def _run_inference(self, summary, threshold=0.5): """ Run inference for the dataset and write the inference related data into summary. Args: summary (SummaryRecord): The summary object to store the data threshold (float): The threshold for prediction. Returns: dict, The map of sample d to the union of its ground truth and predicted labels. """ sample_id_labels = {} self._sample_index = 0 ds.config.set_seed(self._DATASET_SEED) for j, next_element in enumerate(self._dataset): now = time() inputs, labels, _ = self._unpack_next_element(next_element) prob = self._full_network(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') original_image = _np_to_image(_normalize(data_np), mode='RGB') original_image_path = self._save_original_image(self._sample_index, original_image) predicted_labels = [int(i) for i in (prob[idx] > threshold).nonzero()[0]] predicted_probs = [float(prob[idx][i]) for i in predicted_labels] union_labs = list(set(gt_labels + predicted_labels)) sample_id_labels[str(self._sample_index)] = union_labs explain = Explain() explain.sample_id = self._sample_index explain.image_path = original_image_path summary.add_value("explainer", "sample", explain) explain = Explain() explain.sample_id = self._sample_index 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._sample_index += 1 self._spaced_print("Finish running and writing {}-th batch inference data." " Time elapsed: {:.3f} s".format(j, time() - now), end='') return sample_id_labels
def _run_exp_step(self, next_element, explainer, sample_id_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. sample_id_labels (dict): A dict that maps the sample id and its union labels. summary (SummaryRecord): The summary object to store the data Returns: list, List of dict that maps label to its corresponding saliency map. """ inputs, labels, _ = self._unpack_next_element(next_element) sample_index = self._sample_index unions = [] for _ in range(len(labels)): unions_labels = sample_id_labels[str(sample_index)] unions.append(unions_labels) sample_index += 1 batch_unions = self._make_label_batch(unions) saliency_dict_lst = [] if isinstance(explainer, RISE): batch_saliency_full = explainer(inputs, batch_unions) else: batch_saliency_full = [] for i in range(len(batch_unions[0])): batch_saliency = explainer(inputs, batch_unions[:, i]) batch_saliency_full.append(batch_saliency) concat = ms.ops.operations.Concat(1) batch_saliency_full = concat(tuple(batch_saliency_full)) for idx, union in enumerate(unions): saliency_dict = {} explain = Explain() explain.sample_id = self._sample_index for k, lab in enumerate(union): saliency = batch_saliency_full[idx:idx + 1, k:k + 1] saliency_dict[lab] = saliency saliency_np = _normalize(saliency.asnumpy().squeeze()) saliency_image = _np_to_image(saliency_np, mode='L') heatmap_path = self._save_heatmap(explainer.__class__.__name__, lab, self._sample_index, saliency_image) explanation = explain.explanation.add() explanation.explain_method = explainer.__class__.__name__ explanation.heatmap_path = heatmap_path explanation.label = lab summary.add_value("explainer", "explanation", explain) summary.record(1) self._sample_index += 1 saliency_dict_lst.append(saliency_dict) return saliency_dict_lst
def _run_hoc(self, summary, sample_id, sample_input, prob): """ Run HOC search for a sample image, and then save the result to summary. Args: summary (SummaryRecord): The summary object to store the data. sample_id (int): The sample ID. sample_input (Union[Tensor, np.ndarray]): Sample image tensor in CHW or NCWH(N=1). prob (Union[Tensor, np.ndarray]): List of sample's classification prediction output, HOC will run for labels with prediction output strictly larger then HOC searcher's threshold(0.5 by default). """ if isinstance(sample_input, ms.Tensor): sample_input = sample_input.asnumpy() if len(sample_input.shape) == 3: sample_input = np.expand_dims(sample_input, axis=0) has_rec = False explain = Explain() explain.sample_id = sample_id str_mask = hoc.auto_str_mask(sample_input) compiled_mask = None for label_idx, label_prob in enumerate(prob): if label_prob > self._hoc_searcher.threshold: if compiled_mask is None: compiled_mask = hoc.compile_mask(str_mask, sample_input) try: edit_tree, layer_outputs = self._hoc_searcher.search( sample_input, label_idx, compiled_mask) except hoc.NoValidResultError: log.warning( f"No Hierarchical Occlusion result was found in sample#{sample_id} " f"label:{self._labels[label_idx]}, skipped.") continue has_rec = True hoc_rec = explain.hoc.add() hoc_rec.label = label_idx hoc_rec.mask = str_mask layer_count = edit_tree.max_layer + 1 for layer in range(layer_count): steps = edit_tree.get_layer_or_leaf_steps(layer) layer_output = layer_outputs[layer] hoc_layer = hoc_rec.layer.add() hoc_layer.prob = layer_output for step in steps: hoc_layer.box.extend(list(step.box)) if has_rec: summary.add_value("explainer", "hoc", explain) summary.record(1) self._manifest['hierarchical_occlusion'] = True
def _run_hoc(self, summary, sample_id, sample_input, prob): """ Run HOC search for a sample image, and then save the result to summary. Args: summary (SummaryRecord): The summary object to store the data. sample_id (int): The sample ID. sample_input (Union[Tensor, np.ndarray]): Sample image tensor in CHW or NCWH(N=1). prob (Union[Tensor, np.ndarray]): List of sample's classification prediction output, HOC will run for labels with prediction output strictly larger then HOC searcher's threshold(0.5 by default). """ if isinstance(sample_input, ms.Tensor): sample_input = sample_input.asnumpy() if len(sample_input.shape) == 3: sample_input = np.expand_dims(sample_input, axis=0) explain = None str_mask = hoc.auto_str_mask(sample_input) compiled_mask = None for label_idx, label_prob in enumerate(prob): if label_prob <= self._hoc_searcher.threshold: continue if compiled_mask is None: compiled_mask = hoc.compile_mask(str_mask, sample_input) try: edit_tree, layer_outputs = self._hoc_searcher.search( sample_input, label_idx, compiled_mask) except hoc.NoValidResultError: log.warning( f"No Hierarchical Occlusion result was found in sample#{sample_id} " f"label:{self._labels[label_idx]}, skipped.") continue if explain is None: explain = Explain() explain.sample_id = sample_id self._add_hoc_result_to_explain(label_idx, str_mask, edit_tree, layer_outputs, explain) if explain is not None: summary.add_value("explainer", "hoc", explain) summary.record(1) self._manifest['hierarchical_occlusion'] = True
def _add_exp_step_samples(self, explainer, sample_label_sets, batch_saliency_full, summary): """ Add explanation results of samples to summary record. Args: explainer (Attribution): The explainer to be run. sample_label_sets (list[list[int]]): The label sets of samples. batch_saliency_full (Tensor): The saliency output from explainer. summary (SummaryRecord): The summary record. """ saliency_dict_lst = [] has_saliency_rec = False for idx, label_set in enumerate(sample_label_sets): saliency_dict = {} explain = Explain() explain.sample_id = self._sample_index for k, lab in enumerate(label_set): saliency = batch_saliency_full[idx:idx + 1, k:k + 1] saliency_dict[lab] = saliency saliency_np = _normalize(saliency.asnumpy().squeeze()) saliency_image = _np_to_image(saliency_np, mode='L') heatmap_path = self._save_heatmap(explainer.__class__.__name__, lab, self._sample_index, saliency_image) explanation = explain.explanation.add() explanation.explain_method = explainer.__class__.__name__ explanation.heatmap_path = heatmap_path explanation.label = lab has_saliency_rec = True summary.add_value("explainer", "explanation", explain) summary.record(1) self._sample_index += 1 saliency_dict_lst.append(saliency_dict) return saliency_dict_lst, has_saliency_rec
