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):
shape = tuple(input_t[0, 0].shape)
# feed in
self.model.eval()
fmaps, grads = None, None
def hook_forward(module, input, output):
nonlocal fmaps
fmaps = output.detach()
def hook_backward(module, grad_in, grad_out):
nonlocal grads
grads = grad_out[0].detach()
# pass and collect activations + gradient of feature map
forward_handle = self.layer.register_forward_hook(hook_forward)
backward_handle = self.layer.register_backward_hook(hook_backward)
self.model.zero_grad()
preds = self.model(input_t)
forward_handle.remove()
backward_handle.remove()
# calc grads
grad_eval_point = torch.Tensor(1, preds.size()[-1]).zero_()
grad_eval_point[0][preds.argmax().item()] = 1.0
grad_eval_point = grad_eval_point.to(input_t.device)
preds.backward(gradient=grad_eval_point, retain_graph=True)
# weight maps
maps = fmaps.detach().cpu().numpy()[0, ]
weights = grads.detach().cpu().numpy().mean(axis=(2, 3))[0, :]
# avg maps
gcam = np.zeros(maps.shape[0:], dtype=np.float32)
# sum up weighted fmaps
for i, w in enumerate(weights):
gcam += w * maps[i, :, :]
# avg pool over feature maps
gcam = np.mean(gcam, axis=0)
# relu
gcam = np.maximum(gcam, 0)
# to input shape
gcam = resize(gcam, shape, interp=self.interp)
# rescale to max 1
gcam = gcam / (gcam.max() + self.eps)
return gcam
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: torch.Tensor, target_t: torch.Tensor):
self.model.eval()
# create baseline to get patches from
baseline_t = to_img_tensor(self.baseline.apply(to_np_img(input_t)))
# img: 1xCxNxN
rows = input_t.shape[2]
cols = input_t.shape[3]
rsteps = 1 + int(np.floor((rows-self.size) / self.stride))
csteps = 1 + int(np.floor((cols-self.size) / self.stride))
steps = csteps * rsteps
target = target_t.cpu().numpy()
with torch.no_grad():
initial_score = self.eval_np(input_t, target)
hmap = np.zeros((rsteps, csteps))
rstep_list = []
cstep_list = []
occluded = input_t.repeat(steps, 1, 1, 1).clone()
for step in tqdm(range(steps), ncols=100, desc="calc score", disable=not self.progbar):
# calc patch position
cstep = step % csteps
rstep = int((step - cstep) / csteps)
r = rstep * self.stride
c = cstep * self.stride
assert((r + self.size) <= rows)
assert((c + self.size) <= cols)
# occlude
# occluded.copy_(input_t)
occluded[step, :, r:r + self.size, c:c + self.size] = baseline_t[0, :, r:r + self.size, c:c + self.size]
rstep_list.append(rstep)
cstep_list.append(cstep)
# measure score drop
#print(occluded.shape)
score = call_batched(self.model, occluded, batch_size=100)[:, target]
for i, (rstep, cstep) in enumerate(zip(rstep_list, cstep_list)):
hmap[rstep, cstep] += initial_score - score[i]
#for step in tqdm(range(steps), ncols=100, desc="calc score", disable=not self.progbar):
# calc patch position
# cstep = step % csteps
# rstep = int((step - cstep) / csteps)
# r = rstep * self.stride
# c = cstep * self.stride
# assert((r + self.size) <= rows)
# assert((c + self.size) <= cols)
# occlude
# occluded.copy_(input_t)
# occluded[0, :, r:r + self.size, c:c + self.size] = baseline_t[0, :, r:r + self.size, c:c + self.size]
# measure score drop
# score = self.eval_np(occluded, target)
# hmap[rstep, cstep] += initial_score - score
hmap = resize(hmap, (rows, cols), interp=self.interp)
return hmap
