art.estimators.object_detection¶
Module containing estimators for object detection.
Mixin Base Class Object Detector¶
Object Detector PyTorch¶
- class art.estimators.object_detection.PyTorchObjectDetector(model, clip_values: Optional[CLIP_VALUES_TYPE] = None, channels_first: Optional[bool] = None, preprocessing_defences: Optional[Union[Preprocessor, List[Preprocessor]]] = None, postprocessing_defences: Optional[Union[Postprocessor, List[Postprocessor]]] = None, preprocessing: PREPROCESSING_TYPE = None, attack_losses: Tuple[str, ...] = ('loss_classifier', 'loss_box_reg', 'loss_objectness', 'loss_rpn_box_reg'), device_type: str = 'gpu')¶
This module implements the task specific estimator for PyTorch object detectors.
- __init__(model, clip_values: Optional[CLIP_VALUES_TYPE] = None, channels_first: Optional[bool] = None, preprocessing_defences: Optional[Union[Preprocessor, List[Preprocessor]]] = None, postprocessing_defences: Optional[Union[Postprocessor, List[Postprocessor]]] = None, preprocessing: PREPROCESSING_TYPE = None, attack_losses: Tuple[str, ...] = ('loss_classifier', 'loss_box_reg', 'loss_objectness', 'loss_rpn_box_reg'), device_type: str = 'gpu')¶
Initialization.
- Parameters
model –
Object detection model. The output of the model is List[Dict[Tensor]], one for each input image. The fields of the Dict are as follows:
boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format, with values between 0 and H and 0 and W
labels (Int64Tensor[N]): the predicted labels for each image
scores (Tensor[N]): the scores or each prediction
clip_values – Tuple of the form (min, max) of floats or np.ndarray representing the minimum and maximum values allowed for features. If floats are provided, these will be used as the range of all features. If arrays are provided, each value will be considered the bound for a feature, thus the shape of clip values needs to match the total number of features.
channels_first – Set channels first or last.
preprocessing_defences – Preprocessing defence(s) to be applied by the classifier.
postprocessing_defences – Postprocessing defence(s) to be applied by the classifier.
preprocessing – Tuple of the form (subtrahend, divisor) of floats or np.ndarray of values to be used for data preprocessing. The first value will be subtracted from the input. The input will then be divided by the second one.
attack_losses (
Tuple) – Tuple of any combination of strings of loss components: ‘loss_classifier’, ‘loss_box_reg’, ‘loss_objectness’, and ‘loss_rpn_box_reg’.device_type (
str) – Type of device to be used for model and tensors, if cpu run on CPU, if gpu run on GPU if available otherwise run on CPU.
- property channels_first: bool¶
- Returns
Boolean to indicate index of the color channels in the sample x.
- property clip_values: Optional[CLIP_VALUES_TYPE]¶
Return the clip values of the input samples.
- Returns
Clip values (min, max).
- compute_loss(x: numpy.ndarray, y: List[Dict[str, Union[numpy.ndarray, torch.Tensor]]], **kwargs) Union[numpy.ndarray, torch.Tensor]¶
Compute the loss of the neural network for samples x.
- Parameters
x (
ndarray) – Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2, nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).y (
List) –Target values of format List[Dict[Tensor]], one for each input image. The fields of the Dict are as follows:
boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format, with values between 0 and H and 0 and W
labels (Int64Tensor[N]): the predicted labels for each image
scores (Tensor[N]): the scores or each prediction.
- Returns
Loss.
- compute_losses(x: numpy.ndarray, y: List[Dict[str, Union[numpy.ndarray, torch.Tensor]]]) Dict[str, numpy.ndarray]¶
Compute all loss components.
- Return type
Dict- Parameters
x (
ndarray) – Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2, nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).y (
List) –Target values of format List[Dict[Tensor]], one for each input image. The fields of the Dict are as follows:
boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format, with values between 0 and H and 0 and W
labels (Int64Tensor[N]): the predicted labels for each image
scores (Tensor[N]): the scores or each prediction.
- Returns
Dictionary of loss components.
- property device: torch.device¶
Get current used device.
- Returns
Current used device.
- property device_type: str¶
Return the type of device on which the estimator is run.
- Returns
Type of device on which the estimator is run, either gpu or cpu.
