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eval_meshes.py
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eval_meshes.py
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import argparse
# import numpy as np
import os
from tqdm import tqdm
import pandas as pd
import trimesh
import torch
from im2mesh import config, data
from im2mesh.eval import MeshEvaluator
parser = argparse.ArgumentParser(
description='Evaluate mesh algorithms.'
)
parser.add_argument('config', type=str, help='Path to config file.')
parser.add_argument('--no-cuda', action='store_true', help='Do not use cuda.')
args = parser.parse_args()
cfg = config.load_config(args.config, 'configs/default.yaml')
is_cuda = (torch.cuda.is_available() and not args.no_cuda)
device = torch.device("cuda" if is_cuda else "cpu")
mesh_extension = cfg['generation']['mesh_extension']
# Shorthands
out_dir = cfg['training']['out_dir']
generation_dir = os.path.join(out_dir, cfg['generation']['generation_dir'])
eval_dir = os.path.join(out_dir, 'eval')
eval_file_name = cfg['test']['eval_file_name']
out_file = os.path.join(generation_dir, eval_file_name + 'full.pkl')
out_file_class = os.path.join(generation_dir, eval_file_name + '.csv')
pointcloud_field = data.PointCloudField(
cfg['data']['pointcloud_chamfer_file']
)
fields = {
'pointcloud_chamfer': pointcloud_field,
'idx': data.IndexField(),
}
test_split = cfg['data']['test_split']
print('Test split: ', cfg['data']['test_split'])
dataset_folder = cfg['data']['path']
dataset = data.Shapes3dDataset(
dataset_folder, fields,
cfg['data']['test_split'],
categories=cfg['data']['classes'])
# Evaluator
evaluator = MeshEvaluator(n_points=100000)
# Loader
torch.manual_seed(0)
test_loader = torch.utils.data.DataLoader(
dataset, batch_size=1, num_workers=0, shuffle=True)
if True and not os.path.exists(eval_dir):
os.makedirs(eval_dir)
# Evaluate all classes
eval_dicts = []
print('Evaluating meshes...')
for it, data in enumerate(tqdm(test_loader)):
if data is None:
print('Invalid data.')
continue
# Output folders
mesh_dir = os.path.join(generation_dir, 'meshes')
# pointcloud_dir = os.path.join(generation_dir, 'pointcloud')
# Get index etc.
idx = data['idx'].item()
try:
model_dict = dataset.get_model_dict(idx)
except AttributeError:
model_dict = {'model': str(idx), 'category': 'n/a'}
modelname = model_dict['model']
category_id = model_dict['category']
try:
category_name = dataset.metadata[category_id].get('name', 'n/a')
except AttributeError:
category_name = 'n/a'
if category_id != 'n/a':
mesh_dir = os.path.join(mesh_dir, category_id)
# pointcloud_dir = os.path.join(pointcloud_dir, category_id)
# Evaluate
pointcloud_tgt = data['pointcloud_chamfer'].squeeze(0).numpy()
normals_tgt = data['pointcloud_chamfer.normals'].squeeze(0).numpy()
# Evaluating mesh
# Start row and put basic information inside
eval_dict = {
'idx': idx,
'class id': category_id,
'class name': category_name,
'modelname': modelname,
}
eval_dicts.append(eval_dict)
# Evaluate mesh
mesh_file = os.path.join(mesh_dir, '%s.%s' % (modelname, mesh_extension))
if os.path.exists(mesh_file):
try:
mesh = trimesh.load(mesh_file, process=False)
eval_dict_mesh = evaluator.eval_mesh(
mesh, pointcloud_tgt, normals_tgt)
for k, v in eval_dict_mesh.items():
eval_dict[k + ' (mesh)'] = v
except Exception as e:
print('Warning: mesh cannot be loaded: %s (%s)' % (mesh_file, e))
else:
print('Warning: mesh does not exist: %s' % mesh_file)
# Create pandas dataframe and save
eval_df = pd.DataFrame(eval_dicts)
eval_df.set_index(['idx'], inplace=True)
eval_df.to_pickle(out_file)
# Create CSV file with main statistics
eval_df_class = eval_df.groupby(by=['class name']).mean()
eval_df_class.to_csv(out_file_class)
# Print results
eval_df_class.loc['mean'] = eval_df_class.mean()
print(eval_df_class)