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was_main_uda.py
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was_main_uda.py
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import os
import time
import argparse
from tqdm import tqdm
import numpy as np
import torch
import torch.optim as optim
from model import WarnConv, WarnMLP, WarnConv_digits
from solver import Convex, BBSL, NLLSL
from load_data import (
load_numpy_data,
load_shifted_data,
data_loader,
multi_data_loader,
shift_trainset,
)
import utils
parser = argparse.ArgumentParser()
parser.add_argument(
"--name",
help="Name of the dataset: [amazon|digits].",
type=str,
choices=["amazon", "digits"],
default="amazon",
)
parser.add_argument("--result_path", help="Where to save results.", type=str, default="./results")
parser.add_argument("--data_path", help="Where to find the data.", type=str, default="./datasets")
parser.add_argument("--lr", help="Learning rate.", type=float, default=0.5)
parser.add_argument(
"--mu",
help="Hyperparameter of the coefficient for the domain adversarial loss.",
type=float,
default=1e-2,
)
parser.add_argument(
"--gp_coef", help="Coefficent of gradient penality loss(mu * gp_coef).", type=float, default=1.0
)
parser.add_argument(
"--sem_coef", help="Coefficent of semantic loss(mu * sem_coef).", type=float, default=1.0
)
parser.add_argument("--gamma", help="Inverse temperature hyperparameter.", type=float, default=1.0)
parser.add_argument("--epoch", help="Number of training epochs.", type=int, default=50)
parser.add_argument("--batch_size", help="Batch size during training.", type=int, default=20)
parser.add_argument("--cuda", help="Which cuda device to use.", type=int, default=0)
parser.add_argument("--seed", help="Random seed.", type=int, default=0)
parser.add_argument(
"--alpha_solver",
help="solver type used to resolve alpha.",
choices=["bbsl", "nllsl"],
default="nllsl",
)
parser.add_argument("--data_shift", help="use shifted data or not", action="store_true")
args = parser.parse_args()
device = torch.device("cuda:%d" % args.cuda if torch.cuda.is_available() else "cpu")
batch_size = args.batch_size
exp_flags = "lr_{:g}_mu_{:g}_gp_{:g}_sem_{:g}_seed_{:d}_{}_{}".format(
args.lr,
args.mu,
args.gp_coef,
args.sem_coef,
args.seed,
args.alpha_solver,
"shift" if args.data_shift else "noshift",
)
result_path = os.path.join(
args.result_path,
args.name,
exp_flags
# args.method,
# args.mode
)
if not os.path.exists(result_path):
os.makedirs(result_path)
logger = utils.get_logger(os.path.join(result_path, "log_{}.log".format(exp_flags)))
logger.info("Hyperparameter setting = %s" % args)
# Set random number seed.
np.random.seed(args.seed)
torch.manual_seed(args.seed)
#################### Loading the datasets ####################
print(torch.__version__)
time_start = time.time()
data_names, train_insts, train_labels, test_insts, test_labels, configs = load_numpy_data(
args.name, args.data_path, logger
)
# number of srouce classes,
num_classes_dict = {"digits": 10, "office_home": 65, "amazon": 2}
num_src_classes = num_classes_dict[args.name]
# configs["mode"] = args.mode
### this is the required feature space dimension (digits 2304, office 100, amazon review: 100)
feature_dim_dict = {
# 'digits':2304,
"digits": 100,
"office_home": 100,
"amazon": 100,
}
configs["feauture_dim"] = feature_dim_dict[args.name]
configs["mu"] = args.mu
configs["gp_coef"] = args.gp_coef
configs["sem_coef"] = args.sem_coef
configs["gamma"] = args.gamma
configs["num_src_domains"] = len(data_names) - 1
configs["num_src_classes"] = num_src_classes
num_datasets = len(data_names)
logger.info("Time used to process the %s = %g seconds." % (args.name, time.time() - time_start))
logger.info("-" * 100)
test_results = {}
np_test_results = np.zeros(num_datasets)
#################### Model ####################
