neuro-symbolic-visual-dialog/constraints_splitA.py

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2022-08-10 16:49:55 +02:00
"""
author: Adnen Abdessaied
maintainer: "Adnen Abdessaied"
website: adnenabdessaied.de
version: 1.0.1
"""
# --------------------------------------------------------
# adapted from https://github.com/satwikkottur/clevr-dialog/blob/master/constraints.py
# --------------------------------------------------------
import copy
import json
import random
import numpy as np
import global_vars as gvars
# Some quick methods.
def apply_immediate(hist): return (len(hist['objects']) == 1 and
hist['mergeable'] and
'exist' not in hist['template'])
def apply_group(hist): return (len(hist['objects']) >= 2 and
hist['mergeable'] and
'count' not in prev_group)
def caption(scene, templates):
"""Constraints for caption generation.
Args:
scene: CLEVR Scene graphs to generate captions with constraints
template: List of caption templates
Returns:
sample_captions: Samples from caption hypotheses
"""
caption_hypotheses = {}
# Sweep through all templates to extract 'interesting' captions.
n_objs = len(scene['objects'])
rels = scene['relationships']
# Caption Type 1: Extreme locations.
ext_loc_templates = [ii for ii in templates if ii['type'] == 'extreme-loc']
# number of objects in the scene
filter_objs = copy.deepcopy(scene['objects'])
attr_counts = get_attribute_counts_for_objects(scene, filter_objs)
hypotheses = []
for template in ext_loc_templates:
# absolute location based constraint
constraint = template['constraints'][0]
extreme_type = constraint['args'][0]
# check if there is an object that is at the center of the image
# roughly in the middle along front-back and right-left dim
if extreme_type == 'center':
for ii, obj in enumerate(filter_objs):
bla = [len(rels[kk][ii]) <= n_objs / 2
for kk in ['front', 'behind', 'right', 'left']]
matches = np.sum([len(rels[kk][ii]) <= n_objs / 2
for kk in ['front', 'behind', 'right', 'left']])
if matches == 4:
hypotheses.append((extreme_type, copy.deepcopy(obj)))
else:
for ii, obj in enumerate(filter_objs):
if len(rels[extreme_type][ii]) == 0:
hypotheses.append((extreme_type, copy.deepcopy(obj)))
# sample one at random, and create the graph item
# Filter hypothesis which are ambiguous otherwise.
for index, (_, hypothesis) in enumerate(hypotheses):
uniq_attr = [attr for attr in gvars.METAINFO['attributes']
if attr_counts[(attr, hypothesis[attr])] == 1]
for attr in uniq_attr:
del hypotheses[index][1][attr]
hypotheses = [ii for ii in hypotheses if len(ii[1]) > 1]
caption_hypotheses['extreme-loc'] = hypotheses
# Caption Type 2: Unique object and attribute.
filter_objs = copy.deepcopy(scene['objects'])
# each hypothesis is (object, attribute) pair
hypotheses = []
for ii, obj in enumerate(filter_objs):
# get unique set of attributes
uniq_attrs = [ii for ii in gvars.METAINFO['attributes']
if attr_counts[(ii, obj[ii])] == 1]
# for each, add it to hypothesis
for attr in uniq_attrs:
hypotheses.append((obj, attr))
caption_hypotheses['unique-obj'] = hypotheses
# Caption Type 3: Unique attribute count based caption.
# count unique object based constraint
# Each hypothesis is object collection.
caption_hypotheses['count-attr'] = [(attr_val, count)
for attr_val, count in attr_counts.items()
if count > 1]
# Caption Type 4: Relation between two objects.
# Out of the two, one has a unique attribute.
# find a pair of objects sharing a relation, unique
# filter_objs = copy.deepcopy(scene['objects'])
# n_objs = len(filter_objs)
# # get a dict of unique attributes for each object
# uniq_attr = [[] for ii in range(n_objs)]
# non_uniq_attr = [[] for ii in range(n_objs)]
# for ind, obj in enumerate(filter_objs):
# uniq_attr[ind] = [attr for attr in gvars.METAINFO['attributes']
# if attr_counts[(attr, obj[attr])] == 1]
# non_uniq_attr[ind] = [attr for attr in gvars.METAINFO['attributes']
# if attr_counts[(attr, obj[attr])] > 1]
# uniqueness = [len(ii) > 0 for ii in uniq_attr]
# # Hypothesis is a uniq object and non-unique obj2 sharing relation R
# # global ordering for uniqueness
# hypotheses = []
# for rel, order in scene['relationships'].items():
# num_rel = [(ii, len(order[ii])) for ii in range(n_objs)]
# num_rel = sorted(num_rel, key=lambda x: x[1], reverse=True)
# # take only the ids
# num_rel = [ii[0] for ii in num_rel]
# for index, obj_id in enumerate(num_rel[:-1]):
# next_obj_id = num_rel[index + 1]
# # if unique, check if the next one has non-unique attributes
# if uniqueness[obj_id]:
# if len(non_uniq_attr[next_obj_id]) > 0:
# obj1 = (obj_id, random.choice(uniq_attr[obj_id]))
# obj2 = (next_obj_id, random.choice(non_uniq_attr[next_obj_id]))
# hypotheses.append((obj1, rel, obj2))
# # if not unique, check if the next one has unique attributes
# else:
# if len(uniq_attr[next_obj_id]) > 0:
# obj1 = (obj_id, random.choice(non_uniq_attr[obj_id]))
# obj2 = (next_obj_id, random.choice(uniq_attr[next_obj_id]))
# hypotheses.append((obj1, rel, obj2))
# caption_hypotheses['obj-relation'] = hypotheses
sample_captions = sample_from_hypotheses(
caption_hypotheses, scene, templates)
return sample_captions
def question(scene, dialog, template):
"""Constraints question generation.
Inputs:
scene:Partial scene graphs on CLEVR images with generated captions
template: List of question templates to use
Output:
list of object groups
"""
ques_round = len(dialog['graph']['history']) - 1
graph = dialog['graph']
# check for constraints and answer question
if 'group' in template['label']:
groups = []
# Pick a group hypothesis
for ii in graph['history']:
if 'count' in ii or len(ii['objects']) == 0:
groups.append(ii)
if template['label'] == 'count-all':
# Preliminary checks:
# (A) count-all cannot follow count-all, count-other
for prev_history in graph['history'][1:]:
if prev_history['template'] in ['count-all', 'count-other']:
return []
# create object group
obj_group = []
new_obj = {'required': [], 'optional': []}
for obj_id, ii in enumerate(scene['objects']):
obj_copy = copy.deepcopy(new_obj)
obj_copy['id'] = ii['id']
obj_group.append(obj_copy)
# create graph item
graph_item = {'round': ques_round + 1,
'objects': copy.deepcopy(obj_group),
'template': template['label'],
'mergeable': True, 'count': len(obj_group)}
# clean graph item
graph_item = clean_graph_item(graph_item)
# no constraints, count the number of objects in true scene
return [{'answer': len(obj_group), 'group_id': ques_round + 1,
'objects': [], 'graph': graph_item}]
elif (template['label'] == 'count-other' or
template['label'] == 'exist-other'):
# preliminary checks:
# (A) exist-other cannot follow exist-other, count-all, count-other
# (B) count-other cannot follow count-all, count-other
for prev_history in graph['history'][1:]:
if prev_history['template'] in ['count-all', 'count-other']:
return []
if (prev_history['template'] == 'exist-other' and
template['label'] == 'exist-other'):
return []
# get a list of all objects we know
known_ids = [jj['id'] for ii in graph['history'] for jj in ii['objects']]
known_ids = list(set(known_ids))
n_objs = len(scene['objects'])
difference = n_objs - len(known_ids)
diff_ids = [ii for ii in range(n_objs) if ii not in known_ids]
# create empty objects for these
obj_group = [{'id': ii} for ii in diff_ids]
# create graph item
graph_item = {'round': ques_round + 1, 'objects': obj_group,
'template': template['label'], 'mergeable': False}
if 'count' in template['label']:
graph_item['count'] = difference
graph_item['mergeable'] = True # merge if count is known
answer = difference
elif 'exist' in template['label']:
# If heads (> 0.5) -- difference > 0
if random.random() > 0.5:
if difference > 0:
answer = 'yes'
else:
return []
else:
if difference == 0:
answer = 'no'
else:
return []
# no constraints, count the number of objects in true scene
return [{'answer': answer, 'group_id': ques_round + 1,
