def test_one_of():
transforms = [Mock(p=1) for _ in range(10)]
augmentation = OneOf(transforms, p=1)
image = np.ones((8, 8))
augmentation(image=image)
assert len([transform for transform in transforms
if transform.called]) == 1
def __init__(
self,
prob=0.7,
blur_prob=0.7,
jitter_prob=0.7,
rotate_prob=0.7,
flip_prob=0.7,
):
super().__init__()
self.prob = prob
self.blur_prob = blur_prob
self.jitter_prob = jitter_prob
self.rotate_prob = rotate_prob
self.flip_prob = flip_prob
self.transforms = al.Compose(
[
transforms.RandomRotate90(),
transforms.Flip(),
transforms.HueSaturationValue(),
transforms.RandomBrightnessContrast(),
transforms.Transpose(),
OneOf([
transforms.RandomCrop(220, 220, p=0.5),
transforms.CenterCrop(220, 220, p=0.5)
],
p=0.5),
# transforms.Resize(352,352),
# transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
],
p=self.prob)
def oneof_always_apply_crash():
aug = Compose(
[HorizontalFlip(),
Rotate(),
OneOf([Blur(), MedianBlur()], p=1)], p=1)
image = np.ones((8, 8))
data = aug(image=image)
assert data
def test_deterministic_oneof():
aug = ReplayCompose([OneOf([HorizontalFlip(), Blur()])], p=1)
for i in range(10):
image = (np.random.random((8, 8)) * 255).astype(np.uint8)
image2 = np.copy(image)
data = aug(image=image)
assert "replay" in data
data2 = ReplayCompose.replay(data["replay"], image=image2)
assert np.array_equal(data["image"], data2["image"])
def __init__(self, aug: DictConfig):
color_tfms = [aug.tfms.brightness_contrast, aug.tfms.hue_sat]
color_transform = OneOf([instantiate(tfm) for tfm in color_tfms],
p=0.9)
tfm_list = [color_transform] + [
instantiate(tfm)
for tfm in aug.tfms.values() if tfm not in color_tfms
]
self.transforms = Compose(tfm_list,
bbox_params=instantiate(aug.bbox_params))
def create_transform(self, input_dtype):
weights = self.weights.detach().cpu().numpy().tolist()
probabilities = [op.probability.item() for op in self.operations]
true_probabilities = [w * p for (w, p) in zip(weights, probabilities)]
assert sum(true_probabilities) <= 1.0
transforms = []
p_sum = 0
for operation, p in zip(self.operations, true_probabilities):
transforms.append(operation.create_transform(input_dtype, p))
p_sum += p
transforms.append(A.NoOp(p=1.0 - p_sum))
return OneOf(transforms, p=1)
def create_transform(self, input_dtype="float32", preprocessing_transforms=()):
sub_policy_p = 1 / len(self.sub_policies)
return Compose(
[
*preprocessing_transforms,
OneOf([sp.create_transform(input_dtype, p=sub_policy_p) for sp in self.sub_policies], p=1),
A.Normalize(
mean=self._mean.tolist(),
std=self._std.tolist(),
max_pixel_value=MAX_VALUES_BY_INPUT_DTYPE[input_dtype],
),
ToTensorV2(transpose_mask=True),
]
)
def create_transform(self, input_dtype):
weights = self.weights.detach().cpu().numpy().tolist()
probabilities = [op.probability.item() for op in self.operations]
true_probabilities = [w * p for (w, p) in zip(weights, probabilities)]
p_sum = sum(true_probabilities)
if p_sum > 1.0 + PROBABILITY_EPS:
warnings.warn(
f"Sum of all augmentation probabilities exceeds 1.0 and equals {p_sum}. "
"This may indicate an error in AutoAlbument. "
"Please report an issue at https://github.com/albumentations-team/autoalbument/issues.",
RuntimeWarning,
)
transforms = []
for operation, p in zip(self.operations, true_probabilities):
transforms.append(operation.create_transform(input_dtype, p))
if p_sum < 1.0:
transforms.append(A.NoOp(p=1.0 - p_sum))
return OneOf(transforms, p=1.0)
train_img_paths.extend(glob(os.path.join(i, "images", "*")))
train_mask_paths.extend(glob(os.path.join(i, "masks", "*")))
train_img_paths.sort()
train_mask_paths.sort()
transforms = al.Compose(
[
transforms.RandomRotate90(),
transforms.Flip(),
transforms.HueSaturationValue(),
transforms.RandomBrightnessContrast(),
transforms.Transpose(),
OneOf(
[
transforms.RandomCrop(220, 220, p=0.5),
transforms.CenterCrop(220, 220, p=0.5),
],
p=1,
),
# transforms.Resize(352,352),
# transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
],
p=1,
)
dataset = KvasirDataset(train_img_paths,
train_mask_paths,
352,
transform=transforms,
type="train")
fig, ax = plt.subplots(1, 2, figsize=(10, 9))
def main():
config = vars(parse_args())
if config['name'] is None:
config['name'] = '%s_%s' % (config['arch'],
datetime.now().strftime('%m%d%H'))
config['num_filters'] = [int(n) for n in config['num_filters'].split(',')]
if not os.path.exists('models/detection/%s' % config['name']):
os.makedirs('models/detection/%s' % config['name'])
if config['resume']:
with open('models/detection/%s/config.yml' % config['name'], 'r') as f:
config = yaml.load(f, Loader=yaml.FullLoader)
config['resume'] = True
with open('models/detection/%s/config.yml' % config['name'], 'w') as f:
yaml.dump(config, f)
print('-' * 20)
for key in config.keys():
print('- %s: %s' % (key, str(config[key])))
print('-' * 20)
cudnn.benchmark = True
df = pd.read_csv('inputs/train.csv')
img_paths = np.array('inputs/train_images/' + df['ImageId'].values +
'.jpg')
