chenxuanhong
210 lines
8.5 KiB
Python
210 lines
8.5 KiB
Python
#!/usr/bin/env python3
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# -*- coding:utf-8 -*-
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#############################################################
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# File: tester_ID_Pose.py
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# Created Date: Friday March 4th 2022
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# Author: Liu Naiyuan
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# Email: chenxuanhongzju@outlook.com
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# Last Modified: Saturday, 5th March 2022 1:00:29 am
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# Modified By: Chen Xuanhong
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# Copyright (c) 2022 Shanghai Jiao Tong University
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#############################################################
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import os
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import cv2
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import time
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import glob
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from tqdm import tqdm
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import torch
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import torch.nn.functional as F
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from torchvision import transforms
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from torch.utils import data
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import numpy as np
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import PIL
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from PIL import Image
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class TotalDataset(data.Dataset):
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"""Dataset class for the vggface dataset with precalulated face landmarks."""
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def __init__(self,image_dir,content_transform):
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self.image_dir= image_dir
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self.content_transform= content_transform
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self.dataset = []
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self.preprocess()
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self.num_images = len(self.dataset)
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def preprocess(self):
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"""Preprocess the Face++ original frames."""
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filenames = sorted(glob.glob(os.path.join(self.image_dir, '*'), recursive=False))
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# self.total_num = len(lines)
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for filename in filenames:
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self.dataset.append(filename)
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print('Finished preprocessing the Face++ original frames dataset...')
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def __getitem__(self, index):
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"""Return two src domain images and two dst domain images."""
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src_filename = self.dataset[index]
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split_tmp = src_filename.split('/')
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save_filename = split_tmp[-1]
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src_image1 = self.content_transform(Image.open(src_filename))
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return src_image1, save_filename
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def __len__(self):
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"""Return the number of images."""
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return len(self.dataset)
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def getLoader(c_image_dir, batch_size=16):
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"""Build and return a data loader."""
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num_workers = 8
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c_transforms = []
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c_transforms.append(transforms.ToTensor())
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c_transforms.append(transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]))
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# c_transforms.append(T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)))
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c_transforms = transforms.Compose(c_transforms)
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content_dataset = TotalDataset(c_image_dir, c_transforms)
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content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size,
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drop_last=False,shuffle=False,num_workers=num_workers,pin_memory=True)
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return content_data_loader, len(content_dataset)
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class Tester(object):
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def __init__(self, config, reporter):
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self.config = config
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# logger
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self.reporter = reporter
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self.imagenet_std = torch.tensor([0.229, 0.224, 0.225]).cuda().view(3,1,1)
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self.imagenet_mean = torch.tensor([0.485, 0.456, 0.406]).cuda().view(3,1,1)
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def __init_framework__(self):
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'''
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This function is designed to define the framework,
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and print the framework information into the log file
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'''
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#===============build models================#
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print("build models...")
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# TODO [import models here]
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model_config = self.config["model_configs"]
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gscript_name = self.config["com_base"] + model_config["g_model"]["script"]
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class_name = model_config["g_model"]["class_name"]
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package = __import__(gscript_name, fromlist=True)
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gen_class = getattr(package, class_name)
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self.network = gen_class(**model_config["g_model"]["module_params"])
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# TODO replace below lines to define the model framework
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self.network = gen_class(**model_config["g_model"]["module_params"])
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self.network = self.network.eval()
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# print and recorde model structure
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self.reporter.writeInfo("Model structure:")
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self.reporter.writeModel(self.network.__str__())
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arcface1 = torch.load(self.arcface_ckpt, map_location=torch.device("cpu"))
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self.arcface = arcface1['model'].module
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self.arcface.eval()
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self.arcface.requires_grad_(False)
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model_path = os.path.join(self.config["project_checkpoints"],
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"step%d_%s.pth"%(self.config["checkpoint_step"],
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self.config["checkpoint_names"]["generator_name"]))
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self.network.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))
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print('loaded trained backbone model step {}...!'.format(self.config["checkpoint_step"]))
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# train in GPU
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if self.config["cuda"] >=0:
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self.network = self.network.cuda()
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self.arcface = self.arcface.cuda()
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def test(self):
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save_dir = self.config["test_samples_path"]
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version = self.config["version"]
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batch_size = self.config["batch_size"]
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specified_save_path = self.config["specified_save_path"]
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self.arcface_ckpt= self.config["arcface_ckpt"]
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self.reporter.writeInfo("Version %s"%version)
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if os.path.isdir(specified_save_path):
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print("Input a legal specified save path!")
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save_dir = specified_save_path
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save_dir = os.path.join(save_dir,"v_%s_step_%d"%(version,self.config["checkpoint_step"]))
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if not os.path.exists(save_dir):
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os.makedirs(save_dir)
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source_loader, dataet_len = getLoader(
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self.config["env_config"]["dataset_paths"]["id_pose_source_root"], batch_size=batch_size)
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target_loader, dataet_len = getLoader(
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self.config["env_config"]["dataset_paths"]["id_pose_source_root"], batch_size=batch_size)
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source_iter = iter(source_loader)
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target_iter = iter(target_loader)
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# models
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self.__init_framework__()
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# Start time
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import datetime
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print("Start to test at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
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print('Start =================================== test...')
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start_time = time.time()
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self.network.eval()
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with torch.no_grad():
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for profile_batch, filename_batch in tqdm(source_iter):
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profile_batch = profile_batch.cuda()
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profile_id_downsample = F.interpolate(profile_batch, (112,112), mode='bicubic')
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profile_latent_id = self.arcface(profile_id_downsample)
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profile_latent_id = F.normalize(profile_latent_id, p=2, dim=1)
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if init_batch ==True:
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wholeid_batch = profile_latent_id.cpu()
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init_batch = False
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else:
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wholeid_batch = torch.cat([wholeid_batch,profile_latent_id.cpu()],dim=0)
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target_source_pair_dict = np.load(
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self.config["env_config"]["dataset_paths"]["pairs_dict"] ,allow_pickle=True).item()
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for target_batch, filename_batch in tqdm(target_iter):
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target_index_list = []
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init_id_batch = True
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for filename_tmp in filename_batch:
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source_index = int(filename_tmp.split('_')[0])
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target_index = target_source_pair_dict[source_index]
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target_index_list.append(target_index)
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if init_id_batch:
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batch_id = wholeid_batch[target_index][None].cuda()
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init_id_batch = False
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else:
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batch_id = torch.cat([batch_id, wholeid_batch[target_index][None].cuda()],dim = 0)
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img_fakes = self.network(target_batch.cuda(), batch_id)
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for img_fake, target_index_tmp,filename_tmp in zip(img_fakes, target_index_list,filename_batch):
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filename_tmp_split = filename_tmp.split('_')
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final_filename = filename_tmp_split[0] + '_' +str(target_index_tmp) + '_' + filename_tmp_split[-1]
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save_path = os.path.join(save_dir,final_filename)
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img_fake = img_fake * self.imagenet_std + self.imagenet_mean
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img_fake = img_fake.numpy().transpose(1,2,0)
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img_fake = np.clip(img_fake,0.0,1.0) * 255
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PIL.Image.fromarray(img_fake).save(save_path,quality=100)
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elapsed = time.time() - start_time
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elapsed = str(datetime.timedelta(seconds=elapsed))
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print("Elapsed [{}]".format(elapsed)) |