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jax_CNN_on_CIFAR10.ipynb
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jax_cnn_on_cifar10.ipynb
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"<a href=\"https://colab.research.google.com/gist/yonetaniryo/37e7023bc55db275737ec1f8e5851551/jax_cnn_on_cifar10.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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{
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"source": [
"# additional packages\n",
"!pip install --quiet flax==0.3.5\n",
"!pip install --quiet jax[cuda111]"
],
"execution_count": 1,
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{
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"\u001b[K |████████████████████████████████| 193 kB 12.6 MB/s \n",
"\u001b[K |████████████████████████████████| 118 kB 46.1 MB/s \n",
"\u001b[K |████████████████████████████████| 57 kB 5.1 MB/s \n",
"\u001b[?25h"
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},
"id": "El-Ryp_9odXg",
"outputId": "392b0c95-61d0-41ea-b6f3-11f79cd08540"
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"source": [
"# pre-loading dataset\n",
"\n",
"import tensorflow as tf\n",
"import tensorflow_datasets as tfds\n",
"\n",
"# dataloader (tfds)\n",
"ds = tfds.load(\"cifar10\", as_supervised=True, shuffle_files=False, download=True)"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1mDownloading and preparing dataset cifar10/3.0.2 (download: 162.17 MiB, generated: 132.40 MiB, total: 294.58 MiB) to /root/tensorflow_datasets/cifar10/3.0.2...\u001b[0m\n"
]
},
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"text/plain": [
"Dl Completed...: 0 url [00:00, ? url/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
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"version_major": 2
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"text/plain": [
"Dl Size...: 0 MiB [00:00, ? MiB/s]"
]
},
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},
{
"output_type": "display_data",
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"text/plain": [
"Extraction completed...: 0 file [00:00, ? file/s]"
]
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{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"\n",
"\n"
]
},
{
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"text/plain": [
"0 examples [00:00, ? examples/s]"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Shuffling and writing examples to /root/tensorflow_datasets/cifar10/3.0.2.incompleteIM910N/cifar10-train.tfrecord\n"
]
},
{
"output_type": "display_data",
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"model_id": "cd413186f8604def8371fe38bf1f5ccb",
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"text/plain": [
" 0%| | 0/50000 [00:00<?, ? examples/s]"
]
},
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},
{
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"text/plain": [
"0 examples [00:00, ? examples/s]"
]
},
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},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Shuffling and writing examples to /root/tensorflow_datasets/cifar10/3.0.2.incompleteIM910N/cifar10-test.tfrecord\n"
]
},
{
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"text/plain": [
" 0%| | 0/10000 [00:00<?, ? examples/s]"
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},
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{
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"text": [
"\u001b[1mDataset cifar10 downloaded and prepared to /root/tensorflow_datasets/cifar10/3.0.2. Subsequent calls will reuse this data.\u001b[0m\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EVs8eX27t0w0"
},
"source": [
"## Training CNN on CIFAR10 (PyTorch)"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dLtpd4VW8yzV",
"outputId": "3ae39648-61d6-48cc-d694-fe09cbbc6f1a"
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"source": [
"import time\n",
"\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"\n",
"import tensorflow as tf\n",
"import tensorflow_datasets as tfds\n",
"\n",
"\n",
"def preprocessing(x, y):\n",
" x = tf.cast(x, tf.float32) / 255.\n",
" return x, y\n",
"\n",
"ds = tfds.load(\"cifar10\", as_supervised=True, shuffle_files=False, download=True)\n",
"train_set = ds[\"train\"]\n",
"train_set = train_set.shuffle(len(train_set), seed=0, reshuffle_each_iteration=True).batch(32).map(preprocessing).prefetch(1)\n",
"val_set = ds[\"test\"]\n",