- fit(x: numpy.ndarray, y, batch_size: int = 128, nb_epochs: int = 20, **kwargs) None¶
Fit the model of the estimator on the training data x and y.
- Parameters
x (
ndarray) – Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2, nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).y (Format as expected by the model) – Target values.
batch_size (
int) – Batch size.nb_epochs (
int) – Number of training epochs.
- fit_generator(generator: DataGenerator, nb_epochs: int = 20, **kwargs) None¶
Fit the estimator using a generator yielding training batches. Implementations can provide framework-specific versions of this function to speed-up computation.
- Parameters
generator – Batch generator providing (x, y) for each epoch.
nb_epochs (
int) – Number of training epochs.
- get_activations(x: numpy.ndarray, layer: Union[int, str], batch_size: int, framework: bool = False) numpy.ndarray¶
Return the output of a specific layer for samples x where layer is the index of the layer between 0 and nb_layers - 1 or the name of the layer. The number of layers can be determined by counting the results returned by calling `layer_names.
- Return type
ndarray- Parameters
x (
ndarray) – Sampleslayer – Index or name of the layer.
batch_size (
int) – Batch size.framework (
bool) – If true, return the intermediate tensor representation of the activation.
- Returns
The output of layer, where the first dimension is the batch size corresponding to x.
- get_params() Dict[str, Any]¶
Get all parameters and their values of this estimator.
- Returns
A dictionary of string parameter names to their value.
- property input_shape: Tuple[int, ...]¶
Return the shape of one input sample.
- Returns
Shape of one input sample.
- property layer_names: Optional[List[str]]¶
Return the names of the hidden layers in the model, if applicable.
- Returns
The names of the hidden layers in the model, input and output layers are ignored.
Warning
layer_names tries to infer the internal structure of the model. This feature comes with no guarantees on the correctness of the result. The intended order of the layers tries to match their order in the model, but this is not guaranteed either.
- loss_gradient(x: numpy.ndarray, y: List[Dict[str, Union[numpy.ndarray, torch.Tensor]]], **kwargs) numpy.ndarray¶
Compute the gradient of the loss function w.r.t. x.
- Return type
ndarray- Parameters
x (
ndarray) – Samples of shape (nb_samples, height, width, nb_channels).y (
List) –Target values of format List[Dict[Tensor]], one for each input image. The fields of the Dict are as follows:
boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format, with values between 0 and H and 0 and W
labels (Int64Tensor[N]): the predicted labels for each image
scores (Tensor[N]): the scores or each prediction.
- Returns
Loss gradients of the same shape as x.
- property model¶
Return the model.
- Returns
The model.
- property native_label_is_pytorch_format: bool¶
Are the native labels in PyTorch format [x1, y1, x2, y2]?
- predict(x: numpy.ndarray, batch_size: int = 128, **kwargs) List[Dict[str, numpy.ndarray]]¶
Perform prediction for a batch of inputs.
- Return type
List- Parameters
x (
ndarray) – Samples of shape (nb_samples, height, width, nb_channels).batch_size (
int) – Batch size.
- Returns
Predictions of format List[Dict[str, np.ndarray]], one for each input image. The fields of the Dict are as follows:
boxes [N, 4]: the boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H.
labels [N]: the labels for each image
scores [N]: the scores or each prediction.
- set_batchnorm(train: bool) None¶
Set all batch normalization layers into train or eval mode.
- Parameters
train (
bool) – False for evaluation mode.
- set_dropout(train: bool) None¶
Set all dropout layers into train or eval mode.
- Parameters
train (
bool) – False for evaluation mode.
- set_params(**kwargs) None¶
Take a dictionary of parameters and apply checks before setting them as attributes.
- Parameters
kwargs – A dictionary of attributes.
Object Detector PyTorch Faster-RCNN¶
- class art.estimators.object_detection.PyTorchFasterRCNN(model: Optional[torchvision.models.detection.fasterrcnn_resnet50_fpn] = None, clip_values: Optional[CLIP_VALUES_TYPE] = None, channels_first: Optional[bool] = None, preprocessing_defences: Optional[Union[Preprocessor, List[Preprocessor]]] = None, postprocessing_defences: Optional[Union[Postprocessor, List[Postprocessor]]] = None, preprocessing: PREPROCESSING_TYPE = None, attack_losses: Tuple[str, ...] = ('loss_classifier', 'loss_box_reg', 'loss_objectness', 'loss_rpn_box_reg'), device_type: str = 'gpu')¶
This class implements a model-specific object detector using Faster-RCNN and PyTorch.
