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Add codeformer and update license

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This commit is contained in:
Felipe Daragon
2025-04-09 23:03:26 +01:00
parent f40ffde905
commit 3549b7e50c
84 changed files with 12545 additions and 19 deletions
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370
facelib/detection/retinaface/retinaface.py Normal file
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import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from PIL import Image
from torchvision.models._utils import IntermediateLayerGetter as IntermediateLayerGetter
from facelib.detection.align_trans import get_reference_facial_points, warp_and_crop_face
from facelib.detection.retinaface.retinaface_net import FPN, SSH, MobileNetV1, make_bbox_head, make_class_head, make_landmark_head
from facelib.detection.retinaface.retinaface_utils import (PriorBox, batched_decode, batched_decode_landm, decode, decode_landm,
py_cpu_nms)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def generate_config(network_name):
cfg_mnet = {
'name': 'mobilenet0.25',
'min_sizes': [[16, 32], [64, 128], [256, 512]],
'steps': [8, 16, 32],
'variance': [0.1, 0.2],
'clip': False,
'loc_weight': 2.0,
'gpu_train': True,
'batch_size': 32,
'ngpu': 1,
'epoch': 250,
'decay1': 190,
'decay2': 220,
'image_size': 640,
'return_layers': {
'stage1': 1,
'stage2': 2,
'stage3': 3
},
'in_channel': 32,
'out_channel': 64
}
cfg_re50 = {
'name': 'Resnet50',
'min_sizes': [[16, 32], [64, 128], [256, 512]],
'steps': [8, 16, 32],
'variance': [0.1, 0.2],
'clip': False,
'loc_weight': 2.0,
'gpu_train': True,
'batch_size': 24,
'ngpu': 4,
'epoch': 100,
'decay1': 70,
'decay2': 90,
'image_size': 840,
'return_layers': {
'layer2': 1,
'layer3': 2,
'layer4': 3
},
'in_channel': 256,
'out_channel': 256
}
if network_name == 'mobile0.25':
return cfg_mnet
elif network_name == 'resnet50':
return cfg_re50
else:
raise NotImplementedError(f'network_name={network_name}')
class RetinaFace(nn.Module):
def __init__(self, network_name='resnet50', half=False, phase='test'):
super(RetinaFace, self).__init__()
self.half_inference = half
cfg = generate_config(network_name)
self.backbone = cfg['name']
self.model_name = f'retinaface_{network_name}'
self.cfg = cfg
self.phase = phase
self.target_size, self.max_size = 1600, 2150
self.resize, self.scale, self.scale1 = 1., None, None
self.mean_tensor = torch.tensor([[[[104.]], [[117.]], [[123.]]]]).to(device)
self.reference = get_reference_facial_points(default_square=True)
# Build network.
backbone = None
if cfg['name'] == 'mobilenet0.25':
backbone = MobileNetV1()
self.body = IntermediateLayerGetter(backbone, cfg['return_layers'])
elif cfg['name'] == 'Resnet50':
import torchvision.models as models
backbone = models.resnet50(pretrained=False)
self.body = IntermediateLayerGetter(backbone, cfg['return_layers'])
in_channels_stage2 = cfg['in_channel']
in_channels_list = [
in_channels_stage2 * 2,
in_channels_stage2 * 4,
in_channels_stage2 * 8,
]
out_channels = cfg['out_channel']
self.fpn = FPN(in_channels_list, out_channels)
self.ssh1 = SSH(out_channels, out_channels)
self.ssh2 = SSH(out_channels, out_channels)
self.ssh3 = SSH(out_channels, out_channels)
self.ClassHead = make_class_head(fpn_num=3, inchannels=cfg['out_channel'])
self.BboxHead = make_bbox_head(fpn_num=3, inchannels=cfg['out_channel'])
self.LandmarkHead = make_landmark_head(fpn_num=3, inchannels=cfg['out_channel'])
self.to(device)
self.eval()
if self.half_inference:
self.half()
def forward(self, inputs):
out = self.body(inputs)
if self.backbone == 'mobilenet0.25' or self.backbone == 'Resnet50':
out = list(out.values())
# FPN
fpn = self.fpn(out)
# SSH
feature1 = self.ssh1(fpn[0])
feature2 = self.ssh2(fpn[1])
feature3 = self.ssh3(fpn[2])
features = [feature1, feature2, feature3]
bbox_regressions = torch.cat([self.BboxHead[i](feature) for i, feature in enumerate(features)], dim=1)
classifications = torch.cat([self.ClassHead[i](feature) for i, feature in enumerate(features)], dim=1)
