def create_instrumented_model(args, **kwargs):
'''
Creates an instrumented model out of a namespace of arguments that
correspond to ArgumentParser command-line args:
model: a string to evaluate as a constructor for the model.
pthfile: (optional) filename of .pth file for the model.
layers: a list of layers to instrument, defaulted if not provided.
edit: True to instrument the layers for editing.
gen: True for a generator model. One-pixel input assumed.
imgsize: For non-generator models, (y, x) dimensions for RGB input.
cuda: True to use CUDA.
The constructed model will be decorated with the following attributes:
input_shape: (usually 4d) tensor shape for single-image input.
output_shape: 4d tensor shape for output.
feature_shape: map of layer names to 4d tensor shape for featuremaps.
retained: map of layernames to tensors, filled after every evaluation.
ablation: if editing, map of layernames to [0..1] alpha values to fill.
replacement: if editing, map of layernames to values to fill.
When editing, the feature value x will be replaced by:
`x = (replacement * ablation) + (x * (1 - ablation))`
'''
args = EasyDict(vars(args), **kwargs)
# Construct the network
if args.model is None:
print_progress('No model specified')
return None
if isinstance(args.model, torch.nn.Module):
model = args.model
else:
model = autoimport_eval(args.model)
# Unwrap any DataParallel-wrapped model
if isinstance(model, torch.nn.DataParallel):
model = next(model.children())
# Load its state dict
meta = {}
if getattr(args, 'pthfile', None) is not None:
data = torch.load(args.pthfile)
if 'state_dict' in data:
meta = {}
for key in data:
if isinstance(data[key], numbers.Number):
meta[key] = data[key]
data = data['state_dict']
model.load_state_dict(data)
model.meta = meta
# Decide which layers to instrument.
if getattr(args, 'layer', None) is not None:
args.layers = [args.layer]
if getattr(args, 'layers', None) is None:
# Skip wrappers with only one named model
container = model
prefix = ''
while len(list(container.named_children())) == 1:
name, container = next(container.named_children())
prefix += name + '.'
# Default to all nontrivial top-level layers except last.
args.layers = [
prefix + name for name, module in container.named_children()
if type(module).__module__ not in [
# Skip ReLU and other activations.
'torch.nn.modules.activation',
# Skip pooling layers.
'torch.nn.modules.pooling'
]
][:-1]
print_progress('Defaulting to layers: %s' % ' '.join(args.layers))
# Instrument the layers.
retain_layers(model, args.layers)
if getattr(args, 'edit', False):
edit_layers(model, args.layers)
model.eval()
if args.cuda:
model.cuda()
# Annotate input, output, and feature shapes
annotate_model_shapes(model,
gen=getattr(args, 'gen', False),
imgsize=getattr(args, 'imgsize', None))
return model
def main():
# Training settings
def strpair(arg):
p = tuple(arg.split(':'))
if len(p) == 1:
p = p + p
return p
def intpair(arg):
p = arg.split(',')
if len(p) == 1:
p = p + p
return tuple(int(v) for v in p)
parser = argparse.ArgumentParser(
description='Net dissect utility',
prog='python -m netdissect',
epilog=textwrap.dedent(help_epilog),
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('--model',
type=str,
default=None,
help='constructor for the model to test')
parser.add_argument('--pthfile',
type=str,
default=None,
help='filename of .pth file for the model')
parser.add_argument('--outdir',
type=str,
default='dissect',
help='directory for dissection output')
parser.add_argument('--layers',
type=strpair,
nargs='+',
help='space-separated list of layer names to dissect' +
', in the form layername[:reportedname]')
parser.add_argument('--segments',
type=str,
default='dataset/broden',
help='directory containing segmentation dataset')
parser.add_argument('--download',
action='store_true',
default=False,
help='downloads Broden dataset if needed')
parser.add_argument('--imgsize',
type=intpair,
default=(227, 227),
help='input image size to use')
parser.add_argument('--netname',
type=str,
default=None,
help='name for network in generated reports')
parser.add_argument('--meta',
type=str,
nargs='+',
help='json files of metadata to add to report')
parser.add_argument('--examples',
type=int,
default=20,
help='number of image examples per unit')
parser.add_argument('--size',
type=int,
default=10000,
help='dataset subset size to use')
parser.add_argument('--broden_version',
type=int,
default=1,
help='broden version number')
parser.add_argument('--batch_size',
type=int,
default=100,
help='batch size for forward pass')
parser.add_argument('--num_workers',
type=int,
default=24,
help='number of DataLoader workers')
parser.add_argument('--quantile_threshold',
type=float,
default=None,
help='quantile to use for masks')
parser.add_argument('--no-labels',
action='store_true',
default=False,
help='disables labeling of units')
parser.add_argument('--ablation',
action='store_true',
default=False,
help='enables single unit ablation of units')
parser.add_argument('--iqr',
action='store_true',
default=False,
help='enables iqr calculation')
parser.add_argument('--maxiou',
action='store_true',
default=False,
help='enables maxiou calculation')
parser.add_argument('--covariance',
action='store_true',
default=False,
help='enables covariance calculation')
parser.add_argument('--no-images',
action='store_true',
default=False,
help='disables generation of unit images')
parser.add_argument('--single-images',
action='store_true',
default=False,
help='generates single images also')
parser.add_argument('--no-report',
action='store_true',
default=False,
help='disables generation report summary')
parser.add_argument('--no-cuda',
action='store_true',
default=False,
help='disables CUDA usage')
parser.add_argument('--gan',
type=str,
default=None,
help='netdissect.GanImageSegmenter() to probe a GAN')
parser.add_argument('--perturbation',
default=None,
help='filename of perturbation attack to apply')
parser.add_argument('--add_scale_offset',
action='store_true',
default=None,
help='offsets masks according to stride and padding')
parser.add_argument('--quiet',
action='store_true',
default=False,
help='silences console output')
if len(sys.argv) == 1:
parser.print_usage(sys.stderr)
sys.exit(1)
args = parser.parse_args()
args.images = not args.no_images
args.report = not args.no_report
args.labels = not args.no_labels
# Set up console output
verbose_progress(not args.quiet)
