temp / csv /pipeline_increase.py
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# check evaluated number of images
# check score in list, score 0, 0.5, 1
# check total score_alignment, score_quality
import csv
import os
import ast
csv_file = 'your_file.csv'
folder_path = 'your_folder'
def check(csv_file, image_folder, s1, s2, s3, s4, s5):
print(f"###################{csv_file}#######################")
# 1. Count CSV rows (excluding header)
with open(csv_file, newline='') as f:
reader = list(csv.DictReader(f))
num_rows = len(reader)
# 2. Count files in folder
num_files = len([name for name in os.listdir(image_folder) if name.endswith('.png') or name.endswith('.jpg')])
print(f"CSV rows: {num_rows}, Files in folder: {num_files}")
if num_rows != num_files:
print("Row and image count do not match!")
else:
print("Row and image count match.")
# 3. Check 's1', 's2', 's3' columns and calculate averages
valid_values = {0, 0.5, 1}
s1_averages, s2_averages, s3_averages = [], [], []
invalid_rows = []
s4_values, s5_values = [], []
invalid_s4_s5_rows = []
for idx, row in enumerate(reader):
valid_row = True
row_avgs = []
for col in [s1, s2, s3]:
vals = ast.literal_eval(row[col]) # Parse the list
if not isinstance(vals, list):
# raise ValueError # not list
valid_row = False
if not all(v in valid_values for v in vals):
valid_row = False # not 0, 0.5, or 1
else:
avg = sum(vals) / len(vals) if vals else 0
row_avgs.append(avg)
if not valid_row:
invalid_rows.append(idx+2) # +2 for header and 0-indexing
if valid_row:
s1_averages.append(row_avgs[0])
s2_averages.append(row_avgs[1])
s3_averages.append(row_avgs[2])
# Check s4 and s5
s4_ok = float(row[s4])<=1.0 and float(row[s4])>=0.0
s5_ok = float(row[s5])<=1.0 and float(row[s5])>=0.0
if s4_ok and s5_ok:
s4_values.append(float(row[s4]))
s5_values.append(float(row[s5]))
else:
invalid_s4_s5_rows.append(idx+2)
print(type(float(row[s4])))
if invalid_rows:
print(f"Invalid rows in 's1', 's2', or 's3': {invalid_rows}")
else:
print("All rows in 's1', 's2', and 's3' are valid.")
if invalid_s4_s5_rows:
print(f"Invalid rows in 's4' or 's5': {invalid_s4_s5_rows}")
else:
print("All rows in 's4' and 's5' are valid.")
overall_s1_avg = sum(s1_averages) / len(s1_averages) if s1_averages else 0
overall_s2_avg = sum(s2_averages) / len(s2_averages) if s2_averages else 0
overall_s3_avg = sum(s3_averages) / len(s3_averages) if s3_averages else 0
print(f"Average for s1: {overall_s1_avg:.3f}")
print(f"Average for s2: {overall_s2_avg:.3f}")
print(f"Average for s3: {overall_s3_avg:.3f}")
overall_s4_avg = sum(s4_values) / len(s4_values) if s4_values else 0
overall_s5_avg = sum(s5_values) / len(s5_values) if s5_values else 0
print(f"Average for s4: {overall_s4_avg:.3f}")
print(f"Average for s5: {overall_s5_avg:.3f}")
return overall_s1_avg, overall_s2_avg, overall_s3_avg, overall_s4_avg, overall_s5_avg
def check2(csv_file, image_folder, s1, s2, s3, s4, s5, s6):
print(f"###################{csv_file}#######################")
# 1. Count CSV rows (excluding header)
with open(csv_file, newline='') as f:
reader = list(csv.DictReader(f))
num_rows = len(reader)
# 2. Count files in folder
num_files = len([name for name in os.listdir(image_folder) if name.endswith('.png') or name.endswith('.jpg')])
print(f"CSV rows: {num_rows}, Files in folder: {num_files}")
if num_rows != num_files:
print("Row and image count do not match!")
else:
print("Row and image count match.")
