{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "KjYlO80JL3j1" }, "source": [ "Copyright 2022 Google LLC\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\");\n", "you may not use this file except in compliance with the License.\n", "You may obtain a copy of the License at\n", "\n", " https://www.apache.org/licenses/LICENSE-2.0\n", "\n", "Unless required by applicable law or agreed to in writing, software\n", "distributed under the License is distributed on an \"AS IS\" BASIS,\n", "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "See the License for the specific language governing permissions and\n", "limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "uJHywE_oL3j2" }, "source": [ "# ResNet on CIFAR10 with Flax and JAXopt.\n", "\n", "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google/jaxopt/blob/main/docs/notebooks/deep_learning/resnet_flax.ipynb)\n", "\n", "In this notebook, we'll go through training a deep residual network with jaxopt." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "gzQc20SyL3j2" }, "outputs": [], "source": [ "%%capture\n", "%pip install jaxopt flax tqdm" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "VaYIiCnjL3j3" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2023-02-16 11:50:30.930308: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/usr/local/nccl2/lib:/usr/local/cuda/extras/CUPTI/lib64:/usr/local/cuda/lib64:/usr/local/nccl2/lib:/usr/local/cuda/extras/CUPTI/lib64\n", "2023-02-16 11:50:30.930416: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/usr/local/nccl2/lib:/usr/local/cuda/extras/CUPTI/lib64:/usr/local/cuda/lib64:/usr/local/nccl2/lib:/usr/local/cuda/extras/CUPTI/lib64\n", "2023-02-16 11:50:30.930428: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "TPU not found, continuing without it.\n" ] } ], "source": [ "from datetime import datetime\n", "\n", "from functools import partial\n", "from typing import Any, Callable, Sequence, Tuple\n", "\n", "from flax import linen as nn\n", "\n", "import jax\n", "import jax.numpy as jnp\n", "from tqdm.notebook import trange\n", "import numpy as np\n", "\n", "import optax\n", "import tensorflow_datasets as tfds\n", "import tensorflow as tf\n", "\n", "from matplotlib import pyplot as plt\n", "\n", "from jaxopt import loss\n", "from jaxopt import OptaxSolver\n", "from jaxopt import tree_util\n", "\n", "# activate TPUs if available\n", "try:\n", " import jax.tools.colab_tpu\n", " jax.tools.colab_tpu.setup_tpu()\n", "except KeyError:\n", " print(\"TPU not found, continuing without it.\")\n", "\n", "tf.config.experimental.set_visible_devices([], 'GPU')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "jve2h810L3j3" }, "outputs": [], "source": [ "#@markdown Total number of epochs to train for:\n", "MAX_EPOCH = 200 #@param{type:\"integer\"}\n", "#@markdown Number of samples in each batch:\n", "BATCH_SIZE = 128 #@param{type:\"integer\"}\n", "#@markdown The initial learning rate for the optimizer:\n", "INIT_LR = 0.05 #@param{type:\"number\"}\n", "#@markdown The model architecture for the neural network. Could be either `'resnet1'`, `'resnet18'`, `'resnet34'`, `'resnet50'` or `'resnet101'`:\n", "MODEL = \"resnet34\" #@param{type:\"string\"}\n", "#@markdown The dataset to use. Could be either `'cifar10'` or `'cifar100'`:\n", "DATASET = \"cifar10\" #@param{type:\"string\"}\n", "#@markdown The amount of L2 regularization (aka weight decay) to use:\n", "L2_REG = 5e-4 #@param{type:\"number\"}" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "CIFAR10 and CIFAR100 are composed of 32x32 images with 3 channels (RGB). We'll now load the dataset using `tensorflow_datasets` and display a few of the first samples." