def main():
parser = argparse.ArgumentParser(description='Loads a pickled NetMaxTracker and outputs one or more of {the patches of the image, a deconv patch, a backprop patch} associated with the maxes.')
parser.add_argument('--N', type = int, default = 9, help = 'Note and save top N activations.')
parser.add_argument('--gpu', action = 'store_true', help = 'Use gpu.')
parser.add_argument('--do-maxes', action = 'store_true', help = 'Output max patches.')
parser.add_argument('--do-deconv', action = 'store_true', help = 'Output deconv patches.')
parser.add_argument('--do-deconv-norm', action = 'store_true', help = 'Output deconv-norm patches.')
parser.add_argument('--do-backprop', action = 'store_true', help = 'Output backprop patches.')
parser.add_argument('--do-backprop-norm', action = 'store_true', help = 'Output backprop-norm patches.')
parser.add_argument('--do-info', action = 'store_true', help = 'Output info file containing max filenames and labels.')
parser.add_argument('--idx-begin', type = int, default = None, help = 'Start at this unit (default: all units).')
parser.add_argument('--idx-end', type = int, default = None, help = 'End at this unit (default: all units).')
parser.add_argument('nmt_pkl', type = str, help = 'Which pickled NetMaxTracker to load.')
parser.add_argument('net_prototxt', type = str, help = 'Network prototxt to load')
parser.add_argument('net_weights', type = str, help = 'Network weights to load')
parser.add_argument('datadir', type = str, help = 'Directory to look for files in')
parser.add_argument('filelist', type = str, help = 'List of image files to consider, one per line. Must be the same filelist used to produce the NetMaxTracker!')
parser.add_argument('outdir', type = str, help = r'Which output directory to use. Files are output into outdir/layer/unit_%%04d/{maxes,deconv,backprop}_%%03d.png')
parser.add_argument('layer', type = str, help = 'Which layer to output')
#parser.add_argument('--mean', type = str, default = '', help = 'data mean to load')
args = parser.parse_args()
if args.gpu:
caffe.set_mode_gpu()
else:
caffe.set_mode_cpu()
imagenet_mean = load_imagenet_mean()
net = caffe.Classifier(args.net_prototxt, args.net_weights,
mean=imagenet_mean,
channel_swap=(2,1,0),
raw_scale=255,
image_dims=(256, 256))
assert args.do_maxes or args.do_deconv or args.do_deconv_norm or args.do_backprop or args.do_backprop_norm or args.do_info, 'Specify at least one do_* option to output.'
with open(args.nmt_pkl, 'rb') as ff:
nmt = pickle.load(ff)
mt = nmt.max_trackers[args.layer]
if args.idx_begin is None:
args.idx_begin = 0
if args.idx_end is None:
args.idx_end = mt.max_vals.shape[0]
with WithTimer('Saved %d images per unit for %s units %d:%d.' % (args.N, args.layer, args.idx_begin, args.idx_end)):
output_max_patches(mt, net, args.layer, args.idx_begin, args.idx_end,
args.N, args.datadir, args.filelist, args.outdir,
(args.do_maxes, args.do_deconv, args.do_deconv_norm, args.do_backprop, args.do_backprop_norm, args.do_info))
def main():
parser = argparse.ArgumentParser(
description='Loads a pickled NetMaxTracker and outputs one or more of {the patches of the image, a deconv patch, a backprop patch} associated with the maxes.')
parser.add_argument('--model')
parser.add_argument('--idx-begin', type=int, default=None, help='Start at this unit (default: all units).')
parser.add_argument('--idx-end', type=int, default=None, help='End at this unit (default: all units).')
args = parser.parse_args()
settings = Settings.Settings()
settings.load_settings(args.model)
net = load_network(settings)
# set network batch size
current_input_shape = net.blobs[net.inputs[0]].shape
current_input_shape[0] = settings.max_tracker_batch_size
net.blobs[net.inputs[0]].reshape(*current_input_shape)
net.reshape()
assert settings.max_tracker_do_maxes or settings.max_tracker_do_deconv or settings.max_tracker_do_deconv_norm or settings.max_tracker_do_backprop or settings.max_tracker_do_backprop_norm, 'Specify at least one do_* option to output.'