# 3. Check 's1', 's2', 's3' columns and calculate averages
valid_values = {0, 0.5, 1}
s1_averages, s2_averages, s3_averages = [], [], []
invalid_rows = []
s4_values, s5_values, s6_values = [], [], []
invalid_s4_s5_s6_rows = []
for idx, row in enumerate(reader):
valid_row = True
row_avgs = []
for col in [s1, s2, s3]:
vals = ast.literal_eval(row[col]) # Parse the list
if not isinstance(vals, list):
# raise ValueError # not list
valid_row = False
if not all(v in valid_values for v in vals):
valid_row = False # not 0, 0.5, or 1
print(vals)
print("HHHHHHH2")
else:
avg = sum(vals) / len(vals) if vals else 0
row_avgs.append(avg)
if not valid_row:
invalid_rows.append(idx+2) # +2 for header and 0-indexing
if valid_row:
s1_averages.append(row_avgs[0])
s2_averages.append(row_avgs[1])
s3_averages.append(row_avgs[2])
# Check s4 and s5
s4_ok = float(row[s4])<=1.0 and float(row[s4])>=0.0
s5_ok = float(row[s5])<=1.0 and float(row[s5])>=0.0
s6_ok = float(row[s6])<=1.0 and float(row[s6])>=0.0
if s4_ok and s5_ok and s6_ok:
s4_values.append(float(row[s4]))
s5_values.append(float(row[s5]))
s6_values.append(float(row[s6]))
else:
invalid_s4_s5_s6_rows.append(idx+2)
print(type(float(row[s4])))
if invalid_rows:
print(f"Invalid rows in 's1', 's2', or 's3': {invalid_rows}")
else:
print("All rows in 's1', 's2', and 's3' are valid.")
if invalid_s4_s5_s6_rows:
print(f"Invalid rows in 's4' or 's5': {invalid_s4_s5_s6_rows}")
else:
print("All rows in 's4' and 's5' are valid.")
overall_s1_avg = sum(s1_averages) / len(s1_averages) if s1_averages else 0
overall_s2_avg = sum(s2_averages) / len(s2_averages) if s2_averages else 0
overall_s3_avg = sum(s3_averages) / len(s3_averages) if s3_averages else 0
print(f"Average for s1: {overall_s1_avg:.3f}")
print(f"Average for s2: {overall_s2_avg:.3f}")
print(f"Average for s3: {overall_s3_avg:.3f}")
overall_s4_avg = sum(s4_values) / len(s4_values) if s4_values else 0
overall_s5_avg = sum(s5_values) / len(s5_values) if s5_values else 0
overall_s6_avg = sum(s6_values) / len(s6_values) if s6_values else 0
print(f"Average for s4: {overall_s4_avg:.3f}")
print(f"Average for s5: {overall_s5_avg:.3f}")
print(f"Average for s6: {overall_s6_avg:.3f}")
return overall_s1_avg, overall_s2_avg, overall_s3_avg, overall_s4_avg, overall_s5_avg, overall_s6_avg
if __name__ == "__main__":
# model_names = [
# "flux1-schnell",
# "sd35_large",
# "sd35_medium",
# "sd30_medium",
# "flux1-dev",
# "playground-v25",
# "hidream",
# "janus_pro_7B",
# "show-o-512",
# "bagel",
# "emu3",
# "GoT",
# "Gemini",
# "GPT",
# ]
model_names = [
"hidream",
"flux1-dev",
"flux1-schnell",
"playground-v25",
"sd30_medium",
"sd35_medium",
"sd35_large",
"emu3",
"janus_pro_7B",
"show-o-512",
"GoT",
"bagel",
