From 1125788f422a77b63c6c5ff2e848344713fd98ce Mon Sep 17 00:00:00 2001 From: Q-c7 Date: Tue, 21 Jan 2025 17:56:20 +0100 Subject: [PATCH] =?UTF-8?q?=E2=9C=A8=20Improve=20week=2002=20seminar=20and?= =?UTF-8?q?=20return=20old=20hometask?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- week02_management_and_testing/README.md | 18 + .../example_project/compute_metrics.py | 54 + .../example_project/dvc.yaml | 24 + .../example_project/hparams.py | 7 + .../example_project/prepare_data.py | 5 + .../example_project/pyproject.toml | 24 + .../example_project/test_basic.py | 38 + .../example_project/train.py | 91 + .../example_project/uv.lock | 2496 +++++++++++++++++ .../homework/README.md | 92 + .../homework/main.py | 40 + .../homework/modeling/__init__.py | 0 .../homework/modeling/diffusion.py | 69 + .../homework/modeling/training.py | 34 + .../homework/modeling/unet.py | 119 + .../homework/requirements.txt | 5 + .../homework/tests/__init__.py | 0 .../homework/tests/test_model.py | 49 + .../homework/tests/test_pipeline.py | 46 + 19 files changed, 3211 insertions(+) create mode 100644 week02_management_and_testing/README.md create mode 100644 week02_management_and_testing/example_project/compute_metrics.py create mode 100644 week02_management_and_testing/example_project/dvc.yaml create mode 100644 week02_management_and_testing/example_project/hparams.py create mode 100644 week02_management_and_testing/example_project/prepare_data.py create mode 100644 week02_management_and_testing/example_project/pyproject.toml create mode 100644 week02_management_and_testing/example_project/test_basic.py create mode 100644 week02_management_and_testing/example_project/train.py create mode 100644 week02_management_and_testing/example_project/uv.lock create mode 100644 week02_management_and_testing/homework/README.md create mode 100644 week02_management_and_testing/homework/main.py create mode 100644 week02_management_and_testing/homework/modeling/__init__.py create mode 100644 week02_management_and_testing/homework/modeling/diffusion.py create mode 100644 week02_management_and_testing/homework/modeling/training.py create mode 100644 week02_management_and_testing/homework/modeling/unet.py create mode 100644 week02_management_and_testing/homework/requirements.txt create mode 100644 week02_management_and_testing/homework/tests/__init__.py create mode 100644 week02_management_and_testing/homework/tests/test_model.py create mode 100644 week02_management_and_testing/homework/tests/test_pipeline.py diff --git a/week02_management_and_testing/README.md b/week02_management_and_testing/README.md new file mode 100644 index 0000000..170997f --- /dev/null +++ b/week02_management_and_testing/README.md @@ -0,0 +1,18 @@ +# Week 2: Experiment tracking and testing + +[//]: # (* Lecture: [slides](./lecture.pdf)) +* Lecture: TBD +* Seminar: see the [example_project](./example_project) directory +* Homework: see [homework/README.md](homework/README.md) + +## Further reading +* Tools for experiment tracking: [Aim](https://github.com/aimhubio/aim), [Comet](https://www.comet.ml/site/), [Neptune](https://neptune.ai/), [Sacred](https://github.com/IDSIA/sacred), [Weights and Biases](https://wandb.ai/), [ClearML](https://clear.ml/) +* [DVC](https://dvc.org/) and [Pachyderm](https://www.pachyderm.com/) for artifact versioning +* [Hydra documentation](https://hydra.cc/docs/intro/) +* [Unittest](https://docs.python.org/3/library/unittest.html) built-in module +* [Doctest](https://docs.python.org/3/library/doctest.html) built-in module (useful for testing docstrings!) +* [Pytest](https://github.com/pytest-dev/pytest/) repository +* Pytest plugins: [pytest-xdist](https://pypi.org/project/pytest-xdist/) for parallel execution, [pytest-cov](https://pytest-cov.readthedocs.io/en/latest/readme.html) for coverage