def visualize(batch_data, model):
i_datum = 0
# mod_layout_choice = model.module_layout_choices[i_datum]
# print model.apollo_net.blobs.keys()
# att_blob_name = "Find_%d_softmax" % (mod_layout_choice * 100 + 1)
#
datum = batch_data[i_datum]
question = (" ".join([QUESTION_INDEX.get(w) for w in datum.question[1:-1]]),)
preds = model.prediction_data[i_datum, :]
top = np.argsort(preds)[-5:]
top_answers = reversed([ANSWER_INDEX.get(p) for p in top])
# att_data = model.apollo_net.blobs[att_blob_name].data[i_datum,...]
# att_data = att_data.reshape((14, 14))
att_data = np.zeros((14, 14))
chosen_parse = datum.parses[model.layout_ids[i_datum]]
fields = [
question,
str(chosen_parse),
"<img src='../../%s'>" % datum.image_path,
att_data,
", ".join(top_answers),
", ".join([ANSWER_INDEX.get(a) for a in datum.answers]),
]
visualizer.show(fields)
def prepare_indices(config):
set_name = "train2014"
word_counts = defaultdict(lambda: 0)
with open(QUESTION_FILE % set_name) as question_f:
questions = json.load(question_f)["questions"]
for question in questions:
words = proc_question(question["question"])
for word in words:
word_counts[word] += 1
for word, count in word_counts.items():
if count >= MIN_COUNT:
QUESTION_INDEX.index(word)
pred_counts = defaultdict(lambda: 0)
with open(MULTI_PARSE_FILE % set_name) as parse_f:
for line in parse_f:
parts = line.strip().replace("(",
"").replace(")",
"").replace(";",
" ").split()
for part in parts:
pred_counts[part] += 1
for pred, count in pred_counts.items():
if count >= 10 * MIN_COUNT:
MODULE_INDEX.index(pred)
answer_counts = defaultdict(lambda: 0)
with open(ANN_FILE % set_name) as ann_f:
annotations = json.load(ann_f)["annotations"]
for ann in annotations:
for answer in ann["answers"]:
if answer["answer_confidence"] != "yes":
continue
word = answer["answer"]
if re.search(r"[^\w\s]", word):
continue
answer_counts[word] += 1
keep_answers = reversed(sorted([(c, a) for a, c in answer_counts.items()]))
keep_answers = list(keep_answers)[:config.answers]
for count, answer in keep_answers:
ANSWER_INDEX.index(answer)
def prepare_indices():
set_name = "train2014"
word_counts = defaultdict(lambda: 0)
with open(QUESTION_FILE % set_name) as question_f:
questions = json.load(question_f)["questions"]
for question in questions:
words = proc_question(question["question"])
for word in words:
word_counts[word] += 1
for word, count in word_counts.items():
if count >= MIN_COUNT:
QUESTION_INDEX.index(word)
pred_counts = defaultdict(lambda: 0)
with open(MULTI_PARSE_FILE % set_name) as parse_f:
for line in parse_f:
parts = line.strip().replace("(", "").replace(")", "").replace(";", " ").split()
for part in parts:
pred_counts[part] += 1
for pred, count in pred_counts.items():
if count >= 10 * MIN_COUNT:
MODULE_INDEX.index(pred)
answer_counts = defaultdict(lambda: 0)
with open(ANN_FILE % set_name) as ann_f:
annotations = json.load(ann_f)["annotations"]
for ann in annotations:
for answer in ann["answers"]:
if answer["answer_confidence"] != "yes":
continue
word = answer["answer"]
if re.search(r"[^\w\s]", word):
continue
answer_counts[word] += 1
keep_answers = reversed(sorted([(c, a) for a, c in answer_counts.items()]))
keep_answers = list(keep_answers)[:1000]
for count, answer in keep_answers:
ANSWER_INDEX.index(answer)
def visualize(batch_data, model):
i_datum = 0
#mod_layout_choice = model.module_layout_choices[i_datum]
#print model.apollo_net.blobs.keys()
#att_blob_name = "Find_%d_softmax" % (mod_layout_choice * 100 + 1)
#
datum = batch_data[i_datum]
question = " ".join([QUESTION_INDEX.get(w) for w in datum.question[1:-1]]),
preds = model.prediction_data[i_datum, :]
top = np.argsort(preds)[-5:]
top_answers = reversed([ANSWER_INDEX.get(p) for p in top])
#att_data = model.apollo_net.blobs[att_blob_name].data[i_datum,...]
