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evaluation.py
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import os
import numpy as np
import argparse
from data_util.nuscenes_pred_split import get_nuscenes_pred_split
from utils.utils import print_log, AverageMeter, isfile, isfolder, find_unique_common_from_lists, load_list_from_folder, load_txt_file
""" Metrics """
def compute_ADE(pred_arr, gt_arr):
ade = 0.0
for pred, gt in zip(pred_arr, gt_arr):
diff = pred - np.expand_dims(gt, axis=0) # samples x frames x 2
dist = np.linalg.norm(diff, axis=-1) # samples x frames
dist = dist.mean(axis=-1) # samples
ade += dist.min(axis=0) # (1, )
ade /= len(pred_arr)
return ade
def compute_FDE(pred_arr, gt_arr):
fde = 0.0
for pred, gt in zip(pred_arr, gt_arr):
diff = pred - np.expand_dims(gt, axis=0) # samples x frames x 2
dist = np.linalg.norm(diff, axis=-1) # samples x frames
dist = dist[..., -1] # samples
fde += dist.min(axis=0) # (1, )
fde /= len(pred_arr)
return fde
def align_gt(pred, gt):
frame_from_data = pred[0, :, 0].astype('int64').tolist()
frame_from_gt = gt[:, 0].astype('int64').tolist()
common_frames, index_list1, index_list2 = find_unique_common_from_lists(frame_from_gt, frame_from_data)
assert len(common_frames) == len(frame_from_data)
gt_new = gt[index_list1, 2:]
pred_new = pred[:, index_list2, 2:]
return pred_new, gt_new
def plot(pred, gt, log_file):
print(len(pred), "pred", len(gt), "gt", log_file)
def evaluate(dataset, results_dir, split, logger):
logger.info('----Evaluating...')
if dataset == 'nuscenes_pred': # nuscenes
data_root = f'datasets/nuscenes_pred'
gt_dir = f'{data_root}/label/{split}'
seqs_dict = {}
seqs_dict['seq_train'], seqs_dict['seq_val'], seqs_dict['seq_test'] =\
get_nuscenes_pred_split(data_root)
seq_eval = seqs_dict[f'seq_{split}']
else: # ETH/UCY
gt_dir = f'datasets/eth_ucy/{dataset}' # Note how to test with ETH??
seqs_dict = {}
seqs_dict['seq_train'], seqs_dict['seq_val'], seqs_dict['seq_test'] =\
get_ethucy_split(dataset)
seq_eval = seqs_dict[f'seq_{split}']
logger.info('loading results from %s' % results_dir)
logger.info('loading GT from %s' % gt_dir)
stats_func = {
'ADE': compute_ADE,
'FDE': compute_FDE
}
stats_meter = {x: AverageMeter() for x in stats_func.keys()}
seq_list, num_seq = load_list_from_folder(gt_dir)
logger.info('\n\nnumber of sequences to evaluate is %d' % len(seq_eval))
for seq_name in seq_eval:
# load GT raw data
gt_data, _ = load_txt_file(os.path.join(gt_dir, seq_name+'.txt'))
gt_raw = []
for line_data in gt_data:
line_data = np.array([line_data.split(' ')])[:, [0, 1, 13, 15]][0].astype('float32')
if line_data[1] == -1: continue
gt_raw.append(line_data)
gt_raw = np.stack(gt_raw)
data_filelist, _ = load_list_from_folder(os.path.join(results_dir, seq_name))
for data_file in data_filelist: # each example e.g., seq_0001 - frame_000009
# for reconsutrction or deterministic
if isfile(data_file):
all_traj = np.loadtxt(data_file, delimiter=' ', dtype='float32') # (frames x agents) x 4
all_traj = np.expand_dims(all_traj, axis=0) # 1 x (frames x agents) x 4
# for stochastic with multiple samples
elif isfolder(data_file):
sample_list, _ = load_list_from_folder(data_file)
sample_all = []
for sample in sample_list:
sample = np.loadtxt(sample, delimiter=' ', dtype='float32') # (frames x agents) x 4
# print(sample.shape)
sample_all.append(sample)
all_traj = np.stack(sample_all, axis=0) # samples x (framex x agents) x 4
else:
assert False, 'error'
# convert raw data to our format for evaluation
id_list = np.unique(all_traj[:, :, 1])
frame_list = np.unique(all_traj[:, :, 0])
agent_traj = []
gt_traj = []
for idx in id_list:
# GT traj
gt_idx = gt_raw[gt_raw[:, 1] == idx] # frames x 4
# predicted traj
ind = np.unique(np.where(all_traj[:, :, 1] == idx)[1].tolist())
pred_idx = all_traj[:, ind, :] # sample x frames x 4
# filter data
pred_idx, gt_idx = align_gt(pred_idx, gt_idx)
