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(): parser = argparse.ArgumentParser(description='GAN sample making utility') 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='images', help='directory for image output') parser.add_argument('--size', type=int, default=100, help='number of images to output') parser.add_argument('--test_size', type=int, default=None, help='number of images to test') parser.add_argument('--layer', type=str, default=None, help='layer to inspect') parser.add_argument('--seed', type=int, default=1, help='seed') parser.add_argument('--maximize_units', type=int, nargs='+', default=None, help='units to maximize') parser.add_argument('--ablate_units', type=int, nargs='+', default=None, help='units to ablate') 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() verbose_progress(not args.quiet) # Instantiate the model model = autoimport_eval(args.model) if args.pthfile 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) # Unwrap any DataParallel-wrapped model if isinstance(model, torch.nn.DataParallel): model = next(model.children()) # 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)): z_channels = first_layer.in_channels spatialdims = (1, 1) else: z_channels = first_layer.in_features spatialdims = () # Instrument the model if needed if args.maximize_units is not None: retain_layers(model, [args.layer]) model.cuda() # Get the sample of z vectors if args.maximize_units is None: indexes = torch.arange(args.size) z_sample = standard_z_sample(args.size, z_channels, seed=args.seed) z_sample = z_sample.view(tuple(z_sample.shape) + spatialdims) else: # By default, if maximizing units, get a 'top 5%' sample. if args.test_size is None: args.test_size = args.size * 20 z_universe = standard_z_sample(args.test_size, z_channels, seed=args.seed) z_universe = z_universe.view(tuple(z_universe.shape) + spatialdims) indexes = get_highest_znums(model, z_universe, args.maximize_units, args.size, seed=args.seed) z_sample = z_universe[indexes] if args.ablate_units: edit_layers(model, [args.layer]) dims = max(2, max(args.ablate_units) + 1) # >=2 to avoid broadcast model.ablation[args.layer] = torch.zeros(dims) model.ablation[args.layer][args.ablate_units] = 1 save_znum_images(args.outdir, model, z_sample, indexes, args.layer, args.ablate_units) copy_lightbox_to(args.outdir)
def run_command(args): verbose_progress(True) progress = default_progress() classname = args.classname # 'door' layer = args.layer # 'layer4' num_eval_units = 20 assert os.path.isfile(os.path.join(args.outdir, 'dissect.json')), ( "Should be a dissection directory") if args.variant is None: args.variant = 'ace' if args.l2_lambda != 0.005: args.variant = '%s_reg%g' % (args.variant, args.l2_lambda) cachedir = os.path.join(args.outdir, safe_dir_name(layer), args.variant, classname) if pidfile_taken(os.path.join(cachedir, 'lock.pid'), True): sys.exit(0) # Take defaults for model constructor etc from dissect.json settings. with open(os.path.join(args.outdir, 'dissect.json')) as f: dissection = EasyDict(json.load(f)) if args.model is None: args.model = dissection.settings.model if args.pthfile is None: args.pthfile = dissection.settings.pthfile if args.segmenter is None: args.segmenter = dissection.settings.segmenter # Default segmenter class if args.segmenter is None: args.segmenter = ("netdissect.segmenter.UnifiedParsingSegmenter(" + "segsizes=[256], segdiv='quad')") if (not args.no_cache and os.path.isfile(os.path.join(cachedir, 'snapshots', 'epoch-%d.npy' % ( args.train_epochs - 1))) and os.path.isfile(os.path.join(cachedir, 'report.json'))): print('%s already done' % cachedir) sys.exit(0) os.makedirs(cachedir, exist_ok=True) # Instantiate generator model = create_instrumented_model(args, gen=True, edit=True, layers=[args.layer]) if model is None: print('No model specified') sys.exit(1) # Instantiate segmenter segmenter = autoimport_eval(args.segmenter) labelnames, catname = segmenter.get_label_and_category_names() classnum = [i for i, (n, c) in enumerate(labelnames) if n == classname][0] num_classes = len(labelnames) with open(os.path.join(cachedir, 'labelnames.json'), 'w') as f: json.dump(labelnames, f, indent=1) # Sample sets for training. full_sample = netdissect.zdataset.z_sample_for_model(model, args.search_size, seed=10) second_sample = netdissect.zdataset.z_sample_for_model(model, args.search_size, seed=11) # Load any cached data. cache_filename = os.path.join(cachedir, 'corpus.npz') corpus = EasyDict() try: if not args.no_cache: corpus = EasyDict({k: torch.from_numpy(v) for k, v in numpy.load(cache_filename).items()}) except: pass # The steps for the computation. compute_present_locations(args, corpus, cache_filename, model, segmenter, classnum, full_sample) compute_mean_present_features(args, corpus, cache_filename, model) compute_feature_quantiles(args, corpus, cache_filename, model, full_sample) compute_candidate_locations(args, corpus, cache_filename, model, segmenter, classnum, second_sample) # visualize_training_locations(args, corpus, cachedir, model) init_ablation = initial_ablation(args, args.outdir) scores = train_ablation(args, corpus, cache_filename, model, segmenter, classnum, init_ablation) summarize_scores(args, corpus, cachedir, layer, classname, args.variant, scores) if args.variant == 'ace': add_ace_ranking_to_dissection(args.outdir, layer, classname, scores)
