mirror of
https://github.com/clearml/clearml
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Merge pull request #150 from danmalowany-allegro/master
Update Jupiter notebook example scripts
This commit is contained in:
Allegro AI
GitHub
commit
f036213d91
@ -15,7 +15,7 @@
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"! pip install -U torchaudio==0.5.0\n",
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"! pip install -U torchvision==0.6.0\n",
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"! pip install -U matplotlib==3.2.1\n",
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"! pip install -U trains==0.15.0\n",
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"! pip install -U trains>=0.15.0\n",
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"! pip install -U pandas==1.0.4\n",
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"! pip install -U numpy==1.18.4\n",
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"! pip install -U tensorboard==2.2.1"
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@ -283,10 +283,10 @@
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" tensorboard_writer.add_scalar('learning rate/lr', optimizer.param_groups[0]['lr'], iteration)\n",
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" \n",
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" \n",
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" if batch_idx % debug_interval == 0: # report debug image every 500 mini-batches\n",
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" if batch_idx % debug_interval == 0: # report debug image every \"debug_interval\" mini-batches\n",
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" for n, (inp, pred, label) in enumerate(zip(inputs, predicted, labels)):\n",
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" series = 'label_{}_pred_{}'.format(classes[label.cpu()], classes[pred.cpu()])\n",
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" tensorboard_writer.add_image('Train MelSpectrogram samples/{}'.format(n), \n",
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" tensorboard_writer.add_image('Train MelSpectrogram samples/{}_{}_{}'.format(batch_idx, n, series), \n",
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" plot_signal(inp.cpu().numpy().squeeze(), series, 'hot'), iteration)"
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]
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},
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@ -319,12 +319,12 @@
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" class_total[label] += 1\n",
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" \n",
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" iteration = (epoch + 1) * len(train_loader)\n",
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" if idx % debug_interval == 0: # report debug image every 100 mini-batches\n",
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" if idx % debug_interval == 0: # report debug image every \"debug_interval\" mini-batches\n",
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" for n, (sound, inp, pred, label) in enumerate(zip(sounds, inputs, predicted, labels)):\n",
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" series = 'label_{}_pred_{}'.format(classes[label.cpu()], classes[pred.cpu()])\n",
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" tensorboard_writer.add_audio('Test audio samples/{}'.format(n), \n",
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" tensorboard_writer.add_audio('Test audio samples/{}_{}_{}'.format(idx, n, series), \n",
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" sound, iteration, int(sample_rate[n]))\n",
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" tensorboard_writer.add_image('Test MelSpectrogram samples/{}_{}'.format(idx, n), \n",
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" tensorboard_writer.add_image('Test MelSpectrogram samples/{}_{}_{}'.format(idx, n, series), \n",
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" plot_signal(inp.cpu().numpy().squeeze(), series, 'hot'), iteration)\n",
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"\n",
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" total_accuracy = 100 * sum(class_correct)/sum(class_total)\n",
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@ -377,4 +377,4 @@
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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}
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@ -12,7 +12,7 @@
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"! pip install -U torch==1.5.0\n",
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"! pip install -U torchaudio==0.5.0\n",
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"! pip install -U matplotlib==3.2.1\n",
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"! pip install -U trains==0.15.0\n",
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"! pip install -U trains>=0.15.0\n",
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"! pip install -U tensorboard==2.2.1"
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]
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},
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@ -87,10 +87,10 @@
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": true,
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"scrolled": true
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},
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"outputs": [],
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"source": [
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@ -125,4 +125,4 @@
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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}
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@ -133,4 +133,4 @@
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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}
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@ -45,7 +45,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"task = Task.init(project_name='Hyper-Parameter Search', task_name='image_classification_CIFAR10')\n",
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"task = Task.init(project_name='Image Example', task_name='image_classification_CIFAR10')\n",
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"configuration_dict = {'number_of_epochs': 3, 'batch_size': 4, 'dropout': 0.25, 'base_lr': 0.001}\n",
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"configuration_dict = task.connect(configuration_dict) # enabling configuration override by trains\n",
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"print(configuration_dict) # printing actual configuration (after override in remote mode)"
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@ -0,0 +1,317 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"! pip install -U pip\n",
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"! pip install -U torch==1.5.0\n",
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"! pip install -U torchtext==0.6.0\n",
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"! pip install -U matplotlib==3.2.1\n",
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"! pip install -U trains>=0.15.0\n",
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"! pip install -U tensorboard==2.2.1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import time\n",
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"\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"import torch.nn.functional as F\n",
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"import torchtext\n",
