = glcv_get_model(model_name, **kwargs)
elif opt.model_backend == 'gluoncv2':
net = glcv2_get_model(model_name, **kwargs)
else:
raise ValueError(f'Unknown backend: {opt.model_backend}')
# net.cast(opt.dtype)
if opt.resume_params != '':
net.load_parameters(opt.resume_params, ctx=context, cast_dtype=True)
# teacher model for distillation training
if opt.teacher is not None and opt.hard_weight < 1.0:
teacher_name = opt.teacher
if opt.teacher_backend == 'gluoncv':
teacher = glcv_get_model(teacher_name, **kwargs)
elif opt.teacher_backend == 'gluoncv2':
teacher = glcv2_get_model(teacher_name, **kwargs)
else:
raise ValueError(f'Unknown backend: {opt.teacher_backend}')
# teacher = glcv2_get_model(teacher_name, pretrained=True, ctx=context)
# teacher.cast(opt.dtype)
teacher.collect_params().setattr('grad_req', 'null')
distillation = True
else:
distillation = False
# Two functions for reading data from record file or raw images
def get_data_rec(rec_train, rec_val):
rec_train = os.path.expanduser(rec_train)
rec_val = os.path.expanduser(rec_val)
# mean_rgb = [123.68, 116.779, 103.939]
# std_rgb = [58.393, 57.12, 57.375]
train_dataset = ImageRecordDataset(filename=rec_train, flag=1)
val_dataset = ImageRecordDataset(filename=rec_val, flag=1)
return train_dataset, val_dataset
def get_data_loader(data_dir):
train_dataset = ImageNet(data_dir, train=True)
val_dataset = ImageNet(data_dir, train=False)
return train_dataset, val_dataset
def batch_fn(batch, ctx):
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)
return data, label
if opt.use_rec:
train_dataset, val_dataset = get_data_rec(opt.rec_train, opt.rec_val)
else:
train_dataset, val_dataset = get_data_loader(opt.data_dir)
normalize = transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
jitter_param = 0.4
lighting_param = 0.1
if not opt.multi_scale:
train_dataset = train_dataset.transform_first(
transforms.Compose([
transforms.RandomResizedCrop(opt.input_size),
transforms.RandomFlipLeftRight(),
transforms.RandomColorJitter(brightness=jitter_param,
contrast=jitter_param,
saturation=jitter_param),
transforms.RandomLighting(lighting_param),
transforms.ToTensor(), normalize
]))
train_data = gluon.data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
pin_memory=True,
last_batch='rollover',
num_workers=num_workers)
else:
train_data = RandomTransformDataLoader(
[
Transform(
transforms.Compose([
# transforms.RandomResizedCrop(opt.input_size),
transforms.RandomResizedCrop(x * 32),
transforms.RandomFlipLeftRight(),
transforms.RandomColorJitter(brightness=jitter_param,
contrast=jitter_param,
saturation=jitter_param),
transforms.RandomLighting(lighting_param),
transforms.ToTensor(),
normalize
])) for x in range(10, 20)
],
train_dataset,
interval=10 * opt.accumulate,
batch_size=batch_size,
shuffle=False,
pin_memory=True,
last_batch='rollover',
num_workers=num_workers)
val_dataset = val_dataset.transform_first(
transforms.Compose([
transforms.Resize(opt.input_size, keep_ratio=True),
transforms.CenterCrop(opt.input_size),
transforms.ToTensor(), normalize
]))
val_data = gluon.data.DataLoader(val_dataset,
batch_size=batch_size,
shuffle=False,
pin_memory=True,
last_batch='keep',
num_workers=num_workers)
if opt.mixup:
train_metric = mx.metric.RMSE()
else:
train_metric = mx.metric.Accuracy()
train_loss_metric = mx.metric.Loss()
acc_top1 = mx.metric.Accuracy()
acc_top5 = mx.metric.TopKAccuracy(5)
save_frequency = opt.save_frequency
if opt.save_dir and save_frequency:
if opt.wandb:
save_dir = wandb.run.dir
else:
save_dir = opt.save_dir
makedirs(save_dir)
else:
save_dir = ''
save_frequency = 0
def mixup_transform(label, classes, lam=1, eta=0.0):
if isinstance(label, nd.NDArray):
label = [label]
res = []
for l in label:
y1 = l.one_hot(classes,
on_value=1 - eta + eta / classes,
off_value=eta / classes)
y2 = l[::-1].one_hot(classes,
on_value=1 - eta + eta / classes,
off_value=eta / classes)
res.append(lam * y1 + (1 - lam) * y2)
return res
def smooth(label, classes, eta=0.1):
if isinstance(label, nd.NDArray):
label = [label]
smoothed = []
for l in label:
res = l.one_hot(classes,
on_value=1 - eta + eta / classes,
off_value=eta / classes)
smoothed.append(res)
return smoothed
def test(ctx, val_data):
acc_top1.reset()
acc_top5.reset()
for i, batch in tqdm.tqdm(enumerate(val_data),
desc='Validating',
total=num_validating_samples // batch_size):
data, label = batch_fn(batch, ctx)
# outputs = [net(X.astype(opt.dtype, copy=False)) for X in data]
outputs = [net(X) for X in data]
acc_top1.update(label, outputs)
acc_top5.update(label, outputs)
_, top1 = acc_top1.get()
_, top5 = acc_top5.get()
return 1 - top1, 1 - top5
def train(ctx):
if isinstance(ctx, mx.Context):
ctx = [ctx]
if opt.resume_params == '':
import warnings
with warnings.catch_warnings(record=True) as w:
net.initialize(mx.init.MSRAPrelu(), ctx=ctx)
if opt.no_wd:
for k, v in net.collect_params('.*beta|.*gamma|.*bias').items():
v.wd_mult = 0.0
if accumulate > 1:
logger.info(f'accumulate: {accumulate}, using "add" grad_req')
import warnings
with warnings.catch_warnings(record=True) as w:
net.collect_params().setattr('grad_req', 'add')
trainer = gluon.Trainer(net.collect_params(),
optimizer,
optimizer_params,
update_on_kvstore=False if opt.amp else None)
if opt.amp:
amp.init_trainer(trainer)
if opt.resume_states != '':
trainer.load_states(opt.resume_states)
if opt.label_smoothing or opt.mixup:
sparse_label_loss = False
else:
sparse_label_loss = True
if distillation:
L = gcv.loss.DistillationSoftmaxCrossEntropyLoss(
temperature=opt.temperature,
hard_weight=opt.hard_weight,
sparse_label=sparse_label_loss)
else:
L = gluon.loss.SoftmaxCrossEntropyLoss(
sparse_label=sparse_label_loss)
best_val_score = 1
err_top1_val, err_top5_val = test(ctx, val_data)
logger.info('initial validation: err-top1=%f err-top5=%f' %
(err_top1_val, err_top5_val))
for epoch in range(opt.resume_epoch, opt.num_epochs):
tic = time.time()
train_metric.reset()
train_loss_metric.reset()
btic = time.time()
pbar = tqdm.tqdm(total=num_batches,
desc=f'Training [{epoch}]',
leave=True)
for i, batch in enumerate(train_data):
data, label = batch_fn(batch, ctx)
if opt.mixup:
lam = np.random.beta(opt.mixup_alpha, opt.mixup_alpha)
if epoch >= opt.num_epochs - opt.mixup_off_epoch:
lam = 1
data = [lam * X + (1 - lam) * X[::-1] for X in data]
if opt.label_smoothing:
eta = 0.1
else:
eta = 0.0
label = mixup_transform(label, classes, lam, eta)
elif opt.label_smoothing:
hard_label = label
label = smooth(label, classes)
if distillation:
# teacher_prob = [nd.softmax(teacher(X.astype(opt.dtype, copy=False)) / opt.temperature) \
# for X in data]
with ag.predict_mode():
teacher_prob = [
nd.softmax(
teacher(
nd.transpose(
nd.image.resize(
nd.transpose(X, (0, 2, 3, 1)),
size=opt.teacher_imgsize),
(0, 3, 1, 2))) / opt.temperature)
for X in data
]
with ag.record():
# outputs = [net(X.astype(opt.dtype, copy=False)) for X in data]
outputs = [net(X) for X in data]
if distillation:
# loss = [L(yhat.astype('float32', copy=False),
# y.astype('float32', copy=False),
# p.astype('float32', copy=False)) for yhat, y, p in zip(outputs, label, teacher_prob)]
# print([outputs, label, teacher_prob])
loss = [
L(yhat, y, p)
for yhat, y, p in zip(outputs, label, teacher_prob)
]
else:
# loss = [L(yhat, y.astype(opt.dtype, copy=False)) for yhat, y in zip(outputs, label)]
loss = [L(yhat, y) for yhat, y in zip(outputs, label)]
if opt.amp:
