rsp-ml 0.0.87

Machine Learning

RSProduction MachineLearning

This project provides some usefull machine learning functionality.

Table of Contents

• 1 dataset
  • 1.1 TUC_AR : torch.utils.data.dataset.IterableDataset
    • 1.1.1 __init__
• 2 metrics
  • 2.1 AUROC
  • 2.2 F1_Score
  • 2.3 FN
  • 2.4 FP
  • 2.5 FPR
  • 2.6 ROC
  • 2.7 TN
  • 2.8 TP
  • 2.9 TPR
  • 2.10 confusion_matrix
  • 2.11 plot_ROC
  • 2.12 plot_confusion_matrix
  • 2.13 precision
  • 2.14 recall
  • 2.15 top_10_accuracy
  • 2.16 top_1_accuracy
  • 2.17 top_2_accuracy
  • 2.18 top_3_accuracy
  • 2.19 top_5_accuracy
  • 2.20 top_k_accuracy
• 3 model
  • 3.1 MODELS : enum.Enum
  • 3.2 WEIGHTS : enum.Enum
  • 3.3 list_model_weights
  • 3.4 load_model
  • 3.5 publish_model
• 4 multi_transforms
  • 4.1 BGR2GRAY : MultiTransform
    • 4.1.1 __call__
    • 4.1.2 __init__
  • 4.2 BGR2RGB : MultiTransform
    • 4.2.1 __call__
    • 4.2.2 __init__
  • 4.3 Brightness : MultiTransform
    • 4.3.1 __call__
    • 4.3.2 __init__
  • 4.4 CenterCrop : MultiTransform
    • 4.4.1 __call__
    • 4.4.2 __init__
  • 4.5 Color : MultiTransform
    • 4.5.1 __call__
    • 4.5.2 __init__
  • 4.6 Compose : builtins.object
    • 4.6.1 __call__
    • 4.6.2 __init__
  • 4.7 GaussianNoise : MultiTransform
    • 4.7.1 __call__
    • 4.7.2 __init__
  • 4.8 MultiTransform : builtins.object
    • 4.8.1 __call__
    • 4.8.2 __init__
  • 4.9 Normalize : MultiTransform
    • 4.9.1 __call__
    • 4.9.2 __init__
  • 4.10 RGB2BGR : BGR2RGB
    • 4.10.1 __call__
    • 4.10.2 __init__
  • 4.11 RandomCrop : MultiTransform
    • 4.11.1 __call__
    • 4.11.2 __init__
  • 4.12 RandomHorizontalFlip : MultiTransform
    • 4.12.1 __call__
    • 4.12.2 __init__
  • 4.13 RandomVerticalFlip : MultiTransform
    • 4.13.1 __call__
    • 4.13.2 __init__
  • 4.14 Resize : MultiTransform
    • 4.14.1 __call__
    • 4.14.2 __init__
  • 4.15 Rotate : MultiTransform
    • 4.15.1 __call__
    • 4.15.2 __init__
  • 4.16 Satturation : MultiTransform
    • 4.16.1 __call__
    • 4.16.2 __init__
  • 4.17 Scale : MultiTransform
    • 4.17.1 __call__
    • 4.17.2 __init__
  • 4.18 Stack : MultiTransform
    • 4.18.1 __call__
    • 4.18.2 __init__
  • 4.19 ToCVImage : MultiTransform
    • 4.19.1 __call__
    • 4.19.2 __init__
  • 4.20 ToNumpy : MultiTransform
    • 4.20.1 __call__
    • 4.20.2 __init__
  • 4.21 ToPILImage : MultiTransform
    • 4.21.1 __call__
    • 4.21.2 __init__
  • 4.22 ToTensor : MultiTransform
    • 4.22.1 __call__
    • 4.22.2 __init__
• 5 run
  • 5.1 Run : builtins.object
    • 5.1.1 __init__
    • 5.1.2 append
    • 5.1.3 get_avg
    • 5.1.4 get_val
    • 5.1.5 len
    • 5.1.6 load_best_state_dict
    • 5.1.7 load_state_dict
    • 5.1.8 pickle_dump
    • 5.1.9 pickle_load
    • 5.1.10 plot
    • 5.1.11 recalculate_moving_average
    • 5.1.12 save
    • 5.1.13 save_best_state_dict
    • 5.1.14 save_state_dict
    • 5.1.15 train_epoch
    • 5.1.16 validate_epoch

1 dataset

TOC

1.1 TUC_AR : torch.utils.data.dataset.IterableDataset

TOC

Description

Small-scal action recognition dataset.

