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Summary: - Dump instance activations, some statistics about each neuron for model introspection visualization in flow - It is a part of minsuk's summer intern project. See the following link for high-level details: https://www.dropbox.com/s/m89rwpoomqkc9jb/aml-talk-nnvis-minsuk.pptx?dl=0 - Will combine the following two visualizations: https://our.intern.facebook.com/intern/fblearner/c2graphvis/13795371/ and https://our.intern.facebook.com/intern/fblearner/model-introspection-nn/11910201/ Differential Revision: D4303679 fbshipit-source-id: eeac699891b17cea0b29324d584937460a8d7a25
105 lines
4.6 KiB
Python
105 lines
4.6 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import numpy as np
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import json
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class IntrospectVisData():
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def __init__(self, model_name, first_outputs, meta_info, lab_arr):
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self.model_name = model_name
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self.max_num_instances = 1000
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self.count = 0
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self.instances = []
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self.labels = [{"id": i, "name": c} for i, c in enumerate(lab_arr)]
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self.is_multilabel = True if 'multi_label' in meta_info\
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and meta_info['multi_label'] else False
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self.neuron_groups = [{"idx": i, "name": b,
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"size": len(first_outputs[2][i -
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len(meta_info['output_names'])][0])}
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for i, b in enumerate(meta_info['output_names'])]
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self.summaries = map(lambda x: np.array([[0. for _ in range(x['size'])]
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for _ in range(len(self.labels))]),
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self.neuron_groups)
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def getInstanceActivations(self, outputs):
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outputs = outputs[(-1) * len(self.neuron_groups):]
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return [[round(_val, 4) for _val in out[0]] for out in outputs]
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def updateNeuronSummaries(self, activations, true_idxs):
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self.count += 1
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for out_idx in range(len(self.summaries)):
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if self.is_multilabel:
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for true_idx in true_idxs:
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self.summaries[out_idx][true_idx] += activations[out_idx]
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else:
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self.summaries[out_idx][true_idxs] += activations[out_idx]
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def appendInstance(self, instance):
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self.instances.append(instance)
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def processInstance(self, idx, labels, scores, outputs, model_specific):
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activations = []
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if self.model_name in ['DocNN']:
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activations = self.getInstanceActivations(outputs)
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self.updateNeuronSummaries(activations, labels)
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if idx < self.max_num_instances:
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if len(activations) == 0:
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activations = self.getInstanceActivations(outputs),
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instance = {
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"id": idx,
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"labels": labels,
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"scores": scores,
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"activations": activations,
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}
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for key, val in model_specific.items():
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instance[key] = val
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self.appendInstance(instance)
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def updateArrangements(self):
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if self.model_name in ['DocNN']:
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# sort class scores based on score values
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for instance in self.instances:
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instance['scores'] =\
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sorted([{"class_id": j, "score": round(_s, 3)}
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for j, _s in enumerate(instance['scores'])],
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key=lambda x: x['score'], reverse=True)
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# instance positions based on scores
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inst_sort_vals = [[] for _ in range(len(self.labels))]
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for i, x in enumerate(self.instances):
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sort_val = 1.0
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# if multi_label, get the first label
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label = x['labels'] if type(x['labels']) == int\
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else x['labels'][0]
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if label == x['scores'][0]['class_id']:
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# How much score difference from that of rank 2 class
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sort_val = x['scores'][0]['score'] - x['scores'][1]['score']
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else:
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# How much score difference from that of rank 1 class
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sort_val = x['scores'][label]['score'] -\
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x['scores'][0]['score']
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inst_sort_vals[label].append({"inst_id": i, "val": sort_val})
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for class_id, inst_vals in enumerate(inst_sort_vals):
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for i, r in enumerate(sorted(inst_vals, key=lambda x: x['val'],
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reverse=True)):
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self.instances[r['inst_id']]['position'] = i
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def postprocess(self, filepath):
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self.neuron_summaries = [np.around((np.swapaxes(_s, 0, 1) /
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float(self.count)), 4).tolist()
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for _s in self.summaries] if self.count > 0 else None
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self.updateArrangements()
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with open(filepath, 'w') as vf:
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json.dump({
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"model_type": self.model_name,
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"neuron_groups": self.neuron_groups,
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"classes": self.labels,
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"instances": self.instances,
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"neuron_summaries": self.neuron_summaries,
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}, vf)
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