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from typing import Any, List, Optional, Tuple, Union
import numpy as np
from inference.core.entities.responses.inference import (
InferenceResponseImage,
ObjectDetectionInferenceResponse,
ObjectDetectionPrediction,
)
from inference.core.env import FIX_BATCH_SIZE, MAX_BATCH_SIZE
from inference.core.logger import logger
from inference.core.models.defaults import (
DEFAULT_CLASS_AGNOSTIC_NMS,
DEFAULT_CONFIDENCE,
DEFAULT_IOU_THRESH,
DEFAULT_MAX_CANDIDATES,
DEFAUlT_MAX_DETECTIONS,
)
from inference.core.models.roboflow import OnnxRoboflowInferenceModel
from inference.core.models.types import PreprocessReturnMetadata
from inference.core.models.utils.validate import (
get_num_classes_from_model_prediction_shape,
)
from inference.core.nms import w_np_non_max_suppression
from inference.core.utils.postprocess import post_process_bboxes
class ObjectDetectionBaseOnnxRoboflowInferenceModel(OnnxRoboflowInferenceModel):
"""Roboflow ONNX Object detection model. This class implements an object detection specific infer method."""
task_type = "object-detection"
box_format = "xywh"
def infer(
self,
image: Any,
class_agnostic_nms: bool = DEFAULT_CLASS_AGNOSTIC_NMS,
confidence: float = DEFAULT_CONFIDENCE,
disable_preproc_auto_orient: bool = False,
disable_preproc_contrast: bool = False,
disable_preproc_grayscale: bool = False,
disable_preproc_static_crop: bool = False,
iou_threshold: float = DEFAULT_IOU_THRESH,
fix_batch_size: bool = False,
max_candidates: int = DEFAULT_MAX_CANDIDATES,
max_detections: int = DEFAUlT_MAX_DETECTIONS,
return_image_dims: bool = False,
**kwargs,
) -> Any:
"""
Runs object detection inference on one or multiple images and returns the detections.
Args:
image (Any): The input image or a list of images to process.
class_agnostic_nms (bool, optional): Whether to use class-agnostic non-maximum suppression. Defaults to False.
confidence (float, optional): Confidence threshold for predictions. Defaults to 0.5.
iou_threshold (float, optional): IoU threshold for non-maximum suppression. Defaults to 0.5.
fix_batch_size (bool, optional): If True, fix the batch size for predictions. Useful when the model requires a fixed batch size. Defaults to False.
max_candidates (int, optional): Maximum number of candidate detections. Defaults to 3000.
max_detections (int, optional): Maximum number of detections after non-maximum suppression. Defaults to 300.
return_image_dims (bool, optional): Whether to return the dimensions of the processed images along with the predictions. Defaults to False.
disable_preproc_auto_orient (bool, optional): If true, the auto orient preprocessing step is disabled for this call. Default is False.
disable_preproc_contrast (bool, optional): If true, the auto contrast preprocessing step is disabled for this call. Default is False.
disable_preproc_grayscale (bool, optional): If true, the grayscale preprocessing step is disabled for this call. Default is False.
disable_preproc_static_crop (bool, optional): If true, the static crop preprocessing step is disabled for this call. Default is False.
*args: Variable length argument list.
**kwargs: Arbitrary keyword arguments.
Returns:
Union[List[ObjectDetectionInferenceResponse], ObjectDetectionInferenceResponse]: One or multiple object detection inference responses based on the number of processed images. Each response contains a list of predictions. If `return_image_dims` is True, it will return a tuple with predictions and image dimensions.
Raises:
ValueError: If batching is not enabled for the model and more than one image is passed for processing.
"""
return super().infer(
image,
class_agnostic_nms=class_agnostic_nms,
confidence=confidence,
disable_preproc_auto_orient=disable_preproc_auto_orient,
disable_preproc_contrast=disable_preproc_contrast,
disable_preproc_grayscale=disable_preproc_grayscale,
disable_preproc_static_crop=disable_preproc_static_crop,
iou_threshold=iou_threshold,
fix_batch_size=fix_batch_size,
max_candidates=max_candidates,
max_detections=max_detections,
return_image_dims=return_image_dims,
**kwargs,
)
def make_response(
self,
predictions: List[List[float]],
img_dims: List[Tuple[int, int]],
class_filter: Optional[List[str]] = None,
*args,
**kwargs,
) -> List[ObjectDetectionInferenceResponse]:
"""Constructs object detection response objects based on predictions.
Args:
predictions (List[List[float]]): The list of predictions.
img_dims (List[Tuple[int, int]]): Dimensions of the images.
class_filter (Optional[List[str]]): A list of class names to filter, if provided.
Returns:
List[ObjectDetectionInferenceResponse]: A list of response objects containing object detection predictions.
