def __init__(self, lr=1e-3, batchs=8, cuda=True): ''' :param tt: train_test :param tag: 1 - evaluation on testing data, 0 - without evaluation on testing data :param lr: :param batchs: :param cuda: ''' # all the tensor should set the 'volatile' as True, and False when update the network self.hungarian = Munkres() self.device = torch.device("cuda" if cuda else "cpu") self.nEpochs = 999 self.lr = lr self.batchsize = batchs self.numWorker = 4 self.show_process = 0 # interaction self.step_input = 1 print ' Preparing the model...' self.resetU() self.Uphi = uphi().to(self.device) self.Ephi = ephi().to(self.device) self.criterion = nn.MSELoss() if criterion_s else nn.CrossEntropyLoss() self.criterion = self.criterion.to(self.device) self.optimizer = optim.Adam([{ 'params': self.Uphi.parameters() }, { 'params': self.Ephi.parameters() }], lr=lr) # seqs = [2, 4, 5, 9, 10, 11, 13] # lengths = [600, 1050, 837, 525, 654, 900, 750] seqs = [2, 4, 5, 10] lengths = [600, 1050, 837, 654] for i in xrange(len(seqs)): self.writer = SummaryWriter() # print ' Loading Data...' seq = seqs[i] self.seq_index = seq start = time.time() sequence_dir = 'MOT16/train/MOT16-%02d' % seq self.outName = t_dir + 'result_%02d.txt' % seq self.train_set = DatasetFromFolder(sequence_dir, self.outName) self.train_test = lengths[i] self.tag = 0 self.loss_threhold = 0.03 self.update() print ' Logging...' t_data = time.time() - start self.log(t_data)
def __init__(self, lr=5e-3, cuda=True): # all the tensor should set the 'volatile' as True, and False when update the network self.hungarian = Munkres() self.cuda = cuda self.nEpochs = 999 self.tau = 3 # self.frame_end = len(self.edges[0])-1 self.lr = lr self.outName = 'result.txt' self.show_process = 0 # interaction self.step_input = 1 print ' Loading Data...' start = time.time() self.train_set = DatasetFromFolder('MOT16/train/MOT16-05', self.outName) t_data = time.time() - start # print self.tail self.tail = train_test self.tau = 1 self.frame_head = 1 self.frame_end = self.frame_head + self.tau self.loss_threhold = 0.03 print ' Preparing the model...' self.resetU() self.Uphi = uphi() self.Ephi = ephi() self.criterion = nn.MSELoss() if criterion_s else nn.CrossEntropyLoss() self.optimizer = optim.Adam([{ 'params': self.Uphi.parameters() }, { 'params': self.Ephi.parameters() }], lr=lr) print ' Logging...' self.log(t_data) if self.cuda: print ' >>>>>> CUDA <<<<<<' self.Uphi = self.Uphi.cuda() self.Ephi = self.Ephi.cuda() self.criterion = self.criterion.cuda()
def __init__(self, tt, tag, lr=1e-3, batchs=8, cuda=True): ''' :param tt: train_test :param tag: 1 - evaluation on testing data, 0 - without evaluation on testing data :param lr: :param batchs: :param cuda: ''' # all the tensor should set the 'volatile' as True, and False when update the network self.writer = SummaryWriter() self.hungarian = Munkres() self.device = torch.device("cuda" if cuda else "cpu") self.nEpochs = 999 self.lr = lr self.batchsize = batchs self.numWorker = 4 self.outName = t_dir + 'result%s.txt' % name self.show_process = 0 # interaction self.step_input = 1 # print ' Loading Data...' start = time.time() self.train_set = DatasetFromFolder(sequence_dir, self.outName) t_data = time.time() - start self.train_test = tt self.tag = tag self.loss_threhold = 0.03 print ' Preparing the model...' self.resetU() self.Uphi = uphi().to(self.device) self.Ephi = ephi().to(self.device) self.criterion = nn.MSELoss() if criterion_s else nn.CrossEntropyLoss() self.criterion = self.criterion.to(self.device) self.optimizer = optim.Adam([{ 'params': self.Uphi.parameters() }, { 'params': self.Ephi.parameters() }], lr=lr) print ' Logging...' self.log(t_data)
def __init__(self, typeDir, epochs, l, load, dir): print('===> Loading datasets') train_set = DatasetFromFolder(self.typeDir) self.training_data_loader = DataLoader(dataset=train_set, num_workers=4, batch_size=4, shuffle=True)
def get_training_set(): train_dir = join(root_dir, "train") return DatasetFromFolder(train_dir, input_transform=input_transform() #target_transform=target_transform() )
