class Config(object):
gpu_id = 2
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
num_workers = 8
episode_size = 15
test_episode = 600
test_avg_num = 5
feature_encoder, relation_network = CNNEncoder(), RelationNetwork()
# model_path = "../models/two_ic_ufsl_2net_res_sgd_acc"
# model_fe_name = "0_2100_64_5_1_500_200_512_1_1.0_1.0_fe_5way_1shot.pkl"
# model_rn_name = "0_2100_64_5_1_500_200_512_1_1.0_1.0_rn_5way_1shot.pkl"
model_path = "../models/two_ic_ufsl_2net_res_sgd_acc_duli"
model_fe_name = "2_2100_64_5_1_500_200_512_1_1.0_1.0_fe_5way_1shot.pkl"
model_rn_name = "2_2100_64_5_1_500_200_512_1_1.0_1.0_rn_5way_1shot.pkl"
fe_dir = Tools.new_dir(os.path.join(model_path, model_fe_name))
rn_dir = Tools.new_dir(os.path.join(model_path, model_rn_name))
if "Linux" in platform.platform():
data_root = '/mnt/4T/Data/data/miniImagenet'
if not os.path.isdir(data_root):
data_root = '/media/ubuntu/4T/ALISURE/Data/miniImagenet'
else:
data_root = "F:\\data\\miniImagenet"
pass
class Config(object):
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
# train_epoch = 300
train_epoch = 180
learning_rate = 0.001
num_workers = 8
val_freq = 10
num_way = 5
num_shot = 1
batch_size = 64
episode_size = 15
test_episode = 600
feature_encoder, relation_network = CNNEncoder(), RelationNetwork()
# feature_encoder, relation_network = CNNEncoder1(), RelationNetwork1()
model_name = "{}_{}_{}_{}".format(train_epoch, batch_size, num_way,
num_shot)
if "Linux" in platform.platform():
data_root = '/mnt/4T/Data/data/miniImagenet'
if not os.path.isdir(data_root):
data_root = '/media/ubuntu/4T/ALISURE/Data/miniImagenet'
else:
data_root = "F:\\data\\miniImagenet"
fe_dir = Tools.new_dir("../models/fsl/{}_fe_{}way_{}shot.pkl".format(
model_name, num_way, num_shot))
rn_dir = Tools.new_dir("../models/fsl/{}_rn_{}way_{}shot.pkl".format(
model_name, num_way, num_shot))
pass
class Config(object):
gpu_id = 0
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
num_workers = 16
num_way = 5
num_shot = 1
batch_size = 64
val_freq = 10
episode_size = 15
test_episode = 600
feature_encoder, relation_network = CNNEncoder(), RelationNetwork()
# ic
ic_out_dim = 512
# ic_out_dim = 2560
ic_ratio = 1
learning_rate = 0.01
# loss_fsl_ratio = 10.0
# loss_ic_ratio = 0.1
loss_fsl_ratio = 1.0
loss_ic_ratio = 1.0
# train_epoch = 500
# first_epoch, t_epoch = 300, 150
# adjust_learning_rate = RunnerTool.adjust_learning_rate2
train_epoch = 2100
first_epoch, t_epoch = 500, 200
adjust_learning_rate = RunnerTool.adjust_learning_rate1
model_name = "{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}".format(
gpu_id, train_epoch, batch_size, num_way, num_shot, first_epoch,
t_epoch, ic_out_dim, ic_ratio, loss_fsl_ratio, loss_ic_ratio)
if "Linux" in platform.platform():
data_root = '/mnt/4T/Data/data/miniImagenet'
if not os.path.isdir(data_root):
data_root = '/media/ubuntu/4T/ALISURE/Data/miniImagenet'
else:
data_root = "F:\\data\\miniImagenet"
_root_path = "../models/two_ic_ufsl_2net_res_sgd_acc"
fe_dir = Tools.new_dir("{}/{}_fe_{}way_{}shot.pkl".format(
_root_path, model_name, num_way, num_shot))
rn_dir = Tools.new_dir("{}/{}_rn_{}way_{}shot.pkl".format(
_root_path, model_name, num_way, num_shot))
ic_dir = Tools.new_dir("{}/{}_ic_{}way_{}shot.pkl".format(
_root_path, model_name, num_way, num_shot))
pass
class Config(object):
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
learning_rate = 0.01
num_workers = 8
num_way = 5
num_shot = 1
batch_size = 64
val_freq = 10
episode_size = 15
test_episode = 600
# ic
ic_in_dim = 64
ic_out_dim = 512
ic_ratio = 1
loss_fsl_ratio = 10.0
loss_ic_ratio = 0.1
train_epoch = 600
first_epoch, t_epoch = 300, 150
adjust_learning_rate = RunnerTool.adjust_learning_rate2
feature_encoder, relation_network = CNNEncoder(), RelationNetwork()
# feature_encoder, relation_network = CNNEncoder1(), RelationNetwork1()
model_name = "2_{}_{}_{}_{}_{}_{}_{}_{}_{}".format(
train_epoch, batch_size, first_epoch, t_epoch, ic_in_dim, ic_out_dim,
ic_ratio, loss_fsl_ratio, loss_ic_ratio)
if "Linux" in platform.platform():
data_root = '/mnt/4T/Data/data/miniImagenet'
if not os.path.isdir(data_root):
data_root = '/media/ubuntu/4T/ALISURE/Data/miniImagenet'
else:
data_root = "F:\\data\\miniImagenet"
fe_dir = Tools.new_dir(
"../models/two_ic_fsl_sgd/{}_fe_{}way_{}shot.pkl".format(
model_name, num_way, num_shot))
rn_dir = Tools.new_dir(
