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from typing import Any
import torch
from torch import nn
from torch.utils.data import Dataset, DataLoader
import numpy as np
from os import listdir
from os.path import isfile, join
if __package__ == None or __package__ == "":
from utils import tag_training_data, get_upenn_tags_dict, parse_tags
else:
from .utils import tag_training_data, get_upenn_tags_dict, parse_tags
# Model Type 1: LSTM with 1-logit lookahead.
class SegmentorDataset(Dataset):
def __init__(self, datapoints):
self.datapoints = [(torch.from_numpy(k).float(), torch.tensor([t]).float()) for k, t in datapoints]
def __len__(self):
return len(self.datapoints)
def __getitem__(self, idx):
return self.datapoints[idx][0], self.datapoints[idx][1]
class RNN(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, device=None):
super(RNN, self).__init__()
if device == None:
if torch.cuda.is_available():
self.device = "cuda"
else:
self.device = "cpu"
else:
self.device = device
self.num_layers = num_layers
self.hidden_size = hidden_size
self.rnn = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, 1)
def forward(self, x):
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size, device=self.device)
c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size, device=self.device)
out, _ = self.rnn(x, (h0, c0))
out = out[:, -1, :]
out = self.fc(out)
return out
# Model 2: Bidirectional LSTM with entire sequence context (hopefully)
class SegmentorDatasetDirectTag(Dataset):
def __init__(self, document_root: str):
self.tags_dict = get_upenn_tags_dict()
self.datapoints = []
self.eye = np.eye(len(self.tags_dict))
files = listdir(document_root)
for f in files:
if f.endswith(".txt"):
fname = join(document_root, f)
print(f"Loaded datafile: {fname}")
reconstructed_tags = tag_training_data(fname)
input, tag = parse_tags(reconstructed_tags)
self.datapoints.append((
np.array(input),
np.array(tag)
))
def __len__(self):
return len(self.datapoints)
def __getitem__(self, idx):
item = self.datapoints[idx]
return torch.from_numpy(self.eye[item[0]]).float(), torch.from_numpy(item[1]).float()
# The same dataset without one-hot embedding of the input.
class SegmentorDatasetNonEmbed(Dataset):
def __init__(self, document_root: str):
self.datapoints = []
files = listdir(document_root)
for f in files:
if f.endswith(".txt"):
fname = join(document_root, f)
print(f"Loaded datafile: {fname}")
reconstructed_tags = tag_training_data(fname)
input, tag = parse_tags(reconstructed_tags)
self.datapoints.append((
np.array(input),
np.array(tag)
))
def __len__(self):
return len(self.datapoints)
def __getitem__(self, idx):
item = self.datapoints[idx]
return torch.from_numpy(item[0]).int(), torch.from_numpy(item[1]).float()
class BidirLSTMSegmenter(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, device = None):
super(BidirLSTMSegmenter, self).__init__()
if device == None:
if torch.cuda.is_available():
self.device = "cuda"
else:
self.device = "cpu"
else:
self.device = device
self.num_layers = num_layers
self.hidden_size = hidden_size
self.rnn = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, bidirectional=True, device = self.device)
self.fc = nn.Linear(2*hidden_size, 1, device = self.device)
self.final = nn.Sigmoid()
def forward(self, x):
h0 = torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size, device=self.device)
c0 = torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size, device=self.device)
out, _ = self.rnn(x, (h0, c0))
# out_fced = [torch.zeros((out.shape[0], out.shape[1]), device=device)]
# # Shape of out: [batch, seq_length, 256 (num_directions * hidden_size)]
# for i in range(out.shape[1]):
# out_fced[:, i] = self.fc(out[:, i, :])[0]
out_fced = self.fc(out)[:, :, 0]
# Shape of out:
return self.final(out_fced)
class BidirLSTMSegmenterWithEmbedding(nn.Module):
def __init__(self, input_size, embedding_size, hidden_size, num_layers, device = None):
