441 lines
20 KiB
Python
441 lines
20 KiB
Python
from __future__ import print_function
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import os
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import argparse
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import torch
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import torch.optim as optim
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from torch.optim.lr_scheduler import CosineAnnealingLR, StepLR
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from util.data_util import PartNormalDataset
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import torch.nn.functional as F
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import torch.nn as nn
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import model as models
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import numpy as np
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from torch.utils.data import DataLoader
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from util.util import to_categorical, compute_overall_iou, IOStream
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from tqdm import tqdm
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from collections import defaultdict
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from torch.autograd import Variable
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import random
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classes_str = ['aero','bag','cap','car','chair','ear','guitar','knife','lamp','lapt','moto','mug','Pistol','rock','stake','table']
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def _init_():
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if not os.path.exists('checkpoints'):
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os.makedirs('checkpoints')
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if not os.path.exists('checkpoints/' + args.exp_name):
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os.makedirs('checkpoints/' + args.exp_name)
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def weight_init(m):
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if isinstance(m, torch.nn.Linear):
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torch.nn.init.xavier_normal_(m.weight)
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if m.bias is not None:
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torch.nn.init.constant_(m.bias, 0)
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elif isinstance(m, torch.nn.Conv2d):
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torch.nn.init.xavier_normal_(m.weight)
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if m.bias is not None:
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torch.nn.init.constant_(m.bias, 0)
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elif isinstance(m, torch.nn.Conv1d):
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torch.nn.init.xavier_normal_(m.weight)
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if m.bias is not None:
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torch.nn.init.constant_(m.bias, 0)
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elif isinstance(m, torch.nn.BatchNorm2d):
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torch.nn.init.constant_(m.weight, 1)
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torch.nn.init.constant_(m.bias, 0)
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elif isinstance(m, torch.nn.BatchNorm1d):
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torch.nn.init.constant_(m.weight, 1)
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torch.nn.init.constant_(m.bias, 0)
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def train(args, io):
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# ============= Model ===================
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num_part = 50
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device = torch.device("cuda" if args.cuda else "cpu")
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model = models.__dict__[args.model](num_part).to(device)
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io.cprint(str(model))
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model.apply(weight_init)
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model = nn.DataParallel(model)
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print("Let's use", torch.cuda.device_count(), "GPUs!")
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'''Resume or not'''
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if args.resume:
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state_dict = torch.load("checkpoints/%s/best_insiou_model.pth" % args.exp_name,
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map_location=torch.device('cpu'))['model']
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for k in state_dict.keys():
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if 'module' not in k:
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from collections import OrderedDict
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new_state_dict = OrderedDict()
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for k in state_dict:
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new_state_dict['module.' + k] = state_dict[k]
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state_dict = new_state_dict
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break
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model.load_state_dict(state_dict)
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print("Resume training model...")
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print(torch.load("checkpoints/%s/best_insiou_model.pth" % args.exp_name).keys())
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else:
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print("Training from scratch...")