def get_test_set(data_dir, upscale_factor): root_dir = data_dir # download_bsd300() test_dir = join(root_dir, "test") crop_size = calculate_valid_crop_size(256, upscale_factor) #my code # test_images= os.listdir(test_dir) # # for input_image in test_images: # # img = Image.open(test_dir+'/'+input_image).convert('YCbCr') # y, cb, cr = img.split() # target_t=target_transform(crop_size) # target= target_t(y) ## print(target) # out = target.cpu() # print('out.shape', out.shape) # out_img_y = out.detach().numpy() # out_img_y *= 255.0 # out_img_y = out_img_y.clip(0, 255) # out_img_y = Image.fromarray(np.uint8(out_img_y[0]), mode='L') # # out_img_cb = cb.resize(out_img_y.size, Image.BICUBIC) # out_img_cr = cr.resize(out_img_y.size, Image.BICUBIC) # out_img = Image.merge('YCbCr', [out_img_y, out_img_cb, out_img_cr]).convert('RGB') # # # print(input_image) # out_img.save('demo/' + input_image) # print('target shape', target.shape) #torch.Size([1, 255, 255]) # print('cb shape', cb.shape) return DatasetFromFolder(test_dir, input_transform=input_transform(crop_size, upscale_factor), target_transform=target_transform(crop_size))
def get_test_set(upscale_factor): root_dir = download_ms() test_dir = join(root_dir, "test") test_label_dir = join(root_dir, "test_labels") return DatasetFromFolder(test_dir, test_label_dir, input_transform, target_transform)
def get_test_set(dest, crop_size, upscale_factor, jpeg, noise=None, blur=None): root_dir = get_image_dir(dest) test_dir = os.path.join(root_dir, "test") crop_size = calculate_valid_crop_size(crop_size, upscale_factor) return DatasetFromFolder(test_dir, data_transform=test_data_transform(crop_size, upscale_factor))
def get_test_set(upscale_factor): test_dir = "all_images_colab_size_200k" crop_size = calculate_valid_crop_size(256, upscale_factor) return DatasetFromFolder(test_dir, input_transform=input_transform( crop_size, upscale_factor), target_transform=target_transform(crop_size))
def get_test_set(upscale_factor): root_dir = document_dataset() test_dir = join(root_dir, "test") crop_size = calculate_valid_crop_size(256, upscale_factor) return DatasetFromFolder(test_dir, input_transform=input_transform(crop_size, upscale_factor), target_transform=target_transform(crop_size))
def get_test_set(upscale_factor): root_dir = './BSR/BSDS500/data/images' test_dir = join(root_dir, "test") crop_size = calculate_valid_crop_size(256, upscale_factor) return DatasetFromFolder(test_dir, input_transform=input_transform( crop_size, upscale_factor), target_transform=target_transform(crop_size))
def get_test_set(): root_dir = download_bsd300() test_dir = join(root_dir, "test") return DatasetFromFolder(test_dir, LR_transform=LR_transform(crop_size), HR_2_transform=HR_2_transform(crop_size), HR_4_transform=HR_4_transform(crop_size))
def get_test_set(data_dir, dataset, hr, upscale_factor,patch_size): hr_dir = join(data_dir, hr) lr_dir = join(data_dir, dataset) #crop_size = calculate_valid_crop_size(crop_size, upscale_factor) return DatasetFromFolder(hr_dir, lr_dir,patch_size, upscale_factor, dataset, data_augmentation=False, input_transform=input_transform(), target_transform=target_transform())
def get_training_set(data_dir, hr, upscale_factor, patch_size, data_augmentation): hr_dir = join(data_dir, hr) return DatasetFromFolder(hr_dir, patch_size, upscale_factor, data_augmentation, transform=transform())
def get_training_set(data_dir, upscale_factor): root_dir = data_dir # download_bsd300() train_dir = join(root_dir, "train") crop_size = calculate_valid_crop_size(256, upscale_factor) return DatasetFromFolder(train_dir, input_transform=input_transform(crop_size, upscale_factor), target_transform=target_transform(crop_size))
def loadData(self): self.train_test = SEQLEN for camera in xrange(4, 9): # print ' Loading Data...' self.seq_index = camera start = time.time() self.outName = t_dir+'result_%d.txt'%camera out = open(self.outName, 'w') out.close() self.train_set = DatasetFromFolder(camera, self.outName, show=0) self.update() print ' Logging...' t_data = (time.time() - start)/60 self.log(t_data)
def get_test_set(): root_dir = download_bsd300() test_dir = os.path.join(root_dir, "test") return DatasetFromFolder(test_dir, LR_transform=LR_transform(CROP_SIZE), HR_2_transform=HR_2_transform(CROP_SIZE), HR_4_transform=HR_4_transform(CROP_SIZE), HR_8_transform=HR_8_transform(CROP_SIZE))