#att_data = att_data.reshape((14, 14))
att_data = np.zeros((14, 14))
chosen_parse = datum.parses[model.layout_ids[i_datum]]
fields = [
question,
str(chosen_parse),
"<img src='../../%s'>" % datum.image_path, att_data,
", ".join(top_answers),
", ".join([ANSWER_INDEX.get(a) for a in datum.answers])
]
visualizer.show(fields)
def __init__(self, config, set_name, modules):
if set_name == VAL:
self.data = []
return
questions = []
answers = []
parse_lists = []
worlds = []
if config.quant:
ANSWER_INDEX.index(YES)
ANSWER_INDEX.index(NO)
for i_env, environment in enumerate(ENVIRONMENTS):
if i_env == config.fold and set_name == TRAIN:
continue
if i_env != config.fold and set_name == TEST:
continue
places = list()
with open(LOCATION_FILE % environment) as loc_f:
for line in loc_f:
parts = line.strip().split(";")
places.append(parts[0])
cats = {place: np.zeros((len(CATS),)) for place in places}
rels = {(pl1, pl2): np.zeros((len(RELS),)) for pl1 in places for pl2 in places}
with open(WORLD_FILE % environment) as world_f:
for line in world_f:
parts = line.strip().split(";")
if len(parts) < 2:
continue
name = parts[0][1:]
places_here = parts[1].split(",")
if name in CATS:
cat_id = CATS.index(name)
for place in places_here:
cats[place][cat_id] = 1
elif name in RELS:
rel_id = RELS.index(name)
for place_pair in places_here:
pl1, pl2 = place_pair.split("#")
rels[pl1, pl2][rel_id] = 1
rels[pl2, pl1][rel_id] = -1
clean_places = [p.lower().replace(" ", "_") for p in places]
place_index = {place: i for (i, place) in enumerate(places)}
clean_place_index = {place: i for (i, place) in enumerate(clean_places)}
cat_features = np.zeros((len(CATS), DATABASE_SIZE, 1))
rel_features = np.zeros((len(RELS), DATABASE_SIZE, DATABASE_SIZE))
for p1, i_p1 in place_index.items():
cat_features[:, i_p1, 0] = cats[p1]
for p2, i_p2 in place_index.items():
rel_features[:, i_p1, i_p2] = rels[p1, p2]
world = World(environment, clean_place_index, cat_features, rel_features)
for place in clean_places:
ANSWER_INDEX.index(place)
with open(DATA_FILE % environment) as data_f:
for line in data_f:
line = line.strip()
if line == "" or line[0] == "#":
continue
parts = line.split(";")
question = parts[0]
if question[-1] != "?":
question += " ?"