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# =========== Dataloader =================
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train_data = PartNormalDataset(npoints=2048, split='trainval', normalize=False)
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print("The number of training data is:%d", len(train_data))
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test_data = PartNormalDataset(npoints=2048, split='test', normalize=False)
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print("The number of test data is:%d", len(test_data))
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train_loader = DataLoader(train_data, batch_size=args.batch_size, shuffle=True, num_workers=args.workers,
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drop_last=True)
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test_loader = DataLoader(test_data, batch_size=args.test_batch_size, shuffle=False, num_workers=args.workers,
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drop_last=False)
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# ============= Optimizer ================
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if args.use_sgd:
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print("Use SGD")
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opt = optim.SGD(model.parameters(), lr=args.lr*100, momentum=args.momentum, weight_decay=0)
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else:
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print("Use Adam")
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opt = optim.Adam(model.parameters(), lr=args.lr, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)
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if args.scheduler == 'cos':
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print("Use CosLR")
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scheduler = CosineAnnealingLR(opt, args.epochs, eta_min=args.lr if args.use_sgd else args.lr / 100)
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else:
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print("Use StepLR")
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scheduler = StepLR(opt, step_size=args.step, gamma=0.5)
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# ============= Training =================
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best_acc = 0
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best_class_iou = 0
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best_instance_iou = 0
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num_part = 50
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num_classes = 16
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for epoch in range(args.epochs):
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train_epoch(train_loader, model, opt, scheduler, epoch, num_part, num_classes, io)
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test_metrics, total_per_cat_iou = test_epoch(test_loader, model, epoch, num_part, num_classes, io)
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# 1. when get the best accuracy, save the model:
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if test_metrics['accuracy'] > best_acc:
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best_acc = test_metrics['accuracy']
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io.cprint('Max Acc:%.5f' % best_acc)
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state = {
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'model': model.module.state_dict() if torch.cuda.device_count() > 1 else model.state_dict(),
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'optimizer': opt.state_dict(), 'epoch': epoch, 'test_acc': best_acc}
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torch.save(state, 'checkpoints/%s/best_acc_model.pth' % args.exp_name)
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# 2. when get the best instance_iou, save the model:
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if test_metrics['shape_avg_iou'] > best_instance_iou:
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best_instance_iou = test_metrics['shape_avg_iou']
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io.cprint('Max instance iou:%.5f' % best_instance_iou)
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state = {
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'model': model.module.state_dict() if torch.cuda.device_count() > 1 else model.state_dict(),
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'optimizer': opt.state_dict(), 'epoch': epoch, 'test_instance_iou': best_instance_iou}
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torch.save(state, 'checkpoints/%s/best_insiou_model.pth' % args.exp_name)
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# 3. when get the best class_iou, save the model:
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# first we need to calculate the average per-class iou
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class_iou = 0
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for cat_idx in range(16):
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class_iou += total_per_cat_iou[cat_idx]
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avg_class_iou = class_iou / 16
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if avg_class_iou > best_class_iou:
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best_class_iou = avg_class_iou
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# print the iou of each class:
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for cat_idx in range(16):
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io.cprint(classes_str[cat_idx] + ' iou: ' + str(total_per_cat_iou[cat_idx]))
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io.cprint('Max class iou:%.5f' % best_class_iou)
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state = {
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'model': model.module.state_dict() if torch.cuda.device_count() > 1 else model.state_dict(),
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'optimizer': opt.state_dict(), 'epoch': epoch, 'test_class_iou': best_class_iou}
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torch.save(state, 'checkpoints/%s/best_clsiou_model.pth' % args.exp_name)
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# report best acc, ins_iou, cls_iou
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io.cprint('Final Max Acc:%.5f' % best_acc)
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io.cprint('Final Max instance iou:%.5f' % best_instance_iou)
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io.cprint('Final Max class iou:%.5f' % best_class_iou)
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# save last model
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state = {
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'model': model.module.state_dict() if torch.cuda.device_count() > 1 else model.state_dict(),
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'optimizer': opt.state_dict(), 'epoch': args.epochs - 1, 'test_iou': best_instance_iou}
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torch.save(state, 'checkpoints/%s/model_ep%d.pth' % (args.exp_name, args.epochs))
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def train_epoch(train_loader, model, opt, scheduler, epoch, num_part, num_classes, io):
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train_loss = 0.0
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count = 0.0
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accuracy = []
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shape_ious = 0.0
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metrics = defaultdict(lambda: list())
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model.train()
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for batch_id, (points, label, target, norm_plt) in tqdm(enumerate(train_loader), total=len(train_loader), smoothing=0.9):
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batch_size, num_point, _ = points.size()
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points, label, target, norm_plt = Variable(points.float()), Variable(label.long()), Variable(target.long()), \
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Variable(norm_plt.float())
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points = points.transpose(2, 1)
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norm_plt = norm_plt.transpose(2, 1)
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points, label, target, norm_plt = points.cuda(non_blocking=True), label.squeeze(1).cuda(non_blocking=True), \
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target.cuda(non_blocking=True), norm_plt.cuda(non_blocking=True)
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# target: b,n
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seg_pred = model(points, norm_plt, to_categorical(label, num_classes)) # seg_pred: b,n,50
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loss = F.nll_loss(seg_pred.contiguous().view(-1, num_part), target.view(-1, 1)[:, 0])
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# instance iou without considering the class average at each batch_size:
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batch_shapeious = compute_overall_iou(seg_pred, target, num_part) # list of of current batch_iou:[iou1,iou2,...,iou#b_size]
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# total iou of current batch in each process:
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batch_shapeious = seg_pred.new_tensor([np.sum(batch_shapeious)], dtype=torch.float64) # same device with seg_pred!!!