mask_paths = np.array('inputs/train_masks/' + df['ImageId'].values +
'.jpg')
labels = np.array(
[convert_str_to_labels(s) for s in df['PredictionString']])
test_img_paths = None
test_mask_paths = None
test_outputs = None
if config['pseudo_label'] is not None:
test_df = pd.read_csv('inputs/sample_submission.csv')
test_img_paths = np.array('inputs/test_images/' +
test_df['ImageId'].values + '.jpg')
test_mask_paths = np.array('inputs/test_masks/' +
test_df['ImageId'].values + '.jpg')
ext = os.path.splitext(config['pseudo_label'])[1]
if ext == '.pth':
test_outputs = torch.load('outputs/raw/test/%s' %
config['pseudo_label'])
elif ext == '.csv':
test_labels = pd.read_csv('outputs/submissions/test/%s' %
config['pseudo_label'])
null_idx = test_labels.isnull().any(axis=1)
test_img_paths = test_img_paths[~null_idx]
test_mask_paths = test_mask_paths[~null_idx]
test_labels = test_labels.dropna()
test_labels = np.array([
convert_str_to_labels(
s, names=['pitch', 'yaw', 'roll', 'x', 'y', 'z', 'score'])
for s in test_labels['PredictionString']
])
print(test_labels)
else:
raise NotImplementedError
if config['resume']:
checkpoint = torch.load('models/detection/%s/checkpoint.pth.tar' %
config['name'])
heads = OrderedDict([
('hm', 1),
('reg', 2),
('depth', 1),
])
if config['rot'] == 'eular':
heads['eular'] = 3
elif config['rot'] == 'trig':
heads['trig'] = 6
elif config['rot'] == 'quat':
heads['quat'] = 4
else:
raise NotImplementedError
if config['wh']:
heads['wh'] = 2
criterion = OrderedDict()
for head in heads.keys():
criterion[head] = losses.__dict__[config[head + '_loss']]().cuda()
train_transform = Compose([
transforms.ShiftScaleRotate(shift_limit=config['shift_limit'],
scale_limit=0,
rotate_limit=0,
border_mode=cv2.BORDER_CONSTANT,
value=0,
p=config['shift_p'])
if config['shift'] else NoOp(),
OneOf([
transforms.HueSaturationValue(hue_shift_limit=config['hue_limit'],
sat_shift_limit=config['sat_limit'],
val_shift_limit=config['val_limit'],
p=config['hsv_p'])
if config['hsv'] else NoOp(),
transforms.RandomBrightness(
limit=config['brightness_limit'],
p=config['brightness_p'],
) if config['brightness'] else NoOp(),
transforms.RandomContrast(
limit=config['contrast_limit'],
p=config['contrast_p'],
) if config['contrast'] else NoOp(),
],
p=1),
transforms.ISONoise(p=config['iso_noise_p'], )
if config['iso_noise'] else NoOp(),
transforms.CLAHE(p=config['clahe_p'], ) if config['clahe'] else NoOp(),
],
keypoint_params=KeypointParams(
format='xy', remove_invisible=False))
val_transform = None
folds = []
best_losses = []
# best_scores = []
kf = KFold(n_splits=config['n_splits'], shuffle=True, random_state=41)
for fold, (train_idx, val_idx) in enumerate(kf.split(img_paths)):
print('Fold [%d/%d]' % (fold + 1, config['n_splits']))
if (config['resume'] and fold < checkpoint['fold'] - 1) or (
not config['resume']
and os.path.exists('models/%s/model_%d.pth' %
(config['name'], fold + 1))):
log = pd.read_csv('models/detection/%s/log_%d.csv' %
(config['name'], fold + 1))
best_loss = log.loc[log['val_loss'].values.argmin(), 'val_loss']
# best_loss, best_score = log.loc[log['val_loss'].values.argmin(), ['val_loss', 'val_score']].values
folds.append(str(fold + 1))
best_losses.append(best_loss)
# best_scores.append(best_score)
continue
train_img_paths, val_img_paths = img_paths[train_idx], img_paths[
val_idx]
train_mask_paths, val_mask_paths = mask_paths[train_idx], mask_paths[
val_idx]
train_labels, val_labels = labels[train_idx], labels[val_idx]
if config['pseudo_label'] is not None:
train_img_paths = np.hstack((train_img_paths, test_img_paths))
train_mask_paths = np.hstack((train_mask_paths, test_mask_paths))
train_labels = np.hstack((train_labels, test_labels))
# train
train_set = Dataset(
train_img_paths,
train_mask_paths,
train_labels,
input_w=config['input_w'],
input_h=config['input_h'],
transform=train_transform,
lhalf=config['lhalf'],
hflip=config['hflip_p'] if config['hflip'] else 0,
scale=config['scale_p'] if config['scale'] else 0,
scale_limit=config['scale_limit'],
# test_img_paths=test_img_paths,
# test_mask_paths=test_mask_paths,
# test_outputs=test_outputs,
)
train_loader = torch.utils.data.DataLoader(
train_set,
batch_size=config['batch_size'],
shuffle=True,
num_workers=config['num_workers'],
# pin_memory=True,
)
val_set = Dataset(val_img_paths,
val_mask_paths,
val_labels,
input_w=config['input_w'],
input_h=config['input_h'],
transform=val_transform,
lhalf=config['lhalf'])
val_loader = torch.utils.data.DataLoader(
val_set,
batch_size=config['batch_size'],
shuffle=False,
num_workers=config['num_workers'],
# pin_memory=True,
)
# create model
model = get_model(config['arch'],
heads=heads,
head_conv=config['head_conv'],
num_filters=config['num_filters'],
dcn=config['dcn'],
gn=config['gn'],
ws=config['ws'],
freeze_bn=config['freeze_bn'])
model = model.cuda()
if config['load_model'] is not None:
model.load_state_dict(
torch.load('models/detection/%s/model_%d.pth' %
(config['load_model'], fold + 1)))
params = filter(lambda p: p.requires_grad, model.parameters())
