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detect.py
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from collections import defaultdict
import torch.nn.functional as F
import torch
import torch.optim as optim
import torch.nn as nn
from torch.optim import lr_scheduler
import time
import copy
from unet.pytorch_DPCN import FFT2, UNet, LogPolar, PhaseCorr, Corr2Softmax
from data.dataset_DPCN import *
import numpy as np
import shutil
from utils.utils import *
import kornia
from data.dataset import *
from utils.detect_utils import *
def detect_model(template_path, source_path, model_template, model_source, model_corr2softmax,\
model_trans_template, model_trans_source, model_trans_corr2softmax):
batch_size_inner = 1
since = time.time()
# Each epoch has a training and validation phase
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# device = torch.device("cpu")
phase = "val"
model_template.eval() # Set model to evaluate mode
model_source.eval()
model_corr2softmax.eval()
model_trans_template.eval()
model_trans_source.eval()
model_trans_corr2softmax.eval()
with torch.no_grad():
iters = 0
acc = 0.
template, _, _, _, _, _ = default_loader(template_path, 256)
source, _, _, _, _, _ = default_loader(source_path, 256)
template = template.to(device)
source = source.to(device)
template = template.unsqueeze(0)
template = template.permute(1,0,2,3)
source = source.unsqueeze(0)
source = source.permute(1,0,2,3)
iters += 1
since = time.time()
rotation_cal, scale_cal = detect_rot_scale(template, source,\
model_template, model_source, model_corr2softmax, device )
tranformation_y, tranformation_x, image_aligned, source_rotated = detect_translation(template, source, rotation_cal, scale_cal, \
model_trans_template, model_trans_source, model_trans_corr2softmax, device)
time_elapsed = time.time() - since
# print('in detection time {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
print("in detection time", time_elapsed)
plot_and_save_result(template[0,:,:], source[0,:,:], source_rotated[0,:,:], image_aligned)
checkpoint_path = "./checkpoints/checkpoint_simulation_hetero.pt"
template_path = "./demo/temp_1.png"
source_path = "./demo/src_1.png"
load_pretrained =True
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print("The devices that the code is running on:", device)
# device = torch.device("cpu")
batch_size = 1
num_class = 1
start_epoch = 0
model_template = UNet(num_class).to(device)
model_source = UNet(num_class).to(device)
model_corr2softmax = Corr2Softmax(200., 0.).to(device)
model_trans_template = UNet(num_class).to(device)
model_trans_source = UNet(num_class).to(device)
model_trans_corr2softmax = Corr2Softmax(200., 0.).to(device)
optimizer_ft_temp = optim.Adam(filter(lambda p: p.requires_grad, model_template.parameters()), lr=2e-4)
optimizer_ft_src = optim.Adam(filter(lambda p: p.requires_grad, model_source.parameters()), lr=2e-4)
optimizer_c2s = optim.Adam(filter(lambda p: p.requires_grad, model_corr2softmax.parameters()), lr=1e-1)
optimizer_trans_ft_temp = optim.Adam(filter(lambda p: p.requires_grad, model_template.parameters()), lr=2e-4)
optimizer_trans_ft_src = optim.Adam(filter(lambda p: p.requires_grad, model_source.parameters()), lr=2e-4)
optimizer_trans_c2s = optim.Adam(filter(lambda p: p.requires_grad, model_corr2softmax.parameters()), lr=1e-1)
if load_pretrained:
model_template, model_source, model_corr2softmax, model_trans_template, model_trans_source, model_trans_corr2softmax,\
_, _, _, _, _, _,\
start_epoch = load_checkpoint(\
checkpoint_path, model_template, model_source, model_corr2softmax, model_trans_template, model_trans_source, model_trans_corr2softmax,\
optimizer_ft_temp, optimizer_ft_src, optimizer_c2s, optimizer_trans_ft_temp, optimizer_trans_ft_src, optimizer_trans_c2s, device)
detect_model(template_path, source_path, model_template, model_source, model_corr2softmax, model_trans_template, model_trans_source, model_trans_corr2softmax)