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train_feature_projector.py
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train_feature_projector.py
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from __future__ import print_function
import os
import sys
import time
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
import tensorboard_logger as tb_logger
from torchvision import transforms, datasets
from utils.util import adjust_learning_rate, AverageMeter, Tee
from utils import tps
from models.resnet import InsResNet50,InsResNet18,InsResNet34,InsResNet101,InsResNet152
from models.hourglass import HourglassNet
from models.feat_distiller import FeatDistiller
from data_loader.data_loaders_face import CelebAPrunedAligned_MAFLVal, MAFLAligned
import matplotlib.pyplot as plt
from utils.visualization import norm_range
import numpy as np
def parse_option():
parser = argparse.ArgumentParser('argument for training')
parser.add_argument('--print_freq', type=int, default=10, help='print frequency')
parser.add_argument('--tb_freq', type=int, default=500, help='tb frequency')
parser.add_argument('--save_freq', type=int, default=1, help='save frequency')
parser.add_argument('--batch_size', type=int, default=32, help='batch_size')
parser.add_argument('--num_workers', type=int, default=8, help='num of workers to use')
parser.add_argument('--epochs', type=int, default=10, help='number of training epochs')
# optimization
parser.add_argument('--learning_rate', type=float, default=0.001, help='learning rate')
parser.add_argument('--lr_decay_epochs', type=str,
default='30,40,50', help='where to decay lr, can be a list')
parser.add_argument('--lr_decay_rate', type=float,
default=0.2, help='decay rate for learning rate')
parser.add_argument('--momentum', type=float, default=0.9, help='momentum')
parser.add_argument('--weight_decay', type=float, default=0.0005, help='weight decay')
parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for Adam')
parser.add_argument('--beta2', type=float, default=0.999, help='beta2 for Adam')
# model definition
parser.add_argument('--model', type=str, default='resnet50',
choices=['resnet50', 'resnet50_half', 'resnet50x2', 'resnet50x4',
'hourglass','resnet18', 'resnet34', 'resnet101', 'resnet152'])
parser.add_argument('--trained_model_path', type=str, default=None, help='pretrained moco')
parser.add_argument('--train_layer', type=int, default=4, help='num layer in hypercol')
parser.add_argument('--val_layer', type=int, default=4, help='num layer in hypercol')
# crop
parser.add_argument('--image_crop', type=int, default=20, help='image pre-crop')
parser.add_argument('--image_size', type=int, default=136, help='image size')
parser.add_argument('--train_out_size', type=int, default=24, help='output size')
parser.add_argument('--val_out_size', type=int, default=96, help='output size')
# dataset
# fine-tune the pretrained moco on CelebA dataset
parser.add_argument('--dataset', type=str, default='CelebA', choices=['MAFLAligned', 'AFLW_MTFL', 'AFLW', 'ThreeHundredW', 'InatAve', 'CelebA'])
parser.add_argument('--val_dataset', type=str, default='MAFLAligned', choices=['MAFLAligned', 'AFLW_MTFL', 'AFLW', 'ThreeHundredW', 'InatAve', 'CelebA'], help='dataset used for image matching experiments')
# model path and name
parser.add_argument('--model_name', type=str, default='feature projector')
parser.add_argument('--model_path', type=str, default='./logs') # path to store the models
# resume
parser.add_argument('--resume', default='', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
# augmentation
parser.add_argument('--TPS_aug', action='store_true', help='Thin-Plate Spline augmentation')
# add BN
parser.add_argument('--bn', action='store_true', help='use parameter-free BN')
parser.add_argument('--cosine', action='store_true', help='use cosine annealing')
parser.add_argument('--multistep', action='store_true', help='use multistep LR')
parser.add_argument('--adam', action='store_true', help='use adam optimizer')
parser.add_argument('--amsgrad', action='store_true', help='use amsgrad for adam')
# GPU setting
parser.add_argument('--gpu', default=None, type=int, help='GPU id to use.')