if config['optimizer'] == 'Adam':
optimizer = optim.Adam(params,
lr=config['lr'],
weight_decay=config['weight_decay'])
elif config['optimizer'] == 'AdamW':
optimizer = optim.AdamW(params,
lr=config['lr'],
weight_decay=config['weight_decay'])
elif config['optimizer'] == 'RAdam':
optimizer = RAdam(params,
lr=config['lr'],
weight_decay=config['weight_decay'])
elif config['optimizer'] == 'SGD':
optimizer = optim.SGD(params,
lr=config['lr'],
momentum=config['momentum'],
nesterov=config['nesterov'],
weight_decay=config['weight_decay'])
else:
raise NotImplementedError
if config['apex']:
amp.initialize(model, optimizer, opt_level='O1')
if config['scheduler'] == 'CosineAnnealingLR':
scheduler = lr_scheduler.CosineAnnealingLR(
optimizer, T_max=config['epochs'], eta_min=config['min_lr'])
elif config['scheduler'] == 'ReduceLROnPlateau':
scheduler = lr_scheduler.ReduceLROnPlateau(
optimizer,
factor=config['factor'],
patience=config['patience'],
verbose=1,
min_lr=config['min_lr'])
elif config['scheduler'] == 'MultiStepLR':
scheduler = lr_scheduler.MultiStepLR(
optimizer,
milestones=[int(e) for e in config['milestones'].split(',')],
gamma=config['gamma'])
else:
raise NotImplementedError
log = {
'epoch': [],
'loss': [],
# 'score': [],
'val_loss': [],
# 'val_score': [],
}
best_loss = float('inf')
# best_score = float('inf')
start_epoch = 0
if config['resume'] and fold == checkpoint['fold'] - 1:
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
scheduler.load_state_dict(checkpoint['scheduler'])
start_epoch = checkpoint['epoch']
log = pd.read_csv(
'models/detection/%s/log_%d.csv' %
(config['name'], fold + 1)).to_dict(orient='list')
best_loss = checkpoint['best_loss']
for epoch in range(start_epoch, config['epochs']):
print('Epoch [%d/%d]' % (epoch + 1, config['epochs']))
# train for one epoch
train_loss = train(config, heads, train_loader, model, criterion,
optimizer, epoch)
# evaluate on validation set
val_loss = validate(config, heads, val_loader, model, criterion)
if config['scheduler'] == 'CosineAnnealingLR':
scheduler.step()
elif config['scheduler'] == 'ReduceLROnPlateau':
scheduler.step(val_loss)
print('loss %.4f - val_loss %.4f' % (train_loss, val_loss))
# print('loss %.4f - score %.4f - val_loss %.4f - val_score %.4f'
# % (train_loss, train_score, val_loss, val_score))
log['epoch'].append(epoch)
log['loss'].append(train_loss)
# log['score'].append(train_score)
log['val_loss'].append(val_loss)
# log['val_score'].append(val_score)
pd.DataFrame(log).to_csv('models/detection/%s/log_%d.csv' %
(config['name'], fold + 1),
index=False)
if val_loss < best_loss:
torch.save(
model.state_dict(), 'models/detection/%s/model_%d.pth' %
(config['name'], fold + 1))
best_loss = val_loss
# best_score = val_score
print("=> saved best model")
state = {
'fold': fold + 1,
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_loss': best_loss,
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
}
torch.save(
state,
'models/detection/%s/checkpoint.pth.tar' % config['name'])
print('val_loss: %f' % best_loss)
# print('val_score: %f' % best_score)
folds.append(str(fold + 1))
best_losses.append(best_loss)
# best_scores.append(best_score)
results = pd.DataFrame({
'fold':
folds + ['mean'],
'best_loss':
best_losses + [np.mean(best_losses)],
# 'best_score': best_scores + [np.mean(best_scores)],
})
print(results)
results.to_csv('models/detection/%s/results.csv' % config['name'],
index=False)
del model
torch.cuda.empty_cache()
del train_set, train_loader
del val_set, val_loader
gc.collect()
if not config['cv']:
break
def main():
config = vars(parse_args())
if config['name'] is None:
if config['deep_supervision']:
config['name'] = '%s_%s_wDS' % (config['dataset'], config['arch'])
else:
config['name'] = '%s_%s_woDS' % (config['dataset'], config['arch'])
os.makedirs('models/%s' % config['name'], exist_ok=True)
print('-' * 20)
for key in config:
print('%s: %s' % (key, config[key]))
print('-' * 20)
with open('models/%s/config.yml' % config['name'], 'w') as f:
yaml.dump(config, f)
# define loss function (criterion)
if config['loss'] == 'BCEWithLogitsLoss':
criterion = nn.BCEWithLogitsLoss().cuda()
else:
criterion = losses.__dict__[config['loss']]().cuda()
cudnn.benchmark = True
# create model
print("=> creating model %s" % config['arch'])
model = archs.__dict__[config['arch']](config['num_classes'],
config['input_channels'],
config['deep_supervision'])
model = model.cuda()
params = filter(lambda p: p.requires_grad, model.parameters())
if config['optimizer'] == 'Adam':
optimizer = optim.Adam(params,
lr=config['lr'],
weight_decay=config['weight_decay'])
elif config['optimizer'] == 'SGD':
optimizer = optim.SGD(params,
lr=config['lr'],
momentum=config['momentum'],
nesterov=config['nesterov'],
weight_decay=config['weight_decay'])
else:
raise NotImplementedError
if config['scheduler'] == 'CosineAnnealingLR':
scheduler = lr_scheduler.CosineAnnealingLR(optimizer,
T_max=config['epochs'],
eta_min=config['min_lr'])
