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  • License: MIT
  • Author: Robert Schulz
  • Tags python, Machine Learning
Classifiers

Project description

RSProduction MachineLearning

This project provides some usefull machine learning functionality.

Table of Contents

1 metrics

TOC

The module rsp.ml.metrics provides some functionality to quantify the quality of predictions.

1.1 F1_Score

TOC

Description

F1 Score. Expected input shape: (batch_size, num_classes)

Parameters

Name Type Description
Y torch.Tensor Prediction
T torch.Tensor True values
threshold float All values that are greater than or equal to the threshold are considered a positive class.

Returns

F1 Score : float

Equations

$precision = \frac{TP}{TP + FP}$

$recall = \frac{TP}{TP + FN}$

$F_1 = \frac{2 \cdot precision \cdot recall}{precision + recall} = \frac{2 \cdot TP}{2 \cdot TP + FP + FN}$

Example

import rsp.ml.metrics as m

Y = torch.tensor([
  [0.1, 0.1, 0.8],
  [0.03, 0.95, 0.02],
  [0.05, 0.9, 0.05],
  [0.01, 0.87, 0.12],
  [0.04, 0.03, 0.93],
  [0.94, 0.02, 0.06]
])
T = torch.tensor([
  [0, 0, 1],
  [1, 0, 0],
  [0, 1, 0],
  [0, 1, 0],
  [0, 0, 1],
  [1, 0, 0]
])

f1score = m.F1_Score(Y, T)

print(f1score) --> 0.5

1.2 FN

TOC

Description

False negatives. Expected input shape: (batch_size, num_classes)

Parameters

Name Type Description
Y torch.Tensor Prediction
T torch.Tensor True values
threshold float All values that are greater than or equal to the threshold are considered a positive class.

Returns

False negatives : int

Example

import rsp.ml.metrics as m
import torch

Y = torch.tensor([
  [0.1, 0.1, 0.8],
  [0.03, 0.95, 0.02],
  [0.05, 0.9, 0.05],
  [0.01, 0.87, 0.12],
  [0.04, 0.03, 0.93],
  [0.94, 0.02, 0.06]
])
T = torch.tensor([
  [0, 0, 1],
  [1, 0, 0],
  [0, 1, 0],
  [0, 1, 0],
  [0, 0, 1],
  [1, 0, 0]
])

fn = m.FN(Y, T)
print(fn) -> 1

1.3 FP

TOC

Description

False positives. Expected input shape: (batch_size, num_classes)

Parameters

Name Type Description
Y torch.Tensor Prediction
T torch.Tensor True values
threshold float All values that are greater than or equal to the threshold are considered a positive class.

Returns

False positives : int

Example

import rsp.ml.metrics as m
import torch

Y = torch.tensor([
  [0.1, 0.1, 0.8],
  [0.03, 0.95, 0.02],
  [0.05, 0.9, 0.05],
  [0.01, 0.87, 0.12],
  [0.04, 0.03, 0.93],
  [0.94, 0.02, 0.06]
])
T = torch.tensor([
  [0, 0, 1],
  [1, 0, 0],
  [0, 1, 0],
  [0, 1, 0],
  [0, 0, 1],
  [1, 0, 0]
])

fp = m.FP(Y, T)
print(fp) -> 1

1.4 FPR

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Description

False positive rate. Expected input shape: (batch_size, num_classes)

Parameters

Name Type Description
Y torch.Tensor Prediction
T torch.Tensor True values
threshold float All values that are greater than or equal to the threshold are considered a positive class.

Returns

False positive rate : float

Example

import rsp.ml.metrics as m
import torch

Y = torch.tensor([
  [0.1, 0.1, 0.8],
  [0.03, 0.95, 0.02],
  [0.05, 0.9, 0.05],
  [0.01, 0.87, 0.12],
  [0.04, 0.03, 0.93],
  [0.94, 0.02, 0.06]
])
T = torch.tensor([
  [0, 0, 1],
  [1, 0, 0],
  [0, 1, 0],
  [0, 1, 0],
  [0, 0, 1],
  [1, 0, 0]
])

fpr = m.FPR(Y, T)
print(fpr) -> 0.08333333333333333

1.5 ROC

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Description

Calculates the receiver operating characteristic: computes False Positive Rates and True positive Rates for num_thresholds aligned between 0 and 1

Parameters

Name Type Description
Y torch.Tensor Prediction
T torch.Tensor True values
num_thresholds int, default = 100 Number of thresholds to compute.

