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Python utility.quantize方法代码示例

本文整理汇总了Python中utility.quantize方法的典型用法代码示例。如果您正苦于以下问题:Python utility.quantize方法的具体用法?Python utility.quantize怎么用?Python utility.quantize使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在utility的用法示例。


在下文中一共展示了utility.quantize方法的10个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: test

# 需要导入模块: import utility [as 别名]
# 或者: from utility import quantize [as 别名]
def test(self):
        torch.set_grad_enabled(False)

        self.ckp.write_log('\nEvaluation on video:')
        self.model.eval()

        timer_test = utility.timer()
        for idx_scale, scale in enumerate(self.scale):
            vidcap = cv2.VideoCapture(self.args.dir_demo)
            total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
            vidwri = cv2.VideoWriter(
                self.ckp.get_path('{}_x{}.avi'.format(self.filename, scale)),
                cv2.VideoWriter_fourcc(*'XVID'),
                vidcap.get(cv2.CAP_PROP_FPS),
                (
                    int(scale * vidcap.get(cv2.CAP_PROP_FRAME_WIDTH)),
                    int(scale * vidcap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                )
            )

            tqdm_test = tqdm(range(total_frames), ncols=80)
            for _ in tqdm_test:
                success, lr = vidcap.read()
                if not success: break

                lr, = common.set_channel(lr, n_channels=self.args.n_colors)
                lr, = common.np2Tensor(lr, rgb_range=self.args.rgb_range)
                lr, = self.prepare(lr.unsqueeze(0))
                sr = self.model(lr, idx_scale)
                sr = utility.quantize(sr, self.args.rgb_range).squeeze(0)

                normalized = sr * 255 / self.args.rgb_range
                ndarr = normalized.byte().permute(1, 2, 0).cpu().numpy()
                vidwri.write(ndarr)

            vidcap.release()
            vidwri.release()

        self.ckp.write_log(
            'Total: {:.2f}s\n'.format(timer_test.toc()), refresh=True
        )
        torch.set_grad_enabled(True) 
开发者ID:HolmesShuan,项目名称:OISR-PyTorch,代码行数:44,代码来源:videotester.py

示例2: test

# 需要导入模块: import utility [as 别名]
# 或者: from utility import quantize [as 别名]
def test(self):
        torch.set_grad_enabled(False)

        epoch = self.scheduler.last_epoch + 1
        self.ckp.write_log('\nEvaluation:')
        self.ckp.add_log(
            torch.zeros(1, len(self.loader_test), len(self.scale))
        )
        self.model.eval()

        timer_test = utility.timer()
        if self.args.save_results: self.ckp.begin_background()
        for idx_data, d in enumerate(self.loader_test):
            for idx_scale, scale in enumerate(self.scale):
                d.dataset.set_scale(idx_scale)
                for lr, hr, filename, _ in tqdm(d, ncols=80):
                    lr, hr = self.prepare(lr, hr)
                    sr = self.model(lr, idx_scale)
                    sr = utility.quantize(sr, self.args.rgb_range)

                    save_list = [sr]
                    self.ckp.log[-1, idx_data, idx_scale] += utility.calc_psnr(
                        sr, hr, scale, self.args.rgb_range, dataset=d
                    )
                    if self.args.save_gt:
                        save_list.extend([lr, hr])

                    if self.args.save_results:
                        self.ckp.save_results(d, filename[0], save_list, scale)

                self.ckp.log[-1, idx_data, idx_scale] /= len(d)
                best = self.ckp.log.max(0)
                self.ckp.write_log(
                    '[{} x{}]\tPSNR: {:.3f} (Best: {:.3f} @epoch {})'.format(
                        d.dataset.name,
                        scale,
                        self.ckp.log[-1, idx_data, idx_scale],
                        best[0][idx_data, idx_scale],
                        best[1][idx_data, idx_scale] + 1
                    )
                )

        self.ckp.write_log('Forward: {:.2f}s\n'.format(timer_test.toc()))
        self.ckp.write_log('Saving...')