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))
from torchvision import transforms from netdissect.nethook import InstrumentedModel from netdissect.autoeval import autoimport_eval from netdissect.modelconfig import annotate_model_shapes from netdissect.segviz import segment_visualization import PIL batch_size = 1 data = get_segments_dataset('dataset/Adissect_toy') #model = autoimport_eval("p2pgan.from_pth_file('models/pix2pix/p2p_churches.pth')") model = from_pth_file('models/pix2pix/p2p_churches.pth') segmenter = ( "netdissect.segmenter.UnifiedParsingSegmenter(segsizes=[256], segdiv='quad')" ) segrunner = GeneratorSegRunner(autoimport_eval(segmenter)) layer5 = ('model.model.1.model.3.model.3.model.3.model.1', 'layer5') layer9 = ( 'model.model.1.model.3.model.3.model.3.model.3.model.3.model.3.model.3', 'layer9') layer12 = ('model.model.1.model.3.model.3.model.3.model.5', 'layer12') model = InstrumentedModel(model) model.retain_layers([layer5, layer9, layer12]) annotate_model_shapes(model, gen=True, imgsize=None) segloader = torch.utils.data.DataLoader(data, batch_size=batch_size, pin_memory=True)
def main(): # Training settings def strpair(arg): p = tuple(arg.split(':')) if len(p) == 1: p = p + p return p parser = argparse.ArgumentParser( description='Ablation eval', 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', required=True, 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('--unitcount', type=int, default=30, help='number of units to ablate') parser.add_argument('--segmenter', type=str, 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=200, 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 pbar.verbose(not args.quiet) # Speed up pytorch torch.backends.cudnn.benchmark = True # Set up CUDA args.cuda = not args.no_cuda and torch.cuda.is_available() if args.cuda: torch.backends.cudnn.benchmark = True # Take defaults for model constructor etc from dissect.json settings. with open(os.path.join(args.outdir, 'dissect.json')) as f: dissection = EasyDict(json.load(f)) if args.model is None: args.model = dissection.settings.model if args.pthfile is None: args.pthfile = dissection.settings.pthfile if args.segmenter is None: args.segmenter = dissection.settings.segmenter # Instantiate generator model = create_instrumented_model(args, gen=True, edit=True) if model is None: print('No model specified') sys.exit(1) # Instantiate model device = next(model.parameters()).device input_shape = model.input_shape # 4d input if convolutional, 2d input if first layer is linear. raw_sample = standard_z_sample(args.size, input_shape[1], seed=2).view((args.size, ) + input_shape[1:]) dataset = TensorDataset(raw_sample) # Create the segmenter segmenter = autoimport_eval(args.segmenter) # Now do the actual work. labelnames, catnames = (segmenter.get_label_and_category_names(dataset)) label_category = [ catnames.index(c) if c in catnames else 0 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')) # Index the dissection layers by layer name. dissect_layer = {lrec.layer: lrec for lrec in dissection.layers} # First, collect a baseline for l in model.ablation: model.ablation[l] = None # For each sort-order, do an ablation for classname in pbar(args.classes): pbar.post(c=classname) for layername in pbar(model.ablation): pbar.post(l=layername) rankname = '%s-%s' % (classname, args.metric) 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, 'pixablation') 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)): continue if len(data.ablation_effects) >= args.unitcount: continue # file already done. measurements = data.ablation_effects measurements = measure_ablation(segmenter, segloader, model, classnum, layername, ordering[:args.unitcount]) measurements = measurements.cpu().numpy().tolist() 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=measurements[0], layer=layername, metric=args.metric, ablation_units=ordering.tolist(), ablation_effects=measurements[1:]), f)
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 parser = argparse.ArgumentParser(description='Ablation eval', 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', required=True, help='directory for dissection output') parser.add_argument('--layer', type=strpair, help='space-separated list of layer names to edit' + ', in the form layername[:reportedname]') parser.add_argument('--classname', type=str, help='class name to ablate') parser.add_argument('--metric', type=str, default='iou', help='ordering metric for selecting units') parser.add_argument('--unitcount', type=int, default=30, help='number of units to ablate') parser.add_argument('--segmenter', type=str, 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=25, help='batch size for forward pass') parser.add_argument('--mixed_units', action='store_true', default=False, help='true to keep alpha for non-zeroed units') parser.add_argument('--size', type=int, default=200, 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 # Set up CUDA args.cuda = not args.no_cuda