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"""
Edge dataset from temporal complex
"""
from abc import ABC, abstractmethod
from torch.utils.data import Dataset
from PIL import Image
import numpy as np
class DataHandlerAbstractClass(Dataset, ABC):
def __init__(self, edge_to, edge_from, feature_vector) -> None:
super().__init__()
self.edge_to = edge_to
self.edge_from = edge_from
self.data = feature_vector
@abstractmethod
def __getitem__(self, item):
pass
@abstractmethod
def __len__(self):
pass
class DataHandler(Dataset):
def __init__(self, edge_to, edge_from, feature_vector, attention, transform=None):
self.edge_to = edge_to
self.edge_from = edge_from
self.data = feature_vector
self.attention = attention
self.transform = transform
def __getitem__(self, item):
edge_to_idx = self.edge_to[item]
edge_from_idx = self.edge_from[item]
edge_to = self.data[edge_to_idx]
edge_from = self.data[edge_from_idx]
a_to = self.attention[edge_to_idx]
a_from = self.attention[edge_from_idx]
if self.transform is not None:
# TODO correct or not?
edge_to = Image.fromarray(edge_to)
edge_to = self.transform(edge_to)
edge_from = Image.fromarray(edge_from)
edge_from = self.transform(edge_from)
return edge_to, edge_from, a_to, a_from
def __len__(self):
# return the number of all edges
return len(self.edge_to)
class HybridDataHandler(Dataset):
def __init__(self, edge_to, edge_from, feature_vector, attention, embedded, coefficient, transform=None):
self.edge_to = edge_to
self.edge_from = edge_from
self.data = feature_vector
self.attention = attention
self.embedded = embedded # replay of positions generated by previous visuaization
self.coefficient = coefficient # whether samples have generated positions
self.transform = transform
def __getitem__(self, item):
edge_to_idx = self.edge_to[item]
edge_from_idx = self.edge_from[item]
edge_to = self.data[edge_to_idx]
edge_from = self.data[edge_from_idx]
a_to = self.attention[edge_to_idx]
a_from = self.attention[edge_from_idx]
embedded_to = self.embedded[edge_to_idx]
coeffi_to = self.coefficient[edge_to_idx]
if self.transform is not None:
# TODO correct or not?
edge_to = Image.fromarray(edge_to)
edge_to = self.transform(edge_to)
edge_from = Image.fromarray(edge_from)
edge_from = self.transform(edge_from)
return edge_to, edge_from, a_to, a_from, embedded_to, coeffi_to
def __len__(self):
# return the number of all edges
return len(self.edge_to)
class DVIDataHandler(Dataset):
def __init__(self, edge_to, edge_from, feature_vector, attention, transform=None):
self.edge_to = edge_to
self.edge_from = edge_from
self.data = feature_vector
self.attention = attention
self.transform = transform
def __getitem__(self, item):
edge_to_idx = self.edge_to[item]
edge_from_idx = self.edge_from[item]
edge_to = self.data[edge_to_idx]
edge_from = self.data[edge_from_idx]
a_to = self.attention[edge_to_idx]
a_from = self.attention[edge_from_idx]
if self.transform is not None:
# TODO correct or not?
edge_to = Image.fromarray(edge_to)
edge_to = self.transform(edge_to)
edge_from = Image.fromarray(edge_from)
edge_from = self.transform(edge_from)
return edge_to, edge_from, a_to, a_from
def __len__(self):
# return the number of all edges
return len(self.edge_to)
# tf.dataset
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