added sample module

vae/main.py

@@ -14,6 +14,7 @@
 
 
 parser = argparse.ArgumentParser()
+# training
 parser.add_argument('--img_channels', type=int, default=3, help='Numer of channels for images')
 parser.add_argument('--model_dim', type=float, default=128, help='model dimensions multiplier')
 parser.add_argument('--z_dim', type=float, default=100, help='dimension of random noise latent vector')
@@ -32,6 +33,10 @@
 parser.add_argument('--device_ids', type=list, default=[0, 1], help='List of GPU devices')
 parser.add_argument('--img_ext', type=str, default='.jpg', help='Image extentions')
 parser.add_argument('--checkpoint_dir', type=str, default='vae/model_weights', help='Path to where model weights will be saved')
+
+# for sampler
+parser.add_argument('--sample', action="store_true", default=False, help='Sample from VAE')
+parser.add_argument('--walk', action="store_true", default=False, help='Walk through a feature & sample')
 args = parser.parse_args()
 
 

vae/model.py

@@ -46,7 +46,7 @@ def forward(self, x):
 
     def sample(self, z=None, num_samples=50):
         if z is None:
-            z = torch.randn(num_samples, self.args.z_dim, device=self.device)
+            z = torch.randn(num_samples, self.args.z_dim, device=next(self.parameters()).device)
         z = self.projector(z).view(-1, self.args.model_dim * 8,
                                    self.args.img_size // (2**4), self.args.img_size // (2**4))
         return self.decoder(z)

vae/sample.py

@@ -0,0 +1,51 @@
+import argparse
+import torch
+import numpy as np
+from pathlib import Path
+from datetime import datetime
+import torchvision
+
+from vae.model import VAE
+from vae.main import parser, args
+
+
+class Sampler:
+    def __init__(self, sample_path='vae/samples', ext='.jpg'):
+        self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+        self.vae = torch.nn.DataParallel(VAE(args), device_ids=args.device_ids).to(self.device)
+        self.vae.load_state_dict(torch.load(f"{args.checkpoint_dir}/VAE.pth")['model'])
+        self.vae.eval()
+        Path(sample_path).mkdir(parents=True, exist_ok=True)
+
+        self.sample_path = sample_path
+        self.ext = ext
+
+    def sample(self):
+        with torch.no_grad():
+            samples = self.vae.module.sample(num_samples=args.sample_size)
+        torchvision.utils.save_image(
+            samples,
+            self.sample_path + f'/sample_{int(datetime.now().timestamp()*1e6)}' + self.ext)
+
+    def generate_walk_z(self):
+        z = torch.randn(args.z_dim, device=self.device)
+        z = z.repeat(args.sample_size).view(args.sample_size, args.z_dim)
+        walk_dim = np.random.choice(list(range(args.z_dim)))
+        z[:, walk_dim] = torch.linspace(-2, 2, args.sample_size)
+        return z
+
+    def walk(self):
+        z = self.generate_walk_z()
+        with torch.no_grad():
+            samples = self.vae.module.sample(z=z)
+        torchvision.utils.save_image(
+            samples,
+            self.sample_path + f'/walk_{int(datetime.now().timestamp()*1e6)}' + self.ext)
+
+
+if __name__ == '__main__':
+    sampler = Sampler()
+    if args.sample:
+        sampler.sample()
+    if args.walk:
+        sampler.walk()