'objects': [], 'graph': graph_item}]
elif template['label'] == 'count-all-group':
# we need a group in the previous round
prev_group = graph['history'][-1]
prev_label = prev_group['template']
if not (len(prev_group['objects']) > 1 and
'count' not in prev_group and
'obj-relation' not in prev_label):
return []
# check if count is not given before
attrs = [ii for ii in gvars.METAINFO['attributes'] if ii in prev_group]
count = 0
for obj in prev_group['objects']:
count += all([obj[ii] == prev_group['objects'][0][ii] for ii in attrs])
# create object group
obj_group = []
new_obj = {'required': [], 'optional': []}
for obj_id, ii in enumerate(scene['objects']):
obj_copy = copy.deepcopy(new_obj)
obj_copy['id'] = ii['id']
obj_group.append(obj_copy)
# create graph item
graph_item = {'round': ques_round + 1, 'objects': copy.deepcopy(obj_group),
'template': template['label'],
'mergeable': True, 'count': count}
# clean graph item
graph_item = clean_graph_item(graph_item)
# no constraints, count the number of objects in true scene
return [{'answer': count, 'group_id': ques_round + 1,
'objects': [], 'graph': graph_item}]
elif ('count-obj-exclude' in template['label'] or
'exist-obj-exclude' in template['label']):
# placeholder for object description, see below
obj_desc = None
prev_history = graph['history'][-1]
scene_counts = get_attribute_counts_for_objects(scene)
if 'imm' in template['label']:
# we need an immediate group in the previous round
if apply_immediate(prev_history):
focus_id = prev_history['objects'][0]['id']
else:
return []
elif 'early' in template['label']:
# search through history for an object with unique attribute
attr_counts = get_known_attribute_counts(graph)
# get attributes with just one count
single_count = [ii for ii, count in attr_counts.items() if count == 1]
# remove attributes that point to objects in the previous round
# TODO: re-think this again
obj_ids = get_unique_attribute_objects(graph, single_count)
prev_history_obj_ids = [ii['id'] for ii in prev_history['objects']]
single_count = [ii for ii in single_count if
obj_ids[ii] not in prev_history_obj_ids]
if len(single_count) == 0:
return []
# give preference to attributes with multiple counts in scene graph
#scene_counts = get_attribute_counts_for_objects(scene)
ambiguous_attrs = [ii for ii in single_count if scene_counts[ii] > 1]
if len(ambiguous_attrs) > 0:
focus_attr = random.choice(ambiguous_attrs)
else:
focus_attr = random.choice(single_count)
focus_id = obj_ids[focus_attr]
# unique object description
obj_desc = {'required': [focus_attr[0]], 'optional': [],
focus_attr[0]: focus_attr[1]}
# get the known attributes for the current object
focus_obj = graph['objects'][focus_id]
known_attrs = [attr for attr in gvars.METAINFO['attributes']
if attr in focus_obj and
'%s_exclude_count' % attr not in focus_obj]
# for count: only if existence if True, else count it trivially zero
if 'count' in template['label']:
for attr in known_attrs[::-1]:
if not focus_obj.get('%s_exclude_exist' % attr, True):
known_attrs.remove(attr)
# for exist: get relations without exist before
elif 'exist' in template['label']:
known_attrs = [attr for attr in known_attrs
if '%s_exclude_exist' % attr not in focus_obj]
# select an attribute
if len(known_attrs) == 0:
return[]
# split this into zero and non-zero
if 'exist' in template['label']:
focus_attrs = [(ii, scene['objects'][focus_id][ii])
for ii in known_attrs]
zero_count = [ii for ii in focus_attrs if scene_counts[ii] == 1]
nonzero_count = [ii for ii in focus_attrs if scene_counts[ii] > 1]
if random.random() > 0.5:
if len(zero_count) > 0:
attr = random.choice(zero_count)[0]
else:
return []
else:
if len(nonzero_count) > 0:
attr = random.choice(nonzero_count)[0]
else:
return []
else:
attr = random.choice(known_attrs)
# create the object group
obj_group = []
new_obj = {'required': ['attribute'], 'optional': []}
for obj in scene['objects']:
# add if same attribute value and not focus object
if obj[attr] == focus_obj[attr] and obj['id'] != focus_id:
obj_copy = copy.deepcopy(new_obj)
obj_copy['id'] = obj['id']
obj_copy[attr] = focus_obj[attr]
obj_group.append(obj_copy)
answer = len(obj_group)
ref_obj = copy.deepcopy(new_obj)
ref_obj['id'] = focus_id
ref_obj['volatile'] = True
if 'exist' in template['label']:
answer = 'yes' if answer > 0 else 'no'
ref_obj['%s_exclude_exist' % attr] = answer
elif 'count' in template['label']:
ref_obj['%s_exclude_count' % attr] = answer
obj_group.append(ref_obj)
graph_item = {'round': ques_round+1, 'objects': copy.deepcopy(obj_group),
'template': template['label'], 'mergeable': True,