Returns

(False Positive Rates, True Positive Rates) for 100 different thresholds : (List[float], List[float])

Example

import rsp.ml.metrics as m
import torch
import torch.nn.functional as F

num_elements = 100000
num_classes = 7

T = []
for i in range(num_elements):
  true_class = torch.randint(0, num_classes, (1,))
  t = F.one_hot(true_class, num_classes=num_classes)
  T.append(t)
T = torch.cat(T)

dist = torch.normal(T.float(), 1.5)
Y = F.softmax(dist, dim = 1)
FPRs, TPRs = m.ROC(Y, T)

1.6 ROC_AUC

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Description

Calculates the Receiver Operation Chracteristic Area under the Curve.

Parameters

Name Type Description
Y torch.Tensor Prediction
T torch.Tensor True values
num_thresholds int, default = 100 Number of thresholds to compute.

Returns

Receiver Operation Chracteristic Area under the Curve : float

1.7 TN

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Description

True negatives. Expected input shape: (batch_size, num_classes)

Parameters

Name Type Description
Y torch.Tensor Prediction
T torch.Tensor True values
threshold float All values that are greater than or equal to the threshold are considered a positive class.

Returns

True negatives : int

Example

import rsp.ml.metrics as m
import torch

Y = torch.tensor([
  [0.1, 0.1, 0.8],
  [0.03, 0.95, 0.02],
  [0.05, 0.9, 0.05],
  [0.01, 0.87, 0.12],
  [0.04, 0.03, 0.93],
  [0.94, 0.02, 0.06]
])
T = torch.tensor([
  [0, 0, 1],
  [1, 0, 0],
  [0, 1, 0],
  [0, 1, 0],
  [0, 0, 1],
  [1, 0, 0]
])

tn = m.TN(Y, T)
print(tn) -> 11

1.8 TP

TOC

Description

True positives. Expected input shape: (batch_size, num_classes)

Parameters

Name Type Description
Y torch.Tensor Prediction
T torch.Tensor True values
threshold float All values that are greater than or equal to the threshold are considered a positive class.

Returns

True positives : int

Example

import rsp.ml.metrics as m
import torch

Y = torch.tensor([
  [0.1, 0.1, 0.8],
  [0.03, 0.95, 0.02],
  [0.05, 0.9, 0.05],
  [0.01, 0.87, 0.12],
  [0.04, 0.03, 0.93],
  [0.94, 0.02, 0.06]
])
T = torch.tensor([
  [0, 0, 1],
  [1, 0, 0],
  [0, 1, 0],
  [0, 1, 0],
  [0, 0, 1],
  [1, 0, 0]
])

tp = m.TP(Y, T)
print(tp) -> 5

1.9 TPR

TOC

Description

True positive rate. Expected input shape: (batch_size, num_classes)

Parameters

Name Type Description
Y torch.Tensor Prediction
T torch.Tensor True values
threshold float All values that are greater than or equal to the threshold are considered a positive class.

Returns

True positive rate : float

Example

import rsp.ml.metrics as m
import torch

Y = torch.tensor([
  [0.1, 0.1, 0.8],
  [0.03, 0.95, 0.02],
  [0.05, 0.9, 0.05],
  [0.01, 0.87, 0.12],
  [0.04, 0.03, 0.93],
  [0.94, 0.02, 0.06]
])
T = torch.tensor([
  [0, 0, 1],
  [1, 0, 0],
  [0, 1, 0],
  [0, 1, 0],
  [0, 0, 1],
  [1, 0, 0]
])

tpr = m.TPR(Y, T)
print(tpr) -> 0.8333333333333334

1.10 confusion_matrix

TOC

Description

Calculates the confusion matrix. Expected input shape: (batch_size, num_classes)

