def log_prob(self, x: nd.NDArray) -> nd.NDArray:
mean = self.get_param_maybe_repeated('mean')
if x.ndim > mean.ndim:
mean = nd.expand_dims(mean, 0)
np_x = x.asnumpy().astype(np.int32).astype(np.float32)
np.testing.assert_almost_equal(x.asnumpy(), np_x)
return x * nd.log(mean) - mean - nd.gammaln(x + 1.)
def plot_features(features: nd.NDArray, scope: int = 9):
"""
visualize feature maps per channel.
:param features: feature map with shape 1xCxHxW or CxHxW
:param scope: the index of feature maps to visualize is [0, scope)
"""
if len(features.shape) == 4:
scope = scope if scope < features.shape[1] else features.shape[1]
feature_maps = nd.squeeze(features, axis=0).asnumpy()
else:
scope = scope if scope < features.shape[0] else features.shape[0]
feature_maps = features.asnumpy()
feature_map_combination = []
# separate visualization
row, col = get_row_col(scope)
plt.figure()
for i in range(0, scope):
feature_map = feature_maps[i, :, :]
feature_map_combination.append(feature_map)
plt.subplot(row, col, i + 1)
plt.imshow(feature_map)
axis('off')
# title(f"feature map {i}")
plt.show()
# overlaps
feature_map_sum = sum(ele for ele in feature_map_combination)
plt.imshow(feature_map_sum)
axis('off')
plt.show()
def format_to_plot(tensor: nd.NDArray) -> np.ndarray:
"""format the input tensor from NCHW/CHW to HWC/HW for plotting"""
if len(tensor.shape) == 4:
tensor = nd.squeeze(tensor, axis=0)
if tensor.shape[0] == 1:
tensor = nd.squeeze(tensor, axis=0)
else:
tensor = nd.transpose(tensor, axes=(1, 2, 0))
return tensor.asnumpy()
def misclassified_pixels(prob: nd.NDArray,
label: nd.NDArray,
ignore_label: int = -1) -> np.ndarray:
"""
return misclassified pixels.
:param prob: the predicted probability with shape CHW
:param label: the ground truth label with shape HW
:param ignore_label: ignored label
:return: numpy array of shape HW where 0 indicates misclassified pixels
"""
# needs to process on cpu
prob = prob.as_in_context(mx.cpu())
label = label.as_in_context(mx.cpu())
# determine equal or not to get misclassified pixels
pred = nd.squeeze(nd.argmax(prob, axis=0)).astype('int32')
mis_classify = (pred == label).asnumpy()
# deal with ignored label via numpy
label = label.asnumpy()
mis_classify[label == ignore_label] = 1
return mis_classify
def _allclose(a: nd.NDArray, b: nd.NDArray):
return np.allclose(a.asnumpy(), b.asnumpy(), atol=1e-6)