'focus_id': focus_id, 'focus_desc': obj_desc}
if 'count' in template['label']:
graph_item['count'] = answer
graph_item = clean_graph_item(graph_item)
ref_obj['attribute'] = attr
return [{'answer': answer, 'group_id': ques_round + 1,
'required': [], 'optional': [],
'objects': [ref_obj, obj_desc], 'graph': graph_item}]
elif ('count-obj-rel' in template['label'] or
'exist-obj-rel' in template['label']):
# placeholder for object description, see below
obj_desc = None
prev_history = graph['history'][-1]
# we need a single object in the previous round
if 'imm2' in template['label']:
# we need a obj-rel-imm in previous label, same as the current one
prev_label = prev_history['template']
cur_label = template['label']
if 'obj-rel-imm' not in prev_label or cur_label[:5] != prev_label[:5]:
return []
else:
focus_id = prev_history['focus_id']
elif 'imm' in template['label']:
# we need an immediate group in the previous round
if apply_immediate(prev_history):
focus_id = prev_history['objects'][0]['id']
else:
return []
elif 'early' in template['label']:
# search through history for an object with unique attribute
attr_counts = get_known_attribute_counts(graph)
# get attributes with just one count
single_count = [ii for ii, count in attr_counts.items() if count == 1]
# remove attributes that point to objects in the previous round
# TODO: re-think this again
obj_ids = get_unique_attribute_objects(graph, single_count)
prev_history_obj_ids = [ii['id'] for ii in prev_history['objects']]
single_count = [ii for ii in single_count if
obj_ids[ii] not in prev_history_obj_ids]
if len(single_count) == 0:
return []
focus_attr = random.choice(single_count)
for focus_id, obj in graph['objects'].items():
if obj.get(focus_attr[0], None) == focus_attr[1]:
break
# unique object description
obj_desc = {'required': [focus_attr[0]], 'optional': [],
focus_attr[0]: focus_attr[1]}
# get relations with unknown counts
unknown_rels = [rel for rel in gvars.METAINFO['relations']
if '%s_count' % rel not in graph['objects'][focus_id]]
# for count: only if existence if True, else count it trivially zero
if 'count' in template['label']:
for ii in unknown_rels[::-1]:
if not graph['objects'][focus_id].get('%s_exist' % ii, True):
unknown_rels.remove(ii)
# for exist: get relations without exist before
elif 'exist' in template['label']:
unknown_rels = [rel for rel in unknown_rels
if '%s_exist' % rel not in graph['objects'][focus_id]]
# select an object with some known objects
if len(unknown_rels) == 0:
return []
# pick between yes/no for exist questions, 50% of times
if 'exist' in template['label']:
zero_count = [ii for ii in unknown_rels
if len(scene['relationships'][ii][focus_id]) == 0]
nonzero_count = [ii for ii in unknown_rels
if len(scene['relationships'][ii][focus_id]) > 0]
if random.random() > 0.5:
if len(zero_count) > 0:
rel = random.choice(zero_count)
else:
return []
else:
if len(nonzero_count) > 0:
rel = random.choice(nonzero_count)
else:
return []
else:
rel = random.choice(unknown_rels)
# create the object group
obj_group = []
new_obj = {'required': ['relation'], 'optional': []}
obj_pool = scene['relationships'][rel][focus_id]
for obj_id in obj_pool:
obj_copy = copy.deepcopy(new_obj)
obj_copy['id'] = obj_id
obj_group.append(obj_copy)
answer = len(obj_pool)
ref_obj = copy.deepcopy(new_obj)
ref_obj['id'] = focus_id
ref_obj['volatile'] = True
if 'exist' in template['label']:
answer = 'yes' if answer > 0 else 'no'
ref_obj['%s_exist' % rel] = answer
elif 'count' in template['label']:
ref_obj['%s_count' % rel] = answer
obj_group.append(ref_obj)
graph_item = {'round': ques_round+1, 'objects': copy.deepcopy(obj_group),
'template': template['label'], 'mergeable': True,
'focus_id': focus_id, 'focus_desc': obj_desc}
if 'count' in template['label']:
graph_item['count'] = answer
graph_item = clean_graph_item(graph_item)
#ref_obj['relation'] = rel
# add attribute as argument
return [{'answer': answer, 'group_id': ques_round + 1,
'required': [], 'optional': [], 'relation': rel,
'objects': [ref_obj, obj_desc], 'graph': graph_item}]
elif ('count-attribute' in template['label'] or
'exist-attribute' in template['label']):
if 'group' in template['label']:
# we need an immediate group in the previous round
prev_history = graph['history'][-1]
prev_label = prev_history['template']
# if exist: > 0 is good, else > 1 is needed
min_count = 0 if 'exist' in prev_label else 1
if (len(prev_history['objects']) > min_count and
prev_history['mergeable'] and
'obj-relation' not in prev_label):
obj_pool = graph['history'][-1]['objects']
else:
return []
else:
obj_pool = scene['objects']
# get counts for attributes, and sample evenly with 0 and other numbers
counts = get_attribute_counts_for_objects(scene, obj_pool)
# if exist, choose between zero and others wiht 0.5 probability
zero_prob = 0.5 if 'exist' in template['label'] else 0.7
if random.random() > zero_prob:
pool = [ii for ii in counts if counts[ii] == 0]
else:
pool = [ii for ii in counts if counts[ii] != 0]
# check if count is already known
attr_pool = filter_attributes_with_known_counts(graph, pool)
# for exist: get known attributes and remove them
if 'exist' in template['label']:
known_attr = get_known_attributes(graph)
attr_pool = [ii for ii in attr_pool if ii not in known_attr]
# if non-empty, sample it
if len(attr_pool) == 0:
return []
attr, value = random.choice(attr_pool)
# add a hypothesi, and return the answer
count = 0
obj_group = []
new_obj = {attr: value, 'required': [attr], 'optional': []}
for index, obj in enumerate(obj_pool):
if scene['objects'][obj['id']][attr] == value:
obj_copy = copy.deepcopy(new_obj)
obj_copy['id'] = obj['id']
obj_group.append(obj_copy)
count += 1
graph_item = {'round': ques_round + 1, 'objects': copy.deepcopy(obj_group),
'template': template['label'], 'mergeable': True, attr: value}
if 'count' in template['label']:
graph_item['count'] = count
answer = count
elif 'exist' in template['label']:
answer = 'yes' if count > 0 else 'no'
# Clean graph item.
graph_item = clean_graph_item(graph_item)
if count == 0:
# Fake object group, to serve for arguments.
obj_group = [{attr: value, 'required': [attr], 'optional': []}]
return [{'answer': answer, 'group_id': ques_round + 1,
'required': [attr], 'optional': [],
'count': 9999, 'objects': obj_group, 'graph': graph_item}]
elif 'seek-attr-rel' in template['label']:
# Placeholder for object description, see below.
obj_desc = None
prev_history = graph['history'][-1]
if 'imm' in template['label']:
# we need an immediate group in the previous round
if apply_immediate(prev_history):
focus_id = prev_history['objects'][0]['id']
else:
return []
elif 'early' in template['label']:
# search through history for an object with unique attribute
attr_counts = get_known_attribute_counts(graph)
# get attributes with just one count
single_count = [ii for ii, count in attr_counts.items() if count == 1]
# remove attributes that point to objects in the previous round
# TODO: re-think this again
obj_ids = get_unique_attribute_objects(graph, single_count)
prev_history_obj_ids = [ii['id'] for ii in prev_history['objects']]
single_count = [ii for ii in single_count if
obj_ids[ii] not in prev_history_obj_ids]
if len(single_count) == 0:
return []
# give preference to attributes with multiple counts in scene graph
scene_counts = get_attribute_counts_for_objects(scene)
ambiguous_attrs = [ii for ii in single_count if scene_counts[ii] > 1]
if len(ambiguous_attrs) > 0:
focus_attr = random.choice(ambiguous_attrs)
else:
focus_attr = random.choice(single_count)
focus_id = obj_ids[focus_attr]
# unique object description
obj_desc = {'required': [focus_attr[0]], 'optional': [],
focus_attr[0]: focus_attr[1]}
# for each relation, get the object, sample an attribute, and sample
hypotheses = []
for rel in gvars.METAINFO['relations']:
gt_relations = scene['relationships'][rel]
objs = [(ii, len(gt_relations[ii])) for ii in gt_relations[focus_id]]
objs = sorted(objs, key=lambda x: x[1], reverse=True)
if len(objs) == 0:
# add a null hypotheses
# check if the object is known to be extreme
if ('%s_count' % rel not in graph['objects'][focus_id] and
'%s_exist' % rel not in graph['objects'][focus_id]):
random_attr = random.choice(gvars.METAINFO['attributes'])
hypotheses.append((None, rel, random_attr))
continue
closest_obj = objs[0][0]
# check what attributes are known/unknown
known_info = graph['objects'].get(closest_obj, {})
for attr in gvars.METAINFO['attributes']:
if attr not in known_info:
hypotheses.append((closest_obj, rel, attr))
if len(hypotheses) == 0:
return []
sample_id, rel, attr = random.choice(hypotheses)
# add the new attribute to object
new_obj = {'required': ['attribute', 'relation'],
'optional': [], 'id': sample_id}
if sample_id is not None:
answer = scene['objects'][sample_id][attr]
else:
answer = 'none'
new_obj[attr] = answer
graph_item = {'round': ques_round+1, 'objects': [copy.deepcopy(new_obj)],
'template': template['label'], 'mergeable': True,
'focus_id': focus_id, 'focus_desc': obj_desc}
# remove objects if none
if sample_id is None:
graph_item['objects'] = []
graph_item = clean_graph_item(graph_item)
