import torch, sys, os, argparse, textwrap, numbers, numpy, json, PIL from torchvision import transforms from torch.utils.data import TensorDataset from netdissect import pbar from netdissect.nethook import edit_layers from netdissect.zdataset import standard_z_sample from netdissect.autoeval import autoimport_eval from netdissect.easydict import EasyDict from netdissect.modelconfig import create_instrumented_model help_epilog = '''\ Example: python -m netdissect.evalablate \ --segmenter "netdissect.GanImageSegmenter(segvocab='lowres', segsizes=[160,288], segdiv='quad')" \ --model "proggan.from_pth_file('models/lsun_models/${SCENE}_lsun.pth')" \ --outdir dissect/dissectdir \ --classname tree \ --layer layer4 \ --size 1000 Output layout: dissectdir/layer5/ablation/mirror-iqr.json { class: "mirror", classnum: 43, pixel_total: 41342300, class_pixels: 1234531, layer: "layer5", ranking: "mirror-iqr", ablation_units: [341, 23, 12, 142, 83, ...] ablation_pixels: [143242, 132344, 429931, ...] } ''' 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 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 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 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 measure_full_ablation(segmenter, loader, model, classnum, layer, ordering, values): ''' Quick and easy counting of segmented pixels reduced by ablating units. ''' device = next(model.parameters()).device feature_units = model.feature_shape[layer][1] feature_shape = model.feature_shape[layer][2:] repeats = len(ordering) total_scores = torch.zeros(repeats + 1) print(ordering) print(values.tolist()) with torch.no_grad(): for l in model.ablation: model.ablation[l] = None for i, [ibz] in enumerate(pbar(loader)): ibz = ibz.cuda() for num_units in pbar(range(len(ordering) + 1)): ablation = torch.zeros(feature_units, device=device) ablation[ordering[:num_units]] = torch.tensor( values[:num_units]).to(ablation.device, ablation.dtype) model.ablation[layer] = ablation tensor_images = model(ibz) seg = segmenter.segment_batch(tensor_images, downsample=2) mask = (seg == classnum).max(1)[0] total_scores[num_units] += mask.sum().float().cpu() return total_scores if __name__ == '__main__': main()