def run_reconstruction(**kwargs):
parser = argparse.ArgumentParser(
description='Evaluating a trained network')
parser.add_argument('-c',
'--path_to_model',
type=str,
help='path to model weights')
parser.add_argument('-i', '--input_file', type=str)
parser.add_argument('--fixed_duration',
dest='fixed_duration',
action='store_true')
parser.set_defaults(fixed_duration=False)
parser.add_argument(
'-N',
'--window_size',
default=None,
type=int,
help=
"Size of each event window, in number of events. Ignored if --fixed_duration=True"
)
parser.add_argument(
'-T',
'--window_duration',
default=33.33,
type=float,
help=
"Duration of each event window, in milliseconds. Ignored if --fixed_duration=False"
)
parser.add_argument(
'--num_events_per_pixel',
default=0.35,
type=float,
help='in case N (window size) is not specified, it will be \
automatically computed as N = width * height * num_events_per_pixel'
)
parser.add_argument('--skipevents', default=0, type=int)
parser.add_argument('--suboffset', default=0, type=int)
parser.add_argument('--compute_voxel_grid_on_cpu',
dest='compute_voxel_grid_on_cpu',
action='store_true')
parser.add_argument('--channelName', default=None, type=str)
parser.set_defaults(compute_voxel_grid_on_cpu=False)
set_inference_options(parser)
args = parser.parse_args()
argsDict = vars(args)
argsDict.update(kwargs)
# Load model
model = load_model(args.path_to_model)
device = get_device(args.use_gpu)
model = model.to(device)
model.eval()
path_to_events = args.input_file
container = importAe(filePathOrName=path_to_events, **kwargs)
channelName = args.channelName
if channelName is not None:
dvs = container['data'][channelName]['dvs']
else: # use Container functionality to find the right channel
containerObj = Container(container)
dvs = containerObj.getDataType('dvs')
width = dvs.get('dimX', np.max(dvs['x']) + 1)
height = dvs.get('dimY', np.max(dvs['y']) + 1)
reconstructor = ImageReconstructor(model, height, width, model.num_bins,
args)
""" Read chunks of events using Pandas """
# Loop through the events and reconstruct images
N = args.window_size
if not args.fixed_duration:
if N is None:
N = int(width * height * args.num_events_per_pixel)
print(
'Will use {} events per tensor (automatically estimated with num_events_per_pixel={:0.2f}).'
.format(N, args.num_events_per_pixel))
else:
print('Will use {} events per tensor (user-specified)'.format(N))
mean_num_events_per_pixel = float(N) / float(width * height)
if mean_num_events_per_pixel < 0.1:
print(
'!!Warning!! the number of events used ({}) seems to be low compared to the sensor size. \
The reconstruction results might be suboptimal.'.format(N))
elif mean_num_events_per_pixel > 1.5:
print(
'!!Warning!! the number of events used ({}) seems to be high compared to the sensor size. \
The reconstruction results might be suboptimal.'.format(N))
initial_offset = args.skipevents
sub_offset = args.suboffset
start_index = initial_offset + sub_offset
if args.compute_voxel_grid_on_cpu:
print('Will compute voxel grid on CPU.')
if args.fixed_duration:
event_window_iterator = FixedDurationEventReader(
dvs, duration_ms=args.window_duration, start_index=start_index)
else:
event_window_iterator = FixedSizeEventReader(dvs,
num_events=N,
start_index=start_index)
print('Sensor size: {} x {}'.format(width, height))
with Timer('Processing entire dataset'):
for event_window in event_window_iterator:
last_timestamp = event_window[-1, 0]
with Timer('Building event tensor'):
if args.compute_voxel_grid_on_cpu:
event_tensor = events_to_voxel_grid(
event_window,
num_bins=model.num_bins,
width=width,
height=height)
event_tensor = torch.from_numpy(event_tensor)
else:
event_tensor = events_to_voxel_grid_pytorch(
event_window,
num_bins=model.num_bins,
width=width,
height=height,
device=device)
num_events_in_window = event_window.shape[0]
reconstructor.update_reconstruction(
event_tensor, start_index + num_events_in_window,
last_timestamp)
start_index += num_events_in_window
parser.add_argument("--skipevents", default=0, type=int)
parser.add_argument("--suboffset", default=0, type=int)
parser.add_argument(
"--compute_voxel_grid_on_cpu",
dest="compute_voxel_grid_on_cpu",
action="store_true",
)
parser.set_defaults(compute_voxel_grid_on_cpu=False)
set_inference_options(parser)
args = parser.parse_args()
# Loading model
model = load_model(args.path_to_model)
device = get_device(args.use_gpu)
model = model.to(device)
model.eval()
reconstructor = ImageReconstructor(
model, args.height, args.width, model.num_bins, args
)
""" Read chunks of events using Pandas """
path_to_events = args.input_file
# loop through the events and reconstruct images
N = args.window_size
if N is None:
from utils.loading_utils import load_model, get_device
import torch
from model.model import *
from torch2trt import torch2trt
path = './pretrained/firenet_1000.pth.tar'
model = load_model(path)
for x in model.parameters():
x.requires_grad = False
device = get_device('True')
model = model.to(device)
model.half()
model.eval()
curr_state = torch.zeros((1,5,240,320),dtype=torch.float16)
curr_state = curr_state.to(device)
prev_state1 = torch.zeros((1,16,240,320),dtype=torch.float16)
prev_state1 = prev_state1.to(device)
prev_state2 = torch.zeros((1,16,240,320),dtype=torch.float16)
prev_state2 = prev_state2.to(device)
prev_state = [prev_state1,prev_state2]
state = [curr_state,prev_state1,prev_state2]