def get_dataset(dataset):
if dataset == 'imagenet':
transform_train = Compose([
RandomResizedCrop(C.get()['target_size'] + 32,
scale=(0.9, 1.0),
interpolation=PIL.Image.BICUBIC),
])
transform_test = Compose([
Resize(C.get()['target_size'] + 32,
interpolation=PIL.Image.BICUBIC)
])
trainset = ImageNet(root=imagenet_path,
split='train',
transform=transform_train)
testset1 = ImageNet(root=imagenet_path,
split='val',
transform=transform_train)
testset2 = ImageNet(root=imagenet_path,
split='val',
transform=transform_test)
trainset.num_class = testset1.num_class = testset2.num_class = 1000
trainset.targets = [lb for _, lb in trainset.samples]
else:
raise ValueError(dataset)
return trainset, testset1, testset2
def train_model(config, dataroot, augment, cv_ratio_test, cv_fold, save_path=None, skip_exist=False):
C.get()
C.get().conf = config
C.get().aug = augment
result = train_and_eval(config, None, dataroot, cv_ratio_test, cv_fold, save_path=save_path, only_eval=skip_exist)
return C.get()['model'], cv_fold, result
def run_epoch(model,
loader,
loss_fn,
optimizer,
desc_default='',
epoch=0,
writer=None,
verbose=1,
scheduler=None):
if verbose:
loader = tqdm(loader, disable=False)
loader.set_description('[{} {}/{}]'.format(desc_default, epoch,
C.get()['epoch']))
metrics = Accumulator()
cnt = 0
total_steps = len(loader)
steps = 0
for data, label in loader:
steps += 1
data, label = data.cuda(), label.cuda()
if optimizer:
optimizer.zero_grad()
preds = model(data)
loss = loss_fn(preds, label)
if optimizer:
loss.backward()
if C.get()['optimizer'].get('clip', 5) > 0:
nn.utils.clip_grad_norm_(model.parameters(),
C.get()['optimizer'].get('clip', 5))
optimizer.step()
top1, top5 = accuracy(preds, label, (1, 5))
metrics.add_dict({
'loss': loss.item() * len(data),
'top1': top1.item() * len(data),
'top5': top5.item() * len(data),
})
cnt += len(data)
if verbose:
postfix = metrics / cnt
if optimizer:
postfix['lr'] = optimizer.param_groups[0]['lr']
loader.set_postfix(postfix)
if scheduler is not None:
scheduler.step(epoch - 1 + float(steps) / total_steps)
del preds, loss, top1, top5, data, label
metrics /= cnt
if optimizer:
metrics.metrics['lr'] = optimizer.param_groups[0]['lr']
if verbose:
for key, value in metrics.items():
writer.add_scalar(key, value, epoch)
return metrics
def train_model(config,
dataloaders,
dataroot,
augment,
cv_ratio_test,
cv_id,
save_path=None,
skip_exist=False,
evaluation_interval=5,
gr_assign=None,
gr_dist=None):
C.get()
C.get().conf = config
C.get()['aug'] = augment
result = train_and_eval(None,
dataloaders,
dataroot,
cv_ratio_test,
cv_id,
save_path=save_path,
only_eval=skip_exist,
evaluation_interval=evaluation_interval,
gr_assign=gr_assign,
gr_dist=gr_dist)
return C.get()['model']['type'], cv_id, result
def __next__(self):
inputs, labels = next(self.loader_iter)
if self.controller:
# ! original image to controller(only normalized)
# ! augmented image to model
_, _, sampled_policies = self.controller(inputs.cuda())
batch_policies = batch_policy_decoder(
sampled_policies
) # (list:list:list:tuple) [batch, num_policy, n_op, 3]
aug_inputs, applied_policy = augment_data(inputs, batch_policies)
self.applied_policy = applied_policy
else:
aug_inputs = []
for img in inputs:
pil_img = transforms.ToPILImage()(UnNormalize()(img.cpu()))
