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main.py
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main.py
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from __future__ import print_function
import os
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim.lr_scheduler import CosineAnnealingLR
from data import ModelNet40
from model import Pct
import numpy as np
from torch.utils.data import DataLoader
from util import cal_loss, IOStream
import sklearn.metrics as metrics
import torch.nn.functional as F
from torch.autograd import Variable
from sampler import ImbalancedDatasetSampler
import math
from munch import Munch
import time
import json
from unlabeled_sampler import Unlabeled_ImbalancedDatasetSampler
import time
def _init_():
if not os.path.exists('checkpoints'):
os.makedirs('checkpoints')
if not os.path.exists('checkpoints/'+args.exp_name):
os.makedirs('checkpoints/'+args.exp_name)
if not os.path.exists('checkpoints/'+args.exp_name+'/'+'models'):
os.makedirs('checkpoints/'+args.exp_name+'/'+'models')
os.system('cp main.py checkpoints'+'/'+args.exp_name+'/'+'main.py.backup')
os.system('cp model.py checkpoints' + '/' + args.exp_name + '/' + 'model.py.backup')
os.system('cp util.py checkpoints' + '/' + args.exp_name + '/' + 'util.py.backup')
os.system('cp data.py checkpoints' + '/' + args.exp_name + '/' + 'data.py.backup')
def train(args, io):
train_loader_labeled = DataLoader(ModelNet40(partition='train', num_points=args.num_points, data_split='labeled', perceptange = 10), num_workers=8,
batch_size=args.batch_size, shuffle=True, drop_last=True)
train_loader_unlabeled = DataLoader(ModelNet40(partition='train', num_points=args.num_points, data_split='unlabeled', perceptange = 10), num_workers=8,
batch_size=args.batch_size * args.unlabeled_ratio, shuffle=True, drop_last=True)
test_loader = DataLoader(ModelNet40(partition='test', num_points=args.num_points), num_workers=8,
batch_size=args.test_batch_size, shuffle=True, drop_last=False)
validate_loader = DataLoader(ModelNet40(partition='validate', num_points=args.num_points), num_workers=8,
batch_size=args.test_batch_size, shuffle=False, drop_last=False)
device = torch.device("cuda" if args.cuda else "cpu")
model = Pct(args).to(device)
print(str(model))
model = nn.DataParallel(model)
# model_path = '/home//scratch1link/Semi-Vit/Semi/Semi-PCT_base500/checkpoints/train/models/latest_model.t7'
# model.load_state_dict(torch.load(model_path))
if args.use_sgd:
print("Use SGD")
opt = optim.SGD(model.parameters(), lr=args.lr*100, momentum=args.momentum, weight_decay=5e-4)
else:
print("Use Adam")
opt = optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-4)
scheduler = CosineAnnealingLR(opt, args.epochs, eta_min=args.lr)
criterion = cal_loss
best_test_acc = 0
section_interation = args.epochs / args.section_size
for current_section in range(int(section_interation)):
np.random.seed()
if current_section!=0:
train_loader_labeled = DataLoader(
ModelNet40(partition='train', num_points=args.num_points, data_split='labeled', perceptange=10),
num_workers=8, sampler=ImbalancedDatasetSampler(
ModelNet40(partition='train', num_points=args.num_points, data_split='labeled', perceptange=10)),
batch_size=args.batch_size, drop_last=True)
train_loader_unlabeled = DataLoader(
ModelNet40(partition='train', num_points=args.num_points, data_split='unlabeled', perceptange=10),
num_workers=8, sampler=Unlabeled_ImbalancedDatasetSampler(
ModelNet40(partition='train', num_points=args.num_points, data_split='unlabeled', perceptange=10)),
batch_size=args.batch_size*args.unlabeled_ratio, drop_last=True)
for epoch in range(args.section_size):
scheduler.step()
train_loss = 0.0
count = 0.0
model.train()
train_pred = []
train_true = []
idx = 0
total_time = 0.0
for l_data, u_data in zip(train_loader_labeled, train_loader_unlabeled):
data_unaug, data, data_strongaug, label = l_data
data_u_unaug, data_u, data_u_strongaug, label_u = u_data
data_unaug, data, data_strongaug, label = data_unaug.to(device), data.to(device),data_strongaug.to(device), label.to(device).squeeze()
data_u_unaug, data_u, data_u_strongaug, label_u = data_u_unaug.to(device), data_u.to(device), data_u_strongaug.to(device), label_u.to(device).squeeze()
data_u_unaug = data_u_unaug.permute(0, 2, 1)
data = data.permute(0, 2, 1)
data_u = data_u.permute(0, 2, 1)
data_strongaug = data_strongaug.permute(0, 2, 1)
data_u_strongaug = data_u_strongaug.permute(0, 2, 1)
batch_size = data.size()[0]
