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Model/Model.py

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+import torch
+import torch.nn as nn
+
+
+nc = 1
+image_size = 64
+ngpu = 1
+features_d = 64
+features_g = 64
+Z_dim = 250
+channels_noise = Z_dim
+
+
+
+class Critic(nn.Module):
+    def __init__(self, ngpu):
+        super(Critic, self).__init__()
+        self.ngpu = ngpu
+        self.image_size = image_size
+        self.l1 = nn.Linear(200, image_size * image_size * nc)
+        self.disc = nn.Sequential(
+            nn.Conv2d(nc * 2, features_d, kernel_size=4, stride=2, padding=1),
+            nn.LeakyReLU(0.2),
+            self._block(features_d, features_d * 2, 4, 2, 1),
+            self._block(features_d * 2, features_d * 4, 4, 2, 1),
+            self._block(features_d * 4, features_d * 8, 4, 2, 1),
+            nn.Conv2d(features_d * 8, 1, kernel_size=4, stride=1, padding=0),
+            # nn.Sigmoid(),
+        )
+
+    def _block(self, in_channels, out_channels, kernel_size, stride, padding):
+        return nn.Sequential(
+            nn.Conv2d(
+                in_channels, out_channels, kernel_size, stride, padding, bias=False,
+            ),
+            nn.InstanceNorm2d(out_channels, affine=True),
+            nn.LeakyReLU(0.2, inplace=True),
+        )
+
+    def forward(self, img, points21):
+        x1 = img
+        x2 = self.l1(points21)
+        # x2 = x2.reshape(int(b_size / ngpu), nc, image_size, image_size)
+        x2 = x2.reshape(-1, nc, image_size, image_size)
+        combine = torch.cat((x1, x2), dim=1)
+        return self.disc(combine)
+
+
+class Generator(nn.Module):
+    def __init__(self, ngpu):
+        super(Generator, self).__init__()
+        self.ngpu = ngpu
+        self.net = nn.Sequential(
+            # Input: N x channels_noise x 1 x 1
+            self._block(channels_noise, features_g * 16, 4, 1, 0),  # img: 4x4
+            self._block(features_g * 16, features_g * 8, 4, 2, 1),  # img: 8x8
+            self._block(features_g * 8, features_g * 4, 4, 2, 1),  # img: 16x16
+            self._block(features_g * 4, features_g * 2, 4, 2, 1),  # img: 32x32
+            nn.ConvTranspose2d(
+                features_g * 2, nc, kernel_size=4, stride=2, padding=1
+            ),
+            # Output: N x channels_img x 64 x 64
+            nn.Tanh(),
+        )
+
+    def _block(self, in_channels, out_channels, kernel_size, stride, padding):
+        return nn.Sequential(
+            nn.ConvTranspose2d(
+                in_channels, out_channels, kernel_size, stride, padding, bias=False,
+            ),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(),
+        )
+
+    def forward(self, points):
+
+        return self.net(points)
+
+
+def initialize_weights(model):
+    for m in model.modules():
+        if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d, nn.BatchNorm2d)):
+            nn.init.normal_(m.weight.data, 0.0, 0.02)

Model/__init__.py

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+# Disable TF deprecation warnings.
+# Syntax from tf1 is not expected to be compatible with tf2.
+import tensorflow as tf
+tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+
+# Algorithms
+from spinup.algos.tf1.ddpg.ddpg import ddpg as ddpg_tf1
+from spinup.algos.tf1.ppo.ppo import ppo as ppo_tf1
+from spinup.algos.tf1.sac.sac import sac as sac_tf1
+from spinup.algos.tf1.td3.td3 import td3 as td3_tf1
+from spinup.algos.tf1.trpo.trpo import trpo as trpo_tf1
+from spinup.algos.tf1.vpg.vpg import vpg as vpg_tf1
+
+from spinup.algos.pytorch.ddpg.ddpg import ddpg as ddpg_pytorch
+from spinup.algos.pytorch.ppo.ppo import ppo as ppo_pytorch
+from spinup.algos.pytorch.sac.sac import sac as sac_pytorch
+from spinup.algos.pytorch.td3.td3 import td3 as td3_pytorch
+from spinup.algos.pytorch.trpo.trpo import trpo as trpo_pytorch
+from spinup.algos.pytorch.vpg.vpg import vpg as vpg_pytorch
+
+# Loggers
+from spinup.utils.logx import Logger, EpochLogger
+
+# Version
+from spinup.version import __version__