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config_generator_classic_cv.py
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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
"""
Create the .yaml for each experiment
"""
import os
def create_configuration(cfg, cfg_file):
cfg["save_name"] = "{alg}_{dataset}_{num_lb}_{seed}".format(
alg=cfg["algorithm"],
dataset=cfg["dataset"],
num_lb=cfg["num_labels"],
seed=cfg["seed"],
)
# resume
cfg["resume"] = True
cfg["load_path"] = "{}/{}/latest_model.pth".format(
cfg["save_dir"], cfg["save_name"]
)
alg_file = cfg_file + cfg["algorithm"] + "/"
if not os.path.exists(alg_file):
os.mkdir(alg_file)
print(alg_file + cfg["save_name"] + ".yaml")
with open(alg_file + cfg["save_name"] + ".yaml", "w", encoding="utf-8") as w:
lines = []
for k, v in cfg.items():
line = str(k) + ": " + str(v)
lines.append(line)
for line in lines:
w.writelines(line)
w.write("\n")
def create_classic_config(
alg, seed, dataset, net, num_classes, num_labels, img_size, port, weight_decay
):
cfg = {}
cfg["algorithm"] = alg
# save config
cfg["save_dir"] = "./saved_models/classic_cv"
cfg["save_name"] = None
cfg["resume"] = False
cfg["load_path"] = None
cfg["overwrite"] = True
cfg["use_tensorboard"] = True
cfg["use_wandb"] = True
cfg["use_aim"] = False
# algorithm config
cfg["epoch"] = 1024
cfg["num_train_iter"] = 2**20
cfg["num_eval_iter"] = 5120
cfg["num_log_iter"] = 256
cfg["num_labels"] = num_labels
cfg["batch_size"] = 64
cfg["eval_batch_size"] = 256
if alg == "fixmatch":
cfg["hard_label"] = True
cfg["T"] = 0.5
cfg["p_cutoff"] = 0.95
cfg["ulb_loss_ratio"] = 1.0
cfg["uratio"] = 7
elif alg == "adamatch":
cfg["hard_label"] = True
cfg["T"] = 0.5
cfg["p_cutoff"] = 0.95
cfg["ulb_loss_ratio"] = 1.0
cfg["ema_p"] = 0.999
cfg["uratio"] = 7
elif alg == "flexmatch":
cfg["hard_label"] = True
cfg["T"] = 0.5
cfg["thresh_warmup"] = True
cfg["p_cutoff"] = 0.95
cfg["ulb_loss_ratio"] = 1.0
cfg["uratio"] = 7
elif alg == "uda":
cfg["tsa_schedule"] = "none"
cfg["T"] = 0.4
cfg["p_cutoff"] = 0.8
cfg["ulb_loss_ratio"] = 1.0
cfg["uratio"] = 7
elif alg == "pseudolabel":
cfg["p_cutoff"] = 0.95
cfg["ulb_loss_ratio"] = 1.0
cfg["uratio"] = 1
cfg["unsup_warm_up"] = 0.4
elif alg == "mixmatch":
cfg["uratio"] = 1
cfg["mixup_alpha"] = 0.5
cfg["T"] = 0.5
if dataset == "cifar10":
cfg["ulb_loss_ratio"] = 100
elif dataset == "cifar100":
cfg["ulb_loss_ratio"] = 150
else:
cfg["ulb_loss_ratio"] = 100
cfg["unsup_warm_up"] = 0.4 # 16000 / 1024 / 1024
elif alg == "remixmatch":
cfg["mixup_alpha"] = 0.75
cfg["T"] = 0.5
cfg["kl_loss_ratio"] = 0.5
cfg["ulb_loss_ratio"] = 1.5
cfg["rot_loss_ratio"] = 0.5
cfg["unsup_warm_up"] = 1 / 64
cfg["uratio"] = 1
elif alg == "crmatch":
cfg["hard_label"] = True
cfg["p_cutoff"] = 0.95
cfg["ulb_loss_ratio"] = 1.0
cfg["uratio"] = 7
elif alg == "comatch":
cfg["hard_label"] = False
cfg["p_cutoff"] = 0.95
cfg["contrast_p_cutoff"] = 0.8
cfg["contrast_loss_ratio"] = 1.0
cfg["ulb_loss_ratio"] = 1.0
cfg["proj_size"] = 64
cfg["queue_batch"] = 5
cfg["smoothing_alpha"] = 0.9
cfg["uratio"] = 7
cfg["T"] = 0.2
cfg["da_len"] = 32
if dataset == "stl10":
cfg["contrast_loss_ratio"] = 5.0
if dataset == "imagenet":
cfg["p_cutoff"] = 0.6
cfg["contrast_p_cutoff"] = 0.3
cfg["contrast_loss_ratio"] = 10.0
cfg["ulb_loss_ratio"] = 10.0
cfg["smoothing_alpha"] = 0.9
cfg["T"] = 0.1
cfg["proj_size"] = 128
elif alg == "simmatch":
cfg["p_cutoff"] = 0.95
cfg["in_loss_ratio"] = 1.0
cfg["ulb_loss_ratio"] = 1.0
cfg["proj_size"] = 128
cfg["K"] = 256
cfg["da_len"] = 32
cfg["smoothing_alpha"] = 0.9
cfg["uratio"] = 7
if dataset in ["cifar10", "svhn", "cifar100", "stl10"]:
cfg["T"] = 0.1
else:
cfg["T"] = 0.2
elif alg == "meanteacher":
cfg["uratio"] = 1
cfg["ulb_loss_ratio"] = 50
cfg["unsup_warm_up"] = 0.4
elif alg == "pimodel":
cfg["ulb_loss_ratio"] = 10
cfg["uratio"] = 1
cfg["unsup_warm_up"] = 0.4
elif alg == "dash":
cfg["gamma"] = 1.27
cfg["C"] = 1.0001
cfg["rho_min"] = 0.05
cfg["num_wu_iter"] = 2048
cfg["T"] = 0.5
cfg["p_cutoff"] = 0.95