- __init__(model: Optional[torchvision.models.detection.fasterrcnn_resnet50_fpn] = None, clip_values: Optional[CLIP_VALUES_TYPE] = None, channels_first: Optional[bool] = None, preprocessing_defences: Optional[Union[Preprocessor, List[Preprocessor]]] = None, postprocessing_defences: Optional[Union[Postprocessor, List[Postprocessor]]] = None, preprocessing: PREPROCESSING_TYPE = None, attack_losses: Tuple[str, ...] = ('loss_classifier', 'loss_box_reg', 'loss_objectness', 'loss_rpn_box_reg'), device_type: str = 'gpu')¶
Initialization.
- Parameters
model –
Faster-RCNN model. The output of the model is List[Dict[Tensor]], one for each input image. The fields of the Dict are as follows:
boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format, with values between 0 and H and 0 and W
labels (Int64Tensor[N]): the predicted labels for each image
scores (Tensor[N]): the scores or each prediction
clip_values – Tuple of the form (min, max) of floats or np.ndarray representing the minimum and maximum values allowed for features. If floats are provided, these will be used as the range of all features. If arrays are provided, each value will be considered the bound for a feature, thus the shape of clip values needs to match the total number of features.
channels_first – Set channels first or last.
preprocessing_defences – Preprocessing defence(s) to be applied by the classifier.
postprocessing_defences – Postprocessing defence(s) to be applied by the classifier.
preprocessing – Tuple of the form (subtrahend, divisor) of floats or np.ndarray of values to be used for data preprocessing. The first value will be subtracted from the input. The input will then be divided by the second one.
attack_losses (
Tuple) – Tuple of any combination of strings of loss components: ‘loss_classifier’, ‘loss_box_reg’, ‘loss_objectness’, and ‘loss_rpn_box_reg’.device_type (
str) – Type of device to be used for model and tensors, if cpu run on CPU, if gpu run on GPU if available otherwise run on CPU.
- property channels_first: bool¶
- Returns
Boolean to indicate index of the color channels in the sample x.
- property clip_values: Optional[CLIP_VALUES_TYPE]¶
Return the clip values of the input samples.
- Returns
Clip values (min, max).
- compute_loss(x: numpy.ndarray, y: List[Dict[str, Union[numpy.ndarray, torch.Tensor]]], **kwargs) Union[numpy.ndarray, torch.Tensor]¶
Compute the loss of the neural network for samples x.
- Parameters
x (
ndarray) – Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2, nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).y (
List) –Target values of format List[Dict[Tensor]], one for each input image. The fields of the Dict are as follows:
boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format, with values between 0 and H and 0 and W
labels (Int64Tensor[N]): the predicted labels for each image
scores (Tensor[N]): the scores or each prediction.
- Returns
Loss.
- compute_losses(x: numpy.ndarray, y: List[Dict[str, Union[numpy.ndarray, torch.Tensor]]]) Dict[str, numpy.ndarray]¶
Compute all loss components.
- Return type
Dict- Parameters
x (
ndarray) – Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2, nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).y (
List) –Target values of format List[Dict[Tensor]], one for each input image. The fields of the Dict are as follows:
boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format, with values between 0 and H and 0 and W
labels (Int64Tensor[N]): the predicted labels for each image
scores (Tensor[N]): the scores or each prediction.
- Returns
Dictionary of loss components.
- property device: torch.device¶
Get current used device.
- Returns
Current used device.
- property device_type: str¶
Return the type of device on which the estimator is run.
- Returns
Type of device on which the estimator is run, either gpu or cpu.
- fit(x: numpy.ndarray, y, batch_size: int = 128, nb_epochs: int = 20, **kwargs) None¶
Fit the model of the estimator on the training data x and y.
- Parameters
x (
ndarray) – Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2, nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).y (Format as expected by the model) – Target values.
batch_size (
int) – Batch size.nb_epochs (
int) – Number of training epochs.
