def test_dataloader(self):
"""Summary
Returns:
TYPE: Description
"""
ds_test = CustomDataSet(folder=self.hparams.data,
train_or_valid='test',
size=np.inf,
hparams=self.hparams)
ds_test.reset_state()
ag_test = [
imgaug.Resize(self.hparams.shape, interp=cv2.INTER_NEAREST),
imgaug.ToFloat32(),
]
# ds_test = AugmentImageComponent(ds_test, [imgaug.Albumentations(AB.CLAHE(tile_grid_size=(32, 32), always_apply=True, p=1)),], 0)
ds_test = AugmentImageComponents(ds_test, ag_test, [0, 1])
ds_test = BatchData(ds_test, self.hparams.batch, remainder=True)
ds_test = MultiProcessRunner(ds_test, num_proc=4, num_prefetch=16)
ds_test = PrintData(ds_test)
ds_test = MapData(
ds_test, lambda dp: [
torch.tensor(dp[0][:, np.newaxis, :, :]).float(),
torch.tensor(dp[1][:, np.newaxis, :, :]).float(),
])
return ds_test
def read_and_augment_images(ds):
def mapf(dp):
fname = dp[0]
im = cv2.imread(fname, cv2.IMREAD_COLOR).astype('float32')
assert im is not None, dp[0]
dp[0] = im
# assume floatbox as input
assert dp[1].dtype == np.float32
dp[1] = box_to_point8(dp[1])
dp.append(fname)
return dp
ds = MapData(ds, mapf)
augs = [
CustomResize(config.SHORT_EDGE_SIZE, config.MAX_SIZE),
imgaug.Flip(horiz=True)
]
ds = AugmentImageComponents(ds, augs, index=(0, ), coords_index=(1, ))
def unmapf(points):
boxes = point8_to_box(points)
return boxes
ds = MapDataComponent(ds, unmapf, 1)
return ds
def val_dataloader(self):
"""Summary
Returns:
TYPE: Description
"""
ds_valid = CustomDataSet(folder=self.hparams.data,
train_or_valid='valid',
size=np.inf,
hparams=self.hparams)
ds_valid.reset_state()
ag_valid = [
imgaug.Resize(self.hparams.shape, interp=cv2.INTER_NEAREST),
imgaug.ToFloat32(),
]
ds_valid = AugmentImageComponent(ds_valid, [
imgaug.Albumentations(AB.CLAHE(p=1)),
], 0)
if self.hparams.types == 6:
ds_valid = AugmentImageComponents(ds_valid, ag_valid,
[0, 1, 2, 3, 4, 5, 6])
elif self.hparams.types == 1:
ds_valid = AugmentImageComponents(ds_valid, ag_valid, [0, 1])
ds_valid = BatchData(ds_valid, self.hparams.batch, remainder=True)
ds_valid = MultiProcessRunner(ds_valid, num_proc=4, num_prefetch=16)
ds_valid = PrintData(ds_valid)
if self.hparams.types == 6:
ds_valid = MapData(
ds_valid, lambda dp: [
torch.tensor(dp[0][:, np.newaxis, :, :]).float(),
torch.tensor(dp[1][:, np.newaxis, :, :]).float(),
torch.tensor(dp[2][:, np.newaxis, :, :]).float(),
torch.tensor(dp[3][:, np.newaxis, :, :]).float(),
torch.tensor(dp[4][:, np.newaxis, :, :]).float(),
torch.tensor(dp[5][:, np.newaxis, :, :]).float(),
torch.tensor(dp[6][:, np.newaxis, :, :]).float(),
])
elif self.hparams.types == 1:
ds_valid = MapData(
ds_valid, lambda dp: [
torch.tensor(dp[0][:, np.newaxis, :, :]).float(),
torch.tensor(dp[1][:, np.newaxis, :, :]).float(),
])
return ds_valid
def train_generator(ds, shape_aug=None, input_aug=None, label_aug=None, batch_size=16, nr_procs=8):
### augment both the input and label
ds = ds if shape_aug is None else AugmentImageComponents(ds, shape_aug, (0, 1), copy=True)
