BloodAxe / catalyst

Accelerated DL R&D

Home Page:https://catalyst-team.github.io/catalyst

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Accelerated deep learning R&D

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codestyle catalyst catalyst-cv catalyst-nlp

python python python

os os os

PyTorch framework for Deep Learning research and development. It focuses on reproducibility, rapid experimentation, and codebase reuse so you can create something new rather than write another regular train loop.
Break the cycle - use the Catalyst!

Project manifest. Part of PyTorch Ecosystem. Part of Catalyst Ecosystem:

  • Alchemy - experiments logging & visualization
  • Catalyst - accelerated deep learning R&D
  • Reaction - convenient deep learning models serving

Catalyst at AI Landscape.


Getting started

pip install -U catalyst
import os
import torch
from torch.nn import functional as F
from torch.utils.data import DataLoader
from catalyst import dl, metrics
from catalyst.data.cv import ToTensor
from catalyst.contrib.datasets import MNIST

model = torch.nn.Linear(28 * 28, 10)
optimizer = torch.optim.Adam(model.parameters(), lr=0.02)

loaders = {
    "train": DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=ToTensor()), batch_size=32),
    "valid": DataLoader(MNIST(os.getcwd(), train=False, download=True, transform=ToTensor()), batch_size=32),
}

class CustomRunner(dl.Runner):

    def predict_batch(self, batch):
        # model inference step
        return self.model(batch[0].to(self.device).view(batch[0].size(0), -1))

    def _handle_batch(self, batch):
        # model train/valid step
        x, y = batch
        y_hat = self.model(x.view(x.size(0), -1))

        loss = F.cross_entropy(y_hat, y)
        accuracy01, accuracy03 = metrics.accuracy(y_hat, y, topk=(1, 3))
        self.batch_metrics.update(
            {"loss": loss, "accuracy01": accuracy01, "accuracy03": accuracy03}
        )

        if self.is_train_loader:
            loss.backward()
            self.optimizer.step()
            self.optimizer.zero_grad()

runner = CustomRunner()
# model training
runner.train(
    model=model,
    optimizer=optimizer,
    loaders=loaders,
    logdir="./logs",
    num_epochs=5,
    verbose=True,
    load_best_on_end=True,
)
# model inference
for prediction in runner.predict_loader(loader=loaders["valid"]):
    assert prediction.detach().cpu().numpy().shape[-1] == 10
# model tracing
traced_model = runner.trace(loader=loaders["valid"])

Step by step guide

  1. Start with Catalyst 101 — Accelerated PyTorch introduction.
  2. Go through Kittylyst if you would like to dive into the core design concepts of the framework.
  3. Check minimal examples.
  4. Try notebook tutorials with Google Colab.
  5. Read blogposts with use-cases and guides.
  6. Learn machine learning with our "Deep Learning with Catalyst" course.
  7. Or go directly to advanced classification, detection or segmentation pipelines with Config API.
  8. Want more? See Alchemy and Reaction packages.
  9. RL fan? Please follow Catalyst.RL repo.
  10. If you would like to contribute to the project, follow our contribution guidelines.
  11. If you want to support the project, feel free to donate on patreon page or write us with your proposals.
  12. Finally, do not forget to join our slack for collaboration.

Table of Contents

Overview

Catalyst helps you write compact but full-featured Deep Learning pipelines in a few lines of code. You get a training loop with metrics, early-stopping, model checkpointing and other features without the boilerplate.

Installation

Common installation:

pip install -U catalyst
Specific versions with additional requirements

pip install catalyst[cv]         # installs CV-based catalyst
pip install catalyst[nlp]        # installs NLP-based catalyst
pip install catalyst[ecosystem]  # installs Catalyst.Ecosystem
# and master version installation
pip install git+https://github.com/catalyst-team/catalyst@master --upgrade

Catalyst is compatible with: Python 3.6+. PyTorch 1.1+.
Tested on Ubuntu 16.04/18.04/20.04, macOS 10.15, Windows 10 and Windows Subsystem for Linux.