"val_set = val_set.batch(32).map(preprocessing).prefetch(1)\n",
"\n",
"# model\n",
"class CNN(nn.Module):\n",
" def __init__(self):\n",
" super().__init__()\n",
" self.conv1 = nn.Conv2d(3, 32, 3, padding=1)\n",
" self.bn1 = nn.BatchNorm2d(32)\n",
" self.conv2 = nn.Conv2d(32, 64, 3, padding=1)\n",
" self.bn2 = nn.BatchNorm2d(64)\n",
" self.fc1 = nn.Linear(4096, 256)\n",
" self.fc2 = nn.Linear(256, 10)\n",
" \n",
" def forward(self, x):\n",
" h = self.conv1(x)\n",
" h = self.bn1(h)\n",
" h = torch.relu(h)\n",
" h = torch.max_pool2d(h, (2, 2))\n",
" h = self.conv2(h)\n",
" h = self.bn2(h)\n",
" h = torch.relu(h)\n",
" h = torch.max_pool2d(h, (2, 2))\n",
" h = h.reshape(len(h), -1)\n",
" h = self.fc1(h)\n",
" h = torch.relu(h)\n",
" h = self.fc2(h)\n",
" y = torch.log_softmax(h, -1)\n",
"\n",
" return y\n",
"\n",
"\n",
"model = CNN().cuda()\n",
"opt = optim.Adam(model.parameters(), lr=0.001)\n",
"criterion = nn.NLLLoss()\n",
"\n",
"def step(x, y, is_training=True):\n",
" x = x.permute(0, 3, 1, 2) # TFDS loads images in HWC format\n",
" y_pred = model(x)\n",
" loss = criterion(y_pred, y)\n",
" if is_training:\n",
" opt.zero_grad()\n",
" loss.backward()\n",
" opt.step()\n",
" return loss, y_pred\n",
" \n",
"for e in range(5):\n",
" tic = time.time()\n",
" train_loss, val_loss, acc = 0., 0., 0.\n",
" for x, y in train_set.as_numpy_iterator():\n",
" x = torch.from_numpy(x).cuda()\n",
" y = torch.from_numpy(y).cuda()\n",
" model.train()\n",
" loss, y_pred = step(x, y, is_training=True)\n",
" train_loss += loss.item()\n",
" train_loss /= len(train_set)\n",
"\n",
" for x, y in val_set.as_numpy_iterator(): \n",
" x = torch.from_numpy(x).cuda()\n",
" y = torch.from_numpy(y).cuda()\n",
" model.eval()\n",
" with torch.no_grad():\n",
" loss, y_pred = step(x, y, is_training=False)\n",
" val_loss += loss.item()\n",
" acc += (y_pred.max(-1)[1] == y).float().mean()\n",
" val_loss /= len(val_set)\n",
" acc /= len(val_set)\n",
" elapsed = time.time() - tic\n",
" \n",
" print(f\"train_loss: {train_loss:0.2f}, val_loss: {val_loss:0.2f}, val_acc: {acc:0.2f}, elapsed: {elapsed:0.2f}\")"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:68: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:180.)\n",
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:36: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"train_loss: 1.27, val_loss: 1.30, val_acc: 0.54, elapsed: 43.59\n",
"train_loss: 0.92, val_loss: 1.02, val_acc: 0.64, elapsed: 32.64\n",
"train_loss: 0.79, val_loss: 1.07, val_acc: 0.64, elapsed: 32.69\n",
"train_loss: 0.69, val_loss: 0.79, val_acc: 0.72, elapsed: 32.64\n",
"train_loss: 0.61, val_loss: 0.81, val_acc: 0.72, elapsed: 32.68\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "M6VHHlHTt3Z4"
},
"source": [
"## Training CNN on MNIST (JAX)"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JJeGco-XW8Bz",
"outputId": "2207184d-052c-4638-882e-6ca7bbe319fa"
},
"source": [
"\n",
"import time\n",
"from functools import partial\n",
"\n",
"import jax\n",
"import jax.numpy as jnp\n",
"import flax.linen as fnn\n",
"from flax.training.train_state import TrainState\n",
"import optax\n",
"import tensorflow as tf\n",
"import tensorflow_datasets as tfds\n",
"\n",
"def preprocessing(x, y):\n",
" x = tf.cast(x, tf.float32) / 255.\n",
" \n",
" return x, y\n",
"\n",
"ds = tfds.load(\"cifar10\", as_supervised=True, shuffle_files=False, download=True)\n",
"train_set = ds[\"train\"]\n",
"train_set = train_set.shuffle(len(train_set), seed=0, reshuffle_each_iteration=True).batch(32).map(preprocessing).prefetch(1)\n",
"val_set = ds[\"test\"]\n",
"val_set = val_set.batch(32).map(preprocessing).prefetch(1)\n",
"\n",
"# model\n",
"class CNN(fnn.Module):\n",
"\n",
" @fnn.compact\n",
" def __call__(self, x, is_training):\n",
" x = fnn.Conv(features=32, kernel_size=(3, 3))(x)\n",
" x = fnn.BatchNorm(use_running_average=not is_training, momentum=0.1)(x)\n",
" x = fnn.relu(x)\n",
" x = fnn.max_pool(x, window_shape=(2, 2), strides=(2, 2))\n",
" x = fnn.Conv(features=64, kernel_size=(3, 3))(x)\n",
" x = fnn.BatchNorm(use_running_average=not is_training, momentum=0.1)(x)\n",
" x = fnn.relu(x)\n",
" x = fnn.max_pool(x, window_shape=(2, 2), strides=(2, 2))\n",
" x = x.reshape((x.shape[0], -1)) # flatten\n",
" x = fnn.Dense(features=256)(x)\n",
" x = fnn.relu(x)\n",
" x = fnn.Dense(features=10)(x)\n",
" x = fnn.log_softmax(x)\n",
" \n",
" return x\n",
"\n",
"model = CNN()\n",
"variables = model.init(jax.random.PRNGKey(0), jnp.ones([1, 32, 32, 3]), True)\n",