Parameters

Name Type Description
Y torch.Tensor Prediction
T torch.Tensor True values

Returns

Confusion matrix : torch.Tensor

Example

import rsp.ml.metrics as m
import torch

Y = torch.tensor([
  [0.1, 0.1, 0.8],
  [0.03, 0.95, 0.02],
  [0.05, 0.9, 0.05],
  [0.01, 0.87, 0.12],
  [0.04, 0.03, 0.93],
  [0.94, 0.02, 0.06]
])
T = torch.tensor([
  [0, 0, 1],
  [1, 0, 0],
  [0, 1, 0],
  [0, 1, 0],
  [0, 0, 1],
  [1, 0, 0]
])

conf_mat = m.confusion_matrix(Y, T)
print(conf_mat) -> tensor([
  [1, 1, 0],
  [0, 2, 0],
  [0, 0, 2]
])

1.11 plot_ROC

TOC

Description

Plot the receiver operating characteristic.

Parameters

Name Type Description
Y torch.Tensor Prediction
T torch.Tensor True values
num_thresholds int, default = 100 Number of thresholds to compute.
title str, optional, default = 'Confusion Matrix' Title of the plot
class_curves bool, default = False Plot ROC curve for each class
labels str, optional, default = None Class labels -> automatic labeling C000, ..., CXXX if labels is None
plt_show bool, optional, default = False Set to True to show the plot
save_file_name str, optional, default = None If not None, the plot is saved under the specified save_file_name.

Returns

Image of the confusion matrix : np.array

1.12 plot_confusion_matrix

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Description

Plot the confusion matrix

Parameters

Name Type Description
confusion_matrix torch.Tensor Confusion matrix
labels str, optional, default = None Class labels -> automatic labeling C000, ..., CXXX if labels is None
cmap str, optional, default = 'Blues' Seaborn cmap, see https://r02b.github.io/seaborn_palettes/
xlabel str, optional, default = 'Predicted label' X-Axis label
ylabel str, optional, default = 'True label' Y-Axis label
title str, optional, default = 'Confusion Matrix' Title of the plot
plt_show bool, optional, default = False Set to True to show the plot
save_file_name str, optional, default = None If not None, the plot is saved under the specified save_file_name.

Returns

Image of the confusion matrix : np.array

1.13 precision

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Description

Precision. Expected input shape: (batch_size, num_classes)

Parameters

Name Type Description
Y torch.Tensor Prediction
T torch.Tensor True values
threshold float All values that are greater than or equal to the threshold are considered a positive class.

Returns

Precision : float

Equations

$precision = \frac{TP}{TP + FP}$

Example

import rsp.ml.metrics as m
import torch

Y = torch.tensor([
  [0.1, 0.1, 0.8],
  [0.03, 0.95, 0.02],
  [0.05, 0.9, 0.05],
  [0.01, 0.87, 0.12],
  [0.04, 0.03, 0.93],
  [0.94, 0.02, 0.06]
])
T = torch.tensor([
  [0, 0, 1],
  [1, 0, 0],
  [0, 1, 0],
  [0, 1, 0],
  [0, 0, 1],
  [1, 0, 0]
])

precision = m.precision(Y, T)
print(precision) -> 0.8333333333333334

1.14 recall

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Description

Recall. Expected input shape: (batch_size, num_classes)

Parameters

Name Type Description
Y torch.Tensor Prediction
T torch.Tensor True values
threshold float All values that are greater than or equal to the threshold are considered a positive class.