opt.zero_grad()
start_time = time.time()
logits_l_w, tokens_l_w = model(data)
logits_u_unaug, tokens_u_unaug = model(data_u_unaug)
logits_u_s, tokens_u_s = model(data_u_strongaug)
# logits, tokens1 = model(data)
labeled_cross_entropy_loss = criterion(logits_l_w, label)
pseudo_label = torch.softmax(logits_u_unaug.detach(), dim=-1)
max_probs, targets_u = torch.max(pseudo_label, dim=-1)
flex_threshold = torch.zeros(batch_size*args.unlabeled_ratio).to(device)
if epoch + args.section_size*current_section == 0:
mask = max_probs.ge(0.2).float()
else:
z = open("dict_avgconf.txt", "r")
k = z.read()
dict_avgconf = json.loads(k)
z.close()
for current_label in dict_avgconf:
current_label = int(current_label)
if dict_avgconf['%d' %current_label]/(2-dict_avgconf['%d' %current_label])<0.2:
flex_threshold[targets_u==current_label] = 0.2
elif dict_avgconf['%d' %current_label]/(2-dict_avgconf['%d' %current_label])>0.8:
flex_threshold[targets_u==current_label] = 0.8
else:
learning_effect = dict_avgconf['%d' %current_label]
flex_threshold[targets_u==current_label] = learning_effect/(2 - learning_effect)
mask = max_probs.ge(flex_threshold).float()
# print(valid_sample_num.item())
mask_label = torch.ones(mask.shape[0])
mask_label = Variable(mask_label).to('cuda')
pt_pseudo_ce_loss = (F.cross_entropy(logits_u_s, targets_u, reduction='none') * mask).mean()
loss = labeled_cross_entropy_loss + pt_pseudo_ce_loss
loss.backward()
opt.step()
end_time = time.time()
total_time += (end_time - start_time)
preds = logits_l_w.max(dim=1)[1]
count += batch_size
train_loss += loss.item() * batch_size
train_true.append(label.cpu().numpy())
train_pred.append(preds.detach().cpu().numpy())
idx += 1
print ('train total time is',total_time)
train_true = np.concatenate(train_true)
train_pred = np.concatenate(train_pred)
outstr = 'Train %d, loss: %.6f, train acc: %.6f, train avg acc: %.6f' % (epoch,
train_loss*1.0/count,
metrics.accuracy_score(
train_true, train_pred),
metrics.balanced_accuracy_score(
train_true, train_pred))
io.cprint(outstr)
####################
# Test
####################
test_loss = 0.0
count = 0.0
model.eval()
test_pred = []
test_true = []
total_time = 0.0
for data, label in test_loader:
data, label = data.to(device), label.to(device).squeeze()
data = data.permute(0, 2, 1)
batch_size = data.size()[0]
start_time = time.time()
logits, tokens1 = model(data)
end_time = time.time()
total_time += (end_time - start_time)
loss = criterion(logits, label)
preds = logits.max(dim=1)[1]
count += batch_size
test_loss += loss.item() * batch_size
test_true.append(label.cpu().numpy())
test_pred.append(preds.detach().cpu().numpy())
print ('test total time is', total_time)
test_true = np.concatenate(test_true)
test_pred = np.concatenate(test_pred)
test_acc = metrics.accuracy_score(test_true, test_pred)
avg_per_class_acc = metrics.balanced_accuracy_score(test_true, test_pred)
outstr = 'Test %d, loss: %.6f, test acc: %.6f, test avg acc: %.6f' % (epoch + args.section_size*current_section ,
test_loss*1.0/count,
test_acc,
avg_per_class_acc)
io.cprint(outstr)
if test_acc >= best_test_acc:
best_test_acc = test_acc
torch.save(model.state_dict(), 'checkpoints/%s/models/model.t7' % args.exp_name)
torch.save(model.state_dict(), 'checkpoints/%s/models/latest_model.t7' % args.exp_name)
test_true = []
test_pred = []
test_logits = []
test_sec_max = []
model.eval()
for data, label in validate_loader:
data, label = data.to(device), label.to(device).squeeze()
data = data.permute(0, 2, 1)
logits, tokens = model(data)
m = nn.Softmax(dim=1)
output = m(logits)
sec_max, _ = torch.torch.sort(output, -1, descending=True)
max_logits , preds = output.max(dim=1)
sec_max_logits = sec_max[:,1]
if args.test_batch_size == 1:
test_true.append([label.cpu().numpy()])
test_pred.append([preds.detach().cpu().numpy()])
test_logits.append([max_logits.detach().cpu().numpy()])
test_sec_max.append([sec_max_logits.detach().cpu().numpy()])
else:
test_true.append(label.cpu().numpy())
test_pred.append(preds.detach().cpu().numpy())
test_logits.append(max_logits.detach().cpu().numpy())
test_sec_max.append(sec_max_logits.detach().cpu().numpy())
test_true = np.concatenate(test_true)
test_pred = np.concatenate(test_pred)
test_logits = np.concatenate(test_logits)
print(test_logits.shape)
labeled_data = np.arange(0,40)
dict_1 = Munch()
dict_secmax = Munch()
dict_high_conf_index = Munch()
dict_ave_validate_conf = Munch()
for i in labeled_data:
dict_1['%d' % i] = 0