with amp.scale_loss(loss, trainer) as scaled_loss:
ag.backward(scaled_loss)
else:
ag.backward(loss)
if accumulate > 1:
if (i + 1) % accumulate == 0:
trainer.step(batch_size * accumulate)
net.collect_params().zero_grad()
else:
trainer.step(batch_size)
train_loss_metric.update(0, loss)
if opt.mixup:
output_softmax = [nd.SoftmaxActivation(out.astype('float32', copy=False)) \
for out in outputs]
train_metric.update(label, output_softmax)
else:
if opt.label_smoothing:
train_metric.update(hard_label, outputs)
else:
train_metric.update(label, outputs)
_, loss_score = train_loss_metric.get()
train_metric_name, train_metric_score = train_metric.get()
samplers_per_sec = batch_size / (time.time() - btic)
postfix = f'{samplers_per_sec:.1f} imgs/sec, ' \
f'loss: {loss_score:.4f}, ' \
f'acc: {train_metric_score * 100:.2f}, ' \
f'lr: {trainer.learning_rate:.4e}'
if opt.multi_scale:
postfix += f', size: {data[0].shape[-1]}'
pbar.set_postfix_str(postfix)
pbar.update()
btic = time.time()
if opt.log_interval and not (i + 1) % opt.log_interval:
step = epoch * num_batches + i
wandb.log(
{
'samplers_per_sec': samplers_per_sec,
train_metric_name: train_metric_score,
'lr': trainer.learning_rate,
'loss': loss_score
},
step=step)
logger.info(
'Epoch[%d] Batch [%d]\tSpeed: %f samples/sec\t%s=%f\tlr=%f'
% (epoch, i, samplers_per_sec, train_metric_name,
train_metric_score, trainer.learning_rate))
pbar.close()
train_metric_name, train_metric_score = train_metric.get()
throughput = int(batch_size * i / (time.time() - tic))
err_top1_val, err_top5_val = test(ctx, val_data)
wandb.log({
'err1': err_top1_val,
'err5': err_top5_val
},
step=epoch * num_batches)
logger.info('[Epoch %d] training: %s=%f' %
(epoch, train_metric_name, train_metric_score))
logger.info('[Epoch %d] speed: %d samples/sec\ttime cost: %f' %
(epoch, throughput, time.time() - tic))
logger.info('[Epoch %d] validation: err-top1=%f err-top5=%f' %
(epoch, err_top1_val, err_top5_val))
if err_top1_val < best_val_score:
best_val_score = err_top1_val
net.save_parameters(
'%s/%.4f-imagenet-%s-%d-best.params' %
(save_dir, best_val_score, model_name, epoch))
trainer.save_states(
'%s/%.4f-imagenet-%s-%d-best.states' %
(save_dir, best_val_score, model_name, epoch))
if save_frequency and save_dir and (epoch +
1) % save_frequency == 0:
net.save_parameters('%s/imagenet-%s-%d.params' %
(save_dir, model_name, epoch))
trainer.save_states('%s/imagenet-%s-%d.states' %
(save_dir, model_name, epoch))
if save_frequency and save_dir:
net.save_parameters('%s/imagenet-%s-%d.params' %
(save_dir, model_name, opt.num_epochs - 1))
trainer.save_states('%s/imagenet-%s-%d.states' %
(save_dir, model_name, opt.num_epochs - 1))
if opt.mode == 'hybrid':
net.hybridize(static_alloc=True, static_shape=not opt.multi_scale)
if distillation:
teacher.hybridize(static_alloc=True,
static_shape=not opt.multi_scale)
train(context)
from mxnet.image import imread
logging.basicConfig(level=logging.WARNING, format='%(asctime)s %(message)s')
import requests
import json
import binascii
import numpy as np
from pymongo import MongoClient
from requests import ReadTimeout
from pprint import pprint
#image transform
normalize = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
test_transform = T.Compose(
[T.Resize(256), T.CenterCrop(224),
T.ToTensor(), normalize])
# define mongodb connect
def get_db():
mongdb = {}
mongdb['host'] = 'dds-bp10da4305cf39f41.mongodb.rds.aliyuncs.com'
mongdb['port'] = 3717
client = MongoClient(host=mongdb['host'], port=mongdb['port'])
dev = client.get_database('dev')
dev.authenticate(name='nnsearch', password='******')
return dev
@asyncio.coroutine
def download(url, session, semaphore, chunk_size=1 << 15):
def save_and_load_sequential_example():
# Use GPU if one exists, else use CPU.
ctx = mx.gpu() if mx.test_utils.list_gpus() else mx.cpu()
# MNIST images are 28x28. Total pixels in input layer is 28x28 = 784.
#num_inputs = 784
# Clasify the images into one of the 10 digits.
#num_outputs = 10
# 64 images in a batch.
batch_size = 64
# Load the training data.
train_data = gluon.data.DataLoader(gluon.data.vision.MNIST(
train=True).transform_first(transforms.ToTensor()),
batch_size,
shuffle=True)
valid_data = gluon.data.DataLoader(gluon.data.vision.MNIST(
train=False).transform_first(transforms.ToTensor()),
batch_size,
shuffle=True)
# Define a model.
net = build_lenet(gluon.nn.Sequential())
# Initialize the parameters with Xavier initializer.
net.collect_params().initialize(mx.init.Xavier(), ctx=ctx)
# Use cross entropy loss.
softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
# Use Adam optimizer.
trainer = gluon.Trainer(net.collect_params(), 'adam',
{'learning_rate': .001})
train_model(net,
trainer,
softmax_cross_entropy,
train_data,
valid_data,
num_epochs=10)
#--------------------
if True:
model_param_filepath = './lenet.params'
# Save model parameters to file.
net.save_parameters(model_param_filepath)
# Define a model.
new_net = build_lenet(gluon.nn.Sequential())
# Load model parameters from file.
new_net.load_parameters(model_param_filepath, ctx=ctx)
else:
# NOTE [info] >> Sequential models may not be serialized as JSON files.
model_filepath = './lenet.json'
model_param_filepath = './lenet.params'
# Save model architecture to file.
sym_json = net(mx.sym.var('data')).tojson()
sym_json = json.loads(sym_json)
with open(model_filepath, 'w') as fd:
json.dump(sym_json, fd, indent='\t')
# Save model parameters to file.
net.save_parameters(model_param_filepath)
# Load model architecture from file.
with open(model_filepath, 'r') as fd:
sym_json = json.load(fd)
sym_json = json.dumps(sym_json)
new_net = gluon.nn.SymbolBlock(outputs=mx.sym.load_json(sym_json),
inputs=mx.sym.var('data'))
# Load model parameters from file.
new_net.load_parameters(model_param_filepath, ctx=ctx)
verify_loaded_model(new_net, ctx=ctx)
def train_neural_network_example():
mnist_train = datasets.FashionMNIST(train=True)
X, y = mnist_train[0]
print('X shape:', X.shape, 'X dtype:', X.dtype, 'y:', y)
text_labels = [
't-shirt', 'trouser', 'pullover', 'dress', 'coat', 'sandal', 'shirt',
'sneaker', 'bag', 'ankle boot'
]
X, y = mnist_train[0:6]
# Plot images.
_, figs = plt.subplots(1, X.shape[0], figsize=(15, 15))
for f, x, yi in zip(figs, X, y):
# 3D -> 2D by removing the last channel dim.
f.imshow(x.reshape((28, 28)).asnumpy())
ax = f.axes
ax.set_title(text_labels[int(yi)])
ax.title.set_fontsize(20)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
transformer = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize(0.13, 0.31)])
mnist_train = mnist_train.transform_first(transformer)
batch_size = 256
train_data = gluon.data.DataLoader(mnist_train,
batch_size=batch_size,
shuffle=True,
num_workers=4)
for data, label in train_data:
print(data.shape, label.shape)
break
mnist_valid = gluon.data.vision.FashionMNIST(train=False)
valid_data = gluon.data.DataLoader(
mnist_valid.transform_first(transformer),
batch_size=batch_size,
num_workers=4)
# Define a model.
net = build_network(nn.Sequential())
net.initialize(init=init.Xavier())
#net.collect_params().initialize(init.Xavier(), ctx=ctx)
# Define the loss function and optimization method for training.
softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), 'sgd',
{'learning_rate': 0.1})
#trainer = gluon.Trainer(net.collect_params(), 'adam', {'learning_rate': .001})
train_model(net,
trainer,
softmax_cross_entropy,
train_data,
valid_data,
num_epochs=10)