Returns

Recall : float

Equations

$recall = \frac{TP}{TP + FN}$

Example

import rsp.ml.metrics as m
import torch

Y = torch.tensor([
  [0.1, 0.1, 0.8],
  [0.03, 0.95, 0.02],
  [0.05, 0.9, 0.05],
  [0.01, 0.87, 0.12],
  [0.04, 0.03, 0.93],
  [0.94, 0.02, 0.06]
])
T = torch.tensor([
  [0, 0, 1],
  [1, 0, 0],
  [0, 1, 0],
  [0, 1, 0],
  [0, 0, 1],
  [1, 0, 0]
])

recall = m.recall(Y, T)
print(recall) -> 0.8333333333333334

1.15 top_10_accuracy

TOC

Description

Top 10 accuracy. Expected input shape: (batch_size, num_classes)

Parameters

Name Type Description
Y torch.Tensor Prediction
T torch.Tensor True values

Returns

Top 10 accuracy -> top k accuracy | k = 10 : float

Example

import rsp.ml.metrics as m

Y = torch.tensor([
  [0.1, 0.1, 0.8],
  [0.03, 0.95, 0.02],
  [0.05, 0.9, 0.05],
  [0.01, 0.87, 0.12],
  [0.04, 0.03, 0.93],
  [0.94, 0.02, 0.06]
])
T = torch.tensor([
  [0, 0, 1],
  [1, 0, 0],
  [0, 1, 0],
  [0, 1, 0],
  [0, 0, 1],
  [1, 0, 0]
])

top_10_accuracy = m.top_10_accuracy(Y, T, k = 3)

print(top_10_accuracy) --> 1.0

1.16 top_1_accuracy

TOC

Description

Top 1 accuracy. Expected input shape: (batch_size, num_classes)

Parameters

Name Type Description
Y torch.Tensor Prediction
T torch.Tensor True values

Returns

Top 1 accuracy -> top k accuracy | k = 1 : float

Example

import rsp.ml.metrics as m

Y = torch.tensor([
  [0.1, 0.1, 0.8],
  [0.03, 0.95, 0.02],
  [0.05, 0.9, 0.05],
  [0.01, 0.87, 0.12],
  [0.04, 0.03, 0.93],
  [0.94, 0.02, 0.06]
])
T = torch.tensor([
  [0, 0, 1],
  [1, 0, 0],
  [0, 1, 0],
  [0, 1, 0],
  [0, 0, 1],
  [1, 0, 0]
])

top_1_accuracy = m.top_1_accuracy(Y, T, k = 3)

print(top_1_accuracy) --> 0.8333333333333334

1.17 top_2_accuracy

TOC

Description

Top 2 accuracy. Expected input shape: (batch_size, num_classes)

Parameters

Name Type Description
Y torch.Tensor Prediction
T torch.Tensor True values

Returns

Top 2 accuracy -> top k accuracy | k = 2 : float

Example

import rsp.ml.metrics as m

Y = torch.tensor([
  [0.1, 0.1, 0.8],
  [0.03, 0.95, 0.02],
  [0.05, 0.9, 0.05],
  [0.01, 0.87, 0.12],
  [0.04, 0.03, 0.93],
  [0.94, 0.02, 0.06]
])
T = torch.tensor([
  [0, 0, 1],
  [1, 0, 0],
  [0, 1, 0],
  [0, 1, 0],
  [0, 0, 1],
  [1, 0, 0]
])

top_2_accuracy = m.top_2_accuracy(Y, T, k = 3)

print(top_2_accuracy) --> 1.0

1.18 top_3_accuracy

TOC

Description

Top 3 accuracy. Expected input shape: (batch_size, num_classes)

Parameters

Name Type Description
Y torch.Tensor Prediction
T torch.Tensor True values

Returns

Top 3 accuracy -> top k accuracy | k = 3 : float

Example

import rsp.ml.metrics as m

Y = torch.tensor([
  [0.1, 0.1, 0.8],
  [0.03, 0.95, 0.02],
  [0.05, 0.9, 0.05],
  [0.01, 0.87, 0.12],
  [0.04, 0.03, 0.93],
  [0.94, 0.02, 0.06]
])
T = torch.tensor([
  [0, 0, 1],
  [1, 0, 0],
  [0, 1, 0],
  [0, 1, 0],
  [0, 0, 1],
  [1, 0, 0]
])

top_3_accuracy = m.top_3_accuracy(Y, T, k = 3)

print(top_3_accuracy) --> 1.0

1.19 top_5_accuracy

TOC

Description

Top 5 accuracy. Expected input shape: (batch_size, num_classes)