# Speed up pytorch
torch.backends.cudnn.benchmark = True
# Special case: download flag without model to test.
if args.model is None and args.download:
from netdissect.broden import ensure_broden_downloaded
for resolution in [224, 227, 384]:
ensure_broden_downloaded(args.segments, resolution,
args.broden_version)
sys.exit(0)
# Help if broden is not present
if not os.path.isdir(args.segments):
print_progress('Segmentation dataset not found at %s.' % args.segments)
print_progress('Specify dataset directory using --segments [DIR]')
print_progress('To download Broden, run: netdissect --download')
sys.exit(1)
# Default threshold
if args.quantile_threshold is None:
if args.gan:
args.quantile_threshold = 0.01
else:
args.quantile_threshold = 0.005
# Construct the network
if args.model is None:
print_progress('No model specified')
sys.exit(1)
# Set up CUDA
args.cuda = not args.no_cuda and torch.cuda.is_available()
if args.cuda:
torch.backends.cudnn.benchmark = True
model = autoimport_eval(args.model)
# Unwrap any DataParallel-wrapped model
if isinstance(model, torch.nn.DataParallel):
model = next(model.children())
# Default add_scale_offset only for AlexNet-looking models.
if args.add_scale_offset is None and not args.gan:
args.add_scale_offset = ('Alex' in model.__class__.__name__)
# Load its state dict
meta = {}
if args.pthfile is None:
print_progress('Dissecting model without pth file.')
else:
data = torch.load(args.pthfile)
if 'state_dict' in data:
meta = {}
for key in data:
if isinstance(data[key], numbers.Number):
meta[key] = data[key]
data = data['state_dict']
model.load_state_dict(data)
# Update any metadata from files, if any
if args.meta:
for mfilename in args.meta:
with open(mfilename) as f:
meta.update(json.load(f))
# Instrument it and prepare it for eval
if not args.layers:
# Skip wrappers with only one named modele
container = model
prefix = ''
while len(list(container.named_children())) == 1:
name, container = next(container.named_children())
prefix += name + '.'
# Default to all nontrivial top-level layers except last.
args.layers = [
prefix + name for name, module in container.named_children()
if type(module).__module__ not in [
# Skip ReLU and other activations.
'torch.nn.modules.activation',
# Skip pooling layers.
'torch.nn.modules.pooling'
]
][:-1]
print_progress('Defaulting to layers: %s' % ' '.join(args.layers))
retain_layers(model, args.layers, args.add_scale_offset)
if args.gan:
ablate_layers(model, args.layers)
model.eval()
if args.cuda:
model.cuda()
# Set up the output directory, verify write access
if args.outdir is None:
args.outdir = os.path.join('dissect', type(model).__name__)
print_progress('Writing output into %s.' % args.outdir)
os.makedirs(args.outdir, exist_ok=True)
train_dataset = None
if not args.gan:
# Load dataset for ordinary case.
# Load perturbation
perturbation = numpy.load(
args.perturbation) if args.perturbation else None
# Load broden dataset
dataset = try_to_load_broden(args.segments, args.imgsize,
args.broden_version, perturbation,
args.download, args.size)
if dataset is None:
ds = try_to_load_multiseg(args.segments, args.imgsize,
perturbation, args.size)
if dataset is None:
print_progress('No segmentation dataset found in %s' %
args.segements)
print_progress('use --download to download Broden.')
sys.exit(1)
recovery = ReverseNormalize(IMAGE_MEAN, IMAGE_STDEV)
else:
# Examine first conv in model to determine input feature size.
first_layer = [
c for c in model.modules()
if isinstance(c, (torch.nn.Conv2d, torch.nn.ConvTranspose2d,
torch.nn.Linear))
][0]
