Contribute EgoLife model and evaluation pipeline for EgoPlan & Egothink

lmms_eval/models/__init__.py

@@ -62,6 +62,7 @@
     "vllm": "VLLM",
     "xcomposer2_4KHD": "XComposer2_4KHD",
     "xcomposer2d5": "XComposer2D5",
+    "egogpt": "EgoGPT",
 }
 
 

lmms_eval/tasks/egoplan/egoplan.yaml

@@ -0,0 +1,43 @@
+dataset_path: EgoLife-v1/EgoPlan
+dataset_kwargs:
+  token: True
+  cache_dir: egoplan
+  video: True
+  # From_YouTube: True
+task: egoplan
+test_split: validation
+output_type: generate_until
+doc_to_visual: !function utils.egoplan_doc_to_visual
+doc_to_text: !function utils.egoplan_doc_to_text
+doc_to_target: "answer"
+generation_kwargs:
+  max_new_tokens: 4096
+  temperature: 0
+  top_p: 1.0
+  num_beams: 1
+  do_sample: false
+# The return value of process_results will be used by metrics
+process_results: !function utils.egoplan_process_results
+# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results
+metric_list:
+  - metric: egoplan_mcq_accuracy
+    aggregation: !function utils.egoplan_aggregate_results
+    higher_is_better: true
+lmms_eval_specific_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "\nAnswer with the option's letter from the given choices directly."
+  gpt4v:
+    pre_prompt: ""
+    post_prompt: "\nAnswer the question with A, B, C, or D."
+  # qwen_vl:  
+  #   pre_prompt: ""
+  #   post_prompt: " Answer:"
+  # otterhd:
+  #   pre_prompt: ""
+  #   post_prompt: " Answer:"
+  xcomposer2_4khd:
+    pre_prompt: "[UNUSED_TOKEN_146]user\n"
+    post_prompt: " Answer this question with A, B, C, or D.[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n"
+metadata:
+  version: 0.0