transform_img = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(_CIFAR_MEAN, _CIFAR_STD),
])
if C.get()['cutout'] > 0:
transform_img.transforms.append(
CutoutDefault(C.get()['cutout']))
if C.get()['aug'] == 'fa_reduced_cifar10':
transform_img.transforms.insert(
0, Augmentation(fa_reduced_cifar10())) ###
aug_img = transform_img(pil_img)
aug_inputs.append(aug_img)
aug_inputs = torch.stack(aug_inputs)
return (aug_inputs, labels)
def gr_augment(imgs, gr_ids, gr_policies):
"""
imgs: unnormalized np.array
"""
aug_imgs = []
applied_policy = []
for img, gr_id in zip(imgs, gr_ids):
# policy: (list:list:tuple) [num_policy, n_op, 3]
augment = Augmentation(gr_policies[gr_id])
pil_img = transforms.ToPILImage()(img.cpu())
# pil_img = img
aug_img = augment(pil_img)
# apply original training/valid transforms
transform = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(_CIFAR_MEAN, _CIFAR_STD),
])
if C.get()['cutout'] > 0:
transform.transforms.append(CutoutDefault(C.get()['cutout']))
aug_img = transform(aug_img)
aug_imgs.append(aug_img)
applied_policy.append(augment.policy)
aug_imgs = torch.stack(aug_imgs)
assert type(aug_imgs) == torch.Tensor and aug_imgs.shape == imgs.shape, \
"Augmented Image Type Error, type: {}, shape: {}".format(type(aug_imgs), aug_imgs.shape)
return aug_imgs, applied_policy
def get_data(dataset,
dataroot,
resize=608,
split=0.15,
split_idx=0,
multinode=False,
target_lb=-1):
transform_train = transforms.Compose(
[Normalizer(), Augmenter(),
Resizer(min_side=resize)])
transform_test = transforms.Compose(
[Normalizer(), Resizer(min_side=resize)])
if isinstance(C.get().aug, list):
logger.debug('augmentation provided.')
transform_train.transforms.insert(
0, Augmentation(C.get().aug, detection=True))
if dataset == 'coco':
total_trainset = CocoDataset(dataroot,
set_name='train2017',
transform=transform_train)
testset = CocoDataset(dataroot,
set_name='val2017',
transform=transform_test)
return total_trainset, testset
def eval_tta(config, augment):
augment['num_policy'] = 1 # TODO remove
C.get()
C.get().conf = config
cv_ratio_test, cv_fold, save_path = augment['cv_ratio_test'], augment['cv_fold'], augment['save_path']
print(augment)
# setup - provided augmentation rules
C.get().aug = policy_decoder(augment, augment['num_policy'], augment['num_op'])
# eval
ckpt = torch.load(save_path)
model = get_model(ckpt['model_specs']['name'], len(ckpt['labels']), ckpt['model_specs']['training_configs'], local_rank=ckpt['devices']['gpu_index']) #TODO: get model configuration from Retinanet
if 'model' in ckpt:
model.load_state_dict(ckpt['model'])
else:
model.load_state_dict(ckpt)
model.eval()
dataroot = ckpt['model_specs']['data']['home_path']
mAPs = []
start_t = time.time()
for _ in range(augment['num_policy']): # TODO
train_dataset, test_dataset = get_data(ckpt['model_specs']['data']['annotation_type'], dataroot,
split=cv_ratio_test, split_idx=cv_fold)
# mAP = evaluate(dataset_val, model)
mAP = evaluate(train_dataset, model) #TODO: adjust from train to testing on randomely selected perecentage every time
mAPs.append(mAP)
del train_dataset, test_dataset
gpu_secs = (time.time() - start_t) * torch.cuda.device_count()
