Delete 'inference.py'

inference.py

@@ -1,107 +0,0 @@
-from torch import nn
-import torch
-
-
-from torch.utils.data import IterableDataset
-import itertools
-import lzma
-import regex as re
-import pickle
-import scripts
-import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "1"
-
-class SimpleTrigramNeuralLanguageModel(nn.Module):
-  def __init__(self, vocabulary_size, embedding_size):
-      super(SimpleTrigramNeuralLanguageModel, self).__init__()
-      self.embedings = nn.Embedding(vocabulary_size, embedding_size)
-      self.linear = nn.Linear(embedding_size*2, vocabulary_size)
-
-      self.linear_first_layer = nn.Linear(embedding_size*2, embedding_size*2)
-      self.relu = nn.ReLU()
-      self.softmax = nn.Softmax()
-
-    #   self.model = nn.Sequential(
-    #       nn.Embedding(vocabulary_size, embedding_size),
-    #       nn.Linear(embedding_size, vocabulary_size),
-    #       nn.Softmax()
-    #   )
-
-  def forward(self, x):
-      emb_1 = self.embedings(x[0])
-      emb_2 = self.embedings(x[1])
-
-      first_layer = self.linear_first_layer(torch.cat((emb_1, emb_2), dim=1))
-      after_relu = self.relu(first_layer)
-      concated = self.linear(after_relu)
-
-      y = self.softmax(concated)
-
-      return y
-
-vocab_size = scripts.vocab_size
-embed_size = 100
-device = 'cuda'
-
-model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size).to(device)
-
-model.load_state_dict(torch.load('batch_model_epoch_0.bin'))
-model.eval()
-
-with open("vocab.pickle", 'rb') as handle:
-    vocab = pickle.load(handle)
-vocab.set_default_index(vocab['<unk>'])
-
-
-step = 0
-
-
-with lzma.open('dev-0/in.tsv.xz', 'rb') as file:
-    for line in file:
-        line = line.decode('utf-8')
-        line = line.rstrip()
-        # line = line.lower()
-        line = line.replace("\\\\n", ' ')
-
-
-        line_splitted = line.split('\t')[-2:] 
-        
-        prev = list(scripts.get_words_from_line(line_splitted[0]))[-1]
-        next =  list(scripts.get_words_from_line(line_splitted[1]))[0]
-
-        # prev = line[0].split(' ')[-1]
-        # next = line[1].split(' ')[0]
-
-
-        x = torch.tensor(vocab.forward([prev]))
-        z = torch.tensor(vocab.forward([next]))
-        x = x.to(device)
-        z = z.to(device)
-        ypredicted = model([x, z])
-
-        try:
-            
-            top = torch.topk(ypredicted[0], 128)
-        except:
-            print(ypredicted[0])
-            raise Exception('aa')
-        top_indices = top.indices.tolist()
-        top_probs = top.values.tolist()
-        top_words = vocab.lookup_tokens(top_indices)
-
-        string_to_print = ''
-        sum_probs = 0
-
-        for w, p in zip(top_words, top_probs):
-            if '<unk>' in w:
-                continue
-            if re.search(r'\p{L}+', w):
-                string_to_print += f"{w}:{p} "
-                sum_probs += p
-        if string_to_print == '':
-            print(f"the:0.2 a:0.3 :0.5")
-            continue
-        unknow_prob = 1 - sum_probs
-        string_to_print += f":{unknow_prob}"
-
-        print(string_to_print)