images_set_valid = nvl.dataloader.ShapeNet.Images(root ='../../datasets/',categories =args.categories , \
download = True, train = False, split = .7, views=1, transform= preprocess )
valid_set = nvl.dataloader.ShapeNet.Combination([points_set_valid, images_set_valid], root='../../datasets/')
dataloader_val = DataLoader(valid_set, batch_size=args.batch_size, shuffle=False, num_workers=8)
"""
Model settings
"""
meshes = setup_meshes(filename='meshes/386.obj', device=args.device)
encoders = [Encoder().to(args.device) for i in range(3)]
mesh_update_kernels = [963, 1091, 1091]
mesh_updates = [G_Res_Net(mesh_update_kernels[i], hidden = 128, output_features = 3).to(args.device) for i in range(3)]
if args.latent_loss:
mesh_encoder = MeshEncoder(30).to(args.device)
mesh_encoder.load_state_dict(torch.load('log/{}/auto_best_encoder.pth'.format(args.expid)))
parameters = []
for i in range(3):
parameters += list(encoders[i].parameters())
parameters += list(mesh_updates[i].parameters())
optimizer = optim.Adam(parameters, lr=args.lr)
encoding_dims = [56, 28, 14, 7]
def main(args):
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
importer = fileImport()
env_data_path = args.env_data_path
path_data_path = args.path_data_path
pcd_data_path = args.pointcloud_data_path
envs = importer.environments_import(env_data_path + args.envs_file)
print("Loading obstacle data...\n")
dataset_train, targets_train, pc_inds_train, obstacles = load_dataset_end2end(
envs, path_data_path, pcd_data_path, args.path_data_file, importer, NP=1000)
print("Loaded dataset, targets, and pontcloud obstacle vectors: ")
print(str(len(dataset_train)) + " " +
str(len(targets_train)) + " " + str(len(pc_inds_train)))
print("\n")
if not os.path.exists(args.trained_model_path):
os.makedirs(args.trained_model_path)
# Build the models
mlp = MLP(args.mlp_input_size, args.mlp_output_size)
encoder = Encoder(args.enc_input_size, args.enc_output_size)
if torch.cuda.is_available():
encoder.cuda()
mlp.cuda()
# Loss and Optimizer
criterion = nn.MSELoss()
params = list(encoder.parameters())+list(mlp.parameters())
optimizer = torch.optim.Adagrad(params, lr=args.learning_rate)
total_loss = []
epoch = 1
sm = 90 # start saving models after 100 epochs
print("Starting epochs...\n")
# epoch=1
done = False
for epoch in range(args.num_epochs):
# while (not done)
start = time.time()
print("epoch" + str(epoch))
avg_loss = 0
for i in range(0, len(dataset_train), args.batch_size):
# Forward, Backward and Optimize
# zero gradients
encoder.zero_grad()
mlp.zero_grad()
# convert to pytorch tensors and Varialbes
bi, bt, bobs = get_input(
i, dataset_train, targets_train, pc_inds_train, obstacles, args.batch_size)
bi = to_var(bi)
bt = to_var(bt)
bobs = to_var(bobs)
# forward pass through encoder
h = encoder(bobs)
# concatenate encoder output with dataset input
inp = torch.cat((bi, h), dim=1)
# forward pass through mlp
bo = mlp(inp)
# compute overall loss and backprop all the way
loss = criterion(bo, bt)
avg_loss = avg_loss+loss.data
loss.backward()
optimizer.step()
print("--average loss:")
print(avg_loss/(len(dataset_train)/args.batch_size))
total_loss.append(avg_loss/(len(dataset_train)/args.batch_size))
# Save the models
if epoch == sm:
print("\nSaving model\n")
print("time: " + str(time.time() - start))
torch.save(encoder.state_dict(), os.path.join(
args.trained_model_path, 'cae_encoder_'+str(epoch)+'.pkl'))