and torch.cuda.is_available() if args.cuda: torch.backends.cudnn.benchmark = True # Take defaults for model constructor etc from dissect.json settings. with open(os.path.join(args.outdir, 'dissect.json')) as f: dissection = EasyDict(json.load(f)) if args.model is None: args.model = dissection.settings.model if args.pthfile is None: args.pthfile = dissection.settings.pthfile if args.segmenter is None: args.segmenter = dissection.settings.segmenter if args.layer is None: args.layer = dissection.settings.layers[0] args.layers = [args.layer] # Also load specific analysis layername = args.layer[1] if args.metric == 'iou': summary = dissection else: with open(os.path.join(args.outdir, layername, args.metric, args.classname, 'summary.json')) as f: summary = EasyDict(json.load(f)) # Instantiate generator model = create_instrumented_model(args, gen=True, edit=True) if model is None: print('No model specified') sys.exit(1) # Instantiate model device = next(model.parameters()).device input_shape = model.input_shape # 4d input if convolutional, 2d input if first layer is linear. raw_sample = standard_z_sample(args.size, input_shape[1], seed=3).view( (args.size,) + input_shape[1:]) dataset = TensorDataset(raw_sample) # Create the segmenter segmenter = autoimport_eval(args.segmenter) # Now do the actual work. labelnames, catnames = ( segmenter.get_label_and_category_names(dataset)) label_category = [catnames.index(c) if c in catnames else 0 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')) # Index the dissection layers by layer name. # First, collect a baseline for l in model.ablation: model.ablation[l] = None # For each sort-order, do an ablation progress = default_progress() classname = args.classname classnum = labelnum_from_name[classname] # Get iou ranking from dissect.json iou_rankname = '%s-%s' % (classname, 'iou') dissect_layer = {lrec.layer: lrec for lrec in dissection.layers} iou_ranking = next(r for r in dissect_layer[layername].rankings if r.name == iou_rankname) # Get trained ranking from summary.json rankname = '%s-%s' % (classname, args.metric) summary_layer = {lrec.layer: lrec for lrec in summary.layers} ranking = next(r for r in summary_layer[layername].rankings if r.name == rankname) # Get ordering, first by ranking, then break ties by iou. ordering = [t[2] for t in sorted([(s1, s2, i) for i, (s1, s2) in enumerate(zip(ranking.score, iou_ranking.score))])] values = (-numpy.array(ranking.score))[ordering] if not args.mixed_units: values[...] = 1 ablationdir = os.path.join(args.outdir, layername, 'fullablation') measurements = measure_full_ablation(segmenter, segloader, model, classnum, layername, ordering[:args.unitcount], values[:args.unitcount]) measurements = measurements.cpu().numpy().tolist() 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=measurements[0], layer=layername, metric=args.metric, ablation_units=ordering, ablation_values=values.tolist(), ablation_effects=measurements[1:]), f)
def main(): parser = argparse.ArgumentParser( description='GAN output segmentation util') parser.add_argument('--model', type=str, default='netdissect.proggan.from_pth_file("' + 'models/karras/churchoutdoor_lsun.pth")', help='constructor for the model to test') parser.add_argument('--outdir', type=str, default='images', help='directory for image output') parser.add_argument('--size', type=int, default=100, help='number of images to output') parser.add_argument('--seed', type=int, default=1, help='seed') 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() verbose_progress(not args.quiet) # Instantiate the model model = autoimport_eval(args.model) # Make the standard z z_dataset = z_dataset_for_model(model, size=args.size) # Make the segmenter segmenter = UnifiedParsingSegmenter() # Write out text labels labels, cats = segmenter.get_label_and_category_names() with open(os.path.join(args.outdir, 'labels.txt'), 'w') as f: for i, (label, cat) in enumerate(labels): f.write('%s %s\n' % (label, cat)) # Move models to cuda model.cuda() batch_size = 10 progress = default_progress() dirname = args.outdir with torch.no_grad(): # Pass 2: now generate images z_loader = torch.utils.data.DataLoader(z_dataset, batch_size=batch_size, num_workers=2, pin_memory=True) for batch_num, [z ] in enumerate(progress(z_loader, desc='Saving images')): z = z.cuda() start_index = batch_num * batch_size tensor_im = model(z) byte_im = ((tensor_im + 1) / 2 * 255).clamp(0, 255).byte().permute( 0, 2, 3, 1).cpu() seg = segmenter.segment_batch(tensor_im) for i in range(len(tensor_im)): index = i + start_index filename = os.path.join(dirname, '%d_img.jpg' % index) Image.fromarray(byte_im[i].numpy()).save(filename, optimize=True, quality=100) filename = os.path.join(dirname, '%d_seg.mat' % index) savemat(filename, dict(seg=seg[i].cpu().numpy())) filename = os.path.join(dirname, '%d_seg.png' % index) Image.fromarray( segment_visualization(seg[i].cpu().numpy(), tensor_im.shape[2:])).save(filename) srcdir = os.path.realpath( os.path.join(os.getcwd(), os.path.dirname(__file__))) shutil.copy(os.path.join(srcdir, 'lightbox.html'), os.path.join(dirname, '+lightbox.html'))
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)