elif config['scheduler'] == 'ReduceLROnPlateau':
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer,
factor=config['factor'],
patience=config['patience'],
verbose=1,
min_lr=config['min_lr'])
elif config['scheduler'] == 'MultiStepLR':
scheduler = lr_scheduler.MultiStepLR(
optimizer,
milestones=[int(e) for e in config['milestones'].split(',')],
gamma=config['gamma'])
elif config['scheduler'] == 'ConstantLR':
scheduler = None
else:
raise NotImplementedError
# Data loading code
img_ids = glob(
os.path.join('inputs', config['dataset'], 'images',
'*' + config['img_ext']))
img_ids = [os.path.splitext(os.path.basename(p))[0] for p in img_ids]
train_img_ids, val_img_ids = train_test_split(img_ids,
test_size=0.2,
random_state=41)
#######################
train_img_ids = img_ids
#######################
train_transform = Compose([
transforms.RandomRotate90(),
transforms.Flip(),
OneOf([
transforms.HueSaturationValue(),
transforms.RandomBrightness(),
transforms.RandomContrast(),
],
p=1),
transforms.Resize(config['input_h'], config['input_w']),
transforms.Normalize(),
])
val_transform = Compose([
transforms.Resize(config['input_h'], config['input_w']),
transforms.Normalize(),
])
train_dataset = Dataset(img_ids=train_img_ids,
img_dir=os.path.join('inputs', config['dataset'],
'images'),
mask_dir=os.path.join('inputs', config['dataset'],
'masks'),
img_ext=config['img_ext'],
mask_ext=config['mask_ext'],
num_classes=config['num_classes'],
transform=train_transform)
val_dataset = Dataset(img_ids=val_img_ids,
img_dir=os.path.join('inputs', config['dataset'],
'images'),
mask_dir=os.path.join('inputs', config['dataset'],
'masks'),
img_ext=config['img_ext'],
mask_ext=config['mask_ext'],
num_classes=config['num_classes'],
transform=val_transform)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=config['batch_size'],
shuffle=True,
num_workers=config['num_workers'],
drop_last=True)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=config['batch_size'],
shuffle=False,
num_workers=config['num_workers'],
drop_last=False)
log = OrderedDict([
('epoch', []),
('lr', []),
('loss', []),
('iou', []),
('val_loss', []),
('val_iou', []),
])
best_iou = 0
trigger = 0
for epoch in range(config['epochs']):
print('Epoch [%d/%d]' % (epoch, config['epochs']))
# train for one epoch
train_log = train(config, train_loader, model, criterion, optimizer)
# evaluate on validation set
val_log = validate(config, val_loader, model, criterion)
if config['scheduler'] == 'CosineAnnealingLR':
scheduler.step()
elif config['scheduler'] == 'ReduceLROnPlateau':
scheduler.step(val_log['loss'])
print('loss %.4f - iou %.4f - val_loss %.4f - val_iou %.4f' %
(train_log['loss'], train_log['iou'], val_log['loss'],
val_log['iou']))
log['epoch'].append(epoch)
log['lr'].append(config['lr'])
log['loss'].append(train_log['loss'])
log['iou'].append(train_log['iou'])
log['val_loss'].append(val_log['loss'])
log['val_iou'].append(val_log['iou'])
pd.DataFrame(log).to_csv('models/%s/log.csv' % config['name'],
index=False)
trigger += 1
if val_log['iou'] > best_iou:
torch.save(model.state_dict(),
'models/%s/model.pth' % config['name'])
best_iou = val_log['iou']
print("=> saved best model")
trigger = 0
# early stopping
if config['early_stopping'] >= 0 and trigger >= config[
'early_stopping']:
print("=> early stopping")
break
torch.cuda.empty_cache()
# axs[0].imshow(image)
# axs[0].set_title("Original",fontsize=30)
# image_ = transforms.RandomRotate90(always_apply=True)(image=image)["image"]
# axs[1].imshow(image_)
# axs[1].set_title("Rotate",fontsize=30)
# image_ = transforms.Flip(always_apply=True)(image=image)["image"]
# axs[2].imshow(image_)
# axs[2].set_title("Flip",fontsize=30)
image_ = transforms.HueSaturationValue(always_apply=True)(image=image)["image"]
axs[0].imshow(image_)
axs[0].set_title("Saturation", fontsize=30)
image_ = transforms.RandomBrightnessContrast(always_apply=True)(
image=image)["image"]
axs[1].imshow(image_)
axs[1].set_title("Brightness", fontsize=30)
image_ = OneOf(
[
transforms.RandomCrop(204, 250, p=1),
transforms.CenterCrop(204, 250, p=1),
],
p=1,
)(image=image)["image"]
axs[2].imshow(image_)
axs[2].set_title("Random Crop", fontsize=30)
def test_single_transform_compose(
compose_transform: typing.Tuple[typing.Type[BaseCompose], dict],
inner_transform: typing.Tuple[typing.Union[typing.Type[BaseCompose], typing.Type[BasicTransform]], dict],
):
compose_cls, compose_kwargs = compose_transform
cls, kwargs = inner_transform
transform = cls(transforms=[], **kwargs) if issubclass(cls, BaseCompose) else cls(**kwargs)
with pytest.warns(UserWarning):
res_transform = compose_cls(transforms=transform, **compose_kwargs) # type: ignore
assert isinstance(res_transform.transforms, list)
@pytest.mark.parametrize(
"transforms",
[OneOf([Sequential([HorizontalFlip(p=1)])], p=1), SomeOf([Sequential([HorizontalFlip(p=1)])], n=1, p=1)],
)
def test_choice_inner_compositions(transforms):
"""Check that the inner composition is selected without errors."""
