text stringlengths 1 93.6k |
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num_layers (int): Number of layers to create.
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in_size (int): The input size to the first layer.
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state_size (int): The size of the states.
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model (dy.ParameterCollection): The parameter collection for the model.
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name (str, optional): The name of the multilayer LSTM.
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"""
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params = []
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in_size = in_size
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state_size = state_size
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for i in range(num_layers):
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layer_name = name + "-" + str(i)
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print(
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"LSTM " +
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layer_name +
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": " +
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str(in_size) +
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" x " +
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str(state_size) +
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"; default Dynet initialization of hidden weights")
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params.append(dy.VanillaLSTMBuilder(1,
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in_size,
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state_size,
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model))
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in_size = state_size
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return params
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def add_params(model, size, name=""):
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""" Adds parameters to the model.
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Inputs:
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model (dy.ParameterCollection): The parameter collection for the model.
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size (tuple of int): The size to create.
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name (str, optional): The name of the parameters.
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"""
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if len(size) == 1:
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print("vector " + name + ": " +
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str(size[0]) + "; uniform in [-0.1, 0.1]")
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else:
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print("matrix " +
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name +
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": " +
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str(size[0]) +
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" x " +
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str(size[1]) +
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"; uniform in [-0.1, 0.1]")
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return model.add_parameters(size,
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init=dy.UniformInitializer(0.1),
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name=name)
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# <FILESEP>
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"""
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data loder for loading data
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"""
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import os
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import math
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import torch
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import torch.utils.data as data
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import numpy as np
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from PIL import Image
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import torchvision
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import torchvision.datasets as dsets
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import torchvision.transforms as transforms
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import struct
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__all__ = ["DataLoader", "PartDataLoader"]
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class ImageLoader(data.Dataset):
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def __init__(self, dataset_dir, transform=None, target_transform=None):
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class_list = os.listdir(dataset_dir)
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datasets = []
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for cla in class_list:
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cla_path = os.path.join(dataset_dir, cla)
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files = os.listdir(cla_path)
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for file_name in files:
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file_path = os.path.join(cla_path, file_name)
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if os.path.isfile(file_path):
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# datasets.append((file_path, tuple([float(v) for v in int(cla)])))
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datasets.append((file_path, [float(cla)]))
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# print(datasets)
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# assert False
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self.dataset_dir = dataset_dir
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self.datasets = datasets
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self.transform = transform
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self.target_transform = target_transform
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def __getitem__(self, index):
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frames = []
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file_path, label = self.datasets[index]
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noise = torch.load(file_path, map_location=torch.device('cpu'))
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return noise, torch.Tensor(label)
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def __len__(self):
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return len(self.datasets)
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