def on_end(self, data: Data) -> None:
index_summaries = DefaultKeyDict(default=lambda x: Summary(name=x))
for mode in self.mode:
final_scores = sorted([(idx, elem[-1][1]) for idx, elem in self.index_history[mode].items()],
key=lambda x: x[1])
max_idx_list = {elem[0] for elem in final_scores[-1:-self.n_max_to_keep - 1:-1]}
min_idx_list = {elem[0] for elem in final_scores[:self.n_min_to_keep]}
target_idx_list = Set.union(min_idx_list, max_idx_list, self.idx_to_keep)
for idx in target_idx_list:
for step, score in self.index_history[mode][idx]:
index_summaries[idx].history[mode][self.metric_key][step] = score
self.system.add_graph(self.outputs[0], list(index_summaries.values())) # So traceability can draw it
data.write_without_log(self.outputs[0], list(index_summaries.values()))
def on_epoch_end(self, data: Data) -> None:
self.y_true = np.squeeze(np.stack(self.y_true))
self.y_pred = np.stack(self.y_pred)
calibrator = cal.PlattBinnerMarginalCalibrator(num_calibration=len(
self.y_true),
num_bins=10)
calibrator.train_calibration(probs=self.y_pred, labels=self.y_true)
if self.save_path:
if not self.save_key or (self.save_key
and to_number(data[self.save_key]) == 0):
with open(self.save_path, 'wb') as f:
dill.dump(calibrator.calibrate, file=f)
print(
f"FastEstimator-PBMCalibrator: Calibrator written to {self.save_path}"
)
data.write_without_log(self.outputs[0],
calibrator.calibrate(self.y_pred))
def on_end(self, data: Data) -> None:
self.system.add_graph(
self.outputs[0],
list(self.label_summaries.values())) # So traceability can draw it
data.write_without_log(self.outputs[0],
list(self.label_summaries.values()))
def on_epoch_end(self, data: Data) -> None:
mode = self.system.mode
if self.n_found[mode] > 0:
if self.n_required[mode] > 0:
# We are keeping a user-specified number of samples
self.samples[mode] = {
key: concat(val)[:self.n_required[mode]]
for key, val in self.samples[mode].items()
}
else:
# We are keeping one batch of data
self.samples[mode] = {
key: val[0]
for key, val in self.samples[mode].items()
}
# even if you haven't found n_required samples, you're at end of epoch so no point trying to collect more
self.n_found[mode] = 0
self.n_required[mode] = 0
masks = self.salnet.get_masks(self.samples[mode])
smoothed, integrated, smint = {}, {}, {}
if self.smoothing:
smoothed = self.salnet.get_smoothed_masks(self.samples[mode],
nsamples=self.smoothing)
if self.integrating:
if isinstance(self.integrating, Tuple):
n_integration, n_smoothing = self.integrating
else:
n_integration = self.integrating
n_smoothing = self.smoothing
integrated = self.salnet.get_integrated_masks(
self.samples[mode], nsamples=n_integration)
if n_smoothing:
smint = self.salnet.get_smoothed_masks(
self.samples[mode],
nsamples=n_smoothing,
nintegration=n_integration)
# Arrange the outputs
args = {}
if self.class_key:
classes = self.samples[mode][self.class_key]
if self.label_mapping:
classes = np.array([
self.label_mapping[clazz]
for clazz in to_number(squeeze(classes))
])
args[self.class_key] = classes
for key in self.model_outputs:
classes = masks[key]
if self.label_mapping:
classes = np.array([
self.label_mapping[clazz]
for clazz in to_number(squeeze(classes))
])
args[key] = classes
sal = smint or integrated or smoothed or masks
for key, val in self.samples[mode].items():
if key is not self.class_key:
args[key] = val
# Create a linear combination of the original image, the saliency mask, and the product of the two in
# order to highlight regions of importance
min_val = reduce_min(val)
diff = reduce_max(val) - min_val
for outkey in self.outputs:
args["{} {}".format(
key, outkey)] = (0.3 * (sal[outkey] *
(val - min_val) + min_val) +
0.3 * val + 0.4 * sal[outkey] * diff +
min_val)
for key in self.outputs:
args[key] = masks[key]
if smoothed:
args["Smoothed {}".format(key)] = smoothed[key]
if integrated:
args["Integrated {}".format(key)] = integrated[key]
if smint:
args["SmInt {}".format(key)] = smint[key]
result = ImgData(colormap="inferno", **args)
data.write_without_log(self.outputs[0], result)
def on_epoch_end(self, data: Data) -> None:
# Keep only the user-specified number of samples
images = concat(self.images)[:self.n_samples or self.n_found]