- fit_generator(generator: DataGenerator, nb_epochs: int = 20, **kwargs) None¶
Fit the estimator using a generator yielding training batches. Implementations can provide framework-specific versions of this function to speed-up computation.
- Parameters
generator – Batch generator providing (x, y) for each epoch.
nb_epochs (
int) – Number of training epochs.
- get_activations(x: numpy.ndarray, layer: Union[int, str], batch_size: int, framework: bool = False) numpy.ndarray¶
Return the output of a specific layer for samples x where layer is the index of the layer between 0 and nb_layers - 1 or the name of the layer. The number of layers can be determined by counting the results returned by calling `layer_names.
- Return type
ndarray- Parameters
x (
ndarray) – Sampleslayer – Index or name of the layer.
batch_size (
int) – Batch size.framework (
bool) – If true, return the intermediate tensor representation of the activation.
- Returns
The output of layer, where the first dimension is the batch size corresponding to x.
- get_params() Dict[str, Any]¶
Get all parameters and their values of this estimator.
- Returns
A dictionary of string parameter names to their value.
- property input_shape: Tuple[int, ...]¶
Return the shape of one input sample.
- Returns
Shape of one input sample.
- property layer_names: Optional[List[str]]¶
Return the names of the hidden layers in the model, if applicable.
- Returns
The names of the hidden layers in the model, input and output layers are ignored.
Warning
layer_names tries to infer the internal structure of the model. This feature comes with no guarantees on the correctness of the result. The intended order of the layers tries to match their order in the model, but this is not guaranteed either.
- loss_gradient(x: numpy.ndarray, y: List[Dict[str, Union[numpy.ndarray, torch.Tensor]]], **kwargs) numpy.ndarray¶
Compute the gradient of the loss function w.r.t. x.
- Return type
ndarray- Parameters
x (
ndarray) – Samples of shape (nb_samples, height, width, nb_channels).y (
List) –Target values of format List[Dict[Tensor]], one for each input image. The fields of the Dict are as follows:
boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format, with values between 0 and H and 0 and W
labels (Int64Tensor[N]): the predicted labels for each image
scores (Tensor[N]): the scores or each prediction.
- Returns
Loss gradients of the same shape as x.
- property model¶
Return the model.
- Returns
The model.
- property native_label_is_pytorch_format: bool¶
Are the native labels in PyTorch format [x1, y1, x2, y2]?
- predict(x: numpy.ndarray, batch_size: int = 128, **kwargs) List[Dict[str, numpy.ndarray]]¶
Perform prediction for a batch of inputs.
- Return type
List- Parameters
x (
ndarray) – Samples of shape (nb_samples, height, width, nb_channels).batch_size (
int) – Batch size.
- Returns
Predictions of format List[Dict[str, np.ndarray]], one for each input image. The fields of the Dict are as follows:
boxes [N, 4]: the boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H.
labels [N]: the labels for each image
scores [N]: the scores or each prediction.
- set_batchnorm(train: bool) None¶
Set all batch normalization layers into train or eval mode.
- Parameters
train (
bool) – False for evaluation mode.
- set_dropout(train: bool) None¶
Set all dropout layers into train or eval mode.
- Parameters
train (
bool) – False for evaluation mode.
- set_params(**kwargs) None¶
Take a dictionary of parameters and apply checks before setting them as attributes.
- Parameters
kwargs – A dictionary of attributes.
Object Detector TensorFlow Faster-RCNN¶
- class art.estimators.object_detection.TensorFlowFasterRCNN(images: tf.Tensor, model: Optional[FasterRCNNMetaArch] = None, filename: Optional[str] = None, url: Optional[str] = None, sess: Optional[Session] = None, is_training: bool = False, clip_values: Optional[CLIP_VALUES_TYPE] = None, channels_first: bool = False, preprocessing_defences: Optional[Union[Preprocessor, List[Preprocessor]]] = None, postprocessing_defences: Optional[Union[Postprocessor, List[Postprocessor]]] = None, preprocessing: PREPROCESSING_TYPE = (0.0, 1.0), attack_losses: Tuple[str, ...] = ('Loss/RPNLoss/localization_loss', 'Loss/RPNLoss/objectness_loss', 'Loss/BoxClassifierLoss/localization_loss', 'Loss/BoxClassifierLoss/classification_loss'))¶
This class implements a model-specific object detector using Faster-RCNN and TensorFlow.