# reporter(minus_loss=metrics['minus_loss'], top1_valid=metrics['correct'], elapsed_time=gpu_secs, done=True)
# track.log(minus_loss=metrics['minus_loss'], top1_valid=metrics['correct'], elapsed_time=gpu_secs, done=True)
tune.report(top1_valid=np.mean(mAPs))
return np.mean(mAPs)
def run_epoch(model, loader, loss_fn, optimizer, desc_default='', epoch=0, writer=None, verbose=1, scheduler=None):
tqdm_disable = bool(os.environ.get('TASK_NAME', '')) # KakaoBrain Environment
if verbose:
loader = tqdm(loader, disable=tqdm_disable)
loader.set_description('[%s %04d/%04d]' % (desc_default, epoch, C.get()['epoch']))
metrics = Accumulator()
cnt = 0
total_steps = len(loader)
steps = 0
for data, label in loader:
steps += 1
data, label = data.cuda(), label.cuda()
if optimizer:
optimizer.zero_grad()
preds = model(data)
loss = loss_fn(preds, label)
if optimizer:
loss.backward()
if getattr(optimizer, "synchronize", None):
optimizer.synchronize() # for horovod
if C.get()['optimizer'].get('clip', 5) > 0:
nn.utils.clip_grad_norm_(model.parameters(), C.get()['optimizer'].get('clip', 5))
optimizer.step()
top1, top5 = accuracy(preds, label, (1, 5))
metrics.add_dict({
'loss': loss.item() * len(data),
'top1': top1.item() * len(data),
'top5': top5.item() * len(data),
})
cnt += len(data)
if verbose:
postfix = metrics / cnt
if optimizer:
postfix['lr'] = optimizer.param_groups[0]['lr']
loader.set_postfix(postfix)
if scheduler is not None:
scheduler.step(epoch - 1 + float(steps) / total_steps)
del preds, loss, top1, top5, data, label
if tqdm_disable:
if optimizer:
logger.info('[%s %03d/%03d] %s lr=%.6f', desc_default, epoch, C.get()['epoch'], metrics / cnt, optimizer.param_groups[0]['lr'])
else:
logger.info('[%s %03d/%03d] %s', desc_default, epoch, C.get()['epoch'], metrics / cnt)
metrics /= cnt
if optimizer:
metrics.metrics['lr'] = optimizer.param_groups[0]['lr']
if verbose:
for key, value in metrics.items():
writer.add_scalar(key, value, epoch)
return metrics
def __getitem__(self, item):
if not self.aug:
uuid = self.list[item]
else:
uuid = self.list[item // test_aug_sz]
colors = ['red', 'green', 'blue', 'yellow']
flags = cv2.IMREAD_GRAYSCALE
img = [cv2.imread(os.path.join(self.default_path, uuid + '_' + color + self.ext), flags) for color in colors]
if self.resize:
img = [cv2.resize(x, (1024, 1024)) for x in img]
img = np.stack(img, axis=-1)
# TODO : data augmentation zoom/shear/brightness
if 'train' in self.setname:
augment_img = iaa.Sequential([
iaa.OneOf([
iaa.Affine(rotate=0),
iaa.Affine(rotate=90),
iaa.Affine(rotate=180),
iaa.Affine(rotate=270),
iaa.Fliplr(0.5),
iaa.Flipud(0.5),
])
], random_order=True)
img = augment_img.augment_image(img)
# cutout
if C.get()['cutout_p'] > 0.0:
img = cutout(C.get()['cutout_size'], C.get()['cutout_p'], False)(img)
# TODO : channel drop(except green)?
# d_ch = random.choice([0, 2, 3])
# img[:, :, d_ch] = 0
if self.aug:
# teat-time aug. : tta
tta_list = list(itertools.product(
[iaa.Affine(rotate=0), iaa.Affine(rotate=90), iaa.Affine(rotate=180), iaa.Affine(rotate=270)],
[iaa.Fliplr(0.0), iaa.Fliplr(1.0), iaa.Flipud(1.0), iaa.Sequential([iaa.Fliplr(1.0), iaa.Flipud(1.0)])]
))
tta_idx = item % len(tta_list)
img = tta_list[tta_idx][0].augment_image(img)
img = tta_list[tta_idx][1].augment_image(img)
img = img.astype(np.float32)
img /= 255. # TODO : different normalization?