# Save the model parameters.
net.save_parameters('./net.params')
def __init__(self, args):
self.args = args
# image transform
input_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([.485, .456, .406], [.229, .224, .225]),
])
# dataset and dataloader
trainset = get_mxnet_dataset(args.dataset,
split='train',
transform=input_transform)
valset = get_mxnet_dataset(args.dataset,
split='val',
transform=input_transform)
self.train_data = gluon.data.DataLoader(trainset,
args.batch_size,
shuffle=True,
last_batch='rollover',
num_workers=args.workers)
self.eval_data = gluon.data.DataLoader(valset,
args.test_batch_size,
last_batch='keep',
num_workers=args.workers)
# create network
model = get_segmentation_model(model=args.model,
dataset=args.dataset,
backbone=args.backbone,
norm_layer=args.norm_layer,
aux=args.aux)
print(model)
self.net = DataParallelModel(model, args.ctx, args.syncbn)
self.evaluator = DataParallelModel(SegEvalModel(model), args.ctx)
# resume checkpoint if needed
if args.resume is not None:
if os.path.isfile(args.resume):
model.load_params(args.resume, ctx=args.ctx)
else:
raise RuntimeError("=> no checkpoint found at '{}'" \
.format(args.resume))
# create criterion
criterion = SoftmaxCrossEntropyLossWithAux(args.aux)
self.criterion = DataParallelCriterion(criterion, args.ctx,
args.syncbn)
# optimizer and lr scheduling
self.lr_scheduler = LRScheduler(mode='poly',
baselr=args.lr,
niters=len(self.train_data),
nepochs=args.epochs)
kv = mx.kv.create(args.kvstore)
self.optimizer = gluon.Trainer(self.net.module.collect_params(),
'sgd', {
'lr_scheduler': self.lr_scheduler,
'wd': args.weight_decay,
'momentum': args.momentum,
'multi_precision': True
},
kvstore=kv)
time_str = time.strftime("%Y-%m-%d___%H-%M-%S", time.localtime())
net_name = args.model
dataset_name = args.dataset
note = args.backbone
log_dir = os.path.join(self.args.logdir, net_name, dataset_name, note,
time_str)
os.makedirs(log_dir, exist_ok=True)
self.writer = SummaryWriter(log_dir=log_dir)
config_str = json.dumps(self.args, indent=2, sort_keys=True).replace(
'\n', '\n\n').replace(' ', '\t')
self.writer.add_text(tag='config', text_string=config_str)
if not os.path.exists(save_dir):
makedirs(save_dir)
name = opt.name
plot_path = opt.save_plot_dir
logging.basicConfig(level=logging.INFO)
logging.info(opt)
trans, aug_trans = list(), list()
if opt.data_aug:
aug_trans = [
gcv_transforms.RandomCrop(32, pad=4),
transforms.RandomFlipLeftRight()
]
trans.append(transforms.ToTensor())
trans.append(
transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]))
transform_train = transforms.Compose(aug_trans + trans)
transform_test = transforms.Compose(trans)
data_sampler = eval('DataSampler%d' % opt.data_sampler)()
num_data_samples = opt.data_k
use_som = opt.use_som
use_pillars = opt.plc_loss
pillar_sampler = eval('PillarSampler%d' % opt.pillar_sampler)()
num_pillar_samples = opt.pillar_k
w1 = opt.w1
w2 = opt.w2
cumulative = opt.cumulative
def train(net, ctx):
if isinstance(ctx, mx.Context):
ctx = [ctx]
if not opt.resume_from:
net.initialize(mx.init.Xavier(), ctx=ctx)
if opt.dataset == 'NC_CIFAR100':
n = mx.nd.zeros(shape=(1, 3, 32, 32), ctx=ctx[0]) #####init CNN
else:
raise KeyError('dataset keyerror')
for m in range(9):
net(n, m)
def makeSchedule(start_lr, base_lr, length, step, factor):
schedule = mx.lr_scheduler.MultiFactorScheduler(step=step,
factor=factor)
schedule.base_lr = base_lr
schedule = LinearWarmUp(schedule, start_lr=start_lr, length=length)
return schedule
# ==========================================================================
sesses = list(np.arange(opt.sess_num))
epochs = [opt.epoch] * opt.sess_num
lrs = [opt.base_lrs] + [opt.lrs] * (opt.sess_num - 1)
lr_decay = opt.lr_decay
base_decay_epoch = [int(i)
for i in opt.base_decay_epoch.split(',')] + [np.inf]
lr_decay_epoch = [base_decay_epoch
] + [[opt.inc_decay_epoch, np.inf]] * (opt.sess_num - 1)
AL_weight = opt.AL_weight
min_weight = opt.min_weight
oce_weight = opt.oce_weight
pdl_weight = opt.pdl_weight
max_weight = opt.max_weight
temperature = opt.temperature
use_AL = opt.use_AL # anchor loss
use_ng_min = opt.use_ng_min # Neural Gas min loss
use_ng_max = opt.use_ng_max # Neural Gas min loss
ng_update = opt.ng_update # Neural Gas update node
use_oce = opt.use_oce # old samples cross entropy loss
use_pdl = opt.use_pdl # probability distillation loss
use_nme = opt.use_nme # Similarity loss
use_warmUp = opt.use_warmUp
use_ng = opt.use_ng # Neural Gas
fix_conv = opt.fix_conv # fix cnn to train novel classes
fix_epoch = opt.fix_epoch
c_way = opt.c_way
k_shot = opt.k_shot
base_acc = opt.base_acc # base model acc
select_best_method = opt.select_best # select from _best, _best2, _best3
init_class = 60
anchor_num = 400
# ==========================================================================
acc_dict = {}
all_best_e = []
if model_name[-7:] != 'maxhead':
net.fc3.initialize(mx.init.Normal(sigma=0.001),
ctx=ctx,
force_reinit=True)
net.fc4.initialize(mx.init.Normal(sigma=0.001),
ctx=ctx,
force_reinit=True)
net.fc5.initialize(mx.init.Normal(sigma=0.001),
ctx=ctx,
force_reinit=True)
net.fc6.initialize(mx.init.Normal(sigma=0.001),
ctx=ctx,
force_reinit=True)
net.fc7.initialize(mx.init.Normal(sigma=0.001),
ctx=ctx,
force_reinit=True)
net.fc8.initialize(mx.init.Normal(sigma=0.001),
ctx=ctx,
force_reinit=True)
net.fc9.initialize(mx.init.Normal(sigma=0.001),
ctx=ctx,
force_reinit=True)
net.fc10.initialize(mx.init.Normal(sigma=0.001),
ctx=ctx,
force_reinit=True)
for sess in sesses:
logger.info('session : %d' % sess)
schedule = makeSchedule(start_lr=0,
base_lr=lrs[sess],
length=5,
step=lr_decay_epoch[sess],
factor=lr_decay)
# prepare the first anchor batch
if sess == 0 and opt.resume_from:
acc_dict[str(sess)] = list()
acc_dict[str(sess)].append([base_acc, 0])
all_best_e.append(0)
continue
# quick cnn totally unfix, not use data augmentation
if sess == 1 and model_name == 'quick_cnn' and use_AL:
transform_train = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465],
[0.2023, 0.1994, 0.2010])
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465],
[0.2023, 0.1994, 0.2010])
])
anchor_trans = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465],
[0.2023, 0.1994, 0.2010])
])
else:
transform_train = transforms.Compose([
gcv_transforms.RandomCrop(32, pad=4),
transforms.RandomFlipLeftRight(),
transforms.ToTensor(),
transforms.Normalize([0.5071, 0.4866, 0.4409],
[0.2009, 0.1984, 0.2023])
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.5071, 0.4866, 0.4409],
[0.2009, 0.1984, 0.2023])
])
anchor_trans = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.5071, 0.4866, 0.4409],
[0.2009, 0.1984, 0.2023])
])
# ng_init and ng_update
if use_AL or use_nme or use_pdl or use_oce:
if sess != 0:
if ng_update == True:
if sess == 1:
update_anchor1, bmu, variances= \
prepare_anchor(DATASET,logger,anchor_trans,num_workers,feature_size,net,ctx,use_ng,init_class)
update_anchor_data = DataLoader(
update_anchor1,
anchor_trans,
update_anchor1.__len__(),
num_workers,
shuffle=False)
if opt.ng_var:
idx_1 = np.where(variances.asnumpy() > 0.5)
idx_2 = np.where(variances.asnumpy() < 0.5)
variances[idx_1] = 0.9
variances[idx_2] = 1
else:
base_class = init_class + (sess - 1) * 5
new_class = list(init_class + (sess - 1) * 5 +
(np.arange(5)))
new_set = DATASET(train=True,
fine_label=True,
fix_class=new_class,
base_class=base_class,
logger=logger)
update_anchor2 = merge_datasets(
update_anchor1, new_set)
update_anchor_data = DataLoader(
update_anchor2,
anchor_trans,
update_anchor2.__len__(),
num_workers,
shuffle=False)
elif (sess == 1):
update_anchor, bmu, variances = \
prepare_anchor(DATASET,logger,anchor_trans,num_workers,feature_size,net,ctx,use_ng,init_class)
update_anchor_data = DataLoader(update_anchor,
anchor_trans,
update_anchor.__len__(),
num_workers,
shuffle=False)
if opt.ng_var:
idx_1 = np.where(variances.asnumpy() > 0.5)
idx_2 = np.where(variances.asnumpy() < 0.5)
variances[idx_1] = 0.9
variances[idx_2] = 1
for batch in update_anchor_data:
anc_data = gluon.utils.split_and_load(batch[0],
ctx_list=[ctx[0]],
batch_axis=0)
anc_label = gluon.utils.split_and_load(batch[1],
ctx_list=[ctx[0]],
batch_axis=0)
with ag.pause():
anchor_feat, anchor_logit = net(anc_data[0], sess - 1)
anchor_feat = [anchor_feat]
anchor_logit = [anchor_logit]
trainer = gluon.Trainer(net.collect_params(), optimizer, {
'learning_rate': lrs[sess],
'wd': opt.wd,
'momentum': opt.momentum
})
metric = mx.metric.Accuracy()
train_metric = mx.metric.Accuracy()
loss_fn = gluon.loss.SoftmaxCrossEntropyLoss()
# ==========================================================================
# all loss init
if use_nme:
def loss_fn_disG(f1, f2, weight):
f1 = f1.reshape(anchor_num, -1)
f2 = f2.reshape(anchor_num, -1)
similar = mx.nd.sum(f1 * f2, 1)
return (1 - similar) * weight
digG_weight = opt.nme_weight
if use_AL:
if model_name == 'quick_cnn':
AL_w = [120, 75, 120, 100, 50, 60, 90, 90]
AL_weight = AL_w[sess - 1]
else:
AL_weight = opt.AL_weight
if opt.ng_var:
def l2lossVar(feat, anc, weight, var):
dim = feat.shape[1]
feat = feat.reshape(-1, dim)
anc = anc.reshape(-1, dim)
loss = mx.nd.square(feat - anc)
loss = loss * weight * var
return mx.nd.mean(loss, axis=0, exclude=True)
loss_fn_AL = l2lossVar
else:
loss_fn_AL = gluon.loss.L2Loss(weight=AL_weight)
if use_pdl:
loss_fn_pdl = DistillationSoftmaxCrossEntropyLoss(
temperature=temperature, hard_weight=0, weight=pdl_weight)
if use_oce:
loss_fn_oce = gluon.loss.SoftmaxCrossEntropyLoss(weight=oce_weight)
if use_ng_min:
loss_fn_max = NG_Max_Loss(lmbd=max_weight, margin=0.5)
if use_ng_min:
min_loss = NG_Min_Loss(
num_classes=opt.c_way,
feature_size=feature_size,
lmbd=min_weight, # center weight = 0.01 in the paper
ctx=ctx[0])
min_loss.initialize(mx.init.Xavier(magnitude=2.24),
ctx=ctx,
force_reinit=True) # init matrix.