image = np.empty([10, 10, 3], dtype=np.uint8)
transforms(image=image)
@pytest.mark.parametrize(
"transforms",
[
Compose([ChannelShuffle(p=1)], p=1),
Compose([ChannelShuffle(p=0)], p=0),
],
)
def test_contiguous_output(transforms):
def main():
config = vars(parse_args())
now = datetime.datetime.now()
if config["name"] is None:
if config["deep_supervision"]:
config["name"] = "%s_%s_wDS_%s" % (
config["dataset"],
config["arch"],
now.strftime("%Y%m%d_%H%M%S"),
)
else:
config["name"] = "%s_%s_woDS_%s" % (
config["dataset"],
config["arch"],
now.strftime("%Y%m%d_%H%M%S"),
)
output_path = os.path.join(cfg.UNET_RESULTS_DIR, config["name"])
try:
os.makedirs(output_path, exist_ok=True)
except Exception as e:
print(e)
models_path = os.path.join(output_path, "models")
os.mkdir(models_path)
with open(os.path.join(models_path, "config.yml"), "w") as f:
yaml.dump(config, f)
print("-" * 20)
for key in config:
print("%s: %s" % (key, config[key]))
print("-" * 20)
# Tensorboad 用のログを記録するディレクトリパス
log_dir = os.path.join(output_path, "log")
os.mkdir(log_dir)
writer = SummaryWriter(log_dir=log_dir)
# define loss function(criterion)
if config["loss"] == "BCEWithLogitsLoss":
criterion = nn.BCEWithLogitsLoss().cuda()
else:
criterion = losses.__dict__[config["loss"]]().cuda()
cudnn.benchmark = True
# create model
print("=> creating model %s" % config["arch"])
model = archs.__dict__[config["arch"]](config["num_classes"],
config["input_channels"],
config["deep_supervision"])
model = model.cuda()
# モデルを TensorBorad で表示するため，ログに保存
# image = torch.randn(1, 3, 2224, 224)
# writer.add_graph(model, image)
params = filter(lambda p: p.requires_grad, model.parameters())
if config["optimizer"] == "Adam":
optimizer = optim.Adam(params,
lr=config["lr"],
weight_decay=config["weight_decay"])
elif config["optimizer"] == "SGD":
optimizer = optim.SGD(
params,
lr=config["lr"],
momentum=config["momentum"],
nesterov=config["nesterov"],
weight_decay=config["weight_decay"],
)
else:
raise NotImplementedError
# scheduler
if config["scheduler"] == "CosineAnnealingLR":
scheduler = lr_scheduler.CosineAnnealingLR(optimizer=optimizer,
T_max=config["epochs"],
eta_min=config["min_lr"])
elif config["scheduler"] == "ReduceLROnPlateau":
scheduler = lr_scheduler(
optimizer=optimizer,
factor=config["factor"],
patience=config["patience"],
verbose=1,
min_lr=config["min_lr"],
)
elif config["scheduler"] == "MultiStepLR":
scheduler = lr_scheduler.MultiStepLR(
optimizer=optimizer,
milestones=[int(e) for e in config["milestones"].split(",")],
gamma=config["gamma"],
)
elif config["scheduler"] == "ConstantLR":
scheduler = None
else:
raise NotImplementedError
# Data loading code
if config["dataset"] == "dsb2018_96":
input_dir = cfg.DSB2018_96_DIR
img_ids = glob(
os.path.join(input_dir, "images", "*" + config["img_ext"]))
img_ids = [os.path.splitext(os.path.basename(p))[0] for p in img_ids]
train_img_ids, val_img_ids = train_test_split(img_ids,
test_size=0.2,
random_state=41)
train_transform = Compose([
transforms.RandomRotate90(),
transforms.Flip(),
OneOf(
[
transforms.HueSaturationValue(),
transforms.RandomBrightness(),
transforms.RandomContrast(),
],
p=1,
),
transforms.Resize(config["input_h"], config["input_w"]),
transforms.Normalize(),
])
val_transform = Compose([
transforms.Resize(config["input_h"], config["input_w"]),
transforms.Normalize(),
])
train_dataset = Dataset(
img_ids=train_img_ids,
img_dir=os.path.join(input_dir, "images"),
mask_dir=os.path.join(input_dir, "masks"),
img_ext=config["img_ext"],
mask_ext=config["mask_ext"],
num_classes=config["num_classes"],
transform=train_transform,
)
val_dataset = Dataset(
img_ids=val_img_ids,
img_dir=os.path.join(input_dir, "images"),
mask_dir=os.path.join(input_dir, "masks"),
img_ext=config["img_ext"],
mask_ext=config["mask_ext"],
num_classes=config["num_classes"],
transform=val_transform,
)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=config["batch_size"],
shuffle=True,
num_workers=config["num_workers"],
drop_last=True,
)
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=config["batch_size"],
shuffle=False,
num_workers=config["num_workers"],
drop_last=False,
)
log = OrderedDict([
("epoch", []),
("lr", []),
("loss", []),
("iou", []),
("val_loss", []),
("val_iou", []),
])
best_iou = 0
trigger = 0
for epoch in range(config["epochs"] + 1):
print("Epoch [%d/%d]" % (epoch, config["epochs"]))
# train for one epoch
train_log = train(config, train_loader, model, criterion, optimizer)
