MTCNN算法的理解介绍及代码分析
代码地址
这里走的是一个github上的代码mtcnn-pytorch
-
运行效果
如下是效果图,我们可以看到对一张图片内的每一张人脸进行检测,每个人脸有一个边界框与五个标记点。
这里我们使用cv来实现图片显示,以下是TEST.py文件, - 首先从src目录下的detector文件中导入detect_faces函数,从visualization——utils中导入show_bboxes函数.
- 用Image打开图片,这时图片的输出类型是<class’PIL.JpegImagePlugin.JpegImageFile’>,经过detect_faces的处理得到边界框与关键点。
- 用cv2打开处理后的图片,这里的输出是<class ‘numpy.ndarray’> 。这里我们复制一份图片。
- 接着遍历边界框和关键点,这里使关键点为红色,边界框为白色
from src.detector import detect_faces
from src.visualization_utils import show_bboxes
from PIL import Image
import matplotlib.pyplot as plt
import cv2
#
image = Image.open('images/office2.jpg')
bounding_boxes, landmarks = detect_faces(image)
#
image = cv2.imread('images/office2.jpg')
draw = image.copy()
for b in bounding_boxes:
cv2.rectangle(draw, (int(b[0]), int(b[1])), (int(b[2]), int(b[3])), (255, 255, 255))
#
for p in landmarks:
for i in range(5):
cv2.circle(draw, (p[i], p[i + 5]), 1, (0, 0, 255), 2)
cv2.imshow("detection result", draw)
cv2.waitKey(0)
一、上述测试涉及到了一个函数,detect_faces(),它属于detector.py文件。
(1)构建图像金字塔。
- .其中min_face_size=20.0代表最小人脸, thresholds=[0.6, 0.7, 0.8]代表了三个模型的阀值,他们判断是否是人脸的置信越来越高。
nms_thresholds=[0.7, 0.7, 0.7])用于作为去重函数nums的参数(后面分析nums函数) - 接着加载这三个模型,三个模型会有不同的输出,前两个包括分类输出score(人脸的概率)和回归结果bbox(N*4个坐标值)。最后一个比前面多了landmark位置的回归(5个关键点坐标)。
- 接着开始构建图像金字塔。获取图片的长和宽,取最小边。看到最小检测尺寸 min_detection_size = 12,缩小因子 factor = 0.707(根号0.5),通过原图像最小的边来不断使其缩小,直至满足最小检测尺寸,并通过scale保存下来其对应缩小因子。
(2)运行P-Net,对应 run_first_stage(),得到一些框,首先把为空的去除,剩下的不为空的框通过np.vstack()将其由list按照列叠加成一个新的为numpy array,便于后面处理操作。接着用nms(bounding_boxes[:, 0:5], nms_thresholds[0])进行去重操作,并把去重后的框放在边界框列表中。然后使用calibrate_box()通过offset来调整b-box坐标,用convert_to_square()将框调整为正方形,得到的正方形的中心点还是原来矩形的中心点,边长是矩阵宽高的最大值,最后进行一个四舍五入操作。 - 到了R-Net,先把P-Net的输出放到Variable框架内,经过rnet得到两个输出,分别是预测得到的偏移回归信息,以及置信分类信息。有了置信值,就能根据阈值来判断是不是为人脸,从而进行筛选。接着继续进行去重,调整坐标和形状,进行四舍五入。
- 接着使STAGE3,这里是O-Net部分,这里还是先把R-Net的输出放到Varible内,经过onet得到三个输出,除了R-Net的两个输出,这里多了一个关键点landmarks = output[0].data.numpy() # shape [n_boxes, 10],接着还是判断是否为人脸,进行一系列操作。
(3)接着是compute landmark points阶段,即计算关键点位置,因为O-Net得到的关键点是缩放后的关键点位置,所以并不是原本图像的,这里就需要根据width和height去计算原本的关键点位置,最后在进行一次去重操作,得到最终的边界框位置以及关键点位置。
import numpy as np
import torch
from torch.autograd import Variable
from .get_nets import PNet, RNet, ONet
from .box_utils import nms, calibrate_box, get_image_boxes, convert_to_square
from .first_stage import run_first_stage
def detect_faces(image, min_face_size=20.0, # 其中20代表最小的人脸
thresholds=[0.6, 0.7, 0.8],#代表了三个模型的阀值,其判断是人脸的置信越来越高
nms_thresholds=[0.7, 0.7, 0.7]):
"""
Arguments:
image: an instance of PIL.Image.
min_face_size: a float number.
thresholds: a list of length 3.
nms_thresholds: a list of length 3.