"params = variables[\"params\"]\n",
"batch_stats = variables[\"batch_stats\"]\n",
"tx = optax.adam(0.001)\n",
"state = TrainState.create(apply_fn=model.apply, params=params, tx=tx)\n",
"\n",
"@partial(jax.jit, static_argnums=(4,))\n",
"def step(x, y, state, batch_stats, is_training=True):\n",
" def loss_fn(params, batch_stats):\n",
" y_pred, mutated_vars = state.apply_fn({\"params\": params, \"batch_stats\": batch_stats}, x, is_training, mutable=[\"batch_stats\"]) \n",
" new_batch_stats = mutated_vars[\"batch_stats\"]\n",
" loss = optax.softmax_cross_entropy(logits=y_pred, labels=y).mean()\n",
" return loss, (y_pred, new_batch_stats)\n",
" y = jnp.eye(10)[y]\n",
" if is_training:\n",
" grad_fn = jax.value_and_grad(loss_fn, has_aux=True)\n",
" (loss, (y_pred, new_batch_stats)), grads = grad_fn(state.params, batch_stats)\n",
" state = state.apply_gradients(grads=grads)\n",
" else:\n",
" loss, (y_pred, new_batch_stats) = loss_fn(state.params, batch_stats)\n",
" return loss, y_pred, state, new_batch_stats\n",
"\n",
"for e in range(5):\n",
" tic = time.time()\n",
" train_loss, val_loss, acc = 0., 0., 0.\n",
" for x, y in train_set.as_numpy_iterator(): \n",
" loss, y_pred, state, batch_stats = step(x, y, state, batch_stats, is_training=True)\n",
" train_loss += loss\n",
" train_loss /= len(train_set)\n",
"\n",
" for x, y in val_set.as_numpy_iterator(): \n",
" loss, y_pred, state, batch_stats = step(x, y, state, batch_stats, is_training=False)\n",
" val_loss += loss\n",
" acc += (jnp.argmax(y_pred, 1) == y).mean()\n",
" val_loss /= len(val_set)\n",
" acc /= len(val_set)\n",
" elapsed = time.time() - tic\n",
" \n",
" print(f\"train_loss: {train_loss:0.2f}, val_loss: {val_loss:0.2f}, val_acc: {acc:0.2f}, elapsed: {elapsed:0.2f}\")"
],
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"train_loss: 1.28, val_loss: 1.04, val_acc: 0.63, elapsed: 29.05\n",
"train_loss: 0.94, val_loss: 0.97, val_acc: 0.66, elapsed: 13.19\n",
"train_loss: 0.81, val_loss: 0.87, val_acc: 0.69, elapsed: 21.37\n",
"train_loss: 0.73, val_loss: 0.83, val_acc: 0.72, elapsed: 13.03\n",
"train_loss: 0.65, val_loss: 0.87, val_acc: 0.71, elapsed: 12.82\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "O50Faoc70OJq"
},
"source": [
"# Test BatchNorm Layer"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Of-ULkhXLt9h",
"outputId": "e87fedea-a099-40a6-d332-69f095ed5393"
},
"source": [
"import flax.linen as fnn\n",
"import jax.numpy as jnp\n",
"import jax\n",
"\n",
"class BN(fnn.Module):\n",
"\n",
" @fnn.compact\n",
" def __call__(self, x, use_running_average):\n",
" x = fnn.BatchNorm(use_running_average=use_running_average, momentum=0.5)(x)\n",
" return x\n",
"\n",
"model = BN()\n",
"variables = model.init(jax.random.PRNGKey(0), jnp.ones((1, 1)), True)\n",
"print(variables)"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"FrozenDict({\n",
" batch_stats: {\n",
" BatchNorm_0: {\n",
" mean: DeviceArray([0.], dtype=float32),\n",
" var: DeviceArray([1.], dtype=float32),\n",
" },\n",
" },\n",
" params: {\n",
" BatchNorm_0: {\n",
" scale: DeviceArray([1.], dtype=float32),\n",
" bias: DeviceArray([0.], dtype=float32),\n",
" },\n",
" },\n",
"})\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tKXa-g1K0MCY",
"outputId": "dce605bc-c777-4d4f-a261-7fb01af091cd"
},
"source": [
"params = variables[\"params\"]\n",
"batch_stats = variables[\"batch_stats\"]\n",
"x = jnp.array([[1.], [2.], [3.], [4.]])\n",
"y_pred, mutated_vars = model.apply({\"params\": params, \"batch_stats\": batch_stats}, x, use_running_average=False, mutable=[\"batch_stats\"])\n",
"new_batch_stats = mutated_vars[\"batch_stats\"]\n",
"print(new_batch_stats)"
],
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"FrozenDict({\n",
" BatchNorm_0: {\n",
" mean: DeviceArray([1.25], dtype=float32),\n",
" var: DeviceArray([1.125], dtype=float32),\n",
" },\n",
"})\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "r2xUTp9-r_9O",
"outputId": "3d55ffd3-621e-46e2-9cfc-59f9470552c3"
},
"source": [
"model.apply({\"params\": params, \"batch_stats\": new_batch_stats}, x, use_running_average=True)"
],
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DeviceArray([[-0.2357012],\n",
" [ 0.7071036],\n",
" [ 1.6499084],\n",
" [ 2.5927134]], dtype=float32)"
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "w-jtkHZ53tY_"
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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