# evaluate on validation set
val_log = validate(config, val_loader, model, criterion)
if config["scheduler"] == "CosineAnnealingLR":
scheduler.step()
elif config["scheduler"] == "ReduceLROnPlateau":
scheduler.step(val_log["loss"])
print("loss %.4f - iou %.4f - val_loss %.4f - val_iou %.4f" %
(train_log["loss"], train_log["iou"], val_log["loss"],
val_log["iou"]))
log["epoch"].append(epoch)
log["lr"].append(config["lr"])
log["loss"].append(train_log["loss"])
log["iou"].append(train_log["iou"])
log["val_loss"].append(val_log["loss"])
log["val_iou"].append(val_log["iou"])
# Tensorboard用のデータ
writer.add_scalar("training loss", train_log["loss"], epoch)
writer.add_scalar("validation loss", val_log["loss"], epoch)
pd.DataFrame(log).to_csv("%s/log.csv" % (log_dir), index=False)
if epoch == 0:
best_loss = val_log["loss"]
trigger += 1
# Best Model Save
# if val_log['iou'] > best_iou:
if (val_log["iou"] > best_iou) and (val_log["loss"] <= best_loss):
torch.save(model.state_dict(), "%s/model.pth" % (models_path))
best_iou = val_log["iou"]
best_loss = val_log["loss"]
print("=> saved best model")
trigger = 0
# early stopping
if (config["early_stopping"] >= 0 and
trigger >= config["early_stopping"]) or val_log["loss"] < 1e-4:
print("=> early stopping")
break
torch.cuda.empty_cache()
# summary writer を必要としない場合，close()メソッドを呼び出す
writer.close()
# CUDA for PyTorch
use_cuda = torch.cuda.is_available()
torch.device("cuda:0" if use_cuda else "cpu")
torch.backends.cudnn.benchmark = True
from albumentations.pytorch.transforms import ToTensor as py_ToTensor
normalize_dict = {"mean": [0.485, 0.456, 0.406], "std": [0.229, 0.224, 0.225]}
albu_transform = A.Compose([
OneOf([
A.CoarseDropout(always_apply=False,
p=0.7,
max_holes=1,
max_height=64,
max_width=64,
min_holes=1,
min_height=64,
min_width=64,
fill_value=(0, 0, 0),
mask_fill_value=None),
],
p=0.7),
py_ToTensor(),
])
#inv normalization
inv_normalize = T.Compose([
T.Normalize(mean=[-0.485 / 0.229, -0.456 / 0.224, -0.406 / 0.225],
std=[1 / 0.229, 1 / 0.224, 1 / 0.225])
])
def main():
config = vars(parse_args())
if config['name'] is None:
config['name'] = '%s_%s' % (config['arch'], datetime.now().strftime('%m%d%H'))
if not os.path.exists('models/pose/%s' % config['name']):
os.makedirs('models/pose/%s' % config['name'])
if config['resume']:
with open('models/pose/%s/config.yml' % config['name'], 'r') as f:
config = yaml.load(f, Loader=yaml.FullLoader)
config['resume'] = True
with open('models/pose/%s/config.yml' % config['name'], 'w') as f:
yaml.dump(config, f)
print('-'*20)
for key in config.keys():
print('- %s: %s' % (key, str(config[key])))
print('-'*20)
cudnn.benchmark = True
df = pd.read_csv('inputs/train.csv')
img_ids = df['ImageId'].values
pose_df = pd.read_csv('processed/pose_train.csv')
pose_df['img_path'] = 'processed/pose_images/train/' + pose_df['img_path']
if config['resume']:
checkpoint = torch.load('models/pose/%s/checkpoint.pth.tar' % config['name'])
if config['rot'] == 'eular':
num_outputs = 3
elif config['rot'] == 'trig':
num_outputs = 6
elif config['rot'] == 'quat':
num_outputs = 4
else:
raise NotImplementedError
if config['loss'] == 'L1Loss':
criterion = nn.L1Loss().cuda()
elif config['loss'] == 'MSELoss':
criterion = nn.MSELoss().cuda()
else:
raise NotImplementedError
train_transform = Compose([
transforms.ShiftScaleRotate(
shift_limit=config['shift_limit'],
scale_limit=0,
rotate_limit=0,
border_mode=cv2.BORDER_CONSTANT,
value=0,
p=config['shift_p']
) if config['shift'] else NoOp(),
OneOf([
transforms.HueSaturationValue(
hue_shift_limit=config['hue_limit'],
sat_shift_limit=config['sat_limit'],
val_shift_limit=config['val_limit'],
p=config['hsv_p']
) if config['hsv'] else NoOp(),
transforms.RandomBrightness(
limit=config['brightness_limit'],
p=config['brightness_p'],
) if config['brightness'] else NoOp(),
transforms.RandomContrast(
limit=config['contrast_limit'],
p=config['contrast_p'],
) if config['contrast'] else NoOp(),
], p=1),
transforms.ISONoise(
p=config['iso_noise_p'],
) if config['iso_noise'] else NoOp(),
transforms.CLAHE(
p=config['clahe_p'],
) if config['clahe'] else NoOp(),
transforms.Resize(config['input_w'], config['input_h']),
transforms.Normalize(),
ToTensor(),
])
val_transform = Compose([
transforms.Resize(config['input_w'], config['input_h']),
transforms.Normalize(),
ToTensor(),
])
folds = []
best_losses = []
kf = KFold(n_splits=config['n_splits'], shuffle=True, random_state=41)
