diff --git a/docs/tutorials/models/pooled_texture_model.nblink b/docs/tutorials/models/pooled_texture_model.nblink new file mode 100644 index 00000000..98c3b90c --- /dev/null +++ b/docs/tutorials/models/pooled_texture_model.nblink @@ -0,0 +1,3 @@ +{ + "path": "../../../examples/pooled_texture_model.ipynb" +} diff --git a/examples/pooled_texture_model.ipynb b/examples/pooled_texture_model.ipynb new file mode 100644 index 00000000..b4eaddc2 --- /dev/null +++ b/examples/pooled_texture_model.ipynb @@ -0,0 +1,16491 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "69c8b7b3-c0cf-44bf-9a9d-13384fdf993b", + "metadata": {}, + "source": [ + "# Pooled Texture Model\n", + "\n", + "This notebook will be fleshed out over the development of this PR. For now, this assumes you are familiar with the pooled texture model described in Freeman and Simoncelli, 2011 [^1], among other places, and just aims to show how to use the model, so we can get feedback\n", + "\n", + "[^1]: Freeman, J., & Simoncelli, E. P. (2011). Metamers of the ventral stream. Nature Neuroscience, 14(9), 1195–1201. http://dx.doi.org/10.1038/nn.2889. [reprint](https://www.cns.nyu.edu/pub/eero/freeman10-reprint.pdf)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "22fe4861-c6d9-4e16-904d-071afa0d99d2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/sw/nix/store/29h1dijh98y9ar6n8hxv78v8zz2pqfzf-python-3.11.7-view/lib/python3.11/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for type is zero.\n", + " setattr(self, word, getattr(machar, word).flat[0])\n", + "/mnt/sw/nix/store/29h1dijh98y9ar6n8hxv78v8zz2pqfzf-python-3.11.7-view/lib/python3.11/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n", + " return self._float_to_str(self.smallest_subnormal)\n", + "/mnt/sw/nix/store/29h1dijh98y9ar6n8hxv78v8zz2pqfzf-python-3.11.7-view/lib/python3.11/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for type is zero.\n", + " setattr(self, word, getattr(machar, word).flat[0])\n", + "/mnt/sw/nix/store/29h1dijh98y9ar6n8hxv78v8zz2pqfzf-python-3.11.7-view/lib/python3.11/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n", + " return self._float_to_str(self.smallest_subnormal)\n" + ] + } + ], + "source": [ + "import einops\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import torch\n", + "\n", + "import plenoptic as po\n", + "\n", + "# so that relative sizes of axes created by po.imshow and others look right\n", + "plt.rcParams[\"figure.dpi\"] = 72\n", + "\n", + "# set seed for reproducibility\n", + "po.tools.set_seed(1)\n", + "\n", + "# use GPU if available\n", + "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "%matplotlib inline\n", + "\n", + "\n", + "# Animation-related settings\n", + "plt.rcParams[\"animation.html\"] = \"html5\"\n", + "# use single-threaded ffmpeg for animation writer\n", + "plt.rcParams[\"animation.writer\"] = \"ffmpeg\"\n", + "plt.rcParams[\"animation.ffmpeg_args\"] = [\"-threads\", \"1\"]" + ] + }, + { + "cell_type": "markdown", + "id": "d64fe209-3592-4248-a43f-d84bf9a9ba0f", + "metadata": {}, + "source": [ + "First, grab an image. We have several built-in to plenoptic, which can all be found under `po.data`, or you can use `po.load_images` to load your own image in. Images in plenoptic need to be 4d tensors of shape `(batch, channel, height, width)`, where `batch` typically contains multiple images and channel can contain `RGB`, for example. For now, the pooled texture model treats each channel independently and so does not handle color images well. We should thus use a grayscale image, which has a single color channel." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "09387173-7a71-4063-8505-fb21e4aba0bb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "img = po.data.reptile_skin()\n", + "po.imshow(img);" + ] + }, + { + "cell_type": "markdown", + "id": "8cd84e69-94cd-47f3-9fd1-6e9ece41850d", + "metadata": {}, + "source": [ + "The pooled texture model requires a mask as its input. This mask is a list of 3d non-negative tensors, of shape `(masks, height, width)`, where height and width must all match that of the target image (the model downsamples the mask to handle different scales). We will take the product of all masks in the list in order to construct the final mask. This way, users can specify e.g., two tensors each with shape `(4, height, width)` instead of a single tensor with shape `(16, height, width)`, which will be more memory efficient:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "067a6549-6d78-402b-82a0-c15ebd420dfb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[torch.Size([4, 256, 256]), torch.Size([4, 256, 256])]\n" + ] + } + ], + "source": [ + "masks = []\n", + "for i in range(2):\n", + " mask = [torch.zeros_like(img)[0] for i in range(4)]\n", + " mask_sz = int(256 // len(mask))\n", + " for j, m in enumerate(mask):\n", + " if i == 0:\n", + " m[..., j * mask_sz : (j + 1) * mask_sz, :] = 1\n", + " else:\n", + " m[..., j * mask_sz : (j + 1) * mask_sz] = 1\n", + " masks.append(torch.cat(mask, 0))\n", + "print([m.shape for m in masks])" + ] + }, + { + "cell_type": "markdown", + "id": "b9686bed-1fee-48af-9a26-9a84c644e328", + "metadata": {}, + "source": [ + "We can visualize the output of the mask as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "170c32a6-5936-46c6-b30e-36931ec5402a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "masked = einops.einsum(*masks, img, \"m0 h w, m1 h w, b c h w -> b c m0 m1 h w\")\n", + "po.imshow(einops.rearrange(masked, \"b c m0 m1 h w -> b (c m0 m1) h w\"));" + ] + }, + { + "cell_type": "markdown", + "id": "74b834ff-2a3d-49d5-b976-12a3b84392de", + "metadata": {}, + "source": [ + "This is equivalent to, but more memory-efficient than, the following: " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "76c07568-6e54-4f6f-8172-1931327eeabf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([16, 256, 256])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mask = [torch.zeros_like(img)[0] for i in range(16)]\n", + "mask_sz = int(256 // np.sqrt(len(mask)))\n", + "for i, m in enumerate(mask):\n", + " x = int(i // np.sqrt(len(mask)))\n", + " y = int(i % np.sqrt(len(mask)))\n", + " m[..., x * mask_sz : (x + 1) * mask_sz, y * mask_sz : (y + 1) * mask_sz] = 1\n", + "single_masks = torch.cat(mask, 0)\n", + "single_masks.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a6f91221-0644-4012-bf24-2030a93f10be", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "masked = einops.einsum(single_masks, img, \"m0 h w, b c h w -> b c m0 h w\")\n", + "po.imshow(einops.rearrange(masked, \"b c m0 h w -> b (c m0) h w\"));" + ] + }, + { + "cell_type": "markdown", + "id": "b9dbb9fb-3ddb-4b91-b52f-f72136299694", + "metadata": {}, + "source": [ + "Currently, both of these approaches are supported -- should they be? Or should we require the user to pass two masks that we multiply together? I can't think there's any case in which someone would pass a list with three or more mask tensors, but that would work as well. Even if we allow a list of masks, should we allow more than two?\n", + "\n", + "Before moving on, another question: how to handle mask normalization? The model works best if the individual masks (as shown above) sum to approximately 1 because, otherwise, some of the statistics end up being much larger than the others and optimization is hard. Currently, we're **not** normalizing within the model code, but should we? What you want is to have every element of the following end up being roughly equal to 1:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a25d32af-4863-4368-bb32-66531d01cd8a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[4096., 4096., 4096., 4096.],\n", + " [4096., 4096., 4096., 4096.],\n", + " [4096., 4096., 4096., 4096.],\n", + " [4096., 4096., 4096., 4096.]])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "einops.einsum(*masks, \"m0 h w, m1 h w -> m0 m1\")" + ] + }, + { + "cell_type": "markdown", + "id": "08409085-3748-4d84-a9e3-406bdb16debb", + "metadata": {}, + "source": [ + "In this case, that means dividing each mask by `sqrt(4096)=64`:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d4f34ae4-50d0-47e4-ae9d-9586797a1bad", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[1., 1., 1., 1.],\n", + " [1., 1., 1., 1.],\n", + " [1., 1., 1., 1.],\n", + " [1., 1., 1., 1.]])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "masks = [m / 64 for m in masks]\n", + "einops.einsum(*masks, \"m0 h w, m1 h w -> m0 m1\")" + ] + }, + { + "cell_type": "markdown", + "id": "4f5695dd-4386-47d6-8a52-63cbb97f1fd0", + "metadata": {}, + "source": [ + "We do ensure that the sum is constant across scales, when we downsample the masks.\n", + "\n", + "Now that we have our (properly-normalized) masks, we can create out model. It requires the height and width of the image, as well as the list `masks`:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "3c5df099-bcaf-4874-9e85-ba5300764e08", + "metadata": {}, + "outputs": [], + "source": [ + "ps_mask = po.simul.PortillaSimoncelliMasked(img.shape[-2:], masks)" + ] + }, + { + "cell_type": "markdown", + "id": "7553068e-b128-4915-aab0-77231a9f03af", + "metadata": {}, + "source": [ + "To generate the pooled texture statistics, simply pass this object an image:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "5ac88a08-3a89-4a39-b6e3-f0b6a8d07fad", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[[[ 0.3839, 0.0523, 0.0596, ..., 0.1502, 0.1148, 0.0054],\n", + " [ 0.3770, 0.0574, 0.0554, ..., 0.2068, -0.0482, 0.0051],\n", + " [ 0.4332, 0.0603, 0.0119, ..., 0.1555, -0.0754, 0.0043],\n", + " ...,\n", + " [ 0.4184, 0.0460, 0.0072, ..., 0.1884, 0.0434, 0.0047],\n", + " [ 0.4623, 0.0503, -0.0135, ..., 0.2387, 0.0923, 0.0036],\n", + " [ 0.4157, 0.0542, 0.0262, ..., 0.2781, 0.0705, 0.0047]]]])\n" + ] + }, + { + "data": { + "text/plain": [ + "torch.Size([1, 1, 16, 1044])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(ps_mask(img))\n", + "ps_mask(img).shape" + ] + }, + { + "cell_type": "markdown", + "id": "c0bcc3dc-3279-4d85-86de-f8755f2f5474", + "metadata": {}, + "source": [ + "The shape of the models output is `(batch, channel, masks, stats)`, where `batch, channel` match that of our input image, `masks` is the total number of masks (16 here, generally, the product of the first dimension for each element in the list `masks`), and `stats` is the number of statistics, which depends on the other parameters of the texture model (`n_orientations`, `n_scales`, and `spatial_corr_width`).\n", + "\n", + "If we compare this to the regular texture model:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "bcae708c-4618-4f70-997c-503faece5b7d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 1, 1046])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ps = po.simul.PortillaSimoncelli(img.shape[-2:])\n", + "ps(img).shape" + ] + }, + { + "cell_type": "markdown", + "id": "4eb0555b-b586-4eb3-8d8e-ed651b056054", + "metadata": {}, + "source": [ + "This model takes the same initialization arguments, except for `masks`, and returns a tensor of shape `(batch, channel, stats)`, each dimension as above. The regular texture model has two more stats than the pooled ones: the max and min pixel value, which don't make sense to compute (and don't appear to be necessary) for the pooled version of the model.\n", + "\n", + "Once initialized, the model can be called on any image of the appropriate size:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5ec0e2c4-598a-4ed8-b8df-ad5c907d2fd7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 1, 16, 1044])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ps_mask(po.data.einstein()).shape" + ] + }, + { + "cell_type": "markdown", + "id": "34ba7a6c-741d-4750-a2fd-0f3808475db8", + "metadata": {}, + "source": [ + "If available, moving everything over to a GPU will speed things up greatly:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c0205bd4-87ce-49f4-b21b-4fb707a33e1c", + "metadata": {}, + "outputs": [], + "source": [ + "img = img.to(DEVICE)\n", + "ps_mask = ps_mask.to(DEVICE)" + ] + }, + { + "cell_type": "markdown", + "id": "f0bea09e-9736-4377-ad4f-d77b06b5283c", + "metadata": {}, + "source": [ + "To create model metamers, make use of plenoptic's metamer object, which takes the model and target image at intialization (plenoptic has a separate object for coarse-to-fine metamer optimization):" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "6b68631a-35cb-4600-a7fe-d5a5f4eaf889", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/home/wbroderick/plenoptic/src/plenoptic/tools/validate.py:197: UserWarning: model is in training mode, you probably want to call eval() to switch to evaluation mode\n", + " warnings.warn(\n", + "/mnt/home/wbroderick/plenoptic/src/plenoptic/tools/validate.py:232: UserWarning: Validating whether model can work with coarse-to-fine synthesis -- this can take a while!\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "met = po.synth.MetamerCTF(\n", + " img,\n", + " ps_mask,\n", + " loss_function=po.tools.optim.l2_norm,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "83d222db-a6a8-43dc-a50e-573c8d0a90e4", + "metadata": {}, + "source": [ + "Then call the `synthesize()` method, which requires some additional arguments describing how to transition between scales for the coarse-to-fine optimization (see `synthesize` docstring for details):" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "976a33cd-6480-46c3-82d8-20752690efe0", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2dd180cc753f4d05b5577f6c71426035", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/500 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "po.synth.metamer.plot_synthesis_status(\n", + " met, width_ratios={\"plot_representation_error\": 2}\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "a53c9773-c952-4ac7-9a3b-4b705cec1824", + "metadata": {}, + "source": [ + "The leftmost image shows the resulting metamer, with the overall loss in the middle (we can see that we could keep going), and the representation loss on the right. This representation loss shows the error in each class of model statistics.\n", + "\n", + "Just for fun, we can animate this plot (though currently, our ability to update the ylimits on the representation loss plots isn't working):" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "a22631e5-518a-4194-9e68-044776d86a30", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/home/wbroderick/plenoptic/src/plenoptic/synthesize/metamer.py:1814: UserWarning: Looks like representation is image-like, haven't fully thought out how to best handle rescaling color ranges yet!\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "po.synth.metamer.animate(met, width_ratios={\"plot_representation_error\": 2})" + ] + }, + { + "cell_type": "markdown", + "id": "d0acb478-d04d-4cf8-9d81-cd092a6e81d8", + "metadata": {}, + "source": [ + "Let's do this on a different image:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "b505bc40-cd15-469d-a5ff-6ee4fdd6277e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/home/wbroderick/plenoptic/src/plenoptic/tools/validate.py:197: UserWarning: model is in training mode, you probably want to call eval() to switch to evaluation mode\n", + " warnings.warn(\n", + "/mnt/home/wbroderick/plenoptic/src/plenoptic/tools/validate.py:232: UserWarning: Validating whether model can work with coarse-to-fine synthesis -- this can take a while!\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "img = po.data.einstein().to(DEVICE)\n", + "met = po.synth.MetamerCTF(\n", + " img,\n", + " ps_mask,\n", + " loss_function=po.tools.optim.l2_norm,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "8477d4e3-63f2-471a-a00d-6ea99b2ab3af", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "75ffa040bc9a4ea6afcdd0ba9d6edc0d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/500 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "po.synth.metamer.plot_synthesis_status(\n", + " met, width_ratios={\"plot_representation_error\": 2}\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "6b865529-2632-471d-893a-5ca32d0033f5", + "metadata": {}, + "source": [ + "The code *should* work with any set of masks that the user provides, assuming the sum is roughly 1 for any of them. In particular, users may want to use the foveated pooling windows used in Freeman and Simoncelli, 2011, and other places. Those are not present in plenoptic, but a plenoptic-compatible implementation, also written by me, can be found [here](https://github.com/LabForComputationalVision/pooling-windows).\n", + "\n", + "Question: that's currently not a package, and so cannot be installed, but I maintain it -- should I package it up? My hesitation is that it's much more research code than production code.