tmp = [self.LandmarkHead[i](feature) for i, feature in enumerate(features)]
ldm_regressions = (torch.cat(tmp, dim=1))
if self.phase == 'train':
output = (bbox_regressions, classifications, ldm_regressions)
else:
output = (bbox_regressions, F.softmax(classifications, dim=-1), ldm_regressions)
return output
def __detect_faces(self, inputs):
# get scale
height, width = inputs.shape[2:]
self.scale = torch.tensor([width, height, width, height], dtype=torch.float32).to(device)
tmp = [width, height, width, height, width, height, width, height, width, height]
self.scale1 = torch.tensor(tmp, dtype=torch.float32).to(device)
# forawrd
inputs = inputs.to(device)
if self.half_inference:
inputs = inputs.half()
loc, conf, landmarks = self(inputs)
# get priorbox
priorbox = PriorBox(self.cfg, image_size=inputs.shape[2:])
priors = priorbox.forward().to(device)
return loc, conf, landmarks, priors
# single image detection
def transform(self, image, use_origin_size):
# convert to opencv format
if isinstance(image, Image.Image):
image = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)
image = image.astype(np.float32)
# testing scale
im_size_min = np.min(image.shape[0:2])
im_size_max = np.max(image.shape[0:2])
resize = float(self.target_size) / float(im_size_min)
# prevent bigger axis from being more than max_size
if np.round(resize * im_size_max) > self.max_size:
resize = float(self.max_size) / float(im_size_max)
resize = 1 if use_origin_size else resize
# resize
if resize != 1:
image = cv2.resize(image, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR)
# convert to torch.tensor format
# image -= (104, 117, 123)
image = image.transpose(2, 0, 1)
image = torch.from_numpy(image).unsqueeze(0)
return image, resize
def detect_faces(
self,
image,
conf_threshold=0.8,
nms_threshold=0.4,
use_origin_size=True,
):
"""
Params:
imgs: BGR image
"""
image, self.resize = self.transform(image, use_origin_size)
image = image.to(device)
if self.half_inference:
image = image.half()
image = image - self.mean_tensor
loc, conf, landmarks, priors = self.__detect_faces(image)
boxes = decode(loc.data.squeeze(0), priors.data, self.cfg['variance'])
boxes = boxes * self.scale / self.resize
boxes = boxes.cpu().numpy()
scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
landmarks = decode_landm(landmarks.squeeze(0), priors, self.cfg['variance'])
landmarks = landmarks * self.scale1 / self.resize
landmarks = landmarks.cpu().numpy()
# ignore low scores
inds = np.where(scores > conf_threshold)[0]
boxes, landmarks, scores = boxes[inds], landmarks[inds], scores[inds]
# sort
order = scores.argsort()[::-1]
boxes, landmarks, scores = boxes[order], landmarks[order], scores[order]
# do NMS
bounding_boxes = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
keep = py_cpu_nms(bounding_boxes, nms_threshold)
bounding_boxes, landmarks = bounding_boxes[keep, :], landmarks[keep]
# self.t['forward_pass'].toc()
# print(self.t['forward_pass'].average_time)
# import sys
# sys.stdout.flush()
return np.concatenate((bounding_boxes, landmarks), axis=1)
def __align_multi(self, image, boxes, landmarks, limit=None):
if len(boxes) < 1:
return [], []
if limit:
boxes = boxes[:limit]
landmarks = landmarks[:limit]
faces = []
for landmark in landmarks:
facial5points = [[landmark[2 * j], landmark[2 * j + 1]] for j in range(5)]
warped_face = warp_and_crop_face(np.array(image), facial5points, self.reference, crop_size=(112, 112))
faces.append(warped_face)
return np.concatenate((boxes, landmarks), axis=1), faces
def align_multi(self, img, conf_threshold=0.8, limit=None):
rlt = self.detect_faces(img, conf_threshold=conf_threshold)
boxes, landmarks = rlt[:, 0:5], rlt[:, 5:]
return self.__align_multi(img, boxes, landmarks, limit)
# batched detection
def batched_transform(self, frames, use_origin_size):
"""
Arguments:
frames: a list of PIL.Image, or torch.Tensor(shape=[n, h, w, c],
type=np.float32, BGR format).
use_origin_size: whether to use origin size.
