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On/Off filter can be turned on/off

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David Rotermund 2022-05-01 17:03:19 +02:00 committed by GitHub
parent 2929ba2a63
commit 92050f5933
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2 changed files with 99 additions and 50 deletions
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47
Dataset.py
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@ -135,10 +135,13 @@ class DatasetMNIST(DatasetMaster):
pattern = scripted_transforms(pattern)
# => On/Off
if cfg.augmentation.use_on_off_filter is True:
my_on_off_filter: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0])
gray: torch.Tensor = my_on_off_filter(
pattern[:, 0:1, :, :],
)
else:
gray = pattern[:, 0:1, :, :] + torch.finfo(torch.float32).eps
return gray
@ -166,10 +169,13 @@ class DatasetMNIST(DatasetMaster):
pattern = scripted_transforms(pattern)
# => On/Off
if cfg.augmentation.use_on_off_filter is True:
my_on_off_filter: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0])
gray: torch.Tensor = my_on_off_filter(
pattern[:, 0:1, :, :],
)
else:
gray = pattern[:, 0:1, :, :] + torch.finfo(torch.float32).eps
return gray
@ -225,10 +231,13 @@ class DatasetFashionMNIST(DatasetMaster):
pattern = scripted_transforms(pattern)
# => On/Off
if cfg.augmentation.use_on_off_filter is True:
my_on_off_filter: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0])
gray: torch.Tensor = my_on_off_filter(
pattern[:, 0:1, :, :],
)
else:
gray = pattern[:, 0:1, :, :] + torch.finfo(torch.float32).eps
return gray
@ -263,10 +272,13 @@ class DatasetFashionMNIST(DatasetMaster):
pattern = scripted_transforms(pattern)
# => On/Off
if cfg.augmentation.use_on_off_filter is True:
my_on_off_filter: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0])
gray: torch.Tensor = my_on_off_filter(
pattern[:, 0:1, :, :],
)
else:
gray = pattern[:, 0:1, :, :] + torch.finfo(torch.float32).eps
return gray
@ -321,10 +333,16 @@ class DatasetCIFAR(DatasetMaster):
pattern = scripted_transforms(pattern)
# => On/Off
my_on_off_filter_r: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0])
my_on_off_filter_g: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[1])
my_on_off_filter_b: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[2])
if cfg.augmentation.use_on_off_filter is True:
my_on_off_filter_r: OnOffFilter = OnOffFilter(
p=cfg.image_statistics.mean[0]
)
my_on_off_filter_g: OnOffFilter = OnOffFilter(
p=cfg.image_statistics.mean[1]
)
my_on_off_filter_b: OnOffFilter = OnOffFilter(
p=cfg.image_statistics.mean[2]
)
r: torch.Tensor = my_on_off_filter_r(
pattern[:, 0:1, :, :],
)
@ -334,6 +352,10 @@ class DatasetCIFAR(DatasetMaster):
b: torch.Tensor = my_on_off_filter_b(
pattern[:, 2:3, :, :],
)
else:
r = pattern[:, 0:1, :, :] + torch.finfo(torch.float32).eps
g = pattern[:, 1:2, :, :] + torch.finfo(torch.float32).eps
b = pattern[:, 2:3, :, :] + torch.finfo(torch.float32).eps
new_tensor: torch.Tensor = torch.cat((r, g, b), dim=1)
return new_tensor
@ -370,9 +392,16 @@ class DatasetCIFAR(DatasetMaster):
pattern = scripted_transforms(pattern)
# => On/Off
my_on_off_filter_r: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0])
my_on_off_filter_g: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[1])
my_on_off_filter_b: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[2])
if cfg.augmentation.use_on_off_filter is True:
my_on_off_filter_r: OnOffFilter = OnOffFilter(
p=cfg.image_statistics.mean[0]
)
my_on_off_filter_g: OnOffFilter = OnOffFilter(
p=cfg.image_statistics.mean[1]
)
my_on_off_filter_b: OnOffFilter = OnOffFilter(
p=cfg.image_statistics.mean[2]
)
r: torch.Tensor = my_on_off_filter_r(
pattern[:, 0:1, :, :],
)
@ -382,6 +411,10 @@ class DatasetCIFAR(DatasetMaster):
b: torch.Tensor = my_on_off_filter_b(
pattern[:, 2:3, :, :],
)
else:
r = pattern[:, 0:1, :, :] + torch.finfo(torch.float32).eps
g = pattern[:, 1:2, :, :] + torch.finfo(torch.float32).eps
b = pattern[:, 2:3, :, :] + torch.finfo(torch.float32).eps
new_tensor: torch.Tensor = torch.cat((r, g, b), dim=1)
return new_tensor

34
Parameter.py
View file

@ -42,12 +42,14 @@ class Network:
its layers and the number of output neurons."""
number_of_output_neurons: int = field(default=0)
forward_kernel_size: list[list[int]] = field(default_factory=list)
forward_neuron_numbers: list[list[int]] = field(default_factory=list)
is_pooling_layer: list[bool] = field(default_factory=list)
forward_kernel_size: list[list[int]] = field(default_factory=list)
strides: list[list[int]] = field(default_factory=list)
dilation: list[list[int]] = field(default_factory=list)
padding: list[list[int]] = field(default_factory=list)
is_pooling_layer: list[bool] = field(default_factory=list)
w_trainable: list[bool] = field(default_factory=list)
eps_xy_trainable: list[bool] = field(default_factory=list)
eps_xy_mean: list[bool] = field(default_factory=list)
@ -57,24 +59,34 @@ class Network:
class LearningParameters:
"""Parameter required for training"""
learning_active: bool = field(default=True)
loss_coeffs_mse: float = field(default=0.5)
loss_coeffs_kldiv: float = field(default=1.0)
optimizer_name: str = field(default="Adam")
learning_rate_gamma_w: float = field(default=-1.0)
learning_rate_gamma_eps_xy: float = field(default=-1.0)
learning_rate_threshold_w: float = field(default=0.00001)
learning_rate_threshold_eps_xy: float = field(default=0.00001)
learning_active: bool = field(default=True)
lr_schedule_name: str = field(default="ReduceLROnPlateau")
lr_scheduler_factor_w: float = field(default=0.75)
lr_scheduler_patience_w: int = field(default=-1)
lr_scheduler_factor_eps_xy: float = field(default=0.75)
lr_scheduler_patience_eps_xy: int = field(default=-1)
number_of_batches_for_one_update: int = field(default=1)
overload_path: str = field(default="./Previous")
weight_noise_amplitude: float = field(default=0.01)
eps_xy_intitial: float = field(default=0.1)
test_every_x_learning_steps: int = field(default=50)
test_during_learning: bool = field(default=True)
lr_scheduler_factor: float = field(default=0.75)
lr_scheduler_patience: int = field(default=10)
optimizer_name: str = field(default="Adam")
lr_schedule_name: str = field(default="ReduceLROnPlateau")
number_of_batches_for_one_update: int = field(default=1)
alpha_number_of_iterations: int = field(default=0)
overload_path: str = field(default="./Previous")
@dataclass
@ -82,12 +94,16 @@ class Augmentation:
"""Parameters used for data augmentation."""
crop_width_in_pixel: int = field(default=2)
flip_p: float = field(default=0.5)
jitter_brightness: float = field(default=0.5)
jitter_contrast: float = field(default=0.1)
jitter_saturation: float = field(default=0.1)
jitter_hue: float = field(default=0.15)
use_on_off_filter: bool = field(default=True)
@dataclass
class ImageStatistics: