Grid Clusters.
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@ -6,19 +6,21 @@ class BatchToData(object):
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def __init__(self):
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super(BatchToData, self).__init__()
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def __call__(self, batch_x: torch.Tensor, batch_pos: torch.Tensor, batch_y: torch.Tensor):
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def __call__(self, batch_x: torch.Tensor, batch_pos: torch.Tensor,
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batch_y_l: torch.Tensor, batch_y_c: torch.Tensor):
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# Convert to torch_geometric.data.Data type
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# data = data.transpose(1, 2).contiguous()
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batch_size, num_points, _ = batch_x.shape # (batch_size, num_points, 3)
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x = batch_x.reshape(batch_size * num_points, -1)
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pos = batch_pos.reshape(batch_size * num_points, -1)
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batch_y = batch_y.reshape(batch_size * num_points)
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batch_y_l = batch_y_l.reshape(batch_size * num_points)
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batch_y_c = batch_y_c.reshape(batch_size * num_points)
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batch = torch.zeros((batch_size, num_points), device=pos.device, dtype=torch.long)
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for i in range(batch_size):
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batch[i] = i
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batch = batch.view(-1)
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data = Data()
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data.x, data.pos, data.batch, data.y = x, pos, batch, batch_y
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data.x, data.pos, data.batch, data.yl, data.yc = x, pos, batch, batch_y_l, batch_y_c
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return data
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@ -3,20 +3,17 @@ import shelve
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from pathlib import Path
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import numpy as np
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from utils.project_config import GlobalVar
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import torch
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import random
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def to_one_hot(idx_array):
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one_hot = np.zeros((idx_array.size, len(GlobalVar.classes)))
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def to_one_hot(idx_array, max_classes):
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one_hot = np.zeros((idx_array.size, max_classes))
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one_hot[np.arange(idx_array.size), idx_array] = 1
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return one_hot
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def fix_all_random_seeds(config_obj):
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import numpy as np
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import torch
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import random
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np.random.seed(config_obj.main.seed)
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torch.manual_seed(config_obj.main.seed)
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random.seed(config_obj.main.seed)
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@ -39,4 +36,4 @@ def load_from_shelve(file_path, key):
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def check_path(file_path):
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assert isinstance(file_path, Path)
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assert str(file_path).endswith('.pik')
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assert str(file_path).endswith('.pik')
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