/
utils.py
executable file
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/
utils.py
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import torch
import torch.nn as nn
import numpy as np
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class RegressionTransform(nn.Module):
def __init__(self,mean=None,std_box=None,std_ldm=None):
super(RegressionTransform, self).__init__()
if mean is None:
#self.mean = torch.from_numpy(np.array([0, 0, 0, 0]).astype(np.float32)).cuda()
self.mean = torch.from_numpy(np.array([0, 0, 0, 0]).astype(np.float32))
else:
self.mean = mean
if std_box is None:
#self.std_box = torch.from_numpy(np.array([0.1, 0.1, 0.2, 0.2]).astype(np.float32)).cuda()
self.std_box = torch.from_numpy(np.array([0.1, 0.1, 0.2, 0.2]).astype(np.float32))
else:
self.std_box = std_box
if std_ldm is None:
#self.std_ldm = (torch.ones(1,10) * 0.1).cuda()
self.std_ldm = (torch.ones(1,10) * 0.1)
def forward(self,anchors,bbox_deltas,ldm_deltas,img):
widths = anchors[:, :, 2] - anchors[:, :, 0]
heights = anchors[:, :, 3] - anchors[:, :, 1]
ctr_x = anchors[:, :, 0] + 0.5 * widths
ctr_y = anchors[:, :, 1] + 0.5 * heights
# Rescale
ldm_deltas = ldm_deltas * self.std_ldm.cuda()
bbox_deltas = bbox_deltas * self.std_box.cuda()
bbox_dx = bbox_deltas[:, :, 0]
bbox_dy = bbox_deltas[:, :, 1]
bbox_dw = bbox_deltas[:, :, 2]
bbox_dh = bbox_deltas[:, :, 3]
# get predicted boxes
pred_ctr_x = ctr_x + bbox_dx * widths
pred_ctr_y = ctr_y + bbox_dy * heights
pred_w = torch.exp(bbox_dw) * widths
pred_h = torch.exp(bbox_dh) * heights
pred_boxes_x1 = pred_ctr_x - 0.5 * pred_w
pred_boxes_y1 = pred_ctr_y - 0.5 * pred_h
pred_boxes_x2 = pred_ctr_x + 0.5 * pred_w
pred_boxes_y2 = pred_ctr_y + 0.5 * pred_h
pred_boxes = torch.stack([pred_boxes_x1, pred_boxes_y1, pred_boxes_x2, pred_boxes_y2], dim=2)
# get predicted landmarks
pt0_x = ctr_x + ldm_deltas[:,:,0] * widths
pt0_y = ctr_y + ldm_deltas[:,:,1] * heights
pt1_x = ctr_x + ldm_deltas[:,:,2] * widths
pt1_y = ctr_y + ldm_deltas[:,:,3] * heights
pt2_x = ctr_x + ldm_deltas[:,:,4] * widths
pt2_y = ctr_y + ldm_deltas[:,:,5] * heights
pt3_x = ctr_x + ldm_deltas[:,:,6] * widths
pt3_y = ctr_y + ldm_deltas[:,:,7] * heights
pt4_x = ctr_x + ldm_deltas[:,:,8] * widths
pt4_y = ctr_y + ldm_deltas[:,:,9] * heights
pred_landmarks = torch.stack([
pt0_x, pt0_y, pt1_x, pt1_y, pt2_x, pt2_y, pt3_x, pt3_y, pt4_x,pt4_y
],dim=2)
# clip bboxes and landmarks
B,C,H,W = img.shape
pred_boxes[:,:,::2] = torch.clamp(pred_boxes[:,:,::2], min=0, max=W)
pred_boxes[:,:,1::2] = torch.clamp(pred_boxes[:,:,1::2], min=0, max=H)
pred_landmarks[:,:,::2] = torch.clamp(pred_landmarks[:,:,::2], min=0, max=W)
pred_landmarks[:,:,1::2] = torch.clamp(pred_landmarks[:,:,1::2], min=0, max=H)
return pred_boxes, pred_landmarks
def nms(boxes,scores,iou_threshold):
boxes = boxes.cpu().numpy()
score = scores.cpu().numpy()
# coordinates of bounding boxes
start_x = boxes[:, 0]
start_y = boxes[:, 1]
end_x = boxes[:, 2]
end_y = boxes[:, 3]
# Picked bounding boxes
picked_boxes = []
picked_score = []
# Compute areas of bounding boxes
areas = (end_x - start_x + 1) * (end_y - start_y + 1)
# Sort by confidence score of bounding boxes
order = np.argsort(score)
# Iterate bounding boxes
while order.size > 0:
# The index of largest confidence score
index = order[-1]
# Pick the bounding box with largest confidence score
picked_boxes.append(boxes[index])
picked_score.append(score[index])
a=start_x[index]
b=order[:-1]
c=start_x[order[:-1]]
# Compute ordinates of intersection-over-union(IOU)
x1 = np.maximum(start_x[index], start_x[order[:-1]])
x2 = np.minimum(end_x[index], end_x[order[:-1]])
y1 = np.maximum(start_y[index], start_y[order[:-1]])
y2 = np.minimum(end_y[index], end_y[order[:-1]])
# Compute areas of intersection-over-union
w = np.maximum(0.0, x2 - x1 + 1)
h = np.maximum(0.0, y2 - y1 + 1)
intersection = w * h
# Compute the ratio between intersection and union
ratio = intersection / (areas[index] + areas[order[:-1]] - intersection)
left = np.where(ratio < iou_threshold)
order = order[left]
picked_boxes = torch.Tensor(picked_boxes)
picked_score = torch.Tensor(picked_score)
return picked_boxes, picked_score