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

RSProduction MachineLearning

This project provides some usefull machine learning functionality.

Table of Contents

1 dataset

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1.1 HMDB51 : torch.utils.data.dataset.Dataset

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Description

Dataset class for HMDB51.

Example

from rsp.ml.dataset import HMDB51
import rsp.ml.multi_transforms as multi_transforms
import cv2 as cv

transforms = multi_transforms.Compose([
    multi_transforms.Color(1.5, p=0.5),
    multi_transforms.Stack()
])
ds = HMDB51('train', fold=1, transforms=transforms)

for X, T in ds:
  for x in X.permute(0, 2, 3, 1):
    img_color = x[:, :, :3].numpy()
    img_depth = x[:, :, 3].numpy()

    cv.imshow('color', img_color)
    cv.imshow('depth', img_depth)

    cv.waitKey(30)

1.1.1 __init__

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Description

Initializes a new instance.

Parameters

Name Type Description
split str Dataset split [train
fold int Fold number. The dataset is split into 3 folds. If fold is None, all folds will be loaded.
cache_dir str, default = None Directory to store the downloaded files. If set to None, the default cache directory will be used
force_reload bool, default = False If set to True, the dataset will be reloaded
target_size (int, int), default = (400, 400) Size of the frames. The frames will be resized to this size.
sequence_length int, default = 30 Length of the sequences
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.

1.2 Kinetics : torch.utils.data.dataset.Dataset

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Description

Dataset class for the Kinetics dataset.

Example

from rsp.ml.dataset import Kinetics

ds = Kinetics(split='train', type=400)

for X, T in ds:
    print(X)

1.2.1 __init__

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Description

Initializes a new instance.

Parameters

Name Type Description
split str Dataset split [train
type int, default = 400 Type of the kineticts dataset. Currently only 400 is supported.
frame_size (int, int), default = (400, 400) Size of the frames. The frames will be resized to this size.
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.
cache_dir str, default = None Directory to store the downloaded files. If set to None, the default cache directory will be used
num_threads int, default = 0 Number of threads to use for downloading the files.

1.3 TUCRID : torch.utils.data.dataset.Dataset

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Description

Dataset class for the Robot Interaction Dataset by University of Technology Chemnitz (TUCRID).

Example

from rsp.ml.dataset import TUCRID
from rsp.ml.dataset import ReplaceBackgroundRGBD
import rsp.ml.multi_transforms as multi_transforms
import cv2 as cv

backgrounds = TUCRID.load_backgrounds_color()
transforms = multi_transforms.Compose([
    ReplaceBackgroundRGBD(backgrounds),
    multi_transforms.Stack()
])

ds = TUCRID('train', transforms=transforms)

for X, T in ds:
  for x in X.permute(0, 2, 3, 1):
    img_color = x[:, :, :3].numpy()
    img_depth = x[:, :, 3].numpy()

    cv.imshow('color', img_color)
    cv.imshow('depth', img_depth)

    cv.waitKey(30)

1.3.1 __init__

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Description

Initializes a new instance.

Parameters

Name Type Description
phase str Dataset phase [train
load_depth_data bool, default = True Load depth data
sequence_length int, default = 30 Length of the sequences
num_classes int, default = 10 Number of classes
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.

1.3.2 get_uniform_sampler

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1.3.3 load_backgrounds

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Description

Loads the background images.

Parameters

Name Type Description
load_depth_data bool, default = True If set to True, the depth images will be loaded as well.

2 metrics

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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

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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

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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

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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

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

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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

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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

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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

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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

2.13 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

2.14 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

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

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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

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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

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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

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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.

3.2 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__

TOC

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__

TOC

Description

Initializes a new instance.

4.3 Brightness : 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.

4.3.1 __call__

TOC

Description

Call self as a function.

Parameters

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

4.3.2 __init__

TOC

Description

Initializes a new instance.

4.4 CenterCrop : MultiTransform

TOC

Description

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

4.4.1 __call__

TOC

Description

Call self as a function.

Parameters

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

4.4.2 __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.

4.5 Color : 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.

4.5.1 __call__

TOC

Description

Call self as a function.

Parameters

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

4.5.2 __init__

TOC

Description

Initializes a new instance.

4.6 Compose : builtins.object

TOC

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__

TOC

Description

Call self as a function.

4.6.2 __init__

TOC

Description

Initializes a new instance.