\n", + "\n", + "Regardless, if the user clones that github repo and then uses the following line to add it to their path and import it:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "d2e75e39-374a-4d9b-ad89-d5454fff39a5", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "\n", + "sys.path.append(\"../../pooling-windows/\")\n", + "import pooling" + ] + }, + { + "cell_type": "markdown", + "id": "a594e877-8b49-471f-82a2-859643a2b943", + "metadata": {}, + "source": [ + "We can then make use of them. There are two versions, either Freeman's original raised-cosine functional form or the smoother (but more memory intensive) Gaussian ones used in [Broderick et al., 2023](https://elifesciences.org/reviewed-preprints/90554). For the texture model, doesn't seem to matter which:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "3d2f85ee-8517-4169-be18-f7bbff228c8c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/sw/nix/store/29h1dijh98y9ar6n8hxv78v8zz2pqfzf-python-3.11.7-view/lib/python3.11/site-packages/torch/functional.py:507: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /dev/shm/nix-build-py-torch-2.2.2.drv-0/nixbld1/spack-stage-py-torch-2.2.2-sv02268wq1hj65q0cishjs7bjlchg387/spack-src/aten/src/ATen/native/TensorShape.cpp:3549.)\n", + " return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n" + ] + } + ], + "source": [ + "pw = pooling.PoolingWindows(0.5, img.shape[-2:], max_eccentricity=5)\n", + "pooling_windows = [pw.ecc_windows[0], pw.angle_windows[0]]" + ] + }, + { + "cell_type": "markdown", + "id": "8e8dddf6-b455-4692-9af6-cc83e155c11b", + "metadata": {}, + "source": [ + "Note that these windows are specified in degrees of visual angle, and the width of the image in degrees is required. See the above papers for more details.\n", + "\n", + "With these windows, we can see the importance of the rule that \"all masks should sum to approximately 1\". By visualizing the eccentricity windows, we can see that the most eccentric one goes off the image (this was necessary for the models in Broderick et al, 2024, but not for the texture one here):" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "540c550f-bde8-4afb-b282-41360804cbe2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "po.imshow(pooling_windows[0].unsqueeze(0));" + ] + }, + { + "cell_type": "markdown", + "id": "31dbead4-2819-44f2-b29c-99c3f6d02e27", + "metadata": {}, + "source": [ + "Thus, when we take the sum, as we did above, we can see that the sum for these most eccentric windows decreases dramatically:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "dcba3a80-8282-4243-ab6e-46261bb28551", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.stem(\n", + " po.to_numpy(einops.einsum(*pooling_windows, \"m1 h w, m2 h w -> m1 m2\").flatten())\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "a8f7478e-0527-4480-b91c-95e1f04e6c33", + "metadata": {}, + "source": [ + "If we were to create a model using these windows, we can see that the kurtosis recon stats are much higher than the other ones, which will make optimization difficult:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "2cc9af27-e156-4ec2-823e-2d93771bbad0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(
,\n", + " [,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ps_mask = po.simul.PortillaSimoncelliMasked(img.shape[-2:], pooling_windows).to(DEVICE)\n", + "ps_mask.plot_representation(ps_mask(img))" + ] + }, + { + "cell_type": "markdown", + "id": "4ba86054-1a7b-4f4d-ac6d-824581b25516", + "metadata": {}, + "source": [ + "We should thus ignore that final window:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "e290681a-e5bf-4c10-8b8f-f5437ab2adc2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pooling_windows = [pw.ecc_windows[0][:-1], pw.angle_windows[0]]\n", + "plt.stem(\n", + " po.to_numpy(einops.einsum(*pooling_windows, \"m1 h w, m2 h w -> m1 m2\").flatten())\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "4ba5d528-7975-4109-96e5-19892c42fa77", + "metadata": {}, + "source": [ + "When we do that, we can see that we still have some windows whose sum is below one, but they're still at least 0.1 and this seems to work fine." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "fe536528-1bd8-4405-a263-1bedbcff5923", + "metadata": {}, + "outputs": [], + "source": [ + "ps_mask = po.simul.PortillaSimoncelliMasked(img.shape[-2:], pooling_windows).to(DEVICE)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "3c2186bf-75a3-4467-8892-5fc79092f04d", + "metadata": {}, + "outputs": [], + "source": [ + "met = po.synth.MetamerCTF(\n", + " img,\n", + " ps_mask,\n", + " loss_function=po.tools.optim.l2_norm,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "36a414cb-b641-4cb8-9b36-b4d51aedc343", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ec793dc90da74f41aa826eadecab1834", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1000 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "po.synth.metamer.plot_synthesis_status(\n", + " met, width_ratios={\"plot_representation_error\": 2}\n", + ");" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "f0d60823-9806-42d0-b7a1-1482c5dbf2bf", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/home/wbroderick/plenoptic/src/plenoptic/synthesize/metamer.py:1814: UserWarning: Looks like representation is image-like, haven't fully thought out how to best handle rescaling color ranges yet!\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "po.synth.metamer.animate(met, width_ratios={\"plot_representation_error\": 2})" + ] + }, + { + "cell_type": "markdown", + "id": "fee2223b-918f-4b8b-b3b6-6fdfb29eb938", + "metadata": {}, + "source": [ + "Tada! `pw`, the `PoolingWindows` object we created, has some utilities for visualizing the windows:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "18328ee6-cf76-49dc-b03b-5ec2bf8cd2bd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = po.imshow([img, met.metamer], title=[\"Target image\", \"Metamer\"])\n", + "for ax in fig.axes:\n", + " pw.plot_windows(ax, subset=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "plenoptic_venv", + "language": "python", + "name": "plenoptic_venv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/plenoptic/simulate/canonical_computations/__init__.py b/src/plenoptic/simulate/canonical_computations/__init__.py index fe476b20..01dff01c 100644 --- a/src/plenoptic/simulate/canonical_computations/__init__.py +++ b/src/plenoptic/simulate/canonical_computations/__init__.py @@ -5,3 +5,4 @@ from .laplacian_pyramid import LaplacianPyramid from .non_linearities import * from .steerable_pyramid_freq import SteerablePyramidFreq +from .weighted_average import * diff --git a/src/plenoptic/simulate/canonical_computations/weighted_average.py b/src/plenoptic/simulate/canonical_computations/weighted_average.py new file mode 100644 index 00000000..f16b6216 --- /dev/null +++ b/src/plenoptic/simulate/canonical_computations/weighted_average.py @@ -0,0 +1,304 @@ +#!/usr/bin/env python3 + +import torch +from torch import Tensor +import einops +import warnings +from ...tools.conv import blur_downsample +from typing import Literal + + +class SimpleAverage(torch.nn.Module): + """Module to average over the last two dimensions of input""" + + def __init__(self): + super().__init__() + + def forward(self, x: Tensor): + """Average over the last two dimensions of input.""" + return torch.mean(x, dim=(-2, -1)) + + +class WeightedAverage(torch.nn.Module): + """Module to take a weighted average over last two dimensions of tensors. + + - Weights are set at initialization, must be non-negative, and 3d Tensors (different + weighting regions indexed on first dimension, height and width on last two). + + - If two weights are set, they are multiplied together when taking the average + (e.g., separable polar angle and eccentricity weights). + + - Weights are normalized at initialization so that they sum to 1 (as best as + possible). If any weighting region sums to near-zero, an exception is raised. If + there's variation across weighting region sums, a warning is raised. + + Parameters + ---------- + weights_1, weights_2 : + 3d Tensors defining the weights for the average. + image_shape : + Last two dimensions of weights tensors + + Attributes + ---------- + weights : + List of one or two 3d Tensors (depending on whether weights_2 set at + initialization) containing the normalized weights. + + """ + + def __init__(self, weights_1: Tensor, weights_2: Tensor | None = None): + super().__init__() + self._validate_weights(weights_1, "_1") + self.register_buffer("_weights_1", weights_1) + self._n_weights = 1 + input_einsum = "w1 h w" + output_einsum = "w1" + if weights_2 is not None: + self._validate_weights(weights_2, "_2") + if weights_1.shape[-2:] != weights_2.shape[-2:]: + raise ValueError( + "weights_1 and weights_2 must have same height and width!" + ) + self._n_weights += 1 + input_einsum += ", w2 h w" + output_einsum += " w2" + self.register_buffer("_weights_2", weights_2) + self.image_shape = weights_1.shape[-2:] + self._input_einsum = input_einsum + self._output_einsum = output_einsum + self._weight_einsum = f"{input_einsum} -> {output_einsum}" + self._forward_einsum = f"{input_einsum}, b c {{extra_dims}} h w -> b c {output_einsum} {{extra_dims}}" + self._extra_dims = ["", "i1", "i1 i2"] + self._normalize_weights() + + @property + def weights(self): + weights = [self._weights_1] + if self._n_weights > 1: + weights.append(self._weights_2) + return weights + + def _normalize_weights(self): + """Normalize weights. + + Call sum_weights() to multiply and sum all weights, then: + + - Check whether any weighting region sum is near-zero. If so, raise ValueError + + - Check variance of weighting region sums and raise warning if that variance is + not near-zero. (Ideally, all weighting region sums would be the same value.) + + - Take the modal weighting region sum value and divide all weighting regions by + that value to normalize them. If we have two weight tensors, divide each by + the sqrt of the mode instead. + + """ + weight_sums = self.sum_weights() + if torch.isclose(weight_sums, torch.zeros_like(weight_sums)).any(): + raise ValueError( + "Some of the weights sum to zero! This will not work out well." + ) + var = weight_sums.var() + if not torch.isclose(var, torch.zeros_like(var)): + warnings.warn( + "Looks like there's some variation in the sums across your weights." + " That might be fine, but just wanted to make sure you knew..." + ) + mode = torch.mode(weight_sums.flatten()).values + if not torch.isclose(mode, torch.ones_like(mode)): + warnings.warn("Weights don't sum to 1, normalizing...") + if self._n_weights == 1: + self._weights_1 = self._weights_1 / mode + else: + self._weights_1 = self._weights_1 / mode.sqrt() + self._weights_2 = self._weights_2 / mode.sqrt() + + @staticmethod + def _validate_weights(weights: Tensor, idx: "str" = "_1"): + if weights.ndim != 3: + raise ValueError(f"weights{idx} must be 3d!") + if weights.min() < 0: + raise ValueError(f"weights{idx} must be non-negative!") + + def forward(self, image: Tensor) -> Tensor: + """Take the weighted average over last two dimensions of input. + + All other dimensions are preserved. + + Parameters + ---------- + image : + 4d to 6d Tensor. + + Returns + ------- + weighted_avg : + Weighted average. Dimensionality depends on both the input's dimensionality + and ``len(weights)`` + + """ + if image.ndim < 4: + raise ValueError("image must be a tensor of 4 to 6 dimensions!") + try: + extra_dims = self._extra_dims[image.ndim - 4] + except IndexError: + raise ValueError("image must be a tensor of 4 to 6 dimensions!") + einsum_str = self._forward_einsum.format(extra_dims=extra_dims) + return einops.einsum(*self.weights, image, einsum_str).flatten(2, 3) + + def einsum(self, einsum_str: str, *tensors: Tensor) -> Tensor: + """More general version of forward. + + This takes the input einsum_str and prepends self.weights to it and inserts the + weight dimensions into the output after "b c" (for batch, channel). Thus this + will be weird if there's no "b c" dimensions. + + Parameters + ---------- + einsum_str : + String of einsum notation, which must contain "b c" in the output. Intended + use is that this string produces a single output tensor. + tensors : + Any number of tensors + + Returns + ------- + output : + The result of this einsum + + """ + einsum_str = f"{self._input_einsum}, {einsum_str.split('->')[0]} -> b c {self._output_einsum} {einsum_str.split('->')[1].replace('b c', '')}" + return einops.einsum(*self.weights, *tensors, einsum_str).flatten(2, 3) + + def sum_weights(self) -> Tensor: + """Sum weights, largely used for diagnostic purposes. + + Returns + ------- + sum : + 1d or 2d tensor (depending on ``len(weights)``) containing the sum of all + weights. + + """ + return einops.einsum(*self.weights, self._weight_einsum) + + +class WeightedAveragePyramid(torch.nn.Module): + """Module to take weighted average across scales. + + This initializes a ``WeightedAverage`` per scale, down-sampling by a factor of 2 + using the ``blur_downsample`` method (and normalizing independently). + + As with ``WeightedAverage``: + + - Weights are set at initialization, must be non-negative, and 3d Tensors (different + weighting regions indexed on first dimension, height and width on last two). + + - If two weights are set, they are multiplied together when taking the average + (e.g., separable polar angle and eccentricity weights). + + - Weights are normalized at initialization so that they sum to 1 (as best as + possible). If any weighting region sums to near-zero, an exception is raised. If + there's variation across weighting region sums, a warning is raised. + + Parameters + ---------- + weights_1, weights_2 : + 3d Tensors defining the weights for the average. + n_scales : + Number of scales. + + Attributes + ---------- + weights : + ModuleList of ``WeightedAverage`` at each scale + + """ + + def __init__( + self, weights_1: Tensor, weights_2: Tensor | None = None, n_scales: int = 4 + ): + super().__init__() + self._n_weights = 1 if weights_2 is None else 2 + self.n_scales = n_scales + weights = [] + for i in range(n_scales): + if i != 0: + weights_1 = blur_downsample( + weights_1.unsqueeze(0), 1, scale_filter=True + ) + weights_1 = weights_1.squeeze(0).clip(min=0) + if weights_2 is not None: + weights_2 = blur_downsample( + weights_2.unsqueeze(0), 1, scale_filter=True + ) + weights_2 = weights_2.squeeze(0).clip(min=0) + # it's possible negative values will get introduced by the downsampling + # above, in which case we remove them + weights.append(WeightedAverage(weights_1, weights_2)) + self.weights = torch.nn.ModuleList(weights) + + def __getitem__(self, idx: int): + return self.weights[idx] + + def forward(self, image: list[Tensor]) -> list[Tensor]: + """Take the weighted average over last two dimensions of each input in list. + + All other dimensions are preserved. + + Parameters + ---------- + image : + List of 4d to 6d Tensor, each of which has been downsampled by a factor of 2. + + Returns + ------- + weighted_avg : + Weighted average. Dimensionality depends on both the input's dimensionality + and whether ``weights_2`` was set at initialization. Scales are stacked + along last dimension. + + """ + return torch.stack([w(x) for x, w in zip(image, self.weights)], dim=-1) + + def einsum(self, einsum_str: str, *tensors: list[Tensor]) -> list[Tensor]: + """More general version of forward, operates on each + + This takes the input einsum_str and prepends self.weights to it and inserts the + weight dimensions into the output after "b c" (for batch, channel). Thus this + will be weird if there's no "b c" dimensions. + + Parameters + ---------- + einsum_str : + String of einsum notation, which must contain "b c" in the output. Intended + use is that this string produces a single output tensor. + tensors : + Any number of lists of tensors (should all have same number of elements, + each corresponding to a different scale and thus downsampled by factor of 2). + + Returns + ------- + output : + The result of this einsum. Scales are stacked along last dimension. + + """ + return torch.stack( + [w.einsum(einsum_str, *x) for *x, w in zip(*tensors, self.weights)], dim=-1 + ) + + def sum_weights(self) -> Tensor: + """Sum weights, largely used for diagnostic purposes. + + Returns + ------- + sum : + 2d or 3d tensor (depending on whether ``weights_2`` was set at + initialization) containing the sum of all weights on each scale. + + """ + sums = [] + for w in self.weights: + sums.append(w.sum_weights()) + return einops.pack(sums, f"* {self.weights[0]._output_einsum}")[0] diff --git a/src/plenoptic/simulate/models/__init__.py b/src/plenoptic/simulate/models/__init__.py index 64837f31..84c4a88f 100644 --- a/src/plenoptic/simulate/models/__init__.py +++ b/src/plenoptic/simulate/models/__init__.py @@ -3,3 +3,4 @@ from .frontend import * from .naive import * from .portilla_simoncelli import PortillaSimoncelli +from .portilla_simoncelli_masked import PortillaSimoncelliMasked diff --git a/src/plenoptic/simulate/models/portilla_simoncelli.py b/src/plenoptic/simulate/models/portilla_simoncelli.py index cef04614..8b3bc943 100644 --- a/src/plenoptic/simulate/models/portilla_simoncelli.py +++ b/src/plenoptic/simulate/models/portilla_simoncelli.py @@ -14,6 +14,7 @@ import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np +from numpy.typing import NDArray import torch import torch.fft import torch.nn as nn @@ -29,8 +30,606 @@ from ..canonical_computations.steerable_pyramid_freq import ( SteerablePyramidFreq, ) +from ..canonical_computations.weighted_average import ( + WeightedAveragePyramid, + SimpleAverage, +) + +SCALES_TYPE = Literal["pixel_statistics"] | PYR_SCALES_TYPE + + +class _StatsComputer(nn.Module): + """ + + Parameters + ---------- + spatial_corr_width: + The width of the spatial cross- and auto-correlation statistics + + """ + + def __init__( + self, + autocorr_masks: list[Tensor], + spatial_corr_width: int = 9, + ): + super().