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embodied_tasks.py
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embodied_tasks.py
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import pandas as pd
import traceback
from natsort import natsorted
from utils import encode_image, get_image_paths, LM_client, evaluate_texts
import os
import time
import ast
class EmbodiedTasks:
def __init__(self, query_dict, model, api_key):
self.query_dict = query_dict
self.model = model
self.api_key = api_key
def run(self):
"""
Derive the responses from the LLM API
"""
query_dict = self.query_dict
prompt_ = open("prompts/prompt1.txt", "r").read()
# Initializing large model
client = LM_client(self.model, self.api_key)
for qa in query_dict.keys():
# Path of images in dataset
path = 'dataset/imgs'
# Subfolder names corresponding to samples
list_dir = os.listdir(path)
list_dir = natsorted(list_dir)
if isinstance(query_dict[qa], str):
# Perform only single round interaction with the large model
# Initializing prompt
prompt = prompt_ + query_dict[qa]
# Output from LM
label = pd.DataFrame(columns=['folder_name', 'claude3'])
# Run each sample one by one and collect the output of LM
for count in range(len(list_dir)):
error_time = 0
while True:
try:
# Read the embodied image observation
folder_name = list_dir[count]
folder_path = os.path.join(path, folder_name)
imgs = get_image_paths(folder_path)
# Store historical information of large model dialogues
messages = []
# Run LM to obtain output
messages, answer = client.query(messages, prompt, imgs)
print(answer)
# Concat Output from LM
label.loc[label.shape[0], :] = [folder_name, answer]
break
except Exception as e:
# Dealing with abnormal situations caused by frequent LM access
print(f"An error occurred: {e}")
traceback.print_exc()
error_time += 1
time.sleep(10)
if error_time == 3:
label.loc[label.shape[0], :] = [folder_name, str(e)]
break
else:
continue
elif isinstance(query_dict[qa], list):
# Perform multiple rounds of interaction with the large model
# Initializing prompt
prompt1 = prompt_ + query_dict[qa][0]
prompt2 = query_dict[qa][1]
# Output from LM
label = pd.DataFrame(columns=['folder_name', '1', '2'])
# Run each sample one by one and collect the output of LM
for count in range(len(list_dir)):
error_time = 0
while True:
try:
# Read the embodied image observation
folder_name = list_dir[count]
folder_path = os.path.join(path, folder_name)
imgs = get_image_paths(folder_path)
# Store historical information of large model dialogues
messages = []
# Run LM to obtain output
messages, answer1 = client.query(messages, prompt1, imgs)
messages, answer2 = client.query(messages, prompt2)
# Concat Output from LM
label.loc[label.shape[0], :] = [folder_name, answer1, answer2]
break
except Exception as e:
# Dealing with abnormal situations caused by frequent LM access
print(f"An error occurred: {e}")
traceback.print_exc()
error_time += 1
time.sleep(10)
if error_time == 3:
label.loc[label.shape[0], :] = [folder_name, None, None]
break
else:
continue
# Save the results
save_path = 'results/%s_%s.csv' % (qa, model)
label.to_csv(save_path)
def evaluate(self):
"""
Evaluate the model's performance
"""
for qa in self.query_dict.keys():
if qa == 'scene' or qa[:2] == 'qa':
groundtruth_df = pd.read_csv('dataset/imgs_label/%s.csv' % qa, index_col=0)
label = pd.read_csv('results/%s_%s.csv' % (qa, model), index_col=0)
label = label.iloc[:, 1]
groundtruth = groundtruth_df.apply(lambda row: [row[0], row[1], row[2]], axis=1)
results_df = evaluate_texts(groundtruth, label)
elif qa[:6] == 'dialog':
groundtruth_df = pd.read_csv('dataset/imgs_label/%s.csv' % qa, index_col=0)
label = pd.read_csv('results/%s_%s.csv' % (qa, model), index_col=0)
df_extract = label.apply(lambda row: str(row[1]) + ' ' + str(row[2]), axis=1)
label = df_extract
# 将 'items' 列转换为列表
groundtruth_df.iloc[:, 2] = groundtruth_df.iloc[:, 2].apply(ast.literal_eval)
groundtruth_df.iloc[:, 3] = groundtruth_df.iloc[:, 3].apply(ast.literal_eval)
# 合并第二列和第三列成一个列表，并生成一个新的 Series
groundtruth = groundtruth_df.apply(lambda row: [row[0] + ' ' + row[1]] + [row[2][0] + ' ' + row[2][1]] + [row[3][0] + ' ' + row[3][1]], axis=1)
results_df = evaluate_texts(groundtruth, label)
elif qa[:2] == 'tp':
groundtruth_df = pd.read_csv('dataset/imgs_label/%s.csv' % qa, index_col=0)
label = pd.read_csv('results/%s_%s.csv' % (qa, model), index_col=0)
label = label.iloc[:, 1]
groundtruth = groundtruth_df.apply(lambda row: [row[1], row[2], row[3]], axis=1)
results_df = evaluate_texts(groundtruth, label)
print(qa, ': ')
print(results_df['mean'])
if __name__ == '__main__':
# Part of prompt for LM
# Embodied first-view scene understanding ('situation')
# Embodied question answering ('qa1', ..., 'qa10')
# Embodied dialogue ('dialog1', 'dialog2', 'dialog3')
# Embodied task planning ('tp1', 'tp2', 'tp3')
query_dict = {
'scene': 'please describe your current location, including the surrounding environment, your relationship to the environment, and any relevant spatial information.',
'qa1': 'How many traffic lights can be observed around in total?',
'qa2': 'Is there a building on the left side? What color is it?',
'qa3': 'Are you facing the road, the building, or the greenery?',
'dialog1': ['May I ask if there are any prominent waypoints around?',
'Where are they located respectively?'],
'dialog2': ['May I ask what color the building on the left is?',
'Where is it located relative to the road ahead'],
'dialog3': ['How many trees are there in the rear view?', 'What colors are they respectively'],
'tp1': 'I want to have a cup of coffee at ALL-Star coffee shop, but I have not brought any money. What should I do? Please give a chain-like plan.',
'tp2': 'I need to get an emergency medicine from the pharmacy, but I do not know the way. What should I do? Please give a chain-like plan.',
}
model = "xxxx" # LM models, for example: "claude-3-haiku-20240307", "gpt-4o"
api_key = "xxxxxxx" # Fill in API key
embodied_tasks = EmbodiedTasks(query_dict, model, api_key)
embodied_tasks.run()
embodied_tasks.evaluate()