Minimal Examples

ML - linear regression

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst.dl import SupervisedRunner

# data
num_samples, num_features = int(1e4), int(1e1)
X, y = torch.rand(num_samples, num_features), torch.rand(num_samples)
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, 1)
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [3, 6])

# model training
runner = SupervisedRunner()
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    logdir="./logdir",
    num_epochs=8,
    verbose=True,
)

ML - multi-class classification

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl

# sample data
num_samples, num_features, num_classes = int(1e4), int(1e1), 4
X = torch.rand(num_samples, num_features)
y = (torch.rand(num_samples, ) * num_classes).to(torch.int64)

# pytorch loaders
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, num_classes)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [2])

# model training
runner = dl.SupervisedRunner()
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    logdir="./logdir",
    num_epochs=3,
    callbacks=[dl.AccuracyCallback(num_classes=num_classes)]
)

ML - multi-label classification

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl

# sample data
num_samples, num_features, num_classes = int(1e4), int(1e1), 4
X = torch.rand(num_samples, num_features)
y = (torch.rand(num_samples, num_classes) > 0.5).to(torch.float32)

# pytorch loaders
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, num_classes)
criterion = torch.nn.BCEWithLogitsLoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [2])

# model training
runner = dl.SupervisedRunner()
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    logdir="./logdir",
    num_epochs=3,
    callbacks=[dl.MultiLabelAccuracyCallback(threshold=0.5)]
)

CV - MNIST classification

import os
import torch
from torch.nn import functional as F
from torch.utils.data import DataLoader
from catalyst import dl, metrics
from catalyst.data.cv import ToTensor
from catalyst.contrib.datasets import MNIST

model = torch.nn.Linear(28 * 28, 10)
optimizer = torch.optim.Adam(model.parameters(), lr=0.02)

loaders = {
    "train": DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=ToTensor()), batch_size=32),
    "valid": DataLoader(MNIST(os.getcwd(), train=False, download=True, transform=ToTensor()), batch_size=32),
}

class CustomRunner(dl.Runner):

    def _handle_batch(self, batch):
        x, y = batch
        y_hat = self.model(x.view(x.size(0), -1))

        loss = F.cross_entropy(y_hat, y)
        accuracy01, accuracy03, accuracy05 = metrics.accuracy(y_hat, y, topk=(1, 3, 5))
        self.batch_metrics = {
            "loss": loss,
            "accuracy01": accuracy01,
            "accuracy03": accuracy03,
            "accuracy05": accuracy05,
        }
        
        if self.is_train_loader:
            loss.backward()
            self.optimizer.step()
            self.optimizer.zero_grad()

runner = CustomRunner()
runner.train(
    model=model, 
    optimizer=optimizer, 
    loaders=loaders, 
    verbose=True,
)

CV - classification with AutoEncoder

import os
import torch
from torch import nn
from torch.nn import functional as F
from torch.utils.data import DataLoader
from catalyst import dl, metrics
from catalyst.data.cv import ToTensor
from catalyst.contrib.datasets import MNIST

class ClassifyAE(nn.Module):

    def __init__(self, in_features, hid_features, out_features):
        super().__init__()
        self.encoder = nn.Sequential(nn.Linear(in_features, hid_features), nn.Tanh())
        self.decoder = nn.Sequential(nn.Linear(hid_features, in_features), nn.Sigmoid())
        self.clf = nn.Linear(hid_features, out_features)

    def forward(self, x):
        z = self.encoder(x)
        y_hat = self.clf(z)
        x_ = self.decoder(z)
        return y_hat, x_

model = ClassifyAE(28 * 28, 128, 10)
optimizer = torch.optim.Adam(model.parameters(), lr=0.02)

loaders = {
    "train": DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=ToTensor()), batch_size=32),
    "valid": DataLoader(MNIST(os.getcwd(), train=False, download=True, transform=ToTensor()), batch_size=32),
}

class CustomRunner(dl.Runner):

    def _handle_batch(self, batch):
        x, y = batch
        x = x.view(x.size(0), -1)
        y_hat, x_ = self.model(x)

        loss_clf = F.cross_entropy(y_hat, y)
        loss_ae = F.mse_loss(x_, x)
        loss = loss_clf + loss_ae
        accuracy01, accuracy03, accuracy05 = metrics.accuracy(y_hat, y, topk=(1, 3, 5))
        self.batch_metrics = {
            "loss_clf": loss_clf,
            "loss_ae": loss_ae,
            "loss": loss,
            "accuracy01": accuracy01,
            "accuracy03": accuracy03,
            "accuracy05": accuracy05,
        }

        if self.is_train_loader:
            loss.backward()
            self.optimizer.step()
            self.optimizer.zero_grad()

runner = CustomRunner()
runner.train(
    model=model,
    optimizer=optimizer,
    loaders=loaders,
    verbose=True,
)

CV - classification with Variational AutoEncoder

import os
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from torch.utils.data import DataLoader
from catalyst import dl, metrics
from catalyst.data.cv import ToTensor
from catalyst.contrib.datasets import MNIST

LOG_SCALE_MAX = 2
LOG_SCALE_MIN = -10

def normal_sample(loc, log_scale):
    scale = torch.exp(0.5 * log_scale)
    return loc + scale * torch.randn_like(scale)

class ClassifyVAE(torch.nn.Module):

    def __init__(self, in_features, hid_features, out_features):
        super().__init__()
        self.encoder = nn.Linear(in_features, hid_features * 2)
        self.decoder = nn.Sequential(nn.Linear(hid_features, in_features), nn.Sigmoid())
        self.clf = nn.Linear(hid_features, out_features)

    def forward(self, x, deterministic=False):
        z = self.encoder(x)
        bs, z_dim = z.shape

        loc, log_scale = z[:, :z_dim // 2], z[:, z_dim // 2:]
        log_scale = torch.clamp(log_scale, LOG_SCALE_MIN, LOG_SCALE_MAX)

        z_ = loc if deterministic else normal_sample(loc, log_scale)
        z_ = z_.view(bs, -1)
        x_ = self.decoder(z_)

        y_hat = self.clf(z_)

        return y_hat, x_, loc, log_scale

model = ClassifyVAE(28 * 28, 64, 10)
optimizer = torch.optim.Adam(model.parameters(), lr=0.02)

loaders = {
    "train": DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=ToTensor()), batch_size=32),
    "valid": DataLoader(MNIST(os.getcwd(), train=False, download=True, transform=ToTensor()), batch_size=32),
}

class CustomRunner(dl.Runner):

    def _handle_batch(self, batch):
        x, y = batch
        x = x.view(x.size(0), -1)
        y_hat, x_, loc, log_scale = self.model(x, deterministic=not self.is_train_loader)

        loss_clf = F.cross_entropy(y_hat, y)
        loss_ae = F.mse_loss(x_, x)
        loss_kld = (-0.5 * torch.sum(1 + log_scale - loc.pow(2) - log_scale.exp(), dim=1)).mean()
        loss = loss_clf + loss_ae + loss_kld
        accuracy01, accuracy03, accuracy05 = metrics.accuracy(y_hat, y, topk=(1, 3, 5))
        self.batch_metrics = {
            "loss_clf": loss_clf,
            "loss_ae": loss_ae,
            "loss_kld": loss_kld,
            "loss": loss,
            "accuracy01": accuracy01,
            "accuracy03": accuracy03,
            "accuracy05": accuracy05,
        }

        if self.is_train_loader:
            loss.backward()
            self.optimizer.step()
            self.optimizer.zero_grad()

runner = CustomRunner()
runner.train(
    model=model,
    optimizer=optimizer,
    loaders=loaders,
    verbose=True,
)

CV - segmentation with classification auxiliary task

import os
import torch
from torch import nn
from torch.nn import functional as F
from torch.utils.data import DataLoader
from catalyst import dl, metrics
from catalyst.data.cv import ToTensor
from catalyst.contrib.datasets import MNIST

class ClassifyUnet(nn.Module):

    def __init__(self, in_channels, in_hw, out_features):
        super().__init__()
        self.encoder = nn.Sequential(nn.Conv2d(in_channels, in_channels, 3, 1, 1), nn.Tanh())
        self.decoder = nn.Conv2d(in_channels, in_channels, 3, 1, 1)
        self.clf = nn.Linear(in_channels * in_hw * in_hw, out_features)

    def forward(self, x):
        z = self.encoder(x)
        z_ = z.view(z.size(0), -1)
        y_hat = self.clf(z_)
        x_ = self.decoder(z)
        return y_hat, x_

model = ClassifyUnet(1, 28, 10)
optimizer = torch.optim.Adam(model.parameters(), lr=0.02)

