{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Siamese_networks.ipynb",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
""
]
},
{
"cell_type": "code",
"metadata": {
"id": "ffad2gE9AmLs"
},
"source": [
"!pip install -q torch_snippets\n",
"from torch_snippets import *\n",
"!wget -q https://www.dropbox.com/s/ua1rr8btkmpqjxh/face-detection.zip\n",
"!unzip -q face-detection.zip\n",
"device = 'cuda' if torch.cuda.is_available() else 'cpu'"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "xZcDcdb2AocA"
},
"source": [
"class SiameseNetworkDataset(Dataset):\n",
" def __init__(self, folder, transform=None, should_invert=True):\n",
" self.folder = folder\n",
" self.items = Glob(f'{self.folder}/*/*') \n",
" self.transform = transform\n",
" def __getitem__(self, ix):\n",
" itemA = self.items[ix]\n",
" person = fname(parent(itemA))\n",
" same_person = randint(2)\n",
" if same_person:\n",
" itemB = choose(Glob(f'{self.folder}/{person}/*', silent=True))\n",
" else:\n",
" while True:\n",
" itemB = choose(self.items)\n",
" if person != fname(parent(itemB)):\n",
" break\n",
" imgA = read(itemA)\n",
" imgB = read(itemB)\n",
" if self.transform:\n",
" imgA = self.transform(imgA)\n",
" imgB = self.transform(imgB)\n",
" return imgA, imgB, np.array([1-same_person])\n",
" def __len__(self):\n",
" return len(self.items)\n"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "CinJWNg2AvO_",
"outputId": "de794434-58b7-4100-8414-ead41dda1ca4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 52
}
},
"source": [
"from torchvision import transforms\n",
"\n",
"trn_tfms = transforms.Compose([\n",
" transforms.ToPILImage(),\n",
" transforms.RandomHorizontalFlip(),\n",
" transforms.RandomAffine(5, (0.01,0.2),\n",
" scale=(0.9,1.1)),\n",
" transforms.Resize((100,100)),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize((0.5), (0.5))\n",
"])\n",
"\n",
"val_tfms = transforms.Compose([\n",
" transforms.ToPILImage(),\n",
" transforms.Resize((100,100)),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize((0.5), (0.5))\n",
"])\n",
"\n",
"trn_ds = SiameseNetworkDataset(folder=\"./data/faces/training/\", transform=trn_tfms)\n",
"val_ds = SiameseNetworkDataset(folder=\"./data/faces/testing/\", transform=val_tfms)\n",
"\n",
"trn_dl = DataLoader(trn_ds, shuffle=True, batch_size=64)\n",
"val_dl = DataLoader(val_ds, shuffle=False, batch_size=64)"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"2020-10-05 17:18:49.095 | INFO | torch_snippets.loader:Glob:161 - 370 files found at ./data/faces/training//*/*\n",
"2020-10-05 17:18:49.097 | INFO | torch_snippets.loader:Glob:161 - 30 files found at ./data/faces/testing//*/*\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "a_zb_HlTAxY6"
},
"source": [
"def convBlock(ni, no):\n",
" return nn.Sequential(\n",
" nn.Dropout(0.2),\n",
" nn.Conv2d(ni, no, kernel_size=3, padding=1, padding_mode='reflect'),\n",
" nn.ReLU(inplace=True),\n",
" nn.BatchNorm2d(no),\n",
" )"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "_k8qYdmnAzDv"
},
"source": [
"class SiameseNetwork(nn.Module):\n",
" def __init__(self):\n",
" super(SiameseNetwork, self).__init__()\n",
" self.features = nn.Sequential(\n",
" convBlock(1,4),\n",
" convBlock(4,8),\n",
" convBlock(8,8),\n",
" nn.Flatten(),\n",
" nn.Linear(8*100*100, 500), nn.ReLU(inplace=True),\n",
" nn.Linear(500, 500), nn.ReLU(inplace=True),\n",
" nn.Linear(500, 5)\n",
" )\n",
"\n",
" def forward(self, input1, input2):\n",
" output1 = self.features(input1)\n",
" output2 = self.features(input2)\n",
" return output1, output2"
],
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "qOawdFCHA1Ao"
},
"source": [
"class ContrastiveLoss(torch.nn.Module):\n",
" \"\"\"\n",
" Contrastive loss function.\n",
" Based on: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf\n",
" \"\"\"\n",
"\n",
" def __init__(self, margin=2.0):\n",
" super(ContrastiveLoss, self).__init__()\n",
" self.margin = margin\n",
" def forward(self, output1, output2, label):\n",
" euclidean_distance = F.pairwise_distance(output1, output2, keepdim = True)\n",
