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()
if '_h99' in args.variant:
high_replacement = corpus.feature_99[None,:,None,None].cuda()
elif '_tcm' in args.variant:
# variant: top-conditional-mean
high_replacement = (
corpus.mean_present_feature[None,:,None,None].cuda())
else: # default: weighted mean
high_replacement = (
corpus.weighted_mean_present_feature[None,:,None,None].cuda())
fullimage_measurement = False
ablation_only = False
fullimage_ablation = False
if '_fim' in args.variant:
fullimage_measurement = True
elif '_fia' in args.variant:
fullimage_measurement = True
ablation_only = True
fullimage_ablation = True
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
epoch = 0
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
if args.eval_only:
# For eval_only, just load each snapshot and re-run validation eval
# pass on each one.
for epoch in range(-1, args.train_epochs):
snapfile = os.path.join(snapdir, 'epoch-%d.pth' % epoch)
if not os.path.exists(snapfile):
data = {}
if epoch >= 0:
print('No epoch %d' % epoch)
continue
else:
data = torch.load(snapfile)
with torch.no_grad():
ablation[...] = data['ablation'].to(ablation.device)
optimizer.load_state_dict(data['optimizer'])
avg_loss, regularizer, new_extra = eval_loss_and_reg()
# Keep old values, and update any new ones.
extra = {k: v for k, v in data.items()
if k not in ['ablation', 'optimizer', 'avg_loss']}
extra.update(new_extra)
torch.save(dict(ablation=ablation, optimizer=optimizer.state_dict(),
avg_loss=avg_loss, **extra),
os.path.join(snapdir, 'epoch-%d.pth' % epoch))
# Return loaded ablation.
return ablation.view(-1).detach().cpu().numpy()
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
if start_epoch < args.train_epochs:
epoch = start_epoch - 1
avg_loss, regularizer, extra = eval_loss_and_reg()
if epoch == -1:
torch.save(dict(ablation=ablation, optimizer=optimizer.state_dict(),
avg_loss=avg_loss, **extra),
os.path.join(snapdir, 'epoch-%d.pth' % epoch))
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,
ablation_only=ablation_only,
fullimage_measurement=fullimage_measurement)
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
avg_loss, regularizer, extra = eval_loss_and_reg()
torch.save(dict(ablation=ablation, optimizer=optimizer.state_dict(),
avg_loss=avg_loss, **extra),
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 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='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
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
# 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
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)
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
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)