Returns:
two float numpy arrays of shapes [n_boxes, 4] and [n_boxes, 10],
bounding boxes and facial landmarks.
"""
# LOAD MODELS
pnet = PNet()
rnet = RNet()
onet = ONet()
onet.eval()
# BUILD AN IMAGE PYRAMID
width, height = image.size#获取图片的长和宽
min_length = min(height, width)#取最小
min_detection_size = 12#最小检测尺寸
factor = 0.707 # sqrt(0.5) 缩小因子
# scales for scaling the image
scales = []
# scales the image so that
# minimum size that we can detect equals to
# minimum face size that we want to detect
m = min_detection_size/min_face_size#
min_length *= m
factor_count = 0
while min_length > min_detection_size:
scales.append(m*factor**factor_count)
min_length *= factor
factor_count += 1
# STAGE 1
# it will be returned
bounding_boxes = []
# run P-Net on different scales
for s in scales:
boxes = run_first_stage(image, pnet, scale=s, threshold=thresholds[0])
bounding_boxes.append(boxes)
# collect boxes (and offsets, and scores) from different scales
bounding_boxes = [i for i in bounding_boxes if i is not None]
bounding_boxes = np.vstack(bounding_boxes)
keep = nms(bounding_boxes[:, 0:5], nms_thresholds[0])
bounding_boxes = bounding_boxes[keep]
# use offsets predicted by pnet to transform bounding boxes
bounding_boxes = calibrate_box(bounding_boxes[:, 0:5], bounding_boxes[:, 5:])
# shape [n_boxes, 5]
bounding_boxes = convert_to_square(bounding_boxes)
bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])
# STAGE 2
img_boxes = get_image_boxes(bounding_boxes, image, size=24)
with torch.no_grad():
img_boxes = Variable(torch.FloatTensor(img_boxes))
output = rnet(img_boxes)
offsets = output[0].data.numpy() # shape [n_boxes, 4] 预测得到的偏移回归信息
probs = output[1].data.numpy() # shape [n_boxes, 2] 置信度分类信息
keep = np.where(probs[:, 1] > thresholds[1])[0] #置信度是否大于阈值,进一步筛选人脸框
bounding_boxes = bounding_boxes[keep]
bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
offsets = offsets[keep]
keep = nms(bounding_boxes, nms_thresholds[1])
bounding_boxes = bounding_boxes[keep]
bounding_boxes = calibrate_box(bounding_boxes, offsets[keep])
bounding_boxes = convert_to_square(bounding_boxes)
bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])
# STAGE 3
img_boxes = get_image_boxes(bounding_boxes, image, size=48)
if len(img_boxes) == 0:
return [], []
with torch.no_grad():
img_boxes = Variable(torch.FloatTensor(img_boxes))
output = onet(img_boxes)
landmarks = output[0].data.numpy() # shape [n_boxes, 10]
offsets = output[1].data.numpy() # shape [n_boxes, 4]
probs = output[2].data.numpy() # shape [n_boxes, 2]
keep = np.where(probs[:, 1] > thresholds[2])[0]
bounding_boxes = bounding_boxes[keep]
bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
offsets = offsets[keep]
landmarks = landmarks[keep]
# compute landmark points
width = bounding_boxes[:, 2] - bounding_boxes[:, 0] + 1.0
height = bounding_boxes[:, 3] - bounding_boxes[:, 1] + 1.0
xmin, ymin = bounding_boxes[:, 0], bounding_boxes[:, 1]
landmarks[:, 0:5] = np.expand_dims(xmin, 1) + np.expand_dims(width, 1)*landmarks[:, 0:5]
landmarks[:, 5:10] = np.expand_dims(ymin, 1) + np.expand_dims(height, 1)*landmarks[:, 5:10]
bounding_boxes = calibrate_box(bounding_boxes, offsets)
keep = nms(bounding_boxes, nms_thresholds[2], mode='min')
bounding_boxes = bounding_boxes[keep]
landmarks = landmarks[keep]
return bounding_boxes, landmarks
二、接下来说detector.py中STAGE1中用到的函数run_first_stage(),它属于first_stage.py文件,这个文件还有一个函数generate_bboxes()。
- run_first_stage()这个可以看注释,生成边界框,并进行去重操作。其中有四个参数,一个PIL图片,一个神经网络P-Net,图像的宽高规模scale,一个置信度threshold。返回 a float numpy array of shape [n_boxes, 9],bounding boxes with scores and offsets (4 + 1 + 4).
