From 379474fd5e36093b448dceb50a47ae44dfe80233 Mon Sep 17 00:00:00 2001
From: Jean-Luc Parouty <Jean-Luc.Parouty@simap.grenoble-inp.fr>
Date: Mon, 27 Feb 2023 17:16:17 +0100
Subject: [PATCH] Add Generator_2() with upscaling + conv2d
---
DCGAN-PyTorch/01-DCGAN-PL.ipynb | 10 ++++----
DCGAN-PyTorch/modules/Generators.py | 38 +++++++++++++++++++++++++++--
2 files changed, 41 insertions(+), 7 deletions(-)
diff --git a/DCGAN-PyTorch/01-DCGAN-PL.ipynb b/DCGAN-PyTorch/01-DCGAN-PL.ipynb
index a0973d1..ea028f9 100644
--- a/DCGAN-PyTorch/01-DCGAN-PL.ipynb
+++ b/DCGAN-PyTorch/01-DCGAN-PL.ipynb
@@ -79,7 +79,7 @@
"source": [
"latent_dim = 128\n",
" \n",
- "generator_class = 'Generator_1'\n",
+ "generator_class = 'Generator_2'\n",
"discriminator_class = 'Discriminator_1' \n",
" \n",
"scale = .1\n",
@@ -169,7 +169,7 @@
"outputs": [],
"source": [
"print('\\nInstantiation :\\n')\n",
- "generator = Generator_1(latent_dim=latent_dim, data_shape=data_shape)\n",
+ "generator = Generator_2(latent_dim=latent_dim, data_shape=data_shape)\n",
"discriminator = Discriminator_1(latent_dim=latent_dim, data_shape=data_shape)\n",
"\n",
"print('\\nFew tests :\\n')\n",
@@ -308,9 +308,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "pytorch-gpu-1.13.0_py3.10.8",
+ "display_name": "fidle-env",
"language": "python",
- "name": "module-conda-env-pytorch-gpu-1.13.0_py3.10.8"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -322,7 +322,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.8"
+ "version": "3.9.2"
},
"vscode": {
"interpreter": {
diff --git a/DCGAN-PyTorch/modules/Generators.py b/DCGAN-PyTorch/modules/Generators.py
index 0c49adb..cb18782 100644
--- a/DCGAN-PyTorch/modules/Generators.py
+++ b/DCGAN-PyTorch/modules/Generators.py
@@ -22,7 +22,7 @@ class Generator_1(nn.Module):
super().__init__()
self.latent_dim = latent_dim
self.img_shape = data_shape
- print('init generator : ',latent_dim,' to ',data_shape)
+ print('init generator 1 : ',latent_dim,' to ',data_shape)
self.model = nn.Sequential(
@@ -50,4 +50,38 @@ class Generator_1(nn.Module):
def forward(self, z):
img = self.model(z)
img = img.view(img.size(0), *self.img_shape)
- return img
\ No newline at end of file
+ return img
+
+
+
+class Generator_2(nn.Module):
+
+ def __init__(self, latent_dim=None, data_shape=None):
+ super().__init__()
+ self.latent_dim = latent_dim
+ self.img_shape = data_shape
+ print('init generator 2 : ',latent_dim,' to ',data_shape)
+
+ self.model = nn.Sequential(
+
+ nn.Linear(latent_dim, 7*7*64),
+ nn.Unflatten(1, (64,7,7)),
+
+ nn.UpsamplingBilinear2d( scale_factor=2 ),
+ nn.Conv2d( 64,128, (3,3), stride=(1,1), padding=(1,1) ),
+ nn.ReLU(),
+
+ nn.UpsamplingBilinear2d( scale_factor=2 ),
+ nn.Conv2d( 128,256, (3,3), stride=(1,1), padding=(1,1)),
+ nn.ReLU(),
+
+ nn.Conv2d( 256,1, (5,5), stride=(1,1), padding=(2,2)),
+ nn.Sigmoid()
+
+ )
+
+ def forward(self, z):
+ img = self.model(z)
+ img = img.view(img.size(0), *self.img_shape)
+ return img
+
--
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