reports. +* [Hypothesis quick start guide](https://hypothesis.readthedocs.io/en/latest/quickstart.html) and [integration with pytest](https://hypothesis.readthedocs.io/en/latest/details.html#the-hypothesis-pytest-plugin) +* [Full Stack Deep Learning "Troubleshooting & Testing" lecture](https://fullstackdeeplearning.com/course/2022/lecture-3-troubleshooting-and-testing/#4-resources) +* [Made With ML MLOps Course, "Testing Machine Learning Systems: Code, Data and Models"](https://madewithml.com/courses/mlops/testing/) \ No newline at end of file diff --git a/week02_management_and_testing/example_project/compute_metrics.py b/week02_management_and_testing/example_project/compute_metrics.py new file mode 100644 index 0000000..88b3cfd --- /dev/null +++ b/week02_management_and_testing/example_project/compute_metrics.py @@ -0,0 +1,54 @@ +import json +from argparse import ArgumentParser + +import torch +import torchvision.transforms as transforms +from torchvision.datasets import CIFAR10 +from torchvision.models import resnet18 + +from hparams import config + + +def main(args): + transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261)) + ]) + + test_dataset = CIFAR10(root='CIFAR10/test', + train=False, + transform=transform, + download=False, + ) + + test_loader = torch.utils.data.DataLoader(dataset=test_dataset, + batch_size=config["batch_size"]) + + device = torch.device("cuda") + + model = resnet18(pretrained=False, num_classes=10) + model.load_state_dict(torch.load("model.pt")) + model.to(device) + + correct = 0.0 + + for test_images, test_labels in test_loader: + test_images = test_images.to(device) + test_labels = test_labels.to(device) + + with torch.inference_mode(): + outputs = model(test_images) + preds = torch.argmax(outputs, 1) + correct += (preds == test_labels).sum() + + accuracy = correct / len(test_dataset) + + with open("final_metrics.json", "w+") as f: + json.dump({"accuracy": accuracy.item()}, f) + print("\n", file=f) + + +if __name__ == '__main__': + parser = ArgumentParser() + args = parser.parse_args() + main(args) diff --git a/week02_management_and_testing/example_project/dvc.yaml b/week02_management_and_testing/example_project/dvc.yaml new file mode 100644 index 0000000..1f5d7ca --- /dev/null +++ b/week02_management_and_testing/example_project/dvc.yaml @@ -0,0 +1,24 @@ +stages: + prepare_data: + cmd: python prepare_data.py + deps: + - prepare_data.py + outs: + - CIFAR10 + train: + cmd: python train.py + deps: + - CIFAR10 + - hparams.py + - train.py + outs: + - model.pt + compute_metrics: + cmd: python compute_metrics.py + deps: + - CIFAR10 + - compute_metrics.py + - model.pt + metrics: + - final_metrics.json: + cache: false diff --git a/week02_management_and_testing/example_project/hparams.py b/week02_management_and_testing/example_project/hparams.py new file mode 100644 index 0000000..3494b92 --- /dev/null +++ b/week02_management_and_testing/example_project/hparams.py @@ -0,0 +1,7 @@ +config = dict( + batch_size=64, + learning_rate=1e-5, + weight_decay=0.01, + epochs=2, + zero_init_residual=False, +) diff --git a/week02_management_and_testing/example_project/prepare_data.py b/week02_management_and_testing/example_project/prepare_data.py new file mode 100644 index 0000000..392f6af --- /dev/null +++ b/week02_management_and_testing/example_project/prepare_data.py @@ -0,0 +1,5 @@ +from torchvision.datasets import CIFAR10 + +if __name__ == "__main__": + train_dataset = CIFAR10("CIFAR10/train", download=True) + test_dataset = CIFAR10("CIFAR10/test", download=True) diff --git a/week02_management_and_testing/example_project/pyproject.toml b/week02_management_and_testing/example_project/pyproject.toml new file mode 100644 index 0000000..89f64bb --- /dev/null +++ b/week02_management_and_testing/example_project/pyproject.toml @@ -0,0 +1,24 @@ +[project] +name = "homework" +version = "0.1.0" +description = "Sample Text" +authors = [ "YZ " ] +requires-python = ">=3.10" +readme = "README.md" + +dependencies = [ + "torch==2.1.2", + "torchvision==0.16.2", + "wandb>=0.13.10", + "tqdm==4.66.1", + "numpy==1.26.4", + "dvc==2.44.0", + "hydra-core==1.3.1", + "omegaconf==2.3.0", +] + +[tool.uv] +dev-dependencies = [ + "pytest==7.4.4", + "pytest-cov==4.1.0", +] diff --git a/week02_management_and_testing/example_project/test_basic.py b/week02_management_and_testing/example_project/test_basic.py new file mode 100644 index 0000000..63890c1 --- /dev/null +++ b/week02_management_and_testing/example_project/test_basic.py @@ -0,0 +1,38 @@ +import torch +import pytest + +from train import compute_accuracy + +def test_arange_elems(): + arr = torch.arange(0, 10, dtype=torch.float) + assert torch.allclose(arr[-1], torch.tensor([9]).float()) # + +def test_div_zero(): + a = torch.zeros(1,dtype=torch.long) + b = torch.ones(1,dtype=torch.long) + + assert not torch.isfinite(b/a) + + +def test_div_zero_python(): + with pytest.raises(ZeroDivisionError): + 1/0 # + +def test_accuracy(): + preds = torch.randint(0,2,size=(100,)) + targets = preds.clone() + + assert compute_accuracy(preds, targets) == 1.0 + + preds = torch.tensor([1,2,3,0,0,0]) + targets = torch.tensor([1,2,3,4,5,6]) + + assert compute_accuracy(preds, targets) == 0.5 # This is bad - why? + +@pytest.mark.parametrize("preds,targets,result",[ + (torch.tensor([1,2,3]),torch.tensor([1,2,3]), 1.0), + (torch.tensor([1,2,3]),torch.tensor([0,0,0]), 0.0), + (torch.tensor([1,2,3]),torch.tensor([1,2,0]), 2/3), + ]) +def test_accuracy_parametrized(preds, targets, result): + assert torch.allclose(compute_accuracy(preds, targets), torch.tensor([result]), rtol=0, atol=1e-5) diff --git a/week02_management_and_testing/example_project/train.py b/week02_management_and_testing/example_project/train.py new file mode 100644 index 0000000..adb23ef --- /dev/null +++ b/week02_management_and_testing/example_project/train.py @@ -0,0 +1,91 @@ +import torch +import torch.nn as nn +import torchvision.transforms as transforms +import wandb +from torchvision.datasets import CIFAR10 +from torchvision.models import resnet18 +from tqdm import tqdm, trange + +from hparams import config + +def compute_accuracy(preds, targets): + result = (targets == preds).float().sum() + return result + + +def main(): + wandb.init(config=config, project="effdl_example", name="baseline") + + transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261)), + transforms.Resize((224, 224)), + ]) + + train_dataset = CIFAR10(root='CIFAR10/train', + train=True, + transform=transform, + download=False, + ) + + test_dataset = CIFAR10(root='CIFAR10/test', + train=False, + transform=transform, + download=False, + ) + + train_loader = torch.utils.data.DataLoader(dataset=train_dataset, + batch_size=config["batch_size"], + shuffle=True) + + test_loader = torch.utils.data.DataLoader(dataset=test_dataset, + batch_size=config["batch_size"]) + + device = torch.device("cuda") + + model = resnet18(pretrained=False, num_classes=10, zero_init_residual=config["zero_init_residual"]) + model.to(device) + wandb.watch(model) + + criterion = nn.CrossEntropyLoss() + optimizer = torch.optim.AdamW(model.parameters(), lr=config["learning_rate"], weight_decay=config["weight_decay"]) + + for epoch in trange(config["epochs"]): + for i, (images, labels) in enumerate(tqdm(train_loader)): + images = images.to(device) + labels = labels.to(device) + + outputs = model(images) + loss = criterion(outputs, labels) + + loss.backward() + optimizer.step() + optimizer.zero_grad() + + if i % 100 == 0: + all_preds = [] + all_labels = [] + + for test_images, test_labels in test_loader: + test_images = test_images.to(device) + test_labels = test_labels.to(device) + + with torch.inference_mode(): + outputs = model(test_images) + preds = torch.argmax(outputs, 1) + + all_preds.append(preds) + all_labels.append(test_labels) + + accuracy = compute_accuracy(torch.cat(all_preds), torch.cat(all_labels)) + + metrics = {'test_acc': accuracy, 'train_loss': loss} + wandb.log(metrics, step=epoch * len(train_dataset) + (i + 1) * config["batch_size"]) + torch.save(model.state_dict(), "model.pt") + + with open("run_id.txt", "w+") as f: + print(wandb.run.id, file=f) + + +if __name__ == '__main__': + main() diff --git a/week02_management_and_testing/example_project/uv.lock b/week02_management_and_testing/example_project/uv.lock new file mode 100644 index 0000000..0f427f3 --- /dev/null +++ b/week02_management_and_testing/example_project/uv.lock @@ -0,0 +1,2496 @@ +version = 1 +requires-python = ">=3.10" +resolution-markers = [ + "sys_platform != 'linux'", + "sys_platform == 'linux'", +] + +[[package]] +name = "aiohappyeyeballs" +version = "2.4.4" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/7f/55/e4373e888fdacb15563ef6fa9fa8c8252476ea071e96fb46defac9f18bf2/aiohappyeyeballs-2.4.4.tar.gz", hash = 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completing tasks 3 or 4 without the first two will not give you any points. + +# Problem statement +You are given a small codebase that should train an **unconditional** [Denoising Diffusion Probabilistic Model](https://arxiv.org/abs/2006.11239) +on the CIFAR-10 dataset. +However, this project contains several bugs of different severity, and even some of the tests are written incorrectly. +A correct implementation will achieve *somewhat* decent results after training for 100 epochs (~2 hours on an average GPU), +but you should not expect much in terms of quality. +In this homework, we are going to have a deeper look at the training pipeline, try to fix any errors we find and make +the code more reliable and reproducible. + +# Task 1 (6.5 points) +Implement *correct* tests for the training pipeline. +Specifically, have a look at the current [tests](./tests) folder: it contains several files with tests, +some of which fail, fail sometimes or are plainly incorrect. +Your task is to identify the bugs and make the test suite pass deterministically: this will involve changes +both to `modeling` and to `tests`, as some parts of the testing code need to be modified as well. + +In your report, please tell us how you found the bugs in all parts of the code. +You can find the original implementation of DDPM that we use in this assignment, but giving it as an explanation for +your fixes will give you no points. +Obviously, "solving" the assignment by removing all tests or having unreasonably high thresholds will not earn +you a good grade as well. + +After that, implement the `test_training` function in `test_pipeline.py` that runs an integration test for the +entire training procedure with different hyperparameters and expects different outcomes. +This test should increase the coverage of the `modeling.training` file (measured by [pytest-cov](https://github.com/pytest-dev/pytest-cov)) to **>80%**. + +Importantly, you should ensure that your test code running the actual model can run both on CPU and GPU. +Since training on CPU even for 1 epoch might take too long, you need to implement training on a subset of data. + + +# Task 2 (1.5 points) +Implement logging of the metrics and artifacts during training with [Weights and Biases](https://wandb.ai/site). +You should log the following values: +* Training loss and the learning rate +* All training hyperparameters (including batch size, number of epochs etc., as well as all model and diffusion hyperparameters) +* Inputs to the model (1 batch is enough) and samples from it after each epoch + +However, you should **NOT** log the training code for the model. + +Logging the hyperparameters