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "(raw_train_ds, raw_test_ds), info = tfds.load(DATASET, split=['train','test'], as_supervised=True, with_info=True)\n", "\n", "input_shape = (1,) + info.features[\"image\"].shape\n", "num_classes = info.features[\"label\"].num_classes\n", "iter_per_epoch_train = info.splits['train'].num_examples // BATCH_SIZE\n", "iter_per_epoch_test = info.splits['test'].num_examples // BATCH_SIZE\n", "\n", "raw_train_ds_iter = iter(raw_train_ds)\n", "_, axes = plt.subplots(nrows=4, ncols=5, figsize=(6, 4))\n", "for i in range(4):\n", " for j in range(5):\n", " k = i * 4 + j\n", " image, label = next(raw_train_ds_iter)\n", " axes[i, j].imshow(image, cmap=plt.cm.gray_r, interpolation=\"nearest\")\n", " axes[i, j].set_axis_off()\n", " axes[i, j].set_title(info.features['label'].names[label], fontsize=10, y=0.9)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "The accuracy of the model can be improved significantly through data augmentation. That is, instead of training on the above images, we'll generate random modifications of the images and train on those. This is done by using the `transform` argument of `tfds.load` to apply a random crop, random horizontal flip, and random color jittering.\n", "\n", "In the next cell we show an instance of these transformations on the above images." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2023-02-16 11:50:33.041151: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def augment(image, label):\n", " image = tf.image.resize_with_crop_or_pad(image, 40, 40)\n", " image = tf.image.random_crop(image, [32, 32, 3])\n", " image = tf.image.random_flip_left_right(image)\n", " image = tf.image.random_brightness(image, max_delta=0.2)\n", " image = tf.image.random_contrast(image, 0.8, 1.2)\n", " image = tf.image.random_saturation(image, 0.8, 1.2)\n", " return image, label\n", "\n", "raw_train_ds_iter = iter(raw_train_ds)\n", "_, axes = plt.subplots(nrows=4, ncols=5, figsize=(6, 4))\n", "for i in range(4):\n", " for j in range(5):\n", " k = i * 4 + j\n", " image, label = next(raw_train_ds_iter)\n", " augmented_image, _ = augment(image, label)\n", " axes[i, j].imshow(tf.cast(augmented_image, tf.uint8), cmap=plt.cm.gray_r, interpolation=\"nearest\")\n", " axes[i, j].set_axis_off()\n", " axes[i, j].set_title(info.features['label'].names[label], fontsize=10, y=0.9)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Finally, on the test set, we won't be applying data augmentation to keep a reliable evaluation of the model's performance. In the test set we'll only normalize the pixels to be in the range [0, 1]." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def normalize(image, label):\n", " image = tf.cast(image, tf.float32)\n", " image = (image / 255.0)\n", " return image, label\n", "\n", "train_ds = raw_train_ds.map(augment).map(normalize)\n", "test_ds = raw_test_ds.map(normalize)\n", "\n", "train_ds = train_ds.shuffle(5000, reshuffle_each_iteration=True).batch(\n", " BATCH_SIZE, drop_remainder=True).prefetch(tf.data.AUTOTUNE).repeat(2 * MAX_EPOCH).as_numpy_iterator()\n", "test_ds = test_ds.shuffle(5000).batch(\n", " BATCH_SIZE, drop_remainder=True).prefetch(tf.data.AUTOTUNE).repeat(MAX_EPOCH).as_numpy_iterator()\n", "\n", "\n", "NUM_CLASSES = info.features[\"label\"].num_classes\n", "\n", "IMG_SIZE = info.features[\"image\"].shape" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "With the data ready, we can now define the model. Below we define the ResNet architecture that we'll later instantiate. We define different variants of the architecture with different sizes and depths (`'ResNet1'`, `'ResNet18'`, `'ResNet34'`, `'ResNet50'` and `'ResNet101'`). The architecture is based on the [Flax imagenet example](https://github.com/google/flax/blob/main/examples/imagenet/models.py)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "id": "onkVLRw7L3j4" }, "outputs": [], "source": [ "\n", "class ResNetBlock(nn.Module):\n", " \"\"\"ResNet block.