def main():
parser = argparse.ArgumentParser(description='Finds images in a training set that cause max activation for a network; saves results in a pickled NetMaxTracker.')
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('net_prototxt', type = str, default = '', help = 'network prototxt to load')
parser.add_argument('net_weights', type = str, default = '', help = 'network weights to load')
parser.add_argument('datadir', type = str, default = '.', help = 'directory to look for files in')
parser.add_argument('filelist', type = str, help = 'list of image files to consider, one per line')
parser.add_argument('outfile', type = str, help = 'output filename for pkl')
#parser.add_argument('--mean', type = str, default = '', help = 'data mean to load')
args = parser.parse_args()
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))
if args.gpu:
caffe.set_mode_gpu()
else:
caffe.set_mode_cpu()
with WithTimer('Scanning images'):
max_tracker = scan_images_for_maxes(net, args.datadir, args.filelist, args.N)
with WithTimer('Saving maxes'):
with open(args.outfile, 'wb') as ff:
pickle.dump(max_tracker, ff, -1)
def main():
parser = argparse.ArgumentParser(description='Finds images in a training set that cause max activation for a network; saves results in a pickled NetMaxTracker.')
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('net_prototxt', type=str, default='', help='network prototxt to load')
parser.add_argument('net_weights', type=str, default='', help='network weights to load')
parser.add_argument('output_path', type=str, help='output filename for pkl')
parser.add_argument('--mean-path', type=str, default=None, help='path to mean file')
parser.add_argument('--images-dir', type=str, default='.', help='directory to look for files in. Used only together with --filelist-path')
group = parser.add_mutually_exclusive_group()
group.add_argument('--filelist-path', type=str, default=None,
help='list of image files to consider, one per line')
group.add_argument('--images-csv-path', type=str, default=None,
help='path to csv file containing image pathes and labels')
args = parser.parse_args()
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()
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()
net = caffe.Classifier(args.net_prototxt, args.net_weights,
mean=mean,
channel_swap=(2, 1, 0),
raw_scale=255,
image_dims=(256, 256))
layers = ['conv1', 'conv2', 'conv3', 'conv4', 'conv5', 'fc6', 'fc7', 'fc8_exh_all', 'prob']
is_conv_layer = [('conv' in ll) for ll in layers]
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('Scanning images'):
max_tracker = scan_images_for_maxes(net, args.N,
datadir=args.images_dir,
filelist_path=args.filelist_path,
images_df=images_df,
layers=layers, is_conv_layer=is_conv_layer)
with WithTimer('Saving maxes'):
with open(args.output_path, 'wb') as ff:
pickle.dump(max_tracker, ff, -1)
def hardcoded_get():
prototxt = '/home/jyosinsk/results/140311_234854_afadfd3_priv_netbase_upgraded/deploy_1.prototxt'
weights = '/home/jyosinsk/results/140311_234854_afadfd3_priv_netbase_upgraded/caffe_imagenet_train_iter_450000'
datadir = '/home/jyosinsk/imagenet2012/val'
filelist = 'mini_valid.txt'
imagenet_mean = load_imagenet_mean()
net = caffe.Classifier(prototxt, weights,
mean=imagenet_mean,
channel_swap=(2,1,0),
raw_scale=255,
image_dims=(256, 256))
net.set_phase_test()
net.set_mode_cpu()
labels = load_labels()
return net, imagenet_mean, labels, datadir, filelist
def main():
parser = argparse.ArgumentParser(
description='Finds images in a training set that cause max activation for a network; saves results in a pickled NetMaxTracker.')
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('net_prototxt', type=str, default='', help='network prototxt to load')
parser.add_argument('net_weights', type=str, default='', help='network weights to load')
parser.add_argument('datadir', type=str, default='.', help='directory to look for files in')
parser.add_argument('filelist', type=str, help='list of image files to consider, one per line')
parser.add_argument('outfile', type=str, help='output filename for pkl')
parser.add_argument('net_layers', type=str, help='list of layers to compute, separated by comma')
parser.add_argument('net_is_conv', type=str,
help='list of 1 (True) and 0 (False), separated by comma, same length as net_layers')
# parser.add_argument('--mean', type = str, default = '', help = 'data mean to load')
args = parser.parse_args()
# layers, is_conv
layers = [x.strip() for x in args.net_layers.split(',')]
is_conv = [x.strip() for x in args.net_is_conv.split(',')]
is_conv = [True if x == '1' else False for x in is_conv]
assert len(layers) == len(is_conv)
imagenet_mean = load_imagenet_mean()
# this should be rewritten in transformer + net, and some batch operation should be supported.
# whatever, just make it usable first.
# also, this restricts to images with labels, since `filelist` must accept something with label
# and restrict the dataset to those with size 256x256.
net = caffe.Classifier(args.net_prototxt, args.net_weights,
mean=imagenet_mean,
channel_swap=(2, 1, 0),
raw_scale=255,
image_dims=(256, 256))
if args.gpu:
caffe.set_mode_gpu()
else:
caffe.set_mode_cpu()
with WithTimer('Scanning images'):
max_tracker = scan_images_for_maxes(net, args.datadir, args.filelist, args.N,
layers=layers, is_conv=is_conv)
with WithTimer('Saving maxes'):
with open(args.outfile, 'wb') as ff:
pickle.dump(max_tracker, ff, -1)
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)