num_src_domains = configs["num_src_domains"]
logger.info("Model setting = %s." % configs)
if args.name == "amazon":
# for amazon
src_shift_labels = [0]
src_drop_ratios = [0.5]
elif args.name == "digits":
src_shift_labels = [5, 6, 7, 8, 9]
# src_drop_ratios = [0.4, 0.4, 0.4, 0.4, 0.4]
src_drop_ratios = [0.5, 0.5, 0.5, 0.5, 0.5]
else:
logger.info("{} isn't supportted in this setting!".format(args.name))
#################### Train ####################
# we have two parameter lambda and alpha
lambda_list = np.zeros([num_datasets, num_src_domains, args.epoch])
alpha_list = np.zeros([num_datasets, num_src_domains, num_src_classes, args.epoch])
for tar_dom_idx, tar_dom_name in enumerate(data_names):
# collect source data names from full data names except for target data name
src_data_names = [name for name in data_names if name != tar_dom_name]
# display sources v.s. target
logger.info("*" * 100)
logger.info(
"* Source domains: [{}], target domain: [{}] ".format(
"/".join(src_data_names), tar_dom_name
)
)
logger.info("*" * 100)
# Build source instances
source_insts = []
source_labels = []
for j in range(num_datasets):
if j != tar_dom_idx:
if not args.data_shift:
source_insts.append(train_insts[j].astype(np.float32))
source_labels.append(train_labels[j].astype(np.int64))
else:
## apply same label shift on all source domains
train_x_temps, train_y_temps = shift_trainset(
train_insts[j].astype(np.float32),
train_labels[j].astype(np.int64),
src_shift_labels,
src_drop_ratios,
)
source_insts.append(train_x_temps)
source_labels.append(train_y_temps)
# Build target instances
target_insts = train_insts[tar_dom_idx].astype(np.float32)
target_labels = train_labels[tar_dom_idx].astype(np.int64)
# Compute ground truth source label distribution (normalized)
src_true = np.zeros([num_src_domains, num_src_classes])
for tsk in range(num_src_domains):
for j in range(num_src_classes):
src_true[tsk, j] = np.count_nonzero(source_labels[tsk] == j)
src_true[tsk, :] = src_true[tsk, :] / len(source_labels[tsk])
# Model
if args.name in ["amazon"]: # MLP
model = WarnMLP(configs).to(device)
elif args.name == "digits": # ConvNet
model = WarnConv_digits(configs).to(device)
optimizer = optim.Adadelta(model.parameters(), lr=args.lr)
time_start = time.time()
# defining lambda and alpha (global)
task_lambda = np.ones([num_src_domains]) / num_src_domains
task_alpha = np.ones([num_src_domains, num_src_classes], dtype=np.float32)
L2_regularization = 1
global_step = 0
for epoch_idx in range(args.epoch):
# train mode
model.train()
# estimated y label distribution
tar_y_estimated = np.dot(task_lambda, np.multiply(task_alpha, src_true))
# define the confusion matrix, source taget prediction output distribution
C = np.zeros([num_src_classes, num_src_classes, num_src_domains])
tar_pred = np.zeros([num_src_classes])
running_loss = 0.0
# loss_acc (local)
loss_acc = np.zeros(num_src_domains)
train_loader = multi_data_loader(source_insts, source_labels, batch_size)
lam_cuda, alpha_cuda = (
torch.FloatTensor(task_lambda).to(device),
torch.FloatTensor(task_alpha).to(device),
)
src_true_cuda = torch.FloatTensor(src_true).to(device)
for batch_idx, (xs, ys) in enumerate(
tqdm(train_loader, desc="Epoch {}...".format(epoch_idx + 1))
):
global_step += 1
for j in range(num_src_domains):
xs[j] = torch.tensor(xs[j], requires_grad=False).to(device)
ys[j] = torch.tensor(ys[j], requires_grad=False).to(device)
ridx = np.random.choice(target_insts.shape[0], batch_size)
tinputs = target_insts[ridx, :]
tinputs = torch.tensor(tinputs, requires_grad=False).to(device)
optimizer.zero_grad()
train_loss, C_batch, tar_pred_batch, convex_loss, losses_tuple = model(
xs, ys, tinputs, alpha_cuda, src_true_cuda
)
cls_losses, domain_losses, domain_gradient_losses, src_semantic_losses = losses_tuple