Wrapper class for loading SchulzR97/TUC-AR HuggingFace dataset as torch.util.data.IterableDataset.

TUC-AR is a small scale action recognition dataset, containing 6(+1) action categories for human machine interaction.

Facts

• RGB and depth input recorded by Intel RealSense D435 depth camera
• 8 subjects
• 11,031 sequences (train 8,893/ val 2,138)
• 3 perspectives per scene
• 6(+1) action classes
  

Action Classes

Action	Label
A000	None
A001	Waving
A002	Pointing
A003	Clapping
A004	Follow
A005	Walking
A006	Stop

Example

 from rsp.ml.dataset import TUC_AR
 
 transforms = multi_transforms.Compose([multi_transforms.Resize((400, 400))])
 tuc_ar_ds = TUC_AR(
               split='val',
               depth_channel=True,
               transforms=transforms,
               num_actions=10,
               streaming=True)

1.1.1 __init__

TOC

Description

Initializes a new instance.

Parameters

Name	Type	Description
split	str	Dataset split [train
depth_channel	bool	Load depth channel. If set to True, the generated input tensor will have 4 channels instead of 3. (batch_size, sequence_length, channels, width, height)
num_actions	int, default = 7	Number of action classes -> shape[1] of target tensor (batch_size, num_actions)
streaming	bool, default = False	If set to True, don't download the data files. Instead, it streams the data progressively while iterating on the dataset.
sequence_length	int, default = 30	Length of each sequence. -> shape[1] of the generated input tensor. (batch_size, sequence_length, channels, width, height)
transforms	rsp.ml.multi_transforms.Compose = default = rsp.ml.multi_transforms.Compose([])	Transformations, that will be applied to each input sequence. See documentation of rsp.ml.multi_transforms for more details.

2 metrics

TOC

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

2.1 AUROC

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Description

Calculates the Area under the Receiver Operation Chracteristic 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

2.2 F1_Score

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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

2.3 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

2.4 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

2.5 FPR

TOC

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

2.6 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)

2.7 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

2.8 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

2.9 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

2.10 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]
])

2.11 plot_ROC

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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

2.12 plot_confusion_matrix

TOC

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

2.13 precision

TOC

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

2.14 recall

TOC

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

2.15 top_10_accuracy

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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

2.16 top_1_accuracy

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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

2.17 top_2_accuracy

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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

2.18 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

2.19 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

2.20 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

3 model

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

3.1 MODELS : enum.Enum

TOC

Description

Create a collection of name/value pairs.

Example enumeration:

class Color(Enum): ... RED = 1 ... BLUE = 2 ... GREEN = 3

Access them by:

• attribute access::

Color.RED <Color.RED: 1>

• value lookup:

Color(1) <Color.RED: 1>

• name lookup:

Color['RED'] <Color.RED: 1>

Enumerations can be iterated over, and know how many members they have:

len(Color) 3

list(Color) [<Color.RED: 1>, <Color.BLUE: 2>, <Color.GREEN: 3>]

Methods can be added to enumerations, and members can have their own attributes -- see the documentation for details.

3.2 WEIGHTS : enum.Enum

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Description

Create a collection of name/value pairs.

Example enumeration:

class Color(Enum): ... RED = 1 ... BLUE = 2 ... GREEN = 3

Access them by:

• attribute access::

Color.RED <Color.RED: 1>

• value lookup:

Color(1) <Color.RED: 1>

• name lookup:

Color['RED'] <Color.RED: 1>

Enumerations can be iterated over, and know how many members they have:

len(Color) 3

list(Color) [<Color.RED: 1>, <Color.BLUE: 2>, <Color.GREEN: 3>]

Methods can be added to enumerations, and members can have their own attributes -- see the documentation for details.

3.3 list_model_weights

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Description

Lists all available weight files.

