def measure_segclasses_with_zeroed_units(zeroed_units, sample_size=100):
model.remove_edits()
def zero_some_units(x, *args):
x[:, zeroed_units] = 0
return x
model.edit_layer(layername, rule=zero_some_units)
num_seglabels = len(segmodel.get_label_and_category_names()[0])
def compute_mean_seg_in_images(batch_z, *args):
img = model(batch_z.cuda())
seg = segmodel.segment_batch(img, downsample=4)
seg_area = seg.shape[2] * seg.shape[3]
seg_counts = torch.bincount(
(seg + (num_seglabels * torch.arange(
seg.shape[0], dtype=seg.dtype,
device=seg.device)[:, None, None, None])).view(-1),
minlength=num_seglabels * seg.shape[0]).view(seg.shape[0], -1)
seg_fracs = seg_counts.float() / seg_area
return seg_fracs
result = tally.tally_mean(compute_mean_seg_in_images,
dataset,
batch_size=30,
sample_size=sample_size,
pin_memory=True)
model.remove_edits()
return result
def test_generator_segclass_delta_stats(model, dataset, segmodel,
layername=None, zeroed_units=None, sample_size=None, cachefile=None):
model.remove_edits()
def zero_some_units(x, *args):
x[:, zeroed_units] = 0
return x
num_seglabels = len(segmodel.get_label_and_category_names()[0])
def compute_mean_delta_seg_in_images(batch_z, *args):
# First baseline
model.remove_edits()
img = model(batch_z.cuda())
seg = segmodel.segment_batch(img, downsample=4)
seg_area = seg.shape[2] * seg.shape[3]
seg_counts = torch.bincount((seg + (num_seglabels *
torch.arange(seg.shape[0], dtype=seg.dtype, device=seg.device
)[:,None,None,None])).view(-1),
minlength=num_seglabels * seg.shape[0]).view(seg.shape[0], -1)
seg_fracs = seg_counts.float() / seg_area
# Then with changes
model.edit_layer(layername, rule=zero_some_units)
d_img = model(batch_z.cuda())
d_seg = segmodel.segment_batch(d_img, downsample=4)
d_seg_counts = torch.bincount((d_seg + (num_seglabels *
torch.arange(seg.shape[0], dtype=seg.dtype, device=seg.device
)[:,None,None,None])).view(-1),
minlength=num_seglabels * seg.shape[0]).view(seg.shape[0], -1)
d_seg_fracs = d_seg_counts.float() / seg_area
return d_seg_fracs - seg_fracs
result = tally.tally_mean(compute_mean_delta_seg_in_images, dataset,
batch_size=25, sample_size=sample_size,
pin_memory=True, cachefile=cachefile)
model.remove_edits()
return result
u_rq = qd.rq(layername)
def intersect_99_fn(uimg, simg):
s_99 = s_rq.quantiles(0.99)[None, :, None, None].cuda()
u_99 = u_rq.quantiles(0.99)[None, :, None, None].cuda()
with torch.no_grad():
ux, sx = uimg.cuda(), simg.cuda()
inst_net(ux)
ur = inst_net.retained_layer('features.' + layername)
inst_net(sx)
sr = inst_net.retained_layer('features.' + layername)
return ((sr > s_99).float() * (ur > u_99).float()).permute(
0, 2, 3, 1).reshape(-1, ur.size(1))
intersect_99 = tally.tally_mean(intersect_99_fn,
ds,
cachefile=os.path.join(
qd.dir(layername), 'intersect_99.npz'))
def compute_image_max(batch):
inst_net(batch.cuda())
return inst_net.retained_layer('features.' +
layername).max(3)[0].max(2)[0]
s_topk = tally.tally_topk(compute_image_max,
sds,
cachefile=os.path.join(qd.dir(layername),
's_topk.npz'))
def compute_acts(image_batch):
inst_net(image_batch.cuda())
acts_batch = inst_net.retained_layer('features.' + layername)