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print_results.py
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print_results.py
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from datetime import datetime
import numpy as np
import torch
import pandas as pd
import os
import argparse
from functools import cmp_to_key
from tabulate import tabulate
DATASETS = ['davis2016', 'davis2017', 'isic2018', 'duts', 'ap10k', 'eigen']
TASKS = ['vos', 'mvos', 'ds', 'sod', 'animalkp', 'depth']
MODELS = {
0: ['VTM_unified_LARGE_noCont_noCat',
'VTM_unified_LARGE_benchmark_table5_2_base_pseudo',
'VTM_unified_LARGE_noCat', 'VTM_unified_LARGE_noCont',
'VTM_unified_LARGE',
'VTM_unified_LARGE_benchmark',
'VTM_unified_LARGEtable_5_2_segonly',
'VTM_unified_LARGE_table5_2_noseg'],
1: ['VTM_unified_LARGE_benchmark_taskonomy_only',
'VTM_unified_LARGE_benchmark_table5_3_midair_pseudo',
'VTM_unified_LARGE_benchmark_target_domain_pseudo_except_taskonomy',
'VTM_unified_LARGE_benchmark_table5_3_alldata_uniform'],
2: [
'VTM_unified_LARGE_table5_4_nobf',
'VTM_unified_LARGE_benchmark_table5_4_l2b10'],
3: [
'VTM_unified_LARGE_noCont_noCat',
'VTM_unified_LARGE_benchmark_table5_2_base_pseudo',
'VTM_unified_LARGE_noCat',
'VTM_unified_LARGE',
'VTM_unified_LARGE_benchmark',
],
4: [
'VTM_task:all',
'VTM_task:all_LARGE',
'VTM_task:all_LARGE_nl:8_mc:4',
],
5: [
'VTM_unified_LARGE_benchmark'
],
6: [
'VTM_unified_LARGE_benchmark_without_l2b',
'VTM_unified_LARGE_benchmark_table5_4_l2b10',
'VTM_unified_LARGE_benchmark',
]
}
dataset_dict = {
'davis2016': {
'vos': ('Video Object Segmentation', 'J&F-Mean (↑)'),
},
'davis2017': {
'vos': ('Video (Multiple) Object Segmentation', 'J&F-Mean (↑)'),
},
'isic2018': {
'ds': ('Dermoscopic Image Segmentation', 'DICE (↑)'),
},
'duts': {
'sod': ('Salient Object Segmentation', 'Max.F (↑)'),
},
'ap10k': {
'animalkp': ('Animal Keypoint', 'AP (↑)'),
},
'eigen': {
'depth': ('Depth Prediction', 'Abs.Rel (↓)'),
}
}
shots_dict = {
'davis2016': [1, 2, 3, 4],
'davis2017': [1],
'isic2018': [1, 2, 5, 10],
'duts': [1, 2, 5, 10],
'ap10k': [1, 5, 10, 20, 50],
'eigen': [1, 5, 10, 20, 40, 50]
}
shots_dict = {k: [str(i) for i in v] for k, v in shots_dict.items()}
def safe_listdir(path):
try:
return os.listdir(path)
except:
return []
def update_results(table, row, col, value, tag, mode='max'):
if row not in table.index:
table = pd.concat([table, pd.DataFrame(index=[row], columns=table.columns)])
if mode == 'max':
if not isinstance(table.loc[row][col], tuple):
table.loc[row][col] = (f'{value:.04f}', 1, tag)
else:
old_value, count, old_tag = table.loc[row][col]
if value > float(old_value):
table.loc[row][col] = (f'{value:.04f}', count + 1, tag)
else:
table.loc[row][col] = (old_value, count + 1, old_tag)
else:
if not isinstance(table.loc[row][col], tuple):
table.loc[row][col] = (f'{value:.04f}', 1, tag)
else:
old_value, count, old_tag = table.loc[row][col]
if value < float(old_value):
table.loc[row][col] = (f'{value:.04f}', count + 1, tag)
else:
table.loc[row][col] = (old_value, count + 1, old_tag)
return table
def add_results(table, row, col, value, tag, mode='max'):
if row not in table.index:
table = pd.concat([table, pd.DataFrame(index=[row], columns=table.columns)])
if not isinstance(table.loc[row][col], dict):
table.loc[row][col] = {tag:[value], 'mode':mode}
else:
if tag in table.loc[row][col]:
table.loc[row][col][tag].append(value)
else:
table.loc[row][col][tag] = [value]
return table
def process_dups(table):
for row in table.index:
for col in table.columns:
if isinstance(table.loc[row][col], dict):
res_dict = table.loc[row][col]
mode = res_dict.pop('mode')
aggregate = {}
for tag in res_dict:
aggregate[tag] = (np.mean(res_dict[tag]), np.std(res_dict[tag]), len(res_dict[tag]))
if mode == 'max':
best_tag = max(aggregate, key=lambda x: aggregate[x][0])
else:
best_tag = min(aggregate, key=lambda x: aggregate[x][0])
all_results = list(res_dict.values())[0]
all_results = [f'{x:.04f}' for x in all_results]
table.loc[row][col] = (f'{aggregate[best_tag][0]:.04f} \u00B1 {aggregate[best_tag][1]:.04f}' , f'{aggregate[best_tag][2]}', best_tag, all_results)
def create_database(task_dict, exp_name=None, subname_prefix=None, print_failure=False, from_last=False, compact=False):
result_root = os.path.join('experiments', args.result_dir)
# create indices with model names
if exp_name is not None:
model_names = [exp_name]
else:
model_names = sorted(safe_listdir(result_dir))
if args.table_num is not None:
model_names = MODELS[args.table_num]
if args.exp_pattern:
model_names = [model_name for model_name in model_names if (args.exp_pattern in model_name)]
# create multi-columns with dataset, task, and metric
keys = []
for dataset in datasets:
for shot in shots_dict[dataset]:
keys += [(dataset.upper(), shot, task_name, metric, dataset, task)
for task, (task_name, metric) in task_dict[dataset]]
if compact:
break
columns = [key[:4] for key in keys]
columns = pd.MultiIndex.from_tuples(columns, names=['Dataset', 'Shot', 'Task', 'Metric'])
result_ptf = '_fromlast' if from_last else ''
# construct a database
database = pd.DataFrame(index=model_names, columns=columns)
for model_name in model_names:
for (*column, dataset, task) in keys:
column = tuple(column)
exp_name = model_name
exp_dir = os.path.join(result_root, exp_name)
if not os.path.exists(exp_dir):
continue
shot = column[1]
if dataset == 'davis2016':
result_name = f'davis2016_vos_results_shot:{shot}{result_ptf}/global_results-val.csv'
elif dataset == 'davis2017':
result_name = f'davis2017_mvos_results_shot:{shot}{result_ptf}/global_results-val.csv'
elif dataset == 'isic2018':
result_name = f'isic2018_ds_results_shot:{shot}{result_ptf}.pth'
elif dataset == 'duts':
result_name = f'duts_sod_results_shot:{shot}{result_ptf}.pth'
elif dataset == 'ap10k':
result_name = f'ap10k_animalkp_results_shot:{shot}{result_ptf}/result.pth'
elif dataset == 'eigen':
result_name = f'{dataset}_depth_results_shot:{shot}.pth'
else:
raise NotImplementedError
subname_ptf_list = list(set(['_'.join(exp_subname.split('_')[1:]) for exp_subname in safe_listdir(exp_dir)]))
for subname_ptf in subname_ptf_list:
exp_subnames = [exp_subname for exp_subname in safe_listdir(exp_dir)
if '_'.join(exp_subname.split('_')[1:]) == subname_ptf]
if subname_prefix is not None:
exp_subnames = [exp_subname for exp_subname in exp_subnames if all(subname_prefix[i] in exp_subname for i in range(len(subname_prefix)))]
if '_shot:' in subname_ptf:
vis_subname_ptf = subname_ptf.replace(f'_shot:{shot}', '')
else:
vis_subname_ptf = subname_ptf
if '_support_idx' in vis_subname_ptf:
for i in range(1, 6):
vis_subname_ptf = vis_subname_ptf.replace(f'_support_idx:{i}', '')