        if self.args.save_results: self.ckp.end_background()
        if not self.args.test_only:
            self.ckp.save(self, epoch, is_best=(best[1][0, 0] + 1 == epoch))

        self.ckp.write_log(
            'Total: {:.2f}s\n'.format(timer_test.toc()), refresh=True
        )

        torch.set_grad_enabled(True) 
开发者ID:HolmesShuan,项目名称:OISR-PyTorch,代码行数:56,代码来源:trainer.py

示例3: test

# 需要导入模块: import utility [as 别名]
# 或者: from utility import quantize [as 别名]
def test(self):
        torch.set_grad_enabled(False)

        epoch = self.optimizer.get_last_epoch()
        self.ckp.write_log('\nEvaluation:')
        self.ckp.add_log(
            torch.zeros(1, len(self.loader_test), len(self.scale))
        )
        self.model.eval()

        timer_test = utility.timer()
        if self.args.save_results: self.ckp.begin_background()
        for idx_data, d in enumerate(self.loader_test):
            for idx_scale, scale in enumerate(self.scale):
                d.dataset.set_scale(idx_scale)
                for lr, hr, filename in tqdm(d, ncols=80):
                    lr, hr = self.prepare(lr, hr)
                    sr = self.model(lr, idx_scale)
                    sr = utility.quantize(sr, self.args.rgb_range)

                    save_list = [sr]
                    self.ckp.log[-1, idx_data, idx_scale] += utility.calc_psnr(
                        sr, hr, scale, self.args.rgb_range, dataset=d
                    )
                    if self.args.save_gt:
                        save_list.extend([lr, hr])

                    if self.args.save_results:
                        self.ckp.save_results(d, filename[0], save_list, scale)

                self.ckp.log[-1, idx_data, idx_scale] /= len(d)
                best = self.ckp.log.max(0)
                self.ckp.write_log(
                    '[{} x{}]\tPSNR: {:.3f} (Best: {:.3f} @epoch {})'.format(
                        d.dataset.name,
                        scale,
                        self.ckp.log[-1, idx_data, idx_scale],
                        best[0][idx_data, idx_scale],
                        best[1][idx_data, idx_scale] + 1
                    )
                )

        self.ckp.write_log('Forward: {:.2f}s\n'.format(timer_test.toc()))
        self.ckp.write_log('Saving...')

        if self.args.save_results:
            self.ckp.end_background()

        if not self.args.test_only:
            self.ckp.save(self, epoch, is_best=(best[1][0, 0] + 1 == epoch))

        self.ckp.write_log(
            'Total: {:.2f}s\n'.format(timer_test.toc()), refresh=True
        )

        torch.set_grad_enabled(True) 
开发者ID:thstkdgus35,项目名称:EDSR-PyTorch,代码行数:58,代码来源:trainer.py

示例4: test

# 需要导入模块: import utility [as 别名]
# 或者: from utility import quantize [as 别名]
def test(self):
        epoch = self.scheduler.last_epoch + 1
        self.ckp.write_log('\nEvaluation:')
        self.ckp.add_log(torch.zeros(1, len(self.scale)))
        self.model.eval()

        timer_test = utility.timer()
        with torch.no_grad():
            for idx_scale, scale in enumerate(self.scale):
                eval_acc = 0
                self.loader_test.dataset.set_scale(idx_scale)
                tqdm_test = tqdm(self.loader_test, ncols=80)
                for idx_img, (lr, nl, mk, hr, filename, _) in enumerate(tqdm_test):
                    # print('FLAG')
                    # print(filename)
                    filename = filename[0]
                    print(filename)
                    no_eval = (hr.nelement() == 1)
                    if not no_eval:
                        lr, nl, mk, hr = self.prepare([lr, nl, mk, hr])
                    else:
                        lr, nl, mk, = self.prepare([lr, nl, mk])