def _run_inference(self, summary, threshold=0.5): """ Run inference for the dataset and write the inference related data into summary. Args: summary (SummaryRecord): The summary object to store the data. threshold (float): The threshold for prediction. Returns: dict, The map of sample d to the union of its ground truth and predicted labels. """ has_uncertainty_rec = False sample_id_labels = {} self._sample_index = 0 ds.config.set_seed(self._DATASET_SEED) for j, next_element in enumerate(self._dataset): now = time() inputs, labels, _ = self._unpack_next_element(next_element) prob = self._full_network(inputs).asnumpy() if self._uncertainty is not None: prob_var = self._uncertainty.eval_epistemic_uncertainty(inputs) else: prob_var = None for idx, inp in enumerate(inputs): gt_labels = labels[idx] gt_probs = [float(prob[idx][i]) for i in gt_labels] if prob_var is not None: gt_prob_vars = [float(prob_var[idx][i]) for i in gt_labels] gt_itl_lows, gt_itl_his, gt_prob_sds = \ self._calc_beta_intervals(gt_probs, gt_prob_vars) data_np = _convert_image_format(np.expand_dims(inp.asnumpy(), 0), 'NCHW') original_image = _np_to_image(_normalize(data_np), mode='RGB') original_image_path = self._save_original_image(self._sample_index, original_image) predicted_labels = [int(i) for i in (prob[idx] > threshold).nonzero()[0]] predicted_probs = [float(prob[idx][i]) for i in predicted_labels] if prob_var is not None: predicted_prob_vars = [float(prob_var[idx][i]) for i in predicted_labels] predicted_itl_lows, predicted_itl_his, predicted_prob_sds = \ self._calc_beta_intervals(predicted_probs, predicted_prob_vars) union_labs = list(set(gt_labels + predicted_labels)) sample_id_labels[str(self._sample_index)] = union_labs explain = Explain() explain.sample_id = self._sample_index explain.image_path = original_image_path summary.add_value("explainer", "sample", explain) explain = Explain() explain.sample_id = self._sample_index 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) if prob_var is not None: explain.inference.ground_truth_prob_sd.extend(gt_prob_sds) explain.inference.ground_truth_prob_itl95_low.extend(gt_itl_lows) explain.inference.ground_truth_prob_itl95_hi.extend(gt_itl_his) explain.inference.predicted_prob_sd.extend(predicted_prob_sds) explain.inference.predicted_prob_itl95_low.extend(predicted_itl_lows) explain.inference.predicted_prob_itl95_hi.extend(predicted_itl_his) has_uncertainty_rec = True summary.add_value("explainer", "inference", explain) summary.record(1) if self._is_hoc_registered: self._run_hoc(summary, self._sample_index, inputs[idx], prob[idx]) self._sample_index += 1 self._spaced_print("Finish running and writing {}-th batch inference data." " Time elapsed: {:.3f} s".format(j, time() - now)) if has_uncertainty_rec: self._manifest["uncertainty"] = True return sample_id_labels
def _run_inference(self, dataset, summary, threshold=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. """ spacer = '{:120}\r' imageid_labels = {} ds.config.set_seed(_SEED) 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() if self._uncertainty is not None: prob_var = self._uncertainty.eval_epistemic_uncertainty(inputs) prob_sd = np.sqrt(prob_var) else: prob_var = prob_sd = None 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') original_image = _np_to_image(_normalize(data_np), mode='RGB') original_image_path = self._save_original_image( self._count, original_image) predicted_labels = [ int(i) for i in (prob[idx] > threshold).nonzero()[0] ] predicted_probs = [ float(prob[idx][i]) for i in predicted_labels ] has_uncertainty = False gt_prob_sds = gt_prob_itl95_lows = gt_prob_itl95_his = None predicted_prob_sds = predicted_prob_itl95_lows = predicted_prob_itl95_his = None if prob_var is not None: gt_prob_sds = [float(prob_sd[idx][i]) for i in gt_labels] predicted_prob_sds = [ float(prob_sd[idx][i]) for i in