# Add attribute as argument.
new_obj['attribute'] = attr
return [{'answer': new_obj[attr], 'group_id': ques_round + 1,
'required': [], 'optional': [], 'relation': rel,
'objects': [new_obj, obj_desc], 'graph': graph_item}]
elif 'seek-attr' in template['label']:
# placeholder for object description, see below
obj_desc = None
prev_history = graph['history'][-1]
prev_label = prev_history['template']
implicit_attr = None
# we need a single object in the previous round
if 'imm2' in template['label']:
# we need a seek-attr-imm/seek-attr-rel-imm in previous label
if ('seek-attr-imm' not in prev_label and
'seek-attr-rel-imm' not in prev_label):
return []
elif len(prev_history['objects']) == 0:
return []
else:
focus_id = prev_history['objects'][0]['id']
elif 'imm' in template['label']:
# we need an immediate group in the previous round
if apply_immediate(prev_history):
focus_id = prev_history['objects'][0]['id']
else:
return []
elif 'sim' in template['label']:
if 'seek-attr-imm' not in prev_label:
return[]
else:
prev_obj = prev_history['objects'][0]
focus_id = prev_obj['id']
attr = [ii for ii in gvars.METAINFO['attributes'] if ii in prev_obj]
assert len(attr) == 1, 'Something wrong in previous history!'
implicit_attr = attr[0]
if 'early' in template['label']:
# search through history for an object with unique attribute
attr_counts = get_known_attribute_counts(graph)
# get attributes with just one count
single_count = [ii for ii, count in attr_counts.items() if count == 1]
# remove attributes that point to objects in the previous round
# TODO: re-think this again
obj_ids = get_unique_attribute_objects(graph, single_count)
prev_history_obj_ids = [ii['id'] for ii in prev_history['objects']]
single_count = [ii for ii in single_count if
obj_ids[ii] not in prev_history_obj_ids]
# if there is an attribute, eliminate those options
if implicit_attr is not None:
single_count = [ii for ii in single_count if ii[0] != implicit_attr]
obj_ids = get_unique_attribute_objects(graph, single_count)
# again rule out objects whose implicit_attr is known
single_count = [ii for ii in single_count
if implicit_attr not in graph['objects'][obj_ids[ii]]]
if len(single_count) == 0:
return []
# give preference to attributes with multiple counts in scene graph
scene_counts = get_attribute_counts_for_objects(scene)
ambiguous_attrs = [ii for ii in single_count if scene_counts[ii] > 1]
if len(ambiguous_attrs) > 0:
focus_attr = random.choice(ambiguous_attrs)
else:
focus_attr = random.choice(single_count)
focus_id = get_unique_attribute_objects(graph, [focus_attr])[focus_attr]
# unique object description
obj_desc = {'required': [focus_attr[0]], 'optional': [],
focus_attr[0]: focus_attr[1]}
# get unknown attributes, randomly sample one
if implicit_attr is None:
unknown_attrs = [attr for attr in gvars.METAINFO['attributes']
if attr not in graph['objects'][focus_id]]
# TODO: select an object with some known objects
if len(unknown_attrs) == 0:
return []
attr = random.choice(unknown_attrs)
else:
attr = implicit_attr
# add the new attribute to object
new_obj = {'required': ['attribute'], 'optional': [], 'id': focus_id}
if 'sim' in template['label']:
new_obj['required'] = []
new_obj[attr] = scene['objects'][focus_id][attr]
graph_item = {'round': ques_round+1, 'objects': [copy.deepcopy(new_obj)],
'template': template['label'], 'mergeable': True,
'focus_id': focus_id, 'focus_desc': obj_desc}
graph_item = clean_graph_item(graph_item)
# add attribute as argument
new_obj['attribute'] = attr
return [{'answer': new_obj[attr], 'group_id': ques_round + 1,
'required': [], 'optional': [],
'objects': [new_obj, obj_desc], 'graph': graph_item}]
return []
def sample_from_hypotheses(caption_hypotheses, scene, cap_templates):
"""Samples from caption hypotheses given the scene and caption templates.