                    sr = self.model(idx_scale, lr, nl, mk)
                    sr = utility.quantize(sr, self.args.rgb_range)
                    # print(sr.shape)
                    b, c, h, w = sr.shape
                    hr = hr[:, :, :h, :w]
                    save_list = [sr]
                    if not no_eval:
                        eval_acc += utility.calc_psnr(
                            sr, hr, scale, self.args.rgb_range,
                            benchmark=self.loader_test.dataset.benchmark
                        )
                        save_list.extend([lr, hr])

                    if self.args.save_results:
                        self.ckp.save_results(filename, save_list, scale)

                self.ckp.log[-1, idx_scale] = eval_acc / len(self.loader_test)
                best = self.ckp.log.max(0)
                self.ckp.write_log(
                    '[{} x{}]\tPSNR: {:.3f} (Best: {:.3f} @epoch {})'.format(
                        self.args.data_test,
                        scale,
                        self.ckp.log[-1, idx_scale],
                        best[0][idx_scale],
                        best[1][idx_scale] + 1
                    )
                )

        self.ckp.write_log(
            'Total time: {:.2f}s\n'.format(timer_test.toc()), refresh=True
        )
        if not self.args.test_only:
            self.ckp.save(self, epoch, is_best=(best[1][0] + 1 == epoch)) 
开发者ID:ofsoundof,项目名称:3D_Appearance_SR,代码行数:58,代码来源:trainer_finetune.py

示例5: test

# 需要导入模块: import utility [as 别名]
# 或者: from utility import quantize [as 别名]
def test(self):
        epoch = self.scheduler.last_epoch + 1
        self.ckp.write_log('\nEvaluation:')
        self.ckp.add_log(torch.zeros(1, len(self.scale)))
        self.model.eval()

        timer_test = utility.timer()
        with torch.no_grad():
            for idx_scale, scale in enumerate(self.scale):
                eval_acc = 0
                self.loader_test.dataset.set_scale(idx_scale)
                tqdm_test = tqdm(self.loader_test, ncols=80)
                for idx_img, (lr, hr, filename, _) in enumerate(tqdm_test):

                    # from IPython import embed; embed();
                    filename = filename[0]
                    no_eval = (hr.nelement() == 1)
                    if not no_eval:
                        lr, hr = self.prepare([lr, hr])
                    else:
                        lr = self.prepare([lr])[0]

                    sr = self.model(idx_scale, lr)
                    sr = utility.quantize(sr, self.args.rgb_range)

                    save_list = [sr]
                    if not no_eval:
                        eval_acc += utility.calc_psnr(
                            sr, hr, scale, self.args.rgb_range,
                            benchmark=self.loader_test.dataset.benchmark
                        )
                        save_list.extend([lr, hr])

                    if self.args.save_results:
                        self.ckp.save_results(filename, save_list, scale)

                self.ckp.log[-1, idx_scale] = eval_acc / len(self.loader_test)
                best = self.ckp.log.max(0)
                self.ckp.write_log(
                    '[{} x{}]\tPSNR: {:.3f} (Best: {:.3f} @epoch {})'.format(
                        self.args.data_test,
                        scale,
                        self.ckp.log[-1, idx_scale],
                        best[0][idx_scale],
                        best[1][idx_scale] + 1
                    )
                )

        self.ckp.write_log(
            'Total time: {:.2f}s\n'.format(timer_test.toc()), refresh=True
        )
        if not self.args.test_only:
            self.ckp.save(self, epoch, is_best=(best[1][0] + 1 == epoch)) 
开发者ID:ofsoundof,项目名称:3D_Appearance_SR,代码行数:55,代码来源:trainer.py