## updating C, src_true, tar_pred
C += C_batch
tar_pred += tar_pred_batch
# lambda alpha based loss
lambda_loss = torch.sum(train_loss * lam_cuda)
with torch.no_grad():
# convert to L2 optimization mode
loss_np = convex_loss.cpu().numpy()
loss_acc += loss_np
lambda_loss.backward()
optimizer.step()
running_loss += lambda_loss.item()
# updating lamda after each eopch
loss_acc /= batch_idx + 1
if args.name == "digits":
START_EPOCH = 5
elif args.name == "amazon":
START_EPOCH = 1
# update lambda
if args.name == "digits":
L2_regularization = np.sum(loss_acc)
if epoch_idx > START_EPOCH and epoch_idx % 1 == 0:
task_lambda_temp = Convex(loss_acc, L2_regularization)
task_lambda = 0.8 * task_lambda + 0.2 * task_lambda_temp
logger.info(
"Epoch[{}/{}], update lambda with moving average!".format(epoch_idx + 1, args.epoch)
)
else:
logger.info("Epoch[{}/{}], no updates for lambda!".format(epoch_idx + 1, args.epoch))
# updating alpha after each epoch
# IMPORTANT! in the multi-source partial DA, (Cao, ECCV 2018)
# alpha = tar_pred /np.max(tar_pred) (they did not define lambda)
if epoch_idx > 1 and epoch_idx % 1 == 0:
task_alpha_temp = np.ones([num_src_domains, num_src_classes], dtype=np.float32)
tar_pred = tar_pred / np.sum(tar_pred)
for src_dom_idx in range(num_src_domains):
alpha_s = task_alpha[src_dom_idx, :]
Con_s = C[:, :, src_dom_idx]
Con_s = Con_s / np.sum(Con_s)
src_true_s = src_true[src_dom_idx, :]
if args.alpha_solver == "bbsl":
task_alpha_temp[src_dom_idx, :] = BBSL(Con_s, tar_pred, src_true_s)
elif args.alpha_solver == "nllsl":
task_alpha_temp[src_dom_idx, :] = NLLSL(Con_s, tar_pred, src_true_s)
task_alpha = 0.8 * task_alpha + 0.2 * task_alpha_temp
logger.info(
"Epoch[{}/{}], update alpha with moving average!".format(epoch_idx + 1, args.epoch)
)
else:
logger.info("Epoch[{}/{}], no updates for alpha!".format(epoch_idx + 1, args.epoch))
for src_dom_idx, src_dom in enumerate(src_data_names):
logger.info(
"Epoch[{}/{}], alpha[{}] = {}".format(
epoch_idx + 1, args.epoch, src_dom, task_alpha[src_dom_idx, :]
)
)
# display
lambdas_in_str = [
" {}:{:.6f} ".format(dom_name, task_lambda[idx])
for idx, dom_name in enumerate(src_data_names)
]
logger.info(
"Epoch[{}/{}], Lambda=[{}]".format(epoch_idx + 1, args.epoch, ",".join(lambdas_in_str))
)
lambda_list[tar_dom_idx, :, epoch_idx] = task_lambda
alpha_list[tar_dom_idx, :, :, epoch_idx] = task_alpha
logger.info(
"Epoch[{}/{}], running_loss(sum in epoch) = {:.4f}".format(
epoch_idx + 1, args.epoch, running_loss
)
)
logger.info(
"Epoch[{}/{}], convex_loss(avg on epochs) = {}".format(
epoch_idx + 1, args.epoch, loss_acc
)
)
logger.info("Finish training in {:.6g} seconds".format(time.time() - time_start))
model.eval()
# Test (use another hold-out target)
test_loader = data_loader(
test_insts[tar_dom_idx], test_labels[tar_dom_idx], batch_size=1000, shuffle=False
)
test_acc = 0.0
for xt, yt in test_loader:
xt = torch.tensor(xt, requires_grad=False, dtype=torch.float32).to(device)
yt = torch.tensor(yt, requires_grad=False, dtype=torch.int64).to(device)
preds_labels = torch.argmax(model.inference(xt), 1)
test_acc += torch.sum(preds_labels == yt).item()
test_acc /= test_insts[tar_dom_idx].shape[0]
logger.info(
"Epoch[{}/{}], test accuracy on [{}] = {:.6g}".format(
epoch_idx + 1, args.epoch, tar_dom_name, test_acc
)
)
test_results[tar_dom_name] = test_acc
np_test_results[tar_dom_idx] = test_acc
logger.info("All test accuracies: ")
logger.info(test_results)
# Save results to files
with open(
os.path.join(result_path, "test_{}_{}.txt".format(exp_flags, tar_dom_name)), "w"
) as test_file:
for tar_dom_name, test_acc in test_results.items():
test_file.write("{} = {:.6g}\n".format(tar_dom_name, test_acc))
logger.info("Finish {}_{}".format(exp_flags, tar_dom_name))
logger.info("*" * 100)
logger.info("All finished!")