img = np.transpose(img, (2, 0, 1))
img = np.ascontiguousarray(img)
if self.setname == 'tests':
lb = np.zeros(len(name_label_dict), dtype=np.int)
else:
lb = [int(x) for x in self.labels.loc[uuid]['Target'].split()]
lb = np.eye(len(name_label_dict), dtype=np.float)[lb].sum(axis=0)
return img, lb
def run_epoch(model,
loader,
loss_fn,
optimizer,
desc_default='',
epoch=0,
writer=None,
verbose=1):
if verbose:
loader = tqdm(loader)
if optimizer:
curr_lr = optimizer.param_groups[0]['lr']
loader.set_description(
'[%s %04d/%04d] lr=%.4f' %
(desc_default, epoch, C.get()['epoch'], curr_lr))
else:
loader.set_description('[%s %04d/%04d]' %
(desc_default, epoch, C.get()['epoch']))
metrics = Accumulator()
cnt = 0
for data, label in loader:
data, label = data.cuda(), label.cuda()
if optimizer:
optimizer.zero_grad()
preds = model(data)
loss = loss_fn(preds, label)
if optimizer:
nn.utils.clip_grad_norm_(model.parameters(), 5)
loss.backward()
optimizer.step()
top1, top5 = accuracy(preds, label, (1, 5))
metrics.add_dict({
'loss': loss.item() * len(data),
'top1': top1.item() * len(data),
'top5': top5.item() * len(data),
})
cnt += len(data)
if verbose:
loader.set_postfix(metrics / cnt)
del preds, loss, top1, top5, data, label
metrics /= cnt
if optimizer:
metrics.metrics['lr'] = optimizer.param_groups[0]['lr']
if verbose:
for key, value in metrics.items():
writer.add_scalar(key, value, epoch)
return metrics
def __adjust_learning_rate_pyramid(epoch):
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
base_lr = C.get()['lr']
if C.get()['gradual']['lr'] > 1:
base_lr *= C.get()['gradual']['lr'] * min(
1.0, epoch / C.get()['gradual']['epoch'])
lr = base_lr * (0.1**(epoch //
(max_epoch * 0.5))) * (0.1**(epoch //
(max_epoch * 0.75)))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def pretrain_k_folds(copied_conf, cv_ratio, num_fold) -> None:
global MODEL_PATHS, DATASET_ROOT
global logger, watcher
logger.info(
'----- [Phase 1.] Train without Augmentations cv=%d ratio(test)=%.1f -----'
% (num_fold, cv_ratio))
watcher.start(tag='train_no_aug')
reqs = [
train_model.remote(config=copy.deepcopy(copied_conf),
dataroot=DATASET_ROOT,
augment=Config.get()['aug'],
cv_ratio_test=cv_ratio,
cv_fold=fold_idx,
save_path=MODEL_PATHS[fold_idx],
skip_exist=True) for fold_idx in range(num_fold)
]
tqdm_epoch = tqdm(range(Config.get()['epoch']))
is_done = False
for epoch in tqdm_epoch:
while True:
epochs_per_fold = OrderedDict()
for fold_idx in range(num_fold):
try:
latest_ckpt = torch.load(MODEL_PATHS[fold_idx])
if 'epoch' not in latest_ckpt:
epochs_per_fold['fold%d' %
(fold_idx + 1)] = Config.get()['epoch']
continue
epochs_per_fold['fold%d' %
(fold_idx + 1)] = latest_ckpt['epoch']
except Exception as e:
continue
tqdm_epoch.set_postfix(epochs_per_fold)
if len(epochs_per_fold) == num_fold and min(
epochs_per_fold.values()) >= Config.get()['epoch']:
is_done = True
if len(epochs_per_fold) == num_fold and min(
epochs_per_fold.values()) >= epoch:
break
time.sleep(10)
if is_done:
break
logger.info('getting results...')
