Delete old test.py code

1 files changed, 0 insertions, 172 deletions

diff --git a/test.py b/test.py
deleted file mode 100644
index ff88000..0000000
--- a/test.py
+++ /dev/null
@@ -1,172 +0,0 @@
-#!/usr/bin/python3
-# https://github.com/ChunML/NLP/blob/master/text_generation/train_pt.py
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-import numpy as np
-from collections import Counter
-import os
-from argparse import Namespace
-
-
-flags = Namespace(
-    train_file='data',
-    seq_size=32,
-    batch_size=256,
-    embedding_size=64,
-    lstm_size=64,
-    gradients_norm=5,
-    initial_words=['i', 'am'],
-    predict_top_k=3,
-    checkpoint_path='checkpoint',
-)
-
-
-def get_data_from_file(train_file, batch_size, seq_size):
-    with open(train_file, 'r', encoding='utf-8') as f:
-        text = f.read()
-    text = text.split()
-
-    word_counts = Counter(text)
-    sorted_vocab = sorted(word_counts, key=word_counts.get, reverse=True)
-    int_to_vocab = {k: w for k, w in enumerate(sorted_vocab)}
-    vocab_to_int = {w: k for k, w in int_to_vocab.items()}
-    n_vocab = len(int_to_vocab)
-
-    print('Vocabulary size', n_vocab)
-
-    int_text = [vocab_to_int[w] for w in text]
-    num_batches = int(len(int_text) / (seq_size * batch_size))
-    in_text = int_text[:num_batches * batch_size * seq_size]
-    out_text = np.zeros_like(in_text)
-    out_text[:-1] = in_text[1:]
-    out_text[-1] = in_text[0]
-    in_text = np.reshape(in_text, (batch_size, -1))
-    out_text = np.reshape(out_text, (batch_size, -1))
-    return int_to_vocab, vocab_to_int, n_vocab, in_text, out_text
-
-
-def get_batches(in_text, out_text, batch_size, seq_size):
-    num_batches = np.prod(in_text.shape) // (seq_size * batch_size)
-    for i in range(0, num_batches * seq_size, seq_size):
-        yield in_text[:, i:i+seq_size], out_text[:, i:i+seq_size]
-
-
-class RNNModule(nn.Module):
-    def __init__(self, n_vocab, seq_size, embedding_size, lstm_size):
-        super(RNNModule, self).__init__()
-        self.seq_size = seq_size
-        self.lstm_size = lstm_size
-        self.embedding = nn.Embedding(n_vocab, embedding_size)
-        self.lstm = nn.LSTM(embedding_size,
-                            lstm_size,
-                            batch_first=True)
-        self.dense = nn.Linear(lstm_size, n_vocab)
-
-    def forward(self, x, prev_state):
-        embed = self.embedding(x)
-        output, state = self.lstm(embed, prev_state)
-        logits = self.dense(output)
-
-        return logits, state
-
-    def zero_state(self, batch_size):
-        return (torch.zeros(1, batch_size, self.lstm_size),
-                torch.zeros(1, batch_size, self.lstm_size))
-
-
-def get_loss_and_train_op(net, lr=0.001):
-    criterion = nn.CrossEntropyLoss()
-    optimizer = torch.optim.Adam(net.parameters(), lr=lr)
-
-    return criterion, optimizer
-
-
-def predict(device, net, words, n_vocab, vocab_to_int, int_to_vocab, top_k=5):
-    net.eval()
-    words = ['i', 'am']
-
-    state_h, state_c = net.zero_state(1)
-    state_h = state_h.to(device)
-    state_c = state_c.to(device)
-    for w in words:
-        ix = torch.tensor([[vocab_to_int[w]]]).to(device)
-        output, (state_h, state_c) = net(ix, (state_h, state_c))
-
-    _, top_ix = torch.topk(output[0], k=top_k)
-    choices = top_ix.tolist()
-    choice = np.random.choice(choices[0])
-
-    words.append(int_to_vocab[choice])
-
-    for _ in range(100):
-        ix = torch.tensor([[choice]]).to(device)
-        output, (state_h, state_c) = net(ix, (state_h, state_c))
-
-        _, top_ix = torch.topk(output[0], k=top_k)
-        choices = top_ix.tolist()
-        choice = np.random.choice(choices[0])
-        words.append(int_to_vocab[choice])
-
-    print(' '.join(words).encode('utf-8'))
-
-
-def main():
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-    int_to_vocab, vocab_to_int, n_vocab, in_text, out_text = get_data_from_file(
-        flags.train_file, flags.batch_size, flags.seq_size)
-
-    net = RNNModule(n_vocab, flags.seq_size,
-                    flags.embedding_size, flags.lstm_size)
-    net = net.to(device)
-
-    criterion, optimizer = get_loss_and_train_op(net, 0.01)
-
-    iteration = 0
-
-    for e in range(200):
-        batches = get_batches(
-            in_text, out_text, flags.batch_size, flags.seq_size)
-        state_h, state_c = net.zero_state(flags.batch_size)
-        state_h = state_h.to(device)
-        state_c = state_c.to(device)
-        for x, y in batches:
-            iteration += 1
-            net.train()
-
-            optimizer.zero_grad()
-
-            x = torch.tensor(x).to(device)
-            y = torch.tensor(y).to(device)
-
-            logits, (state_h, state_c) = net(x, (state_h, state_c))
-            loss = criterion(logits.transpose(1, 2), y)
-
-            loss_value = loss.item()
-
-            loss.backward()
-
-            state_h = state_h.detach()
-            state_c = state_c.detach()
-
-            _ = torch.nn.utils.clip_grad_norm_(
-                net.parameters(), flags.gradients_norm)
-
-            optimizer.step()
-
-            if iteration % 1 == 0:
-                print('Epoch: {}/{}'.format(e, 20),
-                      'Iteration: {}'.format(iteration),
-                      'Loss: {}'.format(loss_value))
-
-            if iteration % 1000 == 0:
-                predict(device, net, flags.initial_words, n_vocab,
-                        vocab_to_int, int_to_vocab, top_k=3)
-                torch.save(net.state_dict(),
-                           'checkpoint/model-{}.pth'.format(iteration))
-
-
-if __name__ == '__main__':
-    main()