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run_exp.py
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import argparse
import numpy as np
import torch
from torchvision import datasets
from torch import nn, optim, autograd
import torch.nn.functional as F
import copy
import os
from backpack import backpack, extend
from backpack.extensions import BatchGrad
from collections import OrderedDict
from mydatasets import coloredmnist
from models import MLP, TopMLP
from utils import pretty_print, correct_pred,GeneralizedCELoss, EMA, mean_weight, mean_nll, mean_mse, mean_accuracy,validation, parse_bool
from train import get_train_func
def main(flags):
if flags.save_dir is not None and not os.path.exists(flags.save_dir):
os.makedirs(flags.save_dir)
flags.freeze_featurizer = False if flags.freeze_featurizer.lower() == 'false' else True
final_train_accs = []
final_train_losses = []
final_test_accs = []
final_test_losses = []
logs = []
for restart in range(flags.n_restarts):
if flags.verbose:
print("Restart", restart)
### loss function binary_cross_entropy
input_dim = 2 * 14 * 14
if flags.methods in ['rsc', 'lff']:
n_targets = 2
lossf = F.cross_entropy
int_target = True
else:
n_targets = 1
lossf = mean_nll
int_target = False
np.random.seed(restart)
torch.manual_seed(restart)
### load datasets
if flags.dataset == 'coloredmnist025':
envs, test_envs = coloredmnist(0.25, 0.1, 0.2, int_target = int_target)
elif flags.dataset == 'coloredmnist025gray':
envs, test_envs = coloredmnist(0.25, 0.5, 0.5,int_target = int_target)
elif flags.dataset == 'coloredmnist01':
envs, test_envs = coloredmnist(0.1, 0.2, 0.25, int_target = int_target)
elif flags.dataset == 'coloredmnist01gray':
envs, test_envs = coloredmnist(0.1, 0.5, 0.5, int_target = int_target)
else:
raise NotImplementedError
mlp = MLP(hidden_dim = flags.hidden_dim, input_dim=input_dim).cuda()
topmlp = TopMLP(hidden_dim = flags.hidden_dim, n_top_layers=flags.n_top_layers, \
n_targets=n_targets, fishr= flags.methods=='fishr').cuda()
print(mlp, topmlp)
if flags.load_model_dir is not None and os.path.exists(flags.load_model_dir):
device = torch.device("cuda")
state = torch.load(os.path.join(flags.load_model_dir,'mlp%d.pth' % restart), map_location=device)
mlp.load_state_dict(state)
state = torch.load(os.path.join(flags.load_model_dir,'topmlp%d.pth' % restart), map_location=device)
topmlp.load_state_dict(state)
print("Load model from %s" % flags.load_model_dir)
if len(flags.group_dirs)>0:
print('load groups')
x = torch.cat([env['images'] for env in envs])
y = torch.cat([env['labels'] for env in envs])
#print(x.shape, y.shape)
groups = [np.load(os.path.join(group_dir,'group%d.npy' % restart)) for group_dir in flags.group_dirs]
n_groups = len(groups)
new_envs = []
for group in groups:
for val in np.unique(group):
env = {}
env['images'] = x[group == val]
env['labels'] = y[group == val]
new_envs.append(env)
train_envs = new_envs
else:
train_envs = envs
train_func = get_train_func(flags.methods)
params = [mlp, topmlp, flags.steps, train_envs, test_envs,lossf,\
flags.penalty_anneal_iters, flags.penalty_weight, \
flags.anneal_val, flags.lr, \
flags.l2_regularizer_weight, flags.freeze_featurizer, flags.eval_steps, flags.verbose, ]
if flags.methods in ['vrex', 'iga','irm','fishr','gm','lff','erm','dro','ifeat','feat','irmx','ibirm']:
res = train_func(*params)
elif flags.methods in ['clove']:
hparams = {'batch_size': flags.batch_size, 'kernel_scale': flags.kernel_scale}
res = train_func(*params, hparams)
elif flags.methods in ['rsc']:
hparams = {'rsc_f_drop_factor' : flags.rsc_f, 'rsc_b_drop_factor': flags.rsc_b}
res = train_func(*params, hparams)
elif flags.methods in ['sd']:
hparams = {'lr_s2_decay': flags.lr_s2_decay}
res = train_func(*params, hparams)
else:
raise NotImplementedError
hparams['stage2_methods'] = flags.stage2_methods
