def forward_batch(
self, data: Union[Tensor, List[Tensor]],
state: Dict[str, Any]) -> Union[np.ndarray, List[np.ndarray]]:
"""A method which will be invoked in order to transform a batch of data.
This method will be invoked on batches of data during network postprocessing. Note that the inputs may be numpy
arrays or TF/Torch tensors. Outputs are expected to be Numpy arrays, though this is not enforced. Developers
should probably not need to override this implementation unless they are building an op specifically intended
for postprocessing.
Args:
data: The arrays from the data dictionary corresponding to whatever keys this Op declares as its `inputs`.
state: Information about the current execution context, for example {"mode": "train"}.
Returns:
The `data` after applying whatever transform this Op is responsible for. It will be written into the data
dictionary based on whatever keys this Op declares as its `outputs`.
"""
if isinstance(data, List):
data = [to_number(elem) for elem in data]
batch_size = data[0].shape[0]
data = [[elem[i] for elem in data] for i in range(batch_size)]
else:
data = to_number(data)
data = [data[i] for i in range(data.shape[0])]
results = [self.forward(elem, state) for elem in data]
if self.out_list:
results = [
np.array(col) for col in [[row[i] for row in results]
for i in range(len(results[0]))]
]
else:
results = np.array(results)
return results
def on_batch_end(self, data: Data) -> None:
y_true, y_pred = to_number(data[self.true_key]), to_number(
data[self.pred_key])
if y_true.shape[-1] > 1 and y_true.ndim > 1:
y_true = np.argmax(y_true, axis=-1)
assert y_pred.shape[0] == y_true.shape[0]
self.y_true.extend(y_true)
self.y_pred.extend(y_pred)
def on_batch_end(self, data: Data) -> None:
y_pred, y_true = to_number(data['pred']), to_number(data['target_real'])
if y_true.shape[-1] > 1 and y_true.ndim > 2:
y_true = np.argmax(y_true, axis=-1)
if y_pred.shape[-1] > 1 and y_pred.ndim > 2:
y_pred = np.argmax(y_pred, axis=-1)
sentence_level_scores = self.batch_precision_parameters(y_true, y_pred)
data.write_per_instance_log(self.outputs[0], sentence_level_scores)
def on_batch_end(self, data: Data) -> None:
y_true, y_pred = to_number(data[self.true_key]), to_number(data[self.pred_key])
batch_size = y_true.shape[0]
y_true, y_pred = y_true.reshape((batch_size, -1)), y_pred.reshape((batch_size, -1))
prediction_label = (y_pred >= self.threshold).astype(np.int32)
intersection = np.sum(y_true * prediction_label, axis=-1)
area_sum = np.sum(y_true, axis=-1) + np.sum(prediction_label, axis=-1)
dice = (2. * intersection + self.smooth) / (area_sum + self.smooth)
self.dice.extend(list(dice))
def on_batch_end(self, data: Data) -> None:
y_true, y_pred = to_number(data[self.true_key]), to_number(data[self.pred_key])
self.binary_classification = y_pred.shape[-1] == 1
if y_true.shape[-1] > 1 and y_true.ndim > 1:
y_true = np.argmax(y_true, axis=-1)
if y_pred.shape[-1] > 1:
y_pred = np.argmax(y_pred, axis=-1)
else:
y_pred = np.round(y_pred)
assert y_pred.size == y_true.size
self.y_pred.extend(y_pred.ravel())
self.y_true.extend(y_true.ravel())
def on_batch_end(self, data: Data) -> None:
y_true, y_pred = to_number(data[self.true_key]), to_number(
data[self.pred_key])
if y_true.shape[-1] > 1 and y_true.ndim > 1:
y_true = np.argmax(y_true, axis=-1)
if y_pred.shape[-1] > 1:
y_pred = np.argmax(y_pred, axis=-1)
else:
y_pred = np.round(y_pred)
assert y_pred.size == y_true.size
self.correct += np.sum(y_pred.ravel() == y_true.ravel())
self.total += len(y_pred.ravel())
def on_batch_end(self, data: Data) -> None:
y_true, y_pred = to_number(data[self.true_key]), to_number(
data[self.pred_key])
if y_true.shape[-1] > 1 and y_true.ndim > 1:
y_true = np.argmax(y_true, axis=-1)
if y_pred.shape[-1] > 1:
y_pred = np.argmax(y_pred, axis=-1)
else: # binaray classification (pred shape is [batch, 1])
if self.from_logits:
y_pred = 1 / (1 + np.exp(-y_pred))
y_pred = np.round(y_pred)
assert y_pred.size == y_true.size
self.correct += np.sum(y_pred.ravel() == y_true.ravel())
self.total += len(y_pred.ravel())
def on_batch_end(self, data: Data) -> None:
for case in self.instance_cases:
result = case.criteria(
*[data[var_name] for var_name in case.criteria_inputs])
if not isinstance(result, np.ndarray):
raise TypeError(
f"In test with description '{case.description}': "
"Criteria return of per-instance test needs to be ndarray with dtype bool."