# append
agent_traj.append(pred_idx)
gt_traj.append(gt_idx)
# plot(agent_traj, gt_traj, log_file)
"""compute stats"""
for stats_name, meter in stats_meter.items():
func = stats_func[stats_name]
value = func(agent_traj, gt_traj)
meter.update(value, n=len(agent_traj))
logger.info('-' * 30 + ' STATS ' + '-' * 30)
for name, meter in stats_meter.items():
logger.info(f'{meter.count} {name}: {meter.avg:.4f}')
logger.info('-' * 67)
return stats_meter['ADE'].avg, stats_meter['FDE'].avg
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', default='nuscenes_pred')
parser.add_argument('--results_dir', default=None)
parser.add_argument('--data', default='test')
parser.add_argument('--log_file', default=None)
args = parser.parse_args()
dataset = args.dataset.lower()
results_dir = args.results_dir
if dataset == 'nuscenes_pred': # nuscenes
data_root = f'datasets/nuscenes_pred'
gt_dir = f'{data_root}/label/{args.data}'
seq_train, seq_val, seq_test = get_nuscenes_pred_split(data_root)
seq_eval = globals()[f'seq_{args.data}']
else: # ETH/UCY
gt_dir = f'datasets/eth_ucy/{args.dataset}'
seq_train, seq_val, seq_test = get_ethucy_split(args.dataset)
seq_eval = globals()[f'seq_{args.data}']
if args.log_file is None:
log_file = os.path.join(results_dir, 'log_eval.txt')
else:
log_file = args.log_file
log_file = open(log_file, 'a+')
print_log('loading results from %s' % results_dir, log_file)
print_log('loading GT from %s' % gt_dir, log_file)
stats_func = {
'ADE': compute_ADE,
'FDE': compute_FDE
}
stats_meter = {x: AverageMeter() for x in stats_func.keys()}
seq_list, num_seq = load_list_from_folder(gt_dir)
print_log('\n\nnumber of sequences to evaluate is %d' % len(seq_eval), log_file)
for seq_name in seq_eval:
# load GT raw data
gt_data, _ = load_txt_file(os.path.join(gt_dir, seq_name+'.txt'))
gt_raw = []
for line_data in gt_data:
line_data = np.array([line_data.split(' ')])[:, [0, 1, 13, 15]][0].astype('float32')
if line_data[1] == -1: continue
gt_raw.append(line_data)
gt_raw = np.stack(gt_raw)
data_filelist, _ = load_list_from_folder(os.path.join(results_dir, seq_name))
for data_file in data_filelist: # each example e.g., seq_0001 - frame_000009
# for reconsutrction or deterministic
if isfile(data_file):
all_traj = np.loadtxt(data_file, delimiter=' ', dtype='float32') # (frames x agents) x 4
all_traj = np.expand_dims(all_traj, axis=0) # 1 x (frames x agents) x 4
# for stochastic with multiple samples
elif isfolder(data_file):
sample_list, _ = load_list_from_folder(data_file)
sample_all = []
for sample in sample_list:
sample = np.loadtxt(sample, delimiter=' ', dtype='float32') # (frames x agents) x 4
# print(sample.shape)
sample_all.append(sample)
all_traj = np.stack(sample_all, axis=0) # samples x (framex x agents) x 4
else:
assert False, 'error'
# convert raw data to our format for evaluation
id_list = np.unique(all_traj[:, :, 1])
frame_list = np.unique(all_traj[:, :, 0])
agent_traj = []
gt_traj = []
for idx in id_list:
# GT traj
gt_idx = gt_raw[gt_raw[:, 1] == idx] # frames x 4
# predicted traj
ind = np.unique(np.where(all_traj[:, :, 1] == idx)[1].tolist())
pred_idx = all_traj[:, ind, :] # sample x frames x 4
# filter data
pred_idx, gt_idx = align_gt(pred_idx, gt_idx)
# append
agent_traj.append(pred_idx)
gt_traj.append(gt_idx)
# plot(agent_traj, gt_traj, log_file)
"""compute stats"""
for stats_name, meter in stats_meter.items():
func = stats_func[stats_name]
value = func(agent_traj, gt_traj)
meter.update(value, n=len(agent_traj))
stats_str = ' '.join([f'{x}: {y.val:.4f} ({y.avg:.4f})' for x, y in stats_meter.items()])
# print_log(f'evaluating seq {seq_name:s}, forecasting frame {int(frame_list[0]):06d} to {int(frame_list[-1]):06d} {stats_str}', log_file)
print_log('-' * 30 + ' STATS ' + '-' * 30, log_file)
for name, meter in stats_meter.items():
print_log(f'{meter.count} {name}: {meter.avg:.4f}', log_file)
print_log('-' * 67, log_file)
log_file.close()