Interpolations (#38)

* add interpolate and reconstruct methods

* update doc

* update tests

* update demo

* black & isort

* prepare release

* update README

README.md

@@ -307,7 +307,7 @@ And now build the model
 You can also find predefined neural network architectures for the most common data sets (*i.e.* MNIST, CIFAR, CELEBA ...) that can be loaded as follows
 
 ```python
->>> for pythae.models.nn.benchmark.mnist import (
+>>> from pythae.models.nn.benchmark.mnist import (
 ... Encoder_Conv_AE_MNIST, # For AE based model (only return embeddings)
 ... Encoder_Conv_VAE_MNIST, # For VAE based model (return embeddings and log_covariances)
 ... Decoder_Conv_AE_MNIST

docs/source/index.rst

@@ -23,6 +23,12 @@ we can conduct benchmark analysis and reproducible research!
 Setup
 ~~~~~~~~~~~~~
 
+To install the latest stable release of this library run the following using ``pip``
+
+.. code-block:: bash
+
+ $ pip install pythae
+
 To install the latest version of this library run the following using ``pip``
 
 .. code-block:: bash

docs/source/models/autoencoders/auto_model.rst

@@ -0,0 +1,10 @@
+**********************************
+AutoModel
+**********************************
+
+
+.. automodule::
+ pythae.models.auto_model
+
+.. autoclass:: pythae.models.AutoModel
+ :members:

docs/source/models/autoencoders/models.rst

@@ -7,6 +7,7 @@ Autoencoders
  :maxdepth: 1
 
  baseAE
+ auto_model
  ae
  vae
  betavae
@@ -37,6 +38,7 @@ Available Models
 
 .. autosummary::
  ~pythae.models.BaseAE
+ ~pythae.models.AutoModel
  ~pythae.models.AE
  ~pythae.models.VAE
  ~pythae.models.BetaVAE

docs/source/models/pythae.models.rst

@@ -21,6 +21,7 @@ Available Autoencoders
 
 .. autosummary::
  ~pythae.models.BaseAE
+ ~pythae.models.AutoModel
  ~pythae.models.AE
  ~pythae.models.VAE
  ~pythae.models.BetaVAE

examples/notebooks/models_training/adversarial_ae_training.ipynb

@@ -239,6 +239,86 @@
  "source": [
  "## ... the other samplers work the same"
  ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Visualizing reconstructions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "reconstructions = model.reconstruct(eval_dataset[:25]).detach().cpu()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# show reconstructions\n",
+ "fig, axes = plt.subplots(nrows=5, ncols=5, figsize=(10, 10))\n",
+ "\n",
+ "for i in range(5):\n",
+ " for j in range(5):\n",
+ " axes[i][j].imshow(reconstructions[i*5 + j].cpu().squeeze(0), cmap='gray')\n",
+ " axes[i][j].axis('off')\n",
+ "plt.tight_layout(pad=0.)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# show the true data\n",
+ "fig, axes = plt.subplots(nrows=5, ncols=5, figsize=(10, 10))\n",
+ "\n",
+ "for i in range(5):\n",
+ " for j in range(5):\n",
+ " axes[i][j].imshow(eval_dataset[i*5 +j].cpu().squeeze(0), cmap='gray')\n",
+ " axes[i][j].axis('off')\n",
+ "plt.tight_layout(pad=0.)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Visualizing interpolations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "interpolations = model.interpolate(eval_dataset[:5], eval_dataset[5:10], granularity=10).detach().cpu()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# show interpolations\n",
+ "fig, axes = plt.subplots(nrows=5, ncols=10, figsize=(10, 5))\n",
+ "\n",
+ "for i in range(5):\n",
+ " for j in range(10):\n",
+ " axes[i][j].imshow(interpolations[i, j].cpu().squeeze(0), cmap='gray')\n",
+ " axes[i][j].axis('off')\n",
+ "plt.tight_layout(pad=0.)"
+ ]
  }
  ],
  "metadata": {

examples/notebooks/models_training/ae_training.ipynb

@@ -238,6 +238,86 @@
  "source": [
  "## ... the other samplers work the same"
  ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Visualizing reconstructions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "reconstructions = model.reconstruct(eval_dataset[:25]).detach().cpu()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# show reconstructions\n",
+ "fig, axes = plt.subplots(nrows=5, ncols=5, figsize=(10, 10))\n",
+ "\n",
+ "for i in range(5):\n",
+ " for j in range(5):\n",
+ " axes[i][j].imshow(reconstructions[i*5 + j].cpu().squeeze(0), cmap='gray')\n",
+ " axes[i][j].axis('off')\n",
+ "plt.tight_layout(pad=0.)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# show the true data\n",
+ "fig, axes = plt.subplots(nrows=5, ncols=5, figsize=(10, 10))\n",
+ "\n",
+ "for i in range(5):\n",
+ " for j in range(5):\n",
+ " axes[i][j].imshow(eval_dataset[i*5 +j].cpu().squeeze(0), cmap='gray')\n",
+ " axes[i][j].axis('off')\n",
+ "plt.tight_layout(pad=0.)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Visualizing interpolations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "interpolations = model.interpolate(eval_dataset[:5], eval_dataset[5:10], granularity=10).detach().cpu()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# show interpolations\n",
+ "fig, axes = plt.subplots(nrows=5, ncols=10, figsize=(10, 5))\n",
+ "\n",
+ "for i in range(5):\n",
+ " for j in range(10):\n",
+ " axes[i][j].imshow(interpolations[i, j].cpu().squeeze(0), cmap='gray')\n",
+ " axes[i][j].axis('off')\n",
+ "plt.tight_layout(pad=0.)"
+ ]
  }
  ],
  "metadata": {

examples/notebooks/models_training/beta_tc_vae_training.ipynb

@@ -242,6 +242,86 @@
  "source": [
  "## ... the other samplers work the same"
  ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Visualizing reconstructions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "reconstructions = model.reconstruct(eval_dataset[:25]).detach().cpu()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# show reconstructions\n",
+ "fig, axes = plt.subplots(nrows=5, ncols=5, figsize=(10, 10))\n",
+ "\n",
+ "for i in range(5):\n",
+ " for j in range(5):\n",
+ " axes[i][j].imshow(reconstructions[i*5 + j].cpu().squeeze(0), cmap='gray')\n",
+ " axes[i][j].axis('off')\n",
+ "plt.tight_layout(pad=0.)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# show the true data\n",
+ "fig, axes = plt.subplots(nrows=5, ncols=5, figsize=(10, 10))\n",
+ "\n",
+ "for i in range(5):\n",
+ " for j in range(5):\n",
+ " axes[i][j].imshow(eval_dataset[i*5 +j].cpu().squeeze(0), cmap='gray')\n",
+ " axes[i][j].axis('off')\n",
+ "plt.tight_layout(pad=0.)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Visualizing interpolations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "interpolations = model.interpolate(eval_dataset[:5], eval_dataset[5:10], granularity=10).detach().cpu()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# show interpolations\n",
+ "fig, axes = plt.subplots(nrows=5, ncols=10, figsize=(10, 5))\n",
+ "\n",
+ "for i in range(5):\n",
+ " for j in range(10):\n",
+ " axes[i][j].imshow(interpolations[i, j].cpu().squeeze(0), cmap='gray')\n",
+ " axes[i][j].axis('off')\n",
+ "plt.tight_layout(pad=0.)"
+ ]
  }
  ],
  "metadata": {