# 4d input if convolutional, 2d input if first layer is linear.
if isinstance(first_layer,
(torch.nn.Conv2d, torch.nn.ConvTranspose2d)):
sample = standard_z_sample(args.size,
first_layer.in_channels)[:, :, None,
None]
train_sample = standard_z_sample(args.size,
first_layer.in_channels,
seed=2)[:, :, None, None]
else:
sample = standard_z_sample(args.size, first_layer.in_features)
train_sample = standard_z_sample(args.size,
first_layer.in_features,
seed=2)
dataset = TensorDataset(sample)
train_dataset = TensorDataset(train_sample)
recovery = autoimport_eval(args.gan)
# Run dissect
dissect(args.outdir,
model,
dataset,
train_dataset=train_dataset,
recover_image=recovery,
examples_per_unit=args.examples,
netname=args.netname,
quantile_threshold=args.quantile_threshold,
meta=meta,
make_images=args.images or args.single_images,
make_labels=args.labels,
make_ablation=args.ablation,
make_iqr=args.iqr,
make_maxiou=args.maxiou,
make_covariance=args.covariance,
make_report=args.report,
make_row_images=args.images,
make_single_images=args.single_images,
batch_size=args.batch_size,
num_workers=args.num_workers,
settings=vars(args))
def eval_loss_and_reg():
discrete_experiments = dict(
# dpixel=dict(discrete_pixels=True),
# dunits20=dict(discrete_units=20),
# dumix20=dict(discrete_units=20, mixed_units=True),
# dunits10=dict(discrete_units=10),
# abonly=dict(ablation_only=True),
# fimabl=dict(ablation_only=True,
# fullimage_ablation=True,
# fullimage_measurement=True),
dboth20=dict(discrete_units=20, discrete_pixels=True),
# dbothm20=dict(discrete_units=20, mixed_units=True,
# discrete_pixels=True),
# abdisc20=dict(discrete_units=20, discrete_pixels=True,
# ablation_only=True),
# abdiscm20=dict(discrete_units=20, mixed_units=True,
# discrete_pixels=True,
# ablation_only=True),
# fimadp=dict(discrete_pixels=True,
# ablation_only=True,
# fullimage_ablation=True,
# fullimage_measurement=True),
# fimadu10=dict(discrete_units=10,
# ablation_only=True,
# fullimage_ablation=True,
# fullimage_measurement=True),
# fimadb10=dict(discrete_units=10, discrete_pixels=True,
# ablation_only=True,
# fullimage_ablation=True,
# fullimage_measurement=True),
fimadbm10=dict(discrete_units=10, mixed_units=True,
discrete_pixels=True,
ablation_only=True,
fullimage_ablation=True,
fullimage_measurement=True),
# fimadu20=dict(discrete_units=20,
# ablation_only=True,
# fullimage_ablation=True,
# fullimage_measurement=True),
# fimadb20=dict(discrete_units=20, discrete_pixels=True,
# ablation_only=True,
# fullimage_ablation=True,
# fullimage_measurement=True),
fimadbm20=dict(discrete_units=20, mixed_units=True,
discrete_pixels=True,
ablation_only=True,
fullimage_ablation=True,
fullimage_measurement=True)
)
with torch.no_grad():
total_loss = 0
discrete_losses = {k: 0 for k in discrete_experiments}
for [pbatch, ploc, cbatch, cloc] in progress(
torch.utils.data.DataLoader(TensorDataset(
corpus.eval_present_sample,
corpus.eval_present_location,
corpus.eval_candidate_sample,
corpus.eval_candidate_location),
batch_size=args.inference_batch_size, num_workers=10,
shuffle=False, pin_memory=True),
desc="Eval"):
# First, put in zeros for the selected units.
# Loss is amount of remaining object.
total_loss = total_loss + ace_loss(segmenter, classnum,
model, args.layer, high_replacement, ablation,
pbatch, ploc, cbatch, cloc, run_backward=False,
ablation_only=ablation_only,
fullimage_measurement=fullimage_measurement)
for k, config in discrete_experiments.items():
discrete_losses[k] = discrete_losses[k] + ace_loss(
segmenter, classnum,
model, args.layer, high_replacement, ablation,
pbatch, ploc, cbatch, cloc, run_backward=False,
**config)
avg_loss = (total_loss / args.eval_size).item()
avg_d_losses = {k: (d / args.eval_size).item()
for k, d in discrete_losses.items()}
regularizer = (args.l2_lambda * ablation.pow(2).sum())
print_progress('Epoch %d Loss %g Regularizer %g' %
(epoch, avg_loss, regularizer))
print_progress(' '.join('%s: %g' % (k, d)
for k, d in avg_d_losses.items()))
print_progress(scale_summary(ablation.view(-1), 10, 3))
return avg_loss, regularizer, avg_d_losses
def main():
# Training settings
def strpair(arg):
p = tuple(arg.split(':'))
if len(p) == 1:
p = p + p
return p
def intpair(arg):
p = arg.split(',')
if len(p) == 1:
p = p + p
return tuple(int(v) for v in p)
parser = argparse.ArgumentParser(
description='Net dissect utility',
prog='python -m netdissect',
epilog=textwrap.dedent(help_epilog),
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('--model',
type=str,
default=None,
help='constructor for the model to test')
parser.add_argument('--pthfile',
type=str,
default=None,
help='filename of .pth file for the model')
parser.add_argument('--unstrict',
action='store_true',
default=False,
help='ignore unexpected pth parameters')
parser.add_argument('--submodule',
type=str,
default=None,
help='submodule to load from pthfile')
parser.add_argument('--outdir',
type=str,
default='dissect',
help='directory for dissection output')
parser.add_argument('--layers',
type=strpair,
nargs='+',
help='space-separated list of layer names to dissect' +
', in the form layername[:reportedname]')
parser.add_argument('--segments',
type=str,
default='dataset/broden',
help='directory containing segmentation dataset')
parser.add_argument('--segmenter',
type=str,
default=None,
help='constructor for asegmenter class')
parser.add_argument('--download',
action='store_true',