and metrics will likely involve some refactoring of the original codebase. +You can either place the necessary hyperparameters in a config file or simply have them as constants/argparse defaults +defined somewhere reasonable in the training code. + +After finishing this task, train the model for at least 100 epochs with default hyperparameters and attach the link to +your W&B project containing this run to the final report. + +# Task 3 (2 points) +Improve the configuration process of this pipeline using the [Hydra](https://hydra.cc/) library. +You should create a config that allows adjusting at least the following attributes: +* Peak learning rate and optimizer momentum +* Optimizer (Adam by default, at least SGD should be supported) +* Training batch size and the number of epochs +* Number of workers in the dataloader +* Existence of random flip augmentations + +Demonstrate that your integration works by running at least three *complete* runs (less than 100 epochs is OK) +with hyperparameters changed via the config file. +From these runs, it should be evident that changing hyperparameters affects the training procedure. +Here, you should log the config using [run.log_artifact](https://docs.wandb.ai/ref/python/run#log_artifact) +and show that this changes the hyperparameters of the run in W&B. + +# Task 4 (2 points) +Make the pipeline reproducible using [Data Version Control](https://dvc.org/). +You should end up with a `dvc.yaml` that represents two stages of your experiment with corresponding inputs and outputs: +getting the data (yes, you need to refactor that part of the code) and training the model itself. +Also, you should specify the relevant code and configuration as dependencies of the corresponding pipeline stages. +Lastly, after running your code, you should have a `dvc.lock` that stores hashes of all artifacts in your pipeline. +Submit both `dvc.yaml` and `dvc.lock` as parts of your solution. + +Importantly, modifying any of the relevant modules or hyperparameters should trigger an invalidation of the +corresponding pipeline stages: that is, `dvc repro` should do nothing if and only if `dvc.lock` is consistent with +hashes of all dependencies in the pipeline. + +If you have also done the Hydra configuration assignment, make sure to check out [this guide](https://dvc.org/doc/user-guide/experiment-management/hydra-composition) +on integrating Hydra with DVC experiment management. + +# Submission format +When submitting this assignment, you should attach a .zip archive that contains: +- The source code with all your fixes and improvements +- A Markdown/PDF report in the root of the project folder that: + 1. Details the changes you made to the original code (we will run `diff` and see if everything is explained) + 2. Tells how to run the modified code (i.e., which command line arguments you have added and how to use them) + 3. Describes your process of fixing and adding new tests for Task 1 and reports the test coverage + 4. Gives a link to the Weights and Biases project with all necessary logs for tasks 2 and 3 +- If you solved Tasks 3 or 4, please ensure that the archived project contains the corresponding configuration/lock files as well. +- An updated `requirements.txt` file, if your solution requires new dependencies such as `wandb`, `hydra-core` or `dvc`. \ No newline at end of file diff --git a/week02_management_and_testing/homework/main.py b/week02_management_and_testing/homework/main.py new file mode 100644 index 0000000..0dcdb3a --- /dev/null +++ b/week02_management_and_testing/homework/main.py @@ -0,0 +1,40 @@ +import torch +from torch.utils.data import DataLoader +from torchvision import transforms +from torchvision.datasets import CIFAR10 + +from modeling.diffusion import DiffusionModel +from modeling.training import generate_samples, train_epoch +from modeling.unet import UnetModel + + +def main(device: str, num_epochs: int = 100): + ddpm = DiffusionModel( + eps_model=UnetModel(3, 3, hidden_size=128), + betas=(1e-4, 0.02), + num_timesteps=1000, + ) + ddpm.to(device) + + train_transforms = transforms.Compose( + [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))] + ) + + dataset = CIFAR10( + "cifar10", + train=True, + download=True, + transform=train_transforms, + ) + + dataloader = DataLoader(dataset, batch_size=128, num_workers=4, shuffle=True) + optim = torch.optim.Adam(ddpm.parameters(), lr=1e-5) + + for i in range(num_epochs): + train_epoch(ddpm, dataloader, optim, device) + generate_samples(ddpm, device, f"samples/{i:02d}.png") + + +if __name__ == "__main__": + device = "cuda" if torch.cuda.is_available() else "cpu" + main(device=device) diff --git a/week02_management_and_testing/homework/modeling/__init__.py b/week02_management_and_testing/homework/modeling/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/week02_management_and_testing/homework/modeling/diffusion.py b/week02_management_and_testing/homework/modeling/diffusion.py new file mode 100644 index 0000000..84f6b2f --- /dev/null +++ b/week02_management_and_testing/homework/modeling/diffusion.py @@ -0,0 +1,69 @@ +from typing import Dict, Tuple + +import torch +import torch.nn as nn + + +class DiffusionModel(nn.Module): + def __init__( + self, + eps_model: nn.Module, + betas: Tuple[float, float], + num_timesteps: int, + ): + super().__init__() + self.eps_model = eps_model + + for name, schedule in get_schedules(betas[0], betas[1], num_timesteps).items(): + self.register_buffer(name, schedule) + + self.num_timesteps = num_timesteps + self.criterion = nn.MSELoss() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + timestep = torch.randint(1, self.num_timesteps + 1, (x.shape[0],)) + eps = torch.rand_like(x) + + x_t = ( + self.sqrt_alphas_cumprod[timestep, None, None, None] * x + + self.one_minus_alpha_over_prod[timestep, None, None, None] * eps + ) + + return self.criterion(eps, self.eps_model(x_t, timestep / self.num_timesteps)) + + def sample(self, num_samples: int, size, device) -> torch.Tensor: + + x_i = torch.randn(num_samples, *size) + + for i in range(self.num_timesteps, 0, -1): + z = torch.randn(num_samples, *size) if i > 1 else 0 + eps = self.eps_model(x_i, torch.tensor(i / self.num_timesteps).repeat(num_samples, 1).to(device)) + x_i = self.inv_sqrt_alphas[i] * (x_i - eps * self.one_minus_alpha_over_prod[i]) + self.sqrt_betas[i] * z + + return x_i + + +def get_schedules(beta1: float, beta2: float, num_timesteps: int) -> Dict[str, torch.Tensor]: + assert beta1 < beta2 < 1.0, "beta1 and beta2 must be in (0, 1)" + + betas = (beta2 - beta1) * torch.arange(0, num_timesteps + 1, dtype=torch.float32) / num_timesteps + beta1 + sqrt_betas = torch.sqrt(betas) + alphas = 1 - betas + + alphas_cumprod = torch.cumprod(alphas, dim=0) + + sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod) + inv_sqrt_alphas = 1 / torch.sqrt(alphas) + + sqrt_one_minus_alpha_prod = torch.sqrt(1 - alphas_cumprod) + one_minus_alpha_over_prod = (1 - alphas) / sqrt_one_minus_alpha_prod + + return { + "alphas": alphas, + "inv_sqrt_alphas": inv_sqrt_alphas, + "sqrt_betas": sqrt_betas, + "alphas_cumprod": alphas_cumprod, + "sqrt_alphas_cumprod": sqrt_alphas_cumprod, + "sqrt_one_minus_alpha_prod": sqrt_one_minus_alpha_prod, + "one_minus_alpha_over_prod": one_minus_alpha_over_prod, + } diff --git a/week02_management_and_testing/homework/modeling/training.py b/week02_management_and_testing/homework/modeling/training.py new file mode 100644 index 0000000..ddb6fd6 --- /dev/null +++ b/week02_management_and_testing/homework/modeling/training.py @@ -0,0 +1,34 @@ +import torch +from torch.optim.optimizer import Optimizer +from torch.utils.data import DataLoader +from