# log_path
parser.add_argument('--log_path', default='log_tmp', type=str, metavar='PATH', help='path to the log file')
parser.add_argument('--vis_path', type=str, metavar='PATH', help='path to save visualization results')
# use hypercolumn or single layer output
parser.add_argument('--train_use_hypercol', action='store_true', help='use HC as representations during training')
parser.add_argument('--val_use_hypercol', action='store_true', help='use HC as representations during testing')
# feature distillation
parser.add_argument('--feat_distill', action='store_true', help='feature distillation')
parser.add_argument('--distill_mode', type=str, default='softmax',
choices=['softplus', 'softmax', 'linear'], help='mode of heatmap')
parser.add_argument('--kernel_size', type=int, default=1, help='kernel_size in the feature distiller')
parser.add_argument('--out_dim', type=int, default=64, help='dim of feature distiller output')
parser.add_argument('--softargmax_mul', type=float, default=7., help='temparture hyperparameters in feature distiller')
parser.add_argument('--temperature', type=float, default=7., help='temparture hyperparameters for dense corr loss')
parser.add_argument('--trained_feat_model_path', type=str, default=None, help='the pretrained feat model to test')
parser.add_argument('--evaluation_mode', action='store_true', help='evaluate pretrained feature distiller')
parser.add_argument('--visualize_matching', action='store_true', help='evaluate pretrained feature distiller')
opt = parser.parse_args()
# set the path according to the environment
default_roots = {
"MAFLAligned": "./datasets/celeba",
"AFLW_MTFL": "./datasets/face_datasets/aflw-mtfl",
"AFLW": "./datasets/face_datasets/aflw/aflw_release-2",
"ThreeHundredW": "./datasets/face_datasets/300w/300w",
}
eye_idxs = {
"MAFLAligned": [0, 1],
"AFLW_MTFL": [0, 1],
"AFLW": [0, 1],
"ThreeHundredW": [36, 45]
}
num_annotated_points = {
"MAFLAligned": 5,
"AFLW_MTFL": 5,
"AFLW": 5,
"ThreeHundredW": 68
}
opt.data_folder = default_roots[opt.val_dataset]
opt.eye_idx = eye_idxs[opt.val_dataset]
opt.num_points = num_annotated_points[opt.val_dataset]
Tee(opt.log_path, 'a')
iterations = opt.lr_decay_epochs.split(',')
opt.lr_decay_epochs = list([])
for it in iterations:
opt.lr_decay_epochs.append(int(it))
opt.save_path = opt.model_path
opt.tb_path = '%s_tensorboard' % opt.model_path
opt.tb_folder = os.path.join(opt.tb_path, opt.model_name)
if not os.path.isdir(opt.tb_folder):
os.makedirs(opt.tb_folder)
opt.save_folder = os.path.join(opt.save_path, opt.model_name)
if not os.path.isdir(opt.save_folder):
os.makedirs(opt.save_folder)
return opt
def main():
global best_error
best_error = np.Inf
args = parse_option()
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
torch.manual_seed(0)
# train on celebA unlabeled dataset
train_dataset = CelebAPrunedAligned_MAFLVal(args.data_folder,
train=True,
pair_image=False,
do_augmentations=True,
imwidth=args.image_size,
crop = args.image_crop)
print('Number of training images: %d' % len(train_dataset))
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.batch_size, shuffle=True,
num_workers=args.num_workers, pin_memory=True, sampler=None)
# validation set from MAFLAligned trainset for hyperparameter searching
# we sample 2000 images as our val set
val_dataset = MAFLAligned(args.data_folder,
train=True, # train set
pair_image=True,
do_augmentations=False,
TPS_aug = True,
imwidth=args.image_size,
crop=args.image_crop)
print('Initial number of validation images: %d' % len(val_dataset))
val_dataset.restrict_annos(num=2000, outpath=args.save_folder, repeat_flag=False)
print('After restricting the size of validation set: %d' % len(val_dataset))
val_loader = torch.utils.data.DataLoader(
val_dataset, batch_size=2, shuffle=False,
num_workers=args.num_workers, pin_memory=True)
# testing set from MAFLAligned test for evaluating image matching
test_dataset = MAFLAligned(args.data_folder,
train=False, # test set
pair_image=True,
do_augmentations=False,
TPS_aug = True, # match landmark between deformed images
imwidth=args.image_size,
crop=args.image_crop)
print('Number of testing images: %d' % len(test_dataset))
