Colaboratory를 통해 생성됨

Author: Seonghoon-Yu

GAN/pix2pix(2016).ipynb

@@ -6,7 +6,7 @@
       "name": "pix2pix(2016).ipynb",
       "provenance": [],
       "machine_shape": "hm",
-      "authorship_tag": "ABX9TyN2YQrXN7TVuSj3zNIUbCHW",
+      "authorship_tag": "ABX9TyMt3J3nGrabI++eewLqG6Ia",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -64,7 +64,7 @@
         "\n",
         "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
       ],
-      "execution_count": 23,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -88,7 +88,7 @@
       "source": [
         "!git clone https://github.com/mrzhu-cool/pix2pix-pytorch # 깃 클론"
       ],
-      "execution_count": 2,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -110,7 +110,7 @@
       "source": [
         "!mkdir 'data' # 폴더 생성"
       ],
-      "execution_count": 3,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -156,7 +156,7 @@
         "    def __len__(self):\n",
         "        return len(self.img_filenames)"
       ],
-      "execution_count": 69,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -172,7 +172,7 @@
         "                    transforms.Resize((256,256))\n",
         "])"
       ],
-      "execution_count": 70,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -185,7 +185,7 @@
         "path2img = '/content/data/facades/train'\n",
         "train_ds = FacadeDataset(path2img, transform=transform)"
       ],
-      "execution_count": 71,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -209,7 +209,7 @@
         "plt.imshow(to_pil_image(0.5*b+0.5))\n",
         "plt.axis('off')"
       ],
-      "execution_count": 72,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -247,7 +247,7 @@
         "# 데이터 로더 생성하기\n",
         "train_dl = DataLoader(train_ds, batch_size=32, shuffle=True)"
       ],
-      "execution_count": 73,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -296,7 +296,7 @@
         "down_out = model(x)\n",
         "print(down_out.shape)"
       ],
-      "execution_count": 74,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -343,7 +343,7 @@
         "out = model(x,down_out)\n",
         "print(out.shape)"
       ],
-      "execution_count": 75,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -417,7 +417,7 @@
         "out = model(x)\n",
         "print(out.shape)"
       ],
-      "execution_count": 76,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -459,7 +459,7 @@
         "out = model(x)\n",
         "print(out.shape)"
       ],
-      "execution_count": 77,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -482,7 +482,7 @@
       "source": [
         "# Discriminator은 patch gan을 사용합니다.\n",
         "# Patch Gan: 이미지를 16x16의 패치로 분할하여 각 패치가 진짜인지 가짜인지 식별합니다.\n",
-        "# low-frequency에서 정확도가 향상됩니다.\n",
+        "# high-frequency에서 정확도가 향상됩니다.\n",
         "\n",
         "class Discriminator(nn.Module):\n",
         "    def __init__(self, in_channels=3):\n",
@@ -510,7 +510,7 @@
         "out = model(x,x)\n",
         "print(out.shape)"
       ],
-      "execution_count": 78,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -530,7 +530,7 @@
         "model_gen = GeneratorUNet().to(device)\n",
         "model_dis = Discriminator().to(device)"
       ],
-      "execution_count": 79,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -550,7 +550,7 @@
         "model_gen.apply(initialize_weights);\n",
         "model_dis.apply(initialize_weights);"
       ],
-      "execution_count": 80,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -587,7 +587,7 @@
         "opt_dis = optim.Adam(model_dis.parameters(),lr=lr,betas=(beta1,beta2))\n",
         "opt_gen = optim.Adam(model_gen.parameters(),lr=lr,betas=(beta1,beta2))"
       ],
-      "execution_count": 81,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -656,7 +656,7 @@
         "        if batch_count % 100 == 0:\n",
         "            print('Epoch: %.0f, G_Loss: %.6f, D_Loss: %.6f, time: %.2f min' %(epoch, g_loss.item(), d_loss.item(), (time.time()-start_time)/60))"
       ],
-      "execution_count": 82,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -700,7 +700,7 @@
         "plt.legend()\n",
         "plt.show()"
       ],
-      "execution_count": 83,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "display_data",
@@ -732,7 +732,7 @@
         "torch.save(model_gen.state_dict(), path2weights_gen)\n",
         "torch.save(model_dis.state_dict(), path2weights_dis)"
       ],
-      "execution_count": 84,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -758,7 +758,7 @@
         "weights = torch.load(path2weights_gen)\n",
         "model_gen.load_state_dict(weights)"
       ],
-      "execution_count": 85,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -790,7 +790,7 @@
         "        real_imgs = b\n",
         "        break"
       ],
-      "execution_count": 86,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -815,7 +815,7 @@
         "    plt.imshow(to_pil_image(0.5*fake_imgs[ii]+0.5))\n",
         "    plt.axis('off')\n"
       ],
-      "execution_count": 87,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "display_data",
@@ -840,7 +840,7 @@
       "source": [
         ""
       ],
-      "execution_count": 87,
+      "execution_count": null,
       "outputs": []
     }
   ]