"""
from_PIL = True if isinstance(frames[0], Image.Image) else False
# convert to opencv format
if from_PIL:
frames = [cv2.cvtColor(np.asarray(frame), cv2.COLOR_RGB2BGR) for frame in frames]
frames = np.asarray(frames, dtype=np.float32)
# testing scale
im_size_min = np.min(frames[0].shape[0:2])
im_size_max = np.max(frames[0].shape[0:2])
resize = float(self.target_size) / float(im_size_min)
# prevent bigger axis from being more than max_size
if np.round(resize * im_size_max) > self.max_size:
resize = float(self.max_size) / float(im_size_max)
resize = 1 if use_origin_size else resize
# resize
if resize != 1:
if not from_PIL:
frames = F.interpolate(frames, scale_factor=resize)
else:
frames = [
cv2.resize(frame, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR)
for frame in frames
]
# convert to torch.tensor format
if not from_PIL:
frames = frames.transpose(1, 2).transpose(1, 3).contiguous()
else:
frames = frames.transpose((0, 3, 1, 2))
frames = torch.from_numpy(frames)
return frames, resize
def batched_detect_faces(self, frames, conf_threshold=0.8, nms_threshold=0.4, use_origin_size=True):
"""
Arguments:
frames: a list of PIL.Image, or np.array(shape=[n, h, w, c],
type=np.uint8, BGR format).
conf_threshold: confidence threshold.
nms_threshold: nms threshold.
use_origin_size: whether to use origin size.
Returns:
final_bounding_boxes: list of np.array ([n_boxes, 5],
type=np.float32).
final_landmarks: list of np.array ([n_boxes, 10], type=np.float32).
"""
# self.t['forward_pass'].tic()
frames, self.resize = self.batched_transform(frames, use_origin_size)
frames = frames.to(device)
frames = frames - self.mean_tensor
b_loc, b_conf, b_landmarks, priors = self.__detect_faces(frames)
final_bounding_boxes, final_landmarks = [], []
# decode
priors = priors.unsqueeze(0)
b_loc = batched_decode(b_loc, priors, self.cfg['variance']) * self.scale / self.resize
b_landmarks = batched_decode_landm(b_landmarks, priors, self.cfg['variance']) * self.scale1 / self.resize
b_conf = b_conf[:, :, 1]
# index for selection
b_indice = b_conf > conf_threshold
# concat
b_loc_and_conf = torch.cat((b_loc, b_conf.unsqueeze(-1)), dim=2).float()
for pred, landm, inds in zip(b_loc_and_conf, b_landmarks, b_indice):
# ignore low scores
pred, landm = pred[inds, :], landm[inds, :]
if pred.shape[0] == 0:
final_bounding_boxes.append(np.array([], dtype=np.float32))
final_landmarks.append(np.array([], dtype=np.float32))
continue
# sort
# order = score.argsort(descending=True)
# box, landm, score = box[order], landm[order], score[order]
# to CPU
bounding_boxes, landm = pred.cpu().numpy(), landm.cpu().numpy()
# NMS
keep = py_cpu_nms(bounding_boxes, nms_threshold)
bounding_boxes, landmarks = bounding_boxes[keep, :], landm[keep]
# append
final_bounding_boxes.append(bounding_boxes)
final_landmarks.append(landmarks)
# self.t['forward_pass'].toc(average=True)
# self.batch_time += self.t['forward_pass'].diff
# self.total_frame += len(frames)
# print(self.batch_time / self.total_frame)
return final_bounding_boxes, final_landmarks

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facelib/detection/retinaface/retinaface_net.py Normal file
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import torch
import torch.nn as nn
import torch.nn.functional as F
def conv_bn(inp, oup, stride=1, leaky=0):
return nn.Sequential(
nn.Conv2d(inp, oup, 3, stride, 1, bias=False), nn.BatchNorm2d(oup),
nn.LeakyReLU(negative_slope=leaky, inplace=True))
def conv_bn_no_relu(inp, oup, stride):
return nn.Sequential(
nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
nn.BatchNorm2d(oup),
)
def conv_bn1X1(inp, oup, stride, leaky=0):
return nn.Sequential(
nn.Conv2d(inp, oup, 1, stride, padding=0, bias=False), nn.BatchNorm2d(oup),
nn.LeakyReLU(negative_slope=leaky, inplace=True))
def conv_dw(inp, oup, stride, leaky=0.1):
return nn.Sequential(
nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
nn.BatchNorm2d(inp),
nn.LeakyReLU(negative_slope=leaky, inplace=True),
nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
nn.BatchNorm2d(oup),
nn.LeakyReLU(negative_slope=leaky, inplace=True),
)
class SSH(nn.Module):
def __init__(self, in_channel, out_channel):
super(SSH, self).__init__()
assert out_channel % 4 == 0
leaky = 0
if (out_channel <= 64):
leaky = 0.1
self.conv3X3 = conv_bn_no_relu(in_channel, out_channel // 2, stride=1)
self.conv5X5_1 = conv_bn(in_channel, out_channel // 4, stride=1, leaky=leaky)
self.conv5X5_2 = conv_bn_no_relu(out_channel // 4, out_channel // 4, stride=1)
self.conv7X7_2 = conv_bn(out_channel // 4, out_channel // 4, stride=1, leaky=leaky)
self.conv7x7_3 = conv_bn_no_relu(out_channel // 4, out_channel // 4, stride=1)
def forward(self, input):
conv3X3 = self.conv3X3(input)
conv5X5_1 = self.conv5X5_1(input)
conv5X5 = self.conv5X5_2(conv5X5_1)
conv7X7_2 = self.conv7X7_2(conv5X5_1)
conv7X7 = self.conv7x7_3(conv7X7_2)
out = torch.cat([conv3X3, conv5X5, conv7X7], dim=1)
out = F.relu(out)
return out
class FPN(nn.Module):
def __init__(self, in_channels_list, out_channels):
super(FPN, self).__init__()
leaky = 0
if (out_channels <= 64):
leaky = 0.1
self.output1 = conv_bn1X1(in_channels_list[0], out_channels, stride=1, leaky=leaky)
self.output2 = conv_bn1X1(in_channels_list[1], out_channels, stride=1, leaky=leaky)
self.output3 = conv_bn1X1(in_channels_list[2], out_channels, stride=1, leaky=leaky)
self.merge1 = conv_bn(out_channels, out_channels, leaky=leaky)
self.merge2 = conv_bn(out_channels, out_channels, leaky=leaky)
def forward(self, input):
# names = list(input.keys())
# input = list(input.values())
output1 = self.output1(input[0])
output2 = self.output2(input[1])
output3 = self.output3(input[2])
up3 = F.interpolate(output3, size=[output2.size(2), output2.size(3)], mode='nearest')
output2 = output2 + up3
output2 = self.merge2(output2)
up2 = F.interpolate(output2, size=[output1.size(2), output1.size(3)], mode='nearest')
output1 = output1 + up2
output1 = self.merge1(output1)
out = [output1, output2, output3]
return out
class MobileNetV1(nn.Module):
def __init__(self):
super(MobileNetV1, self).__init__()
self.stage1 = nn.Sequential(
conv_bn(3, 8, 2, leaky=0.1), # 3
conv_dw(8, 16, 1), # 7
conv_dw(16, 32, 2), # 11
conv_dw(32, 32, 1), # 19
conv_dw(32, 64, 2), # 27
conv_dw(64, 64, 1), # 43
)
self.stage2 = nn.Sequential(
conv_dw(64, 128, 2), # 43 + 16 = 59
conv_dw(128, 128, 1), # 59 + 32 = 91
conv_dw(128, 128, 1), # 91 + 32 = 123
conv_dw(128, 128, 1), # 123 + 32 = 155
conv_dw(128, 128, 1), # 155 + 32 = 187
conv_dw(128, 128, 1), # 187 + 32 = 219
)
self.stage3 = nn.Sequential(
conv_dw(128, 256, 2), # 219 +3 2 = 241
conv_dw(256, 256, 1), # 241 + 64 = 301
)
self.avg = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(256, 1000)
def forward(self, x):
x = self.stage1(x)
x = self.stage2(x)
x = self.stage3(x)
x = self.avg(x)
# x = self.model(x)
x = x.view(-1, 256)
x = self.fc(x)
return x
class ClassHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=3):
super(ClassHead, self).__init__()
self.num_anchors = num_anchors
self.conv1x1 = nn.Conv2d(inchannels, self.num_anchors * 2, kernel_size=(1, 1), stride=1, padding=0)
def forward(self, x):
out = self.conv1x1(x)
out = out.permute(0, 2, 3, 1).contiguous()
return out.view(out.shape[0], -1, 2)
class BboxHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=3):
super(BboxHead, self).__init__()
self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 4, kernel_size=(1, 1), stride=1, padding=0)
def forward(self, x):
out = self.conv1x1(x)
out = out.permute(0, 2, 3, 1).contiguous()
return out.view(out.shape[0], -1, 4)
class LandmarkHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=3):
super(LandmarkHead, self).__init__()