### augment just the input i.e index 0 within each yield of DatasetSerial
ds = ds if input_aug is None else AugmentImageComponent(ds, input_aug, index=0, copy=False)
### augment just the output i.e index 1 within each yield of DatasetSerial
ds = ds if label_aug is None else AugmentImageComponent(ds, label_aug, index=1, copy=True)
#
ds = BatchDataByShape(ds, batch_size, idx=0)
ds = PrefetchDataZMQ(ds, nr_procs)
return ds
def valid_generator(ds, shape_aug=None, input_aug=None, label_aug=None, batch_size=16, nr_procs=1):
### augment both the input and label
ds = ds if shape_aug is None else AugmentImageComponents(ds, shape_aug, (0, 1), copy=True)
### augment just the input
ds = ds if input_aug is None else AugmentImageComponent(ds, input_aug, index=0, copy=False)
### augment just the output
ds = ds if label_aug is None else AugmentImageComponent(ds, label_aug, index=1, copy=True)
#
ds = BatchData(ds, batch_size, remainder=True)
ds = CacheData(ds) # cache all inference images
return ds
def train_dataloader(self):
"""Summary
Returns:
TYPE: Description
"""
ds_train = CustomDataSet(folder=self.hparams.data,
train_or_valid='train',
size=np.inf,
hparams=self.hparams)
ds_train.reset_state()
ag_train = [
imgaug.Affine(shear=10, interp=cv2.INTER_NEAREST),
imgaug.Affine(translate_frac=(0.01, 0.02),
interp=cv2.INTER_NEAREST),
imgaug.Affine(scale=(0.25, 1.0), interp=cv2.INTER_NEAREST),
imgaug.RotationAndCropValid(max_deg=10, interp=cv2.INTER_NEAREST),
imgaug.GoogleNetRandomCropAndResize(
crop_area_fraction=(0.8, 1.0),
aspect_ratio_range=(0.8, 1.2),
interp=cv2.INTER_NEAREST,
target_shape=self.hparams.shape),
imgaug.Resize(self.hparams.shape, interp=cv2.INTER_NEAREST),
imgaug.Flip(horiz=True, vert=False, prob=0.5),
imgaug.Flip(horiz=False, vert=True, prob=0.5),
imgaug.Transpose(prob=0.5),
imgaug.Albumentations(AB.RandomRotate90(p=1)),
imgaug.ToFloat32(),
]
ds_train = AugmentImageComponent(
ds_train,
[
# imgaug.Float32(),
# imgaug.RandomChooseAug([
# imgaug.Albumentations(AB.IAAAdditiveGaussianNoise(p=0.25)),
# imgaug.Albumentations(AB.GaussNoise(p=0.25)),
# ]),
# imgaug.ToUint8(),
imgaug.RandomChooseAug([
imgaug.Albumentations(AB.Blur(blur_limit=4, p=0.25)),
imgaug.Albumentations(AB.MotionBlur(blur_limit=4, p=0.25)),
imgaug.Albumentations(AB.MedianBlur(blur_limit=4, p=0.25)),
]),
imgaug.RandomChooseAug([
# imgaug.Albumentations(AB.IAASharpen(p=0.5)),
# imgaug.Albumentations(AB.IAAEmboss(p=0.5)),
imgaug.Albumentations(AB.RandomBrightnessContrast(p=0.5)),
]),
imgaug.ToUint8(),
imgaug.Albumentations(AB.CLAHE(tile_grid_size=(32, 32),
p=0.5)),
],
0)
ds_train = AugmentImageComponents(ds_train, ag_train, [0, 1])
ds_train = BatchData(ds_train, self.hparams.batch, remainder=True)
if self.hparams.debug:
ds_train = FixedSizeData(ds_train, 2)
ds_train = MultiProcessRunner(ds_train, num_proc=4, num_prefetch=16)
ds_train = PrintData(ds_train)
ds_train = MapData(
ds_train, lambda dp: [
torch.tensor(dp[0][:, np.newaxis, :, :]).float(),
torch.tensor(dp[1][:, np.newaxis, :, :]).float(),
])
return ds_train