hparams['rounds'] = flags.rounds
hparams['steps_per_round'] = flags.steps_per_round
(train_acc, train_loss, test_worst_acc, test_worst_loss), per_logs = res
logs.extend(per_logs)
final_train_accs.append(train_acc)
final_train_losses.append(train_loss)
final_test_accs.append(test_worst_acc)
final_test_losses.append(test_worst_loss)
if flags.verbose:
print('Final train acc (mean/std across restarts so far):')
print(np.mean(final_train_accs), np.std(final_train_accs))
print('Final train loss (mean/std across restarts so far):')
print(np.mean(final_train_losses), np.std(final_train_losses))
print('Final worest test acc (mean/std across restarts so far):')
print(np.mean(final_test_accs), np.std(final_test_accs))
print('Final worest test loss (mean/std across restarts so far):')
print(np.mean(final_test_losses), np.std(final_test_losses))
results = [np.mean(final_train_accs), np.std(final_train_accs),
np.mean(final_train_losses), np.std(final_train_losses),
np.mean(final_test_accs), np.std(final_test_accs),
np.mean(final_test_losses), np.std(final_test_losses),
]
if flags.save_dir is not None:
state = mlp.state_dict()
torch.save(state, os.path.join(flags.save_dir,'mlp%d.pth' % restart))
state = topmlp.state_dict()
torch.save(state, os.path.join(flags.save_dir,'topmlp%d.pth' % restart))
with torch.no_grad():
x = torch.cat([env['images'] for env in envs])
y = torch.cat([env['labels'] for env in envs])
logits = topmlp(mlp(x))
group, _ = correct_pred(logits, y)
pseudolabel = np.copy(y.cpu().numpy().flatten())
pseudolabel[~group] = 1-pseudolabel[~group]
np.save(os.path.join(flags.save_dir,'group%d.npy' % restart), group)
np.save(os.path.join(flags.save_dir,'pseudolabel%d.npy' % restart), pseudolabel )
logs = np.array(logs)
if flags.save_dir is not None:
np.save(os.path.join(flags.save_dir,'logs.npy'), logs)
return results, logs
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Colored MNIST & CowCamel')
parser.add_argument('--verbose', type=bool, default=False)
parser.add_argument('--n_restarts', type=int, default=10)
parser.add_argument('--dataset', type=str, default='coloredmnist025')
parser.add_argument('--hidden_dim', type=int, default=256)
parser.add_argument('--n_top_layers', type=int, default=1)
parser.add_argument('--l2_regularizer_weight', type=float,default=0.001)
parser.add_argument('--lr', type=float, default=0.001)
parser.add_argument('--steps', type=int, default=501)
parser.add_argument('--lossf', type=str, default='nll')
parser.add_argument('--penalty_anneal_iters', type=int, default=100)
parser.add_argument('--penalty_weight', type=float, default=10000.0)
parser.add_argument('--anneal_val', type=float, default=1)
parser.add_argument('--methods', type=str, default='irmv2')
parser.add_argument('-s2','--stage2_methods', type=str, default='irm')
parser.add_argument('-r','--rounds', type=int,default=2)
parser.add_argument('-sr','--steps_per_round', type=int,default=151)
parser.add_argument('--lr_s2_decay', type=float, default=500)
parser.add_argument('--freeze_featurizer', type=str, default='False')
parser.add_argument('--eval_steps', type=int, default=5)
parser.add_argument('--load_model_dir', type=str, default=None)
parser.add_argument('--save_dir', type=str, default=None)
parser.add_argument('--group_dirs', type=str, nargs='*',default={})
#RSC
parser.add_argument('--rsc_f', type=float, default=0.99)
parser.add_argument('--rsc_b', type=float, default=0.97)
#clove
parser.add_argument('--batch_size', type=int, default=512)
parser.add_argument('--kernel_scale', type=float, default=0.4)
parser.add_argument('--n_examples', type=int, default=18000)
parser.add_argument('--norun',type=parse_bool, default=False)
flags = parser.parse_args()
if flags.norun:
if flags.verbose:
print('Flags:')
for k,v in sorted(vars(flags).items()):
print("\t{}: {}".format(k, v))
else:
main(flags)