Parameters

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

4.7 GaussianNoise : 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.

4.7.1 __call__

TOC

Description

Call self as a function.

Parameters

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

4.7.2 __init__

TOC

Description

Initializes a new instance.

4.8 MultiTransform : builtins.object

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.

4.8.1 __call__

TOC

Description

Call self as a function.

Parameters

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

4.8.2 __init__

TOC

Description

Initializes a new instance.

4.9 Normalize : MultiTransform

TOC

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__

TOC

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__

TOC

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

TOC

Description

Converts sequence of RGB images to BGR images.

4.10.1 __call__

TOC

Description

Call self as a function.

Parameters

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

4.10.2 __init__

TOC

Description

Initializes a new instance.

4.11 RandomCrop : MultiTransform

TOC

Description

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

4.11.1 __call__

TOC

Description

Call self as a function.

Parameters

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

4.11.2 __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.

4.12 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.

4.12.1 __call__

TOC

Description

Call self as a function.

Parameters

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

4.12.2 __init__

TOC

Description

Initializes a new instance.

4.13 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.

4.13.1 __call__

TOC

Description

Call self as a function.

Parameters

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

4.13.2 __init__

TOC

Description

Initializes a new instance.

4.14 ReplaceBackground : MultiTransform

TOC

Description

Transformation for background replacement based on HSV values. Supports depth background replacement. backgrounds have to be passed as list of tuples of rgb and depth images.

Example

from rsp.nl.dataset import TUCRID
import rsp.ml.multi_transforms as multi_transforms

USE_DEPTH_DATA = False
backgrounds = TUCRID.load_backgrounds(USE_DEPTH_DATA)
tranforms_train = multi_transforms.Compose([
    multi_transforms.ReplaceBackground(
        backgrounds = backgrounds,
        hsv_filter=[(69, 87, 139, 255, 52, 255)],
        p = 0.8
    ),
    multi_transforms.Stack()
])
tucrid = TUCRID('train', load_depth_data=USE_DEPTH_DATA, transforms=tranforms_train)

for X, T in tucrid:
    for x in X:
        img = x.permute(1, 2, 0).numpy()

        cv.imshow('img', img)
        cv.waitKey(30)

4.14.1 __call__

TOC

Description

Call self as a function.

Parameters

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

4.14.2 __init__

TOC

Description

Transformation for background replacement based on HSV values. Supports depth background replacement. backgrounds have to be passed as list of tuples of rgb and depth images.

Parameters

Name Type Description
backgrounds List[np.array] List of background images
hsv_filter List[tuple[int, int, int, int, int, int]] List of HSV filters
p float, default = 1. Probability of applying the transformation
rotate float, default = 5 Maximum rotation angle
max_scale float, default = 2 Maximum scaling factor
max_noise float, default = 0.002 Maximum noise level

4.15 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.

4.15.1 __call__

TOC

Description

Call self as a function.

Parameters

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

4.15.2 __init__

TOC

Description

Initializes a new instance.

4.16 Rotate : MultiTransform

TOC

Description

Randomly rotates images.

Equations

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

4.16.1 __call__

TOC

Description

Call self as a function.

Parameters

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

4.16.2 __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.

4.17 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.

4.17.1 __call__

TOC

Description

Call self as a function.

Parameters

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

4.17.2 __init__

TOC

Description

Initializes a new instance.

4.18 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.

4.18.1 __call__

TOC

Description

Call self as a function.

Parameters

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

4.18.2 __init__

TOC

Description

Initializes a new instance.

4.19 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.

4.19.1 __call__

TOC

Description

Call self as a function.

Parameters

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

4.19.2 __init__

TOC

Description

Initializes a new instance.

4.20 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.

4.20.1 __call__

TOC

Description

Call self as a function.

Parameters

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

4.20.2 __init__

TOC

Description

Initializes a new instance.

4.21 ToNumpy : MultiTransform

TOC

Description

Converts a torch.Tensorto numpy

4.21.1 __call__

TOC

Description

Call self as a function.

Parameters

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

4.21.2 __init__

TOC

Description

Initializes a new instance.

4.22 ToPILImage : MultiTransform

TOC

Description

Converts sequence of images to sequence of PIL.Image.

4.22.1 __call__

TOC

Description

Call self as a function.

Parameters

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

4.22.2 __init__

TOC

Description

Initializes a new instance.

4.23 ToTensor : MultiTransform

TOC

Description

Converts a sequence of images to torch.Tensor.

