Refactor

app.py

@@ -3,20 +3,11 @@
 from __future__ import annotations
 
 import argparse
-import os
-import pickle
-import sys
 
 import gradio as gr
-import numpy as np
-import torch
-import torch.nn as nn
 from huggingface_hub import hf_hub_download
 
-sys.path.insert(0, 'StyleGAN-Human')
-
-TOKEN = os.environ['TOKEN']
-
+from model import Model
 
 def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser()
@@ -30,76 +21,9 @@ def parse_args() -> argparse.Namespace:
     return parser.parse_args()
 
 
-class App:
-
-    def __init__(self, device: torch.device):
-        self.device = device
-        self.model = self.load_model('stylegan_human_v2_1024.pkl')
-
-    def load_model(self, file_name: str) -> nn.Module:
-        path = hf_hub_download('hysts/StyleGAN-Human',
-                               f'models/{file_name}',
-                               use_auth_token=TOKEN)
-        with open(path, 'rb') as f:
-            model = pickle.load(f)['G_ema']
-        model.eval()
-        model.to(self.device)
-        with torch.inference_mode():
-            z = torch.zeros((1, model.z_dim)).to(self.device)
-            label = torch.zeros([1, model.c_dim], device=self.device)
-            model(z, label, force_fp32=True)
-        return model
-
-    def generate_z(self, z_dim: int, seed: int) -> torch.Tensor:
-        return torch.from_numpy(np.random.RandomState(seed).randn(
-            1, z_dim)).to(self.device).float()
-
-    @torch.inference_mode()
-    def generate_single_image(self, seed: int,
-                              truncation_psi: float) -> np.ndarray:
-        seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max))
-
-        z = self.generate_z(self.model.z_dim, seed)
-        label = torch.zeros([1, self.model.c_dim], device=self.device)
-
-        out = self.model(z,
-                         label,
-                         truncation_psi=truncation_psi,
-                         force_fp32=True)
-        out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(
-            torch.uint8)
-        return out[0].cpu().numpy()
-
-    @torch.inference_mode()
-    def generate_interpolated_images(
-            self, seed0: int, psi0: float, seed1: int, psi1: float,
-            num_intermediate: int) -> list[np.ndarray]:
-        seed0 = int(np.clip(seed0, 0, np.iinfo(np.uint32).max))
-        seed1 = int(np.clip(seed1, 0, np.iinfo(np.uint32).max))
-
-        z0 = self.generate_z(self.model.z_dim, seed0)
-        z1 = self.generate_z(self.model.z_dim, seed1)
-        vec = z1 - z0
-        dvec = vec / (num_intermediate + 1)
-        zs = [z0 + dvec * i for i in range(num_intermediate + 2)]
-        dpsi = (psi1 - psi0) / (num_intermediate + 1)
-        psis = [psi0 + dpsi * i for i in range(num_intermediate + 2)]
-
-        label = torch.zeros([1, self.model.c_dim], device=self.device)
-
-        res = []
-        for z, psi in zip(zs, psis):
-            out = self.model(z, label, truncation_psi=psi, force_fp32=True)
-            out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(
-                torch.uint8)
-            out = out[0].cpu().numpy()
-            res.append(out)
-        return res
-
-
 def main():
     args = parse_args()
-    app = App(device=torch.device(args.device))
+    app = Model(device=args.device)
 
     with gr.Blocks(theme=args.theme) as demo:
         gr.Markdown('''<center><h1>StyleGAN-Human</h1></center>

model.py

@@ -0,0 +1,81 @@
+from __future__ import annotations
+
+import os
+import pickle
+import sys
+
+import numpy as np
+import torch
+import torch.nn as nn
+from huggingface_hub import hf_hub_download
+
+sys.path.insert(0, 'StyleGAN-Human')
+
+HF_TOKEN = os.environ['HF_TOKEN']
+
+
+class Model:
+
+    def __init__(self, device: str | torch.device):
+        self.device = torch.device(device)
+        self.model = self.load_model('stylegan_human_v2_1024.pkl')
+
+    def load_model(self, file_name: str) -> nn.Module:
+        path = hf_hub_download('hysts/StyleGAN-Human',
+                               f'models/{file_name}',
+                               use_auth_token=HF_TOKEN)
+        with open(path, 'rb') as f:
+            model = pickle.load(f)['G_ema']
+        model.eval()
+        model.to(self.device)
+        with torch.inference_mode():
+            z = torch.zeros((1, model.z_dim)).to(self.device)
+            label = torch.zeros([1, model.c_dim], device=self.device)
+            model(z, label, force_fp32=True)
+        return model
+
+    def generate_z(self, z_dim: int, seed: int) -> torch.Tensor:
+        return torch.from_numpy(np.random.RandomState(seed).randn(
+            1, z_dim)).to(self.device).float()
+
+    @torch.inference_mode()
+    def generate_single_image(self, seed: int,
+                              truncation_psi: float) -> np.ndarray:
+        seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max))
+
+        z = self.generate_z(self.model.z_dim, seed)
+        label = torch.zeros([1, self.model.c_dim], device=self.device)
+
+        out = self.model(z,
+                         label,
+                         truncation_psi=truncation_psi,
+                         force_fp32=True)
+        out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(
+            torch.uint8)
+        return out[0].cpu().numpy()
+
+    @torch.inference_mode()
+    def generate_interpolated_images(
+            self, seed0: int, psi0: float, seed1: int, psi1: float,
+            num_intermediate: int) -> list[np.ndarray]:
+        seed0 = int(np.clip(seed0, 0, np.iinfo(np.uint32).max))
+        seed1 = int(np.clip(seed1, 0, np.iinfo(np.uint32).max))
+
+        z0 = self.generate_z(self.model.z_dim, seed0)
+        z1 = self.generate_z(self.model.z_dim, seed1)
+        vec = z1 - z0
+        dvec = vec / (num_intermediate + 1)
+        zs = [z0 + dvec * i for i in range(num_intermediate + 2)]
+        dpsi = (psi1 - psi0) / (num_intermediate + 1)
+        psis = [psi0 + dpsi * i for i in range(num_intermediate + 2)]
+
+        label = torch.zeros([1, self.model.c_dim], device=self.device)
+
+        res = []
+        for z, psi in zip(zs, psis):
+            out = self.model(z, label, truncation_psi=psi, force_fp32=True)
+            out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(
+                torch.uint8)
+            out = out[0].cpu().numpy()
+            res.append(out)
+        return res