\"\"\"\n", " filters: int\n", " conv: Any\n", " norm: Any\n", " act: Callable\n", " strides: Tuple[int, int] = (1, 1)\n", "\n", " @nn.compact\n", " def __call__(self, x,):\n", " residual = x\n", " y = self.conv(self.filters, (3, 3), self.strides)(x)\n", " y = self.norm()(y)\n", " y = self.act(y)\n", " y = self.conv(self.filters, (3, 3))(y)\n", " y = self.norm(scale_init=nn.initializers.zeros)(y)\n", "\n", " if residual.shape != y.shape:\n", " residual = self.conv(self.filters, (1, 1),\n", " self.strides, name='conv_proj')(residual)\n", " residual = self.norm(name='norm_proj')(residual)\n", "\n", " return self.act(residual + y)\n", "\n", "\n", "class BottleneckResNetBlock(nn.Module):\n", " \"\"\"Bottleneck ResNet block.\"\"\"\n", " filters: int\n", " conv: Any\n", " norm: Any\n", " act: Callable\n", " strides: Tuple[int, int] = (1, 1)\n", "\n", " @nn.compact\n", " def __call__(self, x):\n", " residual = x\n", " y = self.conv(self.filters, (1, 1))(x)\n", " y = self.norm()(y)\n", " y = self.act(y)\n", " y = self.conv(self.filters, (3, 3), self.strides)(y)\n", " y = self.norm()(y)\n", " y = self.act(y)\n", " y = self.conv(self.filters * 4, (1, 1))(y)\n", " y = self.norm(scale_init=nn.initializers.zeros)(y)\n", "\n", " if residual.shape != y.shape:\n", " residual = self.conv(self.filters * 4, (1, 1),\n", " self.strides, name='conv_proj')(residual)\n", " residual = self.norm(name='norm_proj')(residual)\n", "\n", " return self.act(residual + y)\n", "\n", "class ResNet(nn.Module):\n", " \"\"\"ResNetV1.\"\"\"\n", " stage_sizes: Sequence[int]\n", " block_cls: Any\n", " num_classes: int\n", " num_filters: int = 64\n", " dtype: Any = jnp.float32\n", " act: Callable = nn.relu\n", "\n", " @nn.compact\n", " def __call__(self, x, train: bool = True):\n", " conv = partial(nn.Conv, use_bias=False, dtype=self.dtype)\n", " norm = partial(nn.BatchNorm,\n", " # use_running_average=True,\n", " use_running_average=not train,\n", " momentum=0.99,\n", " epsilon=0.001,\n", " dtype=self.dtype)\n", "\n", " x = conv(self.num_filters, (7, 7), (2, 2),\n", " padding=[(3, 3), (3, 3)],\n", " name='conv_init')(x)\n", " x = norm(name='bn_init')(x)\n", " x = nn.relu(x)\n", " x = nn.max_pool(x, (3, 3), strides=(2, 2), padding='SAME')\n", " for i, block_size in enumerate(self.stage_sizes):\n", " for j in range(block_size):\n", " strides = (2, 2) if i > 0 and j == 0 else (1, 1)\n", " x = self.block_cls(self.num_filters * 2 ** i,\n", " strides=strides,\n", " conv=conv,\n", " norm=norm,\n", " act=self.act)(x)\n", " x = jnp.mean(x, axis=(1, 2))\n", " x = nn.Dense(self.num_classes, dtype=self.dtype)(x)\n", " x = jnp.asarray(x, self.dtype)\n", " return x\n", "\n", "\n", "ResNet1 = partial(ResNet, stage_sizes=[1], block_cls=ResNetBlock)\n", "ResNet18 = partial(ResNet, stage_sizes=[2, 2, 2, 2], block_cls=ResNetBlock)\n", "ResNet34 = partial(ResNet, stage_sizes=[3, 4, 6, 3], block_cls=ResNetBlock)\n", "ResNet50 = partial(ResNet, stage_sizes=[3, 4, 6, 3],\n", " block_cls=BottleneckResNetBlock)\n", "ResNet101 = partial(ResNet, stage_sizes=[3, 4, 23, 3],\n", " block_cls=BottleneckResNetBlock)" ] }, { "cell_type": "markdown", "metadata": { "id": "BZPW1DnOL3j4" }, "source": [ "We'll now load our train and test dataset and plot a few of the training images." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "id": "_kbXJT07L3j5" }, "outputs": [], "source": [ "# Set up model.\n", "if MODEL == \"resnet1\":\n", " net = ResNet1(num_classes=num_classes)\n", "elif MODEL == \"resnet18\":\n", " net = ResNet18(num_classes=num_classes)\n", "elif MODEL == \"resnet34\":\n", " net = ResNet34(num_classes=num_classes)\n", "elif MODEL == \"resnet50\":\n", " net = ResNet50(num_classes=num_classes)\n", "else:\n", " raise ValueError(f\"Unknown model {MODEL}.\")\n", "\n", "\n", "def predict(params, inputs, batch_stats, train=False):\n", " x = inputs.astype(jnp.float32) / 255.\n", " all_params = {\"params\": params, \"batch_stats\": batch_stats}\n", " if train:\n", " # Returns logits and net_state (which contains the key \"batch_stats\").\n", " return net.apply(all_params, x, train=train, mutable=[\"batch_stats\"])\n", " else:\n", " # Returns logits only.