add src only exp

experiments/synsigns_gtsrb_src_only.py

@@ -0,0 +1,81 @@
+import os
+import sys
+import datetime
+from tensorboardX import SummaryWriter
+
+import torch
+sys.path.append('../')
+from models.model import GTSRBmodel
+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 = "synsigns-gtsrb"
+    model_base = '/home/wogong/models/pytorch-dann'
+    note = 'src-only'
+    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 = True
+
+    # params for datasets and data loader
+    batch_size = 128
+
+    # params for source dataset
+    src_dataset = "synsigns"
+    src_image_root = os.path.join('/home/wogong/datasets', 'synsigns')
+    src_model_trained = True
+    src_classifier_restore = os.path.join(model_root, src_dataset + '-source-classifier-final.pt')
+
+    # params for target dataset
+    tgt_dataset = "gtsrb"
+    tgt_image_root = os.path.join('/home/wogong/datasets', 'gtsrb')
+    tgt_model_trained = True
+    dann_restore = os.path.join(model_root, src_dataset + '-' + tgt_dataset + '-dann-final.pt')
+
+    # params for training dann
+    gpu_id = '0'
+
+    ## for digit
+    num_epochs = 200
+    log_step = 50
+    save_step = 100
+    eval_step = 5
+
+    manual_seed = None
+    alpha = 0
+
+    # params for optimizing models
+    lr = 0.01
+    momentum = 0.9
+
+    def __init__(self):
+        """save config to model root"""
+        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=GTSRBmodel(), 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)