def _run_inference(self, dataset, summary, threshod=0.5):
"""
Run inference for the dataset and write the inference related data into summary.
Args:
dataset (`ds`): the parsed dataset
summary (`SummaryRecord`): the summary object to store the data
threshold (float): the threshold for prediction.
Returns:
imageid_labels (dict): a dict that maps image_id and the union of its ground truth and predicted labels.
"""
imageid_labels = {}
ds.config.set_seed(58)
self._count = 0
for j, next_element in enumerate(dataset):
now = time()
inputs, labels, _ = self._unpack_next_element(next_element)
prob = self._model(inputs).asnumpy()
for idx, inp in enumerate(inputs):
gt_labels = labels[idx]
gt_probs = [float(prob[idx][i]) for i in gt_labels]
data_np = _convert_image_format(
np.expand_dims(inp.asnumpy(), 0), 'NCHW')
_, _, _, image_string = _make_image(_normalize(data_np))
predicted_labels = [
int(i) for i in (prob[idx] > threshod).nonzero()[0]
]
predicted_probs = [
float(prob[idx][i]) for i in predicted_labels
]
union_labs = list(set(gt_labels + predicted_labels))
imageid_labels[str(self._count)] = union_labs
explain = Explain()
explain.image_id = str(self._count)
explain.image_data = image_string
summary.add_value("explainer", "image", explain)
explain = Explain()
explain.image_id = str(self._count)
explain.ground_truth_label.extend(gt_labels)
explain.inference.ground_truth_prob.extend(gt_probs)
explain.inference.predicted_label.extend(predicted_labels)
explain.inference.predicted_prob.extend(predicted_probs)
summary.add_value("explainer", "inference", explain)
summary.record(1)
self._count += 1
print(
"Finish running and writing {}-th batch inference data. Time elapsed: {}s"
.format(j,
time() - now))
return imageid_labels
def _run_exp_step(self, next_element, explainer, imageid_labels, summary):
"""
Run the explanation for each step and write explanation results into summary.
Args:
next_element (Tuple): data of one step
explainer (_Attribution): an Attribution object to generate saliency maps.
imageid_labels (dict): a dict that maps the image_id and its union labels.
summary (SummaryRecord): the summary object to store the data
Returns:
List of dict that maps label to its corresponding saliency map.
"""
inputs, labels, _ = self._unpack_next_element(next_element)
count = self._count
unions = []
for _ in range(len(labels)):
unions_labels = imageid_labels[str(count)]
unions.append(unions_labels)
count += 1
batch_unions = self._make_label_batch(unions)
saliency_dict_lst = []
batch_saliency_full = []
for i in range(len(batch_unions[0])):
batch_saliency = explainer(inputs, batch_unions[:, i])
batch_saliency_full.append(batch_saliency)
for idx, union in enumerate(unions):
saliency_dict = {}
explain = Explain()
explain.image_id = str(self._count)
for k, lab in enumerate(union):
saliency = batch_saliency_full[k][idx:idx + 1]
saliency_dict[lab] = saliency
saliency_np = _make_rgba(saliency)
_, _, _, saliency_string = _make_image(_normalize(saliency_np))
explanation = explain.explanation.add()
explanation.explain_method = explainer.__class__.__name__
explanation.label = lab
explanation.heatmap = saliency_string
summary.add_value("explainer", "explanation", explain)
summary.record(1)
self._count += 1
saliency_dict_lst.append(saliency_dict)
return saliency_dict_lst