225 lines
8.1 KiB
Python
225 lines
8.1 KiB
Python
#! /usr/bin/env python
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"""
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Copyright (c) Facebook, Inc. and its affiliates.
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All rights reserved.
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This source code is licensed under the license found in the LICENSE file in the
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root directory of this source tree.
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Reads SIMMC 2.1 dataset, creates train, devtest, dev formats for ambiguous candidates.
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Author(s): Satwik Kottur
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"""
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from __future__ import absolute_import, division, print_function, unicode_literals
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import argparse
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import copy
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import json
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import os
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import random
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SPLITS = ["train", "dev", "devtest", "teststd"]
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def get_image_name(scene_ids, turn_ind):
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"""Given scene ids and turn index, get the image name.
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"""
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sorted_scene_ids = sorted(
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((int(key), val) for key, val in scene_ids.items()),
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key=lambda x: x[0],
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reverse=True
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)
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# NOTE: Hardcoded to only two scenes.
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if turn_ind >= sorted_scene_ids[0][0]:
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scene_label = sorted_scene_ids[0][1]
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else:
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scene_label = sorted_scene_ids[1][1]
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image_label = scene_label
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if "m_" in scene_label:
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image_label = image_label.replace("m_", "")
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return f"{image_label}.png", scene_label
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def get_object_mapping(scene_label, args):
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"""Get the object mapping for a given scene.
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"""
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scene_json_path = os.path.join(
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args["scene_json_folder"], f"{scene_label}_scene.json"
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)
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with open(scene_json_path, "r") as file_id:
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scene_objects = json.load(file_id)["scenes"][0]["objects"]
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object_map = [ii["index"] for ii in scene_objects]
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return object_map
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def dictionary_to_string(dictionary):
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result = ""
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for k, v in dictionary.items():
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result += k + ":"
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result += str(v) + " "
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return result
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def get_all_answers(dialogs):
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all_answers = []
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for dialog_id, dialog_datum in enumerate(dialogs["dialogue_data"]):
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for turn_ind, turn_datum in enumerate(dialog_datum["dialogue"]):
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all_answers.append(turn_datum["system_transcript"])
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return all_answers
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def main(args):
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for split in SPLITS:
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read_path = args[f"simmc_{split}_json"]
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print(f"Reading: {read_path}")
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with open(read_path, "r") as file_id:
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dialogs = json.load(file_id)
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# load the metadata files
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with open(args["furniture_prefab_metadata"], "r") as file:
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furniture_metadata = json.load(file)
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with open(args["fashion_prefab_metadata"], "r") as file:
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fashion_metadata = json.load(file)
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# get all answer fromm all dialogues to sample answer candidates from for each dialogue iteration
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all_answers = get_all_answers(dialogs)
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# Reformat into simple strings with positive and negative labels.
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# (dialog string, label)
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ambiguous_candidates_data = []
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for dialog_id, dialog_datum in enumerate(dialogs["dialogue_data"]):
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turns = []
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q_turns = []
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a_turns = []
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for turn_ind, turn_datum in enumerate(dialog_datum["dialogue"]):
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query = [turn_datum["transcript"]]
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answer = [turn_datum["system_transcript"]]
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answer_candidates = []
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# sample random answers from the list of all answers as answer candidates
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# sample n_answer_candidates - 1 wrong answer candidates from the list of all answers
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for _ in range(int(args["n_answer_candidates"]) - 1):
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random_idx = random.randint(0, len(all_answers) - 1)
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answer_candidates.append([all_answers[random_idx]])
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answer_candidates.insert(0, answer)
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#random.shuffle(answer_candidates)
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#annotations = turn_datum["transcript_annotated"]
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#if annotations.get("disambiguation_label", False):
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#label = annotations["disambiguation_candidates"]
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image_name, scene_id = get_image_name(
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dialog_datum["scene_ids"], turn_ind
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)
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# load the scene files and get all the prefab pahts to get the object descriptions for each scene
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prefab_paths = []
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scene_path = os.path.join(args["scene_json_folder"], f"{scene_id}_scene.json")
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with open(scene_path, "r") as scene_file:
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scene_data = json.load(scene_file)
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for scene in scene_data["scenes"]:
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for object in scene["objects"]:
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prefab_paths.append(object["prefab_path"])
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# get the metadata for all objects of the scene (prefab_paths)
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object_metadata = []
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for prefab_path in prefab_paths:
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if scene_id[:11] in ["cloth_store", "m_cloth_sto"]:
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object_dict = fashion_metadata[prefab_path]
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elif scene_id[:7] == "wayfair":
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object_dict = furniture_metadata[prefab_path]
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object_str = dictionary_to_string(object_dict)
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object_metadata.append([object_str])
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# If dialog contains multiple scenes, map it accordingly.
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#object_map = get_object_mapping(scene_label, args)
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new_datum = {
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"query": query,
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"answer": answer,
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"answer_candidates": answer_candidates,
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"q_turns": copy.deepcopy(q_turns),
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"a_turns": copy.deepcopy(a_turns),
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"turns": copy.deepcopy(turns),
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"object_metadata": object_metadata,
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"dialog_id": dialog_datum["dialogue_idx"],
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"turn_id": turn_ind,
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#"input_text": copy.deepcopy(history),
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#"ambiguous_candidates": label,
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"image_name": image_name,
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#"object_map": object_map,
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}
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ambiguous_candidates_data.append(new_datum)
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turns.append([turn_datum["transcript"] + turn_datum["system_transcript"]])
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q_turns.append(query)
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a_turns.append(answer)
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# Ignore if system_transcript is not found (last round teststd).
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# if turn_datum.get("system_transcript", None):
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# history.append(turn_datum["system_transcript"])
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print(f"# instances [{split}]: {len(ambiguous_candidates_data)}")
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save_path = os.path.join(
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args["ambiguous_candidates_save_path"],
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f"simmc2.1_ambiguous_candidates_dstc11_{split}.json"
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)
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print(f"Saving: {save_path}")
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with open(save_path, "w") as file_id:
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json.dump(
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{
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"source_path": read_path,
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"split": split,
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"data": ambiguous_candidates_data,
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},
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file_id
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)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument(
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"--simmc_train_json", default=None, help="Path to SIMMC 2.1 train"
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)
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parser.add_argument(
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"--simmc_dev_json", default=None, help="Path to SIMMC 2.1 dev"
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)
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parser.add_argument(
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"--simmc_devtest_json", default=None, help="Path to SIMMC 2.1 devtest"
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)
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parser.add_argument(
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"--simmc_teststd_json", default=None, help="Path to SIMMC 2.1 teststd (public)"
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)
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parser.add_argument(
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"--scene_json_folder", default=None, help="Path to SIMMC scene jsons"
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)
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parser.add_argument(
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"--ambiguous_candidates_save_path",
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required=True,
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help="Path to save SIMMC disambiguate JSONs",
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)
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parser.add_argument(
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"--fashion_prefab_metadata", required=True,
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help="Path to the file with all metadata for fashion objects"
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)
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parser.add_argument(
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"--furniture_prefab_metadata", required=True,
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help="Path to the file with all metadata for fashion objects"
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)
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parser.add_argument(
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"--n_answer_candidates", required=True,
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help="number of answer candidates for the ranking task"
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)
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try:
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parsed_args = vars(parser.parse_args())
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except (IOError) as msg:
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parser.error(str(msg))
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main(parsed_args)
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