question = question.lower()
questions.append(question)
answer = parts[1].lower().replace(" ", "_")
if config.quant and question.split()[0] in ("is", "are"):
answer = YES if answer else NO
answers.append(answer)
worlds.append(world)
with open(PARSE_FILE % environment) as parse_f:
for line in parse_f:
parse_strs = line.strip().split(";")
trees = [parse_tree(s) for s in parse_strs]
if not config.quant:
trees = [t for t in trees if t[0] != "exists"]
parse_lists.append(trees)
assert len(questions) == len(parse_lists)
data = []
i_datum = 0
for question, answer, parse_list, world in \
zip(questions, answers, parse_lists, worlds):
tokens = ["<s>"] + question.split() + ["</s>"]
parse_list = parse_list[-config.k_best_parses:]
indexed_question = [QUESTION_INDEX.index(w) for w in tokens]
indexed_answer = \
tuple(ANSWER_INDEX[a] for a in answer.split(",") if a != "")
assert all(a is not None for a in indexed_answer)
layouts = [parse_to_layout(p, world, config, modules) for p in parse_list]
data.append(GeoDatum(
i_datum, indexed_question, parse_list, layouts, indexed_answer, world))
i_datum += 1
self.data = data
logging.info("%s:", set_name)
logging.info("%s items", len(self.data))
logging.info("%s words", len(QUESTION_INDEX))
logging.info("%s functions", len(MODULE_INDEX))
logging.info("%s answers", len(ANSWER_INDEX))
def __init__(self, config, set_name, modules):
if set_name == VAL:
self.data = []
return
questions = []
answers = []
parse_lists = []
worlds = []
if config.quant:
ANSWER_INDEX.index(YES)
ANSWER_INDEX.index(NO)
for i_env, environment in enumerate(ENVIRONMENTS):
if i_env == config.fold and set_name == TRAIN:
continue
if i_env != config.fold and set_name == TEST:
continue
places = list()
with open(LOCATION_FILE % environment) as loc_f:
for line in loc_f:
parts = line.strip().split(";")
places.append(parts[0])
cats = {place: np.zeros((len(CATS), )) for place in places}
rels = {(pl1, pl2): np.zeros((len(RELS), ))
for pl1 in places for pl2 in places}
with open(WORLD_FILE % environment) as world_f:
for line in world_f:
parts = line.strip().split(";")
if len(parts) < 2:
continue
name = parts[0][1:]
places_here = parts[1].split(",")
if name in CATS:
cat_id = CATS.index(name)
for place in places_here:
cats[place][cat_id] = 1
elif name in RELS:
rel_id = RELS.index(name)
for place_pair in places_here:
pl1, pl2 = place_pair.split("#")
rels[pl1, pl2][rel_id] = 1
rels[pl2, pl1][rel_id] = -1
clean_places = [p.lower().replace(" ", "_") for p in places]
place_index = {place: i for (i, place) in enumerate(places)}
clean_place_index = {
place: i
for (i, place) in enumerate(clean_places)
}
cat_features = np.zeros((len(CATS), DATABASE_SIZE, 1))
rel_features = np.zeros((len(RELS), DATABASE_SIZE, DATABASE_SIZE))
for p1, i_p1 in place_index.items():
cat_features[:, i_p1, 0] = cats[p1]
for p2, i_p2 in place_index.items():
rel_features[:, i_p1, i_p2] = rels[p1, p2]
world = World(environment, clean_place_index, cat_features,
rel_features)
for place in clean_places:
ANSWER_INDEX.index(place)
with open(DATA_FILE % environment) as data_f:
for line in data_f:
line = line.strip()
if line == "" or line[0] == "#":
continue
parts = line.split(";")
question = parts[0]
if question[-1] != "?":
question += " ?"
question = question.lower()
questions.append(question)
answer = parts[1].lower().replace(" ", "_")
if config.quant and question.split()[0] in ("is", "are"):
answer = YES if answer else NO
answers.append(answer)
worlds.append(world)
with open(PARSE_FILE % environment) as parse_f:
for line in parse_f:
parse_strs = line.strip().split(";")
trees = [parse_tree(s) for s in parse_strs]
if not config.quant:
trees = [t for t in trees if t[0] != "exists"]
parse_lists.append(trees)
assert len(questions) == len(parse_lists)
data = []
i_datum = 0
for question, answer, parse_list, world in \
zip(questions, answers, parse_lists, worlds):
tokens = ["<s>"] + question.split() + ["</s>"]
parse_list = parse_list[-config.k_best_parses:]
indexed_question = [QUESTION_INDEX.index(w) for w in tokens]
indexed_answer = \
tuple(ANSWER_INDEX[a] for a in answer.split(",") if a != "")
assert all(a is not None for a in indexed_answer)
layouts = [
parse_to_layout(p, world, config, modules) for p in parse_list
]
data.append(
GeoDatum(i_datum, indexed_question, parse_list, layouts,
indexed_answer, world))
i_datum += 1
self.data = data
logging.info("%s:", set_name)
logging.info("%s items", len(self.data))
logging.info("%s words", len(QUESTION_INDEX))
logging.info("%s functions", len(MODULE_INDEX))
logging.info("%s answers", len(ANSWER_INDEX))