)
elif result.dtype != np.dtype("bool"):
raise TypeError(
f"In test with description '{case.description}': "
"Criteria return of per-instance test needs to be ndarray with dtype bool."
)
result = result.reshape(-1)
case.result.append(result)
if self.data_id:
data_id = to_number(data[self.data_id]).reshape((-1, ))
if data_id.size != result.size:
raise ValueError(
f"In test with description '{case.description}': "
"Array size of criteria return doesn't match ID array size. Size of criteria"
"return should be equal to the batch_size such that each entry represents the test"
"result of its corresponding data instance.")
case.fail_id.append(data_id[result == False])
def _print_message(self, header: str, data: Data, log_epoch: bool = False) -> None:
"""Print a log message to the screen, and record the `data` into the `system` summary.
Args:
header: The prefix for the log message.
data: A collection of data to be recorded.
log_epoch: Whether epoch information should be included in the log message.
"""
log_message = header
if log_epoch:
log_message += "epoch: {}; ".format(self.system.epoch_idx)
self.system.write_summary('epoch', self.system.epoch_idx)
deferred = []
for key, val in humansorted(data.read_logs().items(), key=lambda x: x[0]):
if isinstance(val, ValWithError):
log_message += "{}: {}; ".format(key, str(val))
else:
val = to_number(val)
if val.size > 1:
deferred.append("\n{}:\n{};".format(key, np.array2string(val, separator=',')))
else:
log_message += "{}: {}; ".format(key, str(val))
self.system.write_summary(key, val)
log_message = log_message.strip()
for elem in deferred:
log_message += elem
print(log_message)
def __init__(self,
loss: LossOp,
threshold: Union[float, str] = 'exp',
regularization: float = 1.0,
average_loss: bool = True,
output_confidence: Optional[str] = None):
if len(loss.outputs) != 1 or loss.out_list:
raise ValueError(
"SuperLoss only supports lossOps which have a single output.")
self.loss = loss
self.loss.average_loss = False
super().__init__(inputs=loss.inputs,
outputs=loss.outputs[0] if not output_confidence else
(loss.outputs[0], output_confidence),
mode=loss.mode,
ds_id=loss.ds_id,
average_loss=average_loss)
if not isinstance(threshold, str):
threshold = to_number(threshold).item()
if not isinstance(threshold, float) and threshold != 'exp':
raise ValueError(
f'SuperLoss threshold parameter must be "exp" or a float, but got {threshold}'
)
self.tau_method = threshold
if regularization <= 0:
raise ValueError(
f"SuperLoss regularization parameter must be greater than 0, but got {regularization}"
)
self.lam = regularization
self.cap = -1.9999998 / e # Slightly more than -2 / e for numerical stability
self.initialized = {}
self.tau = {}
def write_embeddings(
self,
mode: str,
embeddings: Iterable[Tuple[str, Tensor, Optional[List[Any]],
Optional[Tensor]]],
step: int,
):
"""Write embeddings (like UMAP) to TensorBoard.
Args:
mode: The current mode of execution ('train', 'eval', 'test', 'infer').
embeddings: A collection of quadruplets like [("key", <features>, [<label1>, ...], <label_images>)].