def get_test_set(upscale_factor, sr_run): root_dir = download_div2k() #download_bsd300() # test_dir = join(root_dir, "valid_sr/", str(sr_run) ) test_dir2= join(root_dir, "compressed_valid_png") crop_size = calculate_valid_crop_size(128, upscale_factor) return DatasetFromFolder(test_dir, test_dir2, input_transform=input_transform(crop_size, upscale_factor), target_transform=target_transform(crop_size))
def get_test_set(upscale_factor): #root_dir = download_bsd300() test_dir = join(data_folder, "test") crop_size = calculate_valid_crop_size(256, upscale_factor) return DatasetFromFolder(test_dir, input_transform=input_transform( crop_size, upscale_factor), target_transform=target_transform(crop_size))
def get_training_set(training_size=224, image_type='RGB', mean_std=((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), image_source='train'): return DatasetFromFolder('train', training_size, image_type, mean_std, image_source=image_source)
def get_training_set(upscale_factor, folder): root_dir = join("dataset", folder) train_dir = join(root_dir, "train") crop_size = calculate_valid_crop_size(cropsize, upscale_factor) return DatasetFromFolder(train_dir, input_transform=input_transform( crop_size, upscale_factor), target_transform=target_transform(crop_size))
def get_training_set(data_dir, nFrames, upscale_factor, data_augmentation, other_dataset, patch_size, future_frame): return DatasetFromFolder(data_dir, nFrames, upscale_factor, data_augmentation, other_dataset, patch_size, future_frame, transform=transform())
def get_training_set(data_dir, train_dir, patch_size, sr_patch_size, upscale_factor, num_classes, data_augmentation): return DatasetFromFolder(data_dir, train_dir, patch_size, sr_patch_size, upscale_factor, num_classes, data_augmentation, transform=transform())
def get_test_set(): root_dir = 'dataset' test_dir = join(root_dir, "validation") crop_size = (72, 272) return DatasetFromFolder(test_dir, input_transform=Compose([ CenterCrop(crop_size), Resize(crop_size), ToTensor(), ]))
def get_training_set(train_dir=None): if train_dir is None: root_dir = download_bsd300() train_dir = os.path.join(root_dir, "train") return DatasetFromFolder(train_dir, LR_transform=LR_transform(CROP_SIZE), HR_2_transform=HR_2_transform(CROP_SIZE), HR_4_transform=HR_4_transform(CROP_SIZE), HR_8_transform=HR_8_transform(CROP_SIZE))
def get_test_set(upscale_factor, convert_gray=False): root_dir = download_bsd300() test_dir = join(root_dir, "test") crop_size = calculate_valid_crop_size(256, upscale_factor) return DatasetFromFolder(test_dir, input_transform=input_transform( crop_size, upscale_factor), target_transform=target_transform(crop_size), convert_gray=convert_gray)
def get_val_set(): root_dir = os.path.join(os.path.expanduser('~'), 'data/plate_recognition/plate_e2e') val_dir = join(root_dir, "validation") crop_size = (72, 272) return DatasetFromFolder(val_dir, input_transform=Compose([CenterCrop(crop_size), Resize(crop_size), ToTensor(),Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),])) # return DatasetFromFolder(val_dir, # input_transform=Compose([ToTensor(),Normalize(mean=[0.485, 0.456, 0.406], # std=[0.229, 0.224, 0.225]),]))
def get_test_set(upscale_factor): # <FIXED LINE FOR EXPERIMENT> # root_dir = download_bsd300() # test_dir = join(root_dir, "test") root_dir = './data' test_dir = root_dir + '/test' crop_size = calculate_valid_crop_size(256, upscale_factor) return DatasetFromFolder(test_dir, input_transform=input_transform(crop_size, upscale_factor), target_transform=target_transform(crop_size))
def get_val_set(root): root_dir = root val_LR_dir = join(root_dir, "val_LR/") val_HR_2_dir = join(root_dir, "val_HR_2/") val_HR_4_dir = join(root_dir, "val_HR_4/") return DatasetFromFolder(val_LR_dir, val_HR_2_dir, val_HR_4_dir, LR_transform=LR_transform(), HR_2_transform=HR_2_transform(), HR_4_transform=HR_4_transform())
def get_test_set(h=IMAGE_HEIGHT, w=IMAGE_WIDTH, download=download_bsd300, upscale_factor=None): h = calculate_valid_size(h, upscale_factor) w = calculate_valid_size(w, upscale_factor) return DatasetFromFolder( join(download(), 'test'), input_transfrom=input_transform(h, w, upscale_factor), target_transform=target_transform(h, w), )