diff --git a/datasets/syndigits.py b/datasets/syndigits.py new file mode 100644 index 0000000..a5c51e7 --- /dev/null +++ b/datasets/syndigits.py @@ -0,0 +1,27 @@ +"""Dataset setting and data loader for syn-digits.""" + +import os +import torch +from torchvision import datasets, transforms +import torch.utils.data as data + + +def get_syndigits(dataset_root, batch_size, train): + """Get synth digits datasets loader.""" + # image pre-processing + pre_process = transforms.Compose([ + transforms.Resize(32), + transforms.ToTensor(), + transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)) + ]) + + # datasets and data loader + if train: + syndigits_dataset = datasets.ImageFolder(os.path.join(dataset_root, 'TRAIN_separate_dirs'), transform=pre_process) + else: + syndigits_dataset = datasets.ImageFolder(os.path.join(dataset_root, 'TEST_separate_dirs'), transform=pre_process) + + syndigits_dataloader = torch.utils.data.DataLoader( + dataset=syndigits_dataset, batch_size=batch_size, shuffle=True, num_workers=0) + + return syndigits_dataloader \ No newline at end of file diff --git a/experiments/syndigits_svhn.py b/experiments/syndigits_svhn.py new file mode 100644 index 0000000..b21000e --- /dev/null +++ b/experiments/syndigits_svhn.py @@ -0,0 +1,82 @@ +import os +import sys +import datetime +from tensorboardX import SummaryWriter + +import torch +sys.path.append('../') +from models.model import SVHNmodel +from core.train import train_dann +from utils.utils import get_data_loader, init_model, init_random_seed + + +class Config(object): + # params for path + model_name = "syndigits-svhn" + model_base = '/home/wogong/models/pytorch-dann' + note = 'default' + model_root = os.path.join(model_base, model_name, note + '_' + datetime.datetime.now().strftime('%m%d_%H%M%S')) + os.makedirs(model_root) + config = os.path.join(model_root, 'config.txt') + finetune_flag = False + lr_adjust_flag = 'simple' + src_only_flag = False + + # params for datasets and data loader + batch_size = 128 + + # params for source dataset + src_dataset = "syndigits" + src_image_root = os.path.join('/home/wogong/datasets', 'syndigits') + src_model_trained = True + src_classifier_restore = os.path.join(model_root, src_dataset + '-source-classifier-final.pt') + + # params for target dataset + tgt_dataset = "svhn" + tgt_image_root = os.path.join('/home/wogong/datasets', 'svhn') + tgt_model_trained = True + dann_restore = os.path.join(model_root, src_dataset + '-' + tgt_dataset + '-dann-final.pt') + + # params for GPU device + gpu_id = '0' + + ## for digit + num_epochs = 200 + log_step = 200 + save_step = 100 + eval_step = 1 + + manual_seed = 42 + alpha = 0 + + # params for SGD optimizer + lr = 0.01 + momentum = 0.9 + weight_decay = 1e-6 + + def __init__(self): + public_props = (name for name in dir(self) if not name.startswith('_')) + with open(self.config, 'w') as f: + for name in public_props: + f.write(name + ': ' + str(getattr(self, name)) + '\n') + +params = Config() +logger = SummaryWriter(params.model_root) +device = torch.device("cuda:" + params.gpu_id if torch.cuda.is_available() else "cpu") + +# init random seed +init_random_seed(params.manual_seed) + +# load dataset +src_data_loader = get_data_loader(params.src_dataset, params.src_image_root, params.batch_size, train=True) +src_data_loader_eval = get_data_loader(params.src_dataset, params.src_image_root, params.batch_size, train=False) +tgt_data_loader = get_data_loader(params.tgt_dataset, params.tgt_image_root, params.batch_size, train=True) +tgt_data_loader_eval = get_data_loader(params.tgt_dataset, params.tgt_image_root, params.batch_size, train=False) + +# load dann model +dann = init_model(net=SVHNmodel(), restore=None) + +# train dann model +print("Training dann model") +if not (dann.restored and params.dann_restore): + dann = train_dann(dann, params, src_data_loader, tgt_data_loader, tgt_data_loader_eval, device, logger) diff --git a/models/model.py b/models/model.py index ebcdab0..818819f 100644 --- a/models/model.py +++ b/models/model.py @@ -160,12 +160,12 @@ class SVHNmodel(nn.Module): nn.Conv2d(in_channels=3, out_channels=64, kernel_size=(5, 5)), # 28 nn.BatchNorm2d(64), nn.ReLU(inplace=True), - nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2)), # 14 - nn.Conv2d(in_channels=64, out_channels=64, kernel_size=(5, 5)), # 10 + nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2)), # 13 + nn.Conv2d(in_channels=64, out_channels=64, kernel_size=(5, 5)), # 9 nn.BatchNorm2d(64), nn.Dropout2d(), nn.ReLU(inplace=True), - nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2)), # 5 + nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2)), # 4 nn.ReLU(inplace=True), nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(4, 4)), # 1 ) diff --git a/utils/utils.py b/utils/utils.py index 03a3103..d30ad91 100644 --- a/utils/utils.py +++ b/utils/utils.py @@ -7,6 +7,7 @@ import torch.backends.cudnn as cudnn from datasets import get_mnist, get_mnistm, get_svhn from datasets.office import get_office from datasets.officecaltech import get_officecaltech +from datasets.syndigits import get_syndigits from datasets.synsigns import get_synsigns from datasets.gtsrb import get_gtsrb @@ -61,6 +62,8 @@ def get_data_loader(name, dataset_root, batch_size, train=True): return get_office(dataset_root, batch_size, 'webcam') elif name == "webcam10": return get_officecaltech(dataset_root, batch_size, 'webcam') + elif name == "syndigits": + return get_syndigits(dataset_root, batch_size, train) elif name == "synsigns": return get_synsigns(dataset_root, batch_size, train) elif name == "gtsrb":