self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 10, kernel_size=(1, 1), stride=1, padding=0)
def forward(self, x):
out = self.conv1x1(x)
out = out.permute(0, 2, 3, 1).contiguous()
return out.view(out.shape[0], -1, 10)
def make_class_head(fpn_num=3, inchannels=64, anchor_num=2):
classhead = nn.ModuleList()
for i in range(fpn_num):
classhead.append(ClassHead(inchannels, anchor_num))
return classhead
def make_bbox_head(fpn_num=3, inchannels=64, anchor_num=2):
bboxhead = nn.ModuleList()
for i in range(fpn_num):
bboxhead.append(BboxHead(inchannels, anchor_num))
return bboxhead
def make_landmark_head(fpn_num=3, inchannels=64, anchor_num=2):
landmarkhead = nn.ModuleList()
for i in range(fpn_num):
landmarkhead.append(LandmarkHead(inchannels, anchor_num))
return landmarkhead

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facelib/detection/retinaface/retinaface_utils.py Normal file
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import numpy as np
import torch
import torchvision
from itertools import product as product
from math import ceil
class PriorBox(object):
def __init__(self, cfg, image_size=None, phase='train'):
super(PriorBox, self).__init__()
self.min_sizes = cfg['min_sizes']
self.steps = cfg['steps']
self.clip = cfg['clip']
self.image_size = image_size
self.feature_maps = [[ceil(self.image_size[0] / step), ceil(self.image_size[1] / step)] for step in self.steps]
self.name = 's'
def forward(self):
anchors = []
for k, f in enumerate(self.feature_maps):
min_sizes = self.min_sizes[k]
for i, j in product(range(f[0]), range(f[1])):
for min_size in min_sizes:
s_kx = min_size / self.image_size[1]
s_ky = min_size / self.image_size[0]
dense_cx = [x * self.steps[k] / self.image_size[1] for x in [j + 0.5]]
dense_cy = [y * self.steps[k] / self.image_size[0] for y in [i + 0.5]]
for cy, cx in product(dense_cy, dense_cx):
anchors += [cx, cy, s_kx, s_ky]
# back to torch land
output = torch.Tensor(anchors).view(-1, 4)
if self.clip:
output.clamp_(max=1, min=0)
return output
def py_cpu_nms(dets, thresh):
"""Pure Python NMS baseline."""
keep = torchvision.ops.nms(
boxes=torch.Tensor(dets[:, :4]),
scores=torch.Tensor(dets[:, 4]),
iou_threshold=thresh,
)
return list(keep)
def point_form(boxes):
""" Convert prior_boxes to (xmin, ymin, xmax, ymax)
representation for comparison to point form ground truth data.
Args:
boxes: (tensor) center-size default boxes from priorbox layers.
Return:
boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes.
"""
return torch.cat(
(
boxes[:, :2] - boxes[:, 2:] / 2, # xmin, ymin
boxes[:, :2] + boxes[:, 2:] / 2),
1) # xmax, ymax
def center_size(boxes):
""" Convert prior_boxes to (cx, cy, w, h)
representation for comparison to center-size form ground truth data.
Args:
boxes: (tensor) point_form boxes
Return:
boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes.
"""
return torch.cat(
(boxes[:, 2:] + boxes[:, :2]) / 2, # cx, cy
boxes[:, 2:] - boxes[:, :2],
1) # w, h
def intersect(box_a, box_b):
""" We resize both tensors to [A,B,2] without new malloc:
[A,2] -> [A,1,2] -> [A,B,2]
[B,2] -> [1,B,2] -> [A,B,2]
Then we compute the area of intersect between box_a and box_b.
Args:
box_a: (tensor) bounding boxes, Shape: [A,4].
box_b: (tensor) bounding boxes, Shape: [B,4].
Return:
(tensor) intersection area, Shape: [A,B].
"""
A = box_a.size(0)
B = box_b.size(0)
max_xy = torch.min(box_a[:, 2:].unsqueeze(1).expand(A, B, 2), box_b[:, 2:].unsqueeze(0).expand(A, B, 2))
min_xy = torch.max(box_a[:, :2].unsqueeze(1).expand(A, B, 2), box_b[:, :2].unsqueeze(0).expand(A, B, 2))
inter = torch.clamp((max_xy - min_xy), min=0)
return inter[:, :, 0] * inter[:, :, 1]
def jaccard(box_a, box_b):
"""Compute the jaccard overlap of two sets of boxes. The jaccard overlap
is simply the intersection over union of two boxes. Here we operate on
ground truth boxes and default boxes.
