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Cleaned full_text_generation. Identical output as before.
This commit is contained in:
piero
Julien Chaumond
parent
6c9c131780
commit
08c6e456a3
@ -401,74 +401,6 @@ def full_text_generation(
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device
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)
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# if args.discrim == 'clickbait':
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# classifier = ClassificationHead(class_size=2, embed_size=1024).to(device)
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# classifier.load_state_dict(torch.load("discrim_models/clickbait_classifierhead.pt"))
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# classifier.eval()
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# args.label_class = 1 # clickbaity
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#
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# elif args.discrim == 'sentiment':
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# classifier = ClassificationHead(class_size=5, embed_size=1024).to(device)
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# #classifier.load_state_dict(torch.load("discrim_models/sentiment_classifierhead.pt"))
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# classifier.load_state_dict(torch.load("discrim_models/SST_classifier_head_epoch_16.pt"))
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# classifier.eval()
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# if args.label_class < 0:
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# raise Exception('Wrong class for sentiment, use --label-class 2 for *very positive*, 3 for *very negative*')
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# #args.label_class = 2 # very pos
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# #args.label_class = 3 # very neg
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#
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# elif args.discrim == 'toxicity':
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# classifier = ClassificationHead(class_size=2, embed_size=1024).to(device)
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# classifier.load_state_dict(torch.load("discrim_models/toxicity_classifierhead.pt"))
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# classifier.eval()
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# args.label_class = 0 # not toxic
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#
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# elif args.discrim == 'generic':
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# if args.discrim_weights is None:
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# raise ValueError('When using a generic discriminator, '
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# 'discrim_weights need to be specified')
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# if args.discrim_meta is None:
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# raise ValueError('When using a generic discriminator, '
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# 'discrim_meta need to be specified')
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#
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# with open(args.discrim_meta, 'r') as discrim_meta_file:
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# meta = json.load(discrim_meta_file)
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#
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# classifier = ClassificationHead(
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# class_size=meta['class_size'],
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# embed_size=meta['embed_size'],
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# # todo add tokenizer from meta
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# ).to(device)
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# classifier.load_state_dict(torch.load(args.discrim_weights))
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# classifier.eval()
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# if args.label_class == -1:
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# args.label_class = meta['default_class']
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#
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# else:
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# classifier = None
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# Get tokens for the list of positive words
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def list_tokens(word_list):
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token_list = [TOKENIZER.encode(word, add_prefix_space=True) for word in
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word_list]
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# token_list = []
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# for word in word_list:
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# token_list.append(TOKENIZER.encode(" " + word))
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return token_list
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# good_index = []
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# if args.bag_of_words:
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# bags_of_words = args.bag_of_words.split(";")
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# for wordlist in bags_of_words:
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# with open(wordlist, "r") as f:
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# words = f.read().strip()
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# words = words.split('\n')
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# good_index.append(list_tokens(words))
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#
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# for good_list in good_index:
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# good_list = list(filter(lambda x: len(x) <= 1, good_list))
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# actual_words = [(TOKENIZER.decode(ww).strip(),ww) for ww in good_list]
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bow_indices = []
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if bag_of_words:
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bow_indices = get_bag_of_words_indices(bag_of_words.split(";"))
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@ -486,9 +418,9 @@ def full_text_generation(
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print("Using PPLM-Discrim")
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else:
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raise Exception("Specify either --bag_of_words (-B) or --discrim (-D)")
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raise Exception("Specify either a bag of words or a discriminator")
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original, _, _ = generate_text_pplm(
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unpert_gen_tok_text, _, _ = generate_text_pplm(
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model=model,
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context=context,
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device=device,
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@ -497,12 +429,12 @@ def full_text_generation(
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)
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torch.cuda.empty_cache()
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perturbed_list = []
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discrim_loss_list = []
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loss_in_time_list = []
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pert_gen_tok_texts = []
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discrim_losses = []
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losses_in_time = []
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for i in range(num_samples):
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perturbed, discrim_loss, loss_in_time = generate_text_pplm(
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pert_gen_tok_text, discrim_loss, loss_in_time = generate_text_pplm(
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model=model,
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context=context,
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device=device,
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@ -525,14 +457,14 @@ def full_text_generation(
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decay=decay,
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gamma=gamma,
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)
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perturbed_list.append(perturbed)
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pert_gen_tok_texts.append(pert_gen_tok_text)
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if classifier is not None:
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discrim_loss_list.append(discrim_loss.data.cpu().numpy())
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loss_in_time_list.append(loss_in_time)
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discrim_losses.append(discrim_loss.data.cpu().numpy())
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losses_in_time.append(loss_in_time)
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torch.cuda.empty_cache()
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return original, perturbed_list, discrim_loss_list, loss_in_time_list
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return unpert_gen_tok_text, pert_gen_tok_texts, discrim_losses, losses_in_time
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def generate_text_pplm(
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@ -821,11 +753,14 @@ def run_model():
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generated_texts = []
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bow_words = set()
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bow_indices = get_bag_of_words_indices(args.bag_of_words.split(";"))
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for bow_list in bow_indices:
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filtered = list(filter(lambda x: len(x) <= 1, bow_list))
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bow_words.update(w[0] for w in filtered)
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bow_word_ids = set()
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if args.bag_of_words and args.colorama:
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bow_indices = get_bag_of_words_indices(args.bag_of_words.split(";"))
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for single_bow_list in bow_indices:
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# filtering all words in the list composed of more than 1 token
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filtered = list(filter(lambda x: len(x) <= 1, single_bow_list))
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# w[0] because we are sure w has only 1 item because previous fitler
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bow_word_ids.update(w[0] for w in filtered)
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# iterate through the perturbed texts
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for i, pert_gen_tok_text in enumerate(pert_gen_tok_texts):
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@ -836,7 +771,7 @@ def run_model():
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pert_gen_text = ''
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for word_id in pert_gen_tok_text.tolist()[0]:
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if word_id in bow_words:
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if word_id in bow_word_ids:
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pert_gen_text += '{}{}{}'.format(
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colorama.Fore.RED,
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TOKENIZER.decode([word_id]),
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