test_loader = torch.utils.data.DataLoader(
test_dataset, batch_size=2, shuffle=False,
num_workers=args.num_workers, pin_memory=True)
assert len(val_dataset) == 2000
assert len(test_dataset) == 1000
# create model and optimizer
input_size = args.image_size - 2 * args.image_crop
pool_size = int(input_size / 2**5) # 96x96 --> 3; 160x160 --> 5; 224x224 --> 7;
# we use smaller feature map when training the feature distiller for memory issue
args.train_output_shape = (args.train_out_size, args.train_out_size)
# we use the original size of the image (e.g. 96x96 face images) during testing
args.val_output_shape = (args.val_out_size, args.val_out_size)
if args.model == 'resnet50':
model = InsResNet50(pool_size=pool_size)
desc_dim = {1:64, 2:256, 3:512, 4:1024, 5:2048}
elif args.model == 'resnet50_half':
model = InsResNet50(width=0.5, pool_size=pool_size)
desc_dim = {1:int(64/2), 2:int(256/2), 3:int(512/2), 4:int(1024/2), 5:int(2048/2)}
elif args.model == 'resnet50x2':
model = InsResNet50(width=2, pool_size=pool_size)
desc_dim = {1:128, 2:512, 3:1024, 4:2048, 5:4096}
elif args.model == 'resnet50x4':
model = InsResNet50(width=4, pool_size=pool_size)
desc_dim = {1:512, 2:1024, 3:2048, 4:4096, 5:8192}
elif args.model == 'resnet18':
model = InsResNet18(width=1, pool_size=pool_size)
desc_dim = {1:64, 2:64, 3:128, 4:256, 5:512}
elif args.model == 'resnet34':
model = InsResNet34(width=1, pool_size=pool_size)
desc_dim = {1:64, 2:64, 3:128, 4:256, 5:512}
elif args.model == 'resnet101':
model = InsResNet101(width=1, pool_size=pool_size)
desc_dim = {1:64, 2:256, 3:512, 4:1024, 5:2048}
elif args.model == 'resnet152':
model = InsResNet152(width=1, pool_size=pool_size)
desc_dim = {1:64, 2:256, 3:512, 4:1024, 5:2048}
elif args.model == 'hourglass':
model = HourglassNet()
else:
raise NotImplementedError('model not supported {}'.format(args.model))
# xxx_feat_spectral records the feat dim per layer in hypercol
# this information is useful to do layer-wise feat normalization in landmark matching
train_feat_spectral = []
if args.train_use_hypercol:
for i in range(args.train_layer):
train_feat_spectral.append(desc_dim[5-i])
else:
train_feat_spectral.append(desc_dim[args.train_layer])
args.train_feat_spectral = train_feat_spectral
val_feat_spectral = []
if args.val_use_hypercol:
for i in range(args.val_layer):
val_feat_spectral.append(desc_dim[5-i])
else:
val_feat_spectral.append(desc_dim[args.val_layer])
args.val_feat_spectral = val_feat_spectral
# load pretrained moco
if args.trained_model_path != 'none':
print('==> loading pre-trained model')
ckpt = torch.load(args.trained_model_path, map_location='cpu')
model.load_state_dict(ckpt['model'], strict=True)
print("==> loaded checkpoint '{}' (epoch {})".format(
args.trained_model_path, ckpt['epoch']))
print('==> done')
else:
print('==> use randomly initialized model')
# Define feature distiller, set pretrained model to eval mode
if args.feat_distill:
model.eval()
assert np.sum(train_feat_spectral) == np.sum(val_feat_spectral)
feat_distiller = FeatDistiller(np.sum(val_feat_spectral),
kernel_size=args.kernel_size,
mode=args.distill_mode,
out_dim = args.out_dim,
softargmax_mul=args.softargmax_mul)
feat_distiller = nn.DataParallel(feat_distiller)
feat_distiller.train()
print('Feature distillation is used: kernel_size:{}, mode:{}, out_dim:{}'.format(
args.kernel_size, args.distill_mode, args.out_dim))
feat_distiller = feat_distiller.cuda()
else:
feat_distiller = None
# evaluate feat distiller on landmark matching, given pretrained moco and feature distiller
model = model.cuda()
if args.evaluation_mode:
if args.feat_distill:
print("==> use pretrained feature distiller ...")
feat_ckpt = torch.load(args.trained_feat_model_path, map_location='cpu')
# in below, feat_distiller is misspelt, but to use pretrained model, I keep it.
feat_distiller.load_state_dict(feat_ckpt['feat_disiller'], strict=False)
print("==> loaded checkpoint '{}' (epoch {})".format(
args.trained_feat_model_path, feat_ckpt['epoch']))
same_err, diff_err = validate(test_loader, model, args,
feat_distiller=feat_distiller,
visualization=args.visualize_matching)
else:
print("==> use hypercolumn ...")