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"from torchtext.datasets import text_classification\n",
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"from torch.utils.data import DataLoader\n",
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"from torch.utils.tensorboard import SummaryWriter\n",
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"\n",
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"from trains import Task\n",
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"\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"task = Task.init(project_name='Text Example', task_name='text classifier')\n",
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"configuration_dict = {'number_of_epochs': 6, 'batch_size': 16, 'ngrams': 2, 'base_lr': 1.0}\n",
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"configuration_dict = task.connect(configuration_dict) # enabling configuration override by trains\n",
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"print(configuration_dict) # printing actual configuration (after override in remote mode)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"if not os.path.isdir('./data'):\n",
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" os.mkdir('./data')\n",
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"train_dataset, test_dataset = text_classification.DATASETS['AG_NEWS'](root='./data', \n",
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" ngrams=configuration_dict.get('ngrams', 2))\n",
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"vocabulary = train_dataset.get_vocab()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def generate_batch(batch):\n",
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" label = torch.tensor([entry[0] for entry in batch])\n",
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" # original data batch input are packed into a list and concatenated as a single tensor\n",
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" text = [entry[1] for entry in batch]\n",
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" # offsets is a tensor of delimiters to represent the beginning index of each sequence in the text tensor.\n",
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" offsets = [0] + [len(entry) for entry in text] \n",
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" \n",
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" # torch.Tensor.cumsum returns the cumulative sum of elements in the dimension dim.\n",
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" offsets = torch.tensor(offsets[:-1]).cumsum(dim=0)\n",
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" text = torch.cat(text)\n",
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" return text, offsets, label\n",
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"\n",
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"train_loader = torch.utils.data.DataLoader(train_dataset, batch_size = configuration_dict.get('batch_size', 16), \n",
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" shuffle = True, pin_memory=True, collate_fn=generate_batch)\n",
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"test_loader = torch.utils.data.DataLoader(test_dataset, batch_size = configuration_dict.get('batch_size', 16), \n",
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" shuffle = False, pin_memory=True, collate_fn=generate_batch)\n",
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"\n",
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"classes = (\"World\", \"Sports\", \"Business\", \"Sci/Tec\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"class TextSentiment(nn.Module):\n",
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" def __init__(self, vocab_size, embed_dim, num_class):\n",
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" super().__init__()\n",
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" self.embedding = nn.EmbeddingBag(vocab_size, embed_dim, sparse=True)\n",
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" self.fc = nn.Linear(embed_dim, num_class)\n",
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" self.init_weights()\n",
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"\n",
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" def init_weights(self):\n",
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" initrange = 0.5\n",
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" self.embedding.weight.data.uniform_(-initrange, initrange)\n",
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" self.fc.weight.data.uniform_(-initrange, initrange)\n",
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" self.fc.bias.data.zero_()\n",
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"\n",
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" def forward(self, text, offsets):\n",
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" embedded = self.embedding(text, offsets)\n",
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" return self.fc(embedded)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"VOCAB_SIZE = len(train_dataset.get_vocab())\n",
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"EMBED_DIM = 32\n",
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"NUN_CLASS = len(train_dataset.get_labels())\n",
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"model = TextSentiment(VOCAB_SIZE, EMBED_DIM, NUN_CLASS)\n",
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"\n",
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"device = torch.cuda.current_device() if torch.cuda.is_available() else torch.device('cpu')\n",
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"print('Device to use: {}'.format(device))\n",
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"model.to(device)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"criterion = torch.nn.CrossEntropyLoss().to(device)\n",
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"optimizer = torch.optim.SGD(model.parameters(), lr=configuration_dict.get('base_lr', 1.0))\n",
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"scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 2, gamma=0.9)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"tensorboard_writer = SummaryWriter('./tensorboard_logs')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def train_func(data, epoch):\n",
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" # Train the model\n",
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" train_loss = 0\n",
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" train_acc = 0\n",
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" for batch_idx, (text, offsets, cls) in enumerate(data):\n",
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" optimizer.zero_grad()\n",
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" text, offsets, cls = text.to(device), offsets.to(device), cls.to(device)\n",
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" output = model(text, offsets)\n",
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" loss = criterion(output, cls)\n",
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" train_loss += loss.item()\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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" train_acc += (output.argmax(1) == cls).sum().item()\n",
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" \n",
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" iteration = epoch * len(train_loader) + batch_idx\n",
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" if batch_idx % log_interval == 0: \n",