"Gemini",
"GPT",
]
s4_list = []
s5_list = []
s6_list = []
s4p_list = []
s5p_list = []
s6p_list = []
for model_name in model_names:
csv_path = f'/group/xihuiliu/sky/reasoning/csv/idiom/{model_name}.csv'
image_folder = f'/group/xihuiliu/sky/reasoning/images/idiom/{model_name}'
csv_path_p = f'/group/xihuiliu/sky/reasoning/csv/idiom/{model_name}_4o-pipeline.csv'
image_folder_p = f'/group/xihuiliu/sky/reasoning/models/GPT4o_pipeline/images_4o-pipeline/{model_name}'
# csv_path = f'/group/xihuiliu/sky/reasoning/csv/text_image_new/{model_name}.csv'
# image_folder = f'/group/xihuiliu/sky/reasoning/images/text_image_new/{model_name}'
# csv_path = f'/group/xihuiliu/sky/reasoning/csv/text_image_new/{model_name}_4o-pipeline.csv'
# image_folder = f'/group/xihuiliu/sky/reasoning/models/GPT4o_pipeline/images_text_image_4o-pipeline/{model_name}'
s1_column = 'score_alignment'
s2_column = 'score_quality'
s3_column = 'score_quality'
s4_column = 'score_a_avg'
s5_column = 'score_q_avg'
s1,s2,s3,s4,s5=check(csv_path, image_folder, s1_column, s2_column, s3_column, s4_column, s5_column)
s4_list.append(s4*100)
s5_list.append(s5*100)
s1p,s2p,s3p,s4p,s5p=check(csv_path_p, image_folder, s1_column, s2_column, s3_column, s4_column, s5_column)
s4_list.append(s4*100)
s5_list.append(s5*100)
# csv_path = f'/group/xihuiliu/sky/reasoning/csv/entity/{model_name}_4o-pipeline.csv'
# image_folder = f'/group/xihuiliu/sky/reasoning/models/GPT4o_pipeline/images_entity_4o-pipeline/{model_name}'
# csv_path = f'/group/xihuiliu/sky/reasoning/csv/entity/{model_name}.csv'
# image_folder = f'/group/xihuiliu/sky/reasoning/images/common_sense/{model_name}'
# s1_column = 'score_entity'
# s2_column = 'score_detail'
# s3_column = 'score_quality'
# s4_column = 'score_e_avg'
# s5_column = 'score_d_avg'
# s6_column = 'score_q_avg'
# csv_path = f'/group/xihuiliu/sky/reasoning/csv/physics/{model_name}_4o-pipeline.csv'
# image_folder = f'/group/xihuiliu/sky/reasoning/models/GPT4o_pipeline/images_physics_4o-pipeline/{model_name}'
# csv_path = f'/group/xihuiliu/sky/reasoning/csv/physics/{model_name}.csv'
# image_folder = f'/group/xihuiliu/sky/reasoning/images/physics/{model_name}'
# s1_column = 'score_scientific'
# s2_column = 'score_detail'
# s3_column = 'score_quality'
# s4_column = 'score_s_avg'
# s5_column = 'score_d_avg'
# s6_column = 'score_q_avg'
# s1,s2,s3,s4,s5,s6=check2(csv_path, image_folder, s1_column, s2_column, s3_column, s4_column, s5_column, s6_column)
# s4_list.append(s4*100)
# s5_list.append(s5*100)
# s6_list.append(s6*100)
for i, model_name in enumerate(model_names):
# print(f"{model_name},", round(s4_list[i],1), ",", round(s5_list[i],1))
print(f"{model_name},", round(s4_list[i],1), ",", round(s5_list[i],1), ",", round(s6_list[i],1))