vis_subname_ptf = vis_subname_ptf.replace('_skip_crowd:True_ssl:True_top_one:True', '')
row = f'{model_name} ({vis_subname_ptf})' if subname_ptf != '' else model_name
for exp_subname in exp_subnames:
result_path = os.path.join(exp_dir, exp_subname, 'logs', result_name)
if os.path.exists(result_path):
mode = 'max'
if result_path.endswith('.csv'):
result = pd.read_csv(result_path)
else:
result = torch.load(result_path)
if dataset in ['davis2016', 'davis2017']:
value = result['J&F-Mean'][0]
elif dataset == 'linemod':
value = result[2]
elif dataset == 'isic2018':
value = result
elif dataset == 'duts':
value = result['Max.F']
elif dataset == 'ap10k':
value = result[0][1]
elif dataset == 'eigen':
value = result['abs_rel']
mode = 'min'
tag = exp_subname.replace(f'_{subname_ptf}', '')
if args.lr is None or tag == f'lr:{args.lr}':
database = add_results(database, row, column, value, tag, mode)
elif print_failure:
print(result_path)
process_dups(database)
return database
if True:
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', nargs='+', default=None, choices=DATASETS)
parser.add_argument('--result_dir', type=str, default='TEST')
parser.add_argument('--exp_name', type=str, default=None)
parser.add_argument('--exp_pattern', type=str, default=None)
parser.add_argument('--table_num', type=int, default=None)
parser.add_argument('--subname_prefix', '-sprf', type=str, default=None, nargs='+')
parser.add_argument('--verbose', '-v', default=False, action='store_true')
parser.add_argument('--from_last', '-fl', default=False, action='store_true')
parser.add_argument('--compact', '-cp', default=False, action='store_true')
parser.add_argument('--lr', type=str, default=None)
parser.add_argument('--save', action='store_true', default=False)
args = parser.parse_args()
result_dir = os.path.join('experiments', args.result_dir)
# choose datasets to show
if args.dataset is not None:
datasets = args.dataset
else:
datasets = list(set([
log.split('_')[0]
for exp_name in safe_listdir(result_dir)
for exp_subname in safe_listdir(os.path.join(result_dir, exp_name))
for log in safe_listdir(os.path.join(result_dir, exp_name, exp_subname, 'logs'))
]))
datasets = [dataset.replace('result', 'taskonomy') for dataset in datasets]
datasets = [dataset for dataset in datasets if dataset in DATASETS]
# construct a task dictionary
task_dict = {}
for dataset in datasets:
task_dict[dataset] = []
for task in TASKS:
if dataset in dataset_dict and task in dataset_dict[dataset]:
task_dict[dataset].append((task, dataset_dict[dataset][task]))
try:
pd.set_option('max_columns', None)
except:
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 1000)
pd.set_option('display.max_colwidth', 200)
pd.set_option('display.colheader_justify', 'left')
database = create_database(task_dict, args.exp_name,
args.subname_prefix, print_failure=args.verbose, from_last=args.from_last,
compact=args.compact)
database = database[database.columns[database.isna().sum(axis=0) < len(database.index)]]
database = database.loc[database.index[database.isna().sum(axis=1) < len(database.columns)]]
database = database.reindex(sorted(database.columns,
key=cmp_to_key(lambda x, y: DATASETS.index(x[0].lower()) -
DATASETS.index(y[0].lower()))), axis=1)
print(database.to_string(justify='right'))
if args.save:
# current time
database.to_csv(f'results_{datetime.now().strftime("%Y%m%d_%H%M%S") }.csv')