- _generate_bboxes生成可能是人脸的边界框位置,调整下offset的形式,同时计算得到每个scale对应的bbox坐标值。
import torch
from torch.autograd import Variable
import math
from PIL import Image
import numpy as np
from .box_utils import nms, _preprocess
def run_first_stage(image, net, scale, threshold):
"""Run P-Net, generate bounding boxes, and do NMS.
Arguments:
image: an instance of PIL.Image.
net: an instance of pytorch's nn.Module, P-Net.
scale: a float number,
scale width and height of the image by this number.
threshold: a float number,
threshold on the probability of a face when generating
bounding boxes from predictions of the net.
Returns:
a float numpy array of shape [n_boxes, 9],
bounding boxes with scores and offsets (4 + 1 + 4).
"""
# scale the image and convert it to a float array
width, height = image.size
sw, sh = math.ceil(width*scale), math.ceil(height*scale)
img = image.resize((sw, sh), Image.BILINEAR)
img = np.asarray(img, 'float32')
with torch.no_grad():
img = Variable(torch.FloatTensor(_preprocess(img)))
output = net(img)
probs = output[1].data.numpy()[0, 1, :, :]
offsets = output[0].data.numpy()
# probs: probability of a face at each sliding window
# offsets: transformations to true bounding boxes
boxes = _generate_bboxes(probs, offsets, scale, threshold)
if len(boxes) == 0:
return None
keep = nms(boxes[:, 0:5], overlap_threshold=0.5)
return boxes[keep]
def _generate_bboxes(probs, offsets, scale, threshold):
"""Generate bounding boxes at places
where there is probably a face.
Arguments:
probs: a float numpy array of shape [n, m].
offsets: a float numpy array of shape [1, 4, n, m].
scale: a float number,
width and height of the image were scaled by this number.
threshold: a float number.
Returns:
a float numpy array of shape [n_boxes, 9]
"""
# applying P-Net is equivalent, in some sense, to
# moving 12x12 window with stride 2
stride = 2
cell_size = 12
# indices of boxes where there is probably a face
inds = np.where(probs > threshold)
if inds[0].size == 0:
return np.array([])
# transformations of bounding boxes
tx1, ty1, tx2, ty2 = [offsets[0, i, inds[0], inds[1]] for i in range(4)]
# they are defined as:
# w = x2 - x1 + 1
# h = y2 - y1 + 1
# x1_true = x1 + tx1*w
# x2_true = x2 + tx2*w
# y1_true = y1 + ty1*h
# y2_true = y2 + ty2*h
offsets = np.array([tx1, ty1, tx2, ty2])
score = probs[inds[0], inds[1]]
# P-Net is applied to scaled images
# so we need to rescale bounding boxes back
bounding_boxes = np.vstack([
np.round((stride*inds[1] + 1.0)/scale),
np.round((stride*inds[0] + 1.0)/scale),
np.round((stride*inds[1] + 1.0 + cell_size)/scale),
np.round((stride*inds[0] + 1.0 + cell_size)/scale),
score, offsets
])
# why one is added?
return bounding_boxes.T
三、之前一直用到了一个去重函数nms,它属于box_utils.py文件,这个主要有五个函数以及一个_preprocess()对图片进行预处理。
-
其中nms函数,用来非最大值抑制,也就是通过对这些边界框进行判断,选出极大值进行保留,从而达到去重的操作。
-
convert_to_square函数,是吧边界框转换为方形
-
calibrate_box,通过offset使边界框更可能是最终的目的边界框,返回调整后的bbox。
-
get_image_boxes,从框出的图片像素裁剪到指定大小,便于计算。
-
correct_bboxes,通过检查避免框的尺寸超过图像或者为负值,在之前通过offset整合时,尺寸大小容易过大或过小。它会返回十个参数,前四个中dx,dy多设为0,出现正数,此时x,y为负数了,取了下相反数,edx,edy又进行了调整。而 y, x, ey, ex则为原本的始末坐标,h,w为宽和高。
-
最后就是图片的预处理了
Returns:
dy, dx, edy, edx: a int numpy arrays of shape [n],
coordinates of the boxes with respect to the cutouts.
y, x, ey, ex: a int numpy arrays of shape [n],
corrected ymin, xmin, ymax, xmax.
h, w: a int numpy arrays of shape [n],
just heights and widths of boxes.in the following order: [dy, edy, dx, edx, y, ey, x, ex, w, h].
import numpy as np
from PIL import Image
def nms(boxes, overlap_threshold=0.5, mode='union'):
"""Non-maximum suppression.