nmt = load_max_tracker_from_file(settings.find_maxes_output_file)
for layer_name in settings.layers_to_output_in_offline_scripts:
print 'Started work on layer %s' % (layer_name)
mt = nmt.max_trackers[layer_name]
if args.idx_begin is None:
idx_begin = 0
if args.idx_end is None:
idx_end = mt.max_vals.shape[0]
with WithTimer('Saved %d images per unit for %s units %d:%d.' % (settings.N, layer_name, idx_begin, idx_end)):
output_max_patches(settings, mt, net, layer_name, idx_begin, idx_end,
settings.N, settings.data_dir, settings.deepvis_outputs_path, False,
(settings.max_tracker_do_maxes, settings.max_tracker_do_deconv,
settings.max_tracker_do_deconv_norm, settings.max_tracker_do_backprop,
settings.max_tracker_do_backprop_norm, True))
if settings.search_min:
output_max_patches(settings, mt, net, layer_name, idx_begin, idx_end,
settings.N, settings.data_dir, settings.deepvis_outputs_path, True,
(settings.max_tracker_do_maxes, settings.max_tracker_do_deconv,
settings.max_tracker_do_deconv_norm, settings.max_tracker_do_backprop,
settings.max_tracker_do_backprop_norm, True))
def main():
parser = argparse.ArgumentParser(description='Loads a pickled NetMaxTracker and outputs one or more of {the patches of the image, a deconv patch, a backprop patch} associated with the maxes.')
parser.add_argument('--N', type = int, default = 9, help = 'Note and save top N activations.')
parser.add_argument('--gpu', action = 'store_true', default=settings.caffevis_mode_gpu, help = 'Use gpu.')
parser.add_argument('--do-maxes', action = 'store_true', default=settings.max_tracker_do_maxes, help = 'Output max patches.')
parser.add_argument('--do-deconv', action = 'store_true', default=settings.max_tracker_do_deconv, help = 'Output deconv patches.')
parser.add_argument('--do-deconv-norm', action = 'store_true', default=settings.max_tracker_do_deconv_norm, help = 'Output deconv-norm patches.')
parser.add_argument('--do-backprop', action = 'store_true', default=settings.max_tracker_do_backprop, help = 'Output backprop patches.')
parser.add_argument('--do-backprop-norm', action = 'store_true', default=settings.max_tracker_do_backprop_norm, help = 'Output backprop-norm patches.')
parser.add_argument('--do-info', action = 'store_true', default=settings.max_tracker_do_info, help = 'Output info file containing max filenames and labels.')
parser.add_argument('--idx-begin', type = int, default = None, help = 'Start at this unit (default: all units).')
parser.add_argument('--idx-end', type = int, default = None, help = 'End at this unit (default: all units).')
parser.add_argument('--nmt_pkl', type = str, default = os.path.join(settings.caffevis_outputs_dir, 'find_max_acts_output.pickled'), help = 'Which pickled NetMaxTracker to load.')
parser.add_argument('--net_prototxt', type = str, default = settings.caffevis_deploy_prototxt, help = 'network prototxt to load')
parser.add_argument('--net_weights', type = str, default = settings.caffevis_network_weights, help = 'network weights to load')
parser.add_argument('--datadir', type = str, default = settings.static_files_dir, help = 'directory to look for files in')
parser.add_argument('--filelist', type = str, default = settings.static_files_input_file, help = 'List of image files to consider, one per line. Must be the same filelist used to produce the NetMaxTracker!')
parser.add_argument('--outdir', type = str, default = settings.caffevis_outputs_dir, help = 'Which output directory to use. Files are output into outdir/layer/unit_%%04d/{maxes,deconv,backprop}_%%03d.png')
parser.add_argument('--search-min', action='store_true', default=False, help='Should we also search for minimal activations?')
args = parser.parse_args()
settings.caffevis_deploy_prototxt = args.net_prototxt
settings.caffevis_network_weights = args.net_weights
net, data_mean = load_network(settings)
# validate batch size
if settings.is_siamese and settings._calculated_siamese_network_format == 'siamese_batch_pair':
# currently, no batch support for siamese_batch_pair networks
# it can be added by simply handle the batch indexes properly, but it should be thoroughly tested
assert (settings.max_tracker_batch_size == 1)
# set network batch size
current_input_shape = net.blobs[net.inputs[0]].shape
current_input_shape[0] = settings.max_tracker_batch_size
net.blobs[net.inputs[0]].reshape(*current_input_shape)
net.reshape()
assert args.do_maxes or args.do_deconv or args.do_deconv_norm or args.do_backprop or args.do_backprop_norm or args.do_info, 'Specify at least one do_* option to output.'
