s434749/ium_434749
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

cnn

Browse Source
This commit is contained in:
Alagris 2021-04-25 20:55:45 +02:00
parent ca31a63a87
commit 2d8f539aa5
4 changed files with 212 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
Dockerfile
View File

@ -5,7 +5,7 @@ ENV PYTHONIOENCODING=utf-8
# Instalujemy niezbędne zależności. Zwróć uwagę na flagę "-y" (assume yes)
RUN apt update && apt install -y python3 python3-pip git locales
RUN pip3 install requests
RUN pip3 install requests torch==1.8.1+cpu python-Levenshtein
RUN sed -i '/en_US.UTF-8/s/^# //g' /etc/locale.gen && locale-gen en_US.UTF-8
ENV LANG='en_US.UTF-8' LANGUAGE='en_US:en' LC_ALL='en_US.UTF-8'

5
create_dataset.py
View File

@ -13,14 +13,15 @@ with open(DATA_FILE) as f, open(PREPROCESSED, 'w+') as p:
for line in f:
text, phonemes = line.strip().split("\t")
phonemes = phonemes.split(",")[0]
phonemes = '^' + re.sub(r'[/\'ˈˌ]', '', phonemes) + '$'
text = '^' + re.sub(r'[^a-z]', '', text.strip()) + '$'
phonemes = re.sub(r'[/\'ˈˌ]', '', phonemes)
text = re.sub(r'[^a-z]', '', text.strip())
for letter in phonemes:
out_alph.add(letter)
for letter in text:
in_alph.add(letter)
p.write(text + '\t' + phonemes+'\n')
print(in_alph)
print(out_alph)
with open('in_alphabet', 'w+') as p:

21
train-model.Jenkinsfile Normal file
View File

@ -0,0 +1,21 @@
pipeline {
agent {
dockerfile true
}
stages {
stage('Build') {
steps {
git 'https://git.wmi.amu.edu.pl/s434749/ium_434749.git'
copyArtifacts fingerprintArtifacts: true, projectName: 's434749-create-dataset', selector: lastSuccessful()
sh 'python3 train_model.py'
}
post {
success {
archiveArtifacts 'cnn.pth'
}
}
}
}
}