E.g.:
A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B)
Args:
box_a: (tensor) Ground truth bounding boxes, Shape: [num_objects,4]
box_b: (tensor) Prior boxes from priorbox layers, Shape: [num_priors,4]
Return:
jaccard overlap: (tensor) Shape: [box_a.size(0), box_b.size(0)]
"""
inter = intersect(box_a, box_b)
area_a = ((box_a[:, 2] - box_a[:, 0]) * (box_a[:, 3] - box_a[:, 1])).unsqueeze(1).expand_as(inter) # [A,B]
area_b = ((box_b[:, 2] - box_b[:, 0]) * (box_b[:, 3] - box_b[:, 1])).unsqueeze(0).expand_as(inter) # [A,B]
union = area_a + area_b - inter
return inter / union # [A,B]
def matrix_iou(a, b):
"""
return iou of a and b, numpy version for data augenmentation
"""
lt = np.maximum(a[:, np.newaxis, :2], b[:, :2])
rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])
area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2)
area_a = np.prod(a[:, 2:] - a[:, :2], axis=1)
area_b = np.prod(b[:, 2:] - b[:, :2], axis=1)
return area_i / (area_a[:, np.newaxis] + area_b - area_i)
def matrix_iof(a, b):
"""
return iof of a and b, numpy version for data augenmentation
"""
lt = np.maximum(a[:, np.newaxis, :2], b[:, :2])
rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])
area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2)
area_a = np.prod(a[:, 2:] - a[:, :2], axis=1)
return area_i / np.maximum(area_a[:, np.newaxis], 1)
def match(threshold, truths, priors, variances, labels, landms, loc_t, conf_t, landm_t, idx):
"""Match each prior box with the ground truth box of the highest jaccard
overlap, encode the bounding boxes, then return the matched indices
corresponding to both confidence and location preds.
Args:
threshold: (float) The overlap threshold used when matching boxes.
truths: (tensor) Ground truth boxes, Shape: [num_obj, 4].
priors: (tensor) Prior boxes from priorbox layers, Shape: [n_priors,4].
variances: (tensor) Variances corresponding to each prior coord,
Shape: [num_priors, 4].
labels: (tensor) All the class labels for the image, Shape: [num_obj].
landms: (tensor) Ground truth landms, Shape [num_obj, 10].
loc_t: (tensor) Tensor to be filled w/ encoded location targets.
conf_t: (tensor) Tensor to be filled w/ matched indices for conf preds.
landm_t: (tensor) Tensor to be filled w/ encoded landm targets.
idx: (int) current batch index
Return:
The matched indices corresponding to 1)location 2)confidence
3)landm preds.
"""
# jaccard index
overlaps = jaccard(truths, point_form(priors))
# (Bipartite Matching)
# [1,num_objects] best prior for each ground truth
best_prior_overlap, best_prior_idx = overlaps.max(1, keepdim=True)
# ignore hard gt
valid_gt_idx = best_prior_overlap[:, 0] >= 0.2
best_prior_idx_filter = best_prior_idx[valid_gt_idx, :]
if best_prior_idx_filter.shape[0] <= 0:
loc_t[idx] = 0
conf_t[idx] = 0
return
# [1,num_priors] best ground truth for each prior
best_truth_overlap, best_truth_idx = overlaps.max(0, keepdim=True)
best_truth_idx.squeeze_(0)
best_truth_overlap.squeeze_(0)
best_prior_idx.squeeze_(1)
best_prior_idx_filter.squeeze_(1)
best_prior_overlap.squeeze_(1)
best_truth_overlap.index_fill_(0, best_prior_idx_filter, 2) # ensure best prior
# TODO refactor: index best_prior_idx with long tensor
# ensure every gt matches with its prior of max overlap
for j in range(best_prior_idx.size(0)): # 判别此anchor是预测哪一个boxes
best_truth_idx[best_prior_idx[j]] = j
matches = truths[best_truth_idx] # Shape: [num_priors,4] 此处为每一个anchor对应的bbox取出来
conf = labels[best_truth_idx] # Shape: [num_priors] 此处为每一个anchor对应的label取出来
conf[best_truth_overlap < threshold] = 0 # label as background overlap<0.35的全部作为负样本
loc = encode(matches, priors, variances)
matches_landm = landms[best_truth_idx]
landm = encode_landm(matches_landm, priors, variances)
loc_t[idx] = loc # [num_priors,4] encoded offsets to learn
conf_t[idx] = conf # [num_priors] top class label for each prior
landm_t[idx] = landm
def encode(matched, priors, variances):
"""Encode the variances from the priorbox layers into the ground truth boxes
we have matched (based on jaccard overlap) with the prior boxes.