siamese_helper = SiameseHelper(settings.layers_list)
nmt = load_max_tracker_from_file(args.nmt_pkl)
for layer_name in settings.layers_to_output_in_offline_scripts:
print 'Started work on layer %s' % (layer_name)
normalized_layer_name = siamese_helper.normalize_layer_name_for_max_tracker(layer_name)
mt = nmt.max_trackers[normalized_layer_name]
if args.idx_begin is None:
idx_begin = 0
if args.idx_end is None:
idx_end = mt.max_vals.shape[0]
with WithTimer('Saved %d images per unit for %s units %d:%d.' % (args.N, normalized_layer_name, idx_begin, idx_end)):
output_max_patches(settings, mt, net, normalized_layer_name, idx_begin, idx_end,
args.N, args.datadir, args.filelist, args.outdir, False,
(args.do_maxes, args.do_deconv, args.do_deconv_norm, args.do_backprop, args.do_backprop_norm, args.do_info))
if args.search_min:
output_max_patches(settings, mt, net, normalized_layer_name, idx_begin, idx_end,
args.N, args.datadir, args.filelist, args.outdir, True,
(args.do_maxes, args.do_deconv, args.do_deconv_norm, args.do_backprop, args.do_backprop_norm, args.do_info))
def main():
parser = argparse.ArgumentParser(
description=
'Loads a pickled NetMaxTracker and outputs one or more of {the patches of the image, a deconv patch, a backprop patch} associated with the maxes.'
)
parser.add_argument('--N',
type=int,
default=9,
help='Note and save top N activations.')
parser.add_argument('--gpu', action='store_true', help='Use gpu.')
parser.add_argument('--do-maxes',
action='store_true',
help='Output max patches.')
parser.add_argument('--do-deconv',
action='store_true',
help='Output deconv patches.')
parser.add_argument('--do-deconv-norm',
action='store_true',
help='Output deconv-norm patches.')
parser.add_argument('--do-backprop',
action='store_true',
help='Output backprop patches.')
parser.add_argument('--do-backprop-norm',
action='store_true',
help='Output backprop-norm patches.')
parser.add_argument(
'--do-info',
action='store_true',
help='Output info file containing max filenames and labels.')
parser.add_argument('--idx-begin',
type=int,
default=None,
help='Start at this unit (default: all units).')
parser.add_argument('--idx-end',
type=int,
default=None,
help='End at this unit (default: all units).')
parser.add_argument('nmt_pkl',
type=str,
help='Which pickled NetMaxTracker to load.')
parser.add_argument('net_prototxt',
type=str,
help='Network prototxt to load')
parser.add_argument('net_weights',
type=str,
help='Network weights to load')
parser.add_argument('datadir',
type=str,
help='Directory to look for files in')
parser.add_argument(
'filelist',
type=str,
help=
'List of image files to consider, one per line. Must be the same filelist used to produce the NetMaxTracker!'
)
parser.add_argument(
'outdir',
type=str,
help=
r'Which output directory to use. Files are output into outdir/layer/unit_%%04d/{maxes,deconv,backprop}_%%03d.png'
)
parser.add_argument('layer', type=str, help='Which layer to output')
#parser.add_argument('--mean', type = str, default = '', help = 'data mean to load')
args = parser.parse_args()
if args.gpu:
caffe.set_mode_gpu()
else:
caffe.set_mode_cpu()
imagenet_mean = load_imagenet_mean()
net = caffe.Classifier(args.net_prototxt,
args.net_weights,
mean=imagenet_mean,
channel_swap=(2, 1, 0),
raw_scale=255,
image_dims=(256, 256))
assert args.do_maxes or args.do_deconv or args.do_deconv_norm or args.do_backprop or args.do_backprop_norm or args.do_info, 'Specify at least one do_* option to output.'
