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anchors.py
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anchors.py
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""" RetinaNet / EfficientDet Anchor Gen
Adapted for PyTorch from Tensorflow impl at
https://github.com/google/automl/blob/6f6694cec1a48cdb33d5d1551a2d5db8ad227798/efficientdet/anchors.py
Hacked together by Ross Wightman, original copyright below
"""
# Copyright 2020 Google Research. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Anchor definition.
This module is borrowed from TPU RetinaNet implementation:
https://github.com/tensorflow/tpu/blob/master/models/official/retinanet/anchors.py
"""
import collections
import numpy as np
import torch
import torch.nn as nn
from torchvision.ops.boxes import batched_nms, remove_small_boxes
from effdet.object_detection import ArgMaxMatcher, FasterRcnnBoxCoder, BoxList, IouSimilarity, TargetAssigner
# The minimum score to consider a logit for identifying detections.
MIN_CLASS_SCORE = -5.0
# The score for a dummy detection
_DUMMY_DETECTION_SCORE = -1e5
# The maximum number of (anchor,class) pairs to keep for non-max suppression.
MAX_DETECTION_POINTS = 5000
# The maximum number of detections per image.
MAX_DETECTIONS_PER_IMAGE = 100
def decode_box_outputs(rel_codes, anchors, output_xyxy: bool=False):
"""Transforms relative regression coordinates to absolute positions.
Network predictions are normalized and relative to a given anchor; this
reverses the transformation and outputs absolute coordinates for the input image.
Args:
rel_codes: box regression targets.
anchors: anchors on all feature levels.
Returns:
outputs: bounding boxes.
"""
ycenter_a = (anchors[:, 0] + anchors[:, 2]) / 2
xcenter_a = (anchors[:, 1] + anchors[:, 3]) / 2
ha = anchors[:, 2] - anchors[:, 0]
wa = anchors[:, 3] - anchors[:, 1]
ty, tx, th, tw = rel_codes.unbind(dim=1)
w = torch.exp(tw) * wa
h = torch.exp(th) * ha
ycenter = ty * ha + ycenter_a
xcenter = tx * wa + xcenter_a
ymin = ycenter - h / 2.
xmin = xcenter - w / 2.
ymax = ycenter + h / 2.
xmax = xcenter + w / 2.
if output_xyxy:
out = torch.stack([xmin, ymin, xmax, ymax], dim=1)
else:
out = torch.stack([ymin, xmin, ymax, xmax], dim=1)
return out
def _generate_anchor_configs(min_level, max_level, num_scales, aspect_ratios):
"""Generates mapping from output level to a list of anchor configurations.
A configuration is a tuple of (num_anchors, scale, aspect_ratio).
Args:
min_level: integer number of minimum level of the output feature pyramid.
max_level: integer number of maximum level of the output feature pyramid.
num_scales: integer number representing intermediate scales added on each level.
For instances, num_scales=2 adds two additional anchor scales [2^0, 2^0.5] on each level.
aspect_ratios: list of tuples representing the aspect ratio anchors added on each level.
For instances, aspect_ratios = [(1, 1), (1.4, 0.7), (0.7, 1.4)] adds three anchors on each level.
Returns:
anchor_configs: a dictionary with keys as the levels of anchors and
values as a list of anchor configuration.
"""
anchor_configs = {}
for level in range(min_level, max_level + 1):
anchor_configs[level] = []
for scale_octave in range(num_scales):
for aspect in aspect_ratios:
anchor_configs[level].append((2 ** level, scale_octave / float(num_scales), aspect))
return anchor_configs
def _generate_anchor_boxes(image_size, anchor_scale, anchor_configs):
"""Generates multiscale anchor boxes.
Args:
image_size: integer number of input image size. The input image has the same dimension for
width and height. The image_size should be divided by the largest feature stride 2^max_level.
anchor_scale: float number representing the scale of size of the base
anchor to the feature stride 2^level.
anchor_configs: a dictionary with keys as the levels of anchors and
values as a list of anchor configuration.
Returns:
anchor_boxes: a numpy array with shape [N, 4], which stacks anchors on all feature levels.
Raises:
ValueError: input size must be the multiple of largest feature stride.
"""
boxes_all = []
for _, configs in anchor_configs.items():
boxes_level = []
for config in configs:
stride, octave_scale, aspect = config
if image_size % stride != 0:
raise ValueError("input size must be divided by the stride.")
base_anchor_size = anchor_scale * stride * 2 ** octave_scale
anchor_size_x_2 = base_anchor_size * aspect[0] / 2.0
anchor_size_y_2 = base_anchor_size * aspect[1] / 2.0
x = np.arange(stride / 2, image_size, stride)
y = np.arange(stride / 2, image_size, stride)
xv, yv = np.meshgrid(x, y)
xv = xv.reshape(-1)
yv = yv.reshape(-1)
boxes = np.vstack((yv - anchor_size_y_2, xv - anchor_size_x_2,
yv + anchor_size_y_2, xv + anchor_size_x_2))
boxes = np.swapaxes(boxes, 0, 1)
boxes_level.append(np.expand_dims(boxes, axis=1))
# concat anchors on the same level to the reshape NxAx4
boxes_level = np.concatenate(boxes_level, axis=1)
boxes_all.append(boxes_level.reshape([-1, 4]))
anchor_boxes = np.vstack(boxes_all)
return anchor_boxes
def clip_boxes_xyxy(boxes: torch.Tensor, size: torch.Tensor):
boxes = boxes.clamp(min=0)
size = torch.cat([size, size], dim=0)
boxes = boxes.min(size)
return boxes
def generate_detections(
cls_outputs, box_outputs, anchor_boxes, indices, classes, img_scale, img_size,
max_det_per_image: int = MAX_DETECTIONS_PER_IMAGE):
"""Generates detections with RetinaNet model outputs and anchors.