"../models/two_ic_fsl_sgd/{}_rn_{}way_{}shot.pkl".format(
model_name, num_way, num_shot))
ic_dir = Tools.new_dir(
"../models/two_ic_fsl_sgd/{}_ic_{}way_{}shot.pkl".format(
model_name, num_way, num_shot))
pass
class Config(object):
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
num_workers = 8
batch_size = 64
val_freq = 2
learning_rate = 0.001
learning_rate_small = 0.001
num_way = 5
num_shot = 1
episode_size = 15
test_episode = 600
feature_encoder, relation_network = CNNEncoder(), RelationNetwork()
fe_pretrain = "../models/ic/1_64_512_1_500_200_0.01_fe.pkl"
ic_pretrain = "../models/ic/1_64_512_1_500_200_0.01_ic.pkl"
# ic
ic_in_dim = 64
ic_out_dim = 512
ic_ratio = 2
train_epoch = 300
loss_fsl_ratio = 10.0
loss_ic_ratio = 0.1
model_name = "4_{}_{}_{}_{}_{}_{}_{}_{}_{}".format(
train_epoch, batch_size, num_way, num_shot, ic_in_dim, ic_out_dim,
ic_ratio, loss_fsl_ratio, loss_ic_ratio)
if "Linux" in platform.platform():
data_root = '/mnt/4T/Data/data/miniImagenet'
if not os.path.isdir(data_root):
data_root = '/media/ubuntu/4T/ALISURE/Data/miniImagenet'
else:
data_root = "F:\\data\\miniImagenet"
root_path = "../models/two_ic_ufsl_pretrain"
fe_dir = Tools.new_dir("{}/{}_fe_{}way_{}shot.pkl".format(
root_path, model_name, num_way, num_shot))
rn_dir = Tools.new_dir("{}/{}_rn_{}way_{}shot.pkl".format(
root_path, model_name, num_way, num_shot))
ic_dir = Tools.new_dir("{}/{}_ic_{}way_{}shot.pkl".format(
root_path, model_name, num_way, num_shot))
pass
class Config(object):
gpu_id = 3
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
num_workers = 24
batch_size = 64
val_freq = 10
learning_rate = 0.001
num_way = 5
num_shot = 1
episode_size = 15
test_episode = 600
feature_encoder, relation_network = CNNEncoder(), RelationNetwork()
# ic
ic_in_dim = 64
ic_out_dim = 512
ic_ratio = 1
train_epoch = 900
loss_fsl_ratio = 10.0
loss_ic_ratio = 0.1
model_name = "{}_{}_{}_{}_{}_{}_{}_{}_{}_{}".format(
gpu_id, train_epoch, batch_size, num_way, num_shot, ic_in_dim,
ic_out_dim, ic_ratio, loss_fsl_ratio, loss_ic_ratio)
if "Linux" in platform.platform():
data_root = '/mnt/4T/Data/data/miniImagenet'
if not os.path.isdir(data_root):
data_root = '/media/ubuntu/4T/ALISURE/Data/miniImagenet'
else:
data_root = "F:\\data\\miniImagenet"
fe_dir = Tools.new_dir(
"../models/two_ic_ufsl_acc/{}_fe_{}way_{}shot.pkl".format(
model_name, num_way, num_shot))
rn_dir = Tools.new_dir(
"../models/two_ic_ufsl_acc/{}_rn_{}way_{}shot.pkl".format(
model_name, num_way, num_shot))
ic_dir = Tools.new_dir(
"../models/two_ic_ufsl_acc/{}_ic_{}way_{}shot.pkl".format(
model_name, num_way, num_shot))
pass
class Config(object):
gpu_id = 0
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
num_workers = 8
num_way = 5
num_shot = 1
batch_size = 64
val_freq = 10
episode_size = 15
test_episode = 600
learning_rate = 0.01
loss_fsl_ratio = 1.0
loss_ic_ratio = 1.0
train_epoch = 2100
first_epoch, t_epoch = 500, 200
adjust_learning_rate = RunnerTool.adjust_learning_rate1
# ic
ic_out_dim = 512
ic_ratio = 1
##############################################################################################################
# resnet = resnet18
resnet = resnet34
feature_encoder, relation_network = CNNEncoder(), RelationNetwork()
is_png = True
# is_png = False
# modify_head = False
modify_head = True
##############################################################################################################
model_name = "{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}{}{}".format(
gpu_id, train_epoch, batch_size, num_way, num_shot, first_epoch,
t_epoch, ic_out_dim, ic_ratio, loss_fsl_ratio, loss_ic_ratio,
"_head" if modify_head else "", "_png" if is_png else "")
if "Linux" in platform.platform():
data_root = '/mnt/4T/Data/data/miniImagenet'
if not os.path.isdir(data_root):
data_root = '/media/ubuntu/4T/ALISURE/Data/miniImagenet'
else:
data_root = "F:\\data\\miniImagenet"
data_root = os.path.join(data_root,
"miniImageNet_png") if is_png else data_root
Tools.print(data_root)
_root_path = "../models/two_ic_ufsl_2net_res_sgd_acc_duli"
fe_dir = Tools.new_dir("{}/{}_fe_{}way_{}shot.pkl".format(
_root_path, model_name, num_way, num_shot))
rn_dir = Tools.new_dir("{}/{}_rn_{}way_{}shot.pkl".format(
_root_path, model_name, num_way, num_shot))
ic_dir = Tools.new_dir("{}/{}_ic_{}way_{}shot.pkl".format(
_root_path, model_name, num_way, num_shot))
pass