with open(args.nmt_pkl, 'rb') as ff:
nmt = pickle.load(ff)
mt = nmt.max_trackers[args.layer]
if args.idx_begin is None:
args.idx_begin = 0
if args.idx_end is None:
args.idx_end = mt.max_vals.shape[0]
with WithTimer('Saved %d images per unit for %s units %d:%d.' %
(args.N, args.layer, args.idx_begin, args.idx_end)):
output_max_patches(
mt, net, args.layer, args.idx_begin, args.idx_end, args.N,
args.datadir, args.filelist, args.outdir,
(args.do_maxes, args.do_deconv, args.do_deconv_norm,
args.do_backprop, args.do_backprop_norm, args.do_info))
def main():
parser = argparse.ArgumentParser(description='Loads a pickled NetMaxTracker and outputs one or more of {the patches of the image, a deconv patch, a backprop patch} associated with the maxes.')
parser.add_argument('--N', type=int, default=9, help='Note and save top N activations.')
parser.add_argument('--gpu-id', type=int, default=0, help='GPU id. Set -1 to use CPU')
parser.add_argument('--do-maxes', action = 'store_true', help = 'Output max patches.')
parser.add_argument('--do-deconv', action = 'store_true', help = 'Output deconv patches.')
parser.add_argument('--do-deconv-norm', action = 'store_true', help = 'Output deconv-norm patches.')
parser.add_argument('--do-backprop', action = 'store_true', help = 'Output backprop patches.')
parser.add_argument('--do-backprop-norm', action = 'store_true', help = 'Output backprop-norm patches.')
parser.add_argument('--do-info', action = 'store_true', help = 'Output info file containing max filenames and labels.')
parser.add_argument('--idx-begin', type = int, default = None, help = 'Start at this unit (default: all units).')
parser.add_argument('--idx-end', type = int, default = None, help = 'End at this unit (default: all units).')
parser.add_argument('nmt_pkl', type = str, help = 'Which pickled NetMaxTracker to load.')
parser.add_argument('net_prototxt', type = str, help = 'Network prototxt to load')
parser.add_argument('net_weights', type = str, help = 'Network weights to load')
parser.add_argument('--mean-path', type=str, default=None, help='path to mean file')
parser.add_argument('--images-dir', type=str, default=None,
help='directory to look for files in. Used only together with --filelist-path')
parser.add_argument('--output-dir', type = str, help = r'Which output directory to use. Files are output into outdir/layer/unit_%%04d/{maxes,deconv,backprop}_%%03d.png')
parser.add_argument('--layer', type = str, help = 'Which layer to output')
group = parser.add_mutually_exclusive_group()
group.add_argument('--images-csv-path', type=str, default=None,
help='path to csv file containing image pathes and labels')
group.add_argument('--filelist-path', type=str,
help='List of image files to consider, one per line. Must be the same filelist used to produce the NetMaxTracker!')
args = parser.parse_args()
print 'Caffe file path:', caffe.__file__
if args.gpu_id >= 0:
print 'Using GPU: {}'.format(args.gpu_id)
caffe.set_device(args.gpu_id)
caffe.set_mode_gpu()
else:
caffe.set_mode_cpu()
if args.mean_path is not None:
print 'Loading RGB HWC mean and convert it to BRG CWH from', args.mean_path
mean = load_rgb_hwc_mean_and_convert(args.mean_path)
else:
print 'Loading Imagenet mean'
mean = load_imagenet_mean()
net = caffe.Classifier(args.net_prototxt, args.net_weights,
mean=mean,
channel_swap=(2, 1, 0),
raw_scale=255,
image_dims=(256, 256))
assert args.do_maxes or args.do_deconv or args.do_deconv_norm or args.do_backprop or args.do_backprop_norm or args.do_info, 'Specify at least one do_* option to output.'
with open(args.nmt_pkl, 'rb') as ff:
nmt = pkl.load(ff)
mt = nmt.max_trackers[args.layer]
if args.idx_begin is None:
args.idx_begin = 0
if args.idx_end is None:
args.idx_end = mt.max_vals.shape[0]
images_df = None
if args.images_csv_path is not None:
images_df = pd.read_csv(args.images_csv_path, index_col='index')
with WithTimer('Saved %d images per unit for %s units %d:%d.' % (args.N, args.layer, args.idx_begin, args.idx_end)):
output_max_patches(mt, net, args.layer, args.idx_begin, args.idx_end,
args.N, args.output_dir,
(args.do_maxes, args.do_deconv, args.do_deconv_norm,
args.do_backprop, args.do_backprop_norm, args.do_info),
filelist_path=args.filelist_path,
images_dir=args.images_dir,
images_df=images_df)
def main():
parser = argparse.ArgumentParser(
description=
'Loads a pickled NetMaxTracker and outputs one or more of {the patches of the image, a deconv patch, a backprop patch} associated with the maxes.'