Arguments:
boxes: a float numpy array of shape [n, 5],
where each row is (xmin, ymin, xmax, ymax, score).
overlap_threshold: a float number.
mode: 'union' or 'min'.
Returns:
list with indices of the selected boxes
"""
# if there are no boxes, return the empty list
if len(boxes) == 0:
return []
# list of picked indices
pick = []
# grab the coordinates of the bounding boxes
x1, y1, x2, y2, score = [boxes[:, i] for i in range(5)]
area = (x2 - x1 + 1.0)*(y2 - y1 + 1.0)
ids = np.argsort(score) # in increasing order
while len(ids) > 0:
# grab index of the largest value
last = len(ids) - 1
i = ids[last]
pick.append(i)
# compute intersections
# of the box with the largest score
# with the rest of boxes
# left top corner of intersection boxes
ix1 = np.maximum(x1[i], x1[ids[:last]])
iy1 = np.maximum(y1[i], y1[ids[:last]])
# right bottom corner of intersection boxes
ix2 = np.minimum(x2[i], x2[ids[:last]])
iy2 = np.minimum(y2[i], y2[ids[:last]])
# width and height of intersection boxes
w = np.maximum(0.0, ix2 - ix1 + 1.0)
h = np.maximum(0.0, iy2 - iy1 + 1.0)
# intersections' areas
inter = w * h
if mode == 'min':
overlap = inter/np.minimum(area[i], area[ids[:last]])
elif mode == 'union':
# intersection over union (IoU)
overlap = inter/(area[i] + area[ids[:last]] - inter)
# delete all boxes where overlap is too big
ids = np.delete(
ids,
np.concatenate([[last], np.where(overlap > overlap_threshold)[0]])
)
return pick
def convert_to_square(bboxes):
"""Convert bounding boxes to a square form.
Arguments:
bboxes: a float numpy array of shape [n, 5].
Returns:
a float numpy array of shape [n, 5],
squared bounding boxes.
"""
square_bboxes = np.zeros_like(bboxes)
x1, y1, x2, y2 = [bboxes[:, i] for i in range(4)]
h = y2 - y1 + 1.0
w = x2 - x1 + 1.0
max_side = np.maximum(h, w)
square_bboxes[:, 0] = x1 + w*0.5 - max_side*0.5
square_bboxes[:, 1] = y1 + h*0.5 - max_side*0.5
square_bboxes[:, 2] = square_bboxes[:, 0] + max_side - 1.0
square_bboxes[:, 3] = square_bboxes[:, 1] + max_side - 1.0
return square_bboxes
def calibrate_box(bboxes, offsets):
"""Transform bounding boxes to be more like true bounding boxes.
'offsets' is one of the outputs of the nets.
Arguments:
bboxes: a float numpy array of shape [n, 5].
offsets: a float numpy array of shape [n, 4].
Returns:
a float numpy array of shape [n, 5].
"""
x1, y1, x2, y2 = [bboxes[:, i] for i in range(4)]
w = x2 - x1 + 1.0
h = y2 - y1 + 1.0
w = np.expand_dims(w, 1)
h = np.expand_dims(h, 1)
# this is what happening here:
# tx1, ty1, tx2, ty2 = [offsets[:, i] for i in range(4)]
# x1_true = x1 + tx1*w
# y1_true = y1 + ty1*h
# x2_true = x2 + tx2*w
# y2_true = y2 + ty2*h
# below is just more compact form of this
# are offsets always such that
# x1 < x2 and y1 < y2 ?
translation = np.hstack([w, h, w, h])*offsets
bboxes[:, 0:4] = bboxes[:, 0:4] + translation
return bboxes
def get_image_boxes(bounding_boxes, img, size=24):
"""Cut out boxes from the image.
Arguments:
bounding_boxes: a float numpy array of shape [n, 5].
img: an instance of PIL.Image.
size: an integer, size of cutouts.
Returns:
a float numpy array of shape [n, 3, size, size].
