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fedcvt_bhi_exp_run.py
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fedcvt_bhi_exp_run.py
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import numpy as np
import torch
from dataset.bhi_dataset import BHIDataset2Party
from fedcvt_core.fedcvt_parties import VFTLGuest, VFLHost
from fedcvt_core.param import PartyModelParam, FederatedTrainingParam
from models.cnn_models import MyResnet18
from run_experiment import batch_run_experiments
from utils import set_seed
class VFTLGuestConstructor(object):
def __init__(self, party_param: PartyModelParam, fed_model_param: FederatedTrainingParam):
self.party_param = party_param
self.fed_model_param = fed_model_param
def build(self, args=None, debug=False):
print("[INFO] # ===> Build Guest Local Model.")
device = args["device"]
hidden_dim_list = self.party_param.hidden_dim_list
n_classes = self.party_param.n_classes
print("hidden_dim_list[-1]:", hidden_dim_list[-1])
nn_prime = MyResnet18(an_id=0, class_num=n_classes, output_dim=hidden_dim_list[-1]).to(device)
nn = MyResnet18(an_id=1, class_num=n_classes, output_dim=hidden_dim_list[-1]).to(device)
print("[INFO] Guest NN_prime:")
print(nn_prime)
print("[INFO] Guest NN:")
print(nn)
guest = VFTLGuest(party_model_param=self.party_param, debug=debug)
guest.set_model(nn, nn_prime)
return guest
class VFTLHostConstructor(object):
def __init__(self, party_param: PartyModelParam, fed_model_param: FederatedTrainingParam):
self.party_param = party_param
self.fed_model_param = fed_model_param
def build(self, args=None, debug=False):
print("[INFO] # ===> Build Host Local Model.")
device = args["device"]
hidden_dim_list = self.party_param.hidden_dim_list
n_classes = self.party_param.n_classes
print("hidden_dim_list[-1]:", hidden_dim_list[-1])
nn_prime = MyResnet18(an_id=2, class_num=n_classes, output_dim=hidden_dim_list[-1]).to(device)
nn = MyResnet18(an_id=3, class_num=n_classes, output_dim=hidden_dim_list[-1]).to(device)
print("[INFO] Host NN_prime:")
print(nn_prime)
print("[INFO] Host NN:")
print(nn)
host = VFLHost(party_model_param=self.party_param, debug=debug)
host.set_model(nn, nn_prime)
return host
def get_valid_sample_indices(data):
idx_valid_sample_list = []
idx_invalid_sample_list = []
for idx, data in enumerate(data):
if np.all(data == 0):
idx_invalid_sample_list.append(idx)
# print("X_text_all_i", idx, np.sum(X_text_all_i), len(X_text_all_i))
else:
idx_valid_sample_list.append(idx)
print("number of all-zero text sample:", len(idx_invalid_sample_list))
print("number of not all-zero text sample:", len(idx_valid_sample_list))
print("total number of samples: ", len(idx_invalid_sample_list) + len(idx_valid_sample_list))
return idx_valid_sample_list
if __name__ == "__main__":
file_dir = "../../dataset/bhi/"
seed_list = [0]
for seed in seed_list:
train_dataset = BHIDataset2Party(file_dir, 'train', 32, 32, 2, seed)
test_dataset = BHIDataset2Party(file_dir, 'test', 32, 32, 2, seed)
# =====================================
# Prepare optimizer hyper-parameters
# =====================================
learning_rate_list = [0.001]
# learning_rate_list = [0.05]
# learning_rate_list = [0.01]
optim_args = dict()
# optim_args["weight_decay"] = 0
optim_args["weight_decay"] = 0.0
optim_args["learning_rate_list"] = learning_rate_list
# =====================================
# Prepare training hyper-parameters
# =====================================
epoch = 80
estimation_block_size = 4000
ll_overlap_sample_batch_size = 256
ul_overlap_sample_batch_size = 512
non_overlap_sample_batch_size = 512
sharpen_temperature = 0.5
is_hetero_reprs = False
aggregation_mode = "cat"
label_prob_sharpen_temperature = 0.1
fed_label_prob_threshold = 0.9
guest_label_prob_threshold = 0.9
host_label_prob_threshold = 0.9
num_overlap_list = [250]
# num_overlap_list = [250, 500, 1000, 2000, 4000]
num_labeled_overlap_list = num_overlap_list
training_args = dict()
training_args["epoch"] = epoch
training_args["num_overlap_list"] = num_overlap_list
training_args["num_labeled_overlap_list"] = num_labeled_overlap_list
training_args["ul_overlap_sample_batch_size"] = ul_overlap_sample_batch_size
training_args["ll_overlap_sample_batch_size"] = ll_overlap_sample_batch_size
training_args["non_overlap_sample_batch_size"] = non_overlap_sample_batch_size
training_args["estimation_block_size"] = estimation_block_size
training_args["sharpen_temperature"] = sharpen_temperature
training_args["is_hetero_reprs"] = is_hetero_reprs
training_args["label_prob_sharpen_temperature"] = label_prob_sharpen_temperature
training_args["fed_label_prob_threshold"] = fed_label_prob_threshold
training_args["guest_label_prob_threshold"] = guest_label_prob_threshold
training_args["host_label_prob_threshold"] = host_label_prob_threshold
training_args["data_type"] = "img"
training_args["normalize_repr"] = True
training_args["only_use_ll"] = False
training_args["epoch"] = epoch
training_args["hidden_dim"] = 128
training_args["num_class"] = 2
training_args["vfl_guest_constructor"] = VFTLGuestConstructor
training_args["vfl_host_constructor"] = VFTLHostConstructor
# =====================================
# Prepare loss hyper-parameters
# =====================================
lambda_dis_shared_reprs = [0.1]
lambda_sim_shared_reprs_vs_uniq_reprs = [0.1]
lambda_host_dist_ested_lbls_vs_true_lbls = [1]
# lambda_host_dist_ested_lbls_vs_true_lbls = [1]
lambda_dist_ested_reprs_vs_true_reprs = [0.1]
lambda_host_dist_two_ested_lbls = [0.01]
loss_weight_args = dict()
loss_weight_args["lambda_dist_shared_reprs"] = lambda_dis_shared_reprs
loss_weight_args["lambda_sim_shared_reprs_vs_uniq_reprs"] = lambda_sim_shared_reprs_vs_uniq_reprs
loss_weight_args["lambda_host_dist_ested_lbls_vs_true_lbls"] = lambda_host_dist_ested_lbls_vs_true_lbls
loss_weight_args["lambda_dist_ested_reprs_vs_true_reprs"] = lambda_dist_ested_reprs_vs_true_reprs
loss_weight_args["lambda_host_dist_two_ested_lbls"] = lambda_host_dist_two_ested_lbls
# device
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# set gpu
gpu_device_id = 0
if torch.cuda.is_available():
torch.cuda.set_device(gpu_device_id)
print('[INFO] using gpu device = %d' % gpu_device_id)
else:
print('[INFO] using cpu.')
# seed_list = [0, 1]
# seed = 0
set_seed(seed)
other_args = {"seed": seed, "name": "bhi", "aggregation_mode": aggregation_mode,
"monitor_metric": "fscore", "device": device, "valid_iteration_interval": 1}
batch_run_experiments(train_dataset, test_dataset, optim_args, loss_weight_args, training_args, other_args)