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test_4_networks_mix.py
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test_4_networks_mix.py
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from utils.data_loader_four_tuples_mix import ImageDataset, ImageTransform, generate_training_data_list, generate_testing_data_list
from models.UNet import UNet
from torchvision.utils import make_grid
from torchvision.utils import save_image
from torchvision import models
from torchvision import transforms
from torch.autograd import Variable
from collections import OrderedDict
from PIL import Image
from tqdm import tqdm
import matplotlib.pyplot as plt
import torch.optim as optim
import torch.nn as nn
import numpy as np
import argparse
import time
import torch
import os
torch.manual_seed(1)
def get_parser():
parser = argparse.ArgumentParser()
parser.add_argument('-l', '--load', type=str, default=None, help='the number of checkpoints')
parser.add_argument('-s', '--image_size', type=int, default=256)
parser.add_argument('-tedd', '--testing_data_dir', type=str, default='dataset/shapenet_specular_1500/testing_data')
parser.add_argument('-tedlf', '--testing_data_list_file', type=str, default='dataset/shapenet_specular_1500/test.lst')
parser.add_argument('-mn', '--model_name', type=str, default='SSHR')
parser.add_argument('-tdn', '--testing_data_name', type=str, default='SSHR')
return parser
def fix_model_state_dict(state_dict):
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k
if name.startswith('module.'):
name = name[7:]
new_state_dict[name] = v
return new_state_dict
def unnormalize(x):
x = x.transpose(1, 3)
x = x * torch.Tensor((0.5, )) + torch.Tensor((0.5, ))
x = x.transpose(1, 3)
return x
def test(UNet1, UNet2, UNet3, UNet4, model_name, test_dataset):
device = "cuda" if torch.cuda.is_available() else "cpu"
UNet1.to(device)
UNet2.to(device)
UNet3.to(device)
UNet4.to(device)
if device == 'cuda':
UNet1 = torch.nn.DataParallel(UNet1)
UNet2 = torch.nn.DataParallel(UNet2)
UNet3 = torch.nn.DataParallel(UNet3)
UNet4 = torch.nn.DataParallel(UNet4)
print("parallel mode")
print("device:{}".format(device))
UNet1.eval()
UNet2.eval()
UNet3.eval()
UNet4.eval()
# dirs for saving results
dir_t1 = './test_result_' + model_name + '/' + key_dir + '/' + 'estimated_albedo'
dir_t2 = './test_result_' + model_name + '/' + key_dir + '/' + 'estimated_shading'
dir_t3 = './test_result_' + model_name + '/' + key_dir + '/' + 'grid'
dir_t4 = './test_result_' + model_name + '/' + key_dir + '/' + 'estimated_diffuse'
dir_t5 = './test_result_' + model_name + '/' + key_dir + '/' + 'estimated_diffuse_tc'
for n, (input_img, gt_specular_residue, gt_diffuse, gt_diffuse_tc) in enumerate([test_dataset[i] for i in range(test_dataset.__len__())]):
# print(test_dataset.img_list['path_i'][n].split('/')[4][:-4])
input_img = torch.unsqueeze(input_img, dim=0)
gt_diffuse = torch.unsqueeze(gt_diffuse, dim=0)
gt_diffuse_tc = torch.unsqueeze(gt_diffuse_tc, dim=0)
with torch.no_grad():
estimated_diffuse = UNet1(input_img.to(device))
estimated_specular_residue = UNet2(input_img.to(device))
# estimat diffuse
G3_data = torch.cat([estimated_diffuse, input_img.to(device)], dim=1)
estimated_diffuse_refined = UNet3(G3_data.to(device))
# the third stage (tone correction)
input_img = input_img.to(device)
G4_input = torch.cat([estimated_diffuse_refined, estimated_specular_residue, input_img], dim=1)
estimated_diffuse_tc = UNet4(G4_input.to(device))
# to cpu
estimated_diffuse = estimated_diffuse.to(torch.device('cpu'))
estimated_diffuse_refined = estimated_diffuse_refined.to(torch.device('cpu'))
estimated_diffuse_tc = estimated_diffuse_tc.to(torch.device('cpu'))
input_img = input_img.to(torch.device('cpu'))
grid= make_grid(torch.cat((unnormalize(input_img), unnormalize(gt_diffuse), unnormalize(estimated_diffuse_refined), unnormalize(estimated_diffuse_tc)), dim=0))
temp = len(test_dataset.img_list['path_i'][n].split('/'))
r_subdir = test_dataset.img_list['path_i'][n].split('/')[temp-2]
basename = test_dataset.img_list['path_i'][n].split('/')[temp-1]
print(r_subdir)
print(basename)
subdir = os.path.join(dir_t3, r_subdir)
if not os.path.exists(subdir):
os.makedirs(subdir)
grid_name = os.path.join(subdir, basename)
save_image(grid, grid_name)
# save diffuse images
estimated_diffuse_refined = transforms.ToPILImage(mode='RGB')(unnormalize(estimated_diffuse_refined)[0, :, :, :])
subdir = os.path.join(dir_t4, r_subdir)
if not os.path.exists(subdir):
os.makedirs(subdir)
estimated_diffuse_name = os.path.join(subdir, basename)
estimated_diffuse_refined.save(estimated_diffuse_name)
# save tone-corrected diffuse images
estimated_diffuse_tc = transforms.ToPILImage(mode='RGB')(unnormalize(estimated_diffuse_tc)[0, :, :, :])
subdir = os.path.join(dir_t5, r_subdir)
if not os.path.exists(subdir):
os.makedirs(subdir)
estimated_diffuse_tc_name = os.path.join(subdir, basename)
estimated_diffuse_tc.save(estimated_diffuse_tc_name)
def main(parser):
UNet1 = UNet(input_channels=3, output_channels=3)
UNet2 = UNet(input_channels=3, output_channels=3)
UNet3 = UNet(input_channels=6, output_channels=3)
UNet4 = UNet(input_channels=9, output_channels=3)
if parser.load is not None:
print('load checkpoint ' + parser.load)
# load UNet weights
UNet1_weights = torch.load('./checkpoints_'+parser.model_name+'/UNet1_'+parser.load+'.pth')
UNet1.load_state_dict(fix_model_state_dict(UNet1_weights))
UNet2_weights = torch.load('./checkpoints_'+parser.model_name+'/UNet2_'+parser.load+'.pth')
UNet2.load_state_dict(fix_model_state_dict(UNet2_weights))
UNet3_weights = torch.load('./checkpoints_'+parser.model_name+'/UNet3_'+parser.load+'.pth')
UNet3.load_state_dict(fix_model_state_dict(UNet3_weights))
UNet4_weights = torch.load('./checkpoints_'+parser.model_name+'/UNet4_'+parser.load+'.pth')
UNet4.load_state_dict(fix_model_state_dict(UNet4_weights))
mean = (0.5,)
std = (0.5,)
size = parser.image_size
testing_data_dir = parser.testing_data_dir
testing_data_list_file = parser.testing_data_list_file
model_name = parser.model_name
test_img_list = generate_testing_data_list(testing_data_dir, testing_data_list_file)
test_dataset = ImageDataset(img_list=test_img_list, img_transform=ImageTransform(size=size, mean=mean, std=std), phase='test')
test(UNet1, UNet2, UNet3, UNet4, model_name, test_dataset)
if __name__ == "__main__":
parser = get_parser().parse_args()
if parser.load is not None:
load_num = str(parser.load)
model_name = parser.model_name
testing_data_name = parser.testing_data_name
key_dir = testing_data_name + '_' + model_name + '_' + load_num # like SSHR_SSHR_60
main(parser)