__init__() + self.spatial_corr_width = spatial_corr_width + self.autocorr_masks = autocorr_masks + self.weights = [SimpleAverage()] + self._extra_dims = ["", "o"] + + @staticmethod + def compute_pixel_stats(image: Tensor) -> Tensor: + """Compute the pixel stats: first four moments, min, and max. + + Parameters + ---------- + image : + 4d tensor of shape (batch, channel, height, width) containing input + image. Stats are computed indepently for each batch and channel. + + Returns + ------- + pixel_stats : + 3d tensor of shape (batch, channel, 6) containing the mean, + variance, skew, kurtosis, minimum pixel value, and maximum pixel + value (in that order) + + """ + mean = torch.mean(image, dim=(-2, -1), keepdim=True) + # we use torch.var instead of plenoptic.tools.variance, because our + # variance is the uncorrected (or sample) variance and we want the + # corrected one here. + var = torch.var(image, dim=(-2, -1)) + skew = stats.skew(image, mean=mean, var=var, dim=[-2, -1]) + kurtosis = stats.kurtosis(image, mean=mean, var=var, dim=[-2, -1]) + # can't compute min/max over two dims simultaneously with + # torch.min/max, so use einops + img_min = einops.reduce(image, "b c h w -> b c", "min") + img_max = einops.reduce(image, "b c h w -> b c", "max") + # mean needed to be unflattened to be used by skew and kurtosis + # correctly, but we'll want it to be flattened like this in the final + # representation tensor + return einops.rearrange( + [mean.squeeze(2, -1), var, skew, kurtosis, img_min, img_max], + "s b c -> b c 1 s", + ) + + def compute_autocorr(self, coeffs_list: list[Tensor]) -> tuple[Tensor, Tensor]: + """Compute the autocorrelation of some statistics. + + Parameters + ---------- + coeffs_list : + List (of length s) of tensors of shape (batch, channel, *, height, + width), where * is zero or one additional dimensions. Intended use + case: magnitude_pyr_coeffs (which is list of length n_scales of 5d + tensors, with * containing n_orientations) or reconstructed_images + (which is a list of length n_scales+1 of 4d tensors) + + Returns + ------- + autocorrs : + Tensor of shape (batch, channel, spatial_corr_width, + spatial_corr_width, *, s) containing the autocorrelation (up to + distance ``spatial_corr_width//2``) of each element in + ``coeffs_list``, computed independently over all but the final two + dimensions. + vars : + 3d Tensor of shape (batch, channel, *, s) containing the variance + of each element in ``coeffs_list``, computed independently over all + but the final two dimensions. + + """ + if coeffs_list[0].ndim < 4: + raise ValueError( + "coeffs_list must contain tensors of either 4 or 5 dimensions!" + ) + try: + extra_dims = self._extra_dims[coeffs_list[0].ndim - 4] + mask = self.autocorr_masks[coeffs_list[0].ndim - 4] + except IndexError: + raise ValueError( + "coeffs_list must contain tensors of either 4 or 5 dimensions!" + ) + acs = [signal.autocorrelation(coeff) for coeff in coeffs_list] + var = [signal.center_crop(ac, 1) for ac in acs] + acs = [ac / v for ac, v in zip(acs, var)] + var = einops.rearrange(var, f"s b c {extra_dims} 1 1 -> b c 1 {extra_dims} s") + acs = [signal.center_crop(ac, self.spatial_corr_width) for ac in acs] + acs = torch.stack(acs, 2) + acs = einops.rearrange( + acs, f"b c s {extra_dims} a1 a2 -> b c 1 a1 a2 {extra_dims} s" + ) + return acs[..., mask], var + + @staticmethod + def compute_skew_kurtosis_recon( + reconstructed_images: list[Tensor], + var_recon: Tensor, + img_var: Tensor, + ) -> tuple[Tensor, Tensor]: + """Compute the skew and kurtosis of each lowpass reconstructed image. + + For each scale, if the ratio of its variance to the original image's + pixel variance is below a threshold of + torch.finfo(img_var.dtype).resolution (1e-6 for float32, 1e-15 for + float64), skew and kurtosis are assigned default values of 0 or 3, + respectively. + + Parameters + ---------- + reconstructed_images : + List of length n_scales+1 containing the reconstructed unoriented + image at each scale, from fine to coarse. The final image is + reconstructed just from the residual lowpass image. + var_recon : + Tensor of shape (batch, channel, n_scales+1) containing the + variance of each tensor in reconstruced_images + img_var : + Tensor of shape (batch, channel) containing the pixel variance + (from pixel_stats tensor) + + Returns + ------- + skew_recon, kurtosis_recon : + Tensors of shape (batch, channel, n_scales+1) containing the skew + and kurtosis, respectively, of each tensor in + ``reconstructed_images``. + + """ + skew_recon = [ + stats.skew(im, mean=0, var=var_recon[..., i], dim=[-2, -1]) + for i, im in enumerate(reconstructed_images) + ] + skew_recon = torch.stack(skew_recon, -1) + kurtosis_recon = [ + stats.kurtosis(im, mean=0, var=var_recon[..., i], dim=[-2, -1]) + for i, im in enumerate(reconstructed_images) + ] + kurtosis_recon = torch.stack(kurtosis_recon, -1) + skew_default = torch.zeros_like(skew_recon) + kurtosis_default = 3 * torch.ones_like(kurtosis_recon) + # if this variance ratio is too small, then use the default values + # instead. unsqueeze is used here because var_recon is shape (batch, + # channel, scales+1), whereas img_var is just (batch, channel) + res = torch.finfo(img_var.dtype).resolution + unstable_locs = var_recon / img_var.unsqueeze(-1) < res + skew_recon = torch.where(unstable_locs, skew_default, skew_recon) + kurtosis_recon = torch.where(unstable_locs, kurtosis_default, kurtosis_recon) + return skew_recon, kurtosis_recon + + def compute_cross_correlation( + self, + coeffs_tensor: list[Tensor], + coeffs_tensor_other: list[Tensor], + coeffs_var: None | Tensor = None, + coeffs_other_var: None | Tensor = None, + ) -> Tensor: + """Compute cross-correlations. + + Parameters + ---------- + coeffs_tensor, coeffs_tensor_other : + The two lists of length scales, each containing 5d tensors of shape + (batch, channel, n_orientations, height, width) to be correlated. + coeffs_var, coeffs_other_var : + Two optional tensors containing the variances of coeffs_tensor and + coeffs_tensor_other, respectively, in case they've already been computed. + Should be of shape (batch, channel, n_orientations, n_scales). Used to + normalize the covariances into cross-correlations. + + Returns + ------- + cross_corrs : + Tensor of shape (batch, channel, n_orientations, n_orientations, + scales) containing the cross-correlations at each + scale. + + """ + covars = [] + for i, (coeff, coeff_other) in enumerate( + zip(coeffs_tensor, coeffs_tensor_other) + ): + # precompute this, which we'll use for normalization + numel = torch.mul(*coeff.shape[-2:]) + # compute the covariance + covar = einops.einsum( + coeff, coeff_other, "b c o1 h w, b c o2 h w -> b c o1 o2" + ) + covar = covar / numel + # Then normalize it to get the Pearson product-moment correlation + # coefficient, see + # https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html. + if coeffs_var is None: + # First, compute the variances of each coeff + coeff_var = einops.einsum( + coeff, coeff, "b c o1 h w, b c o1 h w -> b c o1" + ) + coeff_var = coeff_var / numel + else: + # coeffs_var will be shape (batch, channel, 1, ..., scales), where the 1 + # is a dummy dimension for the weights + coeff_var = coeffs_var[:, :, 0, ..., i] + if coeffs_other_var is None: + # First, compute the variances of each coeff + coeff_other_var = einops.einsum( + coeff_other, coeff_other, "b c o1 h w, b c o1 h w -> b c o1" + ) + coeff_other_var = coeff_other_var / numel + else: + # coeffs_other_var will be shape (batch, channel, 1, ..., scales), where + # the 1 is a dummy dimension for the weights + coeff_other_var = coeffs_other_var[:, :, 0, ..., i] + # Then compute the outer product of those variances. + var_outer_prod = einops.einsum( + coeff_var, coeff_other_var, "b c o1, b c o2 -> b c o1 o2" + ) + # And the sqrt of this is what we use to normalize the covariance + # into the cross-correlation + covars.append(covar / var_outer_prod.sqrt()) + return einops.rearrange(covars, "s b c o1 o2 -> b c 1 o1 o2 s") + + +class _WeightedComputer(nn.Module): + """ + + Parameters + ---------- + weights: + + n_scales: + The number of pyramid scales used to measure the statistics (default=4) + n_orientations: + The number of orientations used to measure the statistics (default=4) + spatial_corr_width: + The width of the spatial cross- and auto-correlation statistics + + """ + + def __init__( + self, + weights: WeightedAveragePyramid, + autocorr_shifts: list[NDArray, NDArray], + n_scales: int = 4, + n_orientations: int = 4, + ): + super().__init__() + self.weights = weights + self.n_scales = n_scales + image_shape = weights[0].image_shape + # these are each lists of tensors of shape (batch, channel, n_autocorrs, height, + # width), one per scale, where n_autocorrs is approximately + # spatial_corr_width^2 / 2 + rolls_h, rolls_w = self.create_autocorr_idx( + image_shape, autocorr_shifts, n_scales, n_orientations + ) + for i, (h, w) in enumerate(zip(rolls_h, rolls_w)): + self.register_buffer(f"autocorr_5d_rolls_h_scale_{i}", h) + self.register_buffer(f"autocorr_5d_rolls_w_scale_{i}", w) + # these share data with the full version above + for i, (h, w) in enumerate(zip(rolls_h, rolls_w)): + self.register_buffer(f"autocorr_4d_rolls_h_scale_{i}", h.select(3, 0)) + self.register_buffer(f"autocorr_4d_rolls_w_scale_{i}", w.select(3, 0)) + self.n_autocorrs = rolls_h[0].shape[2] + + @property + def autocorr_5d_rolls_h(self): + # inspired by + # https://discuss.pytorch.org/t/why-no-nn-bufferlist-like-function-for-registered-buffer-tensor/18884/10 + return [ + getattr(self, f"autocorr_5d_rolls_h_scale_{i}") + for i in range(self.n_scales + 1) + ] + + @property + def autocorr_5d_rolls_w(self): + # inspired by + # https://discuss.pytorch.org/t/why-no-nn-bufferlist-like-function-for-registered-buffer-tensor/18884/10 + return [ + getattr(self, f"autocorr_5d_rolls_w_scale_{i}") + for i in range(self.n_scales + 1) + ] + + @property + def autocorr_4d_rolls_h(self): + # inspired by + # https://discuss.pytorch.org/t/why-no-nn-bufferlist-like-function-for-registered-buffer-tensor/18884/10 + return [ + getattr(self, f"autocorr_4d_rolls_h_scale_{i}") + for i in range(self.n_scales + 1) + ] + + @property + def autocorr_4d_rolls_w(self): + # inspired by + # https://discuss.pytorch.org/t/why-no-nn-bufferlist-like-function-for-registered-buffer-tensor/18884/10 + return [ + getattr(self, f"autocorr_4d_rolls_w_scale_{i}") + for i in range(self.n_scales + 1) + ] + + @staticmethod + def create_autocorr_idx( + image_shape: tuple[int, int], + autocorr_shifts: list[NDArray, NDArray], + n_scales: int, + n_orientations: int, + ) -> tuple[list[Tensor], list[Tensor]]: + """Create indices used to shift images when computing autocorrelation. + + The autocorrelation of ``img`` is the product of ``img`` with itself shifted by + a small number of pixels. That is: ``einops.einsum(img, img.roll(i, -1).roll(j, + -2))`` for some relevant values of i and j. This method computes the indices + corresponding to those rolls, so that we can simply call ``img.gather(rolls_h, + -2).gather(rolls_w, -1)`` during the forward pass instead of ``img.roll(i, + -1).roll(j, -2)``, which is less efficient. + + Because of the symmetry of autocorrelations (see Portilla-Simoncelli notebook + for details), we do not need the full ``spatial_corr_width**2`` shifts, we only + need everything below the diagonal (e.g., we don't need to roll both 1 pixel to + the left and 1 pixel to the right). + + Parameters + ---------- + spatial_corr_width : + The width of the spatial auto-correlation. + image_shape : + Shape of input image. + + Returns + ------- + rolls_h, rolls_w : + List of tensors of shape ``(1, 1, n_orientations, n_autocorrs, height, + width)`` giving the shifts along the height (``shape[-2]``) and width + (``shape[-1]``) dimensions required for computing the autocorrelations. Each + entry in the list corresponds to a different scale, and thus height and + width decrease. + + """ + img_h, img_w = image_shape + rolls_h, rolls_w = [], [] + # need one additional scale, since we compute the autocorrelation of the + # reconstructed lowpass images as well + for _ in range(n_scales + 1): + arange_h = ( + torch.arange(img_h) + .view((1, 1, 1, img_h, 1)) + .repeat((1, 1, n_orientations, 1, img_h)) + ) + arange_w = ( + torch.arange(img_w) + .view((1, 1, 1, 1, img_w)) + .repeat((1, 1, n_orientations, img_w, 1)) + ) + rolls_h.append( + torch.stack([arange_h.roll(i, -2) for i in autocorr_shifts[0]], 2) + ) + rolls_w.append( + torch.stack([arange_w.roll(i, -1) for i in autocorr_shifts[1]], 2) + ) + img_h = int(img_h // 2) + img_w = int(img_w // 2) + return rolls_h, rolls_w + + def compute_pixel_stats(self, image: Tensor, epsilon: float = 1e-1) -> Tensor: + """Compute the weighted pixel stats: first four moments. + + Note that, in contrast with the non-weighted version, this does not include the + min/max. + + Parameters + ---------- + image : + 4d tensor of shape (batch, channel, height, width) containing input + image. Stats are computed indepently for each batch and channel. + epsilon : + Epsilon value to use in the denominator when computing the skew and + kurtosis, to prevent them from blowing up. + + Returns + ------- + pixel_stats : + 4d tensor of shape (batch, channel, weights, 4) containing the mean, var, + skew, kurtosis. + + """ + weights = self.weights[0] + mean = weights(image) + # these are all non-central moments... + moment_2 = weights(image.pow(2)) + moment_3 = weights(image.pow(3)) + moment_4 = weights(image.pow(4)) + # ... which we use to compute the var, skew, and kurtosis. the formulas we use + # for var and skew here can be found on their respective wikipedia pages, and + # the one for kurtosis comes from Eero working through the algebra + var = moment_2 - mean.pow(2) + skew = (moment_3 - 3 * mean * var - mean.pow(3)) / (var.pow(1.5) + epsilon) + kurtosis = ( + moment_4 + - 4 * mean * moment_3 + + 6 * mean.pow(2) * moment_2 + - 3 * mean.pow(4) + ) / (var.pow(2) + epsilon) + return torch.stack([mean, var, skew, kurtosis], -1) + + def compute_autocorr( + self, coeffs_list: list[Tensor], epsilon: float = 1e-6 + ) -> tuple[Tensor, Tensor]: + """Compute the autocorrelation of some statistics. + + Parameters + ---------- + coeffs_list : + List (of length s) of tensors of shape (batch, channel, *, height, + width), where * is zero or one additional dimensions. Intended use + case: magnitude_pyr_coeffs (which is list of length n_scales of 5d + tensors, with * containing n_orientations) or reconstructed_images + (which is a list of length n_scales+1 of 4d tensors) + epsilon : + Epsilon value to use in the denominator when normalizing the + autocorrelations, to prevent them from blowing up. + + Returns + ------- + autocorrs : + Tensor of shape (batch, channel, weights, n_autocorrs, *, s) containing the + autocorrelation (up to distance ``spatial_corr_width//2``) of each element + in ``coeffs_list``, computed independently over all but the final two + dimensions. ``n_autocorrs`` is the number of unique autocorrelation values, + which is approximately sptial_corr_width^2 / 2. + vars : + Tensor of shape (batch, channel, weights, *, s) containing the variance of + each element in ``coeffs_list``, computed independently over all but the + final two dimensions. + + """ + if coeffs_list[0].ndim == 5: + dims = "o" + rolls_h = self.autocorr_5d_rolls_h + rolls_w = self.autocorr_5d_rolls_w + elif coeffs_list[0].ndim == 4: + dims = "" + rolls_h = self.autocorr_4d_rolls_h + rolls_w = self.autocorr_4d_rolls_w + else: + raise ValueError( + "coeffs_list must contain tensors of either 4 or 5 dimensions!" + ) + # the WeightedAveragePyramid will insert the weight dimension after the channel. + autocorr_expr = f"b c {dims} h w, b c shift {dims} h w -> b c shift {dims}" + acs = [] + vars = [] + # iterate through scales + for coeff, rolls_h, rolls_w, scale_weight in zip( + coeffs_list, rolls_h, rolls_w, self.weights + ): + # the following two lines are equivalent to having two for loops over + # range(-spatial_corr_width//2, spatial_corr_width//2) and using roll along + # the last two indices, but is much more efficient, especially on the gpu. + rolled_coeff = einops.repeat( + coeff, + f"b c {dims} h w -> b c shift {dims} h w", + shift=self.n_autocorrs, + ) + rolled_coeff = rolled_coeff.gather(-2, rolls_h).gather(-1, rolls_w) + autocorr = scale_weight.einsum(autocorr_expr, coeff, rolled_coeff) + # this returns a view of autocorr that just selects out the variance, while + # preserving the number of dims. we have specifically placed the (0, 0) + # shift, which corresponds to the variance, as the last element. This is the + # third dim, because we have inserted the weight dimensiona after the + # channel. + var = torch.narrow(autocorr, 3, -1, 1) + # and then drop the variance from here + acs.append( + torch.narrow(autocorr, 3, 0, self.n_autocorrs - 1) / (var + epsilon) + ) + vars.append(var) + acs = torch.stack(acs, -1) + vars = einops.rearrange( + vars, + # for vars, shift is always 1, so we're really just + # squeezing it out here + f"scales b c w shift {dims} -> b c (w shift) {dims} scales", + ) + return acs, vars + + def compute_skew_kurtosis_recon( + self, + reconstructed_images: list[Tensor], + var_recon: Tensor, + img_var: None = None, + epsilon: float = 1e-6, + ) -> tuple[Tensor, Tensor]: + """Compute the skew and kurtosis of each lowpass reconstructed image. + + Parameters + ---------- + reconstructed_images : + List of length n_scales+1 containing the reconstructed unoriented + image at each scale, from fine to coarse. The final image is + reconstructed just from the residual lowpass image. + var_recon : + Tensor of shape (batch, channel, weights, n_scales+1) containing the + variance of each tensor in reconstruced_images + epsilon : + Epsilon value to use in the denominator when normalizing the + skew and kurtosis, to prevent them from blowing up. + + Returns + ------- + skew_recon, kurtosis_recon : + Tensors of shape (batch, channel, weights, n_scales+1) containing the skew + and kurtosis, respectively, of each tensor in + ``reconstructed_images``. + + """ + skew_recon = self.weights([img.pow(3) for img in reconstructed_images]) + kurtosis_recon = self.weights([img.pow(4) for img in reconstructed_images]) + skew_recon = skew_recon / (var_recon.pow(1.5) + epsilon) + kurtosis_recon = kurtosis_recon / (var_recon.pow(2) + epsilon) + return skew_recon, kurtosis_recon -SCALES_TYPE = Literal["pixel_statistics"] | PYR_SCALES_TYPE + def compute_cross_correlation( + self, + coeffs_tensor: list[Tensor], + coeffs_tensor_other: list[Tensor], + coeffs_var: Tensor | None = None, + coeffs_other_var: Tensor | None = None, + epsilon: float = 1e-6, + ) -> Tensor: + """Compute cross-correlations. + + Parameters + ---------- + coeffs_tensor, coeffs_tensor_other : + The two lists of length scales, each containing 5d tensors of shape + (batch, channel, n_orientations, height, width) to be correlated. + coeffs_var, coeffs_other_var : + Two optional tensors containing the variances of coeffs_tensor and + coeffs_tensor_other, respectively, in case they've already been computed. + Should be of shape (batch, channel, *weights, n_orientations, n_scales). Note + that by *weights, we indicate that the dimensions should not be combined, so + that if ``len(weights)==2``, *weights would hold two dimensions. Used to + normalize the covariances into cross-correlations. Intended use is the + output of ``compute_autocorr``. + epsilon : + Epsilon value to use in the denominator when normalizing the + correlations, to prevent them from blowing up. + + Returns + ------- + cross_corrs : + Tensor of shape (batch, channel, weights, n_orientations, n_orientations, + scales) containing the cross-correlations at each scale. + + """ + # these two get handled by the weights pyramid, which will insert the weights and + # scales dimensions. + covar_expr = " b c o1 h w, b c o2 h w -> b c o1 o2" + var_expr = "b c o1 h w, b c o1 h w -> b c o1" + # this one does not (because it's computed using the weighted variances), and so + # we need to add the weights and scale dimensions ourselves + outer_prod_expr = "b c w o1 s, b c w o2 s -> b c w o1 o2 s" + # compute the covariance + covars = self.weights.einsum(covar_expr, coeffs_tensor, coeffs_tensor_other) + # Then normalize it to get the Pearson product-moment correlation + # coefficient, see + # https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html. + if coeffs_var is None: + coeffs_var = self.weights.einsum(var_expr, coeffs_tensor, coeffs_tensor) + if coeffs_other_var is None: + coeffs_other_var = self.weights.einsum( + var_expr, coeffs_tensor_other, coeffs_tensor_other + ) + # once we have the variances of each coefficient, we compute the outer product + # of those variances. + var_outer_prod = einops.einsum(coeffs_var, coeffs_other_var, outer_prod_expr) + # And the sqrt of this is what we use to normalize the covariance + # into the cross-correlation + std_outer_prod = (var_outer_prod + epsilon).sqrt() + return covars / (std_outer_prod + epsilon) class PortillaSimoncelli(nn.Module): @@ -60,6 +659,11 @@ class PortillaSimoncelli(nn.Module): The number of orientations used to measure the statistics (default=4) spatial_corr_width: The width of the spatial cross- and auto-correlation statistics + weights: + List of one or two 3d tensors to use as weighting regions. If None, we average + texture stats over whole image. Else, we use these tensors as weights to perform + weighted averages of texture stats in regions across the image. See tutorial for + more details. Attributes ---------- @@ -86,6 +690,7 @@ def __init__( n_scales: int = 4, n_orientations: int = 4, spatial_corr_width: int = 9, + weights: list[Tensor] | None = None, ): super().__init__() @@ -114,6 +719,18 @@ def __init__( + [ii for ii in range(n_scales - 1, -1, -1)] + ["residual_highpass"] ) + autocorr_shifts = self._compute_autocorr_shifts(spatial_corr_width) + autocorr_masks = self._compute_autocorr_mask() + # this gives the indices of all non-redundant autocorrelations, including (0, + # 0), which is the variance, and so we substract one. + self._n_autocorrs = autocorr_shifts[0].shape[0] - 1 + if weights is None: + self._stats_computer = _StatsComputer(autocorr_masks, spatial_corr_width) + else: + weights = WeightedAveragePyramid(*weights, n_scales + 1) + self._stats_computer = _WeightedComputer( + weights, autocorr_shifts, n_scales, n_orientations + ) # Dictionary defining shape of the statistics and which scale they're # associated with @@ -121,14 +738,10 @@ def __init__( # Dictionary defining necessary statistics, that is, those that are not # redundant - self._necessary_stats_dict = self._create_necessary_stats_dict( - scales_shape_dict - ) + necessary_stats_dict = self._create_necessary_stats_dict(scales_shape_dict) # turn this into tensor we can use in forward pass. first into a # boolean mask... - _necessary_stats_mask = einops.pack( - list(self._necessary_stats_dict.values()), "*" - )[0] + _necessary_stats_mask = einops.pack(list(necessary_stats_dict.values()), "*")[0] # then into a tensor of indices _necessary_stats_mask = torch.where(_necessary_stats_mask)[0] self.register_buffer("_necessary_stats_mask", _necessary_stats_mask) @@ -146,6 +759,90 @@ def __init__( self._representation_scales = self._representation_scales[ self._necessary_stats_mask ] + # we store this information in order to convert the vector form back to the + # dictionary. + self._stats_shape_dict = necessary_stats_dict + # and if we're looking at in the dictionary, we want the "unfolded" form for the + # autocorrs, which have shape (spatial_corr_width, spatial_corr_width, ...), + # including NaNs, rather than just (n_autocorrs, ...), without NaNs. + self._stats_shape_dict["auto_correlation_reconstructed"] = autocorr_masks[0] + self._stats_shape_dict["auto_correlation_magnitude"] = autocorr_masks[1] + + @staticmethod + def _compute_autocorr_shifts(spatial_corr_width: int) -> list[NDArray, NDArray]: + """ """ + # because of the symmetry of autocorrelation, in order to generate all + # autocorrelations, we only need the lower triangle (so that we take the + # autocorrelation between the image and itself shifted 1 pixel to the left, but + # not also shifted 1 pixel to the right)... + half_width = (spatial_corr_width - 1) // 2 + autocorr_shift_vals = [ + i - half_width for i in np.tril_indices(spatial_corr_width) + ] + # if spatial_corr_width is even, then we also need these shifts: + if np.mod(spatial_corr_width, 2) == 0: + autocorr_shift_vals[0] = np.concatenate( + [ + np.zeros(spatial_corr_width, dtype=int) - half_width, + autocorr_shift_vals[0], + ], + 0, + ) + autocorr_shift_vals[1] = np.concatenate( + [ + np.arange(spatial_corr_width, dtype=int) - half_width, + autocorr_shift_vals[1], + ], + 0, + ) + # and up to the central element on the diagonal. + idx = [i != j or i < 0 for i, j in zip(*autocorr_shift_vals)] + # put the (0, 0) shift, which corresponds to the variance, at the very end, so + # we know where it is + return [ + np.concatenate([i[idx], np.zeros(1, dtype=int)], 0) + for i in autocorr_shift_vals + ] + + def _compute_autocorr_mask(self): + """Compute the boolean mask for necessary autocorrelation stats.""" + # Pre-compute some necessary indices. + # Lower triangular indices (including diagonal), for auto correlations + tril_inds = torch.tril_indices(self.spatial_corr_width, self.spatial_corr_width) + # Get the second half of the diagonal, i.e., everything from the center + # element on. These are all repeated for the auto correlations. (As + # these are autocorrelations (rather than auto-covariance) matrices, + # they've been normalized by the variance and so the center element is + # always 1, and thus uninformative) + diag_repeated = torch.arange( + start=self.spatial_corr_width // 2, end=self.spatial_corr_width + ) + # masks for the reconstructed_lowpass and magnitude autocorrs, respectively + masks = [ + torch.zeros( + (self.spatial_corr_width, self.spatial_corr_width, self.n_scales + 1), + dtype=torch.bool, + ), + torch.zeros( + ( + self.spatial_corr_width, + self.spatial_corr_width, + self.n_orientations, + self.n_scales, + ), + dtype=torch.bool, + ), + ] + for mask in masks: + # Symmetry M_{i,j} = M_{n-i+1, n-j+1} + # Start with all False, then place True in necessary stats. + mask[tril_inds[0], tril_inds[1]] = True + # if spatial_corr_width is even, then the first row is not + # redundant with anything either + if np.mod(self.spatial_corr_width, 2) == 0: + mask[0] = True + mask[diag_repeated, diag_repeated] = False + return masks def _create_scales_shape_dict(self) -> OrderedDict: """Create dictionary defining scales and shape of each stat. @@ -181,7 +878,10 @@ def _create_scales_shape_dict(self) -> OrderedDict: """ shape_dict = OrderedDict() # There are 6 pixel statistics - shape_dict["pixel_statistics"] = np.array(6 * ["pixel_statistics"]) + if isinstance(self._stats_computer, _StatsComputer): + shape_dict["pixel_statistics"] = np.array(6 * ["pixel_statistics"]) + elif isinstance(self._stats_computer, _WeightedComputer): + shape_dict["pixel_statistics"] = np.array(4 * ["pixel_statistics"]) # These are the basic building blocks of the scale assignments for many # of the statistics calculated by the PortillaSimoncelli model. @@ -202,18 +902,13 @@ def _create_scales_shape_dict(self) -> OrderedDict: # corresponding scale. auto_corr_mag = np.ones( - ( - self.spatial_corr_width, - self.spatial_corr_width, - self.n_orientations, - self.n_scales, - ), + (self._n_autocorrs, self.n_orientations, self.n_scales), dtype=int, ) # this rearrange call is turning scales from 1d with shape (n_scales, ) - # to 4d with shape (1, 1, n_scales, 1), so that it matches + # to 3d with shape (1, 1, n_scales), so that it matches # auto_corr_mag. the following rearrange calls do similar. - auto_corr_mag *= einops.rearrange(scales, "s -> 1 1 1 s") + auto_corr_mag *= einops.rearrange(scales, "s -> 1 1 s") shape_dict["auto_correlation_magnitude"] = auto_corr_mag shape_dict["skew_reconstructed"] = scales_with_lowpass @@ -222,13 +917,12 @@ def _create_scales_shape_dict(self) -> OrderedDict: auto_corr = np.ones( ( - self.spatial_corr_width, - self.spatial_corr_width, + self._n_autocorrs, self.n_scales + 1, ), dtype=object, ) - auto_corr *= einops.rearrange(scales_with_lowpass, "s -> 1 1 s") + auto_corr *= einops.rearrange(scales_with_lowpass, "s -> 1 s") shape_dict["auto_correlation_reconstructed"] = auto_corr shape_dict["std_reconstructed"] = scales_with_lowpass @@ -292,37 +986,13 @@ def _create_necessary_stats_dict( """ mask_dict = scales_shape_dict.copy() - # Pre-compute some necessary indices. - # Lower triangular indices (including diagonal), for auto correlations - tril_inds = torch.tril_indices(self.spatial_corr_width, self.spatial_corr_width) - # Get the second half of the diagonal, i.e., everything from the center - # element on. These are all repeated for the auto correlations. (As - # these are autocorrelations (rather than auto-covariance) matrices, - # they've been normalized by the variance and so the center element is - # always 1, and thus uninformative) - diag_repeated = torch.arange( - start=self.spatial_corr_width // 2, end=self.spatial_corr_width - ) # Upper triangle indices, including diagonal. These are redundant stats # for cross_orientation_correlation_magnitude (because we've normalized # this matrix to be true cross-correlations, the diagonals are all 1, # like for the auto-correlations) triu_inds = torch.triu_indices(self.n_orientations, self.n_orientations) for k, v in mask_dict.items(): - if k in [ - "auto_correlation_magnitude", - "auto_correlation_reconstructed", - ]: - # Symmetry M_{i,j} = M_{n-i+1, n-j+1} - # Start with all False, then place True in necessary stats. - mask = torch.zeros(v.shape, dtype=torch.bool) - mask[tril_inds[0], tril_inds[1]] = True - # if spatial_corr_width is even, then the first row is not - # redundant with anything either - if np.mod(self.spatial_corr_width, 2) == 0: - mask[0] = True - mask[diag_repeated, diag_repeated] = False - elif k == "cross_orientation_correlation_magnitude": + if k == "cross_orientation_correlation_magnitude": # Symmetry M_{i,j} = M_{j,i}. # Start with all True, then place False in redundant stats. mask = torch.ones(v.shape, dtype=torch.bool) @@ -398,13 +1068,12 @@ def forward(self, image: Tensor, scales: list[SCALES_TYPE] | None = None) -> Ten # Calculate pixel statistics (mean, variance, skew, kurtosis, min, # max). - pixel_stats = self._compute_pixel_stats(image) + pixel_stats = self._stats_computer.compute_pixel_stats(image) # Compute the central autocorrelation of the coefficient magnitudes. This is a # tensor of shape: (batch, channel, spatial_corr_width, spatial_corr_width, - # n_orientations, n_scales). var_mags is a tensor of shape (batch, channel, - # n_orientations, n_scales) - autocorr_mags, mags_var = self._compute_autocorr(mag_pyr_coeffs) + # n_orientations, n_scales). + autocorr_mags, mags_var = self._stats_computer.compute_autocorr(mag_pyr_coeffs) # mags_var is the variance of the magnitude coefficients at each scale (it's an # intermediary of the computation of the auto-correlations). We take the square # root to get the standard deviation. @@ -415,20 +1084,22 @@ def forward(self, image: Tensor, scales: list[SCALES_TYPE] | None = None) -> Ten # tensor of shape (batch, channel, spatial_corr_width, # spatial_corr_width, n_scales+1), and var_recon is a tensor of shape # (batch, channel, n_scales+1) - autocorr_recon, var_recon = self._compute_autocorr(reconstructed_images) + autocorr_recon, var_recon = self._stats_computer.compute_autocorr( + reconstructed_images + ) # Compute the standard deviation, skew, and kurtosis of each # reconstructed lowpass image. std_recon, skew_recon, and # kurtosis_recon will all end up as tensors of shape (batch, channel, # n_scales+1) std_recon = var_recon.sqrt() - skew_recon, kurtosis_recon = self._compute_skew_kurtosis_recon( + skew_recon, kurtosis_recon = self._stats_computer.compute_skew_kurtosis_recon( reconstructed_images, var_recon, pixel_stats[..., 