Args:
matched: (tensor) Coords of ground truth for each prior in point-form
Shape: [num_priors, 4].
priors: (tensor) Prior boxes in center-offset form
Shape: [num_priors,4].
variances: (list[float]) Variances of priorboxes
Return:
encoded boxes (tensor), Shape: [num_priors, 4]
"""
# dist b/t match center and prior's center
g_cxcy = (matched[:, :2] + matched[:, 2:]) / 2 - priors[:, :2]
# encode variance
g_cxcy /= (variances[0] * priors[:, 2:])
# match wh / prior wh
g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:]
g_wh = torch.log(g_wh) / variances[1]
# return target for smooth_l1_loss
return torch.cat([g_cxcy, g_wh], 1) # [num_priors,4]
def encode_landm(matched, priors, variances):
"""Encode the variances from the priorbox layers into the ground truth boxes
we have matched (based on jaccard overlap) with the prior boxes.
Args:
matched: (tensor) Coords of ground truth for each prior in point-form
Shape: [num_priors, 10].
priors: (tensor) Prior boxes in center-offset form
Shape: [num_priors,4].
variances: (list[float]) Variances of priorboxes
Return:
encoded landm (tensor), Shape: [num_priors, 10]
"""
# dist b/t match center and prior's center
matched = torch.reshape(matched, (matched.size(0), 5, 2))
priors_cx = priors[:, 0].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
priors_cy = priors[:, 1].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
priors_w = priors[:, 2].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
priors_h = priors[:, 3].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
priors = torch.cat([priors_cx, priors_cy, priors_w, priors_h], dim=2)
g_cxcy = matched[:, :, :2] - priors[:, :, :2]
# encode variance
g_cxcy /= (variances[0] * priors[:, :, 2:])
# g_cxcy /= priors[:, :, 2:]
g_cxcy = g_cxcy.reshape(g_cxcy.size(0), -1)
# return target for smooth_l1_loss
return g_cxcy
# Adapted from https://github.com/Hakuyume/chainer-ssd
def decode(loc, priors, variances):
"""Decode locations from predictions using priors to undo
the encoding we did for offset regression at train time.
Args:
loc (tensor): location predictions for loc layers,
Shape: [num_priors,4]
priors (tensor): Prior boxes in center-offset form.
Shape: [num_priors,4].
variances: (list[float]) Variances of priorboxes
Return:
decoded bounding box predictions
"""
boxes = torch.cat((priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1)
boxes[:, :2] -= boxes[:, 2:] / 2
boxes[:, 2:] += boxes[:, :2]
return boxes
def decode_landm(pre, priors, variances):
"""Decode landm from predictions using priors to undo
the encoding we did for offset regression at train time.
Args:
pre (tensor): landm predictions for loc layers,
Shape: [num_priors,10]
priors (tensor): Prior boxes in center-offset form.
Shape: [num_priors,4].
variances: (list[float]) Variances of priorboxes
Return:
decoded landm predictions
"""
tmp = (
priors[:, :2] + pre[:, :2] * variances[0] * priors[:, 2:],
priors[:, :2] + pre[:, 2:4] * variances[0] * priors[:, 2:],
priors[:, :2] + pre[:, 4:6] * variances[0] * priors[:, 2:],
priors[:, :2] + pre[:, 6:8] * variances[0] * priors[:, 2:],
priors[:, :2] + pre[:, 8:10] * variances[0] * priors[:, 2:],
)
landms = torch.cat(tmp, dim=1)
return landms
def batched_decode(b_loc, priors, variances):
"""Decode locations from predictions using priors to undo
the encoding we did for offset regression at train time.
Args:
b_loc (tensor): location predictions for loc layers,
Shape: [num_batches,num_priors,4]
priors (tensor): Prior boxes in center-offset form.