pretrain_results = ray.get(reqs)
for r_model, r_cv, r_dict in pretrain_results:
logger.info(
'model=%s cv=%d top1_train=%.4f top1_valid=%.4f' %
(r_model, r_cv + 1, r_dict['top1_train'], r_dict['top1_valid']))
logger.info('processed in %.4f secs' % watcher.pause('train_no_aug'))
def adjust_learning_rate_resnet(optimizer):
"""
Sets the learning rate to the initial LR decayed by 10 every [90, 180, 240] epochs
Ref: AutoAugment
"""
if C.get()['epoch'] == 90:
return torch.optim.lr_scheduler.MultiStepLR(optimizer, [30, 60, 80])
elif C.get()['epoch'] == 270: # autoaugment
return torch.optim.lr_scheduler.MultiStepLR(optimizer, [90, 180, 240])
else:
raise ValueError('invalid epoch=%d for resnet scheduler' %
C.get()['epoch'])
def test_arguments_boolean(datafiles):
filenames = [str(f) for f in datafiles.listdir()]
parser = ConfigArgumentParser(filename=filenames[0])
args = parser.parse_args(args=['-c', filenames[0], '--var', 'true'])
assert args.var == True
Config.clear()
parser = ConfigArgumentParser(filename=filenames[0])
args = parser.parse_args(args=['-c', filenames[0], '--var', 'false'])
assert args.var == False
Config.clear()
def efficientnet_params(model_name):
""" Map EfficientNet model name to parameter coefficients. """
params_dict = {
# Coefficients: width,depth,res,dropout
'efficientnet-l2t': (1.0, 1.0, C.get()['size'], C.get()['dropout']), # b0 with higher dropout ratio
'efficientnet-b0': (1.0, 1.0, 224, 0.2),
'efficientnet-b1': (1.0, 1.1, 240, 0.2),
'efficientnet-b2': (1.1, 1.2, 260, 0.3),
'efficientnet-b3': (1.2, 1.4, 300, 0.3),
'efficientnet-b4': (1.4, 1.8, 380, 0.4),
'efficientnet-b5': (1.6, 2.2, 456, 0.4),
'efficientnet-b6': (1.8, 2.6, 528, 0.5),
'efficientnet-b7': (2.0, 3.1, 600, 0.5),
}
return params_dict[model_name]
def get_affinity(aug, aff_bases, config, augment):
C.get()
C.get().conf = config
# setup - provided augmentation rules
C.get()['aug'] = aug
load_paths = augment['load_paths']
cv_num = augment["cv_num"]
aug_loaders = []
for cv_id in range(cv_num):
_, tl, validloader, tl2 = get_dataloaders(C.get()['dataset'],
C.get()['batch'],
augment['dataroot'],
augment['cv_ratio_test'],
split_idx=cv_id)
aug_loaders.append(validloader)
del tl, tl2
loss_fn = torch.nn.CrossEntropyLoss(reduction='none')
aug_accs = []
for cv_id, loader in enumerate(aug_loaders):
# eval
model = get_model(C.get()['model'], num_class(C.get()['dataset']))
ckpt = torch.load(load_paths[cv_id])
if 'model' in ckpt:
model.load_state_dict(ckpt['model'])
else:
model.load_state_dict(ckpt)
model.eval()
metrics = Accumulator()
for data, label in loader:
data = data.cuda()
label = label.cuda()
pred = model(data)
loss = loss_fn(pred, label) # (N)
_, pred = pred.topk(1, 1, True, True)
pred = pred.t()
correct = pred.eq(label.view(
1, -1).expand_as(pred)).detach().cpu().numpy() # (1,N)
metrics.add_dict({
'minus_loss':
-1 * np.sum(loss.detach().cpu().numpy()),
'correct':
np.sum(correct),
'cnt':
len(data)
})
del loss, correct, pred, data, label
aug_accs.append(metrics['correct'] / metrics['cnt'])
del model
affs = []
for aug_valid, clean_valid in zip(aug_accs, aff_bases):
affs.append(aug_valid - clean_valid)
return affs
def retrain(self, save_path=None):
if save_path is None:
augment_path = _get_path(
C.get()['dataset'],
C.get()['model']['type'],
'ratio%.1f_augment%d' % (args.cv_ratio, 0))
logger.info('getting results...')
