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| | import os |
| | import torch |
| | import time |
| | import ml_collections |
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| | save_model = True |
| | tensorboard = True |
| | os.environ["CUDA_VISIBLE_DEVICES"] = "0" |
| | use_cuda = torch.cuda.is_available() |
| | seed = 666 |
| | os.environ['PYTHONHASHSEED'] = str(seed) |
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| | cosineLR = True |
| | n_channels = 3 |
| | n_labels = 1 |
| | epochs = 2000 |
| | img_size = 224 |
| | print_frequency = 1 |
| | save_frequency = 5000 |
| | vis_frequency = 10 |
| | early_stopping_patience = 50 |
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| | pretrain = False |
| | task_name = 'MoNuSeg' |
| | |
| | learning_rate = 1e-3 |
| | batch_size = 4 |
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| | model_name = 'UCTransNet_pretrain' |
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| | train_dataset = './datasets/'+ task_name+ '/Train_Folder/' |
| | val_dataset = './datasets/'+ task_name+ '/Val_Folder/' |
| | test_dataset = './datasets/'+ task_name+ '/Test_Folder/' |
| | session_name = 'Test_session' + '_' + time.strftime('%m.%d_%Hh%M') |
| | save_path = task_name +'/'+ model_name +'/' + session_name + '/' |
| | model_path = save_path + 'models/' |
| | tensorboard_folder = save_path + 'tensorboard_logs/' |
| | logger_path = save_path + session_name + ".log" |
| | visualize_path = save_path + 'visualize_val/' |
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| | def get_CTranS_config(): |
| | config = ml_collections.ConfigDict() |
| | config.transformer = ml_collections.ConfigDict() |
| | config.KV_size = 960 |
| | config.transformer.num_heads = 4 |
| | config.transformer.num_layers = 4 |
| | config.expand_ratio = 4 |
| | config.transformer.embeddings_dropout_rate = 0.1 |
| | config.transformer.attention_dropout_rate = 0.1 |
| | config.transformer.dropout_rate = 0 |
| | config.patch_sizes = [16,8,4,2] |
| | config.base_channel = 64 |
| | config.n_classes = 1 |
| | return config |
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| | test_session = "Test_session_07.03_20h39" |