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import json |
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from pathlib import Path |
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import numpy as np |
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import torch |
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import scipy.sparse as sp |
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def scipy_to_torch_sparse(scp_matrix): |
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values = scp_matrix.data |
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indices = np.vstack((scp_matrix.row, scp_matrix.col)) |
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i = torch.LongTensor(indices) |
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v = torch.FloatTensor(values) |
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shape = scp_matrix.shape |
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sparse_tensor = torch.sparse.FloatTensor(i, v, torch.Size(shape)) |
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return sparse_tensor |
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def load_config(dataset_path, hyperparameters = None): |
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config = {} |
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config['DATASET'] = dataset_path |
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with open(Path(dataset_path) / "config.json") as f: |
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data_config = json.load(f) |
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if hyperparameters is None: |
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hyperparameters = {} |
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hyperparameters['latents'] = 64 |
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hyperparameters['initial_filters'] = 16 |
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hyperparameters['flip_h'] = data_config['flip_h'] |
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hyperparameters['flip_v'] = data_config['flip_v'] |
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hyperparameters['transpose'] = data_config['transpose'] |
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hyperparameters['rotate'] = data_config['rotate'] |
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config.update({ |
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'organs': data_config['organs'], |
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'organ_names': data_config['organ_names'], |
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'resolutions': data_config['resolutions'], |
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'inputsize': data_config['inputsize'], |
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'device': torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
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}) |
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config.update(hyperparameters) |
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num_pooling = len(config['resolutions']) |
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config['filters'] = [config['initial_filters'] * (i+1) // 2 for i in range(num_pooling+1)] |
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config['filters'] = [x for x in config['filters'] for _ in (0, 1)] |
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config['filters'][0] = 2 |
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if config['naive']: |
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adj_path = "Naive" |
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else: |
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adj_path = "NonNaive" |
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A_ = [] |
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for res in config['resolutions']: |
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A = np.load(Path(dataset_path) / adj_path / f"adj_{res}_block_diagonal.npy") |
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A = sp.csc_matrix(A).tocoo() |
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A_.extend([A.copy()]) |
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A_.append(A_[-1]) |
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A_t = [scipy_to_torch_sparse(x).to(config['device']) for x in A_] |
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config['n_nodes'] = [A.shape[0] for A in A_] |
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D_ = [] |
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for res in ['to_' + x for x in config['resolutions'][1:]]: |
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D = np.load(Path(dataset_path) / adj_path / f"downsampling_{res}.npy") |
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D_.append(sp.csc_matrix(D).tocoo()) |
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D_t = [scipy_to_torch_sparse(x).to(config['device']) for x in D_] |
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U_ = [] |
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for res in ['to_' + x for x in config['resolutions'][:-1]]: |
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U = np.load(Path(dataset_path) / adj_path / f"upsampling_{res}.npy") |
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U_.append(sp.csc_matrix(U).tocoo()) |
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U_t = [scipy_to_torch_sparse(x).to(config['device']) for x in U_] |
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return config, D_t, U_t, A_t |
