new code

Author: js05212

Real/configs/config_cub_18.py

@@ -0,0 +1,108 @@
+from easydict import EasyDict
+import numpy as np
+import pickle
+
+
+def read_pickle(name):
+    with open(name, "rb") as f:
+        data = pickle.load(f)
+    return data
+
+
+# load/output dir
+opt = EasyDict()
+opt.loadf = "./dump"
+opt.outf = "./dump"
+opt.outr = './data/test result'
+
+# normalize each data domain
+# opt.normalize_domain = False
+opt.print_switch = True
+opt.adj_default = True
+# now it is half circle
+opt.num_domain = 18
+opt.full_domain = list(range(opt.num_domain))
+opt.src_domain = [0,1,2,3,4,5,6,7,8] # [0,1,2,3,10,11,12,13]
+opt.tgt_domain = list(set(opt.full_domain) - set(opt.src_domain))
+opt.num_source = len(opt.src_domain)
+opt.num_target = opt.num_domain - opt.num_source
+opt.src_dmn_num = opt.num_source
+opt.tgt_dmn_num = opt.num_target
+opt.all_domain = opt.src_domain + opt.tgt_domain
+
+opt.test_on_all_dmn = True
+
+opt.sample_neighbour = False
+
+
+#opt.model = "DANN"
+#opt.model = "CDANN"
+#opt.model = "ADDA"
+opt.model = 'TSDA'
+#opt.model = "GDA"
+opt.cond_disc = (
+    False  # whether use conditional discriminator or not (for CDANN)
+)
+
+
+opt.use_visdom = False
+opt.visdom_port = 2000
+
+opt.use_g_encode = False # False  # True
+if opt.use_g_encode:
+    opt.g_encode = read_pickle("g_encode_l7l40.pkl")
+
+
+opt.device = "cuda"
+opt.seed = 2333  # 1# 101 # 1 # 233 # 1
+
+# opt.lambda_gan = 0.5  # 0.5 # 0.3125 # 0.5 # 0.5
+opt.lambda_gan = 0
+
+# for MDD use only
+opt.lambda_src = 0.5
+opt.lambda_tgt = 0.5
+
+# for TDDA use only
+opt.lambda_r = 0.594795294351678
+opt.lambda_d = 0.5
+opt.lambda_e = 0.7786359421472335
+opt.lambda_c = 1
+opt.num_epoch = 300
+opt.batch_size = 5
+opt.lr_d = 1e-5 # 3e-5 # 1e-4 # 2.9 * 1e-5 #3e-5  # 1e-4
+opt.lr_e = 1e-5  # 3e-5 # 1e-4 # 2.9 * 4e-6
+opt.lr_g = 1e-4
+opt.lr_r = 1e-4
+opt.gamma = 100
+opt.beta1 = 0.9
+opt.weight_decay = 5e-4
+opt.wgan = False  # do not use wgan to train
+opt.no_bn = True  # do not use batch normalization # True
+
+# model size configs, used for D, E, F
+opt.nt = 2  # dimension of the vertex embedding
+opt.nc = 2  # number of label class
+opt.nd_out = 2  # dimension of D's output
+opt.nr_out = 2
+opt.num_input = 4096    # the x data dimension
+opt.nh = 4096        # TODO: the hidden states for many modules, be careful
+opt.nv_embed = 2     # the vertex embedding dimension
+# sample how many vertices for training R
+opt.sample_v = opt.num_domain
+
+# # sample how many vertices for training G
+opt.sample_v_g = opt.num_domain
+
+opt.test_interval = 20
+opt.save_interval = 100
+# drop out rate
+opt.p = 0.2
+opt.shuffle = True
+
+
+# dataset
+opt.data_src = "data/"
+opt.data_path = opt.data_src + "feature_upperparts_black.pkl"
+opt.dataset = opt.data_path
+opt.A = read_pickle(opt.data_src + "A_cub_18.pkl")