Features are expected to be batched, and if labels and/or label images are provided they should have the
same batch dimension as the features.
step: The current training step.
"""
for key, features, labels, label_imgs in embeddings:
flat = to_number(reshape(features, [features.shape[0], -1]))
if not isinstance(label_imgs, (torch.Tensor, type(None))):
label_imgs = to_tensor(label_imgs, 'torch')
if len(label_imgs.shape) == 4:
label_imgs = permute(label_imgs, [0, 3, 1, 2])
self.summary_writers[mode].add_embedding(mat=flat,
metadata=labels,
label_img=label_imgs,
tag=key,
global_step=step)
def get_smoothed_masks(
self,
batch: Dict[str, Any],
stdev_spread: float = .15,
nsamples: int = 25,
nintegration: Optional[int] = None,
magnitude: bool = True) -> Dict[str, Union[Tensor, np.ndarray]]:
"""Generates smoothed greyscale saliency mask(s) from a given `batch` of data.
Args:
batch: An input batch of data.
stdev_spread: Amount of noise to add to the input, as fraction of the total spread (x_max - x_min).
nsamples: Number of samples to average across to get the smooth gradient.
nintegration: Number of samples to compute when integrating (None to disable).
magnitude: If true, computes the sum of squares of gradients instead of just the sum.
Returns:
Greyscale saliency mask(s) smoothed via the SmoothGrad method.
"""
# Shallow copy batch since we're going to modify its contents later
batch = {key: val for key, val in batch.items()}
model_inputs = [batch[ins] for ins in self.model_inputs]
stdevs = [
to_number(stdev_spread *
(reduce_max(ins) - reduce_min(ins))).item()
for ins in model_inputs
]
# Adding noise to the image might cause the max likelihood class value to change, so need to keep track of
# which class we're comparing to
response = self._get_mask(batch)
for gather_key, output_key in zip(self.gather_keys,
self.model_outputs):
batch[gather_key] = response[output_key]
if magnitude:
for key in self.outputs:
response[key] = response[key] * response[key]
for _ in range(nsamples - 1):
noisy_batch = {key: batch[key] for key in self.gather_keys}
for idx, input_name in enumerate(self.model_inputs):
noise = random_normal_like(model_inputs[idx], std=stdevs[idx])
x_plus_noise = model_inputs[idx] + noise
noisy_batch[input_name] = x_plus_noise
grads_and_preds = self._get_mask(
noisy_batch
) if not nintegration else self._get_integrated_masks(
noisy_batch, nsamples=nintegration)
for name in self.outputs:
grad = grads_and_preds[name]
if magnitude:
response[name] += grad * grad
else:
response[name] += grad
for key in self.outputs:
grad = response[key]
response[key] = self._convert_for_visualization(grad / nsamples)
return response
def write_scalars(self, mode: str, scalars: Iterable[Tuple[str, Any]], step: int) -> None:
"""Write summaries of scalars to TensorBoard.
Args:
mode: The current mode of execution ('train', 'eval', 'test', 'infer').
scalars: A collection of pairs like [("key", val), ("key2", val2), ...].
step: The current training step.