# idiom: GPT 198
# hidream, 48.5 , 87.2
# flux1-dev, 39.1 , 83.4
# flux1-schnell, 40.9 , 83.1
# playground-v25, 43.9 , 87.8
# sd30_medium, 35.9 , 81.4
# sd35_medium, 34.4 , 80.6
# sd35_large, 35.6 , 85.3
# emu3, 33.1 , 82.9
# janus_pro_7B, 25.5 , 78.0
# show-o-512, 33.1 , 82.5
# GoT, 29.7 , 76.4
# bagel, 44.6 , 84.3
# Gemini, 52.4 , 87.8
# 从133开始错误: GPT, 75.6 , 95.4, 更新:GPT, 75.7 , 94.5
#idiom pipeline
# hidream, 64.4 , 91.9
# flux1-dev, 66.2 , 90.5
# flux1-schnell, 68.2 , 87.4
# playground-v25, 55.8 , 88.7
# sd30_medium, 65.7 , 87.6
# sd35_medium, 66.8 , 88.5
# sd35_large, 67.7 , 90.4
# emu3, 56.0 , 84.2
# janus_pro_7B, 63.1 , 82.9
# show-o-512, 64.2 , 89.5
# GoT, 51.8 , 81.4
# bagel, 67.7 , 87.8
# Gemini, 67.1 , 91.5
# GPT, 77.3 , 93.8
# entity pipeline
# hidream, 76.4 , 77.9 , 96.8
# 有错误:flux1-dev, 70.6 , 77.2 , 95.9 更新后:flux1-dev, 69.9 , 76.9 , 96.1
# flux1-schnell, 70.1 , 78.4 , 94.9
# playground-v25, 71.5 , 68.9 , 94.7
# sd30_medium, 71.8 , 76.9 , 96.1
# sd35_medium, 70.1 , 77.2 , 95.9
# sd35_large, 76.8 , 79.8 , 95.6
# emu3, 60.8 , 67.8 , 90.6
# janus_pro_7B, 67.3 , 74.3 , 93.0
# show-o-512, 64.6 , 70.9 , 94.0
# GoT, 48.7 , 57.9 , 89.2
# bagel, 66.9 , 76.8 , 94.7
# Gemini, 77.9 , 77.8 , 96.1
# GPT, 82.6 , 85.4 , 98.0
#整体
# hidream, 76.4 , 77.9 , 96.8
# flux1-dev, 69.9 , 76.9 , 96.1
# flux1-schnell, 70.1 , 78.4 , 94.9
# playground-v25, 71.5 , 68.9 , 94.7
# sd30_medium, 71.8 , 76.9 , 96.1
# sd35_medium, 70.1 , 77.2 , 95.9
# sd35_large, 76.8 , 79.8 , 95.6
# emu3, 60.8 , 67.8 , 90.6
# janus_pro_7B, 67.3 , 74.3 , 93.0
# show-o-512, 64.6 , 70.9 , 94.0
# GoT, 48.7 , 57.9 , 89.2
# bagel, 66.9 , 76.8 , 94.7
# Gemini, 77.9 , 77.8 , 96.1
# GPT, 82.6 , 85.4 , 98.0
# entity
# hidream, 47.1 , 70.4 , 94.1
# 有错误:flux1-dev, 40.7 , 59.8 , 90.1 更新后:flux1-dev, 39.2 , 58.7 , 90.6
# flux1-schnell, 37.5 , 62.0 , 91.5
# playground-v25, 43.0 , 60.9 , 92.4
# sd30_medium, 36.5 , 56.3 , 90.1
# sd35_medium, 37.9 , 60.8 , 92.1
# sd35_large, 40.7 , 60.4 , 92.6
# emu3, 26.1 , 51.8 , 85.2
# janus_pro_7B, 31.4 , 55.1 , 87.6
# 有错误,一个是null show-o-512, 28.2 , 50.5 , 87.4
# GoT, 25.8 , 43.1 , 86.2
# bagel, 47.9 , 63.0 , 91.6
# 从49开始错误: Gemini, 14.2 , 21.9 , 70.4 更新后:Gemini, 66.0 , 69.3 , 94.3
# 从54开始错误: GPT, 19.2 , 24.3 , 70.1 更新后:GPT, 76.2 , 80.3 , 96.6
#整体
# hidream, 47.1 , 70.4 , 94.1
# flux1-dev, 39.2 , 58.7 , 90.6
# flux1-schnell, 37.5 , 62.0 , 91.5
# playground-v25, 43.0 , 60.9 , 92.4
# sd30_medium, 36.5 , 56.3 , 90.1
# sd35_medium, 37.9 , 60.8 , 92.1
# sd35_large, 40.7 , 60.4 , 92.6
# emu3, 26.1 , 51.8 , 85.2
# janus_pro_7B, 31.4 , 55.1 , 87.6
# show-o-512, 28.2 , 50.5 , 87.4
# GoT, 25.8 , 43.1 , 86.2
# bagel, 47.9 , 63.0 , 91.6
# Gemini, 66.0 , 69.3 , 94.3