default=False,
help='downloads Broden dataset if needed')
parser.add_argument('--imagedir',
type=str,
default=None,
help='directory containing image-only dataset')
parser.add_argument('--imgsize',
type=intpair,
default=(227, 227),
help='input image size to use')
parser.add_argument('--netname',
type=str,
default=None,
help='name for network in generated reports')
parser.add_argument('--meta',
type=str,
nargs='+',
help='json files of metadata to add to report')
parser.add_argument('--merge',
type=str,
help='json file of unit data to merge in report')
parser.add_argument('--examples',
type=int,
default=20,
help='number of image examples per unit')
parser.add_argument('--size',
type=int,
default=10000,
help='dataset subset size to use')
parser.add_argument('--batch_size',
type=int,
default=100,
help='batch size for forward pass')
parser.add_argument('--num_workers',
type=int,
default=24,
help='number of DataLoader workers')
parser.add_argument('--quantile_threshold',
type=strfloat,
default=None,
choices=[FloatRange(0.0, 1.0), 'iqr'],
help='quantile to use for masks')
parser.add_argument('--no-labels',
action='store_true',
default=False,
help='disables labeling of units')
parser.add_argument('--maxiou',
action='store_true',
default=False,
help='enables maxiou calculation')
parser.add_argument('--covariance',
action='store_true',
default=False,
help='enables covariance calculation')
parser.add_argument('--rank_all_labels',
action='store_true',
default=False,
help='include low-information labels in rankings')
parser.add_argument('--no-images',
action='store_true',
default=False,
help='disables generation of unit images')
parser.add_argument('--no-report',
action='store_true',
default=False,
help='disables generation report summary')
parser.add_argument('--no-cuda',
action='store_true',
default=False,
help='disables CUDA usage')
parser.add_argument('--gen',
action='store_true',
default=False,
help='test a generator model (e.g., a GAN)')
parser.add_argument('--gan',
action='store_true',
default=False,
help='synonym for --gen')
parser.add_argument('--perturbation',
default=None,
help='filename of perturbation attack to apply')
parser.add_argument('--add_scale_offset',
action='store_true',
default=None,
help='offsets masks according to stride and padding')
parser.add_argument('--quiet',
action='store_true',
default=False,
help='silences console output')
if len(sys.argv) == 1:
parser.print_usage(sys.stderr)
sys.exit(1)
args = parser.parse_args()
args.images = not args.no_images
args.report = not args.no_report
args.labels = not args.no_labels
if args.gan:
args.gen = args.gan
# Set up console output
verbose_progress(not args.quiet)
# Exit right away if job is already done or being done.
if args.outdir is not None:
exit_if_job_done(args.outdir)
# Speed up pytorch
torch.backends.cudnn.benchmark = True
# Special case: download flag without model to test.
if args.model is None and args.download:
from netdissect.broden import ensure_broden_downloaded
for resolution in [224, 227, 384]:
ensure_broden_downloaded(args.segments, resolution, 1)
from netdissect.segmenter import ensure_upp_segmenter_downloaded
ensure_upp_segmenter_downloaded('dataset/segmodel')
sys.exit(0)
# Help if broden is not present
if not args.gen and not args.imagedir and not os.path.isdir(args.segments):
print_progress('Segmentation dataset not found at %s.' % args.segments)
print_progress('Specify dataset directory using --segments [DIR]')
print_progress('To download Broden, run: netdissect --download')
sys.exit(1)
# Default segmenter class
if args.gen and args.segmenter is None:
args.segmenter = ("netdissect.segmenter.UnifiedParsingSegmenter(" +
"segsizes=[256], segdiv=None)")
# Default threshold
if args.quantile_threshold is None:
if args.gen:
args.quantile_threshold = 'iqr'
else:
args.quantile_threshold = 0.005
# Set up CUDA
args.cuda = not args.no_cuda and torch.cuda.is_available()
if args.cuda:
torch.backends.cudnn.benchmark = True
# Construct the network with specified layers instrumented
if args.model is None:
print_progress('No model specified')
sys.exit(1)
model = create_instrumented_model(args)
# Update any metadata from files, if any
meta = getattr(model, 'meta', {})
if args.meta:
for mfilename in args.meta:
with open(mfilename) as f:
meta.update(json.load(f))
# Load any merge data from files
mergedata = None
if args.merge:
with open(args.merge) as f:
mergedata = json.load(f)
# Set up the output directory, verify write access
if args.outdir is None:
args.outdir = os.path.join('dissect', type(model).__name__)
exit_if_job_done(args.outdir)
print_progress('Writing output into %s.' % args.outdir)
os.makedirs(args.outdir, exist_ok=True)
train_dataset = None
if not args.gen:
# Load dataset for classifier case.
# Load perturbation
perturbation = numpy.load(
args.perturbation) if args.perturbation else None
segrunner = None
# Load broden dataset
if args.imagedir is not None:
dataset = try_to_load_images(args.imagedir, args.imgsize,
perturbation, args.size)
segrunner = ImageOnlySegRunner(dataset)
else:
dataset = try_to_load_broden(args.segments, args.imgsize, 1,
perturbation, args.download,
args.size)
if dataset is None:
dataset = try_to_load_multiseg(args.segments, args.imgsize,
perturbation, args.size)
if dataset is None:
print_progress('No segmentation dataset found in %s',
args.segments)
print_progress('use --download to download Broden.')