示例6: test

# 需要导入模块: import utility [as 别名]
# 或者: from utility import quantize [as 别名]
def test(self):
        epoch = self.scheduler.last_epoch + 1
        self.ckp.write_log('\nEvaluation:')
        self.ckp.add_log(torch.zeros(1, len(self.scale)))
        self.model.eval()

        timer_test = utility.timer()
        with torch.no_grad():
            for idx_scale, scale in enumerate(self.scale):
                eval_psnr = 0
                # eval_ssim = 0
                self.loader_test.dataset.set_scale(idx_scale)
                tqdm_test = tqdm(self.loader_test, ncols=80)
                for idx_img, (lr, hr, filename, _) in enumerate(tqdm_test):
                    filename = filename[0]
                    no_eval = (hr.nelement() == 1)
                    if not no_eval:
                        lr, hr = self.prepare(lr, hr)
                    else:
                        lr, = self.prepare(lr)

                    sr = self.model(lr, idx_scale)

                    sr = utility.quantize(sr, self.args.rgb_range)

                    save_list = [sr]
                    if not no_eval:
                        eval_psnr += utility.calc_psnr(
                            sr, hr, scale, self.args.rgb_range,
                            benchmark=self.loader_test.dataset.benchmark
                        )
                        # eval_ssim += utility.calc_ssim(sr, hr)
                        # save_list.extend([lr, hr])
                        save_list.extend([hr])

                    if self.args.save_results:
                        self.ckp.save_results(filename, save_list, scale)

                self.ckp.log[-1, idx_scale] = eval_psnr / len(self.loader_test)
                # mean_ssim = eval_ssim / len(self.loader_test)
                
                best = self.ckp.log.max(0)
                self.ckp.write_log(
                    '[{} x{}]\tPSNR: {:.3f} (Best PSNR: {:.3f} @epoch {})'.format(
                        self.args.data_test,
                        scale,
                        self.ckp.log[-1, idx_scale],
                        best[0][idx_scale],
                        best[1][idx_scale] + 1
                    )
                )

        self.ckp.write_log(
            'Total time: {:.2f}s\n'.format(timer_test.toc()), refresh=True
        )
        if not self.args.test_only:
            self.ckp.save(self, epoch, is_best=(best[1][0] + 1 == epoch)) 
开发者ID:ChaofWang,项目名称:AWSRN,代码行数:59,代码来源:trainer.py

示例7: test

# 需要导入模块: import utility [as 别名]
# 或者: from utility import quantize [as 别名]
def test(self):
        #if self.args.test_only:
        #    self.scheduler.step() #just for remake the curve of psnr
        epoch = self.scheduler.last_epoch + 1
        self.ckp.write_log('\nEvaluation:')
        self.ckp.add_log(torch.zeros(1, len(self.scale)))
        self.model.eval()

        timer_test = utility.timer()
        with torch.no_grad():
            for idx_scale, scale in enumerate(self.scale):
                eval_acc = 0
                #eval_ssim = 0
                self.loader_test.dataset.set_scale(idx_scale)
                tqdm_test = tqdm(self.loader_test, ncols=80)
                for idx_img, (lr, hr,filename, _) in enumerate(tqdm_test):
                    filename = filename[0]
                    no_eval = (hr.nelement() == 1)
                    if not no_eval:
                        lr, hr = self.prepare(lr, hr)
                    else:
                        lr, = self.prepare(lr)