center_trainer = gluon.Trainer(
min_loss.collect_params(),
optimizer="sgd",
optimizer_params={"learning_rate":
opt.ng_min_lr}) # alpha=0.1 in the paper.
# ==========================================================================
lr_decay_count = 0
# dataloader
if opt.cum and sess == 1:
base_class = list(np.arange(init_class))
joint_data = DATASET(train=True,
fine_label=True,
c_way=init_class,
k_shot=500,
fix_class=base_class,
logger=logger)
if sess == 0:
base_class = list(np.arange(init_class))
new_class = list(init_class + (np.arange(5)))
base_data = DATASET(train=True,
fine_label=True,
c_way=init_class,
k_shot=500,
fix_class=base_class,
logger=logger)
bc_val_data = DataLoader(DATASET(train=False,
fine_label=True,
fix_class=base_class,
logger=logger),
transform_test,
100,
num_workers,
shuffle=False)
nc_val_data = DataLoader(DATASET(train=False,
fine_label=True,
fix_class=new_class,
base_class=len(base_class),
logger=logger),
transform_test,
100,
num_workers,
shuffle=False)
else:
base_class = list(np.arange(init_class + (sess - 1) * 5))
new_class = list(init_class + (sess - 1) * 5 + (np.arange(5)))
train_data_nc = DATASET(train=True,
fine_label=True,
c_way=c_way,
k_shot=k_shot,
fix_class=new_class,
base_class=len(base_class),
logger=logger)
bc_val_data = DataLoader(DATASET(train=False,
fine_label=True,
fix_class=base_class,
logger=logger),
transform_test,
100,
num_workers,
shuffle=False)
nc_val_data = DataLoader(DATASET(train=False,
fine_label=True,
fix_class=new_class,
base_class=len(base_class),
logger=logger),
transform_test,
100,
num_workers,
shuffle=False)
if sess == 0:
train_data = DataLoader(base_data,
transform_train,
min(batch_size, base_data.__len__()),
num_workers,
shuffle=True)
else:
if opt.cum: # cumulative : merge base and novel dataset.
joint_data = merge_datasets(joint_data, train_data_nc)
train_data = DataLoader(joint_data,
transform_train,
min(batch_size, joint_data.__len__()),
num_workers,
shuffle=True)
elif opt.use_all_novel: # use all novel data
if sess == 1:
novel_data = train_data_nc
else:
novel_data = merge_datasets(novel_data, train_data_nc)
train_data = DataLoader(novel_data,
transform_train,
min(batch_size, novel_data.__len__()),
num_workers,
shuffle=True)
else: # basic method
train_data = DataLoader(train_data_nc,
transform_train,
min(batch_size,
train_data_nc.__len__()),
num_workers,
shuffle=True)
for epoch in range(epochs[sess]):
tic = time.time()
train_metric.reset()
metric.reset()
train_loss, train_anchor_loss, train_oce_loss = 0, 0, 0
train_disg_loss, train_pdl_loss, train_min_loss = 0, 0, 0
train_max_loss = 0
num_batch = len(train_data)
if use_warmUp:
lr = schedule(epoch)
trainer.set_learning_rate(lr)
else:
lr = trainer.learning_rate
if epoch == lr_decay_epoch[sess][lr_decay_count]:
trainer.set_learning_rate(trainer.learning_rate * lr_decay)
lr_decay_count += 1
if sess != 0 and epoch < fix_epoch:
fix_cnn = fix_conv
else:
fix_cnn = False
for i, batch in enumerate(train_data):
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)
all_loss = list()
with ag.record():
output_feat, output = net(data[0], sess, fix_cnn)
output_feat = [output_feat]
output = [output]
loss = [loss_fn(yhat, y) for yhat, y in zip(output, label)]
all_loss.extend(loss)
if use_nme:
anchor_h = [net(X, sess, fix_cnn)[0] for X in anc_data]
disg_loss = [
loss_fn_disG(a_h, a, weight=digG_weight)
for a_h, a in zip(anchor_h, anchor_feat)
]
all_loss.extend(disg_loss)
if sess > 0 and use_ng_max:
max_loss = [
loss_fn_max(feat, label, feature_size, epoch, sess,
init_class)
for feat, label in zip(output_feat, label)
]
all_loss.extend(max_loss[0])
if sess > 0 and use_AL: # For anchor loss
anchor_h = [net(X, sess, fix_cnn)[0] for X in anc_data]
if opt.ng_var:
anchor_loss = [
loss_fn_AL(anchor_h[0], anchor_feat[0],
AL_weight, variances)
]
all_loss.extend(anchor_loss)
else:
anchor_loss = [
loss_fn_AL(a_h, a)
for a_h, a in zip(anchor_h, anchor_feat)
]
all_loss.extend(anchor_loss)
if sess > 0 and use_ng_min:
loss_min = min_loss(output_feat[0], label[0])
all_loss.extend(loss_min)
if sess > 0 and use_pdl:
anchor_l = [net(X, sess, fix_cnn)[1] for X in anc_data]
anchor_l = [anchor_l[0][:, :60 + (sess - 1) * 5]]
soft_label = [
mx.nd.softmax(anchor_logit[0][:, :60 +
(sess - 1) * 5] /
temperature)
]
pdl_loss = [
loss_fn_pdl(a_h, a,
soft_a) for a_h, a, soft_a in zip(
anchor_l, anc_label, soft_label)
]
all_loss.extend(pdl_loss)
if sess > 0 and use_oce:
anchorp = [net(X, sess, fix_cnn)[1] for X in anc_data]
oce_Loss = [
loss_fn_oce(ap, a)
for ap, a in zip(anchorp, anc_label)
]
all_loss.extend(oce_Loss)
all_loss = [nd.mean(l) for l in all_loss]
ag.backward(all_loss)
trainer.step(1, ignore_stale_grad=True)
if use_ng_min:
center_trainer.step(opt.c_way * opt.k_shot)
train_loss += sum([l.sum().asscalar() for l in loss])
if sess > 0 and use_AL:
train_anchor_loss += sum(
[al.mean().asscalar() for al in anchor_loss])
if sess > 0 and use_oce:
train_oce_loss += sum(
[al.mean().asscalar() for al in oce_Loss])
if sess > 0 and use_nme:
train_disg_loss += sum(
[al.mean().asscalar() for al in disg_loss])
if sess > 0 and use_pdl:
train_pdl_loss += sum(
[al.mean().asscalar() for al in pdl_loss])
if sess > 0 and use_ng_min:
train_min_loss += sum(
[al.mean().asscalar() for al in loss_min])
if sess > 0 and use_ng_max:
train_max_loss += sum(
[al.mean().asscalar() for al in max_loss[0]])
train_metric.update(label, output)
train_loss /= batch_size * num_batch
name, acc = train_metric.get()
name, bc_val_acc = test(ctx, bc_val_data, net, sess)
name, nc_val_acc = test(ctx, nc_val_data, net, sess)
if epoch == 0:
acc_dict[str(sess)] = list()
acc_dict[str(sess)].append([bc_val_acc, nc_val_acc])
if sess == 0:
overall = bc_val_acc
else:
overall = (bc_val_acc * (init_class + (sess - 1) * 5) +
nc_val_acc * 5) / (init_class + sess * 5)
logger.info(
'[Epoch %d] lr=%.4f train=%.4f | val(base)=%.4f val(novel)=%.4f overall=%.4f | loss=%.8f anc loss=%.8f '
'pdl loss:%.8f oce loss: %.8f time: %.8f' %
(epoch, lr, acc, bc_val_acc, nc_val_acc, overall, train_loss,
train_anchor_loss / AL_weight, train_pdl_loss / pdl_weight,
train_oce_loss / oce_weight, time.time() - tic))
if use_nme:
logger.info('digG loss:%.8f' % (train_disg_loss / digG_weight))
if use_ng_min:
logger.info('min_loss:%.8f' % (train_min_loss / min_weight))
if use_ng_max:
logger.info('max_loss:%.8f' % (train_max_loss / max_weight))
if save_period and save_dir and (epoch + 1) % save_period == 0:
net.save_parameters('%s/sess-%s-%d.params' %
(save_dir, model_name, epoch))
select = eval(select_best_method)
best_e = select(acc_dict, sess)
logger.info('best select : base: %f novel: %f ' %
(acc_dict[str(sess)][best_e][0],
acc_dict[str(sess)][best_e][1]))
if use_AL and model_name == 'quick_cnn':
reload_path = '%s/sess-%s-%d.params' % (save_dir, model_name,
best_e)
net.load_parameters(reload_path, ctx=context)