sys.exit(1)
else:
# For segmenter case the dataset is just a random z
#dataset = z_dataset_for_model(model, args.size)
#train_dataset = z_dataset_for_model(model, args.size, seed=2)
dataset = datasets.ImageFolder('dataset/Adissect',
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(
(0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))
]))
train_dataset = dataset
segrunner = GeneratorSegRunner(autoimport_eval(args.segmenter))
torch.no_grad()
# Run dissect
dissect(args.outdir,
model,
dataset,
train_dataset=train_dataset,
segrunner=segrunner,
examples_per_unit=args.examples,
netname=args.netname,
quantile_threshold=args.quantile_threshold,
meta=meta,
merge=mergedata,
make_images=args.images,
make_labels=args.labels,
make_maxiou=args.maxiou,
make_covariance=args.covariance,
make_report=args.report,
make_row_images=args.images,
make_single_images=True,
rank_all_labels=args.rank_all_labels,
batch_size=args.batch_size,
num_workers=args.num_workers,
settings=vars(args))
# Mark the directory so that it's not done again.
mark_job_done(args.outdir)
def main():
# Training settings
def strpair(arg):
p = tuple(arg.split(':'))
if len(p) == 1:
p = p + p
return p
def intpair(arg):
p = arg.split(',')
if len(p) == 1:
p = p + p
return tuple(int(v) for v in p)
parser = argparse.ArgumentParser(
description='Net dissect utility',
prog='python -m netdissect',
epilog=textwrap.dedent(help_epilog),
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('--model',
type=str,
default=None,
help='constructor for the model to test')
parser.add_argument('--pthfile',
type=str,
default=None,
help='filename of .pth file for the model')
parser.add_argument('--outdir',
type=str,
default='dissect',
help='directory for dissection output')
parser.add_argument('--layers',
type=strpair,
nargs='+',
help='space-separated list of layer names to dissect' +
', in the form layername[:reportedname]')
parser.add_argument('--segments',
type=str,
default='dataset/broden',
help='directory containing segmentation dataset')
parser.add_argument('--download',
action='store_true',
default=False,
help='downloads Broden dataset if needed')
parser.add_argument('--imgsize',
type=intpair,
default=(227, 227),
help='input image size to use')
parser.add_argument('--netname',
type=str,
default=None,
help='name for network in generated reports')
parser.add_argument('--meta',
type=str,
nargs='+',
help='json files of metadata to add to report')
parser.add_argument('--examples',
type=int,
default=20,
help='number of image examples per unit')
parser.add_argument('--size',
type=int,
default=10000,
help='dataset subset size to use')
parser.add_argument('--broden_version',
type=int,
default=1,
help='broden version number')
parser.add_argument('--batch_size',
type=int,
default=100,
help='batch size for forward pass')
parser.add_argument('--num_workers',
type=int,
default=24,
help='number of DataLoader workers')
parser.add_argument('--no-labels',
action='store_true',
default=False,
help='disables labeling of units')
parser.add_argument('--no-images',
action='store_true',
default=False,
help='disables generation of unit images')
parser.add_argument('--single-images',
action='store_true',
default=False,
help='generates single images also')
parser.add_argument('--no-report',
action='store_true',
default=False,
help='disables generation report summary')
parser.add_argument('--no-cuda',
action='store_true',
default=False,
help='disables CUDA usage')
parser.add_argument('--perturbation',
default=None,
help='filename of perturbation attack to apply')
parser.add_argument('--add_scale_offset',
action='store_true',
default=None,
help='offsets masks according to stride and padding')
parser.add_argument('--quiet',
action='store_true',
default=False,
help='silences console output')
if len(sys.argv) == 1:
parser.print_usage(sys.stderr)
sys.exit(1)
args = parser.parse_args()
args.images = not args.no_images
args.report = not args.no_report
args.labels = not args.no_labels
# Set up console output
verbose_progress(not args.quiet)
# Speed up pytorch
torch.backends.cudnn.benchmark = True
# Special case: download flag without model to test.
if args.model is None and args.download:
from netdissect.broden import ensure_broden_downloaded
for resolution in [224, 227, 384]:
ensure_broden_downloaded(args.segments, resolution,
args.broden_version)
sys.exit(0)
# Help if broden is not present
if not os.path.isdir(args.segments):
print_progress('Segmentation dataset not found at %s.' % args.segments)
print_progress('Specify dataset directory using --segments [DIR]')
print_progress('To download Broden, run: netdissect --download')
sys.exit(1)
# Construct the network
if args.model is None:
print_progress('No model specified')
sys.exit(1)
# Set up CUDA
args.cuda = not args.no_cuda and torch.cuda.is_available()
if args.cuda:
torch.backends.cudnn.benchmark = True
model = eval_constructor(args.model)
# Default add_scale_offset only for AlexNet-looking models.
if args.add_scale_offset is None:
args.add_scale_offset = ('Alex' in model.__class__.__name__)
# Load its state dict
meta = {}
if args.pthfile is None:
print_progress('Dissecting model without pth file.')