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# Loss backward
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loss = torch.mean(loss)
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opt.zero_grad()
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loss.backward()
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opt.step()
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# accuracy
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seg_pred = seg_pred.contiguous().view(-1, num_part) # b*n,50
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target = target.view(-1, 1)[:, 0] # b*n
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pred_choice = seg_pred.contiguous().data.max(1)[1] # b*n
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correct = pred_choice.eq(target.contiguous().data).sum() # torch.int64: total number of correct-predict pts
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# sum
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shape_ious += batch_shapeious.item() # count the sum of ious in each iteration
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count += batch_size # count the total number of samples in each iteration
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train_loss += loss.item() * batch_size
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accuracy.append(correct.item()/(batch_size * num_point)) # append the accuracy of each iteration
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# Note: We do not need to calculate per_class iou during training
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if args.scheduler == 'cos':
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scheduler.step()
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elif args.scheduler == 'step':
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if opt.param_groups[0]['lr'] > 0.9e-5:
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scheduler.step()
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if opt.param_groups[0]['lr'] < 0.9e-5:
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for param_group in opt.param_groups:
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param_group['lr'] = 0.9e-5
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io.cprint('Learning rate: %f' % opt.param_groups[0]['lr'])
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metrics['accuracy'] = np.mean(accuracy)
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metrics['shape_avg_iou'] = shape_ious * 1.0 / count
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outstr = 'Train %d, loss: %f, train acc: %f, train ins_iou: %f' % (epoch+1, train_loss * 1.0 / count,
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metrics['accuracy'], metrics['shape_avg_iou'])
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io.cprint(outstr)
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def test_epoch(test_loader, model, epoch, num_part, num_classes, io):
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test_loss = 0.0
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count = 0.0
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accuracy = []
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shape_ious = 0.0
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final_total_per_cat_iou = np.zeros(16).astype(np.float32)
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final_total_per_cat_seen = np.zeros(16).astype(np.int32)
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metrics = defaultdict(lambda: list())
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model.eval()
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# label_size: b, means each sample has one corresponding class
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for batch_id, (points, label, target, norm_plt) in tqdm(enumerate(test_loader), total=len(test_loader), smoothing=0.9):
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batch_size, num_point, _ = points.size()
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points, label, target, norm_plt = Variable(points.float()), Variable(label.long()), Variable(target.long()), \
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Variable(norm_plt.float())
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points = points.transpose(2, 1)
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norm_plt = norm_plt.transpose(2, 1)
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points, label, target, norm_plt = points.cuda(non_blocking=True), label.squeeze(1).cuda(non_blocking=True), \
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target.cuda(non_blocking=True), norm_plt.cuda(non_blocking=True)
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seg_pred = model(points, norm_plt, to_categorical(label, num_classes)) # b,n,50
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# instance iou without considering the class average at each batch_size:
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batch_shapeious = compute_overall_iou(seg_pred, target, num_part) # [b]
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# per category iou at each batch_size:
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for shape_idx in range(seg_pred.size(0)): # sample_idx
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cur_gt_label = label[shape_idx] # label[sample_idx], denotes current sample belongs to which cat
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final_total_per_cat_iou[cur_gt_label] += batch_shapeious[shape_idx] # add the iou belongs to this cat
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final_total_per_cat_seen[cur_gt_label] += 1 # count the number of this cat is chosen
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# total iou of current batch in each process:
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batch_ious = seg_pred.new_tensor([np.sum(batch_shapeious)], dtype=torch.float64) # same device with seg_pred!!!