                    sr = self.model(lr, idx_scale)
                    sr = utility.quantize(sr, self.args.rgb_range)
                    save_list = [sr]
                    if not no_eval:
                        eval_acc += utility.calc_psnr(
                            sr, hr, scale, self.args.rgb_range,
                            benchmark=self.loader_test.dataset.benchmark
                        )
                        #save_list.extend([lr, hr])
                        #eval_ssim += utility.calc_ssim(
                        #    sr, hr, scale
                        #)
                    if self.args.save_results:
                        self.ckp.save_results(filename, save_list, scale,epoch)
                #del lr,hr,sr,idx_img,filename,save_list
                self.ckp.log[-1, idx_scale] = eval_acc / len(self.loader_test)
                best = self.ckp.log.max(0)
                self.ckp.write_log(
                    '[{} x{}]\tPSNR: {:.3f}(Best: {:.3f} @epoch {})'.format(
                        self.args.data_test,
                        scale,
                        self.ckp.log[-1, idx_scale],
                        best[0][idx_scale],
                        best[1][idx_scale] + 1
                    )
                )
            #del idx_scale,scale,tqdm_test
        self.ckp.write_log(
            'Total time: {:.2f}s\n'.format(timer_test.toc()), refresh=True
        )
        if not self.args.test_only:
            self.ckp.save(self, epoch, is_best=(best[1][0] + 1 == epoch))
            #self.ckp.save(self, epoch, is_best=False)
        else:
            self.ckp.save_for_test(epoch) 
开发者ID:yyknight,项目名称:NTIRE2019_EDRN,代码行数:60,代码来源:trainer.py

示例8: test

# 需要导入模块: import utility [as 别名]
# 或者: from utility import quantize [as 别名]
def test(self):
        epoch = self.scheduler.last_epoch + 1
        self.ckp.write_log('\nEvaluation:')
        self.ckp.add_log(torch.zeros(1, len(self.scale)))
        self.model.eval()

        timer_test = utility.timer()
        with torch.no_grad():
            for idx_scale, scale in enumerate(self.scale):
                eval_acc = 0
                self.loader_test.dataset.set_scale(idx_scale)
                tqdm_test = tqdm(self.loader_test, ncols=80)
                for idx_img, (lr, hr, filename, _) in enumerate(tqdm_test):
                    filename = filename[0]
                    no_eval = (hr.nelement() == 1)
                    if not no_eval:
                        lr, hr = self.prepare([lr, hr])
                    else:
                        lr = self.prepare([lr])[0]
                    print(lr.shape)
                    sr = self.model(lr, idx_scale)
                    sr = utility.quantize(sr, self.args.rgb_range)
                       
                    save_list = [sr]
                    if not no_eval:
                        eval_acc += utility.calc_psnr(
                            sr, hr, scale, self.args.rgb_range,
                            benchmark=self.loader_test.dataset.benchmark
                        )
                        save_list.extend([lr, hr])

                    if self.args.save_results:
                        self.ckp.save_results(filename, save_list, scale)

                self.ckp.log[-1, idx_scale] = eval_acc / len(self.loader_test)
                best = self.ckp.log.max(0)
                self.ckp.write_log(
                    '[{} x{}]\tPSNR: {:.3f} (Best: {:.3f} @epoch {})'.format(
                        self.args.data_test,
                        scale,
                        self.ckp.log[-1, idx_scale],
                        best[0][idx_scale],
                        best[1][idx_scale] + 1
                    )
                )

        self.ckp.write_log(
            'Total time: {:.2f}s\n'.format(timer_test.toc()), refresh=True
        )
        if not self.args.test_only:
            self.ckp.save(self, epoch, is_best=(best[1][0] + 1 == epoch)) 
开发者ID:subeeshvasu,项目名称:2018_subeesh_epsr_eccvw,代码行数:53,代码来源:trainer.py

示例9: test

# 需要导入模块: import utility [as 别名]
# 或者: from utility import quantize [as 别名]
def test(self):
        epoch = self.scheduler.last_epoch + 1
        self.ckp.write_log('\nEvaluation:')
        self.ckp.add_log(torch.zeros(1, len(self.scale)))
        self.model.eval()

        timer_test = utility.timer()
        with torch.no_grad():
            for idx_scale, scale in enumerate(self.scale):
                eval_acc = 0
                self.loader_test.dataset.set_scale(idx_scale)
                tqdm_test = tqdm(self.loader_test, ncols=80)
                for idx_img, (lr, hr, filename, _) in enumerate(tqdm_test):
                    filename = filename[0]
                    no_eval = (hr.nelement() == 1)
                    if not no_eval:
                        lr, hr = self.prepare([lr, hr])
                    else:
                        lr = self.prepare([lr])[0]