4.23.1 __call__

TOC

Description

Call self as a function.

Parameters

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

4.23.2 __init__

TOC

Description

Initializes a new instance.

5 run

TOC

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

TOC

Description

Run class to store and manage training

Example

from rsp.ml.run import Run
import rsp.ml.metrics as m

metrics = [
    m.top_1_accuracy
]
config = {
    m.top_1_accuracy.__name__: {
        'ymin': 0,
        'ymax': 1
    }
}
run = Run(id='run0001', metrics=metrics, config=config, ignore_outliers_in_chart_scaling=True)

for epoch in range(100):
    """here goes some training code, giving us inputs, predictions and targets"""
    acc = m.top_1_accuracy(predictions, targets)
    run.append(m.top_1_accuracy.__name__, 'train', acc)

5.1.1 __init__

TOC

Description

Run class to store and manage training

Parameters

Name Type Description
id str, default = None Id of the run. If None, a new id is generated
moving_average_epochs int, default = 1 Number of epochs to average over
metrics list, default = None List of metrics to compute. Each metric should be a function that takes Y and T as input.
device str, default = None torch device to run on
ignore_outliers_in_chart_scaling bool, default = False Ignore outliers when scaling charts
config dict, default = {} Configuration dictionary. Keys are metric names and values are dictionaries with keys 'ymin' and 'ymax'

5.1.2 append

TOC

Description

Append value to key in phase.

Parameters

Name Type Description
key str Key to append to
phase str Phase to append to
value float Value to append

5.1.3 get_avg

TOC

Description

Get last average value of key in phase

Parameters

Name Type Description
key str Key to get
phase str Phase to get from

Returns

Last average value of key in phase : value : float

5.1.4 get_val

TOC

Description

Get last value of key in phase

Parameters

Name Type Description
key str Key to get
phase str Phase to get from

Returns

Last value of key in phase : value : float

5.1.5 len

TOC

Description

Get length of longest phase

5.1.6 load_best_state_dict

TOC

Description

Load best state_dict from runs/{id}/{fname}

Parameters

Name Type Description
model torch.nn.Module Model to load state_dict into
fname str, default = 'state_dict.pt' Filename to load from

5.1.7 load_state_dict

TOC

Description

Load state_dict from runs/{id}/{fname}

Parameters

Name Type Description
model torch.nn.Module Model to load state_dict into
fname str, default = None Filename to load from

5.1.8 pickle_dump

TOC

Description

Pickle model to runs/{id}/{fname}

Parameters

Name Type Description
model torch.nn.Module Model to pickle
fname str, default = 'model.pkl' Filename to save to

5.1.9 pickle_load

TOC

Description

Load model from runs/{id}/{fname}

Parameters

Name Type Description
fname str, default = 'model.pkl' Filename to load from

5.1.10 plot

TOC

Description

Plot all keys to runs/{id}/plot/{key}.jpg

5.1.11 recalculate_moving_average

TOC

Description

Recalculate moving average

5.1.12 save

TOC

Description

Save data to runs/{id}/data.json

5.1.13 save_best_state_dict

TOC

Description

Save state_dict if new_acc is better than previous best

Parameters

Name Type Description
state_dict dict State dict to save
new_acc float New accuracy
epoch int, default = None Epoch to save
fname str, default = 'state_dict.pt' Filename to save to

5.1.14 save_state_dict

TOC

Description

Save state_dict to runs/{id}/{fname}

Parameters

Name Type Description
state_dict dict State dict to save
fname str, default = 'state_dict.pt' Filename to save to

5.1.15 train_epoch

TOC

Description

Train one epoch.

Parameters

Name Type Description
dataloader DataLoader DataLoader to train on
model torch.nn.Module Model to train
optimizer torch.optim.Optimizer Optimizer to use
criterion torch.nn.Module Criterion to use
num_batches int, default = None Number of batches to train on. If None, train on all batches
return_YT bool, default = False Append Y and T to results

Returns

Dictionary with results : results : dict

5.1.16 validate_epoch

TOC

Description

Validate one epoch.

Parameters

Name Type Description
dataloader DataLoader DataLoader to validate on
model torch.nn.Module Model to validate
optimizer torch.optim.Optimizer Optimizer to use
criterion torch.nn.Module Criterion to use
num_batches int, default = None Number of batches to validate on. If None, validate on all batches
return_YT bool, default = False Append Y and T to results

Returns

Dictionary with results : results : dict

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