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dog_classifier.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Dog_Classifier.ipynb",
"provenance": [],
"authorship_tag": "ABX9TyMJcIFLY77tMMg0lxC3z4kT",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/samyumobi/4a2c855d5f9e762f3b6f8ff60266a3c4/dog_classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "9r57X9-Jgkls"
},
"source": [
"!pip install d2l==0.16.1\r\n",
"!pip install -U mxnet-cu101==1.7.0\r\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "1-eTZvlpkhRB"
},
"source": [
"from d2l import mxnet as d2l\r\n",
"from mxnet import autograd, gluon, init, npx\r\n",
"from mxnet.gluon import nn\r\n",
"import os\r\n",
"\r\n",
"npx.set_np()"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "SGcSbWVrklSv"
},
"source": [
"d2l.DATA_HUB['dog_tiny'] = (d2l.DATA_URL + 'kaggle_dog_tiny.zip',\r\n",
" '0cb91d09b814ecdc07b50f31f8dcad3e81d6a86d')\r\n",
"\r\n",
"# If you use the full dataset downloaded for the Kaggle competition, change\r\n",
"# the variable below to False\r\n",
"demo = True\r\n",
"if demo:\r\n",
" data_dir = d2l.download_extract('dog_tiny')\r\n",
"else:\r\n",
" data_dir = os.path.join('..', 'data', 'dog-breed-identification')"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "yglqConPktUl"
},
"source": [
"def reorg_dog_data(data_dir, valid_ratio):\r\n",
" labels = d2l.read_csv_labels(os.path.join(data_dir, 'labels.csv'))\r\n",
" d2l.reorg_train_valid(data_dir, labels, valid_ratio)\r\n",
" d2l.reorg_test(data_dir)\r\n",
"\r\n",
" \r\n",
"batch_size = 4 if demo else 128\r\n",
"valid_ratio = 0.1\r\n",
"reorg_dog_data(data_dir, valid_ratio)"
],
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "gOxSaL3rk1je"
},
"source": [
"transform_train = gluon.data.vision.transforms.Compose([\r\n",
" # Randomly crop the image to obtain an image with an area of 0.08 to 1 of\r\n",
" # the original area and height to width ratio between 3/4 and 4/3. Then,\r\n",
" # scale the image to create a new image with a height and width of 224\r\n",
" # pixels each\r\n",
" gluon.data.vision.transforms.RandomResizedCrop(224, scale=(0.08, 1.0),\r\n",
" ratio=(3.0/4.0, 4.0/3.0)),\r\n",
" gluon.data.vision.transforms.RandomFlipLeftRight(),\r\n",
" # Randomly change the brightness, contrast, and saturation\r\n",
" gluon.data.vision.transforms.RandomColorJitter(brightness=0.4,\r\n",
" contrast=0.4,\r\n",
" saturation=0.4),\r\n",
" # Add random noise\r\n",
" gluon.data.vision.transforms.RandomLighting(0.1),\r\n",
" gluon.data.vision.transforms.ToTensor(),\r\n",
" # Standardize each channel of the image\r\n",
" gluon.data.vision.transforms.Normalize([0.485, 0.456, 0.406],\r\n",
" [0.229, 0.224, 0.225])])"
],
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Fd4ueeyEk5cN"
},
"source": [
"transform_test = gluon.data.vision.transforms.Compose([\r\n",
" gluon.data.vision.transforms.Resize(256),\r\n",
" # Crop a square of 224 by 224 from the center of the image\r\n",
" gluon.data.vision.transforms.CenterCrop(224),\r\n",
" gluon.data.vision.transforms.ToTensor(),\r\n",
" gluon.data.vision.transforms.Normalize([0.485, 0.456, 0.406],\r\n",
" [0.229, 0.224, 0.225])])"
],
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "4AstW8NWk9WI"
},
"source": [
"train_ds, valid_ds, train_valid_ds, test_ds = [\r\n",
" gluon.data.vision.ImageFolderDataset(\r\n",
" os.path.join(data_dir, 'train_valid_test', folder))\r\n",
" for folder in ('train', 'valid', 'train_valid', 'test')]"
],
"execution_count": 8,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "x9mJRjVRk-kE"
},
"source": [
"train_iter, train_valid_iter = [gluon.data.DataLoader(\r\n",
" dataset.transform_first(transform_train), batch_size, shuffle=True, \r\n",
" last_batch='discard') for dataset in (train_ds, train_valid_ds)]\r\n",
"\r\n",
"valid_iter = gluon.data.DataLoader(\r\n",
" valid_ds.transform_first(transform_test), batch_size, shuffle=False, \r\n",
" last_batch='discard')\r\n",
"\r\n",
"test_iter = gluon.data.DataLoader(\r\n",
" test_ds.transform_first(transform_test), batch_size, shuffle=False, \r\n",
" last_batch='keep')"
],
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "WRwpU98flFDC"
},
"source": [
"def get_net(devices):\r\n",
" finetune_net = gluon.model_zoo.vision.resnet34_v2(pretrained=True)\r\n",
" # Define a new output network\r\n",
" finetune_net.output_new = nn.HybridSequential(prefix='')\r\n",
" finetune_net.output_new.add(nn.Dense(256, activation='relu'))\r\n",
" # There are 120 output categories\r\n",
" finetune_net.output_new.add(nn.Dense(120))\r\n",
" # Initialize the output network\r\n",
" finetune_net.output_new.initialize(init.Xavier(), ctx=devices)\r\n",
