4D-Compositional-Representation-master/generate.py

import torch
import os
import argparse
from tqdm import tqdm
import time
from collections import defaultdict
import pandas as pd
from lib import config
from lib.checkpoints import CheckpointIO
from lib.utils.io import export_pointcloud


parser = argparse.ArgumentParser(
    description='Extract meshes from a 4D model.'
)
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('--g', type=str, default='0', help='gpu id')

args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.g

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")

out_dir = cfg['training']['out_dir']
generation_dir = os.path.join(out_dir, cfg['generation']['generation_dir'])
out_time_file = os.path.join(generation_dir, 'time_generation_full.pkl')
out_time_file_class = os.path.join(generation_dir, 'time_generation.pkl')

batch_size = cfg['generation']['batch_size']
input_type = cfg['data']['input_type']
vis_n_outputs = cfg['generation']['vis_n_outputs']
if vis_n_outputs is None:
    vis_n_outputs = -1

# Dataset
dataset = config.get_dataset('test', cfg, return_idx=True)

# Model
model = config.get_model(cfg, device=device, dataset=dataset)

checkpoint_io = CheckpointIO(out_dir, model=model)
checkpoint_io.load(cfg['test']['model_file'])

# Generator
generator = config.get_generator(model, cfg, device=device)

# Determine what to generate
generate_mesh = cfg['generation']['generate_mesh']
generate_pointcloud = cfg['generation']['generate_pointcloud']

# Loader
torch.manual_seed(cfg['generation']['rand_seed'])
test_loader = torch.utils.data.DataLoader(
    dataset, batch_size=1, num_workers=1,
    shuffle=cfg['generation']['shuffle_generation'])

# Statistics
time_dicts = []

# Generate
model.eval()

# Count how many models already created
model_counter = defaultdict(int)
for it, data in enumerate(tqdm(test_loader)):
    # Output folders
    mesh_dir = os.path.join(generation_dir, 'meshes')
    in_dir = os.path.join(generation_dir, 'input')

    # 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']
    start_idx = model_dict.get('start_idx', 0)
    category_name = 'n/a'

    if category_id != 'n/a':
        mesh_dir = os.path.join(mesh_dir, str(category_id))
        in_dir = os.path.join(in_dir, str(category_id))

        folder_name = str(category_id)

    # Create directories if necessary

    if generate_mesh and not os.path.exists(mesh_dir):
        os.makedirs(mesh_dir)

    if not os.path.exists(in_dir) and cfg['generation']['copy_input']:
        os.makedirs(in_dir)

    # Timing dict
    time_dict = {
        'idx': idx,
        'class id': category_id,
        'class name': category_name,
        'modelname': modelname,
    }
    time_dicts.append(time_dict)

    # Generate outputs
    out_file_dict = {}

    t0 = time.time()
    out = generator.generate(data)
    time_dict['mesh'] = time.time() - t0
    # Get statistics
    try:
        mesh, stats_dict = out
    except TypeError:
        mesh, stats_dict = out, {}
    time_dict.update(stats_dict)

    # Save mesh files
    out_files = generator.export(mesh, mesh_dir, modelname, start_idx)
    for nf, f in enumerate(out_files):
        out_file_dict['mesh_%03d' % nf] = f

    # Save input point cloud files
    if cfg['generation']['copy_input']:
        inputs = data['inputs'][0]
        L = inputs.shape[0]
        inputs_base_path = os.path.join(
            in_dir, modelname, '%04d' % start_idx)
        if not os.path.exists(inputs_base_path):
            os.makedirs(inputs_base_path)
        inputs_path = [os.path.join(
            inputs_base_path, '%04d.ply' % i) for i in range(L)]
        for i in range(L):
            export_pointcloud(
                inputs[i].cpu().numpy(), inputs_path[i], False)
            out_file_dict['in_%d' % i] = inputs_path[i]


# Create pandas dataframe and save
time_df = pd.DataFrame(time_dicts)
time_df.set_index(['idx'], inplace=True)
time_df.to_pickle(out_time_file)

# Create pickle files  with main statistics
time_df_class = time_df.groupby(by=['class name']).mean()
time_df_class.to_pickle(out_time_file_class)

# Print results
time_df_class.loc['mean'] = time_df_class.mean()
print('Timings [s]:')
print(time_df_class)