torchvision.utils import make_grid, save_image +from tqdm import tqdm + +from modeling.diffusion import DiffusionModel + + +def train_step(model: DiffusionModel, inputs: torch.Tensor, optimizer: Optimizer, device: str): + optimizer.zero_grad() + inputs = inputs.to(device) + loss = model(inputs) + loss.backward() + optimizer.step() + return loss + + +def train_epoch(model: DiffusionModel, dataloader: DataLoader, optimizer: Optimizer, device: str): + model.train() + pbar = tqdm(dataloader) + loss_ema = None + for x, _ in pbar: + train_loss = train_step(model, x, optimizer, device) + loss_ema = train_loss if loss_ema is None else 0.9 * loss_ema + 0.1 * train_loss + pbar.set_description(f"loss: {loss_ema:.4f}") + + +def generate_samples(model: DiffusionModel, device: str, path: str): + model.eval() + with torch.no_grad(): + samples = model.sample(8, (3, 32, 32), device=device) + grid = make_grid(samples, nrow=4) + save_image(grid, path) diff --git a/week02_management_and_testing/homework/modeling/unet.py b/week02_management_and_testing/homework/modeling/unet.py new file mode 100644 index 0000000..0360af9 --- /dev/null +++ b/week02_management_and_testing/homework/modeling/unet.py @@ -0,0 +1,119 @@ +import torch +import torch.nn as nn + + +class ConvBlock(nn.Module): + def __init__(self, in_channels: int, out_channels: int, residual: bool = False): + super().__init__() + self.main = nn.Sequential( + nn.Conv2d(in_channels, out_channels, 3, 1, 1), + nn.GroupNorm(8, out_channels), + nn.ReLU(), + ) + self.conv = nn.Sequential( + nn.Conv2d(out_channels, out_channels, 3, 1, 1), + nn.GroupNorm(8, out_channels), + nn.ReLU(), + nn.Conv2d(out_channels, out_channels, 3, 1, 1), + nn.GroupNorm(8, out_channels), + nn.ReLU(), + ) + + self.is_res = residual + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.main(x) + if self.is_res: + x = x + self.conv(x) + return x / 1.414 + else: + return self.conv(x) + + +class DownBlock(nn.Module): + def __init__(self, in_channels: int, out_channels: int): + super().__init__() + self.layers = nn.Sequential(ConvBlock(in_channels, out_channels), nn.MaxPool2d(2)) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.layers(x) + + +class UpBlock(nn.Module): + def __init__(self, in_channels: int, out_channels: int): + super().__init__() + self.layers = nn.Sequential( + nn.ConvTranspose2d(in_channels, out_channels, 2, 2), + ConvBlock(out_channels, out_channels), + ConvBlock(out_channels, out_channels), + ) + + def forward(self, x: torch.Tensor, skip: torch.Tensor) -> torch.Tensor: + x = torch.cat((x, skip), 1) + x = self.layers(x) + + return x + + +class TimestepEmbedding(nn.Module): + def __init__(self, emb_dim: int): + super().__init__() + + self.lin1 = nn.Linear(1, emb_dim, bias=False) + self.lin2 = nn.Linear(emb_dim, emb_dim) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = x.view(-1, 1) + x = torch.sin(self.lin1(x)) + x = self.lin2(x) + return x + + +class UnetModel(nn.Module): + def __init__(self, in_channels: int, out_channels: int, hidden_size: int = 256): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + + self.hidden_size = hidden_size + + self.init_conv = ConvBlock(in_channels, hidden_size, residual=True) + + self.down1 = DownBlock(hidden_size, hidden_size) + self.down2 = DownBlock(hidden_size, 2 * hidden_size) + self.down3 = DownBlock(2 * hidden_size, 2 * hidden_size) + + self.to_vec = nn.Sequential(nn.AvgPool2d(4), nn.ReLU()) + + self.timestep_embedding = TimestepEmbedding(2 * hidden_size) + + self.up0 = nn.Sequential( + nn.ConvTranspose2d(2 * hidden_size, 2 * hidden_size, 4, 4), + nn.GroupNorm(8, 2 * hidden_size), + nn.ReLU(), + ) + + self.up1 = UpBlock(4 * hidden_size, 2 * hidden_size) + self.up2 = UpBlock(4 * hidden_size, hidden_size) + self.up3 = UpBlock(2 * hidden_size, hidden_size) + self.out = nn.Conv2d(2 * hidden_size, self.out_channels, 