chenxuanhong
158 lines
6.4 KiB
Python
158 lines
6.4 KiB
Python
#!/usr/bin/env python3
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# -*- coding:utf-8 -*-
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#############################################################
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# File: tester_commonn.py
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# Created Date: Saturday July 3rd 2021
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# Author: Chen Xuanhong
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# Email: chenxuanhongzju@outlook.com
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# Last Modified: Saturday, 29th January 2022 12:41:01 pm
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# Modified By: Chen Xuanhong
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# Copyright (c) 2021 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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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 torchvision.utils import save_image
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import numpy as np
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from PIL import Image
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from insightface_func.face_detect_crop_single import Face_detect_crop
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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.transformer_Arcface = transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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])
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self.imagenet_std = torch.tensor([0.229, 0.224, 0.225]).view(3,1,1)
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self.imagenet_mean = torch.tensor([0.485, 0.456, 0.406]).view(3,1,1)
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if self.config["cuda"] >=0:
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self.imagenet_std = self.imagenet_std .cuda()
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self.imagenet_mean = self.imagenet_mean.cuda()
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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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# 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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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))
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print('loaded trained backbone model step {}...!'.format(self.config["checkpoint_step"]))
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def test(self):
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save_dir = self.config["test_samples_path"]
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ckp_step = self.config["checkpoint_step"]
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version = self.config["version"]
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attr_files = self.config["attr_files"]
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self.arcface_ckpt= self.config["arcface_ckpt"]
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imgs_list = []
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if os.path.isdir(attr_files):
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print("Input a dir....")
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imgs = glob.glob(os.path.join(attr_files,"**"), recursive=True)
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for item in imgs:
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imgs_list.append(item)
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print(imgs_list)
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else:
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print("Input an image....")
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imgs_list.append(attr_files)
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img_num = len(imgs_list)
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# models
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self.__init_framework__()
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mode = None
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self.detect = Face_detect_crop(name='antelope', root='./insightface_func/models')
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self.detect.prepare(ctx_id = 0, det_thresh=0.6, det_size=(640,640),mode = mode)
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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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index = 0
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with torch.no_grad():
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for img in imgs_list[1:]:
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print(img)
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attr_img_ori= cv2.imread(img)
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# try:
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# attr_img_align_crop, mat = self.detect.get(attr_img_ori,512)
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# except:
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# print("No face detected!")
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# continue
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# attr_img_align_crop_pil = Image.fromarray(cv2.cvtColor(attr_img_align_crop[0],cv2.COLOR_BGR2RGB))
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attr_img_align_crop_pil = Image.fromarray(cv2.cvtColor(attr_img_ori,cv2.COLOR_BGR2RGB))
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attr_img = self.transformer_Arcface(attr_img_align_crop_pil).unsqueeze(0).cuda()
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attr_img_arc= F.interpolate(attr_img,size=(112,112), mode='bicubic')
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attr_id = self.arcface(attr_img_arc)
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results = self.network(attr_id)
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results = results * self.imagenet_std + self.imagenet_mean
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results = results.clamp_(0, 1)
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attr = attr_img_arc * self.imagenet_std + self.imagenet_mean
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results = torch.concat((attr, results), dim=2)
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if index == 0:
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final_img = results
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else:
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final_img = torch.concat((final_img, results), dim=0)
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index += 1
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save_filename = os.path.join(save_dir, "ckp_%s_v_%s.png"%(ckp_step, version))
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mark = 0
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while(True):
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if os.path.exists(save_filename):
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save_filename = os.path.join(save_dir, "ckp_%s_v_%s_%d.png"%(ckp_step, version,mark))
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mark += 1
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else:
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break
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save_image(final_img, save_filename, nrow=img_num//8)
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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)) |