else:
data = torch.load(args.pthfile)
if 'state_dict' in data:
meta = {}
for key in data:
if isinstance(data[key], numbers.Number):
meta[key] = data[key]
data = data['state_dict']
model.load_state_dict(data)
# Update any metadata from files, if any
if args.meta:
for mfilename in args.meta:
with open(mfilename) as f:
meta.update(json.load(f))
# Instrument it and prepare it for eval
if not args.layers:
print_progress('No layers specified')
sys.exit(1)
retain_layers(model, args.layers, args.add_scale_offset)
model.eval()
if args.cuda:
model.cuda()
# Set up the output directory, verify write access
if args.outdir is None:
args.outdir = os.path.join('dissect', type(model).__name__)
print_progress('Writing output into %s.' % args.outdir)
os.makedirs(args.outdir, exist_ok=True)
# Load perturbation
perturbation = numpy.load(args.perturbation) if args.perturbation else None
# Load broden dataset
ds = try_to_load_broden(args.segments, args.imgsize, args.broden_version,
perturbation, args.download, args.size)
if ds is None:
ds = try_to_load_multiseg(args.segments, args.imgsize, perturbation,
args.size)
if ds is None:
print_progress('No segmentation dataset found in %s' % args.segements)
print_progress('use --download to download Broden.')
sys.exit(1)
# Run dissect
dissect(args.outdir,
model,
ds,
recover_image=ReverseNormalize(IMAGE_MEAN, IMAGE_STDEV),
examples_per_unit=args.examples,
netname=args.netname,
meta=meta,
make_images=args.images,
make_labels=args.labels,
make_report=args.report,
make_single_images=args.single_images,
batch_size=args.batch_size,
num_workers=args.num_workers,
settings=vars(args))
def create_instrumented_model(args, **kwargs):
'''
Creates an instrumented model out of a namespace of arguments that
correspond to ArgumentParser command-line args:
model: a string to evaluate as a constructor for the model.
pthfile: (optional) filename of .pth file for the model.
layers: a list of layers to instrument, defaulted if not provided.
edit: True to instrument the layers for editing.
gen: True for a generator model. One-pixel input assumed.
imgsize: For non-generator models, (y, x) dimensions for RGB input.
cuda: True to use CUDA.
The constructed model will be decorated with the following attributes:
input_shape: (usually 4d) tensor shape for single-image input.
output_shape: 4d tensor shape for output.
feature_shape: map of layer names to 4d tensor shape for featuremaps.
retained: map of layernames to tensors, filled after every evaluation.
ablation: if editing, map of layernames to [0..1] alpha values to fill.
replacement: if editing, map of layernames to values to fill.
When editing, the feature value x will be replaced by:
`x = (replacement * ablation) + (x * (1 - ablation))`
'''
args = EasyDict(vars(args), **kwargs)
# Construct the network
if args.model is None:
print_progress('No model specified')
return None
#if isinstance(args.model, torch.nn.Module):
# model = args.model
#else:
# model = autoimport_eval(args.model)
model = UnetNormalized()
checkpoint = torch.load('p2p_churches.pth')
model.load_state_dict(checkpoint)
model.cuda()
#model.eval()
torch.no_grad()
# Unwrap any DataParallel-wrapped model
if isinstance(model, torch.nn.DataParallel):
model = next(model.children())
# Load its state dict
#meta = {}
#if getattr(args, 'pthfile', None) is not None:
# data = torch.load(args.pthfile)
# if 'state_dict' in data:
# meta = {}
# for key in data:
# if isinstance(data[key], numbers.Number):
# meta[key] = data[key]
# data = data['state_dict']
# submodule = getattr(args, 'submodule', None)
# if submodule is not None and len(submodule):
# remove_prefix = submodule + '.'
# data = { k[len(remove_prefix):]: v for k, v in data.items()
# if k.startswith(remove_prefix)}
# if not len(data):
# print_progress('No submodule %s found in %s' %
# (submodule, args.pthfile))
# return None
# model.load_state_dict(data, strict=not getattr(args, 'unstrict', False))
# Decide which layers to instrument.
#if getattr(args, 'layer', None) is not None:
# args.layers = [args.layer]
args.layers = [layer5, layer9, layer12]
#if getattr(args, 'layers', None) is None:
# # Skip wrappers with only one named model
# container = model
# prefix = ''
# while len(list(container.named_children())) == 1:
# name, container = next(container.named_children())
# prefix += name + '.'
# # Default to all nontrivial top-level layers except last.
# args.layers = [prefix + name
# for name, module in container.named_children()
# if type(module).__module__ not in [
# # Skip ReLU and other activations.
# 'torch.nn.modules.activation',
# # Skip pooling layers.
# 'torch.nn.modules.pooling']
# ][:-1]
# print_progress('Defaulting to layers: %s' % ' '.join(args.layers))
# Now wrap the model for instrumentation.
model = InstrumentedModel(model)
