paths interactive

interactive_CA/interactive.py

@@ -12,19 +12,7 @@ from torch.utils.tensorboard import SummaryWriter
 from torchvision.utils import save_image
 
 from model import CAModel, CellularAutomataModel
-from utils import load_emoji, to_rgb, adv_attack
-
-
-def make_seed(size, n_channels):
-    x = torch.zeros((1, n_channels, size, size), dtype=torch.float32)
-    x[:, 3:, size // 2, size // 2] = 1
-    return x
-
-
-def to_rgb_ad(img_rgba):
-    rgb, a = img_rgba[:, :3, ...], torch.clamp(img_rgba[:, 3:, ...], 0, 1)
-    return torch.clamp(1.0 - a + rgb, 0, 1)
-
+from utils import load_emoji, to_rgb, adv_attack, make_seed, to_rgb_ad
 
 class Interactive:
     def __init__(self, args):
@@ -94,9 +82,9 @@ class Interactive:
             image = to_rgb_ad(x[:, :4].detach().cpu())
         save_image(image, path, nrow=1, padding=0)
 
-    # Do damage on model using pygame, cannot run through ssh
 
     def interactive(self):
+        """Do damage on model using pygame"""
         x_eval = self.seedclone()
 
         cellsize = 20

interactive_CA/main.py

@@ -29,6 +29,9 @@ adam_sample_models = [
 
 es_nonsample_models = [
     ['final_models\\ES\\NonSamplePools\\9-CARROT-train_05-05-2022_09-14-06\\models\\model_2212000', '🥕', 9, True]
+    ['final_models\\ES\NonSamplePools\\9-RABBIT FACE-train_06-05-2022_12-18-56\models\\model_1998000', '🐰', 9, True]
+    ['final_models\\ES\\NonSamplePools\\15-CARROT-train_06-05-2022_11-06-58\models\model_1998000', '🥕', 15, True]
+    ['rip_rabbit_15_evolution doth not favour the meek', '🐰', 0, None]
 ]
 
 es_sample_models = [

interactive_CA/utils.py

@@ -40,37 +40,13 @@ def to_rgba(x):
     """Return the four first channels (RGBA) of an image."""
     return x[..., :4]
 
-def save_model(ca, base_fn):
-    """Save a PyTorch model to a specific path."""
-    torch.save(ca.state_dict(), base_fn)
-
-def visualize(xs, step_i, nrow=1):
-    """Save a batch of multiple x's to file"""
-    for i in range(len(xs)):
-        xs[i] = to_rgb(xs[i]).permute(0, 3, 1, 2)
-    save_image(torch.cat(xs, dim=0), './logg/pic/p%04d.png' % step_i, nrow=nrow, padding=0)
-
-class Pool:
-    """Class for storing and providing samples of different stages of growth."""
-    def __init__(self, seed, size):
-        self.size = size
-        self.slots = np.repeat([seed], size, 0)
-        self.seed = seed
-
-    def commit(self, batch):
-        """Replace existing slots with a batch."""
-        indices = batch["indices"]
-        for i, x in enumerate(batch["x"]):
-            if (x[:, :, 3] > 0.1).any():  # Avoid committing dead image
-                self.slots[indices[i]] = x.copy()
-
-    def sample(self, c):
-        """Retrieve a batch from the pool."""
-        indices = np.random.choice(self.size, c, False)
-        batch = {
-            "indices": indices,
-            "x": self.slots[indices]
-        }
-        return batch
+def make_seed(size, n_channels):
+    x = torch.zeros((1, n_channels, size, size), dtype=torch.float32)
+    x[:, 3:, size // 2, size // 2] = 1
+    return x
+
+def to_rgb_ad(img_rgba):
+    rgb, a = img_rgba[:, :3, ...], torch.clamp(img_rgba[:, 3:, ...], 0, 1)
+    return torch.clamp(1.0 - a + rgb, 0, 1)
 
     
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