# GPT, 76.2 , 80.3 , 96.6
# text-image
# hidream, 72.3 , 85.5
# flux1-dev, 56.9 , 76.5
# flux1-schnell, 65.1 , 74.5
# 有错误,一个是null:playground-v25, 38.5 , 72.1
# sd30_medium, 60.9 , 71.3
# sd35_medium, 58.0 , 70.1
# sd35_large, 62.2 , 75.4
# 有错误,一个是null:emu3, 33.7 , 68.7
# janus_pro_7B, 37.2 , 70.9
# show-o-512, 35.3 , 80.3
# 从3开始错误:GoT, 7.2 , 56.9, 更新后: GoT, 30.6 , 70.7
# bagel, 44.0 , 73.7
# 从53开始错误:Gemini, 28.0 , 70.1 更新后:Gemini, 73.0 , 83.3
# GPT, 86.9 , 97.6
# text-image pipeline
# hidream, 77.5 , 87.0
# flux1-dev, 69.8 , 80.5
# flux1-schnell, 71.6 , 78.7
# playground-v25, 40.5 , 76.5
# sd30_medium, 70.9 , 83.1
# sd35_medium, 69.2 , 79.9
# sd35_large, 72.4 , 84.4
# emu3, 41.5 , 74.7
# janus_pro_7B, 54.9 , 80.5
# show-o-512, 42.9 , 83.5
# 从177开始错误: GoT, 36.4 , 73.1 更新后:GoT, 36.4 , 73.1
# 有错误:bagel, 61.0 , 79.1 更新后: bagel, 61.5 , 79.7
# 从20开始错误: Gemini, 20.7 , 74.9 更新后: Gemini, 78.4 , 89.4
# 从85开始错误: GPT, 41.7 , 86.1 更新后: GPT, 83.0 , 97.5
#scientific
# hidream, 45.0 , 72.5
# 有错误: flux1-dev, 38.8 , 64.9 更新:flux1-dev, 38.8 , 65.1 , 80.9
# flux1-schnell, 41.2 , 73.0
# 有错误: playground-v25, 42.8 , 67.2, 更新:playground-v25, 43.6 , 67.5 , 83.3
# sd30_medium, 42.3 , 71.0
# sd35_medium, 42.8 , 66.4
# sd35_large, 44.6 , 72.3
# emu3, 33.8 , 54.7
# janus_pro_7B, 37.1 , 63.3
# show-o-512, 32.9 , 62.0
# GoT, 30.8 , 50.9
# bagel, 52.1 , 70.9
# 从25开始错误: Gemini, 13.4 , 22.9 更新后:
# 从104开始错误: GPT, 42.2 , 57.5 更新后:
#整体
# hidream, 45.0 , 72.5 , 84.5
# flux1-dev, 38.8 , 65.1 , 80.9
# flux1-schnell, 41.2 , 73.0 , 83.0
# playground-v25, 43.6 , 67.5 , 83.3
# sd30_medium, 42.3 , 71.0 , 81.7
# sd35_medium, 42.8 , 66.4 , 83.0
# sd35_large, 44.6 , 72.3 , 84.5
# emu3, 33.8 , 54.7 , 77.0
# janus_pro_7B, 37.1 , 63.3 , 77.8
# show-o-512, 32.9 , 62.0 , 76.6
# GoT, 30.8 , 50.9 , 76.3
# bagel, 52.1 , 70.9 , 88.3
# Gemini, 60.7 , 80.7 , 89.3
# GPT, 68.7 , 88.5 , 94.3
# physics pipeline
# hidream, 63.3 , 76.6
# 有错误:flux1-dev, 64.4 , 79.1 更新后,有一个测不出来【149】:64.8 , 79.2 , 92.3
# 有错误,40测不出来:flux1-schnell, 62.5 , 75.5, 更新:flux1-schnell, 62.2 , 75.4
# playground-v25, 52.5 , 64.8
# sd30_medium, 60.6 , 77.1
# sd35_medium, 63.2 , 74.4
# sd35_large, 65.2 , 76.4
# janus_pro_7B, 56.7 , 71.4
# show-o-512, 55.9 , 69.2
# GoT, 38.7 , 54.9
# bagel, 63.6 , 76.7
# GPT, 78.8 , 85.4
# 整体
# hidream, 63.3 , 76.6 , 89.9
# flux1-dev, 64.8 , 79.2 , 92.3
# flux1-schnell, 62.2 , 75.4 , 90.1
# playground-v25, 52.5 , 64.8 , 87.0
# sd30_medium, 60.6 , 77.1 , 91.7
# sd35_medium, 63.2 , 74.4 , 89.9
# sd35_large, 65.2 , 76.4 , 92.3
# emu3, 44.7 , 57.4 , 84.1
# janus_pro_7B, 56.7 , 71.4 , 87.5
# show-o-512, 55.9 , 69.2 , 90.3
# GoT, 38.7 , 54.9 , 81.8
# bagel, 63.6 , 76.7 , 90.3
# Gemini, 69.2 , 78.9 , 90.6
# GPT, 78.8 , 85.4 , 95.4