"""
num_boxes = len(bounding_boxes)
width, height = img.size
[dy, edy, dx, edx, y, ey, x, ex, w, h] = correct_bboxes(bounding_boxes, width, height)
img_boxes = np.zeros((num_boxes, 3, size, size), 'float32')
for i in range(num_boxes):
img_box = np.zeros((h[i], w[i], 3), 'uint8')
img_array = np.asarray(img, 'uint8')
img_box[dy[i]:(edy[i] + 1), dx[i]:(edx[i] + 1), :] =\
img_array[y[i]:(ey[i] + 1), x[i]:(ex[i] + 1), :]
# resize
img_box = Image.fromarray(img_box)
img_box = img_box.resize((size, size), Image.BILINEAR)
img_box = np.asarray(img_box, 'float32')
img_boxes[i, :, :, :] = _preprocess(img_box)
return img_boxes
def correct_bboxes(bboxes, width, height):
"""Crop boxes that are too big and get coordinates
with respect to cutouts.
Arguments:
bboxes: a float numpy array of shape [n, 5],
where each row is (xmin, ymin, xmax, ymax, score).
width: a float number.
height: a float number.
Returns:
dy, dx, edy, edx: a int numpy arrays of shape [n],
coordinates of the boxes with respect to the cutouts.
y, x, ey, ex: a int numpy arrays of shape [n],
corrected ymin, xmin, ymax, xmax.
h, w: a int numpy arrays of shape [n],
just heights and widths of boxes.
in the following order:
[dy, edy, dx, edx, y, ey, x, ex, w, h].
"""
x1, y1, x2, y2 = [bboxes[:, i] for i in range(4)]
w, h = x2 - x1 + 1.0, y2 - y1 + 1.0
num_boxes = bboxes.shape[0]
# 'e' stands for end
# (x, y) -> (ex, ey)
x, y, ex, ey = x1, y1, x2, y2
# we need to cut out a box from the image.
# (x, y, ex, ey) are corrected coordinates of the box
# in the image.
# (dx, dy, edx, edy) are coordinates of the box in the cutout
# from the image.
dx, dy = np.zeros((num_boxes,)), np.zeros((num_boxes,))
edx, edy = w.copy() - 1.0, h.copy() - 1.0
# if box's bottom right corner is too far right
ind = np.where(ex > width - 1.0)[0]
edx[ind] = w[ind] + width - 2.0 - ex[ind]
ex[ind] = width - 1.0
# if box's bottom right corner is too low
ind = np.where(ey > height - 1.0)[0]
edy[ind] = h[ind] + height - 2.0 - ey[ind]
ey[ind] = height - 1.0
# if box's top left corner is too far left
ind = np.where(x < 0.0)[0]
dx[ind] = 0.0 - x[ind]
x[ind] = 0.0
# if box's top left corner is too high
ind = np.where(y < 0.0)[0]
dy[ind] = 0.0 - y[ind]
y[ind] = 0.0
return_list = [dy, edy, dx, edx, y, ey, x, ex, w, h]
return_list = [i.astype('int32') for i in return_list]
return return_list
def _preprocess(img):
"""Preprocessing step before feeding the network.
Arguments:
img: a float numpy array of shape [h, w, c].
Returns:
a float numpy array of shape [1, c, h, w].
"""
img = img.transpose((2, 0, 1))
img = np.expand_dims(img, 0)
img = (img - 127.5)*0.0078125
return img
四、存放网络模型的文件get_nets.py,里面有供我们使用的P,R,O三种网络模型。其原理与mtcnn论文一致。
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
import numpy as np
class Flatten(nn.Module):
def __init__(self):
super(Flatten, self).__init__()
def forward(self, x):
"""
Arguments:
x: a float tensor with shape [batch_size, c, h, w].
Returns:
a float tensor with shape [batch_size, c*h*w].
"""
# without this pretrained model isn't working
x = x.transpose(3, 2).contiguous()
return x.view(x.size(0), -1)
class PNet(nn.Module):
def __init__(self):
super(PNet, self).__init__()
# suppose we have input with size HxW, then
# after first layer: H - 2,
# after pool: ceil((H - 2)/2),
# after second conv: ceil((H - 2)/2) - 2,
# after last conv: ceil((H - 2)/2) - 4,
# and the same for W
self.features = nn.Sequential(OrderedDict([
('conv1', nn.Conv2d(3, 10, 3, 1)),
('prelu1', nn.PReLU(10)),
('pool1', nn.MaxPool2d(2, 2, ceil_mode=True)),
('conv2', nn.Conv2d(10, 16, 3, 1)),
('prelu2', nn.PReLU(16)),
('conv3', nn.Conv2d(16, 32, 3, 1)),
('prelu3', nn.PReLU(32))
]))
self.conv4_1 = nn.Conv2d(32, 2, 1, 1)
self.conv4_2 = nn.Conv2d(32, 4, 1, 1)
weights = np.load('src/weights/pnet.npy')[()]
for n, p in self.named_parameters():
p.data = torch.FloatTensor(weights[n])
def forward(self, x):
"""
Arguments:
x: a float tensor with shape [batch_size, 3, h, w].