" loss_contrastive = torch.mean((1-label) * torch.pow(euclidean_distance, 2) +\n",
" (label) * torch.pow(torch.clamp(self.margin - euclidean_distance, min=0.0), 2))\n",
" acc = ((euclidean_distance > 0.6) == label).float().mean()\n",
" return loss_contrastive, acc"
],
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "r0PZEC-0A4aD"
},
"source": [
"def train_batch(model, data, optimizer, criterion):\n",
" imgsA, imgsB, labels = [t.to(device) for t in data]\n",
" optimizer.zero_grad()\n",
" codesA, codesB = model(imgsA, imgsB)\n",
" loss, acc = criterion(codesA, codesB, labels)\n",
" loss.backward()\n",
" optimizer.step()\n",
" return loss.item(), acc.item()\n",
"\n",
"@torch.no_grad()\n",
"def validate_batch(model, data, criterion):\n",
" imgsA, imgsB, labels = [t.to(device) for t in data]\n",
" codesA, codesB = model(imgsA, imgsB)\n",
" loss, acc = criterion(codesA, codesB, labels)\n",
" return loss.item(), acc.item()"
],
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "O99n1-N6A6Vh"
},
"source": [
"model = SiameseNetwork().to(device)\n",
"criterion = ContrastiveLoss()\n",
"optimizer = optim.Adam(model.parameters(),lr = 0.001)"
],
"execution_count": 8,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "d8u-q8l7A70U",
"outputId": "4f932e2e-a537-4045-dd5d-90c2909d484e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 190
}
},
"source": [
"n_epochs = 200\n",
"log = Report(n_epochs)\n",
"\n",
"for epoch in range(n_epochs):\n",
" N = len(trn_dl)\n",
" for i, data in enumerate(trn_dl):\n",
" loss, acc = train_batch(model, data, optimizer, criterion)\n",
" log.record(epoch+(1+i)/N, trn_loss=loss, trn_acc=acc, end='\\r')\n",
" N = len(val_dl)\n",
" for i, data in enumerate(val_dl):\n",
" loss, acc = validate_batch(model, data, criterion)\n",
" log.record(epoch+(1+i)/N, val_loss=loss, val_acc=acc, end='\\r')\n",
" if (epoch+1)%20==0: log.report_avgs(epoch+1)\n",
" if epoch==10: optimizer = optim.Adam(model.parameters(), lr=0.0005)"
],
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"text": [
"EPOCH: 20.000\ttrn_loss: 0.663\ttrn_acc: 0.660\tval_loss: 0.602\tval_acc: 0.600\t(16.50s - 148.47s remaining)\n",
"EPOCH: 40.000\ttrn_loss: 0.430\ttrn_acc: 0.762\tval_loss: 0.465\tval_acc: 0.667\t(32.57s - 130.28s remaining)\n",
"EPOCH: 60.000\ttrn_loss: 0.395\ttrn_acc: 0.746\tval_loss: 0.371\tval_acc: 0.700\t(48.54s - 113.26s remaining)\n",
"EPOCH: 80.000\ttrn_loss: 0.255\ttrn_acc: 0.851\tval_loss: 0.220\tval_acc: 0.867\t(64.58s - 96.87s remaining)\n",
"EPOCH: 100.000\ttrn_loss: 0.252\ttrn_acc: 0.852\tval_loss: 0.381\tval_acc: 0.767\t(80.24s - 80.24s remaining)\n",
"EPOCH: 120.000\ttrn_loss: 0.212\ttrn_acc: 0.900\tval_loss: 0.214\tval_acc: 0.800\t(95.98s - 63.99s remaining)\n",
"EPOCH: 140.000\ttrn_loss: 0.251\ttrn_acc: 0.879\tval_loss: 0.385\tval_acc: 0.833\t(111.54s - 47.80s remaining)\n",
"EPOCH: 160.000\ttrn_loss: 0.201\ttrn_acc: 0.891\tval_loss: 0.257\tval_acc: 0.833\t(127.11s - 31.78s remaining)\n",
"EPOCH: 180.000\ttrn_loss: 0.156\ttrn_acc: 0.920\tval_loss: 0.400\tval_acc: 0.733\t(142.95s - 15.88s remaining)\n",
"EPOCH: 200.000\ttrn_loss: 0.174\ttrn_acc: 0.909\tval_loss: 0.377\tval_acc: 0.767\t(158.47s - 0.00s remaining)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "v7PNY-3SA-9o",
"outputId": "a93b8ff7-61e7-489d-e71c-de2c48e372d7",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 905
}
},
"source": [
"log.plot_epochs(['trn_loss', 'val_loss'], log=True, title='Variation in training and validation loss')\n",
"log.plot_epochs(['trn_acc', 'val_acc'], title='Variation in training and validation accuracy')"
],
"execution_count": 21,
"outputs": [
{
"output_type": "stream",
"text": [
" 0%| | 0/200 [00:00, ?it/s]/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py:3335: RuntimeWarning: Mean of empty slice.\n",
" out=out, **kwargs)\n",
"/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
" ret = ret.dtype.type(ret / rcount)\n",
"100%|██████████| 200/200 [00:00<00:00, 5808.36it/s]\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
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\n",
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