                    sr = self.model(lr, idx_scale)
                    sr = utility.quantize(sr, self.args.rgb_range)

                    save_list = [sr]
                    if not no_eval:
                        eval_acc += utility.calc_psnr(
                            sr, hr, scale, self.args.rgb_range,
                            benchmark=self.loader_test.dataset.benchmark
                        )
                        save_list.extend([lr, hr])

                    if self.args.save_results:
                        #self.ckp.save_results(filename, save_list, scale)
                        self.ckp.save_results_nopostfix(filename, save_list, scale)

                self.ckp.log[-1, idx_scale] = eval_acc / len(self.loader_test)
                best = self.ckp.log.max(0)
                self.ckp.write_log(
                    '[{} x{}]\tPSNR: {:.3f} (Best: {:.3f} @epoch {})'.format(
                        self.args.data_test,
                        scale,
                        self.ckp.log[-1, idx_scale],
                        best[0][idx_scale],
                        best[1][idx_scale] + 1
                    )
                )

        self.ckp.write_log(
            'Total time: {:.2f}s, ave time: {:.2f}s\n'.format(timer_test.toc(), timer_test.toc()/len(self.loader_test)), refresh=True
        )
        if not self.args.test_only:
            self.ckp.save(self, epoch, is_best=(best[1][0] + 1 == epoch)) 
开发者ID:MIVRC,项目名称:MSRN-PyTorch,代码行数:54,代码来源:trainer.py

示例10: test

# 需要导入模块: import utility [as 别名]
# 或者: from utility import quantize [as 别名]
def test(self):
        epoch = self.scheduler.last_epoch + 1
        self.ckp.write_log('\nEvaluation:')
        self.ckp.add_log(torch.zeros(1, len(self.scale)))
        self.model.eval()

        timer_test = utility.timer()
        with torch.no_grad():
            for idx_scale, scale in enumerate(self.scale):
                eval_acc = 0
                self.loader_test.dataset.set_scale(idx_scale)
                tqdm_test = tqdm(self.loader_test, ncols=80)
                for idx_img, (lr, hr, filename, _) in enumerate(tqdm_test):
                    filename = filename[0]
                    no_eval = (hr.nelement() == 1)
                    if not no_eval:
                        lr, hr = self.prepare(lr, hr)
                    else:
                        lr, = self.prepare(lr)

                    sr = self.model(lr, idx_scale)
                    sr = utility.quantize(sr, self.args.rgb_range)

                    save_list = [sr]
                    if not no_eval:
                        eval_acc += utility.calc_psnr(
                            sr, hr, scale, self.args.rgb_range,
                            benchmark=self.loader_test.dataset.benchmark
                        )
                        save_list.extend([lr, hr])

                    if self.args.save_results:
                        self.ckp.save_results(filename, save_list, scale)

                self.ckp.log[-1, idx_scale] = eval_acc / len(self.loader_test)
                best = self.ckp.log.max(0)
                self.ckp.write_log(
                    '[{} x{}]\tPSNR: {:.3f} (Best: {:.3f} @epoch {})'.format(
                        self.args.data_test,
                        scale,
                        self.ckp.log[-1, idx_scale],
                        best[0][idx_scale],
                        best[1][idx_scale] + 1
                    )
                )

        self.ckp.write_log(
            'Total time: {:.2f}s\n'.format(timer_test.toc()), refresh=True
        )
        if not self.args.test_only:
            self.ckp.save(self, epoch, is_best=(best[1][0] + 1 == epoch)) 
开发者ID:MIVRC,项目名称:MSRN-PyTorch,代码行数:53,代码来源:trainer.py


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