same_err, diff_err = validate(test_loader, model, args,
feat_distiller=None,
visualization=args.visualize_matching)
exit()
## define optimizer for feature distiller
if not args.adam:
if not args.feat_distill:
optimizer = torch.optim.SGD(model.parameters(),
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
else:
optimizer = torch.optim.SGD(feat_distiller.parameters(),
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
else:
if not args.feat_distill:
optimizer = torch.optim.Adam(model.parameters(),
lr=args.learning_rate,
betas=(args.beta1, args.beta2),
weight_decay=args.weight_decay,
eps=1e-8,
amsgrad=args.amsgrad)
else:
optimizer = torch.optim.Adam(feat_distiller.parameters(),
lr=args.learning_rate,
betas=(args.beta1, args.beta2),
weight_decay=args.weight_decay,
eps=1e-8,
amsgrad=args.amsgrad)
# set lr scheduler
if args.cosine: # we use cosine scheduler by default
eta_min = args.learning_rate * (args.lr_decay_rate ** 3) * 0.1
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, args.epochs, eta_min, -1)
elif args.multistep:
scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[100, 250], gamma=0.1)
# tensorboard
logger = tb_logger.Logger(logdir=args.tb_folder, flush_secs=2)
cudnn.benchmark = True
# report the performance of hypercol on landmark matching tasks
print("==> Testing of initial model on validation set...")
same_err, diff_err = validate(val_loader, model, args, feat_distiller=None)
print("==> Testing of initial model on test set...")
same_err, diff_err = validate(test_loader, model, args, feat_distiller=None)
# training loss for feature projector
criterion = dense_corr_loss
# training feature distiller
for epoch in range(1, args.epochs + 1):
if args.cosine or args.multistep:
scheduler.step()
else:
adjust_learning_rate(epoch, args, optimizer)
print("==> training ...")
time1 = time.time()
train_loss = train_point_contrast(epoch, train_loader, model, criterion, optimizer, args,
feat_distiller=feat_distiller)
time2 = time.time()
print('train epoch {}, total time {:.2f}, train_loss {:.4f}'.format(epoch,
time2 - time1, train_loss))
logger.log_value('train_loss', train_loss, epoch)
logger.log_value('learning_rate', optimizer.param_groups[0]['lr'], epoch)
print("==> validation ...")
val_same_err, val_diff_err = validate(val_loader, model, args,
feat_distiller=feat_distiller)
print("==> testing ...")
test_same_err, test_diff_err = validate(test_loader, model, args,
feat_distiller=feat_distiller)
# save model
if epoch % args.save_freq == 0:
print('==> Saving...')
state = {
'opt': args,
'epoch': epoch,
'feat_disiller': feat_distiller.state_dict(),
'val_error': [val_same_err, val_diff_err],
'test_error': [test_same_err, test_diff_err],
}
save_name = 'ckpt_epoch_{epoch}.pth'.format(epoch=epoch)
save_name = os.path.join(args.save_folder, save_name)
print('saving regular model!')
torch.save(state, save_name)
if val_diff_err < best_error:
best_error = val_diff_err
save_name = 'best.pth'
save_name = os.path.join(args.save_folder, save_name)
print('saving best model! val_same: {} val_diff: {} test_same: {} test_diff: {}'.format(val_same_err, val_diff_err, test_same_err, test_diff_err))
torch.save(state, save_name)
def set_lr(optimizer, lr):
"""
set the learning rate
"""
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def train_point_contrast(epoch,
train_loader,
model, # pretrained moco
criterion, # dense correspondence loss by default
optimizer,
opt,
feat_distiller=None):
"""
one epoch training
"""
if feat_distiller is None:
model.train()
else:
model.eval()
feat_distiller.train()
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
end = time.time()
for idx, (input, _, _, index) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
input = input.cuda(opt.gpu, non_blocking=True)
input = input.float()
# ===================forward=====================
if feat_distiller is not None:
with torch.no_grad():
feat = model(input, opt.train_layer, opt.train_use_hypercol,
opt.train_output_shape)
feat.detach()
feat = feat_distiller(feat)
train_feat_spectral = [opt.out_dim]
else:
feat = model(input, opt.train_layer, opt.train_use_hypercol, opt.train_output_shape)
train_feat_spectral = opt.train_feat_spectral
loss = criterion(feat, input.size(), opt, train_feat_spectral)
if idx == 0:
print('Layer:{0}, shape of input:{1}, feat:{2}'.format(
opt.train_layer, input.size(), feat.size()))
losses.update(loss.item(), input.size(0))
# ===================backward=====================
optimizer.zero_grad()
loss.backward()
optimizer.step()
# ===================meters=====================
batch_time.update(time.time() - end)
end = time.time()
# print info
if idx % opt.print_freq == 0:
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})'.format(
epoch, idx, len(train_loader), batch_time=batch_time,
data_time=data_time, loss=losses))
sys.stdout.flush()
return losses.avg
def dense_corr_loss(feat, input_size, opt, feat_spectral, pow=0.5, normalize_vectors=True):
# feat_spectral is a list of dimensions of features from different layers
B, C, H, W = input_size
b, c, h, w = feat.size()
device = feat.device
stride = H // h
with torch.no_grad():
yyxx = tps.spatial_grid_unnormalized(H, W).to(device)
diff = yyxx[::stride, ::stride, None, None, :] - yyxx[None, None, ::stride, ::stride, :]
diff = (diff * diff).sum(4).sqrt()
diff = diff.pow(pow)
loss = 0.