Returns:
b: a float tensor with shape [batch_size, 4, h', w'].
a: a float tensor with shape [batch_size, 2, h', w'].
"""
x = self.features(x)
a = self.conv4_1(x)
b = self.conv4_2(x)
a = F.softmax(a,dim=1)
return b, a
class RNet(nn.Module):
def __init__(self):
super(RNet, self).__init__()
self.features = nn.Sequential(OrderedDict([
('conv1', nn.Conv2d(3, 28, 3, 1)),
('prelu1', nn.PReLU(28)),
('pool1', nn.MaxPool2d(3, 2, ceil_mode=True)),
('conv2', nn.Conv2d(28, 48, 3, 1)),
('prelu2', nn.PReLU(48)),
('pool2', nn.MaxPool2d(3, 2, ceil_mode=True)),
('conv3', nn.Conv2d(48, 64, 2, 1)),
('prelu3', nn.PReLU(64)),
('flatten', Flatten()),
('conv4', nn.Linear(576, 128)),
('prelu4', nn.PReLU(128))
]))
self.conv5_1 = nn.Linear(128, 2)
self.conv5_2 = nn.Linear(128, 4)
weights = np.load('src/weights/rnet.npy')[()]
for n, p in self.named_parameters():
p.data = torch.FloatTensor(weights[n])
def forward(self, x):
"""
Arguments:
x: a float tensor with shape [batch_size, 3, h, w].
Returns:
b: a float tensor with shape [batch_size, 4].
a: a float tensor with shape [batch_size, 2].
"""
x = self.features(x)
a = self.conv5_1(x)
b = self.conv5_2(x)
a = F.softmax(a,dim=1)
return b, a
class ONet(nn.Module):
def __init__(self):
super(ONet, self).__init__()
self.features = nn.Sequential(OrderedDict([
('conv1', nn.Conv2d(3, 32, 3, 1)),
('prelu1', nn.PReLU(32)),
('pool1', nn.MaxPool2d(3, 2, ceil_mode=True)),
('conv2', nn.Conv2d(32, 64, 3, 1)),
('prelu2', nn.PReLU(64)),
('pool2', nn.MaxPool2d(3, 2, ceil_mode=True)),
('conv3', nn.Conv2d(64, 64, 3, 1)),
('prelu3', nn.PReLU(64)),
('pool3', nn.MaxPool2d(2, 2, ceil_mode=True)),
('conv4', nn.Conv2d(64, 128, 2, 1)),
('prelu4', nn.PReLU(128)),
('flatten', Flatten()),
('conv5', nn.Linear(1152, 256)),
('drop5', nn.Dropout(0.25)),
('prelu5', nn.PReLU(256)),
]))
self.conv6_1 = nn.Linear(256, 2)
self.conv6_2 = nn.Linear(256, 4)
self.conv6_3 = nn.Linear(256, 10)
weights = np.load('src/weights/onet.npy')[()]
for n, p in self.named_parameters():
p.data = torch.FloatTensor(weights[n])
def forward(self, x):
"""
Arguments:
x: a float tensor with shape [batch_size, 3, h, w].
Returns:
c: a float tensor with shape [batch_size, 10].
b: a float tensor with shape [batch_size, 4].
a: a float tensor with shape [batch_size, 2].
"""
x = self.features(x)
a = self.conv6_1(x)
b = self.conv6_2(x)
c = self.conv6_3(x)
a = F.softmax(a,dim=1)
return c, b, a
源于百度:
另外,这里还用到了一种激活函数PRelu,是一种带参数的Relu激活函数,其中如果ai=0,那么就是Relu激活函数。如果参数ai很小,则会退化为LRelu。有实验证明,与ReLU相比,LReLU对最终的结果几乎没什么影响。(我觉得这像是一个极限微分问题…)
五、这个项目中还有一个文件visualzation.py是用来显示结果的,因为这里使用了cv2,并且这个文件不涉及算法内容,不做分析。
本文地址:https://blog.csdn.net/LanceHang/article/details/110952809
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