def heatmap(self, input_t: torch.Tensor, target_t: torch.Tensor):
self.model.eval()
self._inject_bottleneck()
with torch.no_grad():
self.model(input_t)
self._remove_bottleneck()
if (input_t.shape[0] > 1):
hmaps = np.zeros(
(input_t.shape[0], input_t.shape[2], input_t.shape[3]))
for i in range(input_t.shape[0]):
htensor = to_np(self.bn_layer.buffer_capacity[i])
hmap = htensor.mean(0)
hmap = resize(hmap, input_t.shape[2:])
hmap = hmap - hmap.min()
hmap = hmap / (max(hmap.max(), 1e-5))
hmaps[i] = hmap
hmap = hmaps
else:
htensor = to_np(self.bn_layer.buffer_capacity)
hmap = htensor.mean(axis=(0, 1))
hmap = resize(hmap, input_t.shape[2:])
hmap = hmap - hmap.min()
hmap = hmap / (max(hmap.max(), 1e-5))
return hmap
def _current_heatmap(self, shape=None):
# Read bottleneck
heatmap = self.bottleneck.buffer_capacity
heatmap = to_np(heatmap[0])
heatmap = heatmap.sum(axis=0) # Sum over channel dim
heatmap = heatmap - heatmap.min() # min=0
heatmap = heatmap / heatmap.max() # max=0
if shape is not None:
heatmap = resize(heatmap, shape)
return heatmap
def heatmap(self, input_t: torch.Tensor, target_t: torch.Tensor):
self.model.eval()
self._inject_bottleneck()
with torch.no_grad():
self.model(input_t)
self._remove_bottleneck()
htensor = to_np(self.bn_layer.buffer_capacity)
hmap = htensor.mean(axis=(0, 1))
hmap = resize(hmap, input_t.shape[2:])
hmap = hmap - hmap.min()
hmap = hmap / (max(hmap.max(), 1e-5))
return hmap
def heatmap(self, input_t, target):
target_t = target if isinstance(
target, torch.Tensor) else torch.tensor(target,
device=input_t.device)
assert input_t.shape[0] == 1, "We can only fit on one sample"
assert target_t.shape[0] == 1, "We can only fit on one label"
assert input_t.shape[
-1] == 224, "with must be 224, otherwise avgpool is not the identity"
avgpool = self.model.avgpool
self.model.avgpool = Identity()
lrp_model = InnvestigateModel(self.model,
epsilon=self.eps,
beta=self.beta,
method=self.method)
lrp_model.to(self.device)
_, relevance = lrp_model.innvestigate(input_t, target_t)
self.model.avgpool = avgpool
heatmap = to_np(relevance[0]).sum(0)
return heatmap
def hook(m, x, z):
self.estimators[i].feed_batch(to_np(z))