for bb in range(b):
f1 = feat[bb].reshape(c, h*w)
if normalize_vectors:
f1 = layer_wise_normalize(f1, feat_spectral)
corr = torch.matmul(f1.t(), f1)
corr = corr.reshape(h, w, h, w)
smcorr = F.softmax(corr.reshape(h, w, -1) * opt.temperature, dim=2).reshape(corr.shape)
L = diff * smcorr
loss += L.sum()
return loss / (h * w * b)
def validate(val_loader, model, opt, feat_distiller=None, visualization=False):
torch.manual_seed(1)
np.random.seed(1)
batch_time = AverageMeter()
# switch to evaluate mode
model.eval()
if feat_distiller is not None:
feat_distiller.eval()
same_errs = []
diff_errs = []
with torch.no_grad():
end = time.time()
for idx, (input, _, target, index) in enumerate(val_loader):
# get A1, A2, B1, B2
# get feats for each images
input = input.cuda(opt.gpu, non_blocking=True)
input = input.float()
target = target.cuda(opt.gpu, non_blocking=True)
# extract dense descriptors
x1, x2 = torch.split(input, [3, 3], dim = 1)
feat_x1 = model(x1, opt.val_layer, opt.val_use_hypercol, opt.val_output_shape)
feat_x1 = feat_x1.detach()
feat_x2 = model(x2, opt.val_layer, opt.val_use_hypercol, opt.val_output_shape)
feat_x2 = feat_x2.detach()
val_feat_spectral = opt.val_feat_spectral
if feat_distiller is not None:
feat_x1 = feat_distiller(feat_x1)
feat_x2 = feat_distiller(feat_x2)
val_feat_spectral = [opt.out_dim]
# images
im_source = x1[0]
im_same = x2[0]
im_diff = x2[1]
# we use batchsize=2
feat_source = feat_x1[0] # C*H*W
feat_same = feat_x2[0]
feat_diff = feat_x2[1]
# feature normalization
# We always do the layer wise normalization for hypercolumn in landmark matching
# otherwise, the performance drops
if True:
fsrc = layer_wise_normalize(feat_source, val_feat_spectral)
fsame = layer_wise_normalize(feat_same, val_feat_spectral)
fdiff = layer_wise_normalize(feat_diff, val_feat_spectral)
else:
fsrc = feat_source.clone()
fsame = feat_same.clone()
fdiff = feat_diff.clone()
kp1, kp2 = torch.split(target, [5, 5], dim = 1)
kp_source = kp1[0]
kp_same = kp2[0]
kp_diff = kp2[1]
B, C, imH, imW = x1.size()
B, C, featH, featW = feat_x1.size()
if idx == 0:
print('image shape: {}; feature shape: {}'.format(x1.size(), feat_x1.size()))
same_match = []
diff_match = []
# get the matching and compute the pixel error
for ki, kp in enumerate(kp_source):
# normalized keypoints
x, y = kp.cpu().numpy()
gt_same_x, gt_same_y = kp_same[ki].cpu().numpy()
gt_diff_x, gt_diff_y = kp_diff[ki].cpu().numpy()
same_x, same_y = find_descriptor(x, y, fsrc, fsame)
err = compute_pixel_err(pred_x=same_x,
pred_y=same_y,
gt_x=gt_same_x,
gt_y=gt_same_y,
insize=featH)
same_errs.append(err)
same_match.append([same_y, same_x])
diff_x, diff_y = find_descriptor(x, y, fsrc, fdiff)
err = compute_pixel_err(pred_x=diff_x,
pred_y=diff_y,
gt_x=gt_diff_x,
gt_y=gt_diff_y,
insize=featH)
diff_errs.append(err)
diff_match.append([diff_y, diff_x])
if visualization:
# visualize the matching for debugging
target_outpath = os.path.join(opt.vis_path, str(idx))
