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print_results_appendix_latex.py
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print_results_appendix_latex.py
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import os
import argparse
import csv
import pandas as pd
import numpy as np
from models import head_names
parser = argparse.ArgumentParser()
DATASETS = ['office31', 'officehome', 'visda', 'domainnet']
DOMAINS = {'office31': ['amazon', 'dslr', 'webcam'],
'officehome': ['Art', 'Clipart', 'Product', 'RealWorld'],
'visda': ['syn', 'real'],
'domainnet': ['painting', 'real', 'sketch']}
domain_name_map = {'amazon':'A', 'dslr':'D', 'webcam':'W',
'Art':'A', 'Clipart':'C', 'Product':'P', 'RealWorld':'R',
'syn':'S',
'painting':'P', 'real':'R', 'sketch':'S'}
# METHODS = ['SO', 'DANCE', 'OVANet', 'UniOT', 'WiSE-FT', 'ClipCrossModel', 'ClipZeroShot', 'AutoDistill']
METHODS = ['SO', 'DANCE', 'OVANet', 'UniOT', 'ClipDistill']
method_name_map = {'SO':'SO', 'DANCE': 'DANCE', 'OVANet':'OVANet', 'UniOT':'UniOT', 'WiSE-FT':'WiSE-FT', 'ClipCrossModel':'CLIP cross-model', 'ClipZeroShot':'CLIP zero-shot', 'AutoDistill':'CLIP distillation (Ours)', 'ClipDistill':'CLIP distillation (Ours)'}
# SETTINGS = ['open-partial', 'open', 'closed', 'partial']
SETTINGS = ['open-partial']
METRICS = ['H-score', 'H3-score', 'OSCR']
# BACKBONES = ['resnet50', 'dinov2_vitl14', 'ViT-L/14@336px']
# BACKBONES = ['ViT-L/14@336px']
# BACKBONES = ['dinov2_vitl14']
BACKBONES = ['resnet50']
STEP = 'final'
DIR = 'results'
def main(args):
for setting in SETTINGS:
for dataset in DATASETS:
# if dataset == 'domainnet' and setting in ('closed','partial'):
# continue
for backbone in BACKBONES:
backbone_name = backbone.replace('/', '')
if backbone == 'resnet50':
fixed_backbone = False
if setting != 'open-partial':
continue
else:
fixed_backbone = True
for metric in METRICS:
if metric in ('H-score', 'H3-score') and setting in ('closed', 'partial'):
metric_ = 'AA'
elif metric == 'OSCR' and setting in ('closed', 'partial'):
metric_ = 'Closed-set OA'
else:
metric_ = metric
method_csv = []
method_names = []
for method in METHODS:
if metric == 'OSCR' and method == 'ClipZeroShot':
DIR = 'results_old'
else:
DIR = 'results'
if backbone in ('resnet50', 'dinov2_vitl14') and method in ('WiSE-FT', 'ClipCrossModel', 'ClipZeroShot', 'AutoDistill'):
continue
# if metric in ('H-score', 'H3-score') and method in ('WiSE-FT', 'ClipCrossModel', 'ClipZeroShot', 'debug0.3'):
# continue
result_path = f'{backbone_name}-{args.optimizer}-{args.base_lr}-{args.classifier_head}-{fixed_backbone}-{args.fixed_BN}-{args.image_augmentation}-{args.batch_size}'
path_load_mean = os.path.join(DIR, setting, f'{STEP}', result_path, 'mean.csv')
df_mean = pd.read_csv(path_load_mean)
path_load_std = os.path.join(DIR, setting, f'{STEP}', result_path, 'std.csv')
df_std = pd.read_csv(path_load_std)
domain_csv = []
domain_csv_head = []
for source_domain in DOMAINS[dataset]:
for target_domain in DOMAINS[dataset]:
if source_domain != target_domain and not (source_domain == 'real' and target_domain == 'syn'):
result_mean = df_mean[(df_mean['method'] == method) & (df_mean['dataset'] == dataset) & (df_mean['source'] == source_domain) & (df_mean['target'] == target_domain)][metric_]
result_std = df_std[(df_std['method'] == method) & (df_std['dataset'] == dataset) & (df_std['source'] == source_domain) & (df_std['target'] == target_domain)][metric_]
domain_csv_head += [f'{domain_name_map[source_domain]}2{domain_name_map[target_domain]}']
domain_csv += [float(result_mean), float(result_std)]
method_names += [method_name_map[method]]
domain_csv += [round(float(np.mean(domain_csv[0::2])),2)]
method_csv.append(domain_csv)
# domain_csv_head += ['Avg']
# method_csv.append(float(np.mean(domain_csv[0::2])))
max_id = np.array(method_csv).argmax(axis=0)
save_csv = [[name, '&'] for name in method_names]
save_csv_head = ['Methods', '&']
for i in range(len(domain_csv_head)):
save_csv_head += [domain_csv_head[i], '&']
for j in range(len(method_csv)):
if j == max_id[2*i]:
save_csv[j] += ['\\textbf{'+f'{method_csv[j][2*i]}'+ '}$\\pm$' + f'{method_csv[j][2*i+1]}', '&']
else:
save_csv[j] += [f'{method_csv[j][2*i]}$\\pm${method_csv[j][2*i+1]}', '&']
i = len(domain_csv_head)
save_csv_head += ['Avg', '&']
for j in range(len(method_csv)):
if j == max_id[2*i]:
save_csv[j] += ['\\textbf{'+f'{method_csv[j][2*i]}'+ '}', '&']
else:
save_csv[j] += [f'{method_csv[j][2*i]}', '&']
save_csv_head[-1] = '\\\\'
for j in range(len(method_csv)):
save_csv[j][-1] = '\\\\'
save_path = os.path.join('latex', 'appendix', setting, dataset, backbone, f'{metric}.csv')
save_all_csv(save_csv_head, save_csv, save_path)
def save_all_csv(all_headers, all_columns, result_path):
result_dir = os.path.dirname(result_path)
if not os.path.exists(result_dir):
os.makedirs(result_dir)
with open(result_path, 'w+') as f:
writer = csv.writer(f)
writer.writerow(all_headers)
writer.writerows(all_columns)
if __name__ == "__main__":
###########################
# Directory Config (modify if using your own paths)
###########################
parser.add_argument(
"--classifier_head",
type=str,
default="prototype",
choices=head_names,
help="classifier head architecture",
)
parser.add_argument(
"--fixed_backbone",
action="store_true",
help="wheather fixed backbone during training",
)
parser.add_argument(
"--fixed_BN",
action="store_true",
help="wheather fixed batch normalization layers during training",
)
parser.add_argument(
"--batch_size",
type=int,
default=32,
help="batch size for test (feature extraction and evaluation)",
)
parser.add_argument(
"--image_augmentation",
type=str,
default='none',
choices=['none', # only a single center crop
'flip', # add random flip view
'randomcrop', # add random crop view
],
help="specify the image augmentation to use.",
)
parser.add_argument(
"--optimizer",
type=str,
default="sgd",
choices=["adamw", "sgd"],
help="optimizer"
)
parser.add_argument(
'--base_lr',
type=float,
default=1e-2,
help='base learning rate'
)
args = parser.parse_args()
main(args)