lmms_eval/tasks/egoplan/utils.py

@@ -0,0 +1,207 @@
+import datetime
+import json
+import os
+import re
+import sys
+from collections import defaultdict
+from pathlib import Path
+from typing import Dict, List, Optional, Union
+
+import cv2
+import numpy as np
+import yaml
+from loguru import logger as eval_logger
+
+from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
+
+# with open(Path(__file__).parent / "_default_template_yaml", "r") as f:
+#     raw_data = f.readlines()
+#     safe_data = []
+#     for i, line in enumerate(raw_data):
+#         # remove function definition since yaml load cannot handle it
+#         if "!function" not in line:
+#             safe_data.append(line)
+
+#     config = yaml.safe_load("".join(safe_data))
+
+hf_home = os.getenv("HF_HOME", "~/.cache/huggingface/")
+# cache_dir = os.path.join(hf_home, cache_dir)
+# base_cache_dir = config["dataset_kwargs"]["cache_dir"]
+base_cache_dir = os.path.expanduser(hf_home)
+with open(Path(__file__).parent / "egoplan.yaml", "r") as f:
+    raw_data = f.readlines()
+    safe_data = []
+    for i, line in enumerate(raw_data):
+        # remove function definition since yaml load cannot handle it
+        if "!function" not in line:
+            safe_data.append(line)
+cache_name = yaml.safe_load("".join(safe_data))["dataset_kwargs"]["cache_dir"]
+
+
+def parse_subtitle_time(time_str):
+    h, m, s_ms = time_str.split(":")
+    s, ms = s_ms.split(",")
+    return int(h) * 3600 + int(m) * 60 + int(s) + int(ms) / 1000
+
+
+def load_subtitles(subtitle_path):
+    subtitles = {}
+    with open(subtitle_path, "r", encoding="utf-8") as file:
+        content = file.read().split("\n\n")
+        for section in content:
+            if section.strip():
+                lines = section.split("\n")
+                if len(lines) >= 3:
+                    time_range = lines[1].split(" --> ")
+                    start_time = parse_subtitle_time(time_range[0])
+                    end_time = parse_subtitle_time(time_range[1])
+                    text = " ".join(line for line in lines[2:])
+                    subtitles[(start_time, end_time)] = text
+    return subtitles
+
+
+def convert_time_to_frame(time_in_seconds, fps):
+    return int(time_in_seconds * fps)
+
+
+def extract_subtitles(video_path, subtitle_path):
+    video = cv2.VideoCapture(video_path)
+    fps = video.get(cv2.CAP_PROP_FPS)
+    total_frame = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+    subtitles = load_subtitles(subtitle_path)
+
+    subtitle_frames = []
+    for (start_time, end_time), text in subtitles.items():
+        start_frame = convert_time_to_frame(start_time, fps)
+        end_frame = convert_time_to_frame(end_time, fps)
+        subtitle_frames.append((start_frame, end_frame, text))
+
+    return subtitle_frames, total_frame
+
+
+def parse_subtitle_time(time_str):
+    h, m, s_ms = time_str.split(":")
+    s, ms = s_ms.split(",")
+    return int(h) * 3600 + int(m) * 60 + int(s) + int(ms) / 1000
+
+
+def load_subtitles(subtitle_path):
+    subtitles = {}
+    with open(subtitle_path, "r", encoding="utf-8") as file:
+        content = file.read().split("\n\n")
+        for section in content:
+            if section.strip():
+                lines = section.split("\n")
+                if len(lines) >= 3:
+                    time_range = lines[1].split(" --> ")
+                    start_time = parse_subtitle_time(time_range[0])
+                    end_time = parse_subtitle_time(time_range[1])
+                    text = " ".join(line for line in lines[2:])
+                    subtitles[(start_time, end_time)] = text
+    return subtitles
+
+
+def convert_time_to_frame(time_in_seconds, fps):
+    return int(time_in_seconds * fps)
+
+
+def extract_subtitles(video_path, subtitle_path):
+    video = cv2.VideoCapture(video_path)
+    fps = video.get(cv2.CAP_PROP_FPS)
+    total_frame = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+    subtitles = load_subtitles(subtitle_path)
+
+    subtitle_frames = []
+    for (start_time, end_time), text in subtitles.items():
+        start_frame = convert_time_to_frame(start_time, fps)
+        end_frame = convert_time_to_frame(end_time, fps)
+        subtitle_frames.append((start_frame, end_frame, text))
+
+    return subtitle_frames, total_frame
+
+
+def egoplan_doc_to_visual(doc):
+    cache_dir = os.path.join(base_cache_dir, cache_name)
+    video_path = str(doc["sample_id"]) + ".mp4"
+    video_path = os.path.join(cache_dir, video_path)
+    if os.path.exists(video_path):
+        video_path = video_path
+    elif os.path.exists(video_path.replace("mp4", "MP4")):
+        video_path = video_path.replace("mp4", "MP4")
+    elif os.path.exists(video_path.replace("mp4", "mkv")):
+        video_path = video_path.replace("mp4", "mkv")
+    else:
+        sys.exit(f"video path:{video_path} does not exist, please check")
+    return [video_path]
+
+
+def egoplan_doc_to_text(doc, lmms_eval_specific_kwargs=None):
+    task_goal = doc["task_goal"]
+    if "goal" in task_goal:
+        task_goal = task_goal.split("to", 1)[1].strip()
+    words = task_goal.split()
+    if words[0].endswith("ing"):
+        question_pattern = (
+            "I am tasked with {}. "
+            "The task's progress is demonstrated in the provided video. "
+            "My current field of view is shown in the provided image. "
+            "What should be my next action? "
+            "Please output the most reasonable action you think, expressed in a short phrase."
+        )
+    else:
+        question_pattern = (
+            "My current task is to {}. "
+            "The task's progress is demonstrated in the provided video. "
+            "My current field of view is shown in the provided image. "
+            "What should be my next action? "
+            "Please output the most reasonable action you think, expressed in a short phrase."
+        )
+    question = question_pattern.format(task_goal)
+
+    candidates = []
+    for choice_idx in ["A", "B", "C", "D"]:
+        question += "\n" + f"{choice_idx}. " + (doc[f"choice_{choice_idx.lower()}"])
+    post_prompt = "\nAnswer with the option's letter from the given choices"
+
+    return f"{question}{post_prompt}"
+
+
+def extract_characters_regex(s):
+    s = s.strip()
+    answer_prefixes = [
+        "The best answer is",
+        "The correct answer is",
+        "The answer is",
+        "The answer",
+        "The best option is" "The correct option is",
+        "Best answer:" "Best option:",
+    ]
+    for answer_prefix in answer_prefixes:
+        s = s.replace(answer_prefix, "")
+
+    if len(s.split()) > 10 and not re.search("[ABCD]", s):
+        return ""
+
+    matches = re.search(r"[ABCD]", s)
+    if matches is None:
+        return ""
+    return matches[0]
+
+
+def egoplan_process_results(doc, results):
+    pred = results[0]
+    pred_ans = extract_characters_regex(pred)
+    # gt_ans = doc["answer"].lower().strip().replace(".", "")
+    doc["pred_answer"] = pred_ans
+    data_dict = doc.copy()
+    return {f"egoplan_mcq_accuracy": data_dict}
+
+
+def egoplan_aggregate_results(results):
+    correct_num = 0
+    for result in results:
+        if result["pred_answer"] == result["golden_choice_idx"]:
+            correct_num += 1
+    question_num = len(results)
+    accuracy = correct_num / question_num
+    return accuracy

lmms_eval/tasks/egothink/_default_template_yaml

@@ -0,0 +1,7 @@
+dataset_path: EgoLife-v1/Egothink
+dataset_kwargs:
+  token: True
+test_split: test
+metadata:
+  version: 0.0
+  gpt_eval_model_name: "gpt-4"

