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main_m3dm.py
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main_m3dm.py
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# import os
# os.environ['CUDA_VISIBLE_DEVICES'] = '6'
import argparse
from M3DM.m3dm_runner import M3DM
from dataset_pc import real3d_classes
import pandas as pd
def run_3d_ads(args):
classes = real3d_classes()
METHOD_NAMES = [args.xyz_backbone_name]
image_rocaucs_df = pd.DataFrame(METHOD_NAMES, columns=['Method'])
pixel_rocaucs_df = pd.DataFrame(METHOD_NAMES, columns=['Method'])
image_ap_df = pd.DataFrame(METHOD_NAMES, columns=['Method'])
pixel_ap_df = pd.DataFrame(METHOD_NAMES, columns=['Method'])
print('Task start: M3DM '+args.xyz_backbone_name)
for cls in classes:
# print(cls)
model = M3DM(args)
model.fit(cls)
image_rocaucs, pixel_rocaucs,image_ap, pixel_ap = model.evaluate(cls)
image_rocaucs_df[cls.title()] = image_rocaucs_df['Method'].map(image_rocaucs)
pixel_rocaucs_df[cls.title()] = pixel_rocaucs_df['Method'].map(pixel_rocaucs)
image_ap_df[cls.title()] = image_ap_df['Method'].map(image_ap)
pixel_ap_df[cls.title()] = pixel_ap_df['Method'].map(pixel_ap)
print('Task:{}, object_auroc:{}, point_auroc:{}, object_aupr:{}, point_aupr:{}'.format
(cls,image_rocaucs_df['Method'].map(image_rocaucs),pixel_rocaucs_df['Method'].map(pixel_rocaucs),image_ap_df['Method'].map(image_ap),pixel_ap_df['Method'].map(pixel_ap)))
# print(f"\nFinished running on class {cls}")
# print("################################################################################\n\n")
image_rocaucs_df['Mean'] = round(image_rocaucs_df.iloc[:, 1:].mean(axis=1),3)
pixel_rocaucs_df['Mean'] = round(pixel_rocaucs_df.iloc[:, 1:].mean(axis=1),3)
image_ap_df['Mean'] = round(image_ap_df.iloc[:, 1:].mean(axis=1),3)
pixel_ap_df['Mean'] = round(pixel_ap_df.iloc[:, 1:].mean(axis=1),3)
# print("\n\n################################################################################")
# print("############################# Object AUROC Results #############################")
# print("################################################################################\n")
# print(image_rocaucs_df.to_markdown(index=False))
# print("\n\n################################################################################")
# print("############################# Point AUROC Results #############################")
# print("################################################################################\n")
# print(pixel_rocaucs_df.to_markdown(index=False))
# print("\n\n################################################################################")
# print("############################# Object AUPR Results #############################")
# print("################################################################################\n")
# print(image_ap_df.to_markdown(index=False))
# print("\n\n################################################################################")
# print("############################# Point AUPR Results #############################")
# print("################################################################################\n")
# print(pixel_ap_df.to_markdown(index=False))
# with open(args.result_md_path+"image_rocauc_results.md", "a") as tf:
# tf.write(image_rocaucs_df.to_markdown(index=False))
# with open(args.result_md_path+"pixel_rocauc_results.md", "a") as tf:
# tf.write(pixel_rocaucs_df.to_markdown(index=False))
# with open(args.result_md_path+"image_ap_results.md", "a") as tf:
# tf.write(image_ap_df.to_markdown(index=False))
# with open(args.result_md_path+"pixel_ap_results.md", "a") as tf:
# tf.write(pixel_ap_df.to_markdown(index=False))
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('--xyz_backbone_name', default='Point_Bert', type=str, choices=['Point_MAE', 'Point_Bert'],
help='Checkpoints name of RGB backbone[Point_MAE, Point_Bert].')
parser.add_argument('--save_checkpoint_path', default = "./checkpoints/Point-BERT.pth", type=str,
choices=["./checkpoints/pointmae_pretrain.pth", "./checkpoints/Point-BERT.pth"],
help='Save feature for training fusion block.')
parser.add_argument('--save_preds', default=False, action='store_true',
help='Save predicts results.')
parser.add_argument('--group_size', default=128, type=int,
help='Point group size of Point Transformer.')
parser.add_argument('--num_group', default=16384, type=int,
help='Point groups number of Point Transformer.')
parser.add_argument('--random_state', default=None, type=int,
help='random_state for random project')
parser.add_argument('--dataset_path', default='./data', type=str,
help='Dataset store path')
parser.add_argument('--save_path_full', default='./benchmark/point_bert/3dad_m3dm_visualization_full/', type=str,
help='Dataset store path')
parser.add_argument('--save_path', default='./benchmark/point_bert/3dad_m3dm_visualization/', type=str,
help='Dataset store path')
parser.add_argument('--result_md_path', default='results/', type=str,
help='Dataset store path')
parser.add_argument('--max_sample', default=400, type=int,
help='Max sample number.')
parser.add_argument('--checkpoint_path', default='./data', type=str,
help='Dataset store path')
parser.add_argument('--img_size', default=224, type=int,
help='Images size for model')
parser.add_argument('--coreset_eps', default=0.9, type=float,
help='eps for sparse project')
parser.add_argument('--f_coreset', default=0.1, type=float,
help='eps for sparse project')
parser.add_argument('--rm_zero_for_project', default=False, action='store_true',
help='Save predicts results.')
args = parser.parse_args()
run_3d_ads(args)
# python main.py --xyz_backbone_name Point_MAE --save_checkpoint_path ./checkpoints/pointmae_pretrain.pth
# python main.py --xyz_backbone_name Point_Bert --save_checkpoint_path ./checkpoints/Point-BERT.pth