Args:
cls_outputs: a torch tensor with shape [N, 1], which has the highest class
scores on all feature levels. The N is the number of selected
top-K total anchors on all levels. (k being MAX_DETECTION_POINTS)
box_outputs: a torch tensor with shape [N, 4], which stacks box regression
outputs on all feature levels. The N is the number of selected top-k
total anchors on all levels. (k being MAX_DETECTION_POINTS)
anchor_boxes: a torch tensor with shape [N, 4], which stacks anchors on all
feature levels. The N is the number of selected top-k total anchors on all levels.
indices: a torch tensor with shape [N], which is the indices from top-k selection.
classes: a torch tensor with shape [N], which represents the class
prediction on all selected anchors from top-k selection.
img_scale: a float tensor representing the scale between original image
and input image for the detector. It is used to rescale detections for
evaluating with the original groundtruth annotations.
max_det_per_image: an int constant, added as argument to make torchscript happy
Returns:
detections: detection results in a tensor with shape [MAX_DETECTION_POINTS, 6],
each row representing [x, y, width, height, score, class]
"""
anchor_boxes = anchor_boxes[indices, :]
# apply bounding box regression to anchors
boxes = decode_box_outputs(box_outputs.float(), anchor_boxes, output_xyxy=True)
boxes = clip_boxes_xyxy(boxes, img_size / img_scale) # clip before NMS better?
scores = cls_outputs.sigmoid().squeeze(1).float()
top_detection_idx = batched_nms(boxes, scores, classes, iou_threshold=0.5)
# keep only topk scoring predictions
top_detection_idx = top_detection_idx[:max_det_per_image]
boxes = boxes[top_detection_idx]
scores = scores[top_detection_idx, None]
classes = classes[top_detection_idx, None]
# xyxy to xywh & rescale to original image
boxes[:, 2] -= boxes[:, 0]
boxes[:, 3] -= boxes[:, 1]
boxes *= img_scale
classes += 1 # back to class idx with background class = 0
# stack em and pad out to MAX_DETECTIONS_PER_IMAGE if necessary
detections = torch.cat([boxes, scores, classes.float()], dim=1)
if len(top_detection_idx) < max_det_per_image:
detections = torch.cat([
detections,
torch.zeros(
(max_det_per_image - len(top_detection_idx), 6), device=detections.device, dtype=detections.dtype)
], dim=0)
return detections
class Anchors(nn.Module):
"""RetinaNet Anchors class."""
def __init__(self, min_level, max_level, num_scales, aspect_ratios, anchor_scale, image_size):
"""Constructs multiscale RetinaNet anchors.
Args:
min_level: integer number of minimum level of the output feature pyramid.
max_level: integer number of maximum level of the output feature pyramid.
num_scales: integer number representing intermediate scales added
on each level. For instances, num_scales=2 adds two additional
anchor scales [2^0, 2^0.5] on each level.
aspect_ratios: list of tuples representing the aspect ratio anchors added
on each level. For instances, aspect_ratios =
[(1, 1), (1.4, 0.7), (0.7, 1.4)] adds three anchors on each level.
anchor_scale: float number representing the scale of size of the base
anchor to the feature stride 2^level.
image_size: integer number of input image size. The input image has the
same dimension for width and height. The image_size should be divided by
the largest feature stride 2^max_level.
"""
super(Anchors, self).__init__()
self.min_level = min_level
self.max_level = max_level
self.num_scales = num_scales
self.aspect_ratios = aspect_ratios
self.anchor_scale = anchor_scale
self.image_size = image_size
self.config = self._generate_configs()
self.register_buffer('boxes', self._generate_boxes())
def _generate_configs(self):
"""Generate configurations of anchor boxes."""
return _generate_anchor_configs(self.min_level, self.max_level, self.num_scales, self.aspect_ratios)
def _generate_boxes(self):
"""Generates multiscale anchor boxes."""
boxes = _generate_anchor_boxes(self.image_size, self.anchor_scale, self.config)
boxes = torch.from_numpy(boxes).float()
return boxes
def get_anchors_per_location(self):
return self.num_scales * len(self.aspect_ratios)
#@torch.jit.script
class AnchorLabeler(object):
"""Labeler for multiscale anchor boxes.
"""
def __init__(self, anchors, num_classes: int, match_threshold: float = 0.5):
"""Constructs anchor labeler to assign labels to anchors.