Parameters

Name Type Description
Y torch.Tensor Prediction
T torch.Tensor True values

Returns

Top 5 accuracy -> top k accuracy | k = 5 : float

Example

import rsp.ml.metrics as m

Y = torch.tensor([
  [0.1, 0.1, 0.8],
  [0.03, 0.95, 0.02],
  [0.05, 0.9, 0.05],
  [0.01, 0.87, 0.12],
  [0.04, 0.03, 0.93],
  [0.94, 0.02, 0.06]
])
T = torch.tensor([
  [0, 0, 1],
  [1, 0, 0],
  [0, 1, 0],
  [0, 1, 0],
  [0, 0, 1],
  [1, 0, 0]
])

top_5_accuracy = m.top_5_accuracy(Y, T, k = 3)

print(top_5_accuracy) --> 1.0

1.20 top_k_accuracy

TOC

Description

Top k accuracy. Expected input shape: (batch_size, num_classes)

Parameters

Name Type Description
Y torch.Tensor Prediction
T torch.Tensor True values

Returns

Top k accuracy : float

Example

import rsp.ml.metrics as m

Y = torch.tensor([
  [0.1, 0.1, 0.8],
  [0.03, 0.95, 0.02],
  [0.05, 0.9, 0.05],
  [0.01, 0.87, 0.12],
  [0.04, 0.03, 0.93],
  [0.94, 0.02, 0.06]
])
T = torch.tensor([
  [0, 0, 1],
  [1, 0, 0],
  [0, 1, 0],
  [0, 1, 0],
  [0, 0, 1],
  [1, 0, 0]
])

top_k_accuracy = m.top_k_accuracy(Y, T, k = 3)

print(top_k_accuracy) --> 1.0

2 model

TOC

The module rsp.ml.model provides some usefull functionality to store and load pytorch models.

2.2 Constants

TOC

Name Value Description
TUC_ActionPrediction_model004 TUC/ActionPrediction/Model4 TUC Action prediction model 4
CNN with Multihead-Self-Attention
Input
- batch size
- sequence length = 30
- channels = 3
- width = 200
- height = 200
Output
- batch size
- number of classes = 10
TUC_ActionPrediction_model005 TUC/ActionPrediction/Model5 TUC Action prediction model 5
CNN with Multihead-Self-Attention
Input
- batch size
- sequence length = 30
- channels = 3
- width = 300
- height = 300
Output
- batch size
- number of classes = 10
URL https://drive.google.com/drive/folders/1ulNnPqg-5wvenRl2CuJMxMMcaiYfHjQ9?usp=share_link Google Drive URL

2.1 load_model

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Description

Loads a model from an pretrained PyTorch external source into memory.

See Constants for available models

Parameters

Name Type Description
model_id str ID of the model
force_reload bool Overwrite local file -> forces downlad.

Returns

Pretrained PyTorch model : torch.nn.Module

Example

import rsp.ml.model as model

model004 = model.load_model(model.TUC_ActionPrediction_model004)

3 multi_transforms

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The module rsp.ml.multi_transforms is based on torchvision.transforms, which is made for single images. rsp.ml.multi_transforms extends this functionality by providing transformations for sequences of images, which could be usefull for video augmentation.

3.1 BGR2GRAY : MultiTransform

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Description

Converts a sequence of BGR images to grayscale images.

3.1.1 __call__

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Description

Call self as a function.

Parameters

Name Type Description
input torch.Tensor
List[PIL.Image]
List[numpy.array]		 Sequence of images

3.1.2 __init__

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Description

Initializes a new instance.

3.2 BGR2RGB : MultiTransform

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Description

Converts sequence of BGR images to RGB images.

3.2.1 __call__

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Description

Call self as a function.

Parameters

Name Type Description
input torch.Tensor
List[PIL.Image]
List[numpy.array]		 Sequence of images

3.2.2 __init__

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Description

Initializes a new instance.

3.3 Brightness : MultiTransform

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Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.3.1 __call__

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Description

Call self as a function.

Parameters

Name Type Description
input torch.Tensor
List[PIL.Image]
List[numpy.array]		 Sequence of images

3.3.2 __init__

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Description

Initializes a new instance.