Returns

List of (MODEL:str, WEIGHT:str) : List[Tuple(str, str)]

Example

import rsp.ml.model as model

model_weight_files = model.list_model_weights()

3.4 load_model

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Description

Loads a pretrained PyTorch model from HuggingFace.

Parameters

Name	Type	Description
model	MODELS	ID of the model
weights	WEIGHTS	ID of the weights

Returns

Pretrained PyTorch model : torch.nn.Module

Example

import rsp.ml.model as model

action_recognition_model = model.load_model(MODEL.TUCARC3D, WEIGHTS.TUCAR)

3.5 publish_model

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4 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.

4.1 BGR2GRAY : MultiTransform

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Description

Converts a sequence of BGR images to grayscale images.

4.1.1 __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

4.1.2 __init__

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Description

Initializes a new instance.

4.2 BGR2RGB : MultiTransform

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Description

Converts sequence of BGR images to RGB images.

4.2.1 __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

4.2.2 __init__

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Description

Initializes a new instance.

4.3 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.

4.3.1 __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

4.3.2 __init__

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Description

Initializes a new instance.

4.4 CenterCrop : MultiTransform

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Description

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

4.4.1 __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

4.4.2 __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.

4.5 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.

4.5.1 __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

4.5.2 __init__

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Description

Initializes a new instance.

4.6 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)
])

4.6.1 __call__

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Description

Call self as a function.

4.6.2 __init__

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Description

Initializes a new instance.

Parameters

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

4.7 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.

4.7.1 __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

4.7.2 __init__

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Description

Initializes a new instance.

4.8 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.

4.8.1 __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

4.8.2 __init__

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Description

Initializes a new instance.

4.9 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

4.9.1 __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

4.9.2 __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.

4.10 RGB2BGR : BGR2RGB

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Description

Converts sequence of RGB images to BGR images.

4.10.1 __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

4.10.2 __init__

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Description

Initializes a new instance.

4.11 RandomCrop : MultiTransform

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Description

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

4.11.1 __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

4.11.2 __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.

4.12 RandomHorizontalFlip : 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.

4.12.1 __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

4.12.2 __init__

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Description

Initializes a new instance.

4.13 RandomVerticalFlip : 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.

4.13.1 __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

4.13.2 __init__

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Description

Initializes a new instance.

4.14 Resize : 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.

4.14.1 __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

4.14.2 __init__

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Description

Initializes a new instance.

4.15 Rotate : MultiTransform

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Description

Randomly rotates images.

Equations

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

4.15.1 __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

4.15.2 __init__

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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.

4.16 Satturation : 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.

4.16.1 __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

4.16.2 __init__

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Description

Initializes a new instance.

4.17 Scale : 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.

4.17.1 __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

4.17.2 __init__

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Description

Initializes a new instance.

4.18 Stack : 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.

4.18.1 __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

4.18.2 __init__

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Description

Initializes a new instance.

4.19 ToCVImage : MultiTransform

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Description

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

4.19.1 __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

4.19.2 __init__

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Description

Initializes a new instance.

4.20 ToNumpy : MultiTransform

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Description

Converts a torch.Tensorto numpy

4.20.1 __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

4.20.2 __init__

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Description

Initializes a new instance.

4.21 ToPILImage : MultiTransform

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Description

Converts sequence of images to sequence of PIL.Image.

4.21.1 __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

4.21.2 __init__

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Description

Initializes a new instance.

4.22 ToTensor : MultiTransform

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Description

Converts a sequence of images to torch.Tensor.

4.22.1 __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

4.22.2 __init__

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Description

Initializes a new instance.

5 run

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

5.1 Run : builtins.object

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5.1.1 __init__

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Description

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

5.1.2 append

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5.1.3 get_avg

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5.1.4 get_val

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5.1.5 len

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5.1.6 load_best_state_dict

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5.1.7 load_state_dict

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5.1.8 pickle_dump

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5.1.9 pickle_load

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5.1.10 plot

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5.1.11 recalculate_moving_average

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5.1.12 save

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5.1.13 save_best_state_dict

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5.1.14 save_state_dict

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5.1.15 train_epoch

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5.1.16 validate_epoch

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