Source code for detectron2.modeling.test_time_augmentation

# Copyright (c) Facebook, Inc. and its affiliates.
import copy
import numpy as np
from contextlib import contextmanager
from itertools import count
from typing import List
import torch
from fvcore.transforms import HFlipTransform, NoOpTransform
from torch import nn
from torch.nn.parallel import DistributedDataParallel

from detectron2.config import configurable
from import read_image
from import (
from detectron2.structures import Boxes, Instances

from .meta_arch import GeneralizedRCNN
from .postprocessing import detector_postprocess
from .roi_heads.fast_rcnn import fast_rcnn_inference_single_image

__all__ = ["DatasetMapperTTA", "GeneralizedRCNNWithTTA"]

class DatasetMapperTTA:
    Implement test-time augmentation for detection data.
    It is a callable which takes a dataset dict from a detection dataset,
    and returns a list of dataset dicts where the images
    are augmented from the input image by the transformations defined in the config.
    This is used for test-time augmentation.

    def __init__(self, min_sizes: List[int], max_size: int, flip: bool):
            min_sizes: list of short-edge size to resize the image to
            max_size: maximum height or width of resized images
            flip: whether to apply flipping augmentation
        self.min_sizes = min_sizes
        self.max_size = max_size
        self.flip = flip

[docs] @classmethod def from_config(cls, cfg): return { "min_sizes": cfg.TEST.AUG.MIN_SIZES, "max_size": cfg.TEST.AUG.MAX_SIZE, "flip": cfg.TEST.AUG.FLIP, }
def __call__(self, dataset_dict): """ Args: dict: a dict in standard model input format. See tutorials for details. Returns: list[dict]: a list of dicts, which contain augmented version of the input image. The total number of dicts is ``len(min_sizes) * (2 if flip else 1)``. Each dict has field "transforms" which is a TransformList, containing the transforms that are used to generate this image. """ numpy_image = dataset_dict["image"].permute(1, 2, 0).numpy() shape = numpy_image.shape orig_shape = (dataset_dict["height"], dataset_dict["width"]) if shape[:2] != orig_shape: # It transforms the "original" image in the dataset to the input image pre_tfm = ResizeTransform(orig_shape[0], orig_shape[1], shape[0], shape[1]) else: pre_tfm = NoOpTransform() # Create all combinations of augmentations to use aug_candidates = [] # each element is a list[Augmentation] for min_size in self.min_sizes: resize = ResizeShortestEdge(min_size, self.max_size) aug_candidates.append([resize]) # resize only if self.flip: flip = RandomFlip(prob=1.0) aug_candidates.append([resize, flip]) # resize + flip # Apply all the augmentations ret = [] for aug in aug_candidates: new_image, tfms = apply_augmentations(aug, np.copy(numpy_image)) torch_image = torch.from_numpy(np.ascontiguousarray(new_image.transpose(2, 0, 1))) dic = copy.deepcopy(dataset_dict) dic["transforms"] = pre_tfm + tfms dic["image"] = torch_image ret.append(dic) return ret class GeneralizedRCNNWithTTA(nn.Module): """ A GeneralizedRCNN with test-time augmentation enabled. Its :meth:`__call__` method has the same interface as :meth:`GeneralizedRCNN.forward`. """ def __init__(self, cfg, model, tta_mapper=None, batch_size=3): """ Args: cfg (CfgNode): model (GeneralizedRCNN): a GeneralizedRCNN to apply TTA on. tta_mapper (callable): takes a dataset dict and returns a list of augmented versions of the dataset dict. Defaults to `DatasetMapperTTA(cfg)`. batch_size (int): batch the augmented images into this batch size for inference. """ super().__init__() if isinstance(model, DistributedDataParallel): model = model.module assert isinstance( model, GeneralizedRCNN ), "TTA is only supported on GeneralizedRCNN. Got a model of type {}".format(type(model)) self.cfg = cfg.clone() assert not self.cfg.MODEL.KEYPOINT_ON, "TTA for keypoint is not supported yet" assert ( not self.cfg.MODEL.LOAD_PROPOSALS ), "TTA for pre-computed proposals is not supported yet" self.model = model if tta_mapper is None: tta_mapper = DatasetMapperTTA(cfg) self.tta_mapper = tta_mapper self.batch_size = batch_size @contextmanager def _turn_off_roi_heads(self, attrs): """ Open a context where some heads in `model.roi_heads` are temporarily turned off. Args: attr (list[str]): the attribute in `model.roi_heads` which can be used to turn off a specific head, e.g., "mask_on", "keypoint_on". """ roi_heads = self.model.roi_heads old = {} for attr in attrs: try: old[attr] = getattr(roi_heads, attr) except AttributeError: # The head may not be implemented in certain ROIHeads pass if len(old.keys()) == 0: yield else: for attr in old.keys(): setattr(roi_heads, attr, False) yield for attr in old.keys(): setattr(roi_heads, attr, old[attr]) def _batch_inference(self, batched_inputs, detected_instances=None): """ Execute inference on a list of inputs, using batch size = self.batch_size, instead of the length of the