Real/configs/config_imagenet_11.py

@@ -0,0 +1,110 @@
+from easydict import EasyDict
+import numpy as np
+import pickle
+
+
+def read_pickle(name):
+    with open(name, "rb") as f:
+        data = pickle.load(f)
+    return data
+
+
+# load/output dir
+opt = EasyDict()
+opt.loadf = "./dump"
+opt.outf = "./dump"
+opt.outr = './data/test result'
+
+# normalize each data domain
+# opt.normalize_domain = False
+opt.print_switch = True
+opt.adj_default = True
+# now it is half circle
+opt.num_domain = 11
+opt.full_domain = list(range(opt.num_domain))
+opt.src_domain = [6,7,8,9,10]
+opt.tgt_domain = list(set(opt.full_domain) - set(opt.src_domain))
+opt.num_source = len(opt.src_domain)
+opt.num_target = opt.num_domain - opt.num_source
+opt.src_dmn_num = opt.num_source
+opt.tgt_dmn_num = opt.num_target
+opt.all_domain = opt.src_domain + opt.tgt_domain
+
+opt.test_on_all_dmn = True
+
+opt.sample_neighbour = False
+
+
+#opt.model = "DANN"
+#opt.model = "CDANN"
+#opt.model = "ADDA"
+opt.model = 'TSDA'
+#opt.model = "GDA"
+opt.cond_disc = (
+    False  # whether use conditional discriminator or not (for CDANN)
+)
+
+
+opt.use_visdom = False
+opt.visdom_port = 2000
+
+opt.use_g_encode = False # False  # True
+if opt.use_g_encode:
+    opt.g_encode = read_pickle("g_encode_l7l40.pkl")
+
+
+opt.device = "cuda"
+opt.seed = 2333  # 1# 101 # 1 # 233 # 1
+
+# opt.lambda_gan = 0.5  # 0.5 # 0.3125 # 0.5 # 0.5
+opt.lambda_gan = 0
+
+# for MDD use only
+opt.lambda_src = 0.5
+opt.lambda_tgt = 0.5
+
+# for TDDA use only
+opt.lambda_r = 0.594795294351678
+opt.lambda_d = 0.2488505602149487
+opt.lambda_e = 0.7786359421472335
+opt.lambda_c = 1
+
+opt.num_epoch = 300
+opt.batch_size = 5
+opt.lr_d = 1e-5 # 3e-5 # 1e-4 # 2.9 * 1e-5 #3e-5  # 1e-4
+opt.lr_e = 1e-5  # 3e-5 # 1e-4 # 2.9 * 4e-6
+opt.lr_g = 1e-4
+opt.lr_r = 1e-4
+opt.gamma = 100
+opt.beta1 = 0.9
+opt.weight_decay = 5e-4
+opt.wgan = False  # do not use wgan to train
+opt.no_bn = True  # do not use batch normalization # True
+
+# model size configs, used for D, E, F
+opt.nt = 2  # dimension of the vertex embedding
+opt.nc = 2  # number of label class
+opt.nd_out = 2  # dimension of D's output
+opt.nr_out = 2
+opt.num_input = 4096    # the x data dimension
+opt.nh = 4096        # TODO: the hidden states for many modules, be careful
+opt.nv_embed = 2     # the vertex embedding dimension
+# sample how many vertices for training R
+opt.sample_v = opt.num_domain
+
+# # sample how many vertices for training G
+opt.sample_v_g = opt.num_domain
+
+opt.test_interval = 20
+opt.save_interval = 100
+# drop out rate
+opt.p = 0.2
+opt.shuffle = True
+
+
+# dataset
+opt.data_src = "data/"
+opt.data_path = opt.data_src + "feature_brown.pkl"
+opt.dataset = opt.data_path
+opt.A_root = read_pickle(opt.data_src + "A_brown_root.pkl")
+opt.A = read_pickle(opt.data_src + "A_brown.pkl")