DCGAN.Lightning/modules/Generators.py

+
+# ------------------------------------------------------------------
+#     _____ _     _ _
+#    |  ___(_) __| | | ___
+#    | |_  | |/ _` | |/ _ \
+#    |  _| | | (_| | |  __/
+#    |_|   |_|\__,_|_|\___|                         GAN / Generators
+# ------------------------------------------------------------------
+# Formation Introduction au Deep Learning  (FIDLE)
+# CNRS/MIAI - https://fidle.cnrs.fr
+# ------------------------------------------------------------------
+# JL Parouty (Mars 2024)
+
+
+
+import numpy as np
+import torch.nn as nn
+
+
+# -----------------------------------------------------------------------------
+# -- Generator n°1
+# -----------------------------------------------------------------------------
+#
+class Generator_1(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 1         : ',latent_dim,' to ',data_shape)
+
+        self.model = nn.Sequential(
+            
+            nn.Linear(latent_dim, 128),
+            nn.ReLU(),
+
+            nn.Linear(128,256),
+            nn.BatchNorm1d(256, 0.8),
+            nn.ReLU(),
+
+            nn.Linear(256, 512),
+            nn.BatchNorm1d(512, 0.8),
+            nn.ReLU(),
+
+            nn.Linear(512, 1024),
+            nn.BatchNorm1d(1024, 0.8),
+            nn.ReLU(),
+
+            nn.Linear(1024, int(np.prod(data_shape))),
+            nn.Sigmoid()
+
+        )
+
+    def forward(self, z):
+        img = self.model(z)
+        img = img.view(img.size(0), *self.img_shape)
+        return img
+
+
+# -----------------------------------------------------------------------------
+# -- Generator n°1
+# -----------------------------------------------------------------------------
+#
+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.UpsamplingNearest2d( scale_factor=2 ),
+            nn.UpsamplingBilinear2d( scale_factor=2 ),
+            nn.Conv2d( 64,128, (3,3), stride=(1,1), padding=(1,1) ),
+            nn.ReLU(),
+            nn.BatchNorm2d(128),
+
+            # nn.UpsamplingNearest2d( scale_factor=2 ),
+            nn.UpsamplingBilinear2d( scale_factor=2 ),
+            nn.Conv2d( 128,256, (3,3), stride=(1,1), padding=(1,1)),
+            nn.ReLU(),
+            nn.BatchNorm2d(256),
+
+            nn.Conv2d( 256,1, (5,5), stride=(1,1), padding=(2,2)),
+            nn.Sigmoid()
+
+        )
+
+    def forward(self, z):
+        img_nchw = self.model(z)
+        img_nhwc = img_nchw.permute(0, 2, 3, 1) # reformat from NCHW to NHWC
+        # img = img.view(img.size(0), *self.img_shape) # reformat from NCHW to NHWC
+        return img_nhwc
+
+
+