- __init__(images: tf.Tensor, model: Optional[FasterRCNNMetaArch] = None, filename: Optional[str] = None, url: Optional[str] = None, sess: Optional[Session] = None, is_training: bool = False, clip_values: Optional[CLIP_VALUES_TYPE] = None, channels_first: bool = False, preprocessing_defences: Optional[Union[Preprocessor, List[Preprocessor]]] = None, postprocessing_defences: Optional[Union[Postprocessor, List[Postprocessor]]] = None, preprocessing: PREPROCESSING_TYPE = (0.0, 1.0), attack_losses: Tuple[str, ...] = ('Loss/RPNLoss/localization_loss', 'Loss/RPNLoss/objectness_loss', 'Loss/BoxClassifierLoss/localization_loss', 'Loss/BoxClassifierLoss/classification_loss'))¶
Initialization of an instance TensorFlowFasterRCNN.
- Parameters
images – Input samples of shape (nb_samples, height, width, nb_channels).
model –
A TensorFlow Faster-RCNN model. The output that can be computed from the model includes a tuple of (predictions, losses, detections):
predictions: a dictionary holding “raw” prediction tensors.
- losses: a dictionary mapping loss keys (Loss/RPNLoss/localization_loss,
Loss/RPNLoss/objectness_loss, Loss/BoxClassifierLoss/localization_loss, Loss/BoxClassifierLoss/classification_loss) to scalar tensors representing corresponding loss values.
detections: a dictionary containing final detection results.
filename – Filename of the detection model without filename extension.
url – URL to download archive of detection model including filename extension.
sess – Computation session.
is_training (
bool) – A boolean indicating whether the training version of the computation graph should be constructed.clip_values – Tuple of the form (min, max) of floats or np.ndarray representing the minimum and maximum values allowed for input image features. If floats are provided, these will be used as the range of all features. If arrays are provided, each value will be considered the bound for a feature, thus the shape of clip values needs to match the total number of features.
channels_first (
bool) – Set channels first or last.preprocessing_defences – Preprocessing defence(s) to be applied by the classifier.
postprocessing_defences – Postprocessing defence(s) to be applied by the classifier.
preprocessing – Tuple of the form (subtractor, divider) of floats or np.ndarray of values to be used for data preprocessing. The first value will be subtracted from the input. The input will then be divided by the second one.
attack_losses (
Tuple) – Tuple of any combination of strings of the following loss components: first_stage_localization_loss, first_stage_objectness_loss, second_stage_localization_loss, second_stage_classification_loss.
- property channels_first: bool¶
- Returns
Boolean to indicate index of the color channels in the sample x.
- property clip_values: Optional[CLIP_VALUES_TYPE]¶
Return the clip values of the input samples.
- Returns
Clip values (min, max).
- compute_loss(x: numpy.ndarray, y: numpy.ndarray, **kwargs) numpy.ndarray¶
Compute the loss.
- Return type
ndarray- Parameters
x (
ndarray) – Sample input with shape as expected by the model.y (
ndarray) – Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape (nb_samples,).
- Returns
Array of losses of the same shape as x.
- compute_losses(x: numpy.ndarray, y: numpy.ndarray) Dict[str, numpy.ndarray]¶
Compute all loss components.
- Return type
Dict- Parameters
x (
ndarray) – Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2, nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).y (
ndarray) – Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape (nb_samples,).
- Returns
Dictionary of loss components.
- property detections: Dict[str, tf.Tensor]¶
Get the _detections attribute.
- Returns
A dictionary containing final detection results.
- fit(x: numpy.ndarray, y, batch_size: int = 128, nb_epochs: int = 20, **kwargs) None¶
Fit the model of the estimator on the training data x and y.
- Parameters
x (
ndarray) – Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2, nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).y (Format as expected by the model) – Target values.
batch_size (
int) – Batch size.nb_epochs (
int) – Number of training epochs.
- fit_generator(generator: DataGenerator, nb_epochs: int = 20, **kwargs) None¶
Fit the estimator using a generator yielding training batches. Implementations can provide framework-specific versions of this function to speed-up computation.
- Parameters
generator – Batch generator providing (x, y) for each epoch.
nb_epochs (
int) – Number of training epochs.