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# prepare seg_pred and target for later calculating loss and acc:
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seg_pred = seg_pred.contiguous().view(-1, num_part)
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target = target.view(-1, 1)[:, 0]
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# Loss
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loss = F.nll_loss(seg_pred.contiguous(), target.contiguous())
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# accuracy:
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pred_choice = seg_pred.data.max(1)[1] # b*n
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correct = pred_choice.eq(target.data).sum() # torch.int64: total number of correct-predict pts
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loss = torch.mean(loss)
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shape_ious += batch_ious.item() # count the sum of ious in each iteration
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count += batch_size # count the total number of samples in each iteration
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test_loss += loss.item() * batch_size
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accuracy.append(correct.item() / (batch_size * num_point)) # append the accuracy of each iteration
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for cat_idx in range(16):
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if final_total_per_cat_seen[cat_idx] > 0: # indicating this cat is included during previous iou appending
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final_total_per_cat_iou[cat_idx] = final_total_per_cat_iou[cat_idx] / final_total_per_cat_seen[cat_idx] # avg class iou across all samples
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metrics['accuracy'] = np.mean(accuracy)
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metrics['shape_avg_iou'] = shape_ious * 1.0 / count
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outstr = 'Test %d, loss: %f, test acc: %f test ins_iou: %f' % (epoch + 1, test_loss * 1.0 / count,
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metrics['accuracy'], metrics['shape_avg_iou'])
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io.cprint(outstr)
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return metrics, final_total_per_cat_iou
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def test(args, io):
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# Dataloader
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test_data = PartNormalDataset(npoints=2048, split='test', normalize=False)
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print("The number of test data is:%d", len(test_data))
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test_loader = DataLoader(test_data, batch_size=args.test_batch_size, shuffle=False, num_workers=args.workers,
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drop_last=False)
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# Try to load models
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num_part = 50
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device = torch.device("cuda" if args.cuda else "cpu")
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model = models.__dict__[args.model](num_part).to(device)
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io.cprint(str(model))
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from collections import OrderedDict
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state_dict = torch.load("checkpoints/%s/best_%s_model.pth" % (args.exp_name, args.model_type),
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map_location=torch.device('cpu'))['model']
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new_state_dict = OrderedDict()
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for layer in state_dict:
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new_state_dict[layer.replace('module.', '')] = state_dict[layer]
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model.load_state_dict(new_state_dict)
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model.eval()
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num_part = 50
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num_classes = 16
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metrics = defaultdict(lambda: list())
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hist_acc = []
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shape_ious = []
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total_per_cat_iou = np.zeros((16)).astype(np.float32)
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total_per_cat_seen = np.zeros((16)).astype(np.int32)
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for batch_id, (points, label, target, norm_plt) in tqdm(enumerate(test_loader), total=len(test_loader), smoothing=0.9):
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batch_size, num_point, _ = points.size()
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points, label, target, norm_plt = Variable(points.float()), Variable(label.long()), Variable(target.long()), Variable(norm_plt.float())
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points = points.transpose(2, 1)
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norm_plt = norm_plt.transpose(2, 1)
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points, label, target, norm_plt = points.cuda(non_blocking=True), label.squeeze().cuda(
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non_blocking=True), target.cuda(non_blocking=True), norm_plt.cuda(non_blocking=True)
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with torch.no_grad():
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seg_pred = model(points, norm_plt, to_categorical(label, num_classes)) # b,n,50
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# instance iou without considering the class average at each batch_size:
