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utils.py
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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 BBoxTransform(nn.Module):
def __init__(self, mean=None, std=None):
super(BBoxTransform, self).__init__()
if mean is None:
self.mean = torch.from_numpy(np.array([0, 0, 0, 0]).astype(np.float32)).cuda()
else:
self.mean = mean
if std is None:
self.std = torch.from_numpy(np.array([0.1, 0.1, 0.2, 0.2]).astype(np.float32)).cuda()
else:
self.std = std
def forward(self, boxes, deltas):
# print(boxes.shape)
# print(deltas.shape)
# print(deltas[0,0,:])
# print(deltas[0,1,:])
# print(deltas[0,2,:])
# print(deltas[0,3,:])
# print(deltas[0,4,:])
# print(deltas[0,5,:])
# print(deltas[0,6,:])
# print(deltas[0,7,:])
widths = boxes[:, :, 2::4] - boxes[:, :, 0::4]
heights = boxes[:, :, 3::4] - boxes[:, :, 1::4]
ctr_x = boxes[:, :, 0::4] + 0.5 * widths
ctr_y = boxes[:, :, 1::4] + 0.5 * heights
dx = deltas[:, :, 0::4] * self.std[0] + self.mean[0]
dy = deltas[:, :, 1::4] * self.std[1] + self.mean[1]
dw = deltas[:, :, 2::4] * self.std[2] + self.mean[2]
dh = deltas[:, :, 3::4] * self.std[3] + self.mean[3]
# print(ctr_x.shape)
# print(dx.shape)
# print(widths.shape)
pred_ctr_x = ctr_x + dx * widths
pred_ctr_y = ctr_y + dy * heights
pred_w = torch.exp(dw) * widths
pred_h = torch.exp(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_x1 = pred_boxes_x1[:, :, :, np.newaxis]
pred_boxes_y1 = pred_boxes_y1[:, :, :, np.newaxis]
pred_boxes_x2 = pred_boxes_x2[:, :, :, np.newaxis]
pred_boxes_y2 = pred_boxes_y2[:, :, :, np.newaxis]
pred_boxes = torch.stack([pred_boxes_x1, pred_boxes_y1, pred_boxes_x2, pred_boxes_y2], dim=3).reshape(boxes.shape)
#pred_boxes = torch.stack([pred_boxes_x1, pred_boxes_y1, pred_boxes_x2, pred_boxes_y2], dim=2)
return pred_boxes
class ClipBoxes(nn.Module):
def __init__(self, width=None, height=None):
super(ClipBoxes, self).__init__()
def forward(self, boxes, img):
batch_size, num_channels, height, width = img.shape
boxes[:, :, 0::4] = torch.clamp(boxes[:, :, 0::4], min=0)
boxes[:, :, 1::4] = torch.clamp(boxes[:, :, 1::4], min=0)
boxes[:, :, 2::4] = torch.clamp(boxes[:, :, 2::4], max=width)
boxes[:, :, 3::4] = torch.clamp(boxes[:, :, 3::4], max=height)
return boxes
# 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 BBoxTransform(nn.Module):
# def __init__(self, mean=None, std=None):
# super(BBoxTransform, self).__init__()
# if mean is None:
# self.mean = torch.from_numpy(np.array([0, 0, 0, 0]).astype(np.float32)).cuda()
# else:
# self.mean = mean
# if std is None:
# self.std = torch.from_numpy(np.array([0.1, 0.1, 0.2, 0.2]).astype(np.float32)).cuda()
# else:
# self.std = std
# def forward(self, boxes, deltas):
# widths = boxes[:, :, 2] - boxes[:, :, 0]
# heights = boxes[:, :, 3] - boxes[:, :, 1]
# ctr_x = boxes[:, :, 0] + 0.5 * widths
# ctr_y = boxes[:, :, 1] + 0.5 * heights
# dx = deltas[:, :, 0] * self.std[0] + self.mean[0]
# dy = deltas[:, :, 1] * self.std[1] + self.mean[1]
# dw = deltas[:, :, 2] * self.std[2] + self.mean[2]
# dh = deltas[:, :, 3] * self.std[3] + self.mean[3]
# pred_ctr_x = ctr_x + dx * widths
# pred_ctr_y = ctr_y + dy * heights
# pred_w = torch.exp(dw) * widths
# pred_h = torch.exp(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)
# return pred_boxes
# class ClipBoxes(nn.Module):
# def __init__(self, width=None, height=None):
# super(ClipBoxes, self).__init__()
# def forward(self, boxes, img):
# batch_size, num_channels, height, width = img.shape
# boxes[:, :, 0] = torch.clamp(boxes[:, :, 0], min=0)
# boxes[:, :, 1] = torch.clamp(boxes[:, :, 1], min=0)
# boxes[:, :, 2] = torch.clamp(boxes[:, :, 2], max=width)
# boxes[:, :, 3] = torch.clamp(boxes[:, :, 3], max=height)
# return boxes