187
train_model.py Normal file
View File

@ -0,0 +1,187 @@
# https://arxiv.org/pdf/2001.11692.pdf
import numpy as np
import unicodedata
from time import sleep
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import matplotlib.pyplot as plt
import re
import random
import os
from tqdm import tqdm
from Levenshtein import distance as levenshtein_distance
DATA_FILE = 'preprocessed.tsv'
EPOCHS = 14
TEACHER_FORCING_PROBABILITY = 0.4
LEARNING_RATE = 0.01
BATCH_SIZE = 512
DEVICE = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
OUT_LOOKUP = ['', 'b', 'a', 'ʊ', 't', 'k', 'ə', 'z', 'ɔ', 'ɹ', 's', 'j', 'u', 'm', 'f', 'ɪ', 'o', 'ɡ', 'ɛ', 'n',
'e', 'd',
'ɫ', 'w', 'i', 'p', 'ɑ', 'ɝ', 'θ', 'v', 'h', 'æ', 'ŋ', 'ʃ', 'ʒ', 'ð']
IN_LOOKUP = ['', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't',
'u', 'v', 'w', 'x', 'y', 'z']
IN_ALPHABET = {letter: idx for idx, letter in enumerate(IN_LOOKUP)}
OUT_ALPHABET = {letter: idx for idx, letter in enumerate(OUT_LOOKUP)}
TOTAL_OUT_LEN = 0
DATA: [(torch.tensor, torch.tensor)] = []
TEXT: [str] = []
MAX_LEN = 32
with open(DATA_FILE) as f:
for line in f:
text, phonemes = line.split("\t")
TEXT.append(text)
assert len(text) <= MAX_LEN, text
text = torch.tensor([IN_ALPHABET[letter] for letter in text], dtype=torch.int)
DATA.append((text, phonemes))
def collate(batch: [(torch.tensor, str)]):
batch_text = torch.zeros((len(batch), len(IN_ALPHABET), MAX_LEN))
batch_phonemes = list(map(lambda x: x[1], batch))
for i, (sample, _) in enumerate(batch):
for chr_pos, index in enumerate(sample):
batch_text[i, index, chr_pos] = 1
return batch_text, batch_phonemes
class CNN(nn.Module):
def __init__(self, kernel_size, hidden_layers, channels, embedding_size):
super(CNN, self).__init__()
self.input_conv = nn.Conv1d(in_channels=len(IN_ALPHABET), out_channels=channels, kernel_size=kernel_size)
self.conv_hidden = nn.ModuleList(
[nn.Conv1d(in_channels=channels, out_channels=channels, kernel_size=kernel_size) for _ in
range(hidden_layers)])
self.last_layer_size = (MAX_LEN - (kernel_size - 1) * (hidden_layers + 1)) * channels
self.lin = nn.Linear(self.last_layer_size, embedding_size)
def forward(self, x):
x = self.input_conv(x)
x = F.relu(x, inplace=True)
for c in self.conv_hidden:
x = c(x)
x = F.relu(x, inplace=True)
x = x.view(x.size()[0], self.last_layer_size)
x = self.lin(x)
return x
outer_bar = tqdm(total=EPOCHS, position=0)
inner_bar = tqdm(total=len(DATA), position=1)
def dist(a: [str], b: [str]):
return torch.tensor([levenshtein_distance(a[i], b[i]) for i in range(len(a))], dtype=torch.float, device=DEVICE)
def train_model(model):
plt.ion()
optimizer = optim.Adam(filter(lambda x: x.requires_grad, model.parameters()),
lr=LEARNING_RATE)
loss_snapshots = []
outer_bar.reset()
outer_bar.set_description("Epochs")
data_loader = DataLoader(dataset=DATA, drop_last=True,
batch_size=3 * BATCH_SIZE,
collate_fn=collate,
shuffle=True)
for epoch in range(EPOCHS):
total_loss = 0
inner_bar.reset()
for batch_text, batch_phonemes in data_loader:
optimizer.zero_grad()
anchor, positive, negative = batch_text.to(DEVICE).split(BATCH_SIZE)
ph_anchor = batch_phonemes[:BATCH_SIZE]
ph_positive = batch_phonemes[BATCH_SIZE:2 * BATCH_SIZE]
ph_negative = batch_phonemes[2 * BATCH_SIZE:]
embedded_anchor = model(anchor)
embedded_positive = model(positive)
embedded_negative = model(negative)
estimated_pos_dist = torch.linalg.norm(embedded_anchor - embedded_positive, dim=1)
estimated_neg_dist = torch.linalg.norm(embedded_anchor - embedded_negative, dim=1)
estimated_pos_neg_dist = torch.linalg.norm(embedded_positive - embedded_negative, dim=1)
actual_pos_dist = dist(ph_anchor, ph_positive)
actual_neg_dist = dist(ph_anchor, ph_negative)
actual_pos_neg_dist = dist(ph_positive, ph_negative)
loss = sum(abs(estimated_neg_dist - actual_neg_dist)
+ abs(estimated_pos_dist - actual_pos_dist)
+ abs(estimated_pos_neg_dist - actual_pos_neg_dist)
+ (estimated_pos_dist - estimated_neg_dist - (actual_pos_dist - actual_neg_dist)).clip(min=0))
loss.backward()
optimizer.step()
inner_bar.update(3 * BATCH_SIZE)
loss_scalar = loss.item()
total_loss += loss_scalar
inner_bar.set_description("loss %.2f" % loss_scalar)
loss_snapshots.append(total_loss / len(DATA) * 3)
plt.clf()
plt.plot(loss_snapshots, label="Avg loss ")
plt.legend(loc="upper left")
plt.pause(interval=0.01)
# print()
# print("Total epoch loss:", total_loss)
# print("Total epoch avg loss:", total_loss / TOTAL_TRAINING_OUT_LEN)
# print("Training snapshots:", train_snapshots)
# print("Training snapshots(%):", train_snapshots_percentage)
# print("Evaluation snapshots:", eval_snapshots)
# print("Evaluation snapshots(%):", eval_snapshots_percentage)
outer_bar.set_description("Epochs")
outer_bar.update(1)
plt.ioff()
def evaluate_monte_carlo(model, repeats):
with torch.no_grad():
i = 0
diff = 0
outer_bar.reset(total=repeats)
outer_bar.set_description("Epochs")
for _ in range(repeats):
data_loader = DataLoader(dataset=DATA, drop_last=True,
batch_size=2 * BATCH_SIZE,
collate_fn=collate,
shuffle=True)
inner_bar.reset()
for batch_text, batch_phonemes in data_loader:
positive, negative = batch_text.to(DEVICE).split(BATCH_SIZE)
ph_positive = batch_phonemes[0:BATCH_SIZE]
ph_negative = batch_phonemes[BATCH_SIZE:]
embedded_positive = model(positive)
embedded_negative = model(negative)
estimated_dist = torch.linalg.norm(embedded_negative - embedded_positive, dim=1)
actual_dist = dist(ph_negative, ph_positive)
diff += sum(abs(estimated_dist - actual_dist))
i += BATCH_SIZE
inner_bar.update(2 * BATCH_SIZE)
outer_bar.update(1)
print("Average estimation error " + str(diff.item() / i))
cnn = CNN(kernel_size=3, hidden_layers=14, channels=MAX_LEN, embedding_size=MAX_LEN).to(DEVICE)
if os.path.isfile('cnn.pth'):
cnn.load_state_dict(torch.load('cnn.pth', map_location=torch.device('cpu')))
else:
train_model(cnn)
torch.save(cnn.state_dict(), 'cnn.pth')
cnn.eval()
print("Training finished! Starting evaluation!")
evaluate_monte_carlo(cnn, 1)