Open lpc-97 opened 1 month ago
parser = argparse.ArgumentParser() parser.add_argument('--root_path', type=str, default='/data/LarryXu/Synapse/preprocessed_data/train_npz', help='root dir for data') parser.add_argument('--output', type=str, default='/output/sam/results') parser.add_argument('--dataset', type=str, default='Synapse', help='experiment_name') parser.add_argument('--list_dir', type=str, default='./lists/lists_Synapse', help='list dir') parser.add_argument('--num_classes', type=int, default=8, help='output channel of network') parser.add_argument('--max_iterations', type=int, default=30000, help='maximum epoch number to train') parser.add_argument('--max_epochs', type=int, default=200, help='maximum epoch number to train') parser.add_argument('--stop_epoch', type=int, default=160, help='maximum epoch number to train') parser.add_argument('--batch_size', type=int, default=8, help='batch_size per gpu') parser.add_argument('--n_gpu', type=int, default=1, help='total gpu') parser.add_argument('--deterministic', type=int, default=1, help='whether use deterministic training') parser.add_argument('--base_lr', type=float, default=0.005, help='segmentation network learning rate') parser.add_argument('--img_size', type=int, default=512, help='input patch size of network input') parser.add_argument('--seed', type=int, default=1234, help='random seed') parser.add_argument('--vit_name', type=str, default='vit_b', help='select one vit model') parser.add_argument('--ckpt', type=str, default='checkpoints/sam_vit_b_01ec64.pth',# sam_vit_b_01ec64 medsam_vit_b help='Pretrained checkpoint') parser.add_argument('--lora_ckpt', type=str, default=None, help='Finetuned lora checkpoint') parser.add_argument('--rank', type=int, default=4, help='Rank for LoRA adaptation') parser.add_argument('--warmup', action='store_true', help='If activated, warp up the learning from a lower lr to the base_lr') parser.add_argument('--warmup_period', type=int, default=250, help='Warp up iterations, only valid whrn warmup is activated') parser.add_argument('--AdamW', action='store_true', help='If activated, use AdamW to finetune SAM model') parser.add_argument('--module', type=str, default='sam_lora_image_encoder') parser.add_argument('--dice_param', type=float, default=0.8) args = parser.parse_args()
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Namespace(config=None, volume_path='testset/test_vol_h5/', dataset='Synapse', num_classes=8, list_dir='./lists/lists_Synapse/', output_dir='/mnt/d/Linux/SAMed-main/output', img_size=512, input_size=224, seed=1234, is_savenii=True, deterministic=1, ckpt='checkpoints/sam_vit_b_01ec64.pth', lora_ckpt='checkpoints/epoch_159.pth', vit_name='vit_b', rank=4, module='sam_lora_image_encoder') 12 test iterations per epoch 0it [00:00, ?it/s]idx 0 case case0008 mean_dice 0.441581 mean_hd95 34.595021 1it [02:15, 135.34s/it]idx 1 case case0022 mean_dice 0.684595 mean_hd95 3.275303 2it [03:32, 101.11s/it]idx 2 case case0038 mean_dice 0.669412 mean_hd95 27.522629 3it [05:01, 95.56s/it] idx 3 case case0036 mean_dice 0.681733 mean_hd95 19.580403 4it [07:59, 128.04s/it]idx 4 case case0032 mean_dice 0.621131 mean_hd95 6.248785 5it [10:06, 127.68s/it]idx 5 case case0002 mean_dice 0.690973 mean_hd95 2.605073 6it [12:07, 125.58s/it]idx 6 case case0029 mean_dice 0.671065 mean_hd95 18.496830 7it [13:33, 112.38s/it]idx 7 case case0003 mean_dice 0.428255 mean_hd95 71.256974 8it [16:39, 135.90s/it]idx 8 case case0001 mean_dice 0.653609 mean_hd95 26.554855 9it [18:54, 135.68s/it]idx 9 case case0004 mean_dice 0.666332 mean_hd95 14.264169 10it [20:58, 132.16s/it]idx 10 case case0025 mean_dice 0.639110 mean_hd95 7.775622 11it [22:11, 113.85s/it]idx 11 case case0035 mean_dice 0.662067 mean_hd95 7.006532 12it [23:28, 117.42s/it] Mean class 1 name spleen mean_dice 0.863648 mean_hd95 9.370867 Mean class 2 name right kidney mean_dice 0.805086 mean_hd95 38.663117 Mean class 3 name left kidney mean_dice 0.804298 mean_hd95 42.883881 Mean class 4 name gallbladder mean_dice 0.000000 mean_hd95 0.000000 Mean class 5 name liver mean_dice 0.922219 mean_hd95 36.088610 Mean class 6 name stomach mean_dice 0.763024 mean_hd95 23.126339 Mean class 7 name aorta mean_dice 0.848302 mean_hd95 9.321985 Mean class 8 name pancreas mean_dice 0.000000 mean_hd95 0.000000 Testing performance in best val model: mean_dice : 0.625822 mean_hd95 : 19.931850 Testing Finished! ————