3, 1, 1) + + def forward(self, x: torch.Tensor, t: torch.Tensor) -> torch.Tensor: + x = self.init_conv(x) + + down1 = self.down1(x) + down2 = self.down2(down1) + down3 = self.down3(down2) + + thro = self.to_vec(down3) + temb = self.timestep_embedding(t) + + thro = self.up0(thro + temb) + + up1 = self.up1(thro, down3) + temb + up2 = self.up2(up1, down2) + up3 = self.up3(up2, down1) + + out = self.out(torch.cat((up3, x), 1)) + + return out diff --git a/week02_management_and_testing/homework/requirements.txt b/week02_management_and_testing/homework/requirements.txt new file mode 100644 index 0000000..1d93459 --- /dev/null +++ b/week02_management_and_testing/homework/requirements.txt @@ -0,0 +1,5 @@ +torch==2.1.2 +torchvision==0.16.2 +tqdm==4.66.1 +pytest==7.4.4 +pytest-cov==4.1.0 diff --git a/week02_management_and_testing/homework/tests/__init__.py b/week02_management_and_testing/homework/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/week02_management_and_testing/homework/tests/test_model.py b/week02_management_and_testing/homework/tests/test_model.py new file mode 100644 index 0000000..1baed72 --- /dev/null +++ b/week02_management_and_testing/homework/tests/test_model.py @@ -0,0 +1,49 @@ +import pytest +import torch + +from modeling.diffusion import DiffusionModel +from modeling.unet import UnetModel + + +@pytest.mark.parametrize( + [ + "input_tensor", + "num_timesteps", + ], + [ + ( + torch.randn(2, 3, 32, 32), + 10, + ), + ( + torch.randn(2, 3, 64, 64), + 20, + ), + ( + torch.randn(2, 3, 128, 128), + 30, + ), + ( + torch.randn(2, 3, 256, 256), + 40, + ), + ], +) +def test_unet(input_tensor, num_timesteps): + B, C, H, W = input_tensor.shape + net = UnetModel(C, C, hidden_size=128) + timestep = torch.randint(1, num_timesteps + 1, (B,)) / num_timesteps + out = net(input_tensor, timestep) + assert out.shape == input_tensor.shape + + +def test_diffusion(num_channels=3, batch_size=4): + # note: you should not need to change the thresholds or the hyperparameters + net = UnetModel(num_channels, num_channels, hidden_size=128) + model = DiffusionModel(eps_model=net, betas=(1e-4, 0.02), num_timesteps=20) + + input_data = torch.randn((batch_size, num_channels, 32, 32)) + + output = model(input_data) + assert output.ndim == 0 + assert 1.0 <= output <= 1.2 diff --git a/week02_management_and_testing/homework/tests/test_pipeline.py b/week02_management_and_testing/homework/tests/test_pipeline.py new file mode 100644 index 0000000..e35682c --- /dev/null +++ b/week02_management_and_testing/homework/tests/test_pipeline.py @@ -0,0 +1,46 @@ +import pytest +import torch +from torch.utils.data import DataLoader +from torchvision.transforms import Compose, ToTensor, Normalize +from torchvision.datasets import CIFAR10 + +from modeling.diffusion import DiffusionModel +from modeling.training import train_step +from modeling.unet import UnetModel + + +@pytest.fixture +def train_dataset(): + transforms = Compose([ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) + dataset = CIFAR10( + "./data", + train=True, + download=True, + transform=transforms, + ) + return dataset + + +@pytest.mark.parametrize(["device"], [["cpu"], ["cuda"]]) +def test_train_on_one_batch(device, train_dataset): + # note: you should not need to increase the threshold or change the hyperparameters + ddpm = DiffusionModel( + eps_model=UnetModel(3, 3, hidden_size=32), + betas=(1e-4, 0.02), + num_timesteps=1000, + ) + ddpm.to(device) + + optim = torch.optim.Adam(ddpm.parameters(), lr=5e-4) + dataloader = DataLoader(train_dataset, batch_size=4, shuffle=True) + + x, _ = next(iter(dataloader)) + loss = None + for i in range(50): + loss = train_step(ddpm, x, optim, device) + assert loss < 0.5 + + +def test_training(): + # note: implement and test a complete training procedure (including sampling) + pass