Merge pull request #40 from Dok11/generator_update

Made generated files in separate files

README.md

@@ -152,9 +152,16 @@ Also one flag to use `--multi-gpus`
 Once you have finished training, you can generate samples with one command. You can select which checkpoint number to load from. If `--load-from` is not specified, will default to the latest.
 
 ```bash
-$ lightweight_gan --name {name of run} --load-from {checkpoint num} --generate
+$ lightweight_gan \
+  --name {name of run} \
+  --load-from {checkpoint num} \
+  --generate \
+  --generate-types {types of result, default: [default,ema]} \
+  --num-image-tiles {count of image result}
 ```
 
+After run this command you will get folder near results image folder with postfix "-generated-{checkpoint num}".
+
 You can also generate interpolations
 
 ```bash

lightweight_gan/cli.py

@@ -85,6 +85,7 @@ def train_from_folder(
     save_every = 1000,
     evaluate_every = 1000,
     generate = False,
+    generate_types = ['default', 'ema'],
     generate_interpolation = False,
     aug_test = False,
     attn_res_layers = [32],
@@ -131,8 +132,9 @@ def train_from_folder(
         model = Trainer(**model_args)
         model.load(load_from)
         samples_name = timestamped_filename()
-        model.evaluate(samples_name, num_image_tiles)
-        print(f'sample images generated at {results_dir}/{name}/{samples_name}')
+        checkpoint = model.checkpoint_num
+        dir_result = model.generate(samples_name, num_image_tiles, checkpoint, generate_types)
+        print(f'sample images generated at {dir_result}')
         return
 
     if generate_interpolation:

lightweight_gan/lightweight_gan.py

@@ -1088,6 +1088,36 @@ def evaluate(self, num = 0, num_image_tiles = 8, trunc = 1.0):
         generated_images = self.generate_truncated(self.GAN.GE, latents)
         torchvision.utils.save_image(generated_images, str(self.results_dir / self.name / f'{str(num)}-ema.{ext}'), nrow=num_rows)
 
+    @torch.no_grad()
+    def generate(self, num=0, num_image_tiles=4, checkpoint=None, types=['default', 'ema']):
+        self.GAN.eval()
+
+        latent_dim = self.GAN.latent_dim
+        dir_name = self.name + str('-generated-') + str(checkpoint)
+        dir_full = Path().absolute() / self.results_dir / dir_name
+        ext = self.image_extension
+
+        if not dir_full.exists():
+            os.mkdir(dir_full)
+
+        # regular
+        if 'default' in types:
+            for i in tqdm(range(num_image_tiles), desc='Saving generated default images'):
+                latents = torch.randn((1, latent_dim)).cuda(self.rank)
+                generated_image = self.generate_truncated(self.GAN.G, latents)
+                path = str(self.results_dir / dir_name / f'{str(num)}-{str(i)}.{ext}')
+                torchvision.utils.save_image(generated_image[0], path, nrow=1)
+
+        # moving averages
+        if 'ema' in types:
+            for i in tqdm(range(num_image_tiles), desc='Saving generated EMA images'):
+                latents = torch.randn((1, latent_dim)).cuda(self.rank)
+                generated_image = self.generate_truncated(self.GAN.GE, latents)
+                path = str(self.results_dir / dir_name / f'{str(num)}-{str(i)}-ema.{ext}')
+                torchvision.utils.save_image(generated_image[0], path, nrow=1)
+
+        return dir_full
+
     @torch.no_grad()
     def calculate_fid(self, num_batches):
         from pytorch_fid import fid_score