diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 8aa26455d..000000000 --- a/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) [year] [fullname] - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/doc/.quarto/idx/imagenet.ipynb.json b/doc/.quarto/idx/imagenet.ipynb.json new file mode 100644 index 000000000..058ec5092 --- /dev/null +++ b/doc/.quarto/idx/imagenet.ipynb.json @@ -0,0 +1 @@ +{"title":"ImageNet","markdown":{"yaml":{"title":"ImageNet"},"headingText":"Image Compression","containsRefs":false,"markdown":"\n\n\n\n\nTo train a diffusion model to generate images conditioned on activations, we need to extract those activations from some model that is looking at some dataset.\nI have a trained GoogLeNet look at ImageNet images, but due to compute constraints, I could not have GoogLeNet look at all ImageNet images, but only at the first 17760 (which is about 1% of the entire dataset).\nHere I look at whether that is a problem.\n\n\n\nThe sample of the dataset seems reasonably balanced. But a few labels are over- or underrepresented and only by a magnitude of 2 from the mean.\n\n\nLet's see how much smaller we can make the images without losing recognisabillity.\n\nNot all images are of the same original size, or have all 3 colors.\nI used the same preprocessing steps as the [pytorch implementation](https://github.com/Lornatang/GoogLeNet-PyTorch) to have all images be in the same shape before passing them on to GoogLeNet.\n\nWe need to undo some of those steps, in order for matplotlib to present the image well.\n\nThe 64x64 image seems to still be recognisable enough for a human. For testing purposes I will go with the 28x28 and probably go for 64x64 when training the Conditional Diffusion Model.\n","srcMarkdownNoYaml":"\n\n\n\n\nTo train a diffusion model to generate images conditioned on activations, we need to extract those activations from some model that is looking at some dataset.\nI have a trained GoogLeNet look at ImageNet images, but due to compute constraints, I could not have GoogLeNet look at all ImageNet images, but only at the first 17760 (which is about 1% of the entire dataset).\nHere I look at whether that is a problem.\n\n\n\nThe sample of the dataset seems reasonably balanced. But a few labels are over- or underrepresented and only by a magnitude of 2 from the mean.\n\n## Image Compression\n\nLet's see how much smaller we can make the images without losing recognisabillity.\n\nNot all images are of the same original size, or have all 3 colors.\nI used the same preprocessing steps as the [pytorch implementation](https://github.com/Lornatang/GoogLeNet-PyTorch) to have all images be in the same shape before passing them on to GoogLeNet.\n\nWe need to undo some of those steps, in order for matplotlib to present the image well.\n\nThe 64x64 image seems to still be recognisable enough for a human. For testing purposes I will go with the 28x28 and probably go for 64x64 when training the Conditional Diffusion Model.\n"},"formats":{"html":{"identifier":{"display-name":"HTML","target-format":"html","base-format":"html"},"execute":{"fig-width":7,"fig-height":5,"fig-format":"retina","fig-dpi":96,"df-print":"default","error":false,"eval":true,"cache":null,"freeze":false,"echo":true,"output":true,"warning":false,"include":true,"keep-md":false,"keep-ipynb":false,"ipynb":null,"enabled":false,"daemon":null,"daemon-restart":false,"debug":false,"ipynb-filters":[],"ipynb-shell-interactivity":null,"plotly-connected":true,"engine":"jupyter"},"render":{"keep-tex":false,"keep-typ":false,"keep-source":false,"keep-hidden":false,"prefer-html":false,"output-divs":true,"output-ext":"html","fig-align":"default","fig-pos":null,"fig-env":null,"code-fold":"none","code-overflow":"scroll","code-link":false,"code-line-numbers":false,"code-tools":false,"tbl-colwidths":"auto","merge-includes":true,"inline-includes":false,"preserve-yaml":false,"latex-auto-mk":true,"latex-auto-install":true,"latex-clean":true,"latex-min-runs":1,"latex-max-runs":10,"latex-makeindex":"makeindex","latex-makeindex-opts":[],"latex-tlmgr-opts":[],"latex-input-paths":[],"latex-output-dir":null,"link-external-icon":false,"link-external-newwindow":false,"self-contained-math":false,"format-resources":[],"notebook-links":true},"pandoc":{"standalone":true,"wrap":"none","default-image-extension":"png","to":"html","output-file":"imagenet.html"},"language":{"toc-title-document":"Table 