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train.py
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train.py
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"""
@Origin : main.py by Yue Wang
@Contact: yuewangx@mit.edu
@Time: 2018/10/13 10:39 PM
modified by {Sanghyeok Lee, Sihyeon Kim}
@Contact: {cat0626, sh_bs15}@korea.ac.kr
@File: train.py
@Time: 2021.09.29
"""
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim.lr_scheduler import CosineAnnealingLR
import numpy as np
from torch.utils.data import DataLoader
import sklearn.metrics as metrics
import torch.nn.functional as F
from data import ModelNet40
from model import PointNet, DGCNN
from util import cal_loss
def train_vanilla(args, io):
train_loader = DataLoader(ModelNet40(args, partition='train'), num_workers=8,
batch_size=args.batch_size, shuffle=True, drop_last=True)
test_loader = DataLoader(ModelNet40(args, partition='test'), num_workers=8,
batch_size=args.test_batch_size, shuffle=True, drop_last=False)
device = torch.device("cuda" if args.cuda else "cpu")
#Try to load models
if args.model == 'pointnet':
model = PointNet(args).to(device)
elif args.model == 'dgcnn':
model = DGCNN(args).to(device)
else:
raise Exception("Not implemented")
print(str(model))
model = nn.DataParallel(model)
print("Let's use", torch.cuda.device_count(), "GPUs!")
if args.use_sgd:
print("Use SGD")
opt = optim.SGD(model.parameters(), lr=args.lr*100, momentum=args.momentum, weight_decay=1e-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
for epoch in range(args.epochs):
####################
# Train
####################
train_loss = 0.0
count = 0.0
model.train()
train_pred = []
train_true = []
for data, label in train_loader:
data, label = data.to(device), label.to(device).squeeze()
data = data.permute(0, 2, 1)
batch_size = data.size()[0]
opt.zero_grad()
logits = model(data)
loss = criterion(logits, label)
loss.backward()
opt.step()
preds = logits.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())
scheduler.step()
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 = []
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]
logits = model(data)
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())
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,
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)
def train_AugTune(args, io):
train_loader = DataLoader(ModelNet40(args, partition='train'), num_workers=8,
batch_size=args.batch_size, shuffle=True, drop_last=True)
test_loader = DataLoader(ModelNet40(args, partition='test'), num_workers=8,
batch_size=args.test_batch_size, shuffle=True, drop_last=False)
device = torch.device("cuda" if args.cuda else "cpu")
#Try to load models
if args.model == 'pointnet':
model = PointNet(args).to(device)
elif args.model == 'dgcnn':
model = DGCNN(args).to(device)
else:
raise Exception("Not implemented")
print(str(model))
model = nn.DataParallel(model)
print("Let's use", torch.cuda.device_count(), "GPUs!")
if args.use_sgd:
print("Use SGD")
opt = optim.SGD(model.parameters(), lr=args.lr*100, momentum=args.momentum, weight_decay=1e-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
for epoch in range(args.epochs):
####################
# Train
####################
train_loss = 0.0
count = 0.0
model.train()
train_pred = []
train_true = []
for origin, data, label in train_loader:
origin, data, label = origin.to(device), data.to(device), label.to(device).squeeze()
origin = origin.permute(0, 2, 1)
data = data.permute(0, 2, 1)
batch_size = data.size()[0]
#Forward original & augmented sample to get confidence score
with torch.no_grad():
pred_origin = model(origin)
pred_data = model(data)
c_origin = (pred_origin.exp() * F.one_hot(label, pred_origin.shape[-1])).sum(1) #(B)
c_data = (pred_data.exp() * F.one_hot(label, pred_data.shape[-1])).sum(1) #(B)
#Calculate Target Confidence Score
c_target = torch.max((1-args.l) * c_origin, c_data) #(B)
alpha = ((c_target-c_data)/(c_origin-c_data + 1e-4)).unsqueeze(1)
alpha = torch.clamp(alpha, min=0, max=1).reshape(-1,1,1)
#Tune the Sample with alpha
data = alpha * origin + (1-alpha) * data
#Re-normalize Tuned Sample
data = normalize_point_cloud_batch(data)
#CDA
data = translate_pointcloud_batch(data)
opt.zero_grad()
logits = model(data)
loss = criterion(logits, label)
loss.backward()
opt.step()
preds = logits.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())
scheduler.step()
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 = []
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]
logits = model(data)
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())
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,
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)
def test(args, io):
test_loader = DataLoader(ModelNet40(args, partition='test'),
batch_size=args.test_batch_size, shuffle=True, drop_last=False)
device = torch.device("cuda" if args.cuda else "cpu")
#Try to load models
model = DGCNN(args).to(device)
model = nn.DataParallel(model)
model.load_state_dict(torch.load(args.model_path))
model = model.eval()
test_acc = 0.0
count = 0.0
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)
batch_size = data.size()[0]
logits = model(data)
preds = logits.max(dim=1)[1]
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)
def normalize_point_cloud_batch(pointcloud):
"""
input :
pointcloud([B,3,N])
output :
pointcloud([B,3,N]) : Normalized Pointclouds
"""
pointcloud = pointcloud - pointcloud.mean(dim=-1, keepdim=True) #(B,3,N)
scale = 1/torch.sqrt((pointcloud**2).sum(1)).max(axis=1)[0]*0.999999 # (B)
pointcloud = scale.view(-1, 1, 1) * pointcloud
return pointcloud
def translate_pointcloud_batch(pointcloud):
"""
input :
pointcloud([B,3,N])
output :
translated_pointcloud([B,3,N]) : Pointclouds after CDA
"""
B, _, _ = pointcloud.shape
xyz1 = torch.FloatTensor(B,3,1).uniform_(2./3., 3./2.).to(pointcloud.device)
xyz2 = torch.FloatTensor(B,3,1).uniform_(-0.2, 0.2).to(pointcloud.device)
translated_pointcloud = xyz1 * pointcloud + xyz2
return translated_pointcloud