chenxuanhong
123 lines
4.9 KiB
Python
123 lines
4.9 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: Tuesday, 12th October 2021 8:22:37 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 torch
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from utilities.utilities import tensor2img
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# from utilities.Reporter import Reporter
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from tqdm import tqdm
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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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#============build evaluation dataloader==============#
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print("Prepare the test dataloader...")
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dlModulename = config["test_dataloader"]
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package = __import__("data_tools.test_dataloader_%s"%dlModulename, fromlist=True)
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dataloaderClass = getattr(package, 'TestDataset')
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dataloader = dataloaderClass(config["test_data_path"],
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1,
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["png","jpg"])
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self.test_loader= dataloader
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self.test_iter = len(dataloader)
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# if len(dataloader)%config["batch_size"]>0:
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# self.test_iter+=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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script_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__(script_name, fromlist=True)
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network_class = getattr(package, class_name)
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# TODO replace below lines to define the model framework
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self.network = network_class(**model_config["g_model"]["module_params"])
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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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# 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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model_path = os.path.join(self.config["project_checkpoints"],
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"epoch%d_%s.pth"%(self.config["checkpoint_epoch"],
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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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# self.network.load_state_dict(torch.load(pathwocao))
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print('loaded trained backbone model epoch {}...!'.format(self.config["project_checkpoints"]))
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def test(self):
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# save_result = self.config["saveTestResult"]
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save_dir = self.config["test_samples_path"]
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ckp_epoch = self.config["checkpoint_epoch"]
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version = self.config["version"]
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batch_size = self.config["batch_size"]
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win_size = self.config["model_configs"]["g_model"]["module_params"]["window_size"]
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# models
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self.__init_framework__()
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total = len(self.test_loader)
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print("total:", total)
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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 _ in tqdm(range(total)):
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contents, img_names = self.test_loader()
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B, C, H, W = contents.shape
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crop_h = H - H%32
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crop_w = W - W%32
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crop_s = min(crop_h, crop_w)
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contents = contents[:,:,(H//2 - crop_s//2):(crop_s//2 + H//2),
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(W//2 - crop_s//2):(crop_s//2 + W//2)]
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if self.config["cuda"] >=0:
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contents = contents.cuda()
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res = self.network(contents, (crop_s, crop_s))
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print("res shape:", res.shape)
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res = tensor2img(res.cpu())
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temp_img = res[0,:,:,:]
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temp_img = cv2.cvtColor(temp_img, cv2.COLOR_RGB2BGR)
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print(save_dir)
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print(img_names[0])
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cv2.imwrite(os.path.join(save_dir,'{}_version_{}_step{}.png'.format(
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img_names[0], version, ckp_epoch)),temp_img)
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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)) |