loaders = {
    "train": DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=ToTensor()), batch_size=32),
    "valid": DataLoader(MNIST(os.getcwd(), train=False, download=True, transform=ToTensor()), batch_size=32),
}

class CustomRunner(dl.Runner):

    def _handle_batch(self, batch):
        x, y = batch
        x_noise = (x + torch.rand_like(x)).clamp_(0, 1)
        y_hat, x_ = self.model(x_noise)

        loss_clf = F.cross_entropy(y_hat, y)
        iou = metrics.iou(x_, x)
        loss_iou = 1 - iou
        loss = loss_clf + loss_iou
        accuracy01, accuracy03, accuracy05 = metrics.accuracy(y_hat, y, topk=(1, 3, 5))
        self.batch_metrics = {
            "loss_clf": loss_clf,
            "loss_iou": loss_iou,
            "loss": loss,
            "iou": iou,
            "accuracy01": accuracy01,
            "accuracy03": accuracy03,
            "accuracy05": accuracy05,
        }
        
        if self.is_train_loader:
            loss.backward()
            self.optimizer.step()
            self.optimizer.zero_grad()

runner = CustomRunner()
runner.train(
    model=model, 
    optimizer=optimizer, 
    loaders=loaders, 
    verbose=True,
)

CV - MNIST with Metric Learning

Open In Colab

from torch.optim import Adam
from torch.utils.data import DataLoader

from catalyst import data, dl, utils
from catalyst.contrib import datasets, models, nn
import catalyst.data.cv.transforms.torch as t


# 1. train and valid datasets
dataset_root = "."
transforms = t.Compose([t.ToTensor(), t.Normalize((0.1307,), (0.3081,))])

dataset_train = datasets.MnistMLDataset(root=dataset_root, download=True, transform=transforms)
sampler = data.BalanceBatchSampler(labels=dataset_train.get_labels(), p=5, k=10)
train_loader = DataLoader(dataset=dataset_train, sampler=sampler, batch_size=sampler.batch_size)

dataset_val = datasets.MnistQGDataset(root=dataset_root, transform=transforms, gallery_fraq=0.2)
val_loader = DataLoader(dataset=dataset_val, batch_size=1024)

# 2. model and optimizer
model = models.SimpleConv(features_dim=16)
optimizer = Adam(model.parameters(), lr=0.001)

# 3. criterion with triplets sampling
sampler_inbatch = data.HardTripletsSampler(norm_required=False)
criterion = nn.TripletMarginLossWithSampler(margin=0.5, sampler_inbatch=sampler_inbatch)

# 4. training with catalyst Runner
callbacks = [
    dl.ControlFlowCallback(dl.CriterionCallback(), loaders="train"),
    dl.ControlFlowCallback(dl.CMCScoreCallback(topk_args=[1]), loaders="valid"),
    dl.PeriodicLoaderCallback(valid=100),
]

runner = dl.SupervisedRunner(device=utils.get_device())
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    callbacks=callbacks,
    loaders={"train": train_loader, "valid": val_loader},
    minimize_metric=False,
    verbose=True,
    valid_loader="valid",
    num_epochs=200,
    main_metric="cmc01",
)   

GAN - MNIST, flatten version

import os
import torch
from torch import nn
from torch.nn import functional as F
from torch.utils.data import DataLoader
from catalyst import dl
from catalyst.data.cv import ToTensor
from catalyst.contrib.datasets import MNIST
from catalyst.contrib.nn.modules import Flatten, GlobalMaxPool2d, Lambda

latent_dim = 128
generator = nn.Sequential(
    # We want to generate 128 coefficients to reshape into a 7x7x128 map
    nn.Linear(128, 128 * 7 * 7),
    nn.LeakyReLU(0.2, inplace=True),
    Lambda(lambda x: x.view(x.size(0), 128, 7, 7)),
    nn.ConvTranspose2d(128, 128, (4, 4), stride=(2, 2), padding=1),
    nn.LeakyReLU(0.2, inplace=True),
    nn.ConvTranspose2d(128, 128, (4, 4), stride=(2, 2), padding=1),
    nn.LeakyReLU(0.2, inplace=True),
    nn.Conv2d(128, 1, (7, 7), padding=3),
    nn.Sigmoid(),
)
discriminator = nn.Sequential(
    nn.Conv2d(1, 64, (3, 3), stride=(2, 2), padding=1),
    nn.LeakyReLU(0.2, inplace=True),
    nn.Conv2d(64, 128, (3, 3), stride=(2, 2), padding=1),
    nn.LeakyReLU(0.2, inplace=True),
    GlobalMaxPool2d(),
    Flatten(),
    nn.Linear(128, 1)
)