super(BidirLSTMSegmenterWithEmbedding, self).__init__()
if device == None:
if torch.cuda.is_available():
self.device = "cuda"
else:
self.device = "cpu"
else:
self.device = device
self.num_layers = num_layers
self.hidden_size = hidden_size
self.embedding_size = embedding_size
self.embedding = nn.Embedding(input_size, embedding_dim=embedding_size, device = self.device)
self.rnn = nn.LSTM(embedding_size, hidden_size, num_layers, batch_first=True, bidirectional=True, device = self.device)
self.fc = nn.Linear(2*hidden_size, 1, device = self.device)
self.final = nn.Sigmoid()
def forward(self, x):
h0 = torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size, device=self.device)
c0 = torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size, device=self.device)
embedded = self.embedding(x)
out, _ = self.rnn(embedded, (h0, c0))
# out_fced = [torch.zeros((out.shape[0], out.shape[1]), device=device)]
# # Shape of out: [batch, seq_length, 256 (num_directions * hidden_size)]
# for i in range(out.shape[1]):
# out_fced[:, i] = self.fc(out[:, i, :])[0]
out_fced = self.fc(out)[:, :, 0]
# Shape of out:
return self.final(out_fced)
def collate_fn_padd(batch):
'''
Padds batch of variable length
note: it converts things ToTensor manually here since the ToTensor transform
assume it takes in images rather than arbitrary tensors.
'''
## get sequence lengths
inputs = [i[0] for i in batch]
tags = [i[1] for i in batch]
padded_input = torch.nn.utils.rnn.pad_sequence(inputs, batch_first=True)
combined_outputs = torch.nn.utils.rnn.pad_sequence(tags, batch_first=True)
## compute mask
return (padded_input, combined_outputs)
def get_dataloader(dataset: SegmentorDataset, batch_size):
return DataLoader(dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn_padd)
def train_model(model: RNN,
dataset,
lr = 1e-3,
num_epochs = 3,
batch_size = 100,
):
train_loader = get_dataloader(dataset, batch_size=batch_size)
n_total_steps = len(train_loader)
criterion = nn.MSELoss()
optimizer = torch.optim.AdamW(model.parameters(), lr=lr)
device = model.device
for epoch in range(num_epochs):
for i, (input, tags) in enumerate(train_loader):
input = input.to(device)
tags = tags.to(device)
outputs = model(input)
loss = criterion(outputs, tags)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if i%100 == 0:
print(f"Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{n_total_steps}], Loss [{loss.item():.4f}]")
def train_bidirlstm_model(model: BidirLSTMSegmenter,
dataset: SegmentorDatasetDirectTag,
lr = 1e-3,
num_epochs = 3,
batch_size = 1,
):
train_loader = get_dataloader(dataset, batch_size=batch_size)
n_total_steps = len(train_loader)
criterion = nn.BCELoss()
optimizer = torch.optim.AdamW(model.parameters(), lr=lr)
device = model.device
for epoch in range(num_epochs):
for i, (input, tags) in enumerate(train_loader):
input = input.to(device)
tags = tags.to(device)
optimizer.zero_grad()
outputs = model(input)
loss = criterion(outputs, tags)
loss.backward()
optimizer.step()
if i%10 == 0:
print(f"Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{n_total_steps}], Loss [{loss.item():.4f}]")
def train_bidirlstm_embedding_model(model: BidirLSTMSegmenterWithEmbedding,
dataset: SegmentorDatasetNonEmbed,
lr = 1e-3,
num_epochs = 3,
batch_size = 1,
):
train_loader = get_dataloader(dataset, batch_size=batch_size)
n_total_steps = len(train_loader)
criterion = nn.BCELoss()
optimizer = torch.optim.AdamW(model.parameters(), lr=lr)
device = model.device
for epoch in range(num_epochs):
for i, (input, tags) in enumerate(train_loader):
input = input.to(device)
tags = tags.to(device)
optimizer.zero_grad()
outputs = model(input)
loss = criterion(outputs, tags)
loss.backward()
optimizer.step()
if i%10 == 0:
print(f"Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{n_total_steps}], Loss [{loss.item():.4f}]")
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