" # Distribute the model parameters to the CPUs or GPUs used for computation\r\n",
" finetune_net.collect_params().reset_ctx(devices)\r\n",
" return finetune_net"
],
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "jcdfaFgTlKgO"
},
"source": [
"loss = gluon.loss.SoftmaxCrossEntropyLoss()\r\n",
"\r\n",
"def evaluate_loss(data_iter, net, devices):\r\n",
" l_sum, n = 0.0, 0\r\n",
" for features, labels in data_iter:\r\n",
" X_shards, y_shards = d2l.split_batch(features, labels, devices)\r\n",
" output_features = [net.features(X_shard) for X_shard in X_shards]\r\n",
" outputs = [net.output_new(feature) for feature in output_features]\r\n",
" ls = [loss(output, y_shard).sum() for output, y_shard\r\n",
" in zip(outputs, y_shards)]\r\n",
" l_sum += sum([float(l.sum()) for l in ls])\r\n",
" n += labels.size\r\n",
" return l_sum / n"
],
"execution_count": 11,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "7pf9lirWlLVP"
},
"source": [
"def train(net, train_iter, valid_iter, num_epochs, lr, wd, devices, lr_period,\r\n",
" lr_decay):\r\n",
" # Only train the small custom output network\r\n",
" trainer = gluon.Trainer(net.output_new.collect_params(), 'sgd',\r\n",
" {'learning_rate': lr, 'momentum': 0.9, 'wd': wd})\r\n",
" num_batches, timer = len(train_iter), d2l.Timer()\r\n",
" animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs],\r\n",
" legend=['train loss', 'valid loss'])\r\n",
" for epoch in range(num_epochs):\r\n",
" metric = d2l.Accumulator(2)\r\n",
" if epoch > 0 and epoch % lr_period == 0:\r\n",
" trainer.set_learning_rate(trainer.learning_rate * lr_decay)\r\n",
" for i, (features, labels) in enumerate(train_iter):\r\n",
" timer.start()\r\n",
" X_shards, y_shards = d2l.split_batch(features, labels, devices)\r\n",
" output_features = [net.features(X_shard) for X_shard in X_shards]\r\n",
" with autograd.record():\r\n",
" outputs = [net.output_new(feature)\r\n",
" for feature in output_features]\r\n",
" ls = [loss(output, y_shard).sum() for output, y_shard\r\n",
" in zip(outputs, y_shards)]\r\n",
" for l in ls:\r\n",
" l.backward()\r\n",
" trainer.step(batch_size)\r\n",
" metric.add(sum([float(l.sum()) for l in ls]), labels.shape[0])\r\n",
" timer.stop()\r\n",
" if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:\r\n",
" animator.add(epoch + (i + 1) / num_batches, \r\n",
" (metric[0] / metric[1], None))\r\n",
" if valid_iter is not None:\r\n",
" valid_loss = evaluate_loss(valid_iter, net, devices)\r\n",
" animator.add(epoch + 1, (None, valid_loss))\r\n",
" if valid_iter is not None:\r\n",
" print(f'train loss {metric[0] / metric[1]:.3f}, '\r\n",
" f'valid loss {valid_loss:.3f}')\r\n",
" else:\r\n",
" print(f'train loss {metric[0] / metric[1]:.3f}')\r\n",
" print(f'{metric[1] * num_epochs / timer.sum():.1f} examples/sec '\r\n",
" f'on {str(devices)}')"
],
"execution_count": 12,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 301
},
"id": "jNUBIRkqlN9T",
"outputId": "a1e635d2-3a15-4879-c6aa-3990ccc6bdab"
},
"source": [
"devices, num_epochs, lr, wd = d2l.try_all_gpus(), 5, 0.01, 1e-4\r\n",
"lr_period, lr_decay, net = 10, 0.1, get_net(devices)\r\n",
"net.hybridize()\r\n",
"train(net, train_iter, valid_iter, num_epochs, lr, wd, devices, lr_period,\r\n",
" lr_decay)"
],
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"text": [
"train loss 2.265, valid loss 3.017\n",
"8.1 examples/sec on [cpu(0)]\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 297
},
"id": "uHvFXBYClQjJ",
"outputId": "b1fd6273-2497-4f11-b8f3-3ba0c96f9ad1"
},
"source": [
"net = get_net(devices)\r\n",
"net.hybridize()\r\n",
"train(net, train_valid_iter, None, num_epochs, lr, wd, devices, lr_period,\r\n",
" lr_decay)\r\n",
"\r\n",
"preds = []\r\n",
"for data, label in test_iter:\r\n",
" output_features = net.features(data.as_in_ctx(devices[0]))\r\n",
" output = npx.softmax(net.output_new(output_features))\r\n",
" preds.extend(output.asnumpy())\r\n",
"ids = sorted(os.listdir(\r\n",
" os.path.join(data_dir, 'train_valid_test', 'test', 'unknown')))\r\n",
"with open('submission.csv', 'w') as f:\r\n",
" f.write('id,' + ','.join(train_valid_ds.synsets) + '\\n')\r\n",
" for i, output in zip(ids, preds):\r\n",
" f.write(i.split('.')[0] + ',' + ','.join(\r\n",
" [str(num) for num in output]) + '\\n')"
],
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"text": [
"train loss 2.439\n",
"8.1 examples/sec on [cpu(0)]\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": [
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},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "hbU98ZoalTmA"
},
"source": [
""
],
"execution_count": 14,
"outputs": []
}
]
}
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