model = {"generator": generator, "discriminator": discriminator}
optimizer = {
    "generator": torch.optim.Adam(generator.parameters(), lr=0.0003, betas=(0.5, 0.999)),
    "discriminator": torch.optim.Adam(discriminator.parameters(), lr=0.0003, betas=(0.5, 0.999)),
}
loaders = {
    "train": DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=ToTensor()), batch_size=32),
}

class CustomRunner(dl.Runner):

    def _handle_batch(self, batch):
        real_images, _ = batch
        batch_metrics = {}
        
        # Sample random points in the latent space
        batch_size = real_images.shape[0]
        random_latent_vectors = torch.randn(batch_size, latent_dim).to(self.device)
        
        # Decode them to fake images
        generated_images = self.model["generator"](random_latent_vectors).detach()
        # Combine them with real images
        combined_images = torch.cat([generated_images, real_images])
        
        # Assemble labels discriminating real from fake images
        labels = torch.cat([
            torch.ones((batch_size, 1)), torch.zeros((batch_size, 1))
        ]).to(self.device)
        # Add random noise to the labels - important trick!
        labels += 0.05 * torch.rand(labels.shape).to(self.device)
        
        # Train the discriminator
        predictions = self.model["discriminator"](combined_images)
        batch_metrics["loss_discriminator"] = \
          F.binary_cross_entropy_with_logits(predictions, labels)
        
        # Sample random points in the latent space
        random_latent_vectors = torch.randn(batch_size, latent_dim).to(self.device)
        # Assemble labels that say "all real images"
        misleading_labels = torch.zeros((batch_size, 1)).to(self.device)
        
        # Train the generator
        generated_images = self.model["generator"](random_latent_vectors)
        predictions = self.model["discriminator"](generated_images)
        batch_metrics["loss_generator"] = \
          F.binary_cross_entropy_with_logits(predictions, misleading_labels)
        
        self.batch_metrics.update(**batch_metrics)

runner = CustomRunner()
runner.train(
    model=model, 
    optimizer=optimizer,
    loaders=loaders,
    callbacks=[
        dl.OptimizerCallback(
            optimizer_key="generator", 
            metric_key="loss_generator"
        ),
        dl.OptimizerCallback(
            optimizer_key="discriminator", 
            metric_key="loss_discriminator"
        ),
    ],
    main_metric="loss_generator",
    num_epochs=20,
    verbose=True,
    logdir="./logs_gan",
)

ML - multi-class classification (fp16 training version)

Open In Colab

# pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" git+https://github.com/NVIDIA/apex
import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl

# sample data
num_samples, num_features, num_classes = int(1e4), int(1e1), 4
X = torch.rand(num_samples, num_features)
y = (torch.rand(num_samples, ) * num_classes).to(torch.int64)

# pytorch loaders
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, num_classes)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [2])

# model training
runner = dl.SupervisedRunner()
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    logdir="./logdir",
    num_epochs=3,
    callbacks=[dl.AccuracyCallback(num_classes=num_classes)],
    fp16=True,
)

ML - multi-class classification (advanced fp16 training version)

Open In Colab

# pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" git+https://github.com/NVIDIA/apex
import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl

# sample data
num_samples, num_features, num_classes = int(1e4), int(1e1), 4
X = torch.rand(num_samples, num_features)
y = (torch.rand(num_samples, ) * num_classes).to(torch.int64)

# pytorch loaders
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, num_classes)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [2])

# model training
runner = dl.SupervisedRunner()
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    logdir="./logdir",
    num_epochs=3,
    callbacks=[dl.AccuracyCallback(num_classes=num_classes)],
    fp16=dict(opt_level="O1"),
)

ML - Linear Regression (distributed training version)

