188 lines
6.4 KiB
Python
188 lines
6.4 KiB
Python
import cv2
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import math
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import torch
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import pickle
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import numpy as np
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import torch.nn as nn
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import torch.nn.functional as F
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def im2single(im):
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im = im.astype(np.float32) / 255
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return im
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def single2im(im):
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im *= 255
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im = im.astype(np.uint8)
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return im
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def load_interest_points(eval_file):
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"""
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This function is provided for development and debugging but cannot be used in
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the final handin. It 'cheats' by generating interest points from known
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correspondences. It will only work for the 3 image pairs with known
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correspondences.
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Args:
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- eval_file: string representing the file path to the list of known correspondences
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- scale_factor: Python float representing the scale needed to map from the original
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image coordinates to the resolution being used for the current experiment.
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Returns:
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- x1: A numpy array of shape (k,) containing ground truth x-coordinates of imgA correspondence pts
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- y1: A numpy array of shape (k,) containing ground truth y-coordinates of imgA correspondence pts
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- x2: A numpy array of shape (k,) containing ground truth x-coordinates of imgB correspondence pts
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- y2: A numpy array of shape (k,) containing ground truth y-coordinates of imgB correspondence pts
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"""
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with open(eval_file, 'rb') as f:
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d = pickle.load(f, encoding='latin1')
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scale_factor = 1.0
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return d['x1'] * scale_factor, d['y1'] * scale_factor, d['x2'] * scale_factor, d['y2'] * scale_factor
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def show_interest_points(img, X, Y):
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"""
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Visualized interest points on an image with random colors
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Args:
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- img: A numpy array of shape (M,N,C)
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- X: A numpy array of shape (k,) containing x-locations of interest points
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- Y: A numpy array of shape (k,) containing y-locations of interest points
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Returns:
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- newImg: A numpy array of shape (M,N,C) showing the original image with
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colored circles at keypoints plotted on top of it
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"""
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newImg = img.copy()
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for x, y in zip(X.astype(int), Y.astype(int)):
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cur_color = np.random.rand(3)
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newImg = cv2.circle(newImg, (int(x), int(y)), 10, cur_color, -1)
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return newImg
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def conv3x3(in_planes, out_planes, stride=1):
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"""
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3x3 convolution with padding
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"""
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return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
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class BasicBlock(nn.Module):
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expansion = 1
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def __init__(self, inplanes, planes, stride=1, downsample=None):
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super(BasicBlock, self).__init__()
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self.conv1 = conv3x3(inplanes, planes, stride)
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self.bn1 = nn.BatchNorm2d(planes)
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self.relu = nn.ReLU(inplace=True)
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self.conv2 = conv3x3(planes, planes)
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self.bn2 = nn.BatchNorm2d(planes)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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else:
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residual = x
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out += residual
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out = self.relu(out)
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return out
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class Bottleneck(nn.Module):
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expansion = 4
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def __init__(self, inplanes, planes, stride=1, downsample=None):
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super(Bottleneck, self).__init__()
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self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
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self.bn1 = nn.BatchNorm2d(planes)
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(planes)
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self.conv3 = nn.Conv2d(planes, planes*4, kernel_size=1, bias=False)
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self.bn3 = nn.BatchNorm2d(planes*4)
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self.relu = nn.ReLU(inplace=True)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu(out)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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else:
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residual = x
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out += residual
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out = self.relu(out)
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return out
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class ResNet(nn.Module):
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def __init__(self, block, layers, in_channels=3, channels=[16, 32, 64], num_classes=10, flatten=True):
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super(ResNet, self).__init__()
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self.name = "resnet"
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self.flatten = flatten
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self.channels = channels
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self.inplanes = channels[0]
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self.conv1 = nn.Conv2d(in_channels, channels[0], kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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self.bn1 = nn.BatchNorm2d(channels[0])
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self.relu = nn.ReLU(inplace=True)
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self.layer1 = self._make_layer(block, channels[0], layers[0])
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self.layer2 = self._make_layer(block, channels[1], layers[1], stride=2)
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self.layer3 = self._make_layer(block, channels[2], layers[2], stride=2)
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self.avgpool = nn.AdaptiveAvgPool2d(1) # global pooling
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self.fc = nn.Linear(channels[2], num_classes) # global pooling
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if flatten:
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self.feature_size = channels[2]*block.expansion
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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m.weight.data.normal_(0, math.sqrt(2. / n))
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elif isinstance(m, nn.BatchNorm2d):
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m.weight.data.fill_(1)
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m.bias.data.zero_()
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def _make_layer(self, block, planes, blocks, stride=1):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(planes * block.expansion)
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)
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layers = []
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layers.append(block(self.inplanes, planes, stride, downsample))
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self.inplanes = planes * block.expansion
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for _ in range(1, blocks):
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layers.append(block(self.inplanes, planes))
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return nn.Sequential(*layers)
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def forward(self, x):
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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if self.flatten:
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x = self.avgpool(x)
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x = torch.flatten(x, 1)
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x = self.fc(x)
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return x
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