Args:
caption_hypotheses: List of hypotheses for objects/object pairs
scene: CLEVR image scene graph
cap_templates: List of caption templates to sample captions
Returns:
obj_groups: List of object groups and corresponding sampled captions
"""
obj_groups = []
# Caption Type 1: Extreme location.
hypotheses = caption_hypotheses['extreme-loc']
if len(hypotheses) > 0:
# extreme location hypotheses
extreme_type, focus_obj = random.choice(hypotheses)
# sample optional attributes
obj_attrs = [attr for attr in gvars.METAINFO['attributes']
if attr in focus_obj]
focus_attr = random.choice(obj_attrs)
optional_attrs = [ii for ii in obj_attrs if ii != focus_attr]
sampled_attrs = sample_optional_tags(optional_attrs,
gvars.METAINFO['probabilities'])
# add additional attributes
req_attrs = sampled_attrs + [focus_attr]
filter_obj = {attr: val for attr, val in focus_obj.items()
if attr in req_attrs}
filter_obj['required'] = req_attrs
filter_obj['optional'] = req_attrs
filter_obj['id'] = focus_obj['id']
obj_group = {'required': req_attrs, 'optional': [], 'group_id': 0,
'objects': [filter_obj]}
# also create a clean graph object
graph_item = copy.deepcopy(obj_group)
graph_item = clean_graph_item(graph_item)
graph_item['mergeable'] = True
graph_item['objects'][0]['%s_count' % extreme_type] = 0
graph_item['objects'][0]['%s_exist' % extreme_type] = False
graph_item['template'] = 'extreme-%s' % extreme_type
obj_group['graph'] = graph_item
obj_groups.append([obj_group])
# Caption Type 2: Unique object.
hypotheses = caption_hypotheses['unique-obj']
if len(hypotheses) > 0:
# sample one at random, and create the graph item
focus_obj, focus_attr = random.choice(hypotheses)
# sample optional attributes
optional_attrs = [ii for ii in gvars.METAINFO['attributes']
if ii != focus_attr]
sampled_attrs = sample_optional_tags(optional_attrs,
gvars.METAINFO['probabilities'])
# add additional attributes
req_attrs = sampled_attrs + [focus_attr]
filter_obj = {attr: val for attr, val in focus_obj.items()
if attr in req_attrs}
filter_obj['required'] = req_attrs
filter_obj['optional'] = req_attrs
filter_obj['id'] = focus_obj['id']
obj_group = {'required': req_attrs, 'optional': [], 'group_id': 0,
'objects': [filter_obj]}
# also create a clean graph object
graph_item = copy.deepcopy(obj_group)
graph_item = clean_graph_item(graph_item)
graph_item['mergeable'] = True
graph_item['objects'][0]['unique'] = True
graph_item['template'] = 'unique-obj'
obj_group['graph'] = graph_item
obj_groups.append([obj_group])
# Caption Type 3: Unique attribute count based caption.
hypotheses = caption_hypotheses['count-attr']
if len(hypotheses) > 0:
# Randomly sample one hypothesis and one template.
(attr, value), count = random.choice(hypotheses)
# Segregate counting templates.
count_templates = [ii for ii in cap_templates if 'count' in ii['type']]
template = random.choice(count_templates)
obj_group = {'group_id': 0, 'count': count, attr: value,
'optional': [], 'required': [], 'objects': []}
# get a list of objects which are part of this collection
for ii, obj in enumerate(scene['objects']):
if obj[attr] == value:
new_obj = {'id': obj['id'], attr: value}
new_obj['required'] = [attr]
new_obj['optional'] = []
obj_group['objects'].append(new_obj)
if 'no' in template['label']:
# Count is not mentioned.
del obj_group['count']
graph_item = copy.deepcopy(obj_group)
graph_item['mergeable'] = False
else:
# Count is mentioned.
for index, ii in enumerate(obj_group['objects']):
obj_group['objects'][index]['required'].append('count')
graph_item = copy.deepcopy(obj_group)
graph_item['mergeable'] = True
# clean up graph item
graph_item['template'] = template['label']
graph_item = clean_graph_item(graph_item)
obj_group['graph'] = graph_item
obj_group['use_plural'] = True
obj_groups.append([obj_group])
# Caption Type 4: Relation between two objects (one of them is unique).
# hypotheses = caption_hypotheses['obj-relation']
# if len(hypotheses) > 0:
# (obj_id1, attr1), rel, (obj_id2, attr2) = random.choice(hypotheses)
# obj_group = {'group_id': 0, 'relation': rel}
# # create object dictionaries
# obj1 = {'optional': [], 'required': [attr1], 'id': obj_id1,
# attr1: scene['objects'][obj_id1][attr1]}
# obj2 = {'optional': [], 'required': [attr2], 'id': obj_id2,
# attr2: scene['objects'][obj_id2][attr2]}
# obj_group['objects'] = [obj2, obj1]
# # also create a clean graph object
# graph_item = copy.deepcopy(obj_group)
# graph_item = clean_graph_item(graph_item)
# graph_item['mergeable'] = True
# graph_item['template'] = 'obj-relation'
# obj_group['graph'] = graph_item
# obj_groups.append([obj_group])
return obj_groups
def get_known_attributes(graph):
"""Fetches a list of known attributes given the scene graph.