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top","search-no-results-text":"No results","search-matching-documents-text":"matching documents","search-copy-link-title":"Copy link to search","search-hide-matches-text":"Hide additional matches","search-more-match-text":"more match in this document","search-more-matches-text":"more matches in this document","search-clear-button-title":"Clear","search-text-placeholder":"","search-detached-cancel-button-title":"Cancel","search-submit-button-title":"Submit","search-label":"Search","toggle-section":"Toggle section","toggle-sidebar":"Toggle sidebar navigation","toggle-dark-mode":"Toggle dark mode","toggle-reader-mode":"Toggle reader mode","toggle-navigation":"Toggle navigation","crossref-fig-title":"Figure","crossref-tbl-title":"Table","crossref-lst-title":"Listing","crossref-thm-title":"Theorem","crossref-lem-title":"Lemma","crossref-cor-title":"Corollary","crossref-prp-title":"Proposition","crossref-cnj-title":"Conjecture","crossref-def-title":"Definition","crossref-exm-title":"Example","crossref-exr-title":"Exercise","crossref-ch-prefix":"Chapter","crossref-apx-prefix":"Appendix","crossref-sec-prefix":"Section","crossref-eq-prefix":"Equation","crossref-lof-title":"List of Figures","crossref-lot-title":"List of Tables","crossref-lol-title":"List of Listings","environment-proof-title":"Proof","environment-remark-title":"Remark","environment-solution-title":"Solution","listing-page-order-by":"Order By","listing-page-order-by-default":"Default","listing-page-order-by-date-asc":"Oldest","listing-page-order-by-date-desc":"Newest","listing-page-order-by-number-desc":"High to Low","listing-page-order-by-number-asc":"Low to 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No newline at end of file diff --git a/doc/.quarto/xref/c213c7e9 b/doc/.quarto/xref/c213c7e9 new file mode 100644 index 000000000..f6ead1e6a --- /dev/null +++ b/doc/.quarto/xref/c213c7e9 @@ -0,0 +1 @@ +{"entries":[{"caption":"","order":{"number":1,"section":[0,0,0,0,0,0,0]},"key":"fig-label-balance"}],"headings":["image-compression"]} \ No newline at end of file diff --git a/doc/reconstruction-error.qmd b/doc/reconstruction-error.qmd new file mode 100644 index 000000000..32d6da723 --- /dev/null +++ b/doc/reconstruction-error.qmd @@ -0,0 +1,245 @@ + +Load the features and the generations. + +```{python} +from pathlib import Path +features2image_diffusion_dir = Path("../features2image_diffusion") +run_dir = features2image_diffusion_dir / "xyz/run/4409b6282a7d05f0b08880228d6d6564011fa40be412073ff05aff8bf2dc49fa" +batch_size = 64 +shuffle = True +``` + +```{python} +from collections import OrderedDict +from itertools import islice + +import jax.numpy as jnp +import numpy as np +import torchvision +import torch +from torch.utils.data import Dataset, DataLoader +from tqdm import tqdm + +class EditedFeaturesAndGenerations(Dataset): + + def __init__(self, gen_path, mnist_path="./res/MNIST", progress_bar=True, transform=None): + self.dir = gen_path + self.subdirs = OrderedDict() + + mnist_transform = torchvision.transforms.Compose([ + torchvision.transforms.ToTensor(), + torchvision.transforms.Normalize((0.5,), (0.5,)), + ]) + self.mnist = torchvision.datasets.MNIST( + str(mnist_path), + train=True, + download=True, + transform=mnist_transform, + ) + + a_generations_file = next(iter(filter( + lambda p: p.name.split("-")[-1] == "generations.npy", + Path(gen_path).rglob("*") + ))) + self.generations_per_file = self.load_numpy(a_generations_file).shape[0] + + self.len = 0 + subdir_iter = filter(lambda d: d.is_dir(), Path(gen_path).iterdir()) + if progress_bar: + subdir_iter = tqdm( + list(subdir_iter), + desc="loading features and generations", + postfix={"len": self.len} + ) + + for d in subdir_iter: + # convert /dir/parent/id-type.suffix to ./parent/id + files = sorted(map( + lambda f: (f.parent / f.stem.split("-")[0]).relative_to(self.dir), + # only iterate over generated images + filter(lambda f: f.suffix == ".png", d.rglob("*")) + )) + self.subdirs[int(d.name)] = files + self.len += len(files) * self.generations_per_file + if progress_bar: + subdir_iter.set_postfix({"len": self.len}) # pyright: ignore[reportAttributeAccessIssue] + + self.transform = torchvision.transforms.Compose( + [ + # torchvision.transforms.ToTensor(), + torchvision.transforms.Normalize((0.5,), (0.5,)), + ] + ) if transform is None else transform + + @staticmethod + def load_numpy(path): + return np.load(path, allow_pickle=False) + + def __getitem__(self, idx): + # find which directory to sample from + subdirs = iter(self.subdirs.items()) + d, files = next(subdirs) + while idx // self.generations_per_file > len(files): + idx -= len(files) * self.generations_per_file + d, files = next(subdirs) + + original, label = self.mnist[d] + file = files[idx // self.generations_per_file] + features = self.load_numpy(self.dir / file.parent / (file.stem + "-features.npy")) + generation = self.load_numpy(self.dir / file.parent / (file.stem + "-generations.npy")) + # We only get a single generation from the file that contains multiple generations. + generation = generation[idx % self.generations_per_file] + # Temporarily convert the array to a torch tensor to apply the transformations. + generation = self.transform(torch.Tensor(generation)).numpy() + + return str(file), generation, original, features, label + + def iter_unedited(self): + for d in self.subdirs: + original, label = self.mnist[d] + d = str(d) + features = self.load_numpy(self.dir / d / "unedited-features.npy") + for generation in self.load_numpy(self.dir / d / "unedited-generations.npy"): + generation = self.transform(torch.Tensor(generation)).numpy() + yield d + "/unedited", generation, original, features, label + + def __len__(self): + return self.len + +dataset = EditedFeaturesAndGenerations(run_dir) +data_loader = DataLoader( + dataset, + collate_fn=lambda batch: tuple(map(np.array, zip(*batch))), + batch_size=batch_size, + shuffle=shuffle +) +path, generation, original, features, label = next(iter(data_loader)) +print(path.shape, generation.shape, original.shape, features.shape, label.shape) +``` + +```{python} +_, gen_img, orig_img, _, _ = next(dataset.iter_unedited()) +print(orig_img.max(), gen_img.max()) +print(orig_img.min(), gen_img.min()) +``` + +```{python} +import jo3mnist +import matplotlib.pyplot as plt +_, axes = plt.subplots(1, 2, figsize=(10, 5)) +axes[0].imshow(jo3mnist.to_img(orig_img)) +axes[1].imshow(jo3mnist.to_img(gen_img)) +(ax.axis('off') for ax in axes) +plt.show() +``` + +Load the CNN and the SAE and sow the CNN. + +```{python} +import tomllib +# the config.toml in the features2image_diffusion's run_dir describes which SAE run results it used. +sae_run_dir = features2image_diffusion_dir / tomllib.loads((run_dir / "config.toml").read_text())["eval"][0]["feature_dir"] +# the sae_run_dir contains configuration infarmation. +# We need this for knowing which cnn and sae to load and at what place to intercept cnn activations. +sae_config = tomllib.loads((sae_run_dir / "config.toml").read_text()) +cnn_path = sae_config["cnn_storage"] +sae_path = sae_run_dir / "sae.eqx" +sow_layer = sae_config["sae"][0]["layer"] + +print(cnn_path, sae_path, sow_layer, sep="\n") +``` + +```{python} +import jax +import equinox as eqx +import jo3util.eqx as jo3eqx + +from src.sae import SAE +from src.cnn import CNN + +sae = jo3eqx.load(sae_path, SAE) + +# equinox wants to see an example cnn before loading the weights into it +cnn = CNN(jax.random.PRNGKey(0)) +# load the weights into the example cnn +cnn = eqx.tree_deserialise_leaves(cnn_path, cnn) +# sow the cnn, so we can intercept intermediate activations every forward pass +cnn = jo3eqx.sow(lambda m: m.layers[sow_layer], cnn) +``` + +Now reconstruct features from the generated images and compare the reconstructed features with the original. + +```{python} +path, gens, orig, orig_features, label = tuple(map(np.array, zip(*dataset.iter_unedited()))) +print(path.shape, gens.shape, orig_features.shape) +# Pass the images generated by the diffusion through the cnn and fetch the intermediate activations. +activ, pred = jax.vmap(cnn)(gens) # pyright: ignore[reportArgumentType] +print(activ.shape, pred.shape) +# Reconstruct the features by encoding the intermediate activations with the SAE. +recon_features = jax.vmap(sae.encode)(activ) +print(recon_features.shape) +# Look at the difference between the original features and the reconstructed features. +mse = jnp.sum((orig_features - recon_features)**2, axis=-1) # pyright: ignore[reportAssignmentType] +print(mse) +``` + +If we use generated images from edited features instead, the reconstruction difference is larger. + +```{python} +path, gens, orig, orig_features, label = next(iter(data_loader)) +activ, pred = jax.vmap(cnn)(gens) # pyright: ignore[reportArgumentType] +recon_features = jax.vmap(sae.encode)(activ) +mse = jnp.sum((orig_features - recon_features)**2, axis=-1) # pyright: ignore[reportAssignmentType] +print(mse) +``` + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/res/MNIST/MNIST/raw/t10k-images-idx3-ubyte b/res/MNIST/MNIST/raw/t10k-images-idx3-ubyte deleted file mode 100644 index 1170b2cae..000000000 Binary files a/res/MNIST/MNIST/raw/t10k-images-idx3-ubyte and /dev/null differ diff --git a/res/MNIST/MNIST/raw/t10k-images-idx3-ubyte.gz b/res/MNIST/MNIST/raw/t10k-images-idx3-ubyte.gz deleted file mode 100644 index 5ace8ea93..000000000 Binary files a/res/MNIST/MNIST/raw/t10k-images-idx3-ubyte.gz and /dev/null differ diff --git a/res/MNIST/MNIST/raw/t10k-labels-idx1-ubyte b/res/MNIST/MNIST/raw/t10k-labels-idx1-ubyte deleted file mode 100644 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-cnn_storage = "/home/jo3/p/sparse_autoencoder/sparse_autoencoder/../res/cnn.eqx" -sae = [ - { layer = 6, hidden_size = 256, input_size = 64, learning_rate = 0.0001, l1 = 0.0003 }, -] diff --git a/run/171d21f725b8721bc98f2d334b103d6b0e83570a958c461866fd12c6280a670f/log/events.out.tfevents.1703087668.jo3desktop.22582.0 b/run/171d21f725b8721bc98f2d334b103d6b0e83570a958c461866fd12c6280a670f/log/events.out.tfevents.1703087668.jo3desktop.22582.0 deleted file mode 100644 index 114fb7bd4..000000000 Binary files a/run/171d21f725b8721bc98f2d334b103d6b0e83570a958c461866fd12c6280a670f/log/events.out.tfevents.1703087668.jo3desktop.22582.0 and /dev/null differ diff --git a/run/171d21f725b8721bc98f2d334b103d6b0e83570a958c461866fd12c6280a670f/sae-hyperparams.toml b/run/171d21f725b8721bc98f2d334b103d6b0e83570a958c461866fd12c6280a670f/sae-hyperparams.toml deleted file mode 100644 index a63e5b4a1..000000000 --- a/run/171d21f725b8721bc98f2d334b103d6b0e83570a958c461866fd12c6280a670f/sae-hyperparams.toml 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model_storage="./res/cnn.eqx", + storage="./res/cnn.eqx", ): trainloader, testloader = jo3mnist.load(batch_size=batch_size) model = CNN(key) diff --git a/src/sae.py b/src/sae.py index 293c7ef34..f352ffd86 100644 --- a/src/sae.py +++ b/src/sae.py @@ -78,6 +78,17 @@ class SAE(eqx.Module): return loss, (reconstruction_err, l1, deep_err) +class EmptySAE(SAE): + def __init__(self): + [self.we, self.wd, self.be, self.bd] = [jnp.array([])] * 4 + def __call__(self, x): + return x + def encode(self, x): + return x + def decode(self, fx): + return fx + + def sample_features(cnn, sae, loader): for i, (x, _) in enumerate(loader): x = x.numpy() @@ -186,21 +197,22 @@ def compose_model(cnn, sae, layer): def train_sae( key, cnn, - sae_pos, - activ_size, + sown_layer, + input_size, hidden_size, batch_size, learning_rate, steps, print_every, tensorboard, - λ, + l1, + **_ ): trainloader, testloader = jo3mnist.load(batch_size=batch_size) # print(f"test_accuracy={evaluate(cnn, testloader).item()}") - sae = SAE(activ_size, hidden_size, key) - model, freeze_spec, sae_pos = compose_model(cnn, sae, sae_pos) + sae = SAE(input_size, hidden_size, key) + model, freeze_spec, sae_pos = compose_model(cnn, sae, sown_layer) optim = optax.adamw(learning_rate) return train_loop( model, @@ -212,5 +224,5 @@ def train_sae( steps, print_every, tensorboard, - λ + l1 )