#!/usr/bin/env python
import torch
from torch.utils.data import TensorDataset
from catalyst.dl import SupervisedRunner, utils

def datasets_fn(num_features: int):
    X = torch.rand(int(1e4), num_features)
    y = torch.rand(X.shape[0])
    dataset = TensorDataset(X, y)
    return {"train": dataset, "valid": dataset}

def train():
    num_features = int(1e1)
    # model, criterion, optimizer, scheduler
    model = torch.nn.Linear(num_features, 1)
    criterion = torch.nn.MSELoss()
    optimizer = torch.optim.Adam(model.parameters())
    scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [3, 6])

    runner = SupervisedRunner()
    runner.train(
        model=model,
        datasets={
            "batch_size": 32,
            "num_workers": 1,
            "get_datasets_fn": datasets_fn,
            "num_features": num_features,  # will be passed to datasets_fn
        },
        criterion=criterion,
        optimizer=optimizer,
        scheduler=scheduler,
        logdir="./logs/example_distributed_ml",
        num_epochs=8,
        verbose=True,
        distributed=False,
    )

utils.distributed_cmd_run(train)

CV - classification with AutoEncoder (distributed training version)

#!/usr/bin/env python
import os
import torch
from torch import nn
from torch.nn import functional as F
from catalyst import dl, metrics, utils
from catalyst.data.cv import ToTensor
from catalyst.contrib.datasets import MNIST

class ClassifyAE(nn.Module):

    def __init__(self, in_features, hid_features, out_features):
        super().__init__()
        self.encoder = nn.Sequential(nn.Linear(in_features, hid_features), nn.Tanh())
        self.decoder = nn.Linear(hid_features, in_features)
        self.clf = nn.Linear(hid_features, out_features)

    def forward(self, x):
        z = self.encoder(x)
        y_hat = self.clf(z)
        x_ = self.decoder(z)
        return y_hat, x_

class CustomRunner(dl.Runner):

    def _handle_batch(self, batch):
        x, y = batch
        x = x.view(x.size(0), -1)
        y_hat, x_ = self.model(x)

        loss_clf = F.cross_entropy(y_hat, y)
        loss_ae = F.mse_loss(x_, x)
        loss = loss_clf + loss_ae
        accuracy01, accuracy03, accuracy05 = metrics.accuracy(y_hat, y, topk=(1, 3, 5))
        self.batch_metrics = {
            "loss_clf": loss_clf,
            "loss_ae": loss_ae,
            "loss": loss,
            "accuracy01": accuracy01,
            "accuracy03": accuracy03,
            "accuracy05": accuracy05,
        }

        if self.is_train_loader:
            loss.backward()
            self.optimizer.step()
            self.optimizer.zero_grad()

def datasets_fn():
    dataset = MNIST(os.getcwd(), train=False, download=True, transform=ToTensor())
    return {"train": dataset, "valid": dataset}

def train():
    model = ClassifyAE(28 * 28, 128, 10)
    optimizer = torch.optim.Adam(model.parameters(), lr=0.02)

    runner = CustomRunner()
    runner.train(
        model=model,
        optimizer=optimizer,
        datasets={
            "batch_size": 32,
            "num_workers": 1,
            "get_datasets_fn": datasets_fn,
        },
        logdir="./logs/distributed_ae",
        num_epochs=8,
        verbose=True,
    )

utils.distributed_cmd_run(train)

ML - multi-class classification (TPU version)

Open In Colab

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl, utils

# sample data
num_samples, num_features, num_classes = int(1e4), int(1e1), 4
X = torch.rand(num_samples, num_features)
y = (torch.rand(num_samples, ) * num_classes).to(torch.int64)

# pytorch loaders
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# device (TPU > GPU > CPU)
device = utils.get_device()  # <--------- TPU device

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, num_classes).to(device)
criterion = torch.nn.CrossEntropyLoss().to(device)
optimizer = torch.optim.Adam(model.parameters())

# model training
runner = dl.SupervisedRunner(device=device)
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    loaders=loaders,
    logdir="./logdir",
    num_epochs=3,
    callbacks=[dl.AccuracyCallback(num_classes=num_classes)]
)

