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eval.py
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eval.py
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import argparse
# import numpy as np
import os
from tqdm import tqdm
import pandas as pd
import torch
from im2mesh import config, data
from im2mesh.eval import MeshEvaluator
from im2mesh.utils.io import load_pointcloud, load_mesh
import glob
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.')
parser.add_argument('--eval_input', action='store_true',
help='Evaluate inputs instead.')
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")
dataset_folder = cfg['data']['path']
# Shorthands
out_dir = cfg['training']['out_dir']
generation_dir = os.path.join(out_dir, cfg['generation']['generation_dir'])
project_to_final_mesh = cfg['test']['project_to_final_mesh']
if not args.eval_input:
out_file = os.path.join(generation_dir, 'eval_meshes_full.pkl')
out_file_tmp = os.path.join(generation_dir, 'eval_meshes_full_tmp.pkl')
out_file_class = os.path.join(generation_dir, 'eval_meshes.csv')
out_file_class_tmp = os.path.join(generation_dir, 'eval_meshes_tmp.csv')
else:
out_file = os.path.join(generation_dir, 'eval_input_full.pkl')
out_file_class = os.path.join(generation_dir, 'eval_input.csv')
# Dataset
fields = {
'pointcloud_chamfer': data.PointCloudSubseqField(
cfg['data']['pointcloud_seq_folder'],
seq_len=cfg['data']['length_sequence']),
'idx': data.IndexField(),
}
if cfg['test']['eval_mesh_iou']:
fields['points'] = data.PointsSubseqField(
cfg['data']['points_iou_seq_folder'], all_steps=True,
seq_len=cfg['data']['length_sequence'],
unpackbits=cfg['data']['points_unpackbits'])
print('Test split: ', cfg['data']['test_split'])
dataset = data.HumansDataset(
dataset_folder, fields, split=cfg['data']['test_split'],
categories=cfg['data']['classes'],
length_sequence=cfg['data']['length_sequence'],
n_files_per_sequence=cfg['data']['n_files_per_sequence'],
offset_sequence=cfg['data']['offset_sequence'])
# Evaluator
evaluator = MeshEvaluator(n_points=100000)
# Loader
test_loader = torch.utils.data.DataLoader(
dataset, batch_size=1, num_workers=1, shuffle=True)
# Evaluate all classes
eval_dicts = []
print('Starting evaluation process ...')
for it, data_batch in enumerate(tqdm(test_loader)):
if data_batch is None:
print('Invalid data.')
continue
# Output folders
if not args.eval_input:
mesh_dir = os.path.join(generation_dir, 'meshes')
pointcloud_dir = os.path.join(generation_dir, 'pointcloud')
else:
mesh_dir = os.path.join(generation_dir, 'input')
pointcloud_dir = os.path.join(generation_dir, 'input')
# Get index etc.
idx = data_batch['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']
start_idx = model_dict.get('start_idx', 0)
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_batch['pointcloud_chamfer'].squeeze(0).cpu().numpy()
if cfg['test']['eval_mesh_iou']:
points_tgt = data_batch['points'].squeeze(0).cpu().numpy()
occ_tgt = data_batch['points.occ'].squeeze(0).cpu().numpy()
# Evaluating mesh and pointcloud
# Start row and put basic information inside
eval_dict = {
'idx': idx,
'class id': category_id,
'class name': category_name,
'modelname': modelname,
'start_idx': start_idx,
}
eval_dicts.append(eval_dict)
# Evaluate mesh
if cfg['test']['eval_mesh']:
mesh_folder = os.path.join(mesh_dir, modelname, '%05d' % start_idx)
if os.path.exists(mesh_folder):
off_files = glob.glob(os.path.join(mesh_folder, '*.off'))
off_files.sort()
if cfg['test']['eval_only_end_time_steps']:
off_files = [off_files[0], off_files[-1]]
pointcloud_tgt = pointcloud_tgt[[0, -1]]
if cfg['test']['eval_mesh_correspondences']:
eval_dict_cor = evaluator.eval_correspondences_mesh(
off_files, pointcloud_tgt,
project_to_final_mesh=project_to_final_mesh)
for k, v in eval_dict_cor.items():
eval_dict[k] = v
if cfg['test']['eval_mesh_iou']:
for i, mesh_file in enumerate(off_files):
mesh = load_mesh(mesh_file)
eval_dict_mesh = evaluator.eval_mesh(
mesh, pointcloud_tgt[i], None, points_tgt[i],
occ_tgt[i])
for k, v in eval_dict_mesh.items():
eval_dict['%s %d (mesh)' % (k, i)] = v
else:
print('Warning: mesh does not exist: %s (%d)' %
(modelname, start_idx))
# Evaluate point cloud
if cfg['test']['eval_pointcloud']:
pointcloud_folder = os.path.join(
pointcloud_dir, '%s' % modelname, '%05d' % start_idx)
if os.path.exists(pointcloud_folder):
ply_files = glob.glob(os.path.join(pointcloud_folder, '*.ply'))
ply_files.sort()
# Eval Pointcloud Correspondences
if cfg['test']['eval_pointcloud_correspondences']:
eval_dict_cor = evaluator.eval_correspondences_pointcloud(
ply_files, pointcloud_tgt)
for k, v in eval_dict_cor.items():
eval_dict[k] = v
# Eval Point Cloud Chamfer distances
for i, ply_file in enumerate(ply_files[:17]):
pointcloud = load_pointcloud(ply_file)
eval_dict_pcl = evaluator.eval_pointcloud(
pointcloud, pointcloud_tgt[i])
for k, v in eval_dict_pcl.items():
eval_dict['%s %d (pcl)' % (k, i)] = v
else:
print('Warning: pointcloud does not exist: %s (%d)' %
(modelname, start_idx))
if it > 0 and (it % 50) == 0:
# Create pandas dataframe and save
eval_df = pd.DataFrame(eval_dicts)
eval_df.set_index(['idx'], inplace=True)
eval_df.to_pickle(out_file_tmp)
# Create CSV file with main statistics
eval_df_class = eval_df.groupby(by=['class name']).mean()
eval_df_class.to_csv(out_file_class_tmp)
print('Saved tmp file after %d iterations.' % it)
# 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)