\n", " return net.apply(all_params, x, train=train, mutable=False), {\"batch_stats\": None}\n", "\n", "logistic_loss = jax.vmap(loss.multiclass_logistic_loss)\n", "\n", "\n", "def loss_from_logits(params, logits, labels):\n", " mean_loss = jnp.mean(logistic_loss(labels, logits))\n", " sqnorm = tree_util.tree_l2_norm(params, squared=True)\n", " return mean_loss + 0.5 * L2_REG * sqnorm\n", "\n", "\n", "def _loss_accuracy(params, data, batch_stats, train=True):\n", " \"\"\"Return loss and accuracy.\"\"\"\n", " inputs, labels = data\n", " logits, net_state = predict(params, inputs, batch_stats, train=train)\n", " accuracy = jnp.mean(jnp.argmax(logits, axis=-1) == labels)\n", " loss = loss_from_logits(params, logits, labels)\n", " return loss, (accuracy, net_state[\"batch_stats\"])\n", "\n", "loss_accuracy = jax.jit(_loss_accuracy, static_argnums=3)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 250, "referenced_widgets": [ "e652ba75a82f4cedb58e1f286ca899d5", "e7d2eecf8f7d43f99a5eac4c55519690", "2c190f0cd7024553a8afe883b9f82cb9", "1bbd303d94a04471bc1913f55d688b70", "bf553e81e6c04dfd88ccafd4eceb0d74", "99fd6be7f5fa4498806d136be988d9b1", "72aeeab70a3943e88ec6536893d1e69e", "8b2d2b677e19433cbbec27a2d79121e7", "735020048b88417491750f89429ba3bd", "b2c156180d9f4ee19fe8f1fcff76f6bc", "8917f3c947bb43788d121a84b08fcd05" ] }, "id": "xLTQpLg1L3j5", "outputId": "a62822aa-c011-4f28-ee2c-690e450f40a4" }, "outputs": [], "source": [ "# Initialize solver.\n", "\n", "shedule_fn = optax.cosine_onecycle_schedule(\n", " transition_steps=iter_per_epoch_train * MAX_EPOCH,\n", " peak_value=INIT_LR,\n", ")\n", "\n", "opt = optax.sgd(shedule_fn, momentum=0.9, nesterov=False)\n", "# opt = optax.adam(shedule_fn)\n", "\n", "# We need has_aux=True because loss_fun returns batch_stats.\n", "solver = OptaxSolver(opt=opt, fun=loss_accuracy,\n", " maxiter=MAX_EPOCH * iter_per_epoch_train, has_aux=True)\n", "\n", "# Initialize parameters.\n", "rng = jax.random.PRNGKey(0)\n", "init_vars = net.init({\"params\": rng}, jax.random.normal(rng, input_shape, dtype=net.dtype))\n", "params = init_vars[\"params\"]\n", "batch_stats = init_vars[\"batch_stats\"]\n", "start = datetime.now().replace(microsecond=0)\n", "\n", "# Run training loop.\n", "state = solver.init_state(params, next(train_ds), batch_stats)\n", "jitted_update = jax.jit(solver.update)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we do the actual training. The next cell performs `'MAX_EPOCHS'` epochs of training. Each epoch is split into `'STEPS_PER_EPOCH'` steps. In each step, we sample a batch of `'BATCH_SIZE'` images from the training set and perform a gradient step on the loss function." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "id": "loz4M9I5SLRu" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "02ecc418607a43ba8830864d2b50edcc", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/200 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))\n", "\n", "ax1.plot(all_test_error, lw=3)\n", "ax1.set_ylabel('Error on test set', fontsize=20)\n", "ax1.grid()\n", "ax1.set_xlabel('Epochs', fontsize=20)\n", "\n", "ax2.plot(all_train_loss, lw=3)\n", "ax2.set_ylabel('Loss on train set', fontsize=20)\n", "ax2.grid()\n", "ax2.set_xlabel('Epochs', fontsize=20)\n", "ax2.set_yscale('log')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "id": "pKxQZ13kL3j5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Final accuracy on test set: 0.8811097741127014\n" ] } ], "source": [ "# Finally, let's print the test \n", "print('Final accuracy on test set: ', 1 - all_test_error[-1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "dpg7oiGEQy22" }, "outputs": [], "source": [] } ], "metadata": { "accelerator": "TPU", "colab": { "provenance": [] }, "gpuClass": "standard", "jupytext": { "formats": "ipynb,md:myst" }, "kernelspec": { "display_name": "jax", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.4 (main, Mar 31 2022, 03:38:35) [Clang 12.0.0 ]" }, "vscode": { "interpreter": { "hash": "52d10dda8dd4acbc0136d307f056abb067e83eda640decd801853dd83bdd7356" } }, "widgets": { 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