3.4 CenterCrop : MultiTransform

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Description

Crops Images at the center after upscaling them. Dimensions kept the same.

3.4.1 __call__

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Description

Call self as a function.

Parameters

Name Type Description
input torch.Tensor
List[PIL.Image]
List[numpy.array]		 Sequence of images

3.4.2 __init__

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Description

Initializes a new instance.

Parameters

Name Type Description
max_scale float Images are scaled randomly between 1. and max_scale before cropping to original size.

3.5 Color : MultiTransform

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Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.5.1 __call__

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Description

Call self as a function.

Parameters

Name Type Description
input torch.Tensor
List[PIL.Image]
List[numpy.array]		 Sequence of images

3.5.2 __init__

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Description

Initializes a new instance.

3.6 Compose : builtins.object

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Description

Composes several MultiTransforms together.

Example

import rsp.ml.multi_transforms as t

transforms = t.Compose([
  t.BGR2GRAY(),
  t.Scale(0.5)
])

3.6.1 __call__

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Description

Call self as a function.

3.6.2 __init__

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Description

Initializes a new instance.

Parameters

Name Type Description
children List[MultiTransform] List of MultiTransforms to compose.

3.7 GaussianNoise : MultiTransform

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Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.7.1 __call__

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Description

Call self as a function.

Parameters

Name Type Description
input torch.Tensor
List[PIL.Image]
List[numpy.array]		 Sequence of images

3.7.2 __init__

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Description

Initializes a new instance.

3.8 MultiTransform : builtins.object

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Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.8.1 __call__

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Description

Call self as a function.

Parameters

Name Type Description
input torch.Tensor
List[PIL.Image]
List[numpy.array]		 Sequence of images

3.8.2 __init__

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Description

Initializes a new instance.

3.9 Normalize : MultiTransform

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Description

Normalize images with mean and standard deviation. Given mean: (mean[1],...,mean[n]) and std: (std[1],..,std[n]) for n channels, this transform will normalize each channel of the input torch.*Tensor i.e., output[channel] = (input[channel] - mean[channel]) / std[channel]

Based on torchvision.transforms.Normalize

3.9.1 __call__

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Description

Call self as a function.

Parameters

Name Type Description
input torch.Tensor
List[PIL.Image]
List[numpy.array]		 Sequence of images

3.9.2 __init__

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Description

Initializes a new instance.

Parameters

Name Type Description
mean List[float] Sequence of means for each channel.
std List[float] Sequence of standard deviations for each channel.
inplace bool Set to True make this operation in-place.

3.10 RGB2BGR : BGR2RGB

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Description

Converts sequence of RGB images to BGR images.

3.10.1 __call__

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Description

Call self as a function.

Parameters

Name Type Description
input torch.Tensor
List[PIL.Image]
List[numpy.array]		 Sequence of images

3.10.2 __init__

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Description

Initializes a new instance.

3.11 RandomCrop : MultiTransform

TOC

Description

Crops Images at a random location after upscaling them. Dimensions kept the same.

3.11.1 __call__

TOC

Description

Call self as a function.

Parameters

Name Type Description
input torch.Tensor
List[PIL.Image]
List[numpy.array]		 Sequence of images

3.11.2 __init__

TOC

Description

Initializes a new instance.

Parameters

Name Type Description
max_scale float Images are scaled randomly between 1. and max_scale before cropping to original size.

3.12 RandomHorizontalFlip : MultiTransform

TOC

Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.12.1 __call__

TOC

Description

Call self as a function.

Parameters

Name Type Description
input torch.Tensor
List[PIL.Image]
List[numpy.array]		 Sequence of images

3.12.2 __init__

TOC

Description

Initializes a new instance.

3.13 RandomVerticalFlip : MultiTransform

TOC

Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.13.1 __call__

TOC

Description

Call self as a function.

Parameters

Name Type Description
input torch.Tensor
List[PIL.Image]
List[numpy.array]		 Sequence of images

3.13.2 __init__

TOC

Description

Initializes a new instance.

3.14 Resize : MultiTransform

TOC

Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.14.1 __call__

TOC

Description

Call self as a function.