# model.meta = meta
# Instrument the layers.
model.retain_layers(args.layers)
#model.eval()
#if args.cuda:
# model.cuda()
# Annotate input, output, and feature shapes
annotate_model_shapes(model,
gen=getattr(args, 'gen', False),
imgsize=getattr(args, 'imgsize', None))
return model
def train_ablation(args,
corpus,
cachefile,
model,
segmenter,
classnum,
initial_ablation=None):
progress = default_progress()
cachedir = os.path.dirname(cachefile)
snapdir = os.path.join(cachedir, 'snapshots')
os.makedirs(snapdir, exist_ok=True)
# high_replacement = corpus.feature_99[None,:,None,None].cuda()
high_replacement = (corpus.weighted_mean_present_feature[None, :, None,
None].cuda())
high_replacement.requires_grad = False
for p in model.parameters():
p.requires_grad = False
ablation = torch.zeros(high_replacement.shape).cuda()
if initial_ablation is not None:
ablation.view(-1)[...] = initial_ablation
ablation.requires_grad = True
optimizer = torch.optim.Adam([ablation], lr=0.01)
start_epoch = 0
if not args.no_cache:
for start_epoch in reversed(range(args.train_epochs)):
snapfile = os.path.join(snapdir, 'epoch-%d.pth' % start_epoch)
if os.path.exists(snapfile):
data = torch.load(snapfile)
with torch.no_grad():
ablation[...] = data['ablation'].to(ablation.device)
optimizer.load_state_dict(data['optimizer'])
start_epoch += 1
break
update_size = args.train_update_freq * args.train_batch_size
for epoch in range(start_epoch, args.train_epochs):
candidate_shuffle = torch.randperm(len(corpus.candidate_sample))
train_loss = 0
for batch_num, [pbatch, ploc, cbatch, cloc] in enumerate(
progress(torch.utils.data.DataLoader(
TensorDataset(
corpus.object_present_sample,
corpus.object_present_location,
corpus.candidate_sample[candidate_shuffle],
corpus.candidate_location[candidate_shuffle]),
batch_size=args.train_batch_size,
num_workers=10,
shuffle=True,
pin_memory=True),
desc="ACE opt epoch %d" % epoch)):
if batch_num % args.train_update_freq == 0:
optimizer.zero_grad()
# First, put in zeros for the selected units. Loss is amount
# of remaining object.
loss = ace_loss(segmenter,
classnum,
model,
args.layer,
high_replacement,
ablation,
pbatch,
ploc,
cbatch,
cloc,
run_backward=True)
with torch.no_grad():
train_loss = train_loss + loss
if (batch_num + 1) % args.train_update_freq == 0:
# Third, add some L2 loss to encourage sparsity.
regularizer = (args.l2_lambda * update_size *
ablation.pow(2).sum())
regularizer.backward()
optimizer.step()
with torch.no_grad():
ablation.clamp_(0, 1)
post_progress(l=(train_loss / update_size).item(),
r=(regularizer / update_size).item())
train_loss = 0
with torch.no_grad():
total_loss = 0
for [pbatch, ploc, cbatch,
cloc] in progress(torch.utils.data.DataLoader(
TensorDataset(corpus.eval_present_sample,
corpus.eval_present_location,
corpus.eval_candidate_sample,
corpus.eval_candidate_location),
batch_size=args.inference_batch_size,
num_workers=10,
shuffle=False,
pin_memory=True),
desc="Eval"):
# First, put in zeros for the selected units. Loss is amount
# of remaining object.
total_loss = total_loss + ace_loss(segmenter,
classnum,
model,
args.layer,
high_replacement,
ablation,
pbatch,
ploc,
cbatch,
cloc,
run_backward=False)
avg_loss = (total_loss / args.eval_size).item()
regularizer = (args.l2_lambda * ablation.pow(2).sum())
print_progress('Epoch %d Loss %g, Regularizer %g' %
(epoch, avg_loss, regularizer))
print_progress(scale_summary(ablation.view(-1), 10, 3))
torch.save(
dict(ablation=ablation,
optimizer=optimizer.state_dict(),
avg_loss=avg_loss),
os.path.join(snapdir, 'epoch-%d.pth' % epoch))
numpy.save(os.path.join(snapdir, 'epoch-%d.npy' % epoch),
ablation.detach().cpu().numpy())
# The output of this phase is this set of scores.
return ablation.view(-1).detach().cpu().numpy()
def main():
# Training settings
def strpair(arg):
p = tuple(arg.split(':'))
if len(p) == 1:
p = p + p
return p
parser = argparse.ArgumentParser(
description='Net dissect utility',
epilog=textwrap.dedent(help_epilog),
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('--model',
type=str,
default=None,
help='constructor for the model to test')
parser.add_argument('--pthfile',
type=str,
default=None,
help='filename of .pth file for the model')
parser.add_argument('--outdir',
type=str,
default='dissect',
help='directory for dissection output')
parser.add_argument('--layers',
type=strpair,
nargs='+',
help='space-separated list of layer names to edit' +
', in the form layername[:reportedname]')
parser.add_argument('--classes',
type=str,
nargs='+',
help='space-separated list of class names to ablate')
parser.add_argument('--metric',
type=str,
default='iou',
help='ordering metric for selecting units')
parser.add_argument('--startcount',
type=int,
default=1,
help='number of units to ablate')
parser.add_argument('--unitcount',
type=int,
default=30,
help='number of units to ablate')
parser.add_argument('--segmenter',
type=str,
default='dataset/broden',
help='directory containing segmentation dataset')
parser.add_argument('--netname',
type=str,
default=None,
help='name for network in generated reports')
parser.add_argument('--batch_size',
type=int,
default=5,
help='batch size for forward pass')
parser.add_argument('--size',
type=int,
default=1000,
help='number of images to test')
parser.add_argument('--no-cuda',
action='store_true',
default=False,
help='disables CUDA usage')
parser.add_argument('--quiet',
action='store_true',
default=False,
help='silences console output')
if len(sys.argv) == 1:
parser.print_usage(sys.stderr)
sys.exit(1)
args = parser.parse_args()
# Set up console output
verbose_progress(not args.quiet)
# Speed up pytorch
torch.backends.cudnn.benchmark = True
# Construct the network
if args.model is None:
print_progress('No model specified')
sys.exit(1)
# Set up CUDA
args.cuda = not args.no_cuda and torch.cuda.is_available()
if args.cuda:
torch.backends.cudnn.benchmark = True
model = autoimport_eval(args.model)
# Unwrap any DataParallel-wrapped model
if isinstance(model, torch.nn.DataParallel):
model = next(model.children())
# Load its state dict
meta = {}
if args.pthfile is None:
print_progress('Dissecting model without pth file.')