Args:
graph: Scene graph to check unique attributes from
Returns:
known_attrs: List of known attributes from the scene graph
"""
known_attrs = []
for obj_id, obj_info in graph['objects'].items():
# The attribute is unique already.
# if obj_info.get('unique', False): continue
for attr in gvars.METAINFO['attributes']:
if attr in obj_info:
known_attrs.append((attr, obj_info[attr]))
# also go over the groups
for ii in graph['history']:
# a group of objects, with unknown count
#if 'count' not in ii: continue
for attr in gvars.METAINFO['attributes']:
if attr in ii:
known_attrs.append((attr, ii[attr]))
known_attrs = list(set(known_attrs))
return known_attrs
def get_known_attribute_counts(graph):
"""Fetches a count of known attributes given the scene graph.
Calls get_known_attributes method internally.
Args:
graph: Scene graph to check unique attributes from
Returns:
counts: Count of known attributes from the scene graph
"""
known_attrs = get_known_attributes(graph)
# Go through objects and count.
counts = {ii: 0 for ii in known_attrs}
for _, obj in graph['objects'].items():
for attr, val in known_attrs:
if obj.get(attr, None) == val:
counts[(attr, val)] += 1
return counts
def filter_attributes_with_known_counts(graph, known_attrs):
"""Filters attributes whose counts are known, given the scene graph.
Args:
graph: Scene graph from the dialog generated so far
known_attrs: List of known attributes from the ground truth scene graph
Returns:
known_attrs: List of attributes with unknown counts removed inplace
"""
for attr, val in known_attrs[::-1]:
for ii in graph['history']:
# A group of objects, with unknown count.
if 'count' not in ii:
continue
# Count is absent.
if ii.get(attr, None) == val:
known_attrs.remove((attr, val))
return known_attrs
def clean_graph_item(graph_item):
"""Cleans up graph item (remove 'required' and 'optional' tags).
Args:
graph_item: Input graph item to be cleaned.
Returns:
clean_graph_item: Copy of the graph item after cleaning.
"""
clean_graph_item = copy.deepcopy(graph_item)
if 'optional' in clean_graph_item:
del clean_graph_item['optional']
if 'required' in clean_graph_item:
del clean_graph_item['required']
for index, ii in enumerate(clean_graph_item['objects']):
if 'optional' in ii:
del clean_graph_item['objects'][index]['optional']
if 'required' in ii:
del clean_graph_item['objects'][index]['required']
return clean_graph_item
def get_attribute_counts_for_objects(scene, objects=None):
"""Counts attributes for a given set of objects.
Args:
scene: Scene graph for the dialog generated so far
objects: List of objects. Default = None selects all objects
Returns:
counts: Counts for the attributes for attributes
"""
# Initialize the dictionary.
counts = {}
for attr, vals in gvars.METAINFO['values'].items():
for val in vals:
counts[(attr, val)] = 0
# Now count for each given object.
if objects is None:
objects = scene['objects']
for obj in objects:
for attr in gvars.METAINFO['attributes']:
key = (attr, scene['objects'][obj['id']][attr])
counts[key] = counts.get(key, 0) + 1
return counts
def get_unique_attribute_objects(graph, uniq_attrs):
"""Fetches objects from given scene graph with unique attributes.
Args:
graph: Scene graph constructed from the dialog generated so far
uniq_attrs: List of unique attributes to get attributes
Returns:
obj_ids: List of object ids with the unique attributes
"""
obj_ids = {}
for obj_id, obj in graph['objects'].items():
for attr, val in uniq_attrs:
if obj.get(attr, '') == val:
# At this point the key should not be present.
assert (attr, val) not in obj_ids, 'Attributes not unique!'
obj_ids[(attr, val)] = obj_id
return obj_ids
def sample_optional_tags(optional, sample_probs):
"""Samples additional tags depending on given sample probabilities.
Args:
optional: List of optional tags to sample from.
sample_probs: Probabilities of sampling 'n' tags.
Returns:
sampled: Sampled tags from the optional list
"""
sampled = []
if len(optional) > 0:
n_sample = np.random.choice([0, 1], 1, p=sample_probs[:2])[0]
n_sample = min(n_sample, len(optional))
sampled = random.sample(optional, n_sample)
return sampled