final_results = train_model(copy.deepcopy(self.copied_c),
args.dataroot,
self.final_policy_set,
0.0,
0,
save_path=save_path)
logger.info(w)
return final_results
def get_dataset(oversample=True):
cv_fold = C.get()['cv_fold']
if cv_fold < 0:
with open(os.path.join('./split/tr_names.txt'), 'r') as text_file:
tr_n = text_file.read().split(',')
with open(os.path.join('./split/val_names.txt'), 'r') as text_file:
val_n = text_file.read().split(',')
cval_n = val_n
else:
with open(os.path.join('./split/tr_names_fold%d' % cv_fold), 'r') as text_file:
tr_n = text_file.read().split(',')
with open(os.path.join('./split/val_names_fold%d' % cv_fold), 'r') as text_file:
val_n = text_file.read().split(',')
with open(os.path.join('./split/val_names.txt'), 'r') as text_file:
cval_n = text_file.read().split(',')
# test_names = sorted({f[:36] for f in os.listdir(TEST)})
with open(SAMPLE, 'r') as text_file:
test_names = [x.split(',')[0] for x in text_file.readlines()[1:]]
# print(len(tr_n), len(val_n), len(test_names))
if oversample:
s = Oversampling(os.path.join(PATH, LABELS))
tr_n = [idx for idx in tr_n for _ in range(s.get(idx))]
return tr_n, val_n, cval_n, test_names
def test_arguments_complex(datafiles):
filenames = [str(f) for f in datafiles.listdir()]
parser = ConfigArgumentParser(filename=filenames[0])
args = parser.parse_args(args=['-c', filenames[0]])
_ = args
Config.clear()
filenames = [str(f) for f in datafiles.listdir()]
parser = ConfigArgumentParser(filename=filenames[0])
args = parser.parse_args(args=[]) # -c 옵션이 없어도 filename이 명시적으로 있는 경우 parse 가능해야 함
_ = args
Config.clear()
assert True
def __adjust_learning_rate_pyramid(epoch):
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
base_lr = C.get()['lr']
lr = base_lr * (0.1**(epoch //
(max_epoch * 0.5))) * (0.1**(epoch //
(max_epoch * 0.75)))
return lr
def augmentation(self, data, gr_ids, policy):
aug_imgs = []
if "cifar" in C.get()["dataset"]:
mean, std = _CIFAR_MEAN, _CIFAR_STD
elif "svhn" in C.get()["dataset"]:
mean, std = _SVHN_MEAN, _SVHN_STD
# applied_policies = []
for gr_id, img in zip(gr_ids, data):
pil_img = transforms.ToPILImage()(UnNormalize()(img.cpu()))
_aug = Augmentation(policy[int(gr_id)])
aug_img = _aug(pil_img)
aug_img = self.transform(aug_img)
aug_imgs.append(aug_img)
# applied_policy = _aug.policy # Todo
# applied_policies.append(applied_policy)
aug_imgs = torch.stack(aug_imgs)
return aug_imgs.cuda() #, applied_policies
def build_save_str(args):
optional_tokens = []
if "gmaxup_cifar" in args.dataroot:
optional_tokens.append("gmaxup")
if args.name:
optional_tokens.append(args.name)
optional_str = ""
if len(optional_tokens):
for token in optional_tokens:
optional_str += "{}-".format(token)
return '{}e-{}-{}{}'.format(
C.get()['epoch'],
C.get()['aug'], # augmentation string
optional_str, # optional string
datetime.datetime.now().strftime("%-m.%d.%y-%H:%M:%s:%f"))
def eval_tta3(config, augment, reporter):
C.get()
C.get().conf = config
save_path = augment['save_path']
cv_id, gr_id = augment["cv_id"], augment["gr_id"]
