def CreateDataLoader(opt): from data.custom_dataset_data_loader import CustomDatasetDataLoader data_loader = CustomDatasetDataLoader() data_loader.initialize(opt) return data_loader
def CreateDataLoader(opt): from data.custom_dataset_data_loader import CustomDatasetDataLoader data_loader = CustomDatasetDataLoader() print(data_loader.name()) data_loader.initialize(opt) #print data_loader return data_loader
def CreateDataLoader(opt): ''' Calls CustomDatasetDataLoader and initializes it. ''' from data.custom_dataset_data_loader import CustomDatasetDataLoader data_loader = CustomDatasetDataLoader() print(data_loader.name()) data_loader.initialize(opt) return data_loader
def CreateDataLoader(datafolder, dataroot='./dataset', dataset_mode='2afc', load_size=64, batch_size=1, serial_batches=True, nThreads=4): from data.custom_dataset_data_loader import CustomDatasetDataLoader data_loader = CustomDatasetDataLoader() # print(data_loader.name()) data_loader.initialize(datafolder, dataroot=dataroot + '/' + dataset_mode, dataset_mode=dataset_mode, load_size=load_size, batch_size=batch_size, serial_batches=serial_batches, nThreads=nThreads) return data_loader
def CreateDataLoader(opt, k): if k == 0: from data.custom_dataset_data_loader import CustomDatasetDataLoader data_loader = CustomDatasetDataLoader() print(data_loader.name()) data_loader.initialize(opt) return data_loader else: from data.custom_dataset_data_loader_super import CustomDatasetDataLoader_super data_loader = CustomDatasetDataLoader_super() print(data_loader.name()) data_loader.initialize(opt) return data_loader
def CreateDataLoader( datafolder, dataroot="./dataset", dataset_mode="2afc", load_size=64, batch_size=1, serial_batches=True, ): from data.custom_dataset_data_loader import CustomDatasetDataLoader data_loader = CustomDatasetDataLoader() # print(data_loader.name()) data_loader.initialize( datafolder, dataroot=dataroot + "/" + dataset_mode, dataset_mode=dataset_mode, load_size=load_size, batch_size=batch_size, serial_batches=serial_batches, nThreads=1, ) return data_loader
def CreateDataLoader(opt): from data.custom_dataset_data_loader import CustomDatasetDataLoader data_loader = CustomDatasetDataLoader() print(data_loader.name()) # The name returned is "CustomDatasetDataLoader" data_loader.initialize(opt) # initialization parameters return data_loader
def CreateDataLoader(opt): from data.custom_dataset_data_loader import CustomDatasetDataLoader data_loader = CustomDatasetDataLoader() print(data_loader.name()) data_loader.initialize(opt) return data_loader
def CreateDataLoader(opt, rank): data_loader = CustomDatasetDataLoader() # print(data_loader.name()) data_loader.initialize(opt, rank) return data_loader
def main_task(): # define params opt = BaseOptions().parse() iter_path = os.path.join(opt.checkpoints_dir, 'iter.txt') ioupath_path = os.path.join(opt.checkpoints_dir, 'MIoU.txt') # load training data if opt.continue_train: try: start_epoch, epoch_iter = np.loadtxt(iter_path, delimiter=',', dtype=int) except: start_epoch, epoch_iter = 1, 0 try: best_iou = np.loadtxt(ioupath_path, dtype=float) except: best_iou = 0. else: start_epoch, epoch_iter = 1, 0 best_iou = 0. os.environ["CUDA_VISIBLE_DEVICES"] = str(opt.gpu_ids[0]) # define data mode data_loader = CustomDatasetDataLoader() data_loader.initialize(opt) dataset, dataset_val = data_loader.load_data() dataset_size = len(dataset) # define model model = Deeplab_Solver(opt) total_steps = (start_epoch - 1) * dataset_size + epoch_iter print("starting training model......") for epoch in range(start_epoch, opt.nepochs): if epoch != start_epoch: epoch_iter = epoch_iter % dataset_size # for train opt.isTrain = True model.model.train() for i, data in enumerate(dataset, start=epoch_iter): total_steps += opt.batchSize epoch_iter += opt.batchSize # keep time to watch how times each one epoch epoch_start_time = time.time() # forward and backward pass model.forward(data, isTrain=True) model.backward(total_steps, opt.nepochs * dataset_size * opt.batchSize + 1) # save latest model if total_steps % opt.save_latest_freq == 0: print('saving the latest model (epoch %d, total_steps %d)' % (epoch, total_steps)) model.save('latest') np.savetxt(iter_path, (epoch, epoch_iter), delimiter=',', fmt='%d') if model.trainingavgloss < 0.010: break # for eval opt.isTrain = False model.model.eval() if dataset_val != None: label_trues, labels_preds = [], [] for i, data in enumerate(dataset_val): seggt, segpred = model.forward(data, isTrain=False) seggt = seggt.data.cpu().numpy() segpred = segpred.data.cpu().numpy() label_trues.append(seggt) labels_preds.append(segpred) metrics = util.label_accuracy_score(label_trues, labels_preds, n_class=opt.label_nc) metrics *= 100 print('''\ Validation: Accuracy: {0} AccuracyClass: {1} MeanIOU: {2} FWAVAccuracy: {3} '''.format(*metrics)) # save model for best if metrics[2] > best_iou: best_iou = metrics[2] model.save('best') print('end of epoch %d / %d \t Time Taken: %d sec' % (epoch + 1, opt.nepochs, time.time() - epoch_start_time))
self.resize_or_crop = "resize_and_crop" self.save_epoch_freq = 5 self.save_latest_freq = 5000 self.serial_batches = False self.which_direction = "BtoA" self.which_epoch = "latest" self.checkpoints_dir = "/data/kdabi/CS698O/Autopainter/CS698-cartoon-painter/saved_models" self.results_dir = "/data/kdabi/CS698O/Autopainter/CS698-cartoon-painter/saved_models" opt = Options() # opt = TrainOptions().parse() data_loader = CustomDatasetDataLoader() data_loader.initialize(opt) dataset = data_loader.load_data() dataset_size = len(data_loader) # print('#training images = %d' % dataset_size) model = FeatureLoss(opt) visualizer = Visualizer(opt) total_steps = 0 for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1): epoch_start_time = time.time() epoch_iter = 0 for i, data in enumerate(dataset): iter_start_time = time.time() total_steps += opt.batchSize
def CreateDataLoader(datafolder,dataroot='./dataset',dataset_mode='2afc',load_size=64,batch_size=1,serial_batches=True): from data.custom_dataset_data_loader import CustomDatasetDataLoader data_loader = CustomDatasetDataLoader() # print(data_loader.name()) data_loader.initialize(datafolder,dataroot=dataroot+'/'+dataset_mode,dataset_mode=dataset_mode,load_size=load_size,batch_size=batch_size,serial_batches=serial_batches, nThreads=1) return data_loader
def CreateDataLoader(opt, isVal=False): from data.custom_dataset_data_loader import CustomDatasetDataLoader data_loader = CustomDatasetDataLoader() print(data_loader.name()) data_loader.initialize(opt, isVal) return data_loader
def CreateDataLoader(config, filename): from data.custom_dataset_data_loader import CustomDatasetDataLoader data_loader = CustomDatasetDataLoader() print(data_loader.name()) data_loader.initialize(config, filename) return data_loader
def CreateDataLoader(opt): data_loader = CustomDatasetDataLoader() data_loader.initialize(opt) return data_loader