torch.save(total_loss, 'total_loss_'+str(epoch)+'.dat')
model_path = 'mlp_PReLU_ae_dd'+str(epoch)+'.pkl'
torch.save(mlp.state_dict(), os.path.join(
args.trained_model_path, model_path))
if (epoch != 1):
sm = sm+10 # save model after every 50 epochs from 100 epoch ownwards
torch.save(total_loss, 'total_loss.dat')
model_path = 'mlp_PReLU_ae_dd_final.pkl'
torch.save(mlp.state_dict(), os.path.join(args.trained_model_path, model_path))
valid_set = kal.dataloader.ShapeNet.Combination(
[points_set_valid, images_set_valid], root='../../datasets/')
dataloader_val = DataLoader(valid_set,
batch_size=args.batchsize,
shuffle=False,
num_workers=8)
"""
Model settings
"""
mesh = kal.rep.TriangleMesh.from_obj('386.obj')
if args.device == "cuda":
mesh.cuda()
initial_verts = mesh.vertices.clone()
model = Encoder(4, 5, args.batchsize, 137,
mesh.vertices.shape[0]).to(args.device)
loss_fn = kal.metrics.point.chamfer_distance
loss_edge = kal.metrics.mesh.edge_length
optimizer = optim.Adam(model.parameters(), lr=args.lr)
# Create log directory, if it doesn't already exist
args.logdir = os.path.join(args.logdir, args.expid)
if not os.path.isdir(args.logdir):
os.makedirs(args.logdir)
print('Created dir:', args.logdir)
# Log all commandline args
with open(os.path.join(args.logdir, 'args.txt'), 'w') as f:
json.dump(args.__dict__, f, indent=2)
images_set_valid = kal.dataloader.ShapeNet.Images(root ='../../datasets/',categories =args.categories , \
download = True, train = False, split = .7, views=1, transform= preprocess )
meshes_set_valid = kal.dataloader.ShapeNet.Meshes(root ='../../datasets/', categories =args.categories , \
download = True, train = False, split = .7)
valid_set = kal.dataloader.ShapeNet.Combination([points_set_valid, images_set_valid], root='../../datasets/')
dataloader_val = DataLoader(valid_set, batch_size=args.batchsize, shuffle=False,
num_workers=8)
# Model
mesh = kal.rep.TriangleMesh.from_obj('386.obj')
if args.device == "cuda":
mesh.cuda()
initial_verts = mesh.vertices.clone()
model = Encoder(4, 5, args.batchsize, 137, mesh.vertices.shape[0] ).to(args.device)
# Load saved weights
model.load_state_dict(torch.load('log/{0}/best.pth'.format(args.expid)))
loss_epoch = 0.
f_epoch = 0.
num_batches = 0
num_items = 0
loss_fn = kal.metrics.point.chamfer_distance
model.eval()
with torch.no_grad():
for data in tqdm(dataloader_val):
# data creation
tgt_points = data['points'].to(args.device)
inp_images = data['imgs'].to(args.device)
download = True, train = False, split = .7, num_points=10000 )
images_set_valid = kal.dataloader.ShapeNet.Images(root ='../../datasets/',categories =args.categories , \
download = True, train = False, split = .7, views=1, transform= preprocess )
valid_set = kal.dataloader.ShapeNet.Combination(
[points_set_valid, images_set_valid], root='../../datasets/')
dataloader_val = DataLoader(valid_set,
batch_size=1,
shuffle=False,
num_workers=8)
"""
Model settings
"""
meshes = setup_meshes(filename='meshes/156.obj', device=args.device)
encoder = Encoder().to(args.device)
mesh_update_kernels = [963, 1091, 1091]
mesh_updates = [
G_Res_Net(mesh_update_kernels[i], hidden=128,
output_features=3).to(args.device) for i in range(3)
]
parameters = list(encoder.parameters())
for i in range(3):
parameters += list(mesh_updates[i].parameters())
optimizer = optim.Adam(parameters, lr=args.lr)
encoding_dims = [56, 28, 14, 7]
"""
Initial settings
"""