update torch

labml_nn/experiments/nlp_classification.py

@@ -15,6 +15,7 @@ import torch
 import torchtext
 from torch import nn
 from torch.utils.data import DataLoader
+import torchtext.vocab
 from torchtext.vocab import Vocab
 
 from labml import lab, tracker, monit
@@ -270,7 +271,7 @@ def ag_news(c: NLPClassificationConfigs):
     for (label, line) in valid:
         counter.update(tokenizer(line))
     # Create vocabulary
-    vocab = Vocab(counter, min_freq=1)
+    vocab = torchtext.vocab.vocab(counter, min_freq=1)
 
     # Create training data loader
     train_loader = DataLoader(train, batch_size=c.batch_size, shuffle=True,

requirements.txt

@@ -1,8 +1,9 @@
-torch>=1.7
+torch>=1.10
+torchvision>=0.11
+torchtext>=0.11
 labml>=0.4.133
 labml-helpers>=0.4.84
-torchvision
-numpy>=1.16.3
+numpy>=1.19
 matplotlib>=3.0.3
 einops>=0.3.0
 gym[atari]

setup.py

@@ -5,7 +5,7 @@ with open("readme.md", "r") as f:
 
 setuptools.setup(
     name='labml-nn',
-    version='0.4.116',
+    version='0.4.117',
     author="Varuna Jayasiri, Nipun Wijerathne",
     author_email="vpjayasiri@gmail.com, hnipun@gmail.com",
     description="🧑‍🏫 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit), optimizers (adam, radam, adabelief), gans(dcgan, cyclegan, stylegan2), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, etc. 🧠",
@@ -23,6 +23,8 @@ setuptools.setup(
     install_requires=['labml>=0.4.133',
                       'labml-helpers>=0.4.84',
                       'torch',
+                      'torchtext',
+                      'torchvision',
                       'einops',
                       'numpy'],
     classifiers=[