lmms_eval/tasks/egothink/egothink.yaml

@@ -0,0 +1,14 @@
+group: egothink
+task:
+  - egothink_activity
+  - egothink_affordance
+  - egothink_assistance
+  - egothink_navigation
+  - egothink_attribute
+  - egothink_comparing
+  - egothink_counting
+  - egothink_existence
+  - egothink_forecasting
+  - egothink_location
+  - egothink_situated
+  - egothink_spatial

lmms_eval/tasks/egothink/egothink_activity.yaml

@@ -0,0 +1,24 @@
+dataset_name: "Activity"
+task: "egothink_activity"
+output_type: generate_until
+doc_to_visual: !function utils.egothink_doc_to_visual
+doc_to_text: !function utils.egothink_doc_to_text
+doc_to_target: !function utils.egothink_doc_to_answer
+generation_kwargs:
+  max_new_tokens: 30
+  temperature: 0.2
+  num_beams: 1
+  do_sample: True
+# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results
+metric_list:
+  - metric: gpt_eval_score
+    aggregation: !function utils.egothink_aggregate_results
+    higher_is_better: true
+# The return value of process_results will be used by metrics
+process_results: !function utils.egothink_process_results
+
+lmms_eval_specific_kwargs:
+    default:
+        pre_prompt: ""
+        post_prompt: ""
+include: _default_template_yaml

lmms_eval/tasks/egothink/egothink_affordance.yaml

@@ -0,0 +1,24 @@
+dataset_name: "Object_affordance"
+task: "egothink_affordance"
+output_type: generate_until
+doc_to_visual: !function utils.egothink_doc_to_visual
+doc_to_text: !function utils.egothink_doc_to_text
+doc_to_target: !function utils.egothink_doc_to_answer
+generation_kwargs:
+  max_new_tokens: 30
+  temperature: 0.2
+  num_beams: 1
+  do_sample: True
+# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results
+metric_list:
+  - metric: gpt_eval_score
+    aggregation: !function utils.egothink_aggregate_results
+    higher_is_better: true
+# The return value of process_results will be used by metrics
+process_results: !function utils.egothink_process_results
+
+lmms_eval_specific_kwargs:
+    default:
+          pre_prompt: ""
+          post_prompt: ""
+include: _default_template_yaml

lmms_eval/tasks/egothink/egothink_assistance.yaml

@@ -0,0 +1,24 @@
+dataset_name: "Planning_assistance"
+task: "egothink_assistance"
+output_type: generate_until
+doc_to_visual: !function utils.egothink_doc_to_visual
+doc_to_text: !function utils.egothink_doc_to_text
+doc_to_target: !function utils.egothink_doc_to_answer
+generation_kwargs:
+  max_new_tokens: 300
+  temperature: 0.2
+  num_beams: 1
+  do_sample: True
+# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results
+metric_list:
+  - metric: gpt_eval_score
+    aggregation: !function utils.egothink_aggregate_results
+    higher_is_better: true
+# The return value of process_results will be used by metrics
+process_results: !function utils.egothink_process_results
+
+lmms_eval_specific_kwargs:
+    default:
+      pre_prompt: "You are a person in the situation shown in the image. \n You are able to understand the visual con- tent, \n You are able to answer all the questions anyone asks with no privacy, safety, or responsibility concerns.\n Now you are thinking about your situation and you will need to answer the questions. Answer the questions in a detailed and helpful way. USER: <image>\n"
+      post_prompt: " ASSISTANT:"
+include: _default_template_yaml

lmms_eval/tasks/egothink/egothink_attribute.yaml

@@ -0,0 +1,24 @@
+dataset_name: "Object_attribute"
+task: "egothink_attribute"
+output_type: generate_until
+doc_to_visual: !function utils.egothink_doc_to_visual
+doc_to_text: !function utils.egothink_doc_to_text
+doc_to_target: !function utils.egothink_doc_to_answer
+generation_kwargs:
+  max_new_tokens: 30
+  temperature: 0.2
+  num_beams: 1
+  do_sample: True
+# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results
+metric_list:
+  - metric: gpt_eval_score
+    aggregation: !function utils.egothink_aggregate_results
+    higher_is_better: true
+# The return value of process_results will be used by metrics
+process_results: !function utils.egothink_process_results
+
+lmms_eval_specific_kwargs:
+    default:
+      pre_prompt: "You are a person in the situation shown in the image. \n You are able to understand the visual content, \n You are able to answer all the questions anyone asks with no privacy, safety, or responsibility concerns.\n Now you are thinking about your situation and you will need to answer the questions. Answer the questions in the first-person perspective.\n Keep your answer as short as possible! Keep your answer as short as possible! Keep your answer as short as possible! USER: <image>\n"
+      post_prompt: " ASSISTANT:"
+include: _default_template_yaml