for fold, (train_idx, val_idx) in enumerate(kf.split(img_ids)):
print('Fold [%d/%d]' %(fold + 1, config['n_splits']))
if (config['resume'] and fold < checkpoint['fold'] - 1) or (not config['resume'] and os.path.exists('pose_models/%s/model_%d.pth' % (config['name'], fold+1))):
log = pd.read_csv('models/pose/%s/log_%d.csv' %(config['name'], fold+1))
best_loss = log.loc[log['val_loss'].values.argmin(), 'val_loss']
# best_loss, best_score = log.loc[log['val_loss'].values.argmin(), ['val_loss', 'val_score']].values
folds.append(str(fold + 1))
best_losses.append(best_loss)
# best_scores.append(best_score)
continue
train_img_ids, val_img_ids = img_ids[train_idx], img_ids[val_idx]
train_img_paths = []
train_labels = []
for img_id in train_img_ids:
tmp = pose_df.loc[pose_df.ImageId == img_id]
img_path = tmp['img_path'].values
train_img_paths.append(img_path)
yaw = tmp['yaw'].values
pitch = tmp['pitch'].values
roll = tmp['roll'].values
roll = rotate(roll, np.pi)
if config['rot'] == 'eular':
label = np.array([
yaw,
pitch,
roll
]).T
elif config['rot'] == 'trig':
label = np.array([
np.cos(yaw),
np.sin(yaw),
np.cos(pitch),
np.sin(pitch),
np.cos(roll),
np.sin(roll),
]).T
elif config['rot'] == 'quat':
raise NotImplementedError
else:
raise NotImplementedError
train_labels.append(label)
train_img_paths = np.hstack(train_img_paths)
train_labels = np.vstack(train_labels)
val_img_paths = []
val_labels = []
for img_id in val_img_ids:
tmp = pose_df.loc[pose_df.ImageId == img_id]
img_path = tmp['img_path'].values
val_img_paths.append(img_path)
yaw = tmp['yaw'].values
pitch = tmp['pitch'].values
roll = tmp['roll'].values
roll = rotate(roll, np.pi)
if config['rot'] == 'eular':
label = np.array([
yaw,
pitch,
roll
]).T
elif config['rot'] == 'trig':
label = np.array([
np.cos(yaw),
np.sin(yaw),
np.cos(pitch),
np.sin(pitch),
np.cos(roll),
np.sin(roll),
]).T
elif config['rot'] == 'quat':
raise NotImplementedError
else:
raise NotImplementedError
val_labels.append(label)
val_img_paths = np.hstack(val_img_paths)
val_labels = np.vstack(val_labels)
# train
train_set = PoseDataset(
train_img_paths,
train_labels,
transform=train_transform,
)
train_loader = torch.utils.data.DataLoader(
train_set,
batch_size=config['batch_size'],
shuffle=True,
num_workers=config['num_workers'],
# pin_memory=True,
)
val_set = PoseDataset(
val_img_paths,
val_labels,
transform=val_transform,
)
val_loader = torch.utils.data.DataLoader(
val_set,
batch_size=config['batch_size'],
shuffle=False,
num_workers=config['num_workers'],
# pin_memory=True,
)
# create model
model = get_pose_model(config['arch'],
num_outputs=num_outputs,
freeze_bn=config['freeze_bn'])
model = model.cuda()
params = filter(lambda p: p.requires_grad, model.parameters())
if config['optimizer'] == 'Adam':
optimizer = optim.Adam(params, lr=config['lr'], weight_decay=config['weight_decay'])
elif config['optimizer'] == 'AdamW':
optimizer = optim.AdamW(params, lr=config['lr'], weight_decay=config['weight_decay'])
elif config['optimizer'] == 'RAdam':
optimizer = RAdam(params, lr=config['lr'], weight_decay=config['weight_decay'])
elif config['optimizer'] == 'SGD':
optimizer = optim.SGD(params, lr=config['lr'], momentum=config['momentum'],
nesterov=config['nesterov'], weight_decay=config['weight_decay'])
else:
raise NotImplementedError
if config['scheduler'] == 'CosineAnnealingLR':
scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=config['epochs'], eta_min=config['min_lr'])
elif config['scheduler'] == 'ReduceLROnPlateau':
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, factor=config['factor'], patience=config['patience'],
verbose=1, min_lr=config['min_lr'])
elif config['scheduler'] == 'MultiStepLR':
scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[int(e) for e in config['milestones'].split(',')], gamma=config['gamma'])
else:
raise NotImplementedError
log = {
'epoch': [],
'loss': [],
# 'score': [],
'val_loss': [],
# 'val_score': [],
}
best_loss = float('inf')
# best_score = float('inf')
start_epoch = 0
if config['resume'] and fold == checkpoint['fold'] - 1:
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
scheduler.load_state_dict(checkpoint['scheduler'])
start_epoch = checkpoint['epoch']
log = pd.read_csv('models/pose/%s/log_%d.csv' % (config['name'], fold+1)).to_dict(orient='list')
best_loss = checkpoint['best_loss']
for epoch in range(start_epoch, config['epochs']):
