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step5_predict.py
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step5_predict.py
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import os
import numpy as np
import torch
import torch.nn as nn
from meshsegnet import *
import vedo
import pandas as pd
from losses_and_metrics_for_mesh import *
from scipy.spatial import distance_matrix
if __name__ == '__main__':
#gpu_id = utils.get_avail_gpu()
gpu_id = 0
torch.cuda.set_device(gpu_id) # assign which gpu will be used (only linux works)
model_path = './models'
model_name = 'MeshSegNet_Max_15_classes_72samples_lr1e-2_best.tar'
mesh_path = './' # need to define
sample_filenames = ['Example.stl'] # need to define
output_path = './outputs'
if not os.path.exists(output_path):
os.mkdir(output_path)
num_classes = 15
num_channels = 15
# set model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = MeshSegNet(num_classes=num_classes, num_channels=num_channels).to(device, dtype=torch.float)
# load trained model
checkpoint = torch.load(os.path.join(model_path, model_name), map_location='cpu')
model.load_state_dict(checkpoint['model_state_dict'])
del checkpoint
model = model.to(device, dtype=torch.float)
#cudnn
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.enabled = True
# Predicting
model.eval()
with torch.no_grad():
for i_sample in sample_filenames:
print('Predicting Sample filename: {}'.format(i_sample))
mesh = vedo.load(os.path.join(mesh_path, i_sample))
# pre-processing: downsampling
if mesh.NCells() > 10000:
print('\tDownsampling...')
target_num = 10000
ratio = target_num/mesh.NCells() # calculate ratio
mesh_d = mesh.clone()
mesh_d.decimate(fraction=ratio)
predicted_labels_d = np.zeros([mesh_d.NCells(), 1], dtype=np.int32)
else:
mesh_d = mesh.clone()
predicted_labels_d = np.zeros([mesh_d.NCells(), 1], dtype=np.int32)
# move mesh to origin
print('\tPredicting...')
cells = np.zeros([mesh_d.NCells(), 9], dtype='float32')
for i in range(len(cells)):
cells[i][0], cells[i][1], cells[i][2] = mesh_d._polydata.GetPoint(mesh_d._polydata.GetCell(i).GetPointId(0)) # don't need to copy
cells[i][3], cells[i][4], cells[i][5] = mesh_d._polydata.GetPoint(mesh_d._polydata.GetCell(i).GetPointId(1)) # don't need to copy
cells[i][6], cells[i][7], cells[i][8] = mesh_d._polydata.GetPoint(mesh_d._polydata.GetCell(i).GetPointId(2)) # don't need to copy
original_cells_d = cells.copy()
mean_cell_centers = mesh_d.centerOfMass()
cells[:, 0:3] -= mean_cell_centers[0:3]
cells[:, 3:6] -= mean_cell_centers[0:3]
cells[:, 6:9] -= mean_cell_centers[0:3]
# customized normal calculation; the vtk/vedo build-in function will change number of points
v1 = np.zeros([mesh_d.NCells(), 3], dtype='float32')
v2 = np.zeros([mesh_d.NCells(), 3], dtype='float32')
v1[:, 0] = cells[:, 0] - cells[:, 3]
v1[:, 1] = cells[:, 1] - cells[:, 4]
v1[:, 2] = cells[:, 2] - cells[:, 5]
v2[:, 0] = cells[:, 3] - cells[:, 6]
v2[:, 1] = cells[:, 4] - cells[:, 7]
v2[:, 2] = cells[:, 5] - cells[:, 8]
mesh_normals = np.cross(v1, v2)
mesh_normal_length = np.linalg.norm(mesh_normals, axis=1)
mesh_normals[:, 0] /= mesh_normal_length[:]
mesh_normals[:, 1] /= mesh_normal_length[:]
mesh_normals[:, 2] /= mesh_normal_length[:]
mesh_d.addCellArray(mesh_normals, 'Normal')
# preprae input
points = mesh_d.points().copy()
points[:, 0:3] -= mean_cell_centers[0:3]
normals = mesh_d.getCellArray('Normal').copy() # need to copy, they use the same memory address
barycenters = mesh_d.cellCenters() # don't need to copy
barycenters -= mean_cell_centers[0:3]
#normalized data
maxs = points.max(axis=0)
mins = points.min(axis=0)
means = points.mean(axis=0)
stds = points.std(axis=0)
nmeans = normals.mean(axis=0)
nstds = normals.std(axis=0)
for i in range(3):
cells[:, i] = (cells[:, i] - means[i]) / stds[i] #point 1
cells[:, i+3] = (cells[:, i+3] - means[i]) / stds[i] #point 2
cells[:, i+6] = (cells[:, i+6] - means[i]) / stds[i] #point 3
barycenters[:,i] = (barycenters[:,i] - mins[i]) / (maxs[i]-mins[i])
normals[:,i] = (normals[:,i] - nmeans[i]) / nstds[i]
X = np.column_stack((cells, barycenters, normals))
#X = (X-np.ones((X.shape[0], 1))*np.mean(X, axis=0)) / (np.ones((X.shape[0], 1))*np.std(X, axis=0))
# computing A_S and A_L
A_S = np.zeros([X.shape[0], X.shape[0]], dtype='float32')
A_L = np.zeros([X.shape[0], X.shape[0]], dtype='float32')
D = distance_matrix(X[:, 9:12], X[:, 9:12])
A_S[D<0.1] = 1.0
A_S = A_S / np.dot(np.sum(A_S, axis=1, keepdims=True), np.ones((1, X.shape[0])))
A_L[D<0.2] = 1.0
A_L = A_L / np.dot(np.sum(A_L, axis=1, keepdims=True), np.ones((1, X.shape[0])))
# numpy -> torch.tensor
X = X.transpose(1, 0)
X = X.reshape([1, X.shape[0], X.shape[1]])
X = torch.from_numpy(X).to(device, dtype=torch.float)
A_S = A_S.reshape([1, A_S.shape[0], A_S.shape[1]])
A_L = A_L.reshape([1, A_L.shape[0], A_L.shape[1]])
A_S = torch.from_numpy(A_S).to(device, dtype=torch.float)
A_L = torch.from_numpy(A_L).to(device, dtype=torch.float)
tensor_prob_output = model(X, A_S, A_L).to(device, dtype=torch.float)
patch_prob_output = tensor_prob_output.cpu().numpy()
for i_label in range(num_classes):
predicted_labels_d[np.argmax(patch_prob_output[0, :], axis=-1)==i_label] = i_label
# output downsampled predicted labels
mesh2 = mesh_d.clone()
mesh2.addCellArray(predicted_labels_d, 'Label')
vedo.write(mesh2, os.path.join(output_path, '{}_d_predicted.vtp'.format(i_sample[:-4])))
print('Sample filename: {} completed'.format(i_sample))