all_best_e.append(best_e)
reload_path = '%s/sess-%s-%d.params' % (save_dir, model_name, best_e)
net.load_parameters(reload_path, ctx=context)
with open('%s/acc_dict.json' % save_dir, 'w') as json_file:
json.dump(acc_dict, json_file)
plot_pr(acc_dict, sess, save_dir)
plot_all_sess(acc_dict, save_dir, all_best_e)
def main(im_dir, ckpt_path, ctx, out_dir='result', write_images=True):
east_model = east.EAST(nclass=2, text_scale=1024)
ctx = mx.cpu() if ctx < 0 else mx.gpu()
# east_model.hybridize()
east_model.load_parameters(ckpt_path, ctx)
east_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([.485, .456, .406], [.229, .224, .225])
])
imlist = glob.glob1(im_dir, '*g')
for i, im_name in enumerate(imlist):
im_path = os.path.join(im_dir, im_name)
start_time = time.time()
im = cv2.imread(im_path)
im_resized, (ratio_h, ratio_w) = resize_image(im, max_side_len=2048)
timer = {'net': 0, 'restore': 0, 'nms': 0}
start = time.time()
im_resized = east_transform(
mx.nd.array(np.array(im_resized).astype('float32')))
f_score, f_geometry = east_model.forward(
im_resized.expand_dims(axis=0))
timer['net'] = time.time() - start
score_map = f_score.asnumpy().transpose((0, 2, 3, 1))
cv2.imwrite("score_map{}.png".format(i), score_map[0, :, :, 0] * 255)
geo_map = f_geometry.asnumpy().transpose((0, 2, 3, 1))
boxes, timer = detect(score_map, geo_map, timer)
print('{} : net {:.0f}ms, restore {:.0f}ms, nms {:.0f}ms'.format(
im_name, timer['net'] * 1000, timer['restore'] * 1000,
timer['nms'] * 1000))
if boxes is not None:
boxes = boxes[:, :8].reshape((-1, 4, 2))
boxes[:, :, 0] /= ratio_h
boxes[:, :, 1] /= ratio_w
duration = time.time() - start_time
print('[timing] {}'.format(duration))
# save to file
if boxes is not None:
res_file = os.path.join(
out_dir,
'{}.txt'.format(os.path.basename(im_path).split('.')[0]))
print("num_Boxes:{}".format(len(boxes)))
with open(res_file, 'w') as f:
for box in boxes:
# to avoid submitting errors
box = sort_poly(box.astype(np.int32))
# if np.linalg.norm(box[0] - box[1]) < 5 or np.linalg.norm(box[3] - box[0]) < 5:
# continue
f.write('{},{},{},{},{},{},{},{}\r\n'.format(
box[0, 0],
box[0, 1],
box[1, 0],
box[1, 1],
box[2, 0],
box[2, 1],
box[3, 0],
box[3, 1],
))
cv2.polylines(im[:, :, ::-1].astype(np.uint8),
[box.astype(np.int32).reshape((-1, 1, 2))],
True,
color=(255, 255, 0),
thickness=2)
if write_images:
img_path = os.path.join(out_dir, im_name)
cv2.imwrite(img_path, im)
def main():
mnist_train = datasets.FashionMNIST(train=True)
X, y = mnist_train[0]
#print('X ',X)
#print('y ', y)
print('X shape: ', X.shape, 'X dtype ', X.dtype, 'y:', y)
text_labels = [
't-shirt', 'trouser', 'pullover', 'dress', 'coat', 'sandal', 'shirt',
'sneaker', 'bag', 'ankle boot'
]
X, y = mnist_train[0:10]
# plot images
# display.set_matplotlib_formats('svg')
# _, figs = plt.subplots(1, X.shape[0], figsize=(15, 15))
# for f,x,yi in zip(figs, X,y):
# # 3D->2D by removing the last channel dim
# f.imshow(x.reshape((28,28)).asnumpy())
# ax = f.axes
# ax.set_title(text_labels[int(yi)])
# ax.title.set_fontsize(14)
# ax.get_xaxis().set_visible(False)
# ax.get_yaxis().set_visible(False)
# plt.show()
#
transformer = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize(0.13, 0.31)])
mnist_train = mnist_train.transform_first(transformer)
batch_size = 64
train_data = gluon.data.DataLoader(mnist_train,
batch_size=batch_size,
shuffle=True,
num_workers=4)
mnist_valid = gluon.data.vision.FashionMNIST(train=False)
valid_data = gluon.data.DataLoader(
mnist_valid.transform_first(transformer),
batch_size=batch_size,
num_workers=4)
net = nn.Sequential()
net.add(nn.Conv2D(channels=6, kernel_size=5, activation='relu'),
nn.MaxPool2D(pool_size=2, strides=2),
nn.Conv2D(channels=16, kernel_size=3, activation='relu'),
nn.MaxPool2D(pool_size=2, strides=2), nn.Flatten(),
nn.Dense(120, activation='relu'), nn.Dense(84, activation='relu'),
nn.Dense(10))
net.initialize(init=init.Xavier())
softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
trainer_opt = gluon.Trainer(net.collect_params(), 'sgd',
{'learning_rate': 0.1})
for epoch in range(10):
train_loss, train_acc, valid_acc = 0., 0., 0.,
tic = time.time()
for data, label in train_data:
# forward and backward
with autograd.record():
output = net(data)
loss = softmax_cross_entropy(output, label)
loss.backward()
# update parameters
trainer_opt.step(batch_size)
# calculate training metrics
train_loss += loss.mean().asscalar()
train_acc += acc(output, label)
for data, label in valid_data:
valid_acc += acc(net(data), label)
print(
"epoch %d: loss %.3f, train acc %.3f, test acc %.3f, in %.1f sec" %
(epoch, train_loss / len(train_data), train_acc / len(train_data),
valid_acc / len(valid_data), time.time() - tic))
net.save_parameters('net.params')
def test(model):
if args.dataset == 'cifar10':
transform_train = transforms.Compose([
gcv_transforms.RandomCrop(32, pad=4),
transforms.RandomFlipLeftRight(),
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465],
[0.2023, 0.1994, 0.2010])
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465],
[0.2023, 0.1994, 0.2010])
])
train_data = gluon.data.DataLoader(gluon.data.vision.CIFAR10(
train=True).transform_first(transform_train),
batch_size=batch_size,
shuffle=True,
last_batch='discard',
num_workers=args.num_workers)
val_data = gluon.data.DataLoader(gluon.data.vision.CIFAR10(
train=False).transform_first(transform_test),
batch_size=batch_size,
shuffle=False,
num_workers=args.num_workers)
elif args.dataset == 'cifar100':
transform_train = transforms.Compose([
gcv_transforms.RandomCrop(32, pad=4),
transforms.RandomFlipLeftRight(),
transforms.ToTensor(),
transforms.Normalize([0.5071, 0.4865, 0.4409],
[0.2673, 0.2564, 0.2762])
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.5071, 0.4865, 0.4409],
[0.2673, 0.2564, 0.2762])
])
train_data = gluon.data.DataLoader(gluon.data.vision.CIFAR100(
train=True).transform_first(transform_train),
batch_size=batch_size,
shuffle=True,
last_batch='discard',
num_workers=num_workers)
val_data = gluon.data.DataLoader(gluon.data.vision.CIFAR100(
train=False).transform_first(transform_test),
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
metric = mx.metric.Accuracy()
metric.reset()
for i, batch in enumerate(val_data):
data = gluon.utils.split_and_load(batch[0],
ctx_list=context,
batch_axis=0)
label = gluon.utils.split_and_load(batch[1],
ctx_list=context,
batch_axis=0)
outputs = [model(X.astype(args.dtype, copy=False)) for X in data]
metric.update(label, outputs)
_, acc = metric.get()
print('\nTest-set Accuracy: ', acc)
return acc
def get_built_in_dataset(name,
train=True,
input_size=224,
batch_size=256,
num_workers=32,
shuffle=True,
fine_label=False,
**kwargs):
"""Returns built-in popular image classification dataset based on provided string name ('cifar10', 'cifar100','mnist','imagenet').