print('Epoch [%d/%d]' % (epoch + 1, config['epochs']))
# train for one epoch
train_loss = train(config, train_loader, model, criterion, optimizer, epoch)
# evaluate on validation set
val_loss = validate(config, val_loader, model, criterion)
if config['scheduler'] == 'CosineAnnealingLR':
scheduler.step()
elif config['scheduler'] == 'ReduceLROnPlateau':
scheduler.step(val_loss)
print('loss %.4f - val_loss %.4f' % (train_loss, val_loss))
# print('loss %.4f - score %.4f - val_loss %.4f - val_score %.4f'
# % (train_loss, train_score, val_loss, val_score))
log['epoch'].append(epoch)
log['loss'].append(train_loss)
# log['score'].append(train_score)
log['val_loss'].append(val_loss)
# log['val_score'].append(val_score)
pd.DataFrame(log).to_csv('models/pose/%s/log_%d.csv' % (config['name'], fold+1), index=False)
if val_loss < best_loss:
torch.save(model.state_dict(), 'models/pose/%s/model_%d.pth' % (config['name'], fold+1))
best_loss = val_loss
# best_score = val_score
print("=> saved best model")
state = {
'fold': fold + 1,
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_loss': best_loss,
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
}
torch.save(state, 'models/pose/%s/checkpoint.pth.tar' % config['name'])
print('val_loss: %f' % best_loss)
# print('val_score: %f' % best_score)
folds.append(str(fold + 1))
best_losses.append(best_loss)
# best_scores.append(best_score)
results = pd.DataFrame({
'fold': folds + ['mean'],
'best_loss': best_losses + [np.mean(best_losses)],
# 'best_score': best_scores + [np.mean(best_scores)],
})
print(results)
results.to_csv('models/pose/%s/results.csv' % config['name'], index=False)
del model
torch.cuda.empty_cache()
del train_set, train_loader
del val_set, val_loader
gc.collect()
if not config['cv']:
break
def __init__(
self,
prob=0,
Flip_prob=0,
HueSaturationValue_prob=0,
RandomBrightnessContrast_prob=0,
crop_prob=0,
randomrotate90_prob=0,
elastictransform_prob=0,
gridistortion_prob=0,
opticaldistortion_prob=0,
verticalflip_prob=0,
horizontalflip_prob=0,
randomgamma_prob=0,
CoarseDropout_prob=0,
RGBShift_prob=0,
MotionBlur_prob=0,
MedianBlur_prob=0,
GaussianBlur_prob=0,
GaussNoise_prob=0,
ChannelShuffle_prob=0,
ColorJitter_prob=0,
):
super().__init__()
self.prob = prob
self.randomrotate90_prob = randomrotate90_prob
self.elastictransform_prob = elastictransform_prob
self.transforms = al.Compose(
[
transforms.RandomRotate90(p=randomrotate90_prob),
transforms.Flip(p=Flip_prob),
transforms.HueSaturationValue(p=HueSaturationValue_prob),
transforms.RandomBrightnessContrast(
p=RandomBrightnessContrast_prob),
transforms.Transpose(),
OneOf(
[
transforms.RandomCrop(220, 220, p=0.5),
transforms.CenterCrop(220, 220, p=0.5),
],
p=crop_prob,
),
ElasticTransform(
p=elastictransform_prob,
alpha=120,
sigma=120 * 0.05,
alpha_affine=120 * 0.03,
),
GridDistortion(p=gridistortion_prob),
OpticalDistortion(p=opticaldistortion_prob,
distort_limit=2,
shift_limit=0.5),
VerticalFlip(p=verticalflip_prob),
HorizontalFlip(p=horizontalflip_prob),
RandomGamma(p=randomgamma_prob),
RGBShift(p=RGBShift_prob),
MotionBlur(p=MotionBlur_prob, blur_limit=7),
MedianBlur(p=MedianBlur_prob, blur_limit=9),
GaussianBlur(p=GaussianBlur_prob, blur_limit=9),
GaussNoise(p=GaussNoise_prob),
ChannelShuffle(p=ChannelShuffle_prob),
CoarseDropout(p=CoarseDropout_prob,
max_holes=8,
max_height=32,
max_width=32),
ColorJitter(p=ColorJitter_prob)
# transforms.Resize(352, 352),
# transforms.Normalize(
# mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)
# ),
],
p=self.prob,
)
}}), # type: ignore
)
def test_single_transform_compose(
compose_transform: typing.Tuple[typing.Type[BaseCompose], dict],
inner_transform: typing.Tuple[typing.Union[typing.Type[BaseCompose],
typing.Type[BasicTransform]],
dict],
):
compose_cls, compose_kwargs = compose_transform
cls, kwargs = inner_transform
transform = cls(transforms=[], **kwargs) if issubclass(
cls, BaseCompose) else cls(**kwargs)
with pytest.warns(UserWarning):
res_transform = compose_cls(transforms=transform,
**compose_kwargs) # type: ignore
assert isinstance(res_transform.transforms, list)
@pytest.mark.parametrize(
"transforms",
[
OneOf([Sequential([HorizontalFlip(p=1)])], p=1),
SomeOf([Sequential([HorizontalFlip(p=1)])], n=1, p=1)
],
)
def test_choice_inner_compositions(transforms):
"""Check that the inner composition is selected without errors."""
image = np.empty([10, 10, 3], dtype=np.uint8)
transforms(image=image)
