Pipelines
DetectionPipeline
Bases: Pipeline
Pipeline specifically designed for object detection tasks. The pipeline includes loading images, preprocessing, prediction, and postprocessing.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model |
SgModule
|
The object detection model (instance of SgModule) used for making predictions. |
required |
class_names |
List[str]
|
List of class names corresponding to the model's output classes. |
required |
post_prediction_callback |
DetectionPostPredictionCallback
|
Callback function to process raw predictions from the model. |
required |
image_processor |
Optional[Processing]
|
Single image processor or a list of image processors for preprocessing and postprocessing the images. |
None
|
device |
Optional[str]
|
The device on which the model will be run. If None, will run on current model device. Use "cuda" for GPU support. |
None
|
Source code in training/pipelines/pipelines.py
215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 |
|
Pipeline
Bases: ABC
An abstract base class representing a processing pipeline for a specific task. The pipeline includes loading images, preprocessing, prediction, and postprocessing.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model |
SgModule
|
The model used for making predictions. |
required |
image_processor |
Union[Processing, List[Processing]]
|
A single image processor or a list of image processors for preprocessing and postprocessing the images. |
required |
device |
Optional[str]
|
The device on which the model will be run. If None, will run on current model device. Use "cuda" for GPU support. |
None
|
Source code in training/pipelines/pipelines.py
43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 |
|
__call__(inputs, batch_size=32)
Predict an image or a list of images.
Supported types include: - str: A string representing either a video, an image or an URL. - numpy.ndarray: A numpy array representing the image - torch.Tensor: A PyTorch tensor representing the image - PIL.Image.Image: A PIL Image object - List: A list of images of any of the above image types (list of videos not supported).
Parameters:
Name | Type | Description | Default |
---|---|---|---|
inputs |
Union[str, ImageSource, List[ImageSource]]
|
inputs to the model, which can be any of the above-mentioned types. |
required |
batch_size |
Optional[int]
|
Number of images to be processed at the same time. |
32
|
Returns:
Type | Description |
---|---|
ImagesPredictions
|
Results of the prediction. |
Source code in training/pipelines/pipelines.py
62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
|
predict_images(images, batch_size=32)
Predict an image or a list of images.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
images |
Union[ImageSource, List[ImageSource]]
|
Images to predict. |
required |
batch_size |
Optional[int]
|
The size of each batch. |
32
|
Returns:
Type | Description |
---|---|
ImagesPredictions
|
Results of the prediction. |
Source code in training/pipelines/pipelines.py
84 85 86 87 88 89 90 91 92 93 94 95 |
|
predict_video(video_path, batch_size=32)
Predict on a video file, by processing the frames in batches.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
video_path |
str
|
Path to the video file. |
required |
batch_size |
Optional[int]
|
The size of each batch. |
32
|
Returns:
Type | Description |
---|---|
VideoPredictions
|
Results of the prediction. |
Source code in training/pipelines/pipelines.py
97 98 99 100 101 102 103 104 105 106 |
|
predict_webcam()
Predict using webcam
Source code in training/pipelines/pipelines.py
108 109 110 111 112 113 114 115 116 117 |
|
eval_mode(model)
Set a model in evaluation mode and deactivate gradient computation, undo at the end.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model |
SgModule
|
The model to set in evaluation mode. |
required |
Source code in training/pipelines/pipelines.py
30 31 32 33 34 35 36 37 38 39 40 |
|