"""
if isinstance(img_dims, dict) and "img_dims" in img_dims:
img_dims = img_dims["img_dims"]
predictions = predictions[
: len(img_dims)
] # If the batch size was fixed we have empty preds at the end
responses = [
ObjectDetectionInferenceResponse(
predictions=[
ObjectDetectionPrediction(
# Passing args as a dictionary here since one of the args is 'class' (a protected term in Python)
**{
"x": (pred[0] + pred[2]) / 2,
"y": (pred[1] + pred[3]) / 2,
"width": pred[2] - pred[0],
"height": pred[3] - pred[1],
"confidence": pred[4],
"class": self.class_names[int(pred[6])],
"class_id": int(pred[6]),
}
)
for pred in batch_predictions
if not class_filter
or self.class_names[int(pred[6])] in class_filter
],
image=InferenceResponseImage(
width=img_dims[ind][1], height=img_dims[ind][0]
),
)
for ind, batch_predictions in enumerate(predictions)
]
return responses
def postprocess(
self,
predictions: Tuple[np.ndarray, ...],
preproc_return_metadata: PreprocessReturnMetadata,
class_agnostic_nms=DEFAULT_CLASS_AGNOSTIC_NMS,
confidence: float = DEFAULT_CONFIDENCE,
iou_threshold: float = DEFAULT_IOU_THRESH,
max_candidates: int = DEFAULT_MAX_CANDIDATES,
max_detections: int = DEFAUlT_MAX_DETECTIONS,
return_image_dims: bool = False,
**kwargs,
) -> List[ObjectDetectionInferenceResponse]:
"""Postprocesses the object detection predictions.
Args:
predictions (np.ndarray): Raw predictions from the model.
img_dims (List[Tuple[int, int]]): Dimensions of the images.
class_agnostic_nms (bool): Whether to apply class-agnostic non-max suppression. Default is False.
confidence (float): Confidence threshold for filtering detections. Default is 0.5.
iou_threshold (float): IoU threshold for non-max suppression. Default is 0.5.
max_candidates (int): Maximum number of candidate detections. Default is 3000.
max_detections (int): Maximum number of final detections. Default is 300.
Returns:
List[ObjectDetectionInferenceResponse]: The post-processed predictions.
"""
predictions = predictions[0]
predictions = w_np_non_max_suppression(
predictions,
conf_thresh=confidence,
iou_thresh=iou_threshold,
class_agnostic=class_agnostic_nms,
max_detections=max_detections,
max_candidate_detections=max_candidates,
box_format=self.box_format,
)
infer_shape = (self.img_size_h, self.img_size_w)
img_dims = preproc_return_metadata["img_dims"]
predictions = post_process_bboxes(
predictions,
infer_shape,
img_dims,
self.preproc,
resize_method=self.resize_method,
disable_preproc_static_crop=preproc_return_metadata[
"disable_preproc_static_crop"
],
)
return self.make_response(predictions, img_dims, **kwargs)
def preprocess(
self,
image: Any,
disable_preproc_auto_orient: bool = False,
disable_preproc_contrast: bool = False,
disable_preproc_grayscale: bool = False,
disable_preproc_static_crop: bool = False,
fix_batch_size: bool = False,
**kwargs,
) -> Tuple[np.ndarray, PreprocessReturnMetadata]:
"""Preprocesses an object detection inference request.
Args:
request (ObjectDetectionInferenceRequest): The request object containing images.
Returns:
Tuple[np.ndarray, List[Tuple[int, int]]]: Preprocessed image inputs and corresponding dimensions.
"""
img_in, img_dims = self.load_image(
image,
disable_preproc_auto_orient=disable_preproc_auto_orient,
disable_preproc_contrast=disable_preproc_contrast,
disable_preproc_grayscale=disable_preproc_grayscale,
disable_preproc_static_crop=disable_preproc_static_crop,
)
img_in /= 255.0
if self.batching_enabled:
batch_padding = 0
if FIX_BATCH_SIZE or fix_batch_size:
if MAX_BATCH_SIZE == float("inf"):
logger.warn(
"Requested fix_batch_size but MAX_BATCH_SIZE is not set. Using dynamic batching."
)
batch_padding = 0
else:
batch_padding = MAX_BATCH_SIZE - img_in.shape[0]
if batch_padding < 0:
raise ValueError(
f"Requested fix_batch_size but passed in {img_in.shape[0]} images "
f"when the model's batch size is {MAX_BATCH_SIZE}\n"
f"Consider turning off fix_batch_size, changing `MAX_BATCH_SIZE` in"
f"your inference server config, or passing at most {MAX_BATCH_SIZE} images at a time"
)
width_remainder = img_in.shape[2] % 32
height_remainder = img_in.shape[3] % 32
if width_remainder > 0:
width_padding = 32 - (img_in.shape[2] % 32)
else:
width_padding = 0
if height_remainder > 0:
height_padding = 32 - (img_in.shape[3] % 32)
else:
height_padding = 0
img_in = np.pad(
img_in,
((0, batch_padding), (0, 0), (0, width_padding), (0, height_padding)),
"constant",
)
return img_in, PreprocessReturnMetadata(
{
"img_dims": img_dims,
"disable_preproc_static_crop": disable_preproc_static_crop,
}
)
def predict(self, img_in: np.ndarray, **kwargs) -> Tuple[np.ndarray]:
"""Runs inference on the ONNX model.
Args:
img_in (np.ndarray): The preprocessed image(s) to run inference on.
Returns:
Tuple[np.ndarray]: The ONNX model predictions.
Raises:
NotImplementedError: This method must be implemented by a subclass.
"""
raise NotImplementedError("predict must be implemented by a subclass")
def validate_model_classes(self) -> None:
output_shape = self.get_model_output_shape()
num_classes = get_num_classes_from_model_prediction_shape(
output_shape[2], masks=0
)
try:
assert num_classes == self.num_classes
except AssertionError:
raise ValueError(
f"Number of classes in model ({num_classes}) does not match the number of classes in the environment ({self.num_classes})"
)
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