Shape: [1,num_priors,4].
variances: (list[float]) Variances of priorboxes
Return:
decoded bounding box predictions
"""
boxes = (
priors[:, :, :2] + b_loc[:, :, :2] * variances[0] * priors[:, :, 2:],
priors[:, :, 2:] * torch.exp(b_loc[:, :, 2:] * variances[1]),
)
boxes = torch.cat(boxes, dim=2)
boxes[:, :, :2] -= boxes[:, :, 2:] / 2
boxes[:, :, 2:] += boxes[:, :, :2]
return boxes
def batched_decode_landm(pre, priors, variances):
"""Decode landm from predictions using priors to undo
the encoding we did for offset regression at train time.
Args:
pre (tensor): landm predictions for loc layers,
Shape: [num_batches,num_priors,10]
priors (tensor): Prior boxes in center-offset form.
Shape: [1,num_priors,4].
variances: (list[float]) Variances of priorboxes
Return:
decoded landm predictions
"""
landms = (
priors[:, :, :2] + pre[:, :, :2] * variances[0] * priors[:, :, 2:],
priors[:, :, :2] + pre[:, :, 2:4] * variances[0] * priors[:, :, 2:],
priors[:, :, :2] + pre[:, :, 4:6] * variances[0] * priors[:, :, 2:],
priors[:, :, :2] + pre[:, :, 6:8] * variances[0] * priors[:, :, 2:],
priors[:, :, :2] + pre[:, :, 8:10] * variances[0] * priors[:, :, 2:],
)
landms = torch.cat(landms, dim=2)
return landms
def log_sum_exp(x):
"""Utility function for computing log_sum_exp while determining
This will be used to determine unaveraged confidence loss across
all examples in a batch.
Args:
x (Variable(tensor)): conf_preds from conf layers
"""
x_max = x.data.max()
return torch.log(torch.sum(torch.exp(x - x_max), 1, keepdim=True)) + x_max
# Original author: Francisco Massa:
# https://github.com/fmassa/object-detection.torch
# Ported to PyTorch by Max deGroot (02/01/2017)
def nms(boxes, scores, overlap=0.5, top_k=200):
"""Apply non-maximum suppression at test time to avoid detecting too many
overlapping bounding boxes for a given object.
Args:
boxes: (tensor) The location preds for the img, Shape: [num_priors,4].
scores: (tensor) The class predscores for the img, Shape:[num_priors].
overlap: (float) The overlap thresh for suppressing unnecessary boxes.
top_k: (int) The Maximum number of box preds to consider.
Return:
The indices of the kept boxes with respect to num_priors.
"""
keep = torch.Tensor(scores.size(0)).fill_(0).long()
if boxes.numel() == 0:
return keep
x1 = boxes[:, 0]
y1 = boxes[:, 1]
x2 = boxes[:, 2]
y2 = boxes[:, 3]
area = torch.mul(x2 - x1, y2 - y1)
v, idx = scores.sort(0) # sort in ascending order
# I = I[v >= 0.01]
idx = idx[-top_k:] # indices of the top-k largest vals
xx1 = boxes.new()
yy1 = boxes.new()
xx2 = boxes.new()
yy2 = boxes.new()
w = boxes.new()
h = boxes.new()
# keep = torch.Tensor()
count = 0
while idx.numel() > 0:
i = idx[-1] # index of current largest val
# keep.append(i)
keep[count] = i
count += 1
if idx.size(0) == 1:
break
idx = idx[:-1] # remove kept element from view
# load bboxes of next highest vals
torch.index_select(x1, 0, idx, out=xx1)
torch.index_select(y1, 0, idx, out=yy1)
torch.index_select(x2, 0, idx, out=xx2)
torch.index_select(y2, 0, idx, out=yy2)
# store element-wise max with next highest score
xx1 = torch.clamp(xx1, min=x1[i])
yy1 = torch.clamp(yy1, min=y1[i])
xx2 = torch.clamp(xx2, max=x2[i])
yy2 = torch.clamp(yy2, max=y2[i])
w.resize_as_(xx2)
h.resize_as_(yy2)
w = xx2 - xx1
h = yy2 - yy1
# check sizes of xx1 and xx2.. after each iteration
w = torch.clamp(w, min=0.0)
h = torch.clamp(h, min=0.0)
inter = w * h
# IoU = i / (area(a) + area(b) - i)
rem_areas = torch.index_select(area, 0, idx) # load remaining areas)
union = (rem_areas - inter) + area[i]
IoU = inter / union # store result in iou
# keep only elements with an IoU <= overlap
idx = idx[IoU.le(overlap)]
return keep, count