1] ) # Compute the cross-orientation correlations between the magnitude # coefficients at each scale. this will be a tensor of shape (batch, # channel, n_orientations, n_orientations, n_scales) - cross_ori_corr_mags = self._compute_cross_correlation( + cross_ori_corr_mags = self._stats_computer.compute_cross_correlation( mag_pyr_coeffs, mag_pyr_coeffs, mags_var, mags_var ) @@ -445,19 +1116,22 @@ def forward(self, image: Tensor, scales: list[SCALES_TYPE] | None = None) -> Ten # coefficients at the next-coarsest scale. this will be a tensor of # shape (batch, channel, n_orientations, n_orientations, # n_scales-1) - cross_scale_corr_mags = self._compute_cross_correlation( + cross_scale_corr_mags = self._stats_computer.compute_cross_correlation( mag_pyr_coeffs[:-1], phase_doubled_mags, mags_var[..., :-1] ) # Compute the cross-scale correlations between the real # coefficients and the real and imaginary coefficients at the next # coarsest scale. this will be a tensor of shape (batch, channel, # n_orientations, 2*n_orientations, n_scales-1) - cross_scale_corr_real = self._compute_cross_correlation( + cross_scale_corr_real = self._stats_computer.compute_cross_correlation( real_pyr_coeffs[:-1], phase_doubled_sep ) - # Compute the variance of the highpass residual - var_highpass_residual = highpass.pow(2).mean(dim=(-2, -1)) + # Compute the variance of the highpass residual. the unsqueeze is to make sure + # that this is at least 3d, as required when we call einops.pack below + var_highpass_residual = self._stats_computer.weights[0]( + highpass.pow(2) + ).unsqueeze(-1) # Now, combine all these stats together, first into a list all_stats = [ @@ -474,7 +1148,7 @@ def forward(self, image: Tensor, scales: list[SCALES_TYPE] | None = None) -> Ten all_stats += [cross_scale_corr_mags, cross_scale_corr_real] all_stats += [var_highpass_residual] # And then pack them into a 3d tensor - representation_tensor, pack_info = einops.pack(all_stats, "b c *") + representation_tensor, pack_info = einops.pack(all_stats, "b c w *") # the only time when this is None is during testing, when we make sure # that our assumptions are all valid. @@ -550,8 +1224,13 @@ def convert_to_tensor(self, representation_dict: OrderedDict) -> Tensor: """ rep = einops.pack(list(representation_dict.values()), "b c *")[0] + if rep.shape[-1] == len(self._representation_scales): + mask = self._necessary_stats_mask + else: + mask = einops.pack(list(self._stats_shape_dict.values()), "*")[0] + mask = torch.where(mask)[0].to(rep.device) # then get rid of all the nans / unnecessary stats - return rep.index_select(-1, self._necessary_stats_mask) + return rep.index_select(-1, mask) def convert_to_dict(self, representation_tensor: Tensor) -> OrderedDict: """Convert tensor of statistics to a dictionary. @@ -587,13 +1266,13 @@ def convert_to_dict(self, representation_tensor: Tensor) -> OrderedDict: " convert_to_dict does not support such tensors." ) - rep = self._necessary_stats_dict.copy() + rep = self._stats_shape_dict.copy() n_filled = 0 for k, v in rep.items(): # each statistic is a tensor with batch and channel dimensions as # found in representation_tensor and all the other dimensions # determined by the values in necessary_stats_dict. - shape = (*representation_tensor.shape[:2], *v.shape) + shape = (*representation_tensor.shape[:3], *v.shape) new_v = torch.nan * torch.ones( shape, dtype=representation_tensor.dtype, @@ -657,40 +1336,6 @@ def _compute_pyr_coeffs( ] return pyr_coeffs, coeffs_list, highpass, lowpass - @staticmethod - def _compute_pixel_stats(image: Tensor) -> Tensor: - """Compute the pixel stats: first four moments, min, and max. - - Parameters - ---------- - image : - 4d tensor of shape (batch, channel, height, width) containing input - image. Stats are computed indepently for each batch and channel. - - Returns - ------- - pixel_stats : - 3d tensor of shape (batch, channel, 6) containing the mean, - variance, skew, kurtosis, minimum pixel value, and maximum pixel - value (in that order) - - """ - mean = torch.mean(image, dim=(-2, -1), keepdim=True) - # we use torch.var instead of plenoptic.tools.variance, because our - # variance is the uncorrected (or sample) variance and we want the - # corrected one here. - var = torch.var(image, dim=(-2, -1)) - skew = stats.skew(image, mean=mean, var=var, dim=[-2, -1]) - kurtosis = stats.kurtosis(image, mean=mean, var=var, dim=[-2, -1]) - # can't compute min/max over two dims simultaneously with - # torch.min/max, so use einops - img_min = einops.reduce(image, "b c h w -> b c", "min") - img_max = einops.reduce(image, "b c h w -> b c", "max") - # mean needed to be unflattened to be used by skew and kurtosis - # correctly, but we'll want it to be flattened like this in the final - # representation tensor - return einops.pack([mean, var, skew, kurtosis, img_min, img_max], "b c *")[0] - @staticmethod def _compute_intermediate_representations( pyr_coeffs: Tensor, @@ -776,169 +1421,6 @@ def _reconstruct_lowpass_at_each_scale( ] return reconstructed_images - def _compute_autocorr(self, coeffs_list: list[Tensor]) -> tuple[Tensor, Tensor]: - """Compute the autocorrelation of some statistics. - - Parameters - ---------- - coeffs_list : - List (of length s) of tensors of shape (batch, channel, *, height, - width), where * is zero or one additional dimensions. Intended use - case: magnitude_pyr_coeffs (which is list of length n_scales of 5d - tensors, with * containing n_orientations) or reconstructed_images - (which is a list of length n_scales+1 of 4d tensors) - - Returns - ------- - autocorrs : - Tensor of shape (batch, channel, spatial_corr_width, - spatial_corr_width, *, s) containing the autocorrelation (up to - distance ``spatial_corr_width//2``) of each element in - ``coeffs_list``, computed independently over all but the final two - dimensions. - vars : - 3d Tensor of shape (batch, channel, *, s) containing the variance - of each element in ``coeffs_list``, computed independently over all - but the final two dimensions. - - """ - if coeffs_list[0].ndim == 5: - dims = "o" - elif coeffs_list[0].ndim == 4: - dims = "" - else: - raise ValueError( - "coeffs_list must contain tensors of either 4 or 5 dimensions!" - ) - acs = [signal.autocorrelation(coeff) for coeff in coeffs_list] - var = [signal.center_crop(ac, 1) for ac in acs] - acs = [ac / v for ac, v in zip(acs, var)] - var = einops.rearrange(var, f"s b c {dims} 1 1 -> b c {dims} s") - acs = [signal.center_crop(ac, self.spatial_corr_width) for ac in acs] - acs = torch.stack(acs, 2) - return einops.rearrange(acs, f"b c s {dims} a1 a2 -> b c a1 a2 {dims} s"), var - - @staticmethod - def _compute_skew_kurtosis_recon( - reconstructed_images: list[Tensor], var_recon: Tensor, img_var: Tensor - ) -> tuple[Tensor, Tensor]: - """Compute the skew and kurtosis of each lowpass reconstructed image. - - For each scale, if the ratio of its variance to the original image's - pixel variance is below a threshold of - torch.finfo(img_var.dtype).resolution (1e-6 for float32, 1e-15 for - float64), skew and kurtosis are assigned default values of 0 or 3, - respectively. - - Parameters - ---------- - reconstructed_images : - List of length n_scales+1 containing the reconstructed unoriented - image at each scale, from fine to coarse. The final image is - reconstructed just from the residual lowpass image. - var_recon : - Tensor of shape (batch, channel, n_scales+1) containing the - variance of each tensor in reconstruced_images - img_var : - Tensor of shape (batch, channel) containing the pixel variance - (from pixel_stats tensor) - - Returns - ------- - skew_recon, kurtosis_recon : - Tensors of shape (batch, channel, n_scales+1) containing the skew - and kurtosis, respectively, of each tensor in - ``reconstructed_images``. - - """ - skew_recon = [ - stats.skew(im, mean=0, var=var_recon[..., i], dim=[-2, -1]) - for i, im in enumerate(reconstructed_images) - ] - skew_recon = torch.stack(skew_recon, -1) - kurtosis_recon = [ - stats.kurtosis(im, mean=0, var=var_recon[..., i], dim=[-2, -1]) - for i, im in enumerate(reconstructed_images) - ] - kurtosis_recon = torch.stack(kurtosis_recon, -1) - skew_default = torch.zeros_like(skew_recon) - kurtosis_default = 3 * torch.ones_like(kurtosis_recon) - # if this variance ratio is too small, then use the default values - # instead. unsqueeze is used here because var_recon is shape (batch, - # channel, scales+1), whereas img_var is just (batch, channel) - res = torch.finfo(img_var.dtype).resolution - unstable_locs = var_recon / img_var.unsqueeze(-1) < res - skew_recon = torch.where(unstable_locs, skew_default, skew_recon) - kurtosis_recon = torch.where(unstable_locs, kurtosis_default, kurtosis_recon) - return skew_recon, kurtosis_recon - - def _compute_cross_correlation( - self, - coeffs_tensor: list[Tensor], - coeffs_tensor_other: list[Tensor], - coeffs_var: None | Tensor = None, - coeffs_other_var: None | Tensor = None, - ) -> Tensor: - """Compute cross-correlations. - - Parameters - ---------- - coeffs_tensor, coeffs_tensor_other : - The two lists of length scales, each containing 5d tensors of shape - (batch, channel, n_orientations, height, width) to be correlated. - coeffs_var, coeffs_other_var : - Two optional tensors containing the variances of coeffs_tensor and - coeffs_tensor_other, respectively, in case they've already been computed. - Should be of shape (batch, channel, n_orientations, n_scales). Used to - normalize the covariances into cross-correlations. - - Returns - ------- - cross_corrs : - Tensor of shape (batch, channel, n_orientations, n_orientations, - scales) containing the cross-correlations at each - scale. - - """ - covars = [] - for i, (coeff, coeff_other) in enumerate( - zip(coeffs_tensor, coeffs_tensor_other) - ): - # precompute this, which we'll use for normalization - numel = torch.mul(*coeff.shape[-2:]) - # compute the covariance - covar = einops.einsum( - coeff, coeff_other, "b c o1 h w, b c o2 h w -> b c o1 o2" - ) - covar = covar / numel - # Then normalize it to get the Pearson product-moment correlation - # coefficient, see - # https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html. - if coeffs_var is None: - # First, compute the variances of each coeff - coeff_var = einops.einsum( - coeff, coeff, "b c o1 h w, b c o1 h w -> b c o1" - ) - coeff_var = coeff_var / numel - else: - coeff_var = coeffs_var[..., i] - if coeffs_other_var is None: - # First, compute the variances of each coeff - coeff_other_var = einops.einsum( - coeff_other, coeff_other, "b c o1 h w, b c o1 h w -> b c o1" - ) - coeff_other_var = coeff_other_var / numel - else: - coeff_other_var = coeffs_other_var[..., i] - # Then compute the outer product of those variances. - var_outer_prod = einops.einsum( - coeff_var, coeff_other_var, "b c o1, b c o2 -> b c o1 o2" - ) - # And the sqrt of this is what we use to normalize the covariance - # into the cross-correlation - covars.append(covar / var_outer_prod.sqrt()) - return torch.stack(covars, -1) - @staticmethod def _double_phase_pyr_coeffs( pyr_coeffs: list[Tensor], @@ -991,8 +1473,8 @@ def plot_representation( self, data: Tensor, ax: plt.Axes | None = None, - figsize: tuple[float, float] = (15, 15), - ylim: tuple[float, float] | Literal[False] | None = None, + figsize: tuple[float, float] = (15, 5), + ylim: tuple[float, float] | Literal[False] | None = False, batch_idx: int = 0, title: str | None = None, ) -> tuple[plt.Figure, list[plt.Axes]]: @@ -1119,24 +1601,20 @@ def _representation_for_plotting(self, rep: OrderedDict) -> OrderedDict: Intended as a helper function for plot_representation. """ - if rep["skew_reconstructed"].ndim > 1: - raise ValueError( - "Currently, only know how to plot single batch and channel at" - " a time! Select and/or average over those dimensions" - ) data = OrderedDict() data["pixels+var_highpass"] = torch.cat( - [rep.pop("pixel_statistics"), rep.pop("var_highpass_residual")] + [rep.pop("pixel_statistics"), rep.pop("var_highpass_residual")], -1 ) data["std+skew+kurtosis recon"] = torch.cat( ( rep.pop("std_reconstructed"), rep.pop("skew_reconstructed"), rep.pop("kurtosis_reconstructed"), - ) + ), + -1, ) - data["magnitude_std"] = rep.pop("magnitude_std") + data["magnitude_std"] = rep.pop("magnitude_std").flatten(1) # want to plot these in a specific order all_keys = [ @@ -1154,7 +1632,7 @@ def _representation_for_plotting(self, rep: OrderedDict) -> OrderedDict: continue # we compute L2 norm manually, since there are NaNs (marking # redundant stats) - data[k] = rep[k].pow(2).nansum((0, 1)).sqrt().flatten() + data[k] = rep[k].pow(2).nansum((1, 2)).sqrt().flatten(1) return data @@ -1217,11 +1695,7 @@ def update_plot( rep = {k: v[0, 0] for k, v in self.convert_to_dict(data).items()} rep = self._representation_for_plotting(rep) for ax, d in zip(axes, rep.values()): - if isinstance(d, dict): - vals = np.array([dd.detach() for dd in d.values()]) - else: - vals = d.flatten().detach().numpy() - + vals = to_numpy(d.flatten()) sc = update_stem(ax.containers[0], vals) stem_artists.extend([sc.markerline, sc.stemlines]) return stem_artists diff --git a/src/plenoptic/simulate/models/portilla_simoncelli_masked.py b/src/plenoptic/simulate/models/portilla_simoncelli_masked.py new file mode 100644 index 00000000..041ae27e --- /dev/null +++ b/src/plenoptic/simulate/models/portilla_simoncelli_masked.py @@ -0,0 +1,1469 @@ +"""Portilla-Simoncelli texture statistics. + +The Portilla-Simoncelli (PS) texture statistics are a set of image +statistics, first described in [1]_, that are proposed as a sufficient set +of measurements for describing visual textures. That is, if two texture +images have the same values for all PS texture stats, humans should +consider them as members of the same family of textures. +""" + +from collections import OrderedDict +from typing import Literal + +import einops +import matplotlib as mpl +import matplotlib.pyplot as plt +import numpy as np +import torch +import torch.fft +import torch.nn as nn +from torch import Tensor + +from ...tools import signal +from ...tools.conv import blur_downsample +from ...tools.data import to_numpy +from ...tools.display import clean_stem_plot, clean_up_axes, update_stem +from ...tools.validate import validate_input +from ..canonical_computations.steerable_pyramid_freq import ( + SCALES_TYPE as PYR_SCALES_TYPE, +) +from ..canonical_computations.steerable_pyramid_freq import SteerablePyramidFreq + +SCALES_TYPE = Literal["pixel_statistics"] | PYR_SCALES_TYPE + + +class PortillaSimoncelliMasked(nn.Module): + r"""Portila-Simoncelli texture statistics. + + The Portilla-Simoncelli (PS) texture statistics are a set of image + statistics, first described in [1]_, that are proposed as a sufficient set + of measurements for describing visual textures. That is, if two texture + images have the same values for all PS texture stats, humans should + consider them as members of the same family of textures. + + The PS stats are computed based on the steerable pyramid [2]_. They consist + of the local auto-correlations, cross-scale (within-orientation) + correlations, and cross-orientation (within-scale) correlations of both the + pyramid coefficients and the local energy (as computed by those + coefficients). Additionally, they include the first four global moments + (mean, variance, skew, and kurtosis) of the image and down-sampled versions + of that image. See the paper and notebook for more description. + + Parameters + ---------- + image_shape: + Shape of input image. + mask: + List of 3d tensors. We use the masks to perform sums, and so the masks should be + normalized in order to perform the averages. See tutorial for example. + n_scales: + The number of pyramid scales used to measure the statistics (default=4) + n_orientations: + The number of orientations used to measure the statistics (default=4) + spatial_corr_width: + The width of the spatial cross- and auto-correlation statistics + + Attributes + ---------- + scales: list + The names of the unique scales of coefficients in the pyramid, used for + coarse-to-fine metamer synthesis. + + References + ---------- + .. [1] J Portilla and E P Simoncelli. A Parametric Texture Model based on + Joint Statistics of Complex Wavelet Coefficients. Int'l Journal of + Computer Vision. 40(1):49-71, October, 2000. + https://www.cns.nyu.edu/~eero/ABSTRACTS/portilla99-abstract.html + https://www.cns.nyu.edu/~lcv/texture/ + .. [2] E P Simoncelli and W T Freeman, "The Steerable Pyramid: A Flexible + Architecture for Multi-Scale Derivative Computation," Second Int'l Conf + on Image Processing, Washington, DC, Oct 1995. + + """ + + def __init__( + self, + image_shape: tuple[int, int], + mask: list[Tensor], + n_scales: int = 4, + n_orientations: int = 4, + spatial_corr_width: int = 9, + ): + super().