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" print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'\n",
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" .format(epoch, batch_idx * len(cls), len(train_dataset), \n",
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" 100. * batch_idx / len(train_loader), loss))\n",
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" tensorboard_writer.add_scalar('training loss/loss', loss, iteration)\n",
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" tensorboard_writer.add_scalar('learning rate/lr', optimizer.param_groups[0]['lr'], iteration)\n",
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"\n",
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" # Adjust the learning rate\n",
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" scheduler.step()\n",
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"\n",
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" return train_loss / len(train_dataset), train_acc / len(train_dataset)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"def test(data, epoch):\n",
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" loss = 0\n",
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" acc = 0\n",
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" for idx, (text, offsets, cls) in enumerate(data):\n",
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" text, offsets, cls = text.to(device), offsets.to(device), cls.to(device)\n",
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" with torch.no_grad():\n",
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" output = model(text, offsets)\n",
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" predicted = output.argmax(1)\n",
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" loss = criterion(output, cls)\n",
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" loss += loss.item()\n",
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" acc += (predicted == cls).sum().item()\n",
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" \n",
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" iteration = (epoch + 1) * len(train_loader)\n",
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" if idx % debug_interval == 0: # report debug text every \"debug_interval\" mini-batches\n",
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" offsets = offsets.tolist() + [len(text)]\n",
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" for n, (pred, label) in enumerate(zip(predicted, cls)):\n",
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" ids_to_text = [vocabulary.itos[id] for id in text[offsets[n]:offsets[n+1]]]\n",
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" series = '{}_{}_label_{}_pred_{}'.format(idx, n, classes[label], classes[pred])\n",
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" tensorboard_writer.add_text('Test text samples/{}'.format(series), \n",
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" ' '.join(ids_to_text), iteration)\n",
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"\n",
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" return loss / len(test_dataset), acc / len(test_dataset)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"log_interval = 200\n",
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"debug_interval = 500\n",
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"for epoch in range(configuration_dict.get('number_of_epochs', 6)):\n",
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" start_time = time.time()\n",
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" \n",
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" train_loss, train_acc = train_func(train_loader, epoch)\n",
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" test_loss, test_acc = test(test_loader, epoch)\n",
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" \n",
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" secs = int(time.time() - start_time)\n",
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"\n",
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" print('Epoch: %d' %(epoch + 1), \" | time in %d minutes, %d seconds\" %(secs / 60, secs % 60))\n",
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" print(f'\\tLoss: {train_loss:.4f}(train)\\t|\\tAcc: {train_acc * 100:.1f}%(train)')\n",
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" print(f'\\tLoss: {test_loss:.4f}(test)\\t|\\tAcc: {test_acc * 100:.1f}%(test)')\n",
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" tensorboard_writer.add_scalar('accuracy/train', train_acc, (epoch + 1) * len(train_loader))\n",
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" tensorboard_writer.add_scalar('accuracy/test', test_acc, (epoch + 1) * len(train_loader))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"import re\n",
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"from torchtext.data.utils import ngrams_iterator\n",
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"from torchtext.data.utils import get_tokenizer\n",
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"\n",
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"def predict(text, model, vocab, ngrams):\n",
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" tokenizer = get_tokenizer(\"basic_english\")\n",
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" with torch.no_grad():\n",
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" text = torch.tensor([vocab[token]\n",
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" for token in ngrams_iterator(tokenizer(text), ngrams)])\n",
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" output = model(text, torch.tensor([0]))\n",
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" return output.argmax(1).item()\n",
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"\n",
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"ex_text_str = \"MEMPHIS, Tenn. – Four days ago, Jon Rahm was \\\n",
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" enduring the season’s worst weather conditions on Sunday at The \\\n",
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" Open on his way to a closing 75 at Royal Portrush, which \\\n",
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" considering the wind and the rain was a respectable showing. \\\n",
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" Thursday’s first round at the WGC-FedEx St. Jude Invitational \\\n",
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" was another story. With temperatures in the mid-80s and hardly any \\\n",
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" wind, the Spaniard was 13 strokes better in a flawless round. \\\n",
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" Thanks to his best putting performance on the PGA Tour, Rahm \\\n",
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" finished with an 8-under 62 for a three-stroke lead, which \\\n",
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" was even more impressive considering he’d never played the \\\n",
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" front nine at TPC Southwind.\"\n",
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"\n",
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"ans = predict(ex_text_str, model.to(\"cpu\"), vocabulary, configuration_dict.get('ngrams', 2))\n",
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"print(\"This is a %s news\" %classes[ans])"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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