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moved images to github

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Eric Zelikman 2020-12-10 12:47:59 -05:00
parent d67c88f744
commit a0a54f23b9
1 changed files with 7 additions and 7 deletions
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C3W2_SRGAN_(Optional).ipynb
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@ -393,7 +393,7 @@
"\n", "\n",
"Don't worry if this is confusing! The algorithm is conveniently implemented as `torch.nn.PixelShuffle` in PyTorch, so as long as you have a general idea of how this works, you're set.\n", "Don't worry if this is confusing! The algorithm is conveniently implemented as `torch.nn.PixelShuffle` in PyTorch, so as long as you have a general idea of how this works, you're set.\n",
"\n", "\n",
"> ![Efficient Sub-pixel CNN](https://drive.google.com/uc?export=view&id=136LMcywv1r5W1f9-L-55bJ4AKAHlH8Zr)\n", "> ![Efficient Sub-pixel CNN](https://github.com/https-deeplearning-ai/GANs-Public/blob/master/SRGAN-PixelShuffle.png?raw=true)\n",
"*Efficient sub-pixel CNN, taken from Figure 1 of [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://arxiv.org/abs/1609.05158) (Shi et al. 2016). The PixelShuffle operation (also known as sub-pixel convolution) is shown as the last step on the right.*" "*Efficient sub-pixel CNN, taken from Figure 1 of [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://arxiv.org/abs/1609.05158) (Shi et al. 2016). The PixelShuffle operation (also known as sub-pixel convolution) is shown as the last step on the right.*"
] ]
}, },
@ -418,7 +418,7 @@
"\n", "\n",
"The super-resolution residual network (SRResNet) and the generator are the same thing. The generator network architecture is actually quite simple - just a bunch of convolutional layers, residual blocks, and pixel shuffling layers!\n", "The super-resolution residual network (SRResNet) and the generator are the same thing. The generator network architecture is actually quite simple - just a bunch of convolutional layers, residual blocks, and pixel shuffling layers!\n",
"\n", "\n",
"> ![SRGAN Generator](https://drive.google.com/uc?export=view&id=1wY5YmoYTBzuhWLlTAYCI92xWgkf_uINE)\n", "> ![SRGAN Generator](https://github.com/https-deeplearning-ai/GANs-Public/blob/master/SRGAN-Generator.png?raw=true)\n",
"*SRGAN Generator, taken from Figure 4 of [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://arxiv.org/abs/1609.04802) (Ledig et al. 2017).*" "*SRGAN Generator, taken from Figure 4 of [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://arxiv.org/abs/1609.04802) (Ledig et al. 2017).*"
] ]
}, },
@ -492,7 +492,7 @@
"\n", "\n",
"The discriminator architecture is also relatively straightforward, just one big sequential model - see the diagram below for reference!\n", "The discriminator architecture is also relatively straightforward, just one big sequential model - see the diagram below for reference!\n",
"\n", "\n",
"![SRGAN Generator](https://drive.google.com/uc?export=view&id=1fcfTrXBcODoZa2JSEO8OiMUEo5WHdkef)\n", "![SRGAN Generator](https://github.com/https-deeplearning-ai/GANs-Public/blob/master/SRGAN-Discriminator.png?raw=true)\n",
"*SRGAN Discriminator, taken from Figure 4 of [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://arxiv.org/abs/1609.04802) (Ledig et al. 2017).*" "*SRGAN Discriminator, taken from Figure 4 of [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://arxiv.org/abs/1609.04802) (Ledig et al. 2017).*"
] ]
}, },
@ -894,7 +894,6 @@
"cell_type": "code", "cell_type": "code",
"metadata": { "metadata": {
"id": "ScH0Iok8fAMS", "id": "ScH0Iok8fAMS",
"outputId": "388ba277-76ce-427b-b7b1-57ab74e9b1dd",
"colab": { "colab": {
"base_uri": "https://localhost:8080/", "base_uri": "https://localhost:8080/",
"height": 84, "height": 84,
@ -908,7 +907,8 @@
"0c356917abbf4afb8ef89e5e48930635", "0c356917abbf4afb8ef89e5e48930635",
"c0209daeab5f4a09b01a23370e09d352" "c0209daeab5f4a09b01a23370e09d352"
] ]
} },
"outputId": "388ba277-76ce-427b-b7b1-57ab74e9b1dd"
}, },
"source": [ "source": [
"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
@ -968,11 +968,11 @@
"cell_type": "code", "cell_type": "code",
"metadata": { "metadata": {
"id": "AvqBuRwjZrBq", "id": "AvqBuRwjZrBq",
"outputId": "96ff8cef-0371-4bb3-d4ff-b1c1aee180b6",
"colab": { "colab": {
"base_uri": "https://localhost:8080/", "base_uri": "https://localhost:8080/",
"height": 1000 "height": 1000
} },
"outputId": "96ff8cef-0371-4bb3-d4ff-b1c1aee180b6"
}, },
"source": [ "source": [
"generator = torch.load('srresnet.pt')\n", "generator = torch.load('srresnet.pt')\n",