__init__() + + self.image_shape = image_shape + if any([(image_shape[-1] / 2**i) % 2 for i in range(n_scales)]) or any( + [(image_shape[-2] / 2**i) % 2 for i in range(n_scales)] + ): + raise ValueError( + "Because of how the Portilla-Simoncelli model handles " + "multiscale representations, it only works with images" + " whose shape can be divided by 2 `n_scales` times." + ) + if any([m.ndim != 3 for m in mask]): + raise ValueError("All masks must be 3d!") + if any([m.shape[-2:] != image_shape for m in mask]): + raise ValueError( + "Last two dimensions of mask must be height and width" + " and must match image_shape!" + ) + if any([m.min() < 0 for m in mask]): + raise ValueError("All masks must be non-negative!") + # we need to downsample the masks for each scale, plus one additional scale for + # the reconstructed lowpass image + for i in range(n_scales + 1): + if i == 0: + scale_mask = mask + else: + # multiply by the factor of four in order to keep the sum + # approximately equal across scales. + scale_mask = [ + 4 ** (i / len(mask)) + * blur_downsample(m.unsqueeze(0), i, scale_filter=True).squeeze(0) + for m in mask + ] + for j, m in enumerate(scale_mask): + # it's possible negative values will get introduced by the downsampling + # above, in which case we remove them, since they mess up our + # computations. in particular, they could result in negative variance + # values. + self.register_buffer(f"_mask_{j}_scale_{i}", m.clip(min=0)) + # these indices are used to create the einsum expressions + self._mask_input_idx = ", ".join([f"m{i} h w" for i in range(len(mask))]) + self._n_masks = len(mask) + self._mask_output_idx = f"{' '.join([f'm{i}' for i in range(len(mask))])}" + self.spatial_corr_width = spatial_corr_width + self.n_scales = n_scales + self.n_orientations = n_orientations + # these are each lists of tensors of shape (batch, channel, n_autocorrs, height, + # width), one per scale, where n_autocorrs is approximately + # spatial_corr_width^2 / 2 + rolls_h, rolls_w = self._create_autocorr_idx(spatial_corr_width, image_shape) + for i, (h, w) in enumerate(zip(rolls_h, rolls_w)): + self.register_buffer(f"_autocorr_rolls_h_scale_{i}", h) + self.register_buffer(f"_autocorr_rolls_w_scale_{i}", w) + self._n_autocorrs = rolls_h[0].shape[3] + self._pyr = SteerablePyramidFreq( + self.image_shape, + height=self.n_scales, + order=self.n_orientations - 1, + is_complex=True, + tight_frame=False, + ) + + self.scales = ( + ["pixel_statistics", "residual_lowpass"] + + [ii for ii in range(n_scales - 1, -1, -1)] + + ["residual_highpass"] + ) + + # Dictionary defining shape of the statistics and which scale they're + # associated with + scales_shape_dict = self._create_scales_shape_dict() + + # Dictionary defining necessary statistics, that is, those that are not + # redundant + self._necessary_stats_dict = self._create_necessary_stats_dict( + scales_shape_dict + ) + # turn this into tensor we can use in forward pass. first into a + # boolean mask... + _necessary_stats_mask = einops.pack( + list(self._necessary_stats_dict.values()), "*" + )[0] + # then into a tensor of indices + _necessary_stats_mask = torch.where(_necessary_stats_mask)[0] + self.register_buffer("_necessary_stats_mask", _necessary_stats_mask) + + # This array is composed of the following values: 'pixel_statistics', + # 'residual_lowpass', 'residual_highpass' and integer values from 0 to + # self.n_scales-1. It is the same size as the representation tensor + # returned by this object's forward method. It must be a numpy array so + # we can have a mixture of ints and strs (and so we can use np.in1d + # later) + self._representation_scales = einops.pack( + list(scales_shape_dict.values()), "*" + )[0] + # just select the scales of the necessary stats. + self._representation_scales = self._representation_scales[ + self._necessary_stats_mask + ] + # There are two types of computations where we add a little epsilon to help with + # stability: + # - division of one statistic by another + # - taking the sqrt of one statistic + # In both cases, adding a small epsilon avoids a NaN or (for division) an + # unreasonably large number. + self._stability_epsilon = 1e-6 + # this (much larger) epsilon is used when computing the pixel stats skew and + # kurtosis, which involve a division by the variance to the 1.5 and 2 powers, + # respectively, and have much greater problems with very small values. + self._pixel_epsilon = 1e-1 + + @property + def mask(self): + # inspired by + # https://discuss.pytorch.org/t/why-no-nn-bufferlist-like-function-for-registered-buffer-tensor/18884/10 + return [ + [getattr(self, f"_mask_{j}_scale_{i}") for j in range(self._n_masks)] + for i in range(self.n_scales + 1) + ] + + @property + def _autocorr_rolls_h(self): + # inspired by + # https://discuss.pytorch.org/t/why-no-nn-bufferlist-like-function-for-registered-buffer-tensor/18884/10 + return [ + getattr(self, f"_autocorr_rolls_h_scale_{i}") + for i in range(self.n_scales + 1) + ] + + @property + def _autocorr_rolls_w(self): + # inspired by + # https://discuss.pytorch.org/t/why-no-nn-bufferlist-like-function-for-registered-buffer-tensor/18884/10 + return [ + getattr(self, f"_autocorr_rolls_w_scale_{i}") + for i in range(self.n_scales + 1) + ] + + def _create_autocorr_idx( + self, spatial_corr_width, image_shape + ) -> tuple[list[Tensor], list[Tensor]]: + """Create indices used to shift images when computing autocorrelation. + + The autocorrelation of ``img`` is the product of ``img`` with itself shifted by + a small number of pixels. That is: ``einops.einsum(img, img.roll(i, -1).roll(j, + -2))`` for some relevant values of i and j. This method computes the indices + corresponding to those rolls, so that we can simply call ``img.gather(rolls_h, + -2).gather(rolls_w, -1)`` during the forward pass instead of ``img.roll(i, + -1).roll(j, -2)``, which is less efficient. + + Because of the symmetry of autocorrelations (see Portilla-Simoncelli notebook + for details), we do not need the full ``spatial_corr_width**2`` shifts, we only + need everything below the diagonal (e.g., we don't need to roll both 1 pixel to + the left and 1 pixel to the right). + + Parameters + ---------- + spatial_corr_width : + The width of the spatial auto-correlation. + image_shape : + Shape of input image. + + Returns + ------- + rolls_h, rolls_w : + List of tensors of shape ``(1, 1, n_orientations, n_autocorrs, height, + width)`` giving the shifts along the height (``shape[-2]``) and width + (``shape[-1]``) dimensions required for computing the autocorrelations. Each + entry in the list corresponds to a different scale, and thus height and + width decrease. + + """ + # because of the symmetry of autocorrelation, in order to generate all + # autocorrelations, we only need the lower triangle (so that we take the + # autocorrelation between the image and itself shifted 1 pixel to the left, but + # not also shifted 1 pixel to the right)... + half_width = (spatial_corr_width - 1) // 2 + autocorr_shift_vals = [ + i - half_width for i in np.tril_indices(spatial_corr_width) + ] + # if spatial_corr_width is even, then we also need these shifts: + if np.mod(spatial_corr_width, 2) == 0: + autocorr_shift_vals[0] = np.concatenate( + [ + np.zeros(spatial_corr_width, dtype=int) - half_width, + autocorr_shift_vals[0], + ], + 0, + ) + autocorr_shift_vals[1] = np.concatenate( + [ + np.arange(spatial_corr_width, dtype=int) - half_width, + autocorr_shift_vals[1], + ], + 0, + ) + # and up to the central element on the diagonal. + idx = [i != j or i < 0 for i, j in zip(*autocorr_shift_vals)] + # put the (0, 0) shift, which corresponds to the variance, at the very end, so + # we know where it is + autocorr_shift_vals = [ + np.concatenate([i[idx], np.zeros(1, dtype=int)], 0) + for i in autocorr_shift_vals + ] + + img_h, img_w = image_shape + rolls_h, rolls_w = [], [] + # need one additional scale, since we compute the autocorrelation of the + # reconstructed lowpass images as well + for _ in range(self.n_scales + 1): + arange_h = ( + torch.arange(img_h) + .view((1, 1, 1, img_h, 1)) + .repeat((1, 1, self.n_orientations, 1, img_h)) + ) + arange_w = ( + torch.arange(img_w) + .view((1, 1, 1, 1, img_w)) + .repeat((1, 1, self.n_orientations, img_w, 1)) + ) + rolls_h.append( + torch.stack([arange_h.roll(i, -2) for i in autocorr_shift_vals[0]], 3) + ) + rolls_w.append( + torch.stack([arange_w.roll(i, -1) for i in autocorr_shift_vals[1]], 3) + ) + img_h = int(img_h // 2) + img_w = int(img_w // 2) + return rolls_h, rolls_w + + def _create_scales_shape_dict(self) -> OrderedDict: + """Create dictionary defining scales and shape of each stat. + + This dictionary functions as metadata which is used for two main + purposes: + + - Scale assignment. In order for optimization to work well, we proceed + in a "coarse-to-fine" manner. That is, we start optimization by only + considering the statistics related to the lowest frequencies, and + gradually add in those related to higher and higher frequencies. This + is similar to blurring the objective function and then gradually + adding in finer and finer details. The numbers in this dictionary map + the computed statistics to their corresponding scales, which we use + in remove_scales to throw away some stats as needed. + + - Redundant stat identification. As described at the bottom of the + notebook, the model incidentally computes a whole bunch of redundant + stats, because auto- and cross-correlation matrices have certain + symmetries. the _create_necessary_stats_dict method accepts the + dictionary created here as input and uses the values to get the + shapes of these and insert True/False as necessary. + + Returns + ------- + scales_shape_dict + Dictionary defining shape and associated scales of each computed + statistic. The keys name each statistic, with dummy arrays as + values. These arrays have the same shape as the stat (excluding + batch and channel), with values defining which scale they correspond + to. + + """ + shape_dict = OrderedDict() + # There are 6 pixel statistics + shape_dict["pixel_statistics"] = np.array(4 * ["pixel_statistics"]) + + # These are the basic building blocks of the scale assignments for many + # of the statistics calculated by the PortillaSimoncelli model. + scales = np.arange(self.n_scales) + # the cross-scale correlations exclude the coarsest scale + scales_without_coarsest = np.arange(self.n_scales - 1) + # the statistics computed on the reconstructed bandpass images have an + # extra scale corresponding to the lowpass residual + scales_with_lowpass = np.array( + scales.tolist() + ["residual_lowpass"], dtype=object + ) + + # now we go through each statistic in order and create a dummy array + # full of 1s with the same shape as the actual statistic (excluding the + # batch and channel dimensions, as each stat is computed independently + # across those dimensions). We then multiply it by one of the scales + # arrays above to turn those 1s into values describing the + # corresponding scale. + + auto_corr_mag = np.ones( + (self._n_autocorrs - 1, self.n_orientations, self.n_scales), dtype=int + ) + # this rearrange call is turning scales from 1d with shape (n_scales, ) + # to 4d with shape (1, 1, n_scales, 1), so that it matches + # auto_corr_mag. the following rearrange calls do similar. + auto_corr_mag *= einops.rearrange(scales, "s -> 1 1 s") + shape_dict["auto_correlation_magnitude"] = auto_corr_mag + + shape_dict["skew_reconstructed"] = scales_with_lowpass + + shape_dict["kurtosis_reconstructed"] = scales_with_lowpass + + auto_corr = np.ones((self._n_autocorrs - 1, self.n_scales + 1), dtype=object) + auto_corr *= einops.rearrange(scales_with_lowpass, "s -> 1 s") + shape_dict["auto_correlation_reconstructed"] = auto_corr + + shape_dict["std_reconstructed"] = scales_with_lowpass + + cross_orientation_corr_mag = np.ones( + (self.n_orientations, self.n_orientations, self.n_scales), dtype=int + ) + cross_orientation_corr_mag *= einops.rearrange(scales, "s -> 1 1 s") + shape_dict["cross_orientation_correlation_magnitude"] = ( + cross_orientation_corr_mag + ) + + mags_std = np.ones((self.n_orientations, self.n_scales), dtype=int) + mags_std *= einops.rearrange(scales, "s -> 1 s") + shape_dict["magnitude_std"] = mags_std + + cross_scale_corr_mag = np.ones( + (self.n_orientations, self.n_orientations, self.n_scales - 1), dtype=int + ) + cross_scale_corr_mag *= einops.rearrange(scales_without_coarsest, "s -> 1 1 s") + shape_dict["cross_scale_correlation_magnitude"] = cross_scale_corr_mag + + cross_scale_corr_real = np.ones( + (self.n_orientations, 2 * self.n_orientations, self.n_scales - 1), dtype=int + ) + cross_scale_corr_real *= einops.rearrange(scales_without_coarsest, "s -> 1 1 s") + shape_dict["cross_scale_correlation_real"] = cross_scale_corr_real + + shape_dict["var_highpass_residual"] = np.array(["residual_highpass"]) + + return shape_dict + + def _create_necessary_stats_dict( + self, scales_shape_dict: OrderedDict + ) -> OrderedDict: + """Create mask specifying the necessary statistics. + + Some of the statistics computed by the model are redundant, due to + symmetries. For example, about half of the values in the + autocorrelation matrices are duplicates. See the Portilla-Simoncelli + notebook for more details. + + Parameters + ---------- + scales_shape_dict + Dictionary defining shape and associated scales of each computed + statistic. + + Returns + ------- + necessary_stats_dict + Dictionary defining which statistics are necessary (i.e., not + redundant). Will have the same keys as scales_shape_dict, with the + values being boolean tensors of the same shape as + scales_shape_dict's corresponding values. True denotes the + statistics that will be included in the model's output, while False + denotes the redundant ones we will toss. + + """ + mask_dict = scales_shape_dict.copy() + # Upper triangle indices, including diagonal. These are redundant stats + # for cross_orientation_correlation_magnitude (because we've normalized + # this matrix to be true cross-correlations, the diagonals are all 1, + # like for the auto-correlations) + triu_inds = torch.triu_indices(self.n_orientations, self.n_orientations) + for k, v in mask_dict.items(): + if k == "cross_orientation_correlation_magnitude": + # Symmetry M_{i,j} = M_{j,i}. + # Start with all True, then place False in redundant stats. + mask = torch.ones(v.shape, dtype=torch.bool) + mask[triu_inds[0], triu_inds[1]] = False + else: + # all of the other stats have no redundancies + mask = torch.ones(v.shape, dtype=torch.bool) + mask_dict[k] = mask + return mask_dict + + def forward(self, image: Tensor, scales: list[SCALES_TYPE] | None = None) -> Tensor: + r"""Generate Texture Statistics representation of an image. + + Note that separate batches and channels are analyzed in parallel. + + Parameters + ---------- + image : + A 4d tensor (batch, channel, height, width) containing the image(s) to + analyze. + scales : + Which scales to include in the returned representation. If None, we + include all scales. Otherwise, can contain subset of values present + in this model's ``scales`` attribute, and the returned tensor will + then contain the subset corresponding to those scales. + + Returns + ------- + representation_tensor: + 3d tensor of shape (batch, channel, stats) containing the measured + texture statistics. + + Raises + ------ + ValueError : + If `image` is not 4d or has a dtype other than float or complex. + + """ + validate_input(image) + + # pyr_dict is the dictionary of complex-valued tensors returned by the + # steerable pyramid. pyr_coeffs is a list (length n_scales) of 5d + # tensors, each of shape (batch, channel, scales, n_orientations, + # height, width) containing the complex-valued oriented bands, while + # highpass is a real-valued 4d tensor of shape (batch, channel, height, + # width). Note that the residual lowpass in pyr_dict has been demeaned. + # We keep both the dict and list of pyramid coefficients because we + # need the dictionary for reconstructing the image done later on. + pyr_dict, pyr_coeffs, highpass, _ = self._compute_pyr_coeffs(image) + + # Now, we create several intermediate representations that we'll use to + # compute the texture statistics later. + + # First, two intermediate dictionaries: magnitude_pyr_coeffs and + # real_pyr_coeffs, which contain the demeaned magnitude of the pyramid + # coefficients and the real part of the pyramid coefficients + # respectively. + mag_pyr_coeffs, real_pyr_coeffs = self._compute_intermediate_representations( + pyr_coeffs + ) + + # Then, the reconstructed lowpass image at each scale. (this is a list + # of length