_, height, width = get_image_dims(images)
grads = to_number(concat(self.grads)[:self.n_samples or self.n_found])
if tf.is_tensor(images):
grads = np.moveaxis(grads, source=-1,
destination=1) # grads should be channel first
args = {}
labels = None if not self.labels else concat(
self.labels)[:self.n_samples or self.n_found]
if labels is not None:
if len(labels.shape) > 1:
labels = argmax(labels, axis=-1)
if self.label_mapping:
labels = np.array([
self.label_mapping[clazz]
for clazz in to_number(squeeze(labels))
])
args[self.true_label_key] = labels
preds = None if not self.preds else concat(
self.preds)[:self.n_samples or self.n_found]
if preds is not None:
if len(preds.shape) > 1:
preds = argmax(preds, axis=-1)
if self.label_mapping:
preds = np.array([
self.label_mapping[clazz]
for clazz in to_number(squeeze(preds))
])
args[self.pred_label_key] = preds
args[self.image_key] = images
# Clear memory
self._reset()
# Make the image
# TODO: In future maybe allow multiple different grads to have side-by-side comparisons of classes
components = [np.mean(grads, axis=1)]
components = [np.maximum(component, 0) for component in components]
masks = []
for component_batch in components:
img_batch = []
for img in component_batch:
img = cv2.resize(img, (width, height))
img = img - np.min(img)
img = img / np.max(img)
img = cv2.cvtColor(
cv2.applyColorMap(np.uint8(255 * img), cv2.COLORMAP_JET),
cv2.COLOR_BGR2RGB)
img = np.float32(img) / 255
img_batch.append(img)
img_batch = np.array(img_batch, dtype=np.float32)
# Switch to channel first for pytorch
if isinstance(images, torch.Tensor):
img_batch = np.moveaxis(img_batch, source=-1, destination=1)
masks.append(img_batch)
components = [
images + mask for mask in masks
] # This seems to work even if the image is 1 channel instead of 3
components = [image / reduce_max(image) for image in components]
for elem in components:
args[self.grad_key] = elem
result = ImgData(**args)
data.write_without_log(self.outputs[0], result)
def on_epoch_end(self, data: Data) -> None:
# Keep only the user-specified number of samples
images = concat(self.images)[:self.n_samples or self.n_found]
_, height, width = get_image_dims(images)
activations = to_number(
concat(self.activations)[:self.n_samples or self.n_found])
if tf.is_tensor(images):
activations = np.moveaxis(
activations, source=-1,
destination=1) # Activations should be channel first
args = {}
labels = None if not self.labels else concat(
self.labels)[:self.n_samples or self.n_found]
if labels is not None:
if len(labels.shape) > 1:
labels = argmax(labels, axis=-1)
if self.label_mapping:
labels = np.array([
self.label_mapping[clazz]
for clazz in to_number(squeeze(labels))
])
args[self.true_label_key] = labels
preds = None if not self.preds else concat(
self.preds)[:self.n_samples or self.n_found]
if preds is not None:
if len(preds.shape) > 1:
preds = argmax(preds, axis=-1)
if self.label_mapping:
preds = np.array([
self.label_mapping[clazz]
for clazz in to_number(squeeze(preds))
])
args[self.pred_label_key] = preds
args[self.image_key] = images
# Clear memory
self._reset()
# Make the image
n_components, batch_component_image = self._project_2d(activations)
components = [] # component x image (batch x image)
for component_idx in range(n_components):
batch = []
for base_image, component_image in zip(images,
batch_component_image):
if len(component_image) > component_idx:
mask = component_image[component_idx]
mask = cv2.resize(mask, (width, height))
mask = mask - np.min(mask)
mask = mask / np.max(mask)
mask = cv2.cvtColor(
cv2.applyColorMap(np.uint8(255 * mask),
cv2.COLORMAP_JET), cv2.COLOR_BGR2RGB)
mask = np.float32(mask) / 255
# switch to channel first for pytorch
if isinstance(base_image, torch.Tensor):
mask = np.moveaxis(mask, source=-1, destination=1)
new_image = base_image + mask
new_image = new_image / reduce_max(new_image)
else:
# There's no component for this image, so display an empty image here
new_image = np.ones_like(base_image)
batch.append(new_image)
components.append(np.array(batch, dtype=np.float32))
for idx, elem in enumerate(components):
args[f"Component {idx}"] = elem
result = ImgData(**args)
data.write_without_log(self.outputs[0], result)