cfg["ulb_loss_ratio"] = 1.0
cfg["uratio"] = 7
elif alg == "mpl":
cfg["tsa_schedule"] = "none"
cfg["T"] = 0.7
cfg["p_cutoff"] = 0.6
cfg["ulb_loss_ratio"] = 8.0
cfg["uratio"] = 7
cfg["teacher_lr"] = 0.03
cfg["label_smoothing"] = 0.1
cfg["num_uda_warmup_iter"] = 5000
cfg["num_stu_wait_iter"] = 3000
elif alg == "freematch":
cfg["hard_label"] = True
cfg["T"] = 0.5
cfg["ema_p"] = 0.999
cfg["ent_loss_ratio"] = 0.001
cfg["uratio"] = 7
cfg["use_quantile"] = False
if dataset == "svhn":
cfg["clip_thresh"] = True
elif alg == "softmatch":
cfg["hard_label"] = True
cfg["T"] = 0.5
cfg["dist_align"] = True
cfg["dist_uniform"] = True
cfg["per_class"] = False
cfg["ema_p"] = 0.999
cfg["ulb_loss_ratio"] = 1.0
cfg["n_sigma"] = 2
cfg["uratio"] = 7
if dataset == "imagenet":
cfg["ulb_loss_ratio"] = 1.0
elif alg == "defixmatch":
cfg["hard_label"] = True
cfg["T"] = 0.5
cfg["p_cutoff"] = 0.95
cfg["ulb_loss_ratio"] = 0.5
cfg["uratio"] = 7
# cfg['img']
cfg["ema_m"] = 0.999
cfg["crop_ratio"] = 0.875
cfg["img_size"] = img_size
# optim config
cfg["optim"] = "SGD"
cfg["lr"] = 0.03
cfg["momentum"] = 0.9
cfg["weight_decay"] = weight_decay
cfg["layer_decay"] = 1.0
cfg["amp"] = False
cfg["clip"] = 0.0
cfg["use_cat"] = True
# net config
cfg["net"] = net
cfg["net_from_name"] = False
# data config
cfg["data_dir"] = "./data"
cfg["dataset"] = dataset
cfg["train_sampler"] = "RandomSampler"
cfg["num_classes"] = num_classes
cfg["num_workers"] = 1
# basic config
cfg["seed"] = seed
# distributed config
cfg["world_size"] = 1
cfg["rank"] = 0
cfg["multiprocessing_distributed"] = True
cfg["dist_url"] = "tcp://127.0.0.1:" + str(port)
cfg["dist_backend"] = "nccl"
cfg["gpu"] = None
# other config
cfg["overwrite"] = True
cfg["amp"] = False
cfg["use_wandb"] = False
cfg["use_aim"] = False
return cfg
# prepare the configuration for baseline model, use_penalty == False
def exp_classic_cv(label_amount):
config_file = r"./config/classic_cv/"
save_path = r"./saved_models/classic_cv"
if not os.path.exists(config_file):
os.makedirs(config_file)
if not os.path.exists(save_path):
os.makedirs(save_path)
algs = [
"flexmatch",
"fixmatch",
"uda",
"pseudolabel",
"fullysupervised",
"supervised",
"remixmatch",
"mixmatch",
"meanteacher",
"pimodel",
"vat",
"dash",
"crmatch",
"comatch",
"simmatch",
"adamatch",
"freematch",
"softmatch",
"defixmatch",
]
datasets = ["cifar100", "svhn", "stl10", "cifar10"]
seeds = [0]
dist_port = range(10001, 11120, 1)
count = 0
for alg in algs:
for dataset in datasets:
for seed in seeds:
# change the configuration of each dataset
if dataset == "cifar10":
# net = 'WideResNet'
num_classes = 10
num_labels = label_amount[0]
weight_decay = 5e-4
net = "wrn_28_2"
img_size = 32
elif dataset == "cifar100":
# net = 'WideResNet'
num_classes = 100
num_labels = label_amount[1]
weight_decay = 1e-3
# depth = 28
# widen_factor = 8
# net = 'wrn_28_8'
net = "wrn_28_2"
img_size = 32
elif dataset == "svhn":
# net = 'WideResNet'
num_classes = 10
num_labels = label_amount[2]
weight_decay = 5e-4
# depth = 28
# widen_factor = 2
net = "wrn_28_2"
img_size = 32
elif dataset == "stl10":
# net = 'WideResNetVar'
num_classes = 10
num_labels = label_amount[3]
weight_decay = 5e-4
net = "wrn_var_37_2"
img_size = 96
elif dataset == "imagenet":
if alg not in ["fixmatch", "flexmatch"]:
continue
net = "resnet50"
num_classes = 1000
num_labels = 100000 # 128000
weight_decay = 3e-4
port = dist_port[count]
# prepare the configuration file
cfg = create_classic_config(
alg,
seed,
dataset,
net,
num_classes,
num_labels,
img_size,
port,
weight_decay,
)
count += 1
create_configuration(cfg, config_file)
if __name__ == "__main__":
# if not os.path.exists('./saved_models/classic_cv/'):
# os.mkdir('./saved_models/classic_cv/')
if not os.path.exists("./config/classic_cv/"):
os.mkdir("./config/classic_cv/")
# classic cv
label_amount = {
"s": [40, 400, 40, 40],
"m": [250, 2500, 250, 250],
"l": [4000, 10000, 1000, 1000],
}
for i in label_amount:
exp_classic_cv(label_amount=label_amount[i])