n_scales+1 containing tensors of shape (batch, channel, + # height, width)) + reconstructed_images = self._reconstruct_lowpass_at_each_scale(pyr_dict) + # the reconstructed_images list goes from coarse-to-fine, but we want + # each of the stats computed from it to go from fine-to-coarse, so we + # reverse its direction. + reconstructed_images = reconstructed_images[::-1] + + # Now, start calculating the PS texture stats. + + # Calculate pixel statistics (mean, variance, skew, kurtosis). This is a tensor + # of shape (batch, channel, masks, 4) + pixel_stats = self._compute_pixel_stats(self.mask[0], image) + + # Compute the central autocorrelation of the coefficient magnitudes. This is a + # tensor of shape: (batch, channel, masks, n_autocorrs, n_orientations, + # n_scales). + autocorr_mags, mags_var = self._compute_autocorr(self.mask, mag_pyr_coeffs) + # mags_var is the variance of the magnitude coefficients at each scale (it's an + # intermediary of the computation of the auto-correlations). We take the square + # root to get the standard deviation. After this, mags_std will have shape + # (batch, channel, masks, n_orientations, n_scale) + mags_std = einops.rearrange( + (mags_var + self._stability_epsilon).sqrt(), + f"b c {self._mask_output_idx} o s -> b c ({self._mask_output_idx}) o s", + ) + + # Compute the central autocorrelation of the reconstructed lowpass images at + # each scale (and their variances). autocorr_recon is a tensor of shape (batch, + # channel, masks, n_autocorrs, n_scales+1), and var_recon is a tensor of shape + # (batch, channel, masks, n_scales+1) + autocorr_recon, var_recon = self._compute_autocorr( + self.mask, reconstructed_images + ) + # Compute the standard deviation, skew, and kurtosis of each reconstructed + # lowpass image. std_recon, skew_recon, and kurtosis_recon will all end up as + # tensors of shape (batch, channel, masks, n_scales+1) + std_recon = einops.rearrange( + (var_recon + self._stability_epsilon).sqrt(), + f"b c {self._mask_output_idx} s -> b c ({self._mask_output_idx}) s", + ) + skew_recon, kurtosis_recon = self._compute_skew_kurtosis_recon( + self.mask, reconstructed_images, var_recon + ) + + # Compute the cross-orientation correlations between the magnitude + # coefficients at each scale. this will be a tensor of shape (batch, + # channel, n_orientations, n_orientations, n_scales) + cross_ori_corr_mags = self._compute_cross_correlation( + self.mask, mag_pyr_coeffs, mag_pyr_coeffs, mags_var, mags_var + ) + + # If we have more than one scale, compute the cross-scale correlations + if self.n_scales != 1: + # First, double the phase the coefficients, so we can correctly + # compute correlations across scales. + phase_doubled_mags, phase_doubled_sep = self._double_phase_pyr_coeffs( + pyr_coeffs + ) + # Compute the cross-scale correlations between the magnitude + # coefficients. For each coefficient, we're correlating it with the + # coefficients at the next-coarsest scale. this will be a tensor of + # shape (batch, channel, n_orientations, n_orientations, + # n_scales-1) + cross_scale_corr_mags = self._compute_cross_correlation( + self.mask, mag_pyr_coeffs[:-1], phase_doubled_mags, mags_var[..., :-1] + ) + # Compute the cross-scale correlations between the real + # coefficients and the real and imaginary coefficients at the next + # coarsest scale. this will be a tensor of shape (batch, channel, + # n_orientations, 2*n_orientations, n_scales-1) + cross_scale_corr_real = self._compute_cross_correlation( + self.mask, real_pyr_coeffs[:-1], phase_doubled_sep + ) + + # Compute the variance of the highpass residual + var_highpass_residual = einops.einsum( + *self.mask[0], + highpass.pow(2), + f"{self._mask_input_idx}, b c h w -> b c {self._mask_output_idx}", + ) + var_highpass_residual = einops.rearrange( + var_highpass_residual, + f"b c {self._mask_output_idx} -> b c ({self._mask_output_idx})", + ) + + # Now, combine all these stats together, first into a list + all_stats = [ + pixel_stats, + autocorr_mags, + skew_recon, + kurtosis_recon, + autocorr_recon, + std_recon, + cross_ori_corr_mags, + mags_std, + ] + if self.n_scales != 1: + all_stats += [cross_scale_corr_mags, cross_scale_corr_real] + all_stats += [var_highpass_residual] + # And then pack them into a 3d tensor + representation_tensor, pack_info = einops.pack(all_stats, "b c m *") + + # the only time when this is None is during testing, when we make sure + # that our assumptions are all valid. + if self._necessary_stats_mask is None: + # store this so we can unpack this info (only possible when we've + # discarded no stats) + self._pack_info = pack_info + else: + # Throw away all redundant statistics + representation_tensor = representation_tensor.index_select( + -1, self._necessary_stats_mask + ) + + # Return the subset of stats corresponding to the specified scale. + if scales is not None: + representation_tensor = self.remove_scales(representation_tensor, scales) + + return representation_tensor + + def remove_scales( + self, representation_tensor: Tensor, scales_to_keep: list[SCALES_TYPE] + ) -> Tensor: + """Remove statistics not associated with scales. + + For a given representation_tensor and a list of scales_to_keep, this + attribute removes all statistics *not* associated with those scales. + + Note that calling this method will always remove statistics. + + Parameters + ---------- + representation_tensor: + 3d tensor containing the measured representation statistics. + scales_to_keep: + Which scales to include in the returned representation. Can contain + subset of values present in this model's ``scales`` attribute, and + the returned tensor will then contain the subset of the full + representation corresponding to those scales. + + Returns + ------- + limited_representation_tensor : + Representation tensor with some statistics removed. + + """ + # this is necessary because object is the dtype of + # self._representation_scales + scales_to_keep = np.array(scales_to_keep, dtype=object) + # np.in1d returns a 1d boolean array of the same shape as + # self._representation_scales with True at each location where that + # value appears in scales_to_keep. where then converts this boolean + # array into indices + ind = np.where(np.in1d(self._representation_scales, scales_to_keep))[0] + ind = torch.from_numpy(ind).to(representation_tensor.device) + return representation_tensor.index_select(-1, ind) + + def convert_to_tensor(self, representation_dict: OrderedDict) -> Tensor: + r"""Convert dictionary of statistics to a tensor. + + Parameters + ---------- + representation_dict : + Dictionary of representation. + + Returns + ------- + 3d tensor of statistics. + + See Also + -------- + convert_to_dict: + Convert tensor representation to dictionary. + + """ + rep = einops.pack(list(representation_dict.values()), "b c *")[0] + # then get rid of all the nans / unnecessary stats + return rep.index_select(-1, self._necessary_stats_mask) + + def convert_to_dict(self, representation_tensor: Tensor) -> OrderedDict: + """Convert tensor of statistics to a dictionary. + + While the tensor representation is required by plenoptic's synthesis + objects, the dictionary representation is easier to manually inspect. + + This dictionary will contain NaNs in its values: these are placeholders + for the redundant statistics. + + Parameters + ---------- + representation_tensor + 3d tensor of statistics. + + Returns + ------- + rep + Dictionary of representation, with informative keys. + + See Also + -------- + convert_to_tensor: + Convert dictionary representation to tensor. + + """ + if representation_tensor.shape[-1] != len(self._representation_scales): + raise ValueError( + "representation tensor is the wrong length (expected " + f"{len(self._representation_scales)} but got" + f"{representation_tensor.shape[-1]})!" + " Did you remove some of the scales? (i.e., by setting " + "scales in the forward pass)? convert_to_dict does not " + "support such tensors." + ) + + rep = self._necessary_stats_dict.copy() + n_filled = 0 + for k, v in rep.items(): + # each statistic is a tensor with batch and channel dimensions as + # found in representation_tensor and all the other dimensions + # determined by the values in necessary_stats_dict. + shape = (*representation_tensor.shape[:3], *v.shape) + new_v = torch.nan * torch.ones( + shape, + dtype=representation_tensor.dtype, + device=representation_tensor.device, + ) + # v.sum() gives the number of necessary elements from this stat + this_stat_vec = representation_tensor[..., n_filled : n_filled + v.sum()] + # use boolean indexing to put the values from new_stat_vec in the + # appropriate place + new_v[..., v] = this_stat_vec + rep[k] = new_v + n_filled += v.sum() + return rep + + def _compute_pyr_coeffs( + self, image: Tensor + ) -> tuple[OrderedDict, list[Tensor], Tensor, Tensor]: + """Compute pyramid coefficients of image. + + Note that the residual lowpass has been demeaned independently for each + batch and channel (and this is true of the lowpass returned separately + as well as the one included in pyr_coeffs_dict) + + Parameters + ---------- + image : + 4d tensor of shape (batch, channel, height, width) containing the + image + + Returns + ------- + pyr_coeffs_dict : + OrderedDict of containing all pyramid coefficients. + pyr_coeffs : + List of length n_scales, containing 5d tensors of shape (batch, + channel, n_orientations, height, width) containing the complex-valued + oriented bands (note that height and width shrink by half on each + scale). This excludes the residual highpass and lowpass bands. + highpass : + The residual highpass as a real-valued 4d tensor (batch, channel, + height, width) + lowpass : + The residual lowpass as a real-valued 4d tensor (batch, channel, + height, width). This tensor has been demeaned (independently for + each batch and channel). + + """ + pyr_coeffs = self._pyr.forward(image) + # separate out the residuals and demean the residual lowpass + lowpass = pyr_coeffs["residual_lowpass"] + lowpass = lowpass - lowpass.mean(dim=(-2, -1), keepdim=True) + pyr_coeffs["residual_lowpass"] = lowpass + highpass = pyr_coeffs["residual_highpass"] + + # This is a list of tensors, one for each scale, where each tensor is + # of shape (batch, channel, n_orientations, height, width) (note that + # height and width halves on each scale) + coeffs_list = [ + torch.stack([pyr_coeffs[(i, j)] for j in range(self.n_orientations)], 2) + for i in range(self.n_scales) + ] + return pyr_coeffs, coeffs_list, highpass, lowpass + + def _compute_pixel_stats(self, mask: list[Tensor], image: Tensor) -> Tensor: + """Compute the pixel stats: first four moments. + + Note that for the masked version, these are the *non-central* moments, i.e., + they're just the image raised to the first through fourth powers. + + Parameters + ---------- + mask : + The mask to use for weighting. + image : + 4d tensor of shape (batch, channel, height, width) containing input + image. Stats are computed indepently for each batch and channel. + + Returns + ------- + pixel_stats : + 4d tensor of shape (batch, channel, masks, 4) containing the first four + non-central moments + + """ + weighted_avg_expr = ( + f"{self._mask_input_idx}, b c h w -> b c {self._mask_output_idx}" + ) + mean = einops.einsum(*mask, image, weighted_avg_expr) + # these are all non-central moments... + moment_2 = einops.einsum(*mask, image.pow(2), weighted_avg_expr) + moment_3 = einops.einsum(*mask, image.pow(3), weighted_avg_expr) + moment_4 = einops.einsum(*mask, image.pow(4), weighted_avg_expr) + # ... which we use to compute the var, skew, and kurtosis. the formulas we use + # for var and skew here can be found on their respective wikipedia pages, and + # the one for kurtosis comes from Eero working through the algebra + var = moment_2 - mean.pow(2) + skew = (moment_3 - 3 * mean * var - mean.pow(3)) / ( + var.pow(1.5) + self._pixel_epsilon + ) + kurtosis = ( + moment_4 + - 4 * mean * moment_3 + + 6 * mean.pow(2) * moment_2 + - 3 * mean.pow(4) + ) / (var.pow(2) + self._pixel_epsilon) + return einops.rearrange( + [mean, var, skew, kurtosis], + f"stats b c {self._mask_output_idx} -> b c ({self._mask_output_idx}) stats", + ) + + @staticmethod + def _compute_intermediate_representations( + pyr_coeffs: Tensor, + ) -> tuple[list[Tensor], list[Tensor]]: + """Compute useful intermediate representations. + + These representations are: + 1) demeaned magnitude of the pyramid coefficients, + 2) real part of the pyramid coefficients + + These two are used in computing some of the texture representation. + + Parameters + ---------- + pyr_coeffs : + Complex steerable pyramid coefficients (without residuals), as list + of length n_scales, containing 5d tensors of shape (batch, channel, + n_orientations, height, width) + + Returns + ------- + magnitude_pyr_coeffs : + List of length n_scales, containing 5d tensors of shape (batch, + channel, n_orientations, height, width) (same as ``pyr_coeffs``), + containing the demeaned magnitude of the steerable pyramid + coefficients (i.e., coeffs.abs() - coeffs.abs().mean((-2, -1))) + real_pyr_coeffs : + List of length n_scales, containing 5d tensors of shape (batch, + channel, n_orientations, height, width) (same as ``pyr_coeffs``), + containing the real components of the coefficients (i.e. + coeffs.real) + + """ + magnitude_pyr_coeffs = [coeff.abs() for coeff in pyr_coeffs] + magnitude_means = [ + mag.mean((-2, -1), keepdim=True) for mag in magnitude_pyr_coeffs + ] + magnitude_pyr_coeffs = [ + mag - mn for mag, mn in zip(magnitude_pyr_coeffs, magnitude_means) + ] + real_pyr_coeffs = [coeff.real for coeff in pyr_coeffs] + return magnitude_pyr_coeffs, real_pyr_coeffs + + def _reconstruct_lowpass_at_each_scale( + self, pyr_coeffs_dict: OrderedDict + ) -> list[Tensor]: + """Reconstruct the lowpass unoriented image at each scale. + + The autocorrelation, standard deviation, skew, and kurtosis of each of + these images is part of the texture representation. + + Parameters + ---------- + pyr_coeffs_dict : + Dictionary containing the steerable pyramid coefficients, with the + lowpass residual demeaned. + + Returns + ------- + reconstructed_images : + List of length n_scales+1 containing the reconstructed unoriented + image at each scale, from fine to coarse. The final image is + reconstructed just from the residual lowpass image. Each is a 4d + tensor, this is a list because they are all different heights and + widths. + + """ + reconstructed_images = [ + self._pyr.recon_pyr(pyr_coeffs_dict, levels=["residual_lowpass"]) + ] + # go through scales backwards + for lev in range(self.n_scales - 1, -1, -1): + recon = self._pyr.recon_pyr(pyr_coeffs_dict, levels=[lev]) + reconstructed_images.append(recon + reconstructed_images[-1]) + # now downsample as necessary, so that these end up the same size as + # their corresponding coefficients. We multiply by the factor of 4 here + # in order to approximately equalize the steerable pyramid coefficient + # values across scales. This could also be handled by making the + # pyramid tight frame + reconstructed_images[:-1] = [ + signal.shrink(r, 2 ** (self.n_scales - i)) * 4 ** (self.n_scales - i) + for i, r in enumerate(reconstructed_images[:-1]) + ] + return reconstructed_images + + def _compute_autocorr( + self, mask: list[Tensor], coeffs_list: list[Tensor] + ) -> tuple[Tensor, Tensor]: + """Compute the autocorrelation of some statistics. + + Parameters + ---------- + mask : + The mask to use for weighting. + coeffs_list : + List (of length s) of tensors of shape (batch, channel, *, height, + width), where * is zero or one additional dimensions. Intended use + case: magnitude_pyr_coeffs (which is list of length n_scales of 5d + tensors, with * containing n_orientations) or reconstructed_images + (which is a list of length n_scales+1 of 4d tensors) + + Returns + ------- + autocorrs : + Tensor of shape (batch, channel, masks, n_autocorrs, *, s) containing the + autocorrelation (up to distance ``spatial_corr_width//2``) of each element + in ``coeffs_list``, computed independently over all but the final two + dimensions. ``n_autocorrs`` is the number of unique autocorrelation values, + which is approximately sptial_corr_width^2 / 2. + vars : + Tensor of shape (batch, channel, *masks, *, s) containing the variance of + each element in ``coeffs_list``, computed independently over all but the + final two dimensions. Note that by *masks, we indicate that the dimensions + will not be combined, so that if ``len(masks)==2``, *masks would hold two + dimensions. + + """ + if coeffs_list[0].ndim == 5: + dims = "o" + rolls_h = self._autocorr_rolls_h + rolls_w = self._autocorr_rolls_w + var_dim = -2 + elif coeffs_list[0].ndim == 4: + dims = "" + rolls_h = [r[:, :, 0] for r in self._autocorr_rolls_h] + rolls_w = [r[:, :, 0] for r in self._autocorr_rolls_w] + var_dim = -1 + else: + raise ValueError( + "coeffs_list must contain tensors of either 4 or 5 dimensions!" + ) + autocorr_expr = ( + f"{self._mask_input_idx}, b c {dims} h w, " + f"b c {dims} shift h w ->" + f" b c {self._mask_output_idx} shift {dims}" + ) + acs = [] + vars = [] + # iterate through scales + for coeff, rolls_h, rolls_w, scale_mask in zip( + coeffs_list, rolls_h, rolls_w, mask + ): + # the following two lines are equivalent to having two for loops over + # range(-spatial_corr_width//2, spatial_corr_width//2) and using roll along + # the last two indices, but is much more efficient, especially on the gpu. + rolled_coeff = einops.repeat( + coeff, + f"b c {dims} h w -> b c {dims} shift h w", + shift=self._n_autocorrs, + ) + rolled_coeff = rolled_coeff.gather(-2, rolls_h).gather(-1, rolls_w) + autocorr = einops.einsum(*scale_mask, coeff, rolled_coeff, autocorr_expr) + # this returns a view of autocorr that just selects out the variance, while + # preserving the number of dims. we have specifically placed the (0, 0) + # shift, which corresponds to the variance, as the last element + var = torch.narrow(autocorr, var_dim, -1, 1) + # and then drop the variance from here + acs.append( + torch.narrow(autocorr, var_dim, 0, self._n_autocorrs - 1) + / (var + self._stability_epsilon) + ) + vars.append(var) + acs = einops.rearrange( + acs, + ( + f"scales b c {self._mask_output_idx} shifts {dims} -> " + f"b c ({self._mask_output_idx}) shifts {dims} scales" + ), + ) + vars = einops.rearrange( + vars, + ( + f"scales b c {self._mask_output_idx} shifts {dims} -> " + f"b c {self._mask_output_idx} {dims} (shifts scales)" + ), + shifts=1, + ) + return acs, vars + + def _compute_skew_kurtosis_recon( + self, mask: list[Tensor], reconstructed_images: list[Tensor], var_recon: Tensor + ) -> tuple[Tensor, Tensor]: + """Compute the skew and kurtosis of each lowpass reconstructed image. + + For each scale, if the ratio of its variance to the original image's + pixel variance is below a threshold of + torch.finfo(img_var.dtype).resolution (1e-6 for float32, 1e-15 for + float64), skew and kurtosis are assigned default values of 0 or 3, + respectively. + + Parameters + ---------- + mask : + The mask to use for weighting. + reconstructed_images : + List of length n_scales+1 containing the reconstructed unoriented + image at each scale, from fine to coarse. The final image is + reconstructed just from the residual lowpass image. + var_recon : + Tensor of shape (batch, channel, masks, n_scales+1) containing the + variance of each tensor in reconstruced_images + + Returns + ------- + skew_recon, kurtosis_recon : + Tensors of shape (batch, channel, masks, n_scales+1) containing the skew + and kurtosis, respectively, of each tensor in + ``reconstructed_images``. + + """ + var_recon = einops.rearrange( + var_recon, + ( + f"b c {self._mask_output_idx} scales -> " + f"b c ({self._mask_output_idx}) scales" + ), + ) + skew_recon = [] + kurtosis_recon = [] + for img, scale_mask in zip(reconstructed_images, mask): + skew_recon.append( + einops.einsum( + *scale_mask, + img.pow(3), + ( + f"{self._mask_input_idx}, b c h w -> " + f"b c {self._mask_output_idx}" + ), + ) + ) + kurtosis_recon.append( + einops.einsum( + *scale_mask, + img.pow(4), + ( + f"{self._mask_input_idx}, b c h w -> " + f"b c {self._mask_output_idx}" + ), + ) + ) + skew_recon = einops.rearrange( + skew_recon, + ( + f"scales b c {self._mask_output_idx} ->" + f" b c ({self._mask_output_idx}) scales" + ), + ) + kurtosis_recon = einops.rearrange( + kurtosis_recon, + ( + f"scales b c {self._mask_output_idx} -> " + f"b c ({self._mask_output_idx}) scales" + ), + ) + skew_recon = skew_recon / (var_recon.pow(1.5) + self._stability_epsilon) + kurtosis_recon = kurtosis_recon / (var_recon.pow(2) + self._stability_epsilon) + return skew_recon, kurtosis_recon + + def _compute_cross_correlation( + self, + mask: list[Tensor], + coeffs_tensor: list[Tensor], + coeffs_tensor_other: list[Tensor], + coeffs_var: Tensor | None = None, + coeffs_other_var: Tensor | None = None, + ) -> Tensor: + """Compute cross-correlations. + + Parameters + ---------- + coeffs_tensor, coeffs_tensor_other : + The two lists of length scales, each containing 5d tensors of shape + (batch, channel, n_orientations, height, width) to be correlated. + coeffs_var, coeffs_other_var : + Two optional tensors containing the variances of coeffs_tensor and + coeffs_tensor_other, respectively, in case they've already been computed. + Should be of shape (batch, channel, *masks, n_orientations, n_scales). Note + that by *masks, we indicate that the dimensions should not be combined, so + that if ``len(masks)==2``, *masks would hold two dimensions. Used to + normalize the covariances into cross-correlations. Intended use is the + output of ``_compute_autocorr``. + + Returns + ------- + cross_corrs : + Tensor of shape (batch, channel, masks, n_orientations, n_orientations, + scales) containing the cross-correlations at each scale. + + """ + covars = [] + covar_expr = ( + f"{self._mask_input_idx}, b c o1 h w, b c o2 h w ->" + f" b c {self._mask_output_idx} o1 o2" + ) + var_expr = ( + f"{self._mask_input_idx}, b c o1 h w, b c o1 h w ->" + f" b c {self._mask_output_idx} o1" + ) + outer_prod_expr = ( + f"b c {self._mask_output_idx} o1, " + f"b c {self._mask_output_idx} o2 ->" + f" b c {self._mask_output_idx} o1 o2" + ) + for i, (scale_mask, coeff, coeff_other) in enumerate( + zip(mask, coeffs_tensor, coeffs_tensor_other) + ): + # compute the covariance + covar = einops.einsum(*scale_mask, coeff, coeff_other, covar_expr) + # Then normalize it to get the Pearson product-moment correlation + # coefficient, see + # https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html. + if coeffs_var is None: + # First, compute the variances of each coeff + coeff_var = einops.einsum(*scale_mask, coeff, coeff, var_expr) + else: + coeff_var = coeffs_var[..., i] + if coeffs_other_var is None: + # First, compute the variances of each coeff + coeff_other_var = einops.einsum( + *scale_mask, coeff_other, coeff_other, var_expr + ) + else: + coeff_other_var = coeffs_other_var[..., i] + # Then compute the outer product of those variances. + var_outer_prod = einops.einsum(coeff_var, coeff_other_var, outer_prod_expr) + # And the sqrt of this is what we use to normalize the covariance + # into the cross-correlation + std_outer_prod = (var_outer_prod + self._stability_epsilon).sqrt() + covars.append(covar / (std_outer_prod + self._stability_epsilon)) + return einops.rearrange( + covars, + ( + f"scales b c {self._mask_output_idx} o1 o2 ->" + f" b c ({self._mask_output_idx}) o1 o2 scales" + ), + ) + + @staticmethod + def _double_phase_pyr_coeffs( + pyr_coeffs: list[Tensor], + ) -> tuple[list[Tensor], list[Tensor]]: + """Upsample and double the phase of pyramid coefficients. + + Parameters + ---------- + pyr_coeffs : + Complex steerable pyramid coefficients (without residuals), as list + of length n_scales, containing 5d tensors of shape (batch, channel, + n_orientations, height, width) + + Returns + ------- + doubled_phase_mags : + The demeaned magnitude (i.e., pyr_coeffs.abs()) of each upsampled + double-phased coefficient. List of length n_scales-1 containing + tensors of same shape the input (the finest scale has been + removed). + doubled_phase_separate : + The real and imaginary parts of each double-phased coefficient. + List of length n_scales-1, containing tensors of shape (batch, + channel, 2*n_orientations, height, width), with the real component + found at the same orientation index as the input, and the imaginary + at orientation+self.n_orientations. (The finest scale has been + removed.) + + """ + doubled_phase_mags = [] + doubled_phase_sep = [] + # don't do this for the finest scale + for coeff in pyr_coeffs[1:]: + # We divide by the factor of 4 here in order to approximately + # equalize the steerable pyramid coefficient values across scales. + # This could also be handled by making the pyramid tight frame + doubled_phase = signal.expand(coeff, 2) / 4.0 + doubled_phase = signal.modulate_phase(doubled_phase, 2) + doubled_phase_mag = doubled_phase.abs() + doubled_phase_mag = doubled_phase_mag - doubled_phase_mag.mean( + (-2, -1), keepdim=True + ) + doubled_phase_mags.append(doubled_phase_mag) + doubled_phase_sep.append( + einops.pack([doubled_phase.real, doubled_phase.imag], "b c * h w")[0] + ) + return doubled_phase_mags, doubled_phase_sep + + def plot_representation( + self, + data: Tensor, + ax: plt.Axes | None = None, + figsize: tuple[float, float] = (15, 5), + ylim: tuple[float, float] | Literal[False] | None = False, + batch_idx: int = 0, + title: str | None = None, + ) -> tuple[plt.Figure, list[plt.Axes]]: + r"""Plot the representation in a human viewable format -- stem + plots with data separated out by statistic type. + + This plots the representation of a single batch and averages over all + channels in the representation. + + We create the following axes: + + - pixels+var_highpass: marginal pixel statistics (first four moments, + min, max) and variance of the residual highpass. + + - std+skew+kurtosis recon: the standard deviation, skew, and kurtosis + of the reconstructed lowpass image at each scale + + - magnitude_std: the standard deviation of the steerable pyramid + coefficient magnitudes at each orientation and scale. + + - auto_correlation_reconstructed: the auto-correlation of the + reconstructed lowpass image at each scale (summarized using Euclidean + norm). + + - auto_correlation_magnitude: the auto-correlation of the pyramid + coefficient magnitudes at each scale and orientation (summarized + using Euclidean norm). + + - cross_orientation_correlation_magnitude: the cross-correlations + between each orientation at each scale (summarized using Euclidean + norm) + + If self.n_scales > 1, we also have: + + - cross_scale_correlation_magnitude: the cross-correlations between the + pyramid coefficient magnitude at one scale and the same orientation + at the next-coarsest scale (summarized using Euclidean norm). + + - cross_scale_correlation_real: the cross-correlations between the real + component of the pyramid coefficients and the real and imaginary + components (at the same orientation) at the next-coarsest scale + (summarized using Euclidean norm). + + Parameters + ---------- + data : + The data to show on the plot. Else, should look like the output of + ``self.forward(img)``, with the exact same structure (e.g., as + returned by ``metamer.representation_error()`` or another instance + of this class). + ax : + Axes where we will plot the data. If a ``plt.Axes`` instance, will + subdivide into 6 or 8 new axes (depending on self.n_scales). If + None, we create a new figure. + figsize : + The size of the figure. Ignored if ax is not None. + ylim : + If not None, the y-limits to use for this plot. If None, we use the + default, slightly adjusted so that the minimum is 0. If False, do not + change y-limits. + batch_idx : + Which index to take from the batch dimension (the first one) + title : string + Title for the plot + + Returns + ------- + fig: + Figure containing the plot + axes: + List of 6 or 8 axes containing the plot (depending on self.n_scales) + + """ + if self.n_scales != 1: + n_rows = 3 + n_cols = 3 + else: + # then we don't have any cross-scale correlations, so fewer axes. + n_rows = 2 + n_cols = 3 + + # pick the batch_idx we want (but keep the data 3d), and average over + # channels (but keep the data 3d). We keep data 3d because + # convert_to_dict relies on it. + data = data[batch_idx].unsqueeze(0).mean(1, keepdim=True) + # each of these values should now be a 3d tensor with 1 element in each + # of the first two dims + rep = {k: v[0, 0] for k, v in self.convert_to_dict(data).items()} + data = self._representation_for_plotting(rep) + + # Set up grid spec + if ax is None: + # we add 2 to order because we're adding one to get the + # number of orientations and then another one to add an + # extra column for the mean luminance plot + fig = plt.figure(figsize=figsize) + gs = mpl.gridspec.GridSpec(n_rows, n_cols, fig) + else: + # warnings.warn("ax is not None, so we're ignoring figsize...") + # want to make sure the axis we're taking over is basically invisible. + ax = clean_up_axes( + ax, False, ["top", "right", "bottom", "left"], ["x", "y"] + ) + gs = ax.get_subplotspec().subgridspec(n_rows, n_cols) + fig = ax.figure + + # plot data + axes = [] + for i, (k, v) in enumerate(data.items()): + ax = fig.add_subplot(gs[i // 3, i % 3]) + ax = clean_stem_plot(to_numpy(v).flatten(), ax, k, ylim=ylim) + axes.append(ax) + + if title is not None: + fig.suptitle(title) + + return fig, axes + + def _representation_for_plotting(self, rep: OrderedDict) -> OrderedDict: + r"""Convert the data into a more convenient representation for plotting. + + Intended as a helper function for plot_representation. + + """ + data = OrderedDict() + data["pixels+var_highpass"] = torch.cat( + [rep.pop("pixel_statistics"), rep.pop("var_highpass_residual")], -1 + ) + data["std+skew+kurtosis recon"] = torch.cat( + ( + rep.pop("std_reconstructed"), + rep.pop("skew_reconstructed"), + rep.pop("kurtosis_reconstructed"), + ), + -1, + ) + + data["magnitude_std"] = rep.pop("magnitude_std").flatten(1) + + # want to plot these in a specific order + all_keys = [ + "auto_correlation_reconstructed", + "auto_correlation_magnitude", + "cross_orientation_correlation_magnitude", + "cross_scale_correlation_magnitude", + "cross_scale_correlation_real", + ] + if set(rep.keys()) != set(all_keys): + raise ValueError("representation has unexpected keys!") + for k in all_keys: + # if we only have one scale, no cross-scale stats + if k.startswith("cross_scale") and self.n_scales == 1: + continue + # these will then be 2d, with masks on the first dimension + if k == "cross_orientation_correlation_magnitude": + # this one has nans in it (indicating unnecessary stats), and so we need + # to compute the L2 norm ourselves + data[k] = rep[k].pow(2).nansum((1, 2)).sqrt() + else: + data[k] = torch.linalg.vector_norm(rep[k], ord=2, dim=1).flatten(1) + + return data + + def update_plot( + self, + axes: list[plt.Axes], + data: Tensor, + batch_idx: int = 0, + ) -> list[plt.Artist]: + r"""Update the information in our representation plot. + + This is used for creating an animation of the representation + over time. In order to create the animation, we need to know how + to update the matplotlib Artists, and this provides a simple way + of doing that. It relies on the fact that we've used + ``plot_representation`` to create the plots we want to update + and so know that they're stem plots. + + We take the axes containing the representation information (note that + this is probably a subset of the total number of axes in the figure, if + we're showing other information, as done by ``Metamer.animate``), grab + the representation from plotting and, since these are both lists, + iterate through them, updating them to the values in ``data`` as we go. + + In order for this to be used by ``FuncAnimation``, we need to + return Artists, so we return a list of the relevant artists, the + ``markerline`` and ``stemlines`` from the ``StemContainer``. + + Currently, this averages over all channels in the representation. + + Parameters + ---------- + axes : + A list of axes to update. We assume that these are the axes + created by ``plot_representation`` and so contain stem plots + in the correct order. + batch_idx : + Which index to take from the batch dimension (the first one) + data : + The data to show on the plot. Else, should look like the output of + ``self.forward(img)``, with the exact same structure (e.g., as + returned by ``metamer.representation_error()`` or another instance + of this class). + + Returns + ------- + stem_artists : + A list of the artists used to update the information on the + stem plots + + """ + stem_artists = [] + axes = [ax for ax in axes if len(ax.containers) == 1] + # pick the batch_idx we want (but keep the data 3d), and average over + # channels (but keep the data 3d). We keep data 3d because + # convert_to_dict relies on it. + data = data[batch_idx].unsqueeze(0).mean(1, keepdim=True) + # each of these values should now be a 3d tensor with 1 element in each + # of the first two dims + rep = {k: v[0, 0] for k, v in self.convert_to_dict(data).items()} + rep = self._representation_for_plotting(rep) + for ax, d in zip(axes, rep.values()): + vals = to_numpy(d.flatten()) + sc = update_stem(ax.containers[0], vals) + stem_artists.extend([sc.markerline, sc.stemlines]) + return stem_artists