2021-06-16 12:51:01 +02:00
|
|
|
import os
|
|
|
|
from util import Model
|
|
|
|
import spacy
|
|
|
|
import torch
|
|
|
|
import numpy as np
|
|
|
|
import tqdm
|
|
|
|
|
|
|
|
def clean_string(str):
|
|
|
|
str = str.replace('\n', '')
|
|
|
|
return str
|
|
|
|
|
|
|
|
def extract_word(line):
|
|
|
|
return line.split(" ")[1]
|
|
|
|
|
|
|
|
def line2word(line):
|
|
|
|
word = extract_word(line)
|
|
|
|
word = clean_string(word)
|
|
|
|
return word
|
|
|
|
|
|
|
|
def find_interpunction(line, classes):
|
|
|
|
result = [x for x in classes if x in line]
|
|
|
|
if len(result) > 0:
|
|
|
|
return result[0]
|
|
|
|
else:
|
|
|
|
return ['']
|
|
|
|
|
|
|
|
def words_to_vecs(list_of_words):
|
|
|
|
return [nlp(x).vector for x in list_of_words]
|
|
|
|
|
|
|
|
def softXEnt(input, target):
|
2021-06-16 16:04:38 +02:00
|
|
|
m = torch.nn.LogSoftmax(dim=1)
|
|
|
|
logprobs = m(input)
|
2021-06-16 12:51:01 +02:00
|
|
|
return -(target * logprobs).sum() / input.shape[0]
|
|
|
|
|
|
|
|
def compute_class_vector(mark, classes):
|
|
|
|
result = np.zeros(len(classes))
|
|
|
|
for x in range(len(classes)):
|
|
|
|
if classes[x] == mark[0]:
|
|
|
|
result[x] == 1
|
|
|
|
return torch.tensor(result, dtype=torch.long)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
data_dir = "./fa/poleval_final_dataset/train"
|
|
|
|
data_nopunc_dir = "./fa/poleval_final_dataset1/train"
|
2021-06-16 13:03:28 +02:00
|
|
|
mode = "train"
|
|
|
|
# mode = "evaluate"
|
|
|
|
# mode = "generate"
|
2021-06-16 12:51:01 +02:00
|
|
|
|
|
|
|
data_paths = os.listdir(data_dir)
|
|
|
|
data_paths = [data_dir + "/" + x for x in data_paths]
|
|
|
|
|
|
|
|
classes = [',', '.', '?', '!', '-', ':', '...']
|
|
|
|
nlp = spacy.load("pl_core_news_sm")
|
|
|
|
context_size = 5
|
|
|
|
|
|
|
|
model = Model()
|
|
|
|
epochs = 5
|
|
|
|
output_prefix = "model"
|
|
|
|
hidden_state = torch.randn((2, 1, 300), requires_grad=True)
|
|
|
|
cell_state = torch.randn((2, 1, 300), requires_grad=True)
|
|
|
|
|
|
|
|
model.train()
|
|
|
|
optimizer = torch.optim.AdamW(model.parameters(), lr=0.02)
|
|
|
|
loss_function = softXEnt
|
|
|
|
|
2021-06-16 16:04:38 +02:00
|
|
|
"""
|
|
|
|
TODO
|
|
|
|
1) metoda ewaluacyjna
|
|
|
|
2) przenieść na cude !!!!
|
|
|
|
"""
|
2021-06-16 13:03:28 +02:00
|
|
|
|
|
|
|
if mode == "train":
|
|
|
|
for epoch in range(epochs):
|
|
|
|
for path in tqdm.tqdm(data_paths):
|
|
|
|
with open(path, "r") as file:
|
|
|
|
list = file.readlines()[:-1]
|
|
|
|
for i in range(0, len(list) - context_size - 1):
|
|
|
|
model.zero_grad()
|
|
|
|
x = list[i: i + context_size]
|
|
|
|
x = [line2word(y) for y in x]
|
|
|
|
x_1 = [line2word(list[i + context_size + 1])]
|
|
|
|
mark = find_interpunction(x[-1], classes)
|
|
|
|
# mark = words_to_vecs(mark)
|
|
|
|
|
|
|
|
x = x + x_1
|
|
|
|
x = words_to_vecs(x)
|
|
|
|
|
|
|
|
x = torch.tensor(x, dtype=torch.float)
|
|
|
|
# mark = torch.tensor(mark, dtype=torch.float)
|
|
|
|
|
|
|
|
output, (hidden_state, cell_state) = model.forward(x, hidden_state, cell_state)
|
|
|
|
output = output.squeeze(1)
|
|
|
|
loss = loss_function(output, compute_class_vector(mark, classes))
|
|
|
|
loss.backward()
|
|
|
|
optimizer.step()
|
|
|
|
hidden_state = hidden_state.detach()
|
|
|
|
cell_state = cell_state.detach()
|
|
|
|
|
|
|
|
print("Epoch: {}".format(epoch))
|
|
|
|
torch.save(
|
|
|
|
model.state_dict(),
|
|
|
|
os.path.join("./", f"{output_prefix}-{epoch}.pt"),
|
|
|
|
)
|
|
|
|
|
|
|
|
elif mode == "evaluate":
|
|
|
|
for pathA, pathB in zip(data_nopunc_dir, data_dir):
|
|
|
|
listA = []
|
|
|
|
listB = []
|
|
|
|
with open(pathA, "r") as file:
|
|
|
|
listA = file.readlines()[:-1]
|
|
|
|
with open(pathA, "r") as file:
|
|
|
|
listb = file.readlines()[:-1]
|
|
|
|
for i in range(0, len(list) - context_size - 1):
|
|
|
|
model.zero_grad()
|
|
|
|
x = listA[i: i + context_size]
|
|
|
|
x = [line2word(y) for y in x]
|
|
|
|
x_1 = [line2word(listA[i + context_size + 1])]
|
|
|
|
|
|
|
|
x = x + x_1
|
|
|
|
x = words_to_vecs(x)
|
|
|
|
|
|
|
|
y = listB[i + context_size]
|
|
|
|
y = [line2word(x) for x in y]
|
|
|
|
mark_y = find_interpunction(y)
|
|
|
|
x = torch.tensor(x, dtype=torch.float)
|
|
|
|
|
|
|
|
output, (hidden_state, cell_state) = model.forward(x, hidden_state, cell_state)
|
|
|
|
if classes[np.argmax(output)] == mark_y:
|
|
|
|
print('dupa')
|