)
parser.add_argument('--N',
type=int,
default=9,
help='Note and save top N activations.')
parser.add_argument('--gpu-id',
type=int,
default=0,
help='GPU id. Set -1 to use CPU')
parser.add_argument('--do-maxes',
action='store_true',
help='Output max patches.')
parser.add_argument('--do-deconv',
action='store_true',
help='Output deconv patches.')
parser.add_argument('--do-deconv-norm',
action='store_true',
help='Output deconv-norm patches.')
parser.add_argument('--do-backprop',
action='store_true',
help='Output backprop patches.')
parser.add_argument('--do-backprop-norm',
action='store_true',
help='Output backprop-norm patches.')
parser.add_argument(
'--do-info',
action='store_true',
help='Output info file containing max filenames and labels.')
parser.add_argument('--idx-begin',
type=int,
default=None,
help='Start at this unit (default: all units).')
parser.add_argument('--idx-end',
type=int,
default=None,
help='End at this unit (default: all units).')
parser.add_argument('nmt_pkl',
type=str,
help='Which pickled NetMaxTracker to load.')
parser.add_argument('net_prototxt',
type=str,
help='Network prototxt to load')
parser.add_argument('net_weights',
type=str,
help='Network weights to load')
parser.add_argument('--mean-path',
type=str,
default=None,
help='path to mean file')
parser.add_argument(
'--images-dir',
type=str,
default=None,
help=
'directory to look for files in. Used only together with --filelist-path'
)
parser.add_argument(
'--output-dir',
type=str,
help=
r'Which output directory to use. Files are output into outdir/layer/unit_%%04d/{maxes,deconv,backprop}_%%03d.png'
)
parser.add_argument('--layer', type=str, help='Which layer to output')
group = parser.add_mutually_exclusive_group()
group.add_argument(
'--images-csv-path',
type=str,
default=None,
help='path to csv file containing image pathes and labels')
group.add_argument(
'--filelist-path',
type=str,
help=
'List of image files to consider, one per line. Must be the same filelist used to produce the NetMaxTracker!'
)
args = parser.parse_args()
print 'Caffe file path:', caffe.__file__
if args.gpu_id >= 0:
print 'Using GPU: {}'.format(args.gpu_id)
caffe.set_device(args.gpu_id)
caffe.set_mode_gpu()
else:
caffe.set_mode_cpu()
if args.mean_path is not None:
print 'Loading RGB HWC mean and convert it to BRG CWH from', args.mean_path
mean = load_rgb_hwc_mean_and_convert(args.mean_path)
else:
print 'Loading Imagenet mean'
mean = load_imagenet_mean()
net = caffe.Classifier(args.net_prototxt,
args.net_weights,
mean=mean,
channel_swap=(2, 1, 0),
raw_scale=255,
image_dims=(256, 256))
assert args.do_maxes or args.do_deconv or args.do_deconv_norm or args.do_backprop or args.do_backprop_norm or args.do_info, 'Specify at least one do_* option to output.'
with open(args.nmt_pkl, 'rb') as ff:
nmt = pkl.load(ff)
mt = nmt.max_trackers[args.layer]
if args.idx_begin is None:
args.idx_begin = 0
if args.idx_end is None:
args.idx_end = mt.max_vals.shape[0]
images_df = None
if args.images_csv_path is not None:
images_df = pd.read_csv(args.images_csv_path, index_col='index')
with WithTimer('Saved %d images per unit for %s units %d:%d.' %
(args.N, args.layer, args.idx_begin, args.idx_end)):
output_max_patches(
mt,
net,
args.layer,
args.idx_begin,
args.idx_end,
args.N,
args.output_dir,
(args.do_maxes, args.do_deconv, args.do_deconv_norm,
args.do_backprop, args.do_backprop_norm, args.do_info),
filelist_path=args.filelist_path,
images_dir=args.images_dir,
images_df=images_df)