"""
logger.info(f'get_built_in_dataset {name}')
name = name.lower()
if name in ('cifar10', 'cifar'):
import gluoncv.data.transforms as gcv_transforms
if train:
transform_split = transforms.Compose([
gcv_transforms.RandomCrop(32, pad=4),
transforms.RandomFlipLeftRight(),
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465],
[0.2023, 0.1994, 0.2010])
])
else:
transform_split = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465],
[0.2023, 0.1994, 0.2010])
])
return gluon.data.vision.CIFAR10(
train=train).transform_first(transform_split)
elif name == 'cifar100':
import gluoncv.data.transforms as gcv_transforms
if train:
transform_split = transforms.Compose([
gcv_transforms.RandomCrop(32, pad=4),
transforms.RandomFlipLeftRight(),
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465],
[0.2023, 0.1994, 0.2010])
])
else:
transform_split = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465],
[0.2023, 0.1994, 0.2010])
])
return gluon.data.vision.CIFAR100(
train=train,
fine_label=fine_label).transform_first(transform_split)
elif name == 'mnist':
def transform(data, label):
return nd.transpose(data.astype(np.float32),
(2, 0, 1)) / 255, label.astype(np.float32)
return gluon.data.vision.MNIST(train=train, transform=transform)
elif name == 'fashionmnist':
def transform(data, label):
return nd.transpose(data.astype(np.float32),
(2, 0, 1)) / 255, label.astype(np.float32)
return gluon.data.vision.FashionMNIST(train=train, transform=transform)
elif name == 'imagenet':
print("Loading the imagenet from ~/.mxnet/imagenet/")
# Please setup the ImageNet dataset following the tutorial from GluonCV
if train:
rec_file = '~/.mxnet/imagenet/train.rec'
rec_file_idx = '~/.mxnet/imagenet/train.idx'
else:
rec_file = '~/.mxnet/imagenet/val.rec'
rec_file_idx = '~/.mxnet/imagenet/val.idx'
data_loader = get_data_rec(input_size,
0.875,
rec_file,
rec_file_idx,
batch_size,
num_workers,
train,
shuffle=shuffle,
**kwargs)
return data_loader
else:
raise NotImplementedError
def train():
if config.restart_training:
shutil.rmtree(config.output_dir, ignore_errors=True)
if config.output_dir is None:
config.output_dir = 'output'
if not os.path.exists(config.output_dir):
os.makedirs(config.output_dir)
logger = setup_logger(os.path.join(config.output_dir, 'train_log'))
logger.info('train with gpu %s and mxnet %s' %
(config.gpu_id, mx.__version__))
ctx = mx.gpu(config.gpu_id)
# 设置随机种子
mx.random.seed(2)
mx.random.seed(2, ctx=ctx)
train_transfroms = transforms.Compose(
[transforms.RandomBrightness(0.5),
transforms.ToTensor()])
train_dataset = ImageDataset(config.trainfile,
(config.img_h, config.img_w),
3,
80,
config.alphabet,
phase='train')
train_data_loader = DataLoader(
train_dataset.transform_first(train_transfroms),
config.train_batch_size,
shuffle=True,
last_batch='keep',
num_workers=config.workers)
test_dataset = ImageDataset(config.testfile, (config.img_h, config.img_w),
3,
80,
config.alphabet,
phase='test')
test_data_loader = DataLoader(test_dataset.transform_first(
transforms.ToTensor()),
config.eval_batch_size,
shuffle=True,
last_batch='keep',
num_workers=config.workers)
net = CRNN(len(config.alphabet), hidden_size=config.nh)
net.hybridize()
if not config.restart_training and config.checkpoint != '':
logger.info('load pretrained net from {}'.format(config.checkpoint))
net.load_parameters(config.checkpoint, ctx=ctx)
else:
net.initialize(ctx=ctx)
criterion = gluon.loss.CTCLoss()
all_step = len(train_data_loader)
logger.info('each epoch contains {} steps'.format(all_step))
schedule = mx.lr_scheduler.FactorScheduler(step=config.lr_decay_step *
all_step,
factor=config.lr_decay,
stop_factor_lr=config.end_lr)
# schedule = mx.lr_scheduler.MultiFactorScheduler(step=[15 * all_step, 30 * all_step, 60 * all_step,80 * all_step],
# factor=0.1)
adam_optimizer = mx.optimizer.Adam(learning_rate=config.lr,
lr_scheduler=schedule)
trainer = gluon.Trainer(net.collect_params(), optimizer=adam_optimizer)
sw = SummaryWriter(logdir=config.output_dir)
for epoch in range(config.start_epoch, config.end_epoch):
loss = .0
train_acc = .0
tick = time.time()
cur_step = 0
for i, (data, label) in enumerate(train_data_loader):
data = data.as_in_context(ctx)
label = label.as_in_context(ctx)
with autograd.record():
output = net(data)
loss_ctc = criterion(output, label)
loss_ctc.backward()
trainer.step(data.shape[0])
loss_c = loss_ctc.mean()
cur_step = epoch * all_step + i
sw.add_scalar(tag='ctc_loss',
value=loss_c.asscalar(),
global_step=cur_step // 2)
sw.add_scalar(tag='lr',
value=trainer.learning_rate,
global_step=cur_step // 2)
loss += loss_c
acc = accuracy(output, label, config.alphabet)
train_acc += acc
if (i + 1) % config.display_interval == 0:
acc /= len(label)
sw.add_scalar(tag='train_acc', value=acc, global_step=cur_step)
batch_time = time.time() - tick
logger.info(
'[{}/{}], [{}/{}],step: {}, Speed: {:.3f} samples/sec, ctc loss: {:.4f},acc: {:.4f}, lr:{},'
' time:{:.4f} s'.format(
epoch, config.end_epoch, i, all_step, cur_step,
config.display_interval * config.train_batch_size /
batch_time,
loss.asscalar() / config.display_interval, acc,
trainer.learning_rate, batch_time))
loss = .0
tick = time.time()
nd.waitall()
if epoch == 0:
sw.add_graph(net)
logger.info('start val ....')
train_acc /= train_dataset.__len__()
validation_accuracy = evaluate_accuracy(
net, test_data_loader, ctx,
config.alphabet) / test_dataset.__len__()
sw.add_scalar(tag='val_acc',
value=validation_accuracy,
global_step=cur_step)
logger.info("Epoch {},train_acc {:.4f}, val_acc {:.4f}".format(
epoch, train_acc, validation_accuracy))
net.save_parameters("{}/{}_{:.4f}_{:.4f}.params".format(
config.output_dir, epoch, train_acc, validation_accuracy))
sw.close()
def train_mnist():
mnist_train = get_train_data()
print("type(mnist_train)=", type(mnist_train))
transformer = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(0.13, 0.31)])
mnist_train = mnist_train.transform_first(transformer)
batch_size = 256
train_data = gluon.data.DataLoader(
mnist_train, batch_size=batch_size, shuffle=True, num_workers=4)
mnist_valid = gluon.data.vision.FashionMNIST(train=False)
valid_data = gluon.data.DataLoader(
mnist_valid.transform_first(transformer),
batch_size=batch_size, num_workers=4)
# for data, label in train_data:
# print(data.shape, label.shape)
# break
net = nn.Sequential()
net.add(nn.Conv2D(channels=6, kernel_size=5, activation='relu'),
nn.MaxPool2D(pool_size=2, strides=2),
nn.Conv2D(channels=16, kernel_size=3, activation='relu'),
nn.MaxPool2D(pool_size=2, strides=2),
nn.Flatten(),
nn.Dense(120, activation="relu"),
nn.Dense(84, activation="relu"),
nn.Dense(10))
net.initialize(init=init.Xavier())
softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.1})
for epoch in range(10):
train_loss, train_acc, valid_acc = 0., 0., 0.
