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train_MBD.py
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train_MBD.py
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from dataset.ShapeNetDataset import *
from torch.utils.data import DataLoader
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from losses.emd import emd_module as emd
from losses.chamfer import champfer_loss as chamfer
from models.hole_residual import MSNautoencoder, PointNetRes, MSNmodel, NormalModel
from utils.utils import weights_init, visdom_show_pc, save_paths, save_model, vis_curve
from utils.metrics import AverageValueMeter
from losses.MDS import MDS_module
import visdom
import sys
#Input options
parser = argparse.ArgumentParser()
parser.add_argument('--batchSize', type=int, default=32, help='input batch size')
parser.add_argument('--model', type=str, default = '', help='optional reload model path')
parser.add_argument('--workers', type=int, help='number of data loading workers', default=12)
parser.add_argument('--nepoch', type=int, default=200, help='number of epochs to train for')
parser.add_argument('--num_points', type=int, default = 2048, help='number of points')
parser.add_argument('--loss', type=str, default = "emd", help='loss distance')
parser.add_argument('--visualize', type=bool, default = True, help='bool visualize')
parser.add_argument('--vis_step', type=int, default = 50, help='visualize step')
parser.add_argument('--vis_step_test', type=int, default = 20, help='visualize step')
parser.add_argument('--net_alfa', type=float, default = 2000, help='net loss weight')
parser.add_argument('--vis_port', type=int, default = 8997, help='visdom_port')
parser.add_argument('--vis_port_test', type=int, default = 8998, help='visdom_port')
parser.add_argument('--vis_env', type=str, default = "ENV", help='visdom environment')
parser.add_argument('--gpu_n', type=int, default = 0, help='cuda gpu device number')
parser.add_argument('--lrate', type=float, default = 0.0005, help='learning rate')
parser.add_argument('--n_primitives', type=int, default = 16, help='number of primitives')
opt = parser.parse_args()
#We use Visdom to see the training progress
vis = visdom.Visdom(port = opt.vis_port, env= opt.vis_env + " TRAIN")
vis_test = visdom.Visdom(port = opt.vis_port_test , env= opt.vis_env + " TEST")
# initialize variables
dir_name, logname = save_paths(opt.model, "train_MBD", "ShapeNetDataset", "hole_residual")
rec_loss1_train = AverageValueMeter()
rec_loss1_test = AverageValueMeter()
rec_loss2_train = AverageValueMeter()
rec_loss2_test = AverageValueMeter()
rec_loss_train = AverageValueMeter()
rec_loss_test = AverageValueMeter()
best_loss = 20000
n_models = 10
# Shapenet part dataloader
class_choice = {'Airplane': 0, 'Bag': 1, 'Cap': 2, 'Car': 3, 'Chair': 4, 'Guitar': 6, 'Lamp': 8, 'Laptop': 9, 'Motorbike': 10, 'Mug': 11, 'Pistol': 12, 'Skateboard': 14, 'Table': 15}
dataset_dir = './data/shapenet_part'
dataset_train = ShapeNetDataset(root_dir=dataset_dir, class_choice=class_choice, npoints=2048, split='train')
dataloader_train = DataLoader(dataset_train, batch_size=opt.batchSize, shuffle=True, num_workers=int(opt.workers))
dataset_test = ShapeNetDataset(root_dir=dataset_dir, class_choice=class_choice, npoints=2048, split='test')
dataloader_test = DataLoader(dataset_test, batch_size=opt.batchSize, shuffle=True, num_workers=int(opt.workers))
device = torch.device("cuda:" + str(opt.gpu_n) if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
print("Using cuda device")
torch.cuda.set_device(device)
#Create Ours-MBD model and send it to GPU
network = MSNmodel(opt.num_points, device).to(device)
network.apply(weights_init)
network.cuda()
# Create the optimizers
lrate_m = 0.0005
lrate_r = 0.0005
model_optimizer = optim.Adam(network.model.parameters(), lr = lrate_m)
residual_optimizer = optim.Adam(network.residual.parameters(), lr = lrate_r)
#Load a pretrained model to continue training
if opt.model != '' and os.path.isfile("log/" + opt.model + "/model.pth"):
model_checkpoint = torch.load("log/" + opt.model + "/model.pth")
residual_checkpoint = torch.load("log/" + opt.model + "/residual.pth")
print("Model network weights loaded ")
network.model.load_state_dict(model_checkpoint['state_dict'])
model_optimizer.load_state_dict(model_checkpoint['optimizer'])
print("Residual network weights loaded ")
network.residual.load_state_dict(residual_checkpoint['state_dict'])
residual_optimizer.load_state_dict(residual_checkpoint['optimizer'])
# save model architecture
with open(logname, 'a') as f: #open and append
f.write(str(network.model) + '\n')
f.write(str(network.residual)+'\n')
n_points_out = opt.num_points // 2
labels_generated_points = torch.Tensor(range(1, (opt.n_primitives+1)*(n_points_out//opt.n_primitives)+1)).view(n_points_out//opt.n_primitives,(opt.n_primitives+1)).transpose(0,1)
labels_generated_points = (labels_generated_points)%(opt.n_primitives+1)
