batch_size=args.batch_size,
shuffle=True,
num_workers=4,
pin_memory=True,
)
train_set = ModelNet40Cls(args.num_points, transforms=train_transforms)
train_loader = DataLoader(
train_set,
batch_size=args.batch_size,
shuffle=True,
num_workers=4,
pin_memory=True,
)
model = Pointnet(input_channels=0, num_classes=40, use_xyz=True)
model.cuda()
optimizer = optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
lr_lbmd = lambda it: max(
args.lr_decay**(int(it * args.batch_size / args.decay_step)),
lr_clip / args.lr,
)
bn_lbmd = lambda it: max(
args.bn_momentum * args.bnm_decay**
(int(it * args.batch_size / args.decay_step)),
bnm_clip,
)
batch_size=args.batch_size,
shuffle=True,
num_workers=2,
pin_memory=True,
)
train_set = ModelNet40Cls(args.num_points, transforms=transforms)
train_loader = DataLoader(
train_set,
batch_size=args.batch_size,
shuffle=True,
num_workers=2,
pin_memory=True,
)
model = Pointnet(input_channels=0, num_classes=40, use_xyz=True)
model.cuda()
optimizer = optim.Adam(
model.parameters(), lr=args.lr, weight_decay=args.weight_decay
)
lr_lbmd = lambda it: max(
args.lr_decay ** (int(it * args.batch_size / args.decay_step)),
lr_clip / args.lr,
)
bn_lbmd = lambda it: max(
args.bn_momentum
* args.bnm_decay ** (int(it * args.batch_size / args.decay_step)),
bnm_clip,
)
# default value
division,
absolute_import,
with_statement,
print_function,
unicode_literals,
)
import numpy as np
import time
import glob
import torch
import torch.optim as optim
import torch.optim.lr_scheduler as lr_sched
import torch.nn as nn
from torch.utils.data import DataLoader
from torchvision import transforms
import etw_pytorch_utils as pt_utils
import pprint
import os.path as osp
import os, sys
import argparse
sys.path.append('../..')
from pointnet2.models import Pointnet2ClsMSG as Pointnet
from pointnet2.models.pointnet2_msg_cls import model_fn_decorator
from pointnet2.data import ScanNet
import pointnet2.data.data_utils as d_utils
if __name__ == "__main__":
# model
model = Pointnet(input_channels=3, num_classes=21, use_xyz=True)
print('#parameters %d' % sum([x.nelement() for x in model.parameters()]))
shuffle=True,
num_workers=2,
pin_memory=True,
drop_last=True)
val_loader = DataLoader(val_set,
batch_size=args.batch_size,
shuffle=False,
num_workers=2,
pin_memory=True,
drop_last=False)
# model
device = torch.device("cuda" if args.cuda else "cpu")
model = Pointnet(input_channels=args.input_plane,
num_classes=args.output_plane,
use_xyz=True)
model.to(device)
model = nn.DataParallel(model)
print("Let's use", torch.cuda.device_count(), "GPUs!")
# optimizer
optimizer = optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
# scheduler
lr_lbmd = lambda it: max(
args.lr_decay**(int(it * args.batch_size / args.decay_step)),
lr_clip / args.lr,
)
val_set = ScanNet(val_files,args.num_points,color=True)
else:
val_set = ScanNet(val_files,args.num_points)
val_loader = DataLoader(
val_set,
batch_size=args.batch_size,
shuffle=False,
num_workers=2,
pin_memory=True,
drop_last=False
)
# model
device = torch.device("cuda" if args.cuda else "cpu")
model = Pointnet(input_channels=args.input_plane, num_classes=args.output_plane, use_xyz=True)
model.to(device)
model=nn.DataParallel(model)
print("Let's use", torch.cuda.device_count(), "GPUs!")
# restore model
path_checkpoint=os.path.join('logs',args.exp_name,'model.t7')
checkpoint = torch.load(path_checkpoint) # checkpoint
model.load_state_dict(checkpoint)
####################
# Validate
####################
model.eval()
test_pred = []
test_true = []