predicted_labels ] try: gt_prob_itl95_lows, gt_prob_itl95_his = \ _calc_prob_interval(0.95, gt_probs, [float(prob_var[idx][i]) for i in gt_labels]) predicted_prob_itl95_lows, predicted_prob_itl95_his = \ _calc_prob_interval(0.95, predicted_probs, [float(prob_var[idx][i]) for i in predicted_labels]) has_uncertainty = True except ValueError: log.error(traceback.format_exc()) log.error("Error on calculating uncertainty") union_labs = list(set(gt_labels + predicted_labels)) imageid_labels[str(self._count)] = union_labs explain = Explain() explain.sample_id = self._count explain.image_path = original_image_path summary.add_value("explainer", "sample", explain) explain = Explain() explain.sample_id = 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) if has_uncertainty: explain.inference.ground_truth_prob_sd.extend(gt_prob_sds) explain.inference.ground_truth_prob_itl95_low.extend( gt_prob_itl95_lows) explain.inference.ground_truth_prob_itl95_hi.extend( gt_prob_itl95_his) explain.inference.predicted_prob_sd.extend( predicted_prob_sds) explain.inference.predicted_prob_itl95_low.extend( predicted_prob_itl95_lows) explain.inference.predicted_prob_itl95_hi.extend( predicted_prob_itl95_his) summary.add_value("explainer", "inference", explain) summary.record(1) self._count += 1 print(spacer.format( "Finish running and writing {}-th batch inference data." " Time elapsed: {:.3f} s".format(j, time() - now)), end='') return imageid_labels
def _run_sample(self, summary, next_element, sample_id_labels, threshold): """ Run inference for a sample. Args: summary (SummaryRecord): The summary object to store the data. next_element (tuple): The next dataset sample. sample_id_labels (dict): The sample id to labels dictionary. threshold (float): The threshold for prediction. """ inputs, labels, _ = self._unpack_next_element(next_element) prob = self._full_network(inputs).asnumpy() if self._uncertainty is not None: prob_var = self._uncertainty.eval_epistemic_uncertainty(inputs) else: prob_var = None for idx, inp in enumerate(inputs): gt_labels = labels[idx] gt_probs = [float(prob[idx][i]) for i in gt_labels] if prob_var is not None: gt_prob_vars = [float(prob_var[idx][i]) for i in gt_labels] gt_itl_lows, gt_itl_his, gt_prob_sds = \ self._calc_beta_intervals(gt_probs, gt_prob_vars) data_np = _convert_image_format(np.expand_dims(inp.asnumpy(), 0), 'NCHW') original_image = _np_to_image(_normalize(data_np), mode='RGB') original_image_path = self._save_original_image( self._sample_index, original_image) predicted_labels = [ int(i) for i in (prob[idx] > threshold).nonzero()[0] ] predicted_probs = [float(prob[idx][i]) for i in predicted_labels] if prob_var is not None: predicted_prob_vars = [ float(prob_var[idx][i]) for i in predicted_labels ] predicted_itl_lows, predicted_itl_his, predicted_prob_sds = \ self._calc_beta_intervals(predicted_probs, predicted_prob_vars) union_labs = list(set(gt_labels + predicted_labels)) sample_id_labels[str(self._sample_index)] = union_labs explain = Explain() explain.sample_id = self._sample_index explain.image_path = original_image_path summary.add_value("explainer", "sample", explain) explain = Explain() explain.sample_id = self._sample_index 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) if prob_var is not None: explain.inference.ground_truth_prob_sd.extend(gt_prob_sds) explain.inference.ground_truth_prob_itl95_low.extend( gt_itl_lows) explain.inference.ground_truth_prob_itl95_hi.extend(gt_itl_his) explain.inference.predicted_prob_sd.extend(predicted_prob_sds) explain.inference.predicted_prob_itl95_low.extend( predicted_itl_lows) explain.inference.predicted_prob_itl95_hi.extend( predicted_itl_his) self._manifest["uncertainty"] = True summary.add_value("explainer", "inference", explain) summary.record(1) if self._is_hoc_registered: self._run_hoc(summary, self._sample_index, inputs[idx], prob[idx])