gr_ids = augment["gr_ids"]
# setup - provided augmentation rules
C.get()['aug'] = policy_decoder(augment, augment['num_policy'],
augment['num_op'])
# eval
model = get_model(C.get()['model'], num_class(C.get()['dataset']))
ckpt = torch.load(save_path)
if 'model' in ckpt:
model.load_state_dict(ckpt['model'])
else:
model.load_state_dict(ckpt)
del ckpt
model.eval()
loader = get_post_dataloader(C.get()["dataset"],
C.get()['batch'], augment["dataroot"],
augment['cv_ratio_test'], cv_id, gr_id,
gr_ids)
start_t = time.time()
metrics = Accumulator()
loss_fn = torch.nn.CrossEntropyLoss(reduction='none')
for data, label in loader:
data = data.cuda()
label = label.cuda()
pred = model(data)
loss = loss_fn(pred, label) # (N)
_, pred = pred.topk(1, 1, True, True)
pred = pred.t()
correct = pred.eq(label.view(
1, -1).expand_as(pred)).detach().cpu().numpy() # (1,N)
metrics.add_dict({
'loss': np.sum(loss.detach().cpu().numpy()),
'correct': np.sum(correct),
'cnt': len(data)
})
del loss, correct, pred, data, label
del model, loader
metrics = metrics / 'cnt'
gpu_secs = (time.time() - start_t) * torch.cuda.device_count()
reporter(loss=metrics['loss'],
top1_valid=metrics['correct'],
elapsed_time=gpu_secs,
done=True)
return metrics['correct']
def get_dataloaders(tests_aug=False):
tr, vl, cvl, ts = get_dataset()
if C.get()['extdata']:
if C.get()['cv_fold'] >= 0:
with open(os.path.join('./split/tr_ext_names_fold%d' % C.get()['cv_fold']), 'r') as text_file:
tr_n = text_file.read().split(',')
with open(os.path.join('./split/val_ext_names_fold%d' % C.get()['cv_fold']), 'r') as text_file:
val_n = text_file.read().split(',')
ds_train = CombinedDataset(KaggleDataset('train', tr), HPADataset('train_hpa_v18', tr_n))
ds_valid = CombinedDataset(KaggleDataset('valid', tr), HPADataset('valid_hpa_v18', val_n))
else:
with open(os.path.join('./split/tr_ext_names_fold0'), 'r') as text_file:
tr_n = text_file.read().split(',')
with open(os.path.join('./split/val_ext_names_fold0'), 'r') as text_file:
val_n = text_file.read().split(',')
tr_n += val_n
ds_train = CombinedDataset(KaggleDataset('train', tr), HPADataset('train_hpa_v18', tr_n))
ds_valid = KaggleDataset('valid', tr)
else:
ds_train = KaggleDataset('train', tr)
ds_valid = KaggleDataset('valid', vl, aug=False)
ds_cvalid = KaggleDataset('cvalid', cvl, aug=False)
ds_tests = KaggleDataset('tests', ts, aug=tests_aug)
print('data size=', len(ds_train), len(ds_valid), len(ds_cvalid), len(ds_tests))
d_train = torch.utils.data.DataLoader(ds_train, C.get()['batch'], pin_memory=True, num_workers=16 if C.get()['highres'] else 128, shuffle=True, drop_last=True)
d_valid = torch.utils.data.DataLoader(ds_valid, C.get()['batch'], pin_memory=True, num_workers=4, shuffle=False, drop_last=True)
d_cvalid = torch.utils.data.DataLoader(ds_cvalid, C.get()['batch'], pin_memory=True, num_workers=4, shuffle=False, drop_last=True)
d_tests = torch.utils.data.DataLoader(ds_tests, test_aug_sz if tests_aug else 1, pin_memory=True, num_workers=16, shuffle=False)