labels_generated_points = labels_generated_points.contiguous().view(-1)
#Main loop for training
for epoch in range(opt.nepoch):
network.model.train()
network.residual.train()
# -----------------------------------training phase ----------------------------------
for i, data in enumerate(dataloader_train, 0):
# train generator
model_optimizer.zero_grad()
residual_optimizer.zero_grad()
name, in_partial, in_hole, in_complete = data
in_partial = in_partial.contiguous().float().to(device)
in_hole = in_hole.contiguous().float().to(device)
in_complete = in_complete.contiguous().float().to(device)
output, output2, rec_loss1, rec_loss2, exp_loss, _, _ = network(in_partial, in_hole, in_complete, 0.005, 50)
rec_g_loss = rec_loss1 + rec_loss2 + exp_loss
rec_g_loss.backward()
model_optimizer.step()
residual_optimizer.step()
# values to plot and save
rec_loss_train.update(rec_g_loss.item())
rec_loss1_train.update(rec_loss1.item())
rec_loss2_train.update(rec_loss2.item())
# visualization
if opt.visualize and i % opt.vis_step == 0:
idx = random.randint(0, in_partial.size()[0] - 1)
# print(name[idx])
pc_rec = np.concatenate((in_partial.contiguous()[idx].data.cpu().numpy(), output.contiguous()[idx].data.cpu().numpy()))
visdom_show_pc(in_hole.contiguous()[idx].data.cpu(), "TRAIN_IN_HOLE", "IN_HOLE", vis)
visdom_show_pc(in_complete.contiguous()[idx].data.cpu(), "TRAIN_IN_COMPLETE", "TRAIN_IN_COMPLETE", vis)
visdom_show_pc(in_partial.contiguous()[idx].data.cpu(), "TRAIN_IN_PARTIAL", "TRAIN_IN_PARTIAL", vis)
visdom_show_pc(output.contiguous()[idx].data.cpu(), "TRAIN_OUT_HOLE", "TRAIN_OUT_HOLE", vis, Y =labels_generated_points[0:output.size(1)] )
visdom_show_pc(output2.contiguous()[idx].data.cpu(), "TRAIN_OUT_COMPLETE", "TRAIN_OUT_COMPLETE", vis)
visdom_show_pc(pc_rec, "TRAIN_OUT_MERGE", "TRAIN_OUT_MERGE", vis)
# log per batch
print("train -> E: ", epoch, "/", i, " Loss: ", rec_loss_train.val, " EMD1: ", rec_loss1_train.val, " EMD2: ", rec_loss2_train.val, " penalty: ", exp_loss.item())
# ---------------------------------validation phase--------------------------------------
if epoch % 5 == 0:
network.model.eval()
network.residual.eval()
with torch.no_grad():
for i, data in enumerate(dataloader_test, 0):
name, in_partial, in_hole, in_complete = data
in_partial = in_partial.contiguous().float().to(device)
in_hole = in_hole.contiguous().float().to(device)
in_complete = in_complete.contiguous().float().to(device)
output, output2, rec_loss1, rec_loss2, exp_loss, _, _ = network(in_partial, in_hole, in_complete, 0.005, 50)
rec_g_loss = rec_loss1 + rec_loss2 + exp_loss
# values to plot and save
rec_loss_test.update(rec_g_loss.item())
rec_loss1_test.update(rec_loss1.item())
rec_loss2_test.update(rec_loss2.item())
# visualization
if opt.visualize and i % opt.vis_step == 0:
idx = random.randint(0, in_partial.size()[0] - 1)
pc_rec = np.concatenate((in_partial.contiguous()[idx].data.cpu().numpy(), output.contiguous()[idx].data.cpu().numpy()))
visdom_show_pc(in_complete.contiguous()[idx].data.cpu(), str(i) + " COMPLETE", str(i) + " COMPLETE", vis_test)
visdom_show_pc(output.contiguous()[idx].data.cpu(), str(i) + " OUT_HOLE", str(i) + " OUT_HOLE", vis_test, Y =labels_generated_points[0:output.size(1)] )
visdom_show_pc(pc_rec, str(i) + " OUT_MERGE", str(i) + " OUT_MERGE", vis_test)
visdom_show_pc(output2.contiguous()[idx].data.cpu(), str(i) + " OUT_COMPLETE", str(i) + " OUT_COMPLETE", vis_test)
# log per batch
print("test -> E: ", epoch, "/", i, " Loss: ", rec_loss_test.val, " EMD1: ", rec_loss1_test.val, " EMD2: ", rec_loss2_test.val, " penalty: ", exp_loss.item())
rec_loss_train.end_epoch()
rec_loss_test.end_epoch()
rec_loss1_train.end_epoch()
rec_loss2_train.end_epoch()
rec_loss1_test.end_epoch()
rec_loss2_test.end_epoch()
vis_curve(rec_loss_train.avgs, rec_loss_test.avgs, "rec_loss", "rec_loss", vis)
vis_curve(rec_loss1_train.avgs, rec_loss1_test.avgs, "rec_loss1", "rec_loss1", vis)
vis_curve(rec_loss2_train.avgs, rec_loss2_test.avgs, "rec_loss2", "rec_loss2", vis)
if (epoch % 5 == 0) or (epoch == opt.nepoch - 1):
print("Loss reduced from %8.5f to %8.5f" % (best_loss, rec_loss_test.avg))
best_loss = rec_loss_test.avg
save_model(network.model.state_dict(), model_optimizer.state_dict(), logname, dir_name, rec_loss_train, rec_loss_test, epoch, lrate_m, rec_loss1_train.avgs, rec_loss1_test.avgs)
save_model(network.residual.state_dict(), residual_optimizer.state_dict(), logname, dir_name, rec_loss_train, rec_loss_test, epoch, lrate_r, rec_loss2_train.avgs, rec_loss2_test.avgs, net_name= "residual")
save_model(network.model.state_dict(), model_optimizer.state_dict(), logname, dir_name, rec_loss_train, rec_loss_test, epoch, lrate_m, rec_loss1_train.avgs, rec_loss1_test.avgs,net_name= "model_C")
save_model(network.residual.state_dict(), residual_optimizer.state_dict(), logname, dir_name, rec_loss_train, rec_loss_test, epoch, lrate_r, rec_loss2_train.avgs, rec_loss2_test.avgs, net_name= "residual_C")