Parameters

Name Type Description
input torch.Tensor
List[PIL.Image]
List[numpy.array]		 Sequence of images

3.14.2 __init__

TOC

Description

Initializes a new instance.

3.15 Rotate : MultiTransform

TOC

Description

Randomly rotates images.

Equations

$angle = -max_angle + 2 \cdot random() \cdot max_angle$

3.15.1 __call__

TOC

Description

Call self as a function.

Parameters

Name Type Description
input torch.Tensor
List[PIL.Image]
List[numpy.array]		 Sequence of images

3.15.2 __init__

TOC

Description

Iitializes a new instance.

Parameters

Name Type Description
max_angle float Maximal rotation in degrees
auto_scale bool, default = True Image will be resized when auto scale is activated to avoid black margins.

3.16 Satturation : MultiTransform

TOC

Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.16.1 __call__

TOC

Description

Call self as a function.

Parameters

Name Type Description
input torch.Tensor
List[PIL.Image]
List[numpy.array]		 Sequence of images

3.16.2 __init__

TOC

Description

Initializes a new instance.

3.17 Scale : MultiTransform

TOC

Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.17.1 __call__

TOC

Description

Call self as a function.

Parameters

Name Type Description
input torch.Tensor
List[PIL.Image]
List[numpy.array]		 Sequence of images

3.17.2 __init__

TOC

Description

Initializes a new instance.

3.18 Stack : MultiTransform

TOC

Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.18.1 __call__

TOC

Description

Call self as a function.

Parameters

Name Type Description
input torch.Tensor
List[PIL.Image]
List[numpy.array]		 Sequence of images

3.18.2 __init__

TOC

Description

Initializes a new instance.

3.19 ToCVImage : MultiTransform

TOC

Description

Converts a torch.Tensorto Open CV image by changing dimensions (d0, d1, d2) -> (d1, d2, d0) and converting torch.Tensor to numpy.

3.19.1 __call__

TOC

Description

Call self as a function.

Parameters

Name Type Description
input torch.Tensor
List[PIL.Image]
List[numpy.array]		 Sequence of images

3.19.2 __init__

TOC

Description

Initializes a new instance.

3.20 ToNumpy : MultiTransform

TOC

Description

Converts a torch.Tensorto numpy

3.20.1 __call__

TOC

Description

Call self as a function.

Parameters

Name Type Description
input torch.Tensor
List[PIL.Image]
List[numpy.array]		 Sequence of images

3.20.2 __init__

TOC

Description

Initializes a new instance.

3.21 ToPILImage : MultiTransform

TOC

Description

Converts sequence of images to sequence of PIL.Image.

3.21.1 __call__

TOC

Description

Call self as a function.

Parameters

Name Type Description
input torch.Tensor
List[PIL.Image]
List[numpy.array]		 Sequence of images

3.21.2 __init__

TOC

Description

Initializes a new instance.

3.22 ToTensor : MultiTransform

TOC

Description

Converts a sequence of images to torch.Tensor.

3.22.1 __call__

TOC

Description

Call self as a function.

Parameters

Name Type Description
input torch.Tensor
List[PIL.Image]
List[numpy.array]		 Sequence of images

3.22.2 __init__

TOC

Description

Initializes a new instance.

4 run

TOC

The module rsp.ml.run provides some tools for storing, loading and visualizing data during training of models using PyTorch.

4.1 Run : builtins.object

TOC

4.1.1 __init__

TOC

Description

Initialize self. See help(type(self)) for accurate signature.

4.1.2 append

TOC

4.1.3 get_avg

TOC

4.1.4 get_val

TOC

4.1.5 len

TOC

4.1.6 load_best_state_dict

TOC

4.1.7 load_state_dict

TOC

4.1.8 pickle_dump

TOC

4.1.9 pickle_load

TOC

4.1.10 plot

TOC

4.1.11 recalculate_moving_average

TOC

4.1.12 save

TOC

4.1.13 save_best_state_dict

TOC

4.1.14 save_state_dict

TOC

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  • License: MIT
  • Author: Robert Schulz
  • Tags python, Machine Learning
Classifiers

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