return d_train, d_valid, d_cvalid, d_tests
def __init__(self, setname, data_list):
csv = pd.read_csv(LABELS_HPA).set_index('Id')
super().__init__(setname, data_list, aug=False)
if C.get()['highres']:
self.default_path = '/data/public/rw/kaggle-human-protein-atlas/hpa_v18/images_2048'
else:
self.default_path = '/data/public/rw/kaggle-human-protein-atlas/hpa_v18/images'
self.labels = csv
self.ext = '.png'
def train_model(config,
dataroot,
augment,
cv_ratio_test,
cv_fold,
save_path=None,
skip_exist=False): # TODO: 解耦这里的config相关操作
Config.get()
Config.get().conf = config
Config.get()['aug'] = augment
result = train_and_eval(None,
dataroot,
cv_ratio_test,
cv_fold,
save_path=save_path,
only_eval=skip_exist)
return Config.get()['model']['type'], cv_fold, result
def adjust_learning_rate_resnet(optimizer):
epoch = C.get()['epoch']
if epoch == 90:
return torch.optim.lr_scheduler.MultiStepLR(optimizer, [30, 60, 80])
elif epoch == 270:
return torch.optim.lr_scheduler.MultiStepLR(optimizer, [90, 180, 240])
else:
raise ValueError(
'Invalid epoch={} for resnet scheduler.'.format(epoch))
def train_model(config,
dataroot,
augment,
cv_ratio_test,
cv_fold,
save_path=None,
skip_exist=False):
print('into training')
C.get()
C.get().conf = config
C.get()['aug'] = augment
result = train_and_eval(None,
dataroot,
cv_ratio_test,
cv_fold,
save_path=save_path,
only_eval=skip_exist)
return C.get()['model']['type'], cv_fold, result
def eval_tta2(config, augment, reporter):
C.get()
C.get().conf = config
cv_ratio_test, cv_id, save_path = augment['cv_ratio_test'], augment['cv_id'], augment['save_path']
gr_id = augment["gr_id"]
num_repeat = 1
# setup - provided augmentation rules
C.get()['aug'] = policy_decoder(augment, augment['num_policy'], augment['num_op'])
# eval
model = get_model(C.get()['model'], num_class(C.get()['dataset']))
ckpt = torch.load(save_path)
if 'model' in ckpt:
model.load_state_dict(ckpt['model'])
else:
model.load_state_dict(ckpt)
model.eval()
loaders = []
for i in range(num_repeat):
_, tl, validloader, tl2 = get_dataloaders(C.get()['dataset'], C.get()['batch'], augment['dataroot'], cv_ratio_test, split_idx=cv_id, gr_assign=augment["gr_assign"], gr_id=gr_id)
loaders.append(validloader)
del tl, tl2
start_t = time.time()
metrics = Accumulator()
loss_fn = torch.nn.CrossEntropyLoss(reduction='none')
for loader in loaders:
for data, label in loader:
data = data.cuda()
label = label.cuda()
pred = model(data)
loss = loss_fn(pred, label) # (N)
_, pred = pred.topk(1, 1, True, True)
pred = pred.t()
correct = pred.eq(label.view(1, -1).expand_as(pred)).detach().cpu().numpy() # (1,N)
metrics.add_dict({
'minus_loss': -1 * np.sum(loss.detach().cpu().numpy()),
'correct': np.sum(correct),
'cnt': len(data)
})
del loss, correct, pred, data, label
del model
metrics = metrics / 'cnt'
gpu_secs = (time.time() - start_t) * torch.cuda.device_count()
reporter(minus_loss=metrics['minus_loss'], top1_valid=metrics['correct'], elapsed_time=gpu_secs, done=True)
return metrics['correct']