tic = time.time()
for data, label in train_data:
# forward + backward
with autograd.record():
output = net(data)
loss = softmax_cross_entropy(output, label)
loss.backward()
# update parameters
trainer.step(batch_size)
# calculate training metrics
train_loss += loss.mean().asscalar()
train_acc += acc(output, label)
# calculate validation accuracy
for data, label in valid_data:
valid_acc += acc(net(data), label)
print("Epoch %d: loss %.3f, train acc %.3f, test acc %.3f, in %.1f sec" % (
epoch, train_loss / len(train_data), train_acc / len(train_data),
valid_acc / len(valid_data), time.time() - tic))
net.save_parameters('./output_weight/net_%s.params'%(epoch))
def __call__(self, x=None):
x = T.Resize((self.w, self.h))(x)
x = T.ToTensor()(x)
x = T.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225))(x)
return x
import numpy as np
import mxnet as mx
from mxnet import nd
import time
from mxnet.gluon.data.vision import transforms
gpu_total_time = 0.0
transformer = transforms.ToTensor()
count = 1000
for i in range(count):
image = mx.nd.random.uniform(0, 255, (1, 512, 512, 3),
ctx=mx.gpu(0)).astype(np.uint8)
tic = time.time()
#image.as_in_context(mx.gpu(0))
res = transformer(image)
# To force the calculation
#a = res.shape
res.wait_to_read()
tac = time.time()
gpu_total_time += (tac - tic) * 1000
print("Total time for GPU ToTensor - ", gpu_total_time)
print("Average time per ToTensor 1,512,512,3 - ", gpu_total_time / count)
cpu_total_time = 0.0
transformer = transforms.ToTensor()
count = 1000
for i in range(count):
image = mx.nd.random.uniform(0, 255, (1, 512, 512, 3)).astype(np.uint8)
from mxnet.gluon.data import DataLoader, Dataset
from mxnet import nd
from mxnet.image import imread
import os
import numpy as np
import mxnet as mx
import mxnet.gluon.data.vision.transforms as T
from collections import Counter
normalize = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
default_transform = T.Compose([
T.Resize(256),
T.RandomResizedCrop(size=224, scale=(1.0, 1.0),
ratio=(1.0, 1.0)), # just crop,not scale
T.RandomFlipLeftRight(),
T.ToTensor(), # last to swap channel to c,w,h
normalize
])
test_transform = T.Compose(
[T.Resize(256), T.CenterCrop(224),
T.ToTensor(), normalize])
# like the
class DeepInClassFashion(Dataset):
"""
the DeepInClassFashion dataset.read data from list_item_inshop.txt,
"""
def __init__(self,
def __init__(self, args, logger):
self.args = args
self.logger = logger
# image transform
input_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([.485, .456, .406], [.229, .224, .225]),
])
# dataset and dataloader
data_kwargs = {
'transform': input_transform,
'base_size': args.base_size,
'crop_size': args.crop_size
}
trainset = get_segmentation_dataset(args.dataset,
split=args.train_split,
mode='train',
**data_kwargs)
valset = get_segmentation_dataset(args.dataset,
split='val',
mode='val',
**data_kwargs)
self.train_data = gluon.data.DataLoader(trainset,
args.batch_size,
shuffle=True,
last_batch='rollover',
num_workers=args.workers)
self.eval_data = gluon.data.DataLoader(valset,
args.test_batch_size,
last_batch='rollover',
num_workers=args.workers)
# create network
if args.model_zoo is not None:
model = get_model(args.model_zoo,
norm_layer=args.norm_layer,
norm_kwargs=args.norm_kwargs,
aux=args.aux,
base_size=args.base_size,
crop_size=args.crop_size,
pretrained=args.pretrained)
else:
model = get_segmentation_model(model=args.model,
dataset=args.dataset,
backbone=args.backbone,
norm_layer=args.norm_layer,
norm_kwargs=args.norm_kwargs,
aux=args.aux,
base_size=args.base_size,
crop_size=args.crop_size)
# for resnest use only
from gluoncv.nn.dropblock import set_drop_prob
from functools import partial
apply_drop_prob = partial(set_drop_prob, 0.0)
model.apply(apply_drop_prob)
model.cast(args.dtype)
logger.info(model)
self.net = DataParallelModel(model, args.ctx, args.syncbn)
self.evaluator = DataParallelModel(SegEvalModel(model), args.ctx)
# resume checkpoint if needed
if args.resume is not None:
if os.path.isfile(args.resume):
model.load_parameters(args.resume, ctx=args.ctx)
else:
raise RuntimeError("=> no checkpoint found at '{}'".format(
args.resume))
# create criterion
if 'icnet' in args.model:
criterion = ICNetLoss(crop_size=args.crop_size)
elif 'danet' in args.model or (args.model_zoo
and 'danet' in args.model_zoo):
criterion = SegmentationMultiLosses()
else:
criterion = MixSoftmaxCrossEntropyLoss(args.aux,
aux_weight=args.aux_weight)
self.criterion = DataParallelCriterion(criterion, args.ctx,
args.syncbn)
# optimizer and lr scheduling
self.lr_scheduler = LRSequential([
LRScheduler('linear',
base_lr=0,
target_lr=args.lr,
nepochs=args.warmup_epochs,
iters_per_epoch=len(self.train_data)),
LRScheduler(mode='poly',
base_lr=args.lr,
nepochs=args.epochs - args.warmup_epochs,
iters_per_epoch=len(self.train_data),
power=0.9)
])
kv = mx.kv.create(args.kvstore)
if args.optimizer == 'sgd':
optimizer_params = {
'lr_scheduler': self.lr_scheduler,
'wd': args.weight_decay,
'momentum': args.momentum,
'learning_rate': args.lr
}
elif args.optimizer == 'adam':
optimizer_params = {
'lr_scheduler': self.lr_scheduler,
'wd': args.weight_decay,
'learning_rate': args.lr
}
else:
raise ValueError('Unsupported optimizer {} used'.format(
args.optimizer))
if args.dtype == 'float16':
optimizer_params['multi_precision'] = True
if args.no_wd:
for k, v in self.net.module.collect_params(
'.*beta|.*gamma|.*bias').items():
v.wd_mult = 0.0
self.optimizer = gluon.Trainer(self.net.module.collect_params(),
args.optimizer,
optimizer_params,
kvstore=kv)
# evaluation metrics
self.metric = gluoncv.utils.metrics.SegmentationMetric(
trainset.num_class)
def test(args):
# output folder
outdir = 'outdir'
if not os.path.exists(outdir):
os.makedirs(outdir)
# image transform
input_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([.485, .456, .406], [.229, .224, .225]),
])
# dataset and dataloader
if args.eval:
testset = get_segmentation_dataset(args.dataset,
split='val',
mode='testval',
transform=input_transform)
total_inter, total_union, total_correct, total_label = \
np.int64(0), np.int64(0), np.int64(0), np.int64(0)
else:
testset = get_segmentation_dataset(args.dataset,
split='test',
mode='test',
transform=input_transform)
test_data = gluon.data.DataLoader(testset,
args.test_batch_size,
last_batch='keep',
batchify_fn=ms_batchify_fn,
num_workers=args.workers)
# create network
if args.model_zoo is not None:
model = get_model(args.model_zoo, pretrained=True)
else:
model = get_segmentation_model(model=args.model,
dataset=args.dataset,
ctx=args.ctx,
backbone=args.backbone,
norm_layer=args.norm_layer)
# load pretrained weight
assert args.resume is not None, '=> Please provide the checkpoint using --resume'
if os.path.isfile(args.resume):
model.load_params(args.resume, ctx=args.ctx)
else:
raise RuntimeError("=> no checkpoint found at '{}'" \
.format(args.resume))
print(model)
evaluator = MultiEvalModel(model, testset.num_class, ctx_list=args.ctx)
tbar = tqdm(test_data)
for i, (data, dsts) in enumerate(tbar):
if args.eval:
targets = dsts
predicts = evaluator.parallel_forward(data)
for predict, target in zip(predicts, targets):
target = target.as_in_context(predict[0].context)
correct, labeled = batch_pix_accuracy(predict[0], target)
inter, union = batch_intersection_union(
predict[0], target, testset.num_class)
total_correct += correct.astype('int64')
total_label += labeled.astype('int64')
total_inter += inter.astype('int64')
total_union += union.astype('int64')
pixAcc = np.float64(1.0) * total_correct / (
np.spacing(1, dtype=np.float64) + total_label)
IoU = np.float64(1.0) * total_inter / (
np.spacing(1, dtype=np.float64) + total_union)
mIoU = IoU.mean()
tbar.set_description('pixAcc: %.4f, mIoU: %.4f' % (pixAcc, mIoU))
else:
im_paths = dsts
predicts = evaluator.parallel_forward(data)
for predict, impath in zip(predicts, im_paths):
predict = mx.nd.squeeze(mx.nd.argmax(
predict[0], 1)).asnumpy() + testset.pred_offset
mask = get_color_pallete(predict, args.dataset)
outname = os.path.splitext(impath)[0] + '.png'
mask.save(os.path.join(outdir, outname))
def train():
logging.info('Start Training for Task: %s\n' % (task))
# Initialize the net with pretrained model
pretrained_net = gluon.model_zoo.vision.get_model(model_name,
pretrained=True)
finetune_net = gluon.model_zoo.vision.get_model(model_name,
classes=task_num_class)
finetune_net.features = pretrained_net.features
finetune_net.output.initialize(init.Xavier(), ctx=ctx)
finetune_net.collect_params().reset_ctx(ctx)
finetune_net.hybridize()
# Carefully set the 'scale' parameter to make the 'muti-scale train' and 'muti-scale test'
train_transform = transforms.Compose([
transforms.RandomResizedCrop(448,
scale=(0.76, 1.0),
ratio=(0.999, 1.001)),
transforms.RandomFlipLeftRight(),
transforms.RandomBrightness(0.20),
#transforms.RandomColorJitter(brightness=jitter_param, contrast=jitter_param,
# saturation=jitter_param),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
train_dataset = my_customdataset.my_custom_dataset(
imgroot=os.path.join(".", 'train_valid_allset', task, 'train'),
labelmasterpath='label_master.csv')
train_data = gluon.data.DataLoader(
train_dataset.transform_first(train_transform),
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
last_batch='discard')
val_transform = transforms.Compose([
transforms.Resize(480),
transforms.CenterCrop(448),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
val_dataset = my_customdataset.my_custom_dataset(
imgroot=os.path.join(".", 'train_valid_allset', task, 'val'),
labelmasterpath='label_master.csv')
val_data = gluon.data.DataLoader(
val_dataset.transform_first(val_transform),
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
# Define Trainer use ADam to make mdoel converge quickly
trainer = gluon.Trainer(finetune_net.collect_params(), 'adam',
{'learning_rate': lr})
metric = mx.metric.Accuracy()
L = gluon.loss.SoftmaxCrossEntropyLoss()
lr_counter = 0
num_batch = len(train_data)
# Start Training
best_AP = 0
best_acc = 0
for epoch in range(epochs):
train_acc = 0.
#### Load the best model when go to the next training stage
if epoch == lr_steps[lr_counter]:
finetune_net.collect_params().load(best_path, ctx=ctx)
trainer.set_learning_rate(trainer.learning_rate * lr_factor)
lr_counter += 1
tic = time.time()
train_loss = 0
metric.reset()
AP = 0.