"""
for key, val in scalars:
self.summary_writers[mode].add_scalar(tag=key, scalar_value=to_number(val), global_step=step)
def on_batch_end(self, data: Data) -> None:
y_true, y_pred = to_number(data[self.true_key]), to_number(
data[self.pred_key])
if y_true.shape[-1] > 1 and y_true.ndim > 1:
y_true = np.argmax(y_true, axis=-1)
if y_pred.shape[-1] > 1:
y_pred = np.argmax(y_pred, axis=-1)
else:
y_pred = np.round(y_pred)
assert y_pred.size == y_true.size
batch_confusion = confusion_matrix(y_true,
y_pred,
labels=list(
range(0, self.num_classes)))
if self.matrix is None:
self.matrix = batch_confusion
else:
self.matrix += batch_confusion
def forward_batch(self, data: List[Tensor],
state: Dict[str, Any]) -> List[np.ndarray]:
if isinstance(self.calibration_fn, str):
if 'warmup' in state and state['warmup']:
# Don't attempt to load the calibration_fn during warmup
return data
with open(self.calibration_fn, 'rb') as f:
notice = f"FastEstimator-Calibrate: calibration function loaded from {self.calibration_fn}"
self.calibration_fn = dill.load(f)
print(notice)
return [self.calibration_fn(to_number(elem)) for elem in data]
def on_batch_end(self, data: Data):
# begin of reading det and gt
pred = to_number(data[self.pred_key]) # pred is [batch, nms_max_outputs, 7]
pred = self._reshape_pred(pred)
gt = to_number(data[self.true_key]) # gt is np.array (batch, box, 5), box dimension is padded
gt = self._reshape_gt(gt)
ground_truth_bb = []
for gt_item in gt:
idx_in_batch, x1, y1, w, h, label = gt_item
label = int(label)
id_epoch = self._get_id_in_epoch(idx_in_batch)
self.batch_image_ids.append(id_epoch)
self.image_ids.append(id_epoch)
tmp_dict = {'idx': id_epoch, 'x1': x1, 'y1': y1, 'w': w, 'h': h, 'label': label}
ground_truth_bb.append(tmp_dict)
predicted_bb = []
for pred_item in pred:
idx_in_batch, x1, y1, w, h, label, score = pred_item
label = int(label)
id_epoch = self.ids_batch_to_epoch[idx_in_batch]
self.image_ids.append(id_epoch)
tmp_dict = {'idx': id_epoch, 'x1': x1, 'y1': y1, 'w': w, 'h': h, 'label': label, 'score': score}
predicted_bb.append(tmp_dict)
for dict_elem in ground_truth_bb:
self.gt[dict_elem['idx'], dict_elem['label']].append(dict_elem)
for dict_elem in predicted_bb:
self.det[dict_elem['idx'], dict_elem['label']].append(dict_elem)
# end of reading det and gt
# compute iou matrix, matrix index is (img_id, cat_id), each element in matrix has shape (num_det, num_gt)
self.ious = {(img_id, cat_id): self.compute_iou(self.det[img_id, cat_id], self.gt[img_id, cat_id])
for img_id in self.batch_image_ids for cat_id in self.categories}
for cat_id in self.categories:
for img_id in self.batch_image_ids:
self.evalimgs[(cat_id, img_id)] = self.evaluate_img(cat_id, img_id)
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 write_images(self, mode: str, images: Iterable[Tuple[str, Any]], step: int) -> None:
"""Write images to TensorBoard.
Args:
mode: The current mode of execution ('train', 'eval', 'test', 'infer').
images: A collection of pairs like [("key", image1), ("key2", image2), ...].
step: The current training step.
"""
for key, img in images:
if isinstance(img, ImgData):
img = img.paint_figure()
if isinstance(img, plt.Figure):
self.summary_writers[mode].add_figure(tag=key, figure=img, global_step=step)
else:
self.summary_writers[mode].add_images(tag=key,
img_tensor=to_number(img),
global_step=step,
dataformats='NCHW' if isinstance(img, torch.Tensor) else 'NHWC')
def _print_message(self,
header: str,
data: Data,
log_epoch: bool = False) -> None:
"""Print a log message to the screen, and record the `data` into the `system` summary.
Args:
header: The prefix for the log message.
data: A collection of data to be recorded.
log_epoch: Whether epoch information should be included in the log message.
"""
log_message = header
if log_epoch:
log_message += "epoch: {}; ".format(self.system.epoch_idx)
self.system.write_summary('epoch', self.system.epoch_idx)
for key, val in data.read_logs().items():
val = to_number(val)
self.system.write_summary(key, val)
if val.size > 1:
log_message += "\n{}:\n{};".format(
key, np.array2string(val, separator=','))
else:
log_message += "{}: {}; ".format(key, str(val))
print(log_message)
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_batch_end(self, data: Data) -> None:
self.points.append((to_number(data[self.label_key]),
to_number(data[self.metric_key])))
def on_batch_end(self, data: Data) -> None:
if self.system.epoch_idx % self.epoch_frequency == 0:
self.points.append((to_number(data[self.index_key]), to_number(data[self.metric_key])))
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)
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)