AutoML - hyperparameters optimization with Optuna

Open In Colab

import os
import optuna
import torch
from torch import nn
from torch.utils.data import DataLoader
from catalyst import dl
from catalyst.data.cv import ToTensor
from catalyst.contrib.datasets import MNIST
from catalyst.contrib.nn import Flatten
    

def objective(trial):
    lr = trial.suggest_loguniform("lr", 1e-3, 1e-1)
    num_hidden = int(trial.suggest_loguniform("num_hidden", 32, 128))

    loaders = {
        "train": DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=ToTensor()), batch_size=32),
        "valid": DataLoader(MNIST(os.getcwd(), train=False, download=True, transform=ToTensor()), batch_size=32),
    }
    model = nn.Sequential(
        Flatten(), nn.Linear(784, num_hidden), nn.ReLU(), nn.Linear(num_hidden, 10)
    )
    optimizer = torch.optim.Adam(model.parameters(), lr=lr)
    criterion = nn.CrossEntropyLoss()

    runner = dl.SupervisedRunner()
    runner.train(
        model=model,
        loaders=loaders,
        criterion=criterion,
        optimizer=optimizer,
        callbacks=[
            dl.OptunaCallback(trial),
            dl.AccuracyCallback(num_classes=10),
        ],
        num_epochs=10,
        main_metric="accuracy01",
        minimize_metric=False,
    )
    return runner.best_valid_metrics[runner.main_metric]

study = optuna.create_study(
    direction="maximize",
    pruner=optuna.pruners.MedianPruner(
        n_startup_trials=1, n_warmup_steps=0, interval_steps=1
    ),
)
study.optimize(objective, n_trials=10, timeout=300)
print(study.best_value, study.best_params)

Features

  • Universal train/inference loop.
  • Configuration files for model/data hyperparameters.
  • Reproducibility – all source code and environment variables will be saved.
  • Callbacks – reusable train/inference pipeline parts with easy customization.
  • Training stages support.
  • Deep Learning best practices - SWA, AdamW, Ranger optimizer, OneCycle, and more.
  • Developments best practices - fp16 support, distributed training, slurm support.

Structure

  • callbacks - a variety of callbacks for your train-loop customization.
  • contrib - additional modules contributed by Catalyst users.
  • core - framework core with main abstractions - Experiment, Runner and Callback.
  • data - useful tools and scripts for data processing.
  • dl - entrypoint for your deep learning experiments.
  • experiments - a number of useful experiments extensions for Notebook and Config API.
  • metrics – classic ML and CV/NLP/RecSys metrics.
  • registry - Catalyst global registry for Config API.
  • runners - runners extensions for different deep learning tasks.
  • tools - extra tools for Deep Learning research, class-based helpers.
  • utils - typical utils for Deep Learning research, function-based helpers.

Tests

All Catalyst code, features and pipelines are fully tested with our own catalyst-codestyle.

In fact, we train a number of different models for various of tasks - image classification, image segmentation, text classification, GANs training and much more. During the tests, we compare their convergence metrics in order to verify the correctness of the training procedure and its reproducibility.

As a result, Catalyst provides fully tested and reproducible best practices for your deep learning research.

Catalyst

Tutorials

Blogposts

Docs

Projects

Examples, notebooks and starter kits

Competitions

Paper implementations

Tools and pipelines

Talks

Community

Contribution guide

We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us.

User feedback

We have created catalyst.team.core@gmail.com for "user feedback".

  • If you like the project and want to say thanks, this the right place.
  • If you would like to start a collaboration between your team and Catalyst team to do better Deep Learning R&D - you are always welcome.
  • If you just don't like Github issues and this ways suits you better - feel free to email us.
  • Finally, if you do not like something, please, share it with us and we can see how to improve it.

We appreciate any type of feedback. Thank you!

Acknowledgments

Since the beginning of the development of the Сatalyst, a lot of people have influenced it in a lot of different ways.

Catalyst.Team

Catalyst - Metric Learning team

Catalyst.Contributors

Catalyst.Friends

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Citation

Please use this bibtex if you want to cite this repository in your publications:

@misc{catalyst,
    author = {Kolesnikov, Sergey},
    title = {Accelerated deep learning R&D},
    year = {2018},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/catalyst-team/catalyst}},
}

About

Accelerated DL R&D

https://catalyst-team.github.io/catalyst

License:Apache License 2.0


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