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evaluate.py
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evaluate.py
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import argparse
import logging
import time
import numpy as np
import torch.utils.data
from hardware.device import get_device
from inference.post_process import post_process_output
from utils.data import get_dataset
from utils.dataset_processing import evaluation, grasp
from utils.visualisation.plot import save_results,plot_results
import matplotlib.pyplot as plt
logging.basicConfig(level=logging.INFO)
def parse_args():
parser = argparse.ArgumentParser(description='Evaluate networks')
# Network
parser.add_argument('--network', type=str,default='/home/yusheng/code/new_grasp/trained-models/cornell-randsplit-rgbd-grconvnet3-drop1-ch32/epoch_19_iou_0.98',
help='Path to saved networks to evaluate')
# Dataset
parser.add_argument('--dataset', type=str,default='cornell',
help='Dataset Name ("cornell" or "jaquard")')
parser.add_argument('--dataset-path', type=str,default='/home/yusheng/cornnel',
help='Path to dataset')
parser.add_argument('--use-depth', type=int, default=1,
help='Use Depth image for evaluation (1/0)')
parser.add_argument('--use-rgb', type=int, default=1,
help='Use RGB image for evaluation (1/0)')
parser.add_argument('--augment', action='store_true',
help='Whether data augmentation should be applied')
parser.add_argument('--split', type=float, default=0.9,
help='Fraction of data for training (remainder is validation)')
parser.add_argument('--ds-shuffle', action='store_true', default=False,
help='Shuffle the dataset')
parser.add_argument('--ds-rotate', type=float, default=0.0,
help='Shift the start point of the dataset to use a different test/train split')
parser.add_argument('--num-workers', type=int, default=8,
help='Dataset workers')
# Evaluation
parser.add_argument('--n-grasps', type=int, default=1,
help='Number of grasps to consider per image')
parser.add_argument('--iou-threshold', type=float, default=0.25,
help='Threshold for IOU matching')
parser.add_argument('--iou-eval', action='store_true',
help='Compute success based on IoU metric.')
parser.add_argument('--jacquard-output', action='store_true',
help='Jacquard-dataset style output')
# Misc.
parser.add_argument('--vis', action='store_true',
help='Visualise the network output')
parser.add_argument('--cpu', dest='force_cpu', action='store_true', default=False,
help='Force code to run in CPU mode')
parser.add_argument('--random-seed', type=int, default=123,
help='Random seed for numpy')
args = parser.parse_args()
if args.jacquard_output and args.dataset != 'jacquard':
raise ValueError('--jacquard-output can only be used with the --dataset jacquard option.')
if args.jacquard_output and args.augment:
raise ValueError('--jacquard-output can not be used with data augmentation.')
return args
if __name__ == '__main__':
args = parse_args()
# Get the compute device
device = get_device(args.force_cpu)
# Load Dataset
logging.info('Loading {} Dataset...'.format(args.dataset.title()))
Dataset = get_dataset(args.dataset)
test_dataset = Dataset(args.dataset_path,
ds_rotate=args.ds_rotate,
random_rotate=args.augment,
random_zoom=args.augment,
include_depth=args.use_depth,
include_rgb=args.use_rgb)
indices = list(range(test_dataset.length))
split = int(np.floor(args.split * test_dataset.length))
if args.ds_shuffle:
np.random.seed(args.random_seed)
np.random.shuffle(indices)
val_indices = indices[split:]
val_sampler = torch.utils.data.sampler.SubsetRandomSampler(val_indices)
logging.info('Validation size: {}'.format(len(val_indices)))
test_data = torch.utils.data.DataLoader(
test_dataset,
batch_size=1,
num_workers=args.num_workers,
sampler=val_sampler
)
logging.info('Done')
network=args.network
logging.info('\nEvaluating model {}'.format(network))
# Load Network
net = torch.load(network)
results = {'correct': 0, 'failed': 0}
if args.jacquard_output:
jo_fn = network + '_jacquard_output.txt'
with open(jo_fn, 'w') as f:
pass
start_time = time.time()
id = 0
with torch.no_grad():
for idx, (x, y, didx, rot, zoom) in enumerate(test_data):
xc = x.to(device)
yc = [yi.to(device) for yi in y]
lossd = net.compute_loss(xc, yc)
q_img, ang_img, width_img = post_process_output(lossd['pred']['pos'], lossd['pred']['cos'],
lossd['pred']['sin'], lossd['pred']['width'])
if args.iou_eval:
s = evaluation.calculate_iou_match(q_img, ang_img, test_data.dataset.get_gtbb(didx, rot, zoom),
no_grasps=args.n_grasps,
grasp_width=width_img,
threshold=args.iou_threshold
)
if s:
results['correct'] += 1
else:
results['failed'] += 1
if args.jacquard_output:
grasps = grasp.detect_grasps(q_img, ang_img, width_img=width_img, no_grasps=1)
with open(jo_fn, 'a') as f:
for g in grasps:
f.write(test_data.dataset.get_jname(didx) + '\n')
f.write(g.to_jacquard(scale=1024 / 300) + '\n')
# if not s:
# if args.vis:
# save_results(
# rgb_img=test_data.dataset.get_rgb(didx, rot, zoom, normalise=False),
# depth_img=test_data.dataset.get_depth(didx, rot, zoom),
# grasp_q_img=q_img,
# grasp_angle_img=ang_img,
# id = id,
# no_grasps=args.n_grasps,
# grasp_width_img=width_img
# )
# id +=1
if args.vis:
fig = plt.figure(figsize=(20, 20))
plot_results(fig=fig,
rgb_img=test_data.dataset.get_rgb(didx, rot, zoom, normalise=False),
depth_img=test_data.dataset.get_depth(didx, rot, zoom),
grasp_q_img=q_img,
grasp_angle_img=ang_img,
no_grasps=args.n_grasps,
grasp_width_img=width_img
)
fig.savefig('grcnn_cornell/{}.pdf'.format(idx))
fig.savefig('grcnn_cornell/{}.png'.format(idx))
avg_time = (time.time() - start_time) / len(test_data)
logging.info('Average evaluation time per image: {}ms'.format(avg_time * 1000))
if args.iou_eval:
logging.info('IOU Results: %d/%d = %f' % (results['correct'],
results['correct'] + results['failed'],
results['correct'] / (results['correct'] + results['failed'])))
if args.jacquard_output:
logging.info('Jacquard output saved to {}'.format(jo_fn))
del net
torch.cuda.empty_cache()