list. Inputs & outputs have the same format as :meth:`GeneralizedRCNN.inference` """ if detected_instances is None: detected_instances = [None] * len(batched_inputs) outputs = [] inputs, instances = [], [] for idx, input, instance in zip(count(), batched_inputs, detected_instances): inputs.append(input) instances.append(instance) if len(inputs) == self.batch_size or idx == len(batched_inputs) - 1: outputs.extend( self.model.inference( inputs, instances if instances[0] is not None else None, do_postprocess=False, ) ) inputs, instances = [], [] return outputs def __call__(self, batched_inputs): """ Same input/output format as :meth:`GeneralizedRCNN.forward` """ def _maybe_read_image(dataset_dict): ret = copy.copy(dataset_dict) if "image" not in ret: image = read_image(ret.pop("file_name"), self.model.input_format) image = torch.from_numpy(np.ascontiguousarray(image.transpose(2, 0, 1))) # CHW ret["image"] = image if "height" not in ret and "width" not in ret: ret["height"] = image.shape[1] ret["width"] = image.shape[2] return ret return [self._inference_one_image(_maybe_read_image(x)) for x in batched_inputs] def _inference_one_image(self, input): """ Args: input (dict): one dataset dict with "image" field being a CHW tensor Returns: dict: one output dict """ orig_shape = (input["height"], input["width"]) augmented_inputs, tfms = self._get_augmented_inputs(input) # Detect boxes from all augmented versions with self._turn_off_roi_heads(["mask_on", "keypoint_on"]): # temporarily disable roi heads all_boxes, all_scores, all_classes = self._get_augmented_boxes(augmented_inputs, tfms) # merge all detected boxes to obtain final predictions for boxes merged_instances = self._merge_detections(all_boxes, all_scores, all_classes, orig_shape) if self.cfg.MODEL.MASK_ON: # Use the detected boxes to obtain masks augmented_instances = self._rescale_detected_boxes( augmented_inputs, merged_instances, tfms ) # run forward on the detected boxes outputs = self._batch_inference(augmented_inputs, augmented_instances) # Delete now useless variables to avoid being out of memory del augmented_inputs, augmented_instances # average the predictions merged_instances.pred_masks = self._reduce_pred_masks(outputs, tfms) merged_instances = detector_postprocess(merged_instances, *orig_shape) return {"instances": merged_instances} else: return {"instances": merged_instances} def _get_augmented_inputs(self, input): augmented_inputs = self.tta_mapper(input) tfms = [x.pop("transforms") for x in augmented_inputs] return augmented_inputs, tfms def _get_augmented_boxes(self, augmented_inputs, tfms): # 1: forward with all augmented images outputs = self._batch_inference(augmented_inputs) # 2: union the results all_boxes = [] all_scores = [] all_classes = [] for output, tfm in zip(outputs, tfms): # Need to inverse the transforms on boxes, to obtain results on original image pred_boxes = output.pred_boxes.tensor original_pred_boxes = tfm.inverse().apply_box(pred_boxes.cpu().numpy()) all_boxes.append(torch.from_numpy(original_pred_boxes).to(pred_boxes.device)) all_scores.extend(output.scores) all_classes.extend(output.pred_classes) all_boxes =, dim=0) return all_boxes, all_scores, all_classes def _merge_detections(self, all_boxes, all_scores, all_classes, shape_hw): # select from the union of all results num_boxes = len(all_boxes) num_classes = self.cfg.MODEL.ROI_HEADS.NUM_CLASSES # +1 because fast_rcnn_inference expects background scores as well all_scores_2d = torch.zeros(num_boxes, num_classes + 1, device=all_boxes.device) for idx, cls, score in zip(count(), all_classes, all_scores): all_scores_2d[idx, cls] = score merged_instances, _ = fast_rcnn_inference_single_image( all_boxes, all_scores_2d, shape_hw, 1e-8, self.cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST, self.cfg.TEST.DETECTIONS_PER_IMAGE, ) return merged_instances def _rescale_detected_boxes(self, augmented_inputs, merged_instances, tfms): augmented_instances = [] for input, tfm in zip(augmented_inputs, tfms): # Transform the target box to the augmented image's coordinate space pred_boxes = merged_instances.pred_boxes.tensor.cpu().numpy() pred_boxes = torch.from_numpy(tfm.apply_box(pred_boxes)) aug_instances = Instances( image_size=input["image"].shape[1:3], pred_boxes=Boxes(pred_boxes), pred_classes=merged_instances.pred_classes, scores=merged_instances.scores, ) augmented_instances.append(aug_instances) return augmented_instances def _reduce_pred_masks(self, outputs, tfms): # Should apply inverse transforms on masks. # We assume only resize & flip are used. pred_masks is a scale-invariant # representation, so we handle flip specially for output, tfm in zip(outputs, tfms): if any(isinstance(t, HFlipTransform) for t in tfm.transforms): output.pred_masks = output.pred_masks.flip(dims=[3]) all_pred_masks = torch.stack([o.pred_masks for o in outputs], dim=0) avg_pred_masks = torch.mean(all_pred_masks, dim=0) return avg_pred_masks