else:
data = torch.load(args.pthfile)
if 'state_dict' in data:
meta = {}
for key in data:
if isinstance(data[key], numbers.Number):
meta[key] = data[key]
data = data['state_dict']
model.load_state_dict(data)
# Instrument it and prepare it for eval
if not args.layers:
# Skip wrappers with only one named modele
container = model
prefix = ''
while len(list(container.named_children())) == 1:
name, container = next(container.named_children())
prefix += name + '.'
# Default to all nontrivial top-level layers except last.
args.layers = [
prefix + name for name, module in container.named_children()
if type(module).__module__ not in [
# Skip ReLU and other activations.
'torch.nn.modules.activation',
# Skip pooling layers.
'torch.nn.modules.pooling'
]
][:-1]
print_progress('Defaulting to layers: %s' % ' '.join(args.layers))
edit_layers(model, args.layers)
model.eval()
if args.cuda:
model.cuda()
# Set up the output directory, verify write access
if args.outdir is None:
args.outdir = os.path.join('dissect', type(model).__name__)
print_progress('Writing output into %s.' % args.outdir)
os.makedirs(args.outdir, exist_ok=True)
train_dataset = None
# Examine first conv in model to determine input feature size.
first_layer = [
c for c in model.modules()
if isinstance(c, (torch.nn.Conv2d, torch.nn.ConvTranspose2d,
torch.nn.Linear))
][0]
# 4d input if convolutional, 2d input if first layer is linear.
if isinstance(first_layer, (torch.nn.Conv2d, torch.nn.ConvTranspose2d)):
sample = standard_z_sample(args.size,
first_layer.in_channels)[:, :, None, None]
train_sample = standard_z_sample(args.size,
first_layer.in_channels,
seed=2)[:, :, None, None]
else:
sample = standard_z_sample(args.size, first_layer.in_features)
train_sample = standard_z_sample(args.size,
first_layer.in_features,
seed=2)
dataset = TensorDataset(sample)
train_dataset = TensorDataset(train_sample)
recovery = autoimport_eval(args.segmenter)
# Now do the actual work.
device = next(model.parameters()).device
labelnames, catnames = (recovery.get_label_and_category_names(dataset))
label_category = [catnames.index(c) for l, c in labelnames]
labelnum_from_name = {n[0]: i for i, n in enumerate(labelnames)}
segloader = torch.utils.data.DataLoader(dataset,
batch_size=args.batch_size,
num_workers=10,
pin_memory=(device.type == 'cuda'))
with open(os.path.join(args.outdir, 'dissect.json'), 'r') as f:
dissect = EasyDict(json.load(f))
# Index the dissection layers by layer name.
dissect_layer = {lrec.layer: lrec for lrec in dissect.layers}
# First, collect a baseline
for l in model.ablation:
model.ablation[l] = None
baseline = count_segments(recovery, segloader, model)
# For each sort-order, do an ablation
progress = default_progress()
for classname in progress(args.classes):
post_progress(c=classname)
for layername in progress(model.ablation):
post_progress(l=layername)
rankname = '%s-%s' % (classname, args.metric)
measurements = {}
classnum = labelnum_from_name[classname]
try:
ranking = next(r for r in dissect_layer[layername].rankings
if r.name == rankname)
except:
print('%s not found' % rankname)
sys.exit(1)
ordering = numpy.argsort(ranking.score)
# Check if already done
ablationdir = os.path.join(args.outdir, layername, 'ablation')
if os.path.isfile(os.path.join(ablationdir, '%s.json' % rankname)):
with open(os.path.join(ablationdir,
'%s.json' % rankname)) as f:
data = EasyDict(json.load(f))
# If the unit ordering is not the same, something is wrong
if not all(a == o
for a, o in zip(data.ablation_units, ordering)):
import pdb
pdb.set_trace()
continue
if len(data.ablation_effects) >= args.unitcount:
continue # file already done.
measurements = data.ablation_effects
for count in progress(range(args.startcount,
min(args.unitcount, len(ordering)) +
1),
desc='units'):
if str(count) in measurements:
continue
ablation = numpy.zeros(len(ranking.score), dtype='float32')
ablation[ordering[:count]] = 1
for l in model.ablation:
model.ablation[l] = ablation if layername == l else None
m = count_segments(recovery, segloader, model)[classnum].item()
print_progress(
'%s %s %d units (#%d), %g -> %g' %
(layername, rankname, count, ordering[count - 1].item(),
baseline[classnum].item(), m))
measurements[str(count)] = m
os.makedirs(ablationdir, exist_ok=True)
with open(os.path.join(ablationdir, '%s.json' % rankname),
'w') as f:
json.dump(
dict(classname=classname,
classnum=classnum,
baseline=baseline[classnum].item(),
layer=layername,
metric=args.metric,
ablation_units=ordering.tolist(),
ablation_effects=measurements), f)