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batch_shapeious = compute_overall_iou(seg_pred, target, num_part) # [b]
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shape_ious += batch_shapeious # iou +=, equals to .append
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# per category iou at each batch_size:
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for shape_idx in range(seg_pred.size(0)): # sample_idx
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cur_gt_label = label[shape_idx] # label[sample_idx]
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total_per_cat_iou[cur_gt_label] += batch_shapeious[shape_idx]
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total_per_cat_seen[cur_gt_label] += 1
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# accuracy:
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seg_pred = seg_pred.contiguous().view(-1, num_part)
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target = target.view(-1, 1)[:, 0]
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pred_choice = seg_pred.data.max(1)[1]
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correct = pred_choice.eq(target.data).cpu().sum()
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metrics['accuracy'].append(correct.item() / (batch_size * num_point))
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hist_acc += metrics['accuracy']
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metrics['accuracy'] = np.mean(hist_acc)
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metrics['shape_avg_iou'] = np.mean(shape_ious)
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for cat_idx in range(16):
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if total_per_cat_seen[cat_idx] > 0:
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total_per_cat_iou[cat_idx] = total_per_cat_iou[cat_idx] / total_per_cat_seen[cat_idx]
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# First we need to calculate the iou of each class and the avg class iou:
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class_iou = 0
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for cat_idx in range(16):
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class_iou += total_per_cat_iou[cat_idx]
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io.cprint(classes_str[cat_idx] + ' iou: ' + str(total_per_cat_iou[cat_idx])) # print the iou of each class
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avg_class_iou = class_iou / 16
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outstr = 'Test :: test acc: %f test class mIOU: %f, test instance mIOU: %f' % (metrics['accuracy'], avg_class_iou, metrics['shape_avg_iou'])
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io.cprint(outstr)
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if __name__ == "__main__":
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# Training settings
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parser = argparse.ArgumentParser(description='3D Shape Part Segmentation')
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parser.add_argument('--model', type=str, default='PointMLP1')
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parser.add_argument('--exp_name', type=str, default='demo1', metavar='N',
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help='Name of the experiment')
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parser.add_argument('--batch_size', type=int, default=32, metavar='batch_size',
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help='Size of batch)')
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parser.add_argument('--test_batch_size', type=int, default=32, metavar='batch_size',
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help='Size of batch)')
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parser.add_argument('--epochs', type=int, default=350, metavar='N',
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help='number of episode to train')
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parser.add_argument('--use_sgd', type=bool, default=False,
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help='Use SGD')
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parser.add_argument('--scheduler', type=str, default='step',
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help='lr scheduler')
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parser.add_argument('--step', type=int, default=40,
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help='lr decay step')
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parser.add_argument('--lr', type=float, default=0.003, metavar='LR',
|
|
help='learning rate')
|
|
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('--manual_seed', type=int, 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=2048,
|
|
help='num of points to use')
|
|
parser.add_argument('--workers', type=int, default=12)
|
|
parser.add_argument('--resume', type=bool, default=False,
|
|
help='Resume training or not')
|
|
parser.add_argument('--model_type', type=str, default='insiou',
|
|
help='choose to test the best insiou/clsiou/acc model (options: insiou, clsiou, acc)')
|
|
|
|
args = parser.parse_args()
|
|
args.exp_name = args.model+"_"+args.exp_name
|
|
|
|
_init_()
|
|
|
|
if not args.eval:
|
|
io = IOStream('checkpoints/' + args.exp_name + '/%s_train.log' % (args.exp_name))
|
|
else:
|
|
io = IOStream('checkpoints/' + args.exp_name + '/%s_test.log' % (args.exp_name))
|
|
io.cprint(str(args))
|
|
|
|
if args.manual_seed is not None:
|
|
random.seed(args.manual_seed)
|
|
np.random.seed(args.manual_seed)
|
|
torch.manual_seed(args.manual_seed)
|
|
|
|
args.cuda = not args.no_cuda and torch.cuda.is_available()
|
|
|
|
if args.cuda:
|
|
io.cprint('Using GPU')
|
|
if args.manual_seed is not None:
|
|
torch.cuda.manual_seed(args.manual_seed)
|
|
torch.cuda.manual_seed_all(args.manual_seed)
|
|
else:
|
|
io.cprint('Using CPU')
|
|
|
|
if not args.eval:
|
|
train(args, io)
|
|
else:
|
|
test(args, io)
|