- get_activations(x: numpy.ndarray, layer: Union[int, str], batch_size: int, framework: bool = False) numpy.ndarray¶
Return the output of a specific layer for samples x where layer is the index of the layer between 0 and nb_layers - 1 or the name of the layer. The number of layers can be determined by counting the results returned by calling `layer_names.
- Return type
ndarray- Parameters
x (
ndarray) – Sampleslayer – Index or name of the layer.
batch_size (
int) – Batch size.framework (
bool) – If true, return the intermediate tensor representation of the activation.
- Returns
The output of layer, where the first dimension is the batch size corresponding to x.
- get_params() Dict[str, Any]¶
Get all parameters and their values of this estimator.
- Returns
A dictionary of string parameter names to their value.
- property input_images: tf.Tensor¶
Get the images attribute.
- Returns
The input image tensor.
- property input_shape: Tuple[int, ...]¶
Return the shape of one input sample.
- Returns
Shape of one input sample.
- property layer_names: Optional[List[str]]¶
Return the names of the hidden layers in the model, if applicable.
- Returns
The names of the hidden layers in the model, input and output layers are ignored.
Warning
layer_names tries to infer the internal structure of the model. This feature comes with no guarantees on the correctness of the result. The intended order of the layers tries to match their order in the model, but this is not guaranteed either.
- loss_gradient(x: numpy.ndarray, y: List[Dict[str, numpy.ndarray]], standardise_output: bool = False, **kwargs) numpy.ndarray¶
Compute the gradient of the loss function w.r.t. x.
- Return type
ndarray- Parameters
x (
ndarray) – Samples of shape (nb_samples, height, width, nb_channels).y (
List) –- Targets of format List[Dict[str, np.ndarray]], one for each input image. The fields of the Dict are
as follows:
- boxes [N, 4]: the boxes in [y1, x1, y2, x2] in scale [0, 1] (standardise_output=False) or
[x1, y1, x2, y2] in image scale (standardise_output=True) format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H.
- labels [N]: the labels for each image in TensorFlow (standardise_output=False) or PyTorch
(standardise_output=True) format
standardise_output (
bool) – True if y is provided in standardised PyTorch format. Box coordinates will be scaled back to [0, 1], label index will be decreased by 1 and the boxes will be changed from [x1, y1, x2, y2] to [y1, x1, y2, x2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H.
- Returns
Loss gradients of the same shape as x.
- property losses: Dict[str, tf.Tensor]¶
Get the _losses attribute.
- Returns
A dictionary mapping loss keys (Loss/RPNLoss/localization_loss, Loss/RPNLoss/objectness_loss, Loss/BoxClassifierLoss/localization_loss, Loss/BoxClassifierLoss/classification_loss) to scalar tensors representing corresponding loss values.
- property model¶
Return the model.
- Returns
The model.
- property native_label_is_pytorch_format: bool¶
Are the native labels in PyTorch format [x1, y1, x2, y2]?
- predict(x: numpy.ndarray, batch_size: int = 128, standardise_output: bool = False, **kwargs) List[Dict[str, numpy.ndarray]]¶
Perform prediction for a batch of inputs.
- Return type
List- Parameters
x (
ndarray) – Samples of shape (nb_samples, height, width, nb_channels).batch_size (
int) – Batch size.standardise_output (
bool) – True if output should be standardised to PyTorch format. Box coordinates will be scaled from [0, 1] to image dimensions, label index will be increased by 1 to adhere to COCO categories and the boxes will be changed to [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H.
- Returns
Predictions of format List[Dict[str, np.ndarray]], one for each input image. The fields of the Dict are as follows:
- boxes [N, 4]: the boxes in [y1, x1, y2, x2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H.
Can be changed to PyTorch format with standardise_output=True.
- labels [N]: the labels for each image in TensorFlow format. Can be changed to PyTorch format with
standardise_output=True.
scores [N]: the scores or each prediction.
- property predictions: Dict[str, tf.Tensor]¶
Get the _predictions attribute.
- Returns
A dictionary holding “raw” prediction tensors.
- property sess: tf.python.client.session.Session¶
Get current TensorFlow session.
- Returns
The current TensorFlow session.
- set_params(**kwargs) None¶
Take a dictionary of parameters and apply checks before setting them as attributes.
- Parameters
kwargs – A dictionary of attributes.