if not os.path.exists(target_outpath):
os.makedirs(target_outpath)
plot_images(im_source, kp_source[:, [1, 0]].cpu().numpy(),
os.path.join(target_outpath, '1_source.png'))
plot_images(im_same, kp_same[:, [1, 0]].cpu().numpy(),
os.path.join(target_outpath, '2_tsame_gt.png'))
plot_images(im_same, same_match, os.path.join(target_outpath, '3_tsame.png'))
plot_images(im_diff, kp_diff[:, [1, 0]].cpu().numpy(),
os.path.join(target_outpath, '4_tdiff_gt.png'))
plot_images(im_diff, diff_match, os.path.join(target_outpath, '5_tdiff.png'))
batch_time.update(time.time() - end)
end = time.time()
print('Same_error {same_mean_err:.3f}\t'
'Diff_error {diff_mean_err:.3f}'.format(
same_mean_err=np.mean(same_errs),
diff_mean_err=np.mean(diff_errs)))
return np.mean(same_errs), np.mean(diff_errs)
def layer_wise_normalize(feat, feat_spectral):
# we normalize features from different layers seperately for the hypercol representations
feat_out = []
i_prev = 0
for feat_dim in feat_spectral:
i = i_prev + feat_dim
feat_out.append(F.normalize(feat[i_prev: i], p=2, dim=0))
i_prev = i
feat_out = torch.cat(feat_out, 0)
return feat_out
def compute_pixel_err(pred_x, pred_y, gt_x, gt_y, insize):
# this metric follows DVE's implementations:
# https://github.com/jamt9000/DVE/blob/master/test_matching.py
# we normalize the coordinates from [-1, 1] to [0, 1]
canonical_sz = 70
scale = canonical_sz
pred_x = (pred_x + 1.) / 2. * (scale - 1)
pred_y = (pred_y + 1.) / 2. * (scale - 1)
gt_x = (gt_x + 1.) / 2. * (scale - 1)
gt_y = (gt_y + 1.) / 2. * (scale - 1)
return np.sqrt((gt_x - pred_x)**2 + (gt_y - pred_y)**2)
def find_descriptor(x, y, source_descs, target_descs):
# input and output of this function are both normalized coors
C, H, W = source_descs.shape
x = int(np.round((x + 1.) / 2. * (W - 1)))
y = int(np.round((y + 1.) / 2. * (H - 1)))
x = min(W - 1, max(x, 0))
y = min(H - 1, max(y, 0))
query_desc = source_descs[:, y, x]
corr = torch.matmul(query_desc.reshape(-1, C), target_descs.reshape(C, H * W))
maxidx = corr.argmax()
grid = tps.spatial_grid_unnormalized(H, W).reshape(-1, 2)
y, x = grid[maxidx]
x_norm = 2. * x.item() / (W - 1) - 1 # normalize to [-1, 1]
y_norm = 2. * y.item() / (H - 1) - 1
return x_norm, y_norm
def plot_images(image, points, path):
C, H, W = image.size()
points = np.array(points)
points[:, 0] = (points[:, 0] + 1.) / 2. * (H - 1)
points[:, 1] = (points[:, 1] + 1.) / 2. * (W - 1)
fig = plt.figure()
fig.set_size_inches(1., 1, forward = False)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax.imshow(norm_range(image).permute(1, 2, 0).cpu().numpy())
_cmap = plt.cm.get_cmap('gist_rainbow')
K = len(points)
colors = [np.array(_cmap(i)[:3]) for i in np.arange(0,1,1/K)]
for i, point in enumerate(points):
ax.scatter(point[1], point[0], c=[colors[i]], marker='.')
plt.savefig(path, dpi=2*image.shape[1])
plt.close()
if __name__ == '__main__':
main()