dict_secmax['%d' % i] = 0
for current_label in labeled_data:
current_label_index = np.where(test_pred == current_label)
current_label_logit = test_logits[current_label_index]
if len(current_label_index[0])!=0:
dict_ave_validate_conf['%d' % current_label] = np.sum(current_label_logit)/len(current_label_index[0])
else:
dict_ave_validate_conf['%d' % current_label] = 0.2
for current_label in range (40):
label_pos = np.where(test_pred == current_label)
if dict_ave_validate_conf['%d' % current_label]>0.8:
high_conf_label_pos = label_pos[0][np.where(test_logits[label_pos] > dict_ave_validate_conf['%d' % current_label])]
dict_high_conf_index['%d' % current_label] = high_conf_label_pos.tolist()
elif dict_ave_validate_conf['%d' % current_label]<0.8:
low_conf_label_pos = label_pos[0][np.where(test_logits[label_pos] > dict_ave_validate_conf['%d' % current_label])]
dict_high_conf_index['%d_low' % current_label] = low_conf_label_pos.tolist()
f = open("dict_highconf_indx.txt", "w")
js = json.dumps(dict_high_conf_index)
f.write(js)
f.close()
f = open("dict_avgconf.txt", "w")
js = json.dumps(dict_ave_validate_conf)
f.write(js)
f.close()
f = open("dict_logits.txt", "w")
js = json.dumps(test_logits.tolist())
f.write(js)
f.close()
f = open("current_epoch.txt", "w")
js = json.dumps(epoch + args.section_size * current_section)
f.write(js)
f.close()
def test(args, io):
test_loader = DataLoader(ModelNet40(partition='test', num_points=args.num_points),
batch_size=args.test_batch_size, shuffle=True, drop_last=False)
device = torch.device("cuda" if args.cuda else "cpu")
model = Pct(args).to(device)
model = nn.DataParallel(model)
model.load_state_dict(torch.load(args.model_path))
model = model.eval()
test_true = []
test_pred = []
for data, label in test_loader:
data, label = data.to(device), label.to(device).squeeze()
data = data.permute(0, 2, 1)
logits, tokens = model(data)
preds = logits.max(dim=1)[1]
if args.test_batch_size == 1:
test_true.append([label.cpu().numpy()])
test_pred.append([preds.detach().cpu().numpy()])
else:
test_true.append(label.cpu().numpy())
test_pred.append(preds.detach().cpu().numpy())
test_true = np.concatenate(test_true)
test_pred = np.concatenate(test_pred)
test_acc = metrics.accuracy_score(test_true, test_pred)
avg_per_class_acc = metrics.balanced_accuracy_score(test_true, test_pred)
outstr = 'Test :: test acc: %.6f, test avg acc: %.6f'%(test_acc, avg_per_class_acc)
io.cprint(outstr)
if __name__ == "__main__":
# Training settings
parser = argparse.ArgumentParser(description='Point Cloud Recognition')
parser.add_argument('--section_size', type=int, default= 50, metavar='N',
help='how many epoches a section has ')
parser.add_argument('--unlabeled_ratio', type=int, default= 4, metavar='N',
help='unlabeled labeled ratio ')
parser.add_argument('--exp_name', type=str, default='train', metavar='N',
help='Name of the experiment')
parser.add_argument('--dataset', type=str, default='modelnet40', metavar='N',
choices=['modelnet40'])
parser.add_argument('--batch_size', type=int, default=32, metavar='batch_size',
help='Size of batch)')
parser.add_argument('--test_batch_size', type=int, default=16, metavar='batch_size',
help='Size of batch)')
parser.add_argument('--epochs', type=int, default=250, metavar='N',
help='number of episode to train ')
parser.add_argument('--use_sgd', type=bool, default=True,
help='Use SGD')
parser.add_argument('--lr', type=float, default=0.0001, metavar='LR',
help='learning rate (default: 0.001, 0.1 if using sgd)')
parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
help='SGD momentum (default: 0.9)')
parser.add_argument('--no_cuda', type=bool, default=False,
help='enables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--eval', type=bool, default=False,
help='evaluate the model')
parser.add_argument('--num_points', type=int, default=1024,
help='num of points to use')
parser.add_argument('--dropout', type=float, default=0.5,
help='dropout rate')
parser.add_argument('--model_path', type=str, default='', metavar='N',
help='Pretrained model path')
args = parser.parse_args()
_init_()
io = IOStream('checkpoints/' + args.exp_name + '/run.log')
io.cprint(str(args))
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
io.cprint(
'Using GPU : ' + str(torch.cuda.current_device()) + ' from ' + str(torch.cuda.device_count()) + ' devices')
torch.cuda.manual_seed(args.seed)
else:
io.cprint('Using CPU')
if not args.eval:
train(args, io)
else:
test(args, io)