Renamed test.py ->test-system.py because of python

src/test-system.py

@@ -0,0 +1,18 @@
+exec(open("dialrequirements.py").read())
+
+#Print welcome message
+print('------------------------------------------')
+print('       Ongi etorri elkarrizketara.')
+print("Idatz ezazu 'Agur' elkarrizketa bukatzeko.")
+print('------------------------------------------')
+
+#Main system loop
+user = input('- ')
+
+while user != 'Agur' and user != 'agur':
+    sentence = evaluate(' '.join(tokenizer(user)))
+    print('- ' + sentence.strip().capitalize())
+    user = input('- ')
+
+sentence = evaluate(' '.join(tokenizer(user)))
+print('- ' + sentence.strip().capitalize())

src/test.py

@@ -1,74 +0,0 @@
-from torchtext.data.utils import _basic_english_normalize
-import torch
-import random
-from argparse import ArgumentParser
-from model import *
-from spacy.tokenizer import Tokenizer
-from spacy.lang.eu import Basque
-
-nlp = Basque()
-
-
-def tokenizer(s):
-    return list(map(lambda x: x.text, nlp(s)))
-
-
-parser = ArgumentParser(description='Azpitituluetan oinarritutako elkarrizketa \
-sistemaren proba')
-
-parser.add_argument('-decoding_strategy', type=str, default='top1', choices=['top1', 'topk', 'multinomial'])
-
-args = parser.parse_args()
-
-def decode(logits, decoding_strategy='max', k=3, temp=0.4):
-  if decoding_strategy=='top1':
-    target = logits.max(1)[1]
-  elif decoding_strategy=='topk':
-    target = logits.topk(k)[1][0][random.randint(0, k-1)].unsqueeze(-1)
-  else:
-    target = torch.multinomial(logits.squeeze().div(temp).exp().cpu(), 1)
-  return target
-
-def evaluate(sentence):
-  with torch.no_grad():
-    sentence = '<sos> ' + sentence + ' <eos>'
-    sent_len = len(sentence.split())
-    sentence = torch.Tensor([text_field.vocab.stoi[i] for i in sentence.lower().split()]).long().view(sent_len, 1)
-    target = torch.Tensor([text_field.vocab.stoi['<sos>']]).long()
-    output_sentence = ''
-    encoder_outputs, hidden = model.encoder(sentence)
-    for t in range(MAX_LENGTH):
-    # first input to the decoder is the <sos> token
-      output, hidden = model.decoder(target, hidden, encoder_outputs)
-      target = decode(output, decoding_strategy)
-      word = text_field.vocab.itos[target.numpy()[0]]
-      if word == '<eos>':
-        return output_sentence
-      else:
-        output_sentence = output_sentence + ' ' + word
-  return output_sentence
-
-#Load model and fields
-text_field = torch.load('../model/text_field.Field')
-model = torch.load('../model/model.pt', map_location=torch.device('cpu'))
-torch.nn.Module.dump_patches = True
-MAX_LENGTH = 10
-
-#Print welcome message
-print('------------------------------------------')
-print('       Ongi etorri elkarrizketara.')
-print("Idatz ezazu 'Agur' elkarrizketa bukatzeko.")
-print('------------------------------------------')
-
-#Main system loop
-user = input('- ')
-model.eval()
-decoding_strategy = args.decoding_strategy
-
-while user != 'Agur' and user != 'agur':
-    sentence = evaluate(' '.join(tokenizer(user)))
-    print('- ' + sentence.strip().capitalize())
-    user = input('- ')
-
-sentence = evaluate(' '.join(tokenizer(user)))
-print('- ' + sentence.strip().capitalize())