Args:
anchors: an instance of class Anchors.
num_classes: integer number representing number of classes in the dataset.
match_threshold: float number between 0 and 1 representing the threshold
to assign positive labels for anchors.
"""
similarity_calc = IouSimilarity()
matcher = ArgMaxMatcher(
match_threshold,
unmatched_threshold=match_threshold,
negatives_lower_than_unmatched=True,
force_match_for_each_row=True)
box_coder = FasterRcnnBoxCoder()
self.target_assigner = TargetAssigner(similarity_calc, matcher, box_coder)
self.anchors = anchors
self.match_threshold = match_threshold
self.num_classes = num_classes
self.feat_size = {}
for level in range(self.anchors.min_level, self.anchors.max_level + 1):
self.feat_size[level] = int(self.anchors.image_size / 2 ** level)
self.indices_cache = {}
def label_anchors(self, gt_boxes, gt_labels):
"""Labels anchors with ground truth inputs.
Args:
gt_boxes: A float tensor with shape [N, 4] representing groundtruth boxes.
For each row, it stores [y0, x0, y1, x1] for four corners of a box.
gt_labels: A integer tensor with shape [N, 1] representing groundtruth classes.
Returns:
cls_targets_dict: ordered dictionary with keys [min_level, min_level+1, ..., max_level].
The values are tensor with shape [height_l, width_l, num_anchors]. The height_l and width_l
represent the dimension of class logits at l-th level.
box_targets_dict: ordered dictionary with keys [min_level, min_level+1, ..., max_level].
The values are tensor with shape [height_l, width_l, num_anchors * 4]. The height_l and
width_l represent the dimension of bounding box regression output at l-th level.
num_positives: scalar tensor storing number of positives in an image.
"""
cls_targets_out = []
box_targets_out = []
gt_box_list = BoxList(gt_boxes)
anchor_box_list = BoxList(self.anchors.boxes)
# cls_weights, box_weights are not used
cls_targets, _, box_targets, _, matches = self.target_assigner.assign(anchor_box_list, gt_box_list, gt_labels)
# class labels start from 1 and the background class = -1
cls_targets -= 1
cls_targets = cls_targets.long()
# Unpack labels.
"""Unpacks an array of cls/box into multiple scales."""
count = 0
for level in range(self.anchors.min_level, self.anchors.max_level + 1):
feat_size = self.feat_size[level]
steps = feat_size ** 2 * self.anchors.get_anchors_per_location()
indices = torch.arange(count, count + steps, device=cls_targets.device)
count += steps
cls_targets_out.append(
torch.index_select(cls_targets, 0, indices).view([feat_size, feat_size, -1]))
box_targets_out.append(
torch.index_select(box_targets, 0, indices).view([feat_size, feat_size, -1]))
num_positives = (matches.match_results != -1).float().sum()
return cls_targets_out, box_targets_out, num_positives
def _build_indices(self, device):
anchors_per_loc = self.anchors.get_anchors_per_location()
indices_dict = {}
count = 0
for level in range(self.anchors.min_level, self.anchors.max_level + 1):
feat_size = self.feat_size[level]
steps = feat_size ** 2 * anchors_per_loc
indices = torch.arange(count, count + steps, device=device)
indices_dict[level] = indices
count += steps
return indices_dict
def _get_indices(self, device, level):
if device not in self.indices_cache:
self.indices_cache[device] = self._build_indices(device)
return self.indices_cache[device][level]
def batch_label_anchors(self, batch_size: int, gt_boxes, gt_classes):
num_levels = self.anchors.max_level - self.anchors.min_level + 1
cls_targets_out = [[] for _ in range(num_levels)]
box_targets_out = [[] for _ in range(num_levels)]
num_positives_out = []
# FIXME this may be a bottleneck, would be faster if batched, or should be done in loader/dataset?
anchor_box_list = BoxList(self.anchors.boxes)
for i in range(batch_size):
last_sample = i == batch_size - 1
# cls_weights, box_weights are not used
cls_targets, _, box_targets, _, matches = self.target_assigner.assign(
anchor_box_list, BoxList(gt_boxes[i]), gt_classes[i])
# class labels start from 1 and the background class = -1
cls_targets -= 1
cls_targets = cls_targets.long()
# Unpack labels.
"""Unpacks an array of cls/box into multiple scales."""
for level in range(self.anchors.min_level, self.anchors.max_level + 1):
level_index = level - self.anchors.min_level
feat_size = self.feat_size[level]
indices = self._get_indices(cls_targets.device, level)
cls_targets_out[level_index].append(
torch.index_select(cls_targets, 0, indices).view([feat_size, feat_size, -1]))
box_targets_out[level_index].append(
torch.index_select(box_targets, 0, indices).view([feat_size, feat_size, -1]))
if last_sample:
cls_targets_out[level_index] = torch.stack(cls_targets_out[level_index])
box_targets_out[level_index] = torch.stack(box_targets_out[level_index])
num_positives_out.append((matches.match_results != -1).float().sum())
if last_sample:
num_positives_out = torch.stack(num_positives_out)
return cls_targets_out, box_targets_out, num_positives_out