AP_cnt = 0
for i, batch in enumerate(train_data):
data = gluon.utils.split_and_load(batch[0],
ctx_list=ctx,
batch_axis=0,
even_split=False)
label = gluon.utils.split_and_load(batch[1],
ctx_list=ctx,
batch_axis=0,
even_split=False)
with ag.record():
outputs = [finetune_net(X) for X in data]
loss = []
###### Handle 'm' label by soft-softmax function ######
for yhat, y in zip(outputs[0], label[0]):
loss_1 = 0
if y[1] == 99: # only have y [4,0,0,0,0]
loss_1 += L(yhat, y[0])
elif y[2] == 99: #have one m [4,1,0,0,0]
loss_1 = 0.8 * L(yhat, y[0]) + 0.2 * L(yhat, y[1])
elif y[3] == 99: #have two m [4,1,3,0,0]
loss_1 = 0.7 * L(yhat, y[0]) + 0.15 * L(
yhat, y[1]) + 0.15 * L(yhat, y[2])
else: # have many m [4,1,3,2,0]
loss_1 = 0.6 * L(yhat, y[0]) + 0.13 * L(
yhat, y[1]) + 0.13 * L(yhat, y[2]) + 0.13 * L(
yhat, y[3])
loss += [loss_1]
#loss = [L(yhat, y) for yhat, y in zip(outputs, label)
# for l in loss:
# l.backward()
ag.backward(loss) # for soft-softmax
trainer.step(batch_size)
train_loss += sum([l.mean().asscalar() for l in loss]) / len(loss)
#train_acc += accuracy(outputs, label)
metric.update([label[0][:, 0]], outputs)
#ap, cnt = calculate_ap(label, outputs)
#AP += ap
#AP_cnt += cnt
#progressbar(i, num_batch-1)
#train_map = AP / AP_cnt
_, train_acc = metric.get()
train_loss /= num_batch
val_acc, val_loss = validate(finetune_net, val_data, ctx)
logging.info(
'[Epoch %d] Train-acc: %.3f, loss: %.3f | Val-acc: %.3f, loss: %.3f | time: %.1f | learning_rate %.6f'
% (epoch, train_acc, train_loss, val_acc, val_loss,
time.time() - tic, trainer.learning_rate))
f_val.writelines(
'[Epoch %d] Train-acc: %.3f, , loss: %.3f | Val-acc: %.3f, loss: %.3f | time: %.1f | learning_rate %.6f\n'
% (epoch, train_acc, train_loss, val_acc, val_loss,
time.time() - tic, trainer.learning_rate))
### Save the best model every stage
if val_acc > best_acc:
#best_AP = this_AP
best_acc = val_acc
if not os.path.exists(os.path.join('.', 'models')):
os.makedirs(os.path.join('.', 'models'))
if not os.path.exists(
os.path.join(
'.', 'models', '%s_%s_%s_%s_staging.params' %
(task, model_name, epoch, best_acc))):
f = open(
os.path.join(
'.', 'models', '%s_%s_%s_%s_staging.params' %
(task, model_name, epoch, best_acc)), 'w')
f.close()
best_path = os.path.join(
'.', 'models', '%s_%s_%s_%s_staging.params' %
(task, model_name, epoch, best_acc))
finetune_net.collect_params().save(best_path)
logging.info('\n')
finetune_net.collect_params().load(best_path, ctx=ctx)
f_val.writelines(
'Best val acc is :[Epoch %d] Train-acc: %.3f, loss: %.3f | Best-val-acc: %.3f, loss: %.3f | time: %.1f | learning_rate %.6f\n'
% (epoch, train_acc, train_loss, best_acc, val_loss, time.time() - tic,
trainer.learning_rate))
return (finetune_net)
plot_name = opt.save_plot_dir
logging_handlers = [logging.StreamHandler()]
if opt.logging_dir:
logging_dir = opt.logging_dir
makedirs(logging_dir)
logging_handlers.append(logging.FileHandler('%s/train_cifar10_%s.log'%(logging_dir, model_name)))
logging.basicConfig(level=logging.INFO, handlers = logging_handlers)
logging.info(opt)
transform_train = transforms.Compose([
gcv_transforms.RandomCrop(32, pad=4),
transforms.RandomFlipLeftRight(),
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010])
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010])
])
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(ctx, val_data):
def main():
opt = parse_args()
batch_size = opt.batch_size
classes = 10
log_dir = os.path.join(opt.save_dir, "logs")
model_dir = os.path.join(opt.save_dir, "params")
if not os.path.exists(model_dir):
os.makedirs(model_dir)
# Init dataloader
jitter_param = 0.4
transform_train = transforms.Compose([
gcv_transforms.RandomCrop(32, pad=4),
transforms.RandomFlipLeftRight(),
transforms.RandomBrightness(jitter_param),
transforms.RandomColorJitter(jitter_param),
transforms.RandomContrast(jitter_param),
transforms.RandomSaturation(jitter_param),
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465],
[0.2023, 0.1994, 0.2010])
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465],
[0.2023, 0.1994, 0.2010])
])
train_data = gluon.data.DataLoader(
gluon.data.vision.CIFAR10(train=True).transform_first(transform_train),
batch_size=batch_size,
shuffle=True,
last_batch='discard',
num_workers=opt.num_workers)
val_data = gluon.data.DataLoader(
gluon.data.vision.CIFAR10(train=False).transform_first(transform_test),
batch_size=batch_size,
shuffle=False,
num_workers=opt.num_workers)
num_gpus = opt.num_gpus
batch_size *= max(1, num_gpus)
context = [mx.gpu(i)
for i in range(num_gpus)] if num_gpus > 0 else [mx.cpu()]
lr_decay = opt.lr_decay
lr_decay_epoch = [int(i) for i in opt.lr_decay_epoch.split(',')] + [np.inf]
model_name = opt.model
model_name = opt.model
if model_name.startswith('cifar_wideresnet'):
kwargs = {'classes': classes, 'drop_rate': opt.drop_rate}
else:
kwargs = {'classes': classes}
net = get_model(model_name, **kwargs)
if opt.resume_from:
net.load_parameters(opt.resume_from, ctx=context)
optimizer = 'nag'
save_period = opt.save_period
if opt.save_dir and save_period:
save_dir = opt.save_dir
makedirs(save_dir)
else:
save_dir = ''
save_period = 0
def test(ctx, val_loader):
metric = mx.metric.Accuracy()
for i, batch in enumerate(val_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 = [net(X) for X in data]
metric.update(label, outputs)
return metric.get()
def train(train_data, val_data, epochs, ctx):
if isinstance(ctx, mx.Context):
ctx = [ctx]
net.hybridize()
net.initialize(mx.init.Xavier(), ctx=ctx)
net.forward(mx.nd.ones((1, 3, 30, 30), ctx=ctx[0]))
with SummaryWriter(logdir=log_dir, verbose=False) as sw:
sw.add_graph(net)
trainer = gluon.Trainer(net.collect_params(), optimizer, {
'learning_rate': opt.lr,
'wd': opt.wd,
'momentum': opt.momentum
})
metric = mx.metric.Accuracy()
train_metric = mx.metric.Accuracy()
loss_fn = gluon.loss.SoftmaxCrossEntropyLoss()
iteration = 0
lr_decay_count = 0
best_val_score = 0
global_step = 0
for epoch in range(epochs):
tic = time.time()
train_metric.reset()
metric.reset()
train_loss = 0
num_batch = len(train_data)
alpha = 1
if epoch == lr_decay_epoch[lr_decay_count]:
trainer.set_learning_rate(trainer.learning_rate * lr_decay)
lr_decay_count += 1
tbar = tqdm(train_data)
for i, batch in enumerate(tbar):
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)
with ag.record():
output = [net(X) for X in data]
loss = [loss_fn(yhat, y) for yhat, y in zip(output, label)]
for l in loss:
l.backward()
trainer.step(batch_size)
train_loss += sum([l.sum().asscalar() for l in loss])
train_metric.update(label, output)
name, acc = train_metric.get()
iteration += 1
global_step += len(loss)
train_loss /= batch_size * num_batch
name, acc = train_metric.get()
name, val_acc = test(ctx, val_data)
if val_acc > best_val_score:
best_val_score = val_acc
net.save_parameters('{}/{}-{}-{:04.3f}-best.params'.format(
model_dir, model_name, epoch, best_val_score))
with SummaryWriter(logdir=log_dir, verbose=False) as sw:
sw.add_scalar(tag="TrainLos",
value=train_loss,
global_step=global_step)
sw.add_scalar(tag="TrainAcc",
value=acc,
global_step=global_step)
sw.add_scalar(tag="ValAcc",
value=val_acc,
global_step=global_step)
sw.add_graph(net)
logging.info('[Epoch %d] train=%f val=%f loss=%f time: %f' %
(epoch, acc, val_acc, train_loss, time.time() - tic))
if save_period and save_dir and (epoch + 1) % save_period == 0:
net.save_parameters('{}/{}-{}.params'.format(
save_dir, model_name, epoch))
if save_period and save_dir:
net.save_parameters('{}/{}-{}.params'.format(
save_dir, model_name, epochs - 1))
if opt.mode == 'hybrid':
net.hybridize()
train(train_data, val_data, opt.num_epochs, context)