结果复现 无法达到你提供的权重的结果 我自己重新训练的
parser = argparse.ArgumentParser() parser.add_argument('--root_path', type=str, default='/data/LarryXu/Synapse/preprocessed_data/train_npz', help='root dir for data') parser.add_argument('--output', type=str, default='/output/sam/results') parser.add_argument('--dataset', type=str, default='Synapse', help='experiment_name') parser.add_argument('--list_dir', type=str, default='./lists/lists_Synapse', help='list dir') parser.add_argument('--num_classes', type=int, default=8, help='output channel of network') parser.add_argument('--max_iterations', type=int, default=30000, help='maximum epoch number to train') parser.add_argument('--max_epochs', type=int, default=200, help='maximum epoch number to train') parser.add_argument('--stop_epoch', type=int, default=160, help='maximum epoch number to train') parser.add_argument('--batch_size', type=int, default=8, help='batch_size per gpu') parser.add_argument('--n_gpu', type=int, default=1, help='total gpu') parser.add_argument('--deterministic', type=int, default=1, help='whether use deterministic training') parser.add_argument('--base_lr', type=float, default=0.005, help='segmentation network learning rate') parser.add_argument('--img_size', type=int, default=512, help='input patch size of network input') parser.add_argument('--seed', type=int, default=1234, help='random seed') parser.add_argument('--vit_name', type=str, default='vit_b', help='select one vit model') parser.add_argument('--ckpt', type=str, default='checkpoints/sam_vit_b_01ec64.pth',# sam_vit_b_01ec64 medsam_vit_b help='Pretrained checkpoint') parser.add_argument('--lora_ckpt', type=str, default=None, help='Finetuned lora checkpoint') parser.add_argument('--rank', type=int, default=4, help='Rank for LoRA adaptation') parser.add_argument('--warmup', action='store_true', help='If activated, warp up the learning from a lower lr to the base_lr') parser.add_argument('--warmup_period', type=int, default=250, help='Warp up iterations, only valid whrn warmup is activated') parser.add_argument('--AdamW', action='store_true', help='If activated, use AdamW to finetune SAM model') parser.add_argument('--module', type=str, default='sam_lora_image_encoder') parser.add_argument('--dice_param', type=float, default=0.8) args = parser.parse_args()
——
Namespace(config=None, volume_path='testset/test_vol_h5/', dataset='Synapse', num_classes=8, list_dir='./lists/lists_Synapse/', output_dir='/mnt/d/Linux/SAMed-main/output', img_size=512, input_size=224, seed=1234, is_savenii=True, deterministic=1, ckpt='checkpoints/sam_vit_b_01ec64.pth', lora_ckpt='checkpoints/epoch_159.pth', vit_name='vit_b', rank=4, module='sam_lora_image_encoder') 12 test iterations per epoch 0it [00:00, ?it/s]idx 0 case case0008 mean_dice 0.441581 mean_hd95 34.595021 1it [02:15, 135.34s/it]idx 1 case case0022 mean_dice 0.684595 mean_hd95 3.275303 2it [03:32, 101.11s/it]idx 2 case case0038 mean_dice 0.669412 mean_hd95 27.522629 3it [05:01, 95.56s/it] idx 3 case case0036 mean_dice 0.681733 mean_hd95 19.580403 4it [07:59, 128.04s/it]idx 4 case case0032 mean_dice 0.621131 mean_hd95 6.248785 5it [10:06, 127.68s/it]idx 5 case case0002 mean_dice 0.690973 mean_hd95 2.605073 6it [12:07, 125.58s/it]idx 6 case case0029 mean_dice 0.671065 mean_hd95 18.496830 7it [13:33, 112.38s/it]idx 7 case case0003 mean_dice 0.428255 mean_hd95 71.256974 8it [16:39, 135.90s/it]idx 8 case case0001 mean_dice 0.653609 mean_hd95 26.554855 9it [18:54, 135.68s/it]idx 9 case case0004 mean_dice 0.666332 mean_hd95 14.264169 10it [20:58, 132.16s/it]idx 10 case case0025 mean_dice 0.639110 mean_hd95 7.775622 11it [22:11, 113.85s/it]idx 11 case case0035 mean_dice 0.662067 mean_hd95 7.006532 12it [23:28, 117.42s/it] Mean class 1 name spleen mean_dice 0.863648 mean_hd95 9.370867 Mean class 2 name right kidney mean_dice 0.805086 mean_hd95 38.663117 Mean class 3 name left kidney mean_dice 0.804298 mean_hd95 42.883881 Mean class 4 name gallbladder mean_dice 0.000000 mean_hd95 0.000000 Mean class 5 name liver mean_dice 0.922219 mean_hd95 36.088610 Mean class 6 name stomach mean_dice 0.763024 mean_hd95 23.126339 Mean class 7 name aorta mean_dice 0.848302 mean_hd95 9.321985 Mean class 8 name pancreas mean_dice 0.000000 mean_hd95 0.000000 Testing performance in best val model: mean_dice : 0.625822 mean_hd95 : 19.931850 Testing Finished! ————
结果复现 无法达到你提供的权重的结果 我自己重新训练的