asdasd

app.py

@@ -7,68 +7,6 @@ from networks_fastgan import MyGenerator
 import click
 import PIL
 
-@click.command()
-@click.option('--seeds', type=parse_range, help='List of random seeds (e.g., \'0,1,4-6\')', default = 10-15, required=True)
-@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=1, show_default=True)
-@click.option('--class', 'class_idx', type=int, help='Class label (unconditional if not specified)')
-@click.option('--noise-mode', help='Noise mode', type=click.Choice(['const', 'random', 'none']), default='const', show_default=True)
-@click.option('--translate', help='Translate XY-coordinate (e.g. \'0.3,1\')', type=parse_vec2, default='0,0', show_default=True, metavar='VEC2')
-@click.option('--rotate', help='Rotation angle in degrees', type=float, default=0, show_default=True, metavar='ANGLE')
-@click.option('--outdir', help='Where to save the output images', type=str, required=True, metavar='DIR')
-def generate_images(
-    seeds: List[int],
-    truncation_psi: float,
-    noise_mode: str,
-    outdir: str,
-    translate: Tuple[float,float],
-    rotate: float,
-    class_idx: Optional[int]
-):
-    """Generate images using pretrained network pickle.
-
-    Examples:
-
-    \b
-    # Generate an image using pre-trained AFHQv2 model ("Ours" in Figure 1, left).
-    python gen_images.py --outdir=out --trunc=1 --seeds=2 \\
-        --network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-afhqv2-512x512.pkl
-
-    \b
-    # Generate uncurated images with truncation using the MetFaces-U dataset
-    python gen_images.py --outdir=out --trunc=0.7 --seeds=600-605 \\
-        --network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-t-metfacesu-1024x1024.pkl
-    """
-
-    device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
-    G = MyGenerator.from_pretrained("Cropinky/projected_gan_impressionism")
-    print("network loaded")
-    # Labels.
-    label = torch.zeros([1, G.c_dim], device=device)
-    if G.c_dim != 0:
-        if class_idx is None:
-            raise click.ClickException('Must specify class label with --class when using a conditional network')
-        label[:, class_idx] = 1
-    else:
-        if class_idx is not None:
-            print ('warn: --class=lbl ignored when running on an unconditional network')
-
-    # Generate images.
-    for seed_idx, seed in enumerate(seeds):
-        print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds)))
-        z = torch.from_numpy(np.random.RandomState(seed).randn(1, G.z_dim)).to(device).float()
-
-        # Construct an inverse rotation/translation matrix and pass to the generator.  The
-        # generator expects this matrix as an inverse to avoid potentially failing numerical
-        # operations in the network.
-        if hasattr(G.synthesis, 'input'):
-            m = make_transform(translate, rotate)
-            m = np.linalg.inv(m)
-            G.synthesis.input.transform.copy_(torch.from_numpy(m))
-
-        img = G(z, label, truncation_psi=truncation_psi, noise_mode=noise_mode)
-        img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
-        PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').save(f'{outdir}/seed{seed:04d}.png')
-
 
 
 def image_generation(model, number_of_images=1):

image_generator.py

@@ -0,0 +1,143 @@
+# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+"""Generate images using pretrained network pickle."""
+
+import os
+import re
+from typing import List, Optional, Tuple, Union
+
+import click
+import dnnlib
+import numpy as np
+import PIL.Image
+import torch
+import legacy
+
+#----------------------------------------------------------------------------
+
+def parse_range(s: Union[str, List]) -> List[int]:
+    '''Parse a comma separated list of numbers or ranges and return a list of ints.
+
+    Example: '1,2,5-10' returns [1, 2, 5, 6, 7]
+    '''
+    if isinstance(s, list): return s
+    ranges = []
+    range_re = re.compile(r'^(\d+)-(\d+)$')
+    for p in s.split(','):
+        m = range_re.match(p)
+        if m:
+            ranges.extend(range(int(m.group(1)), int(m.group(2))+1))
+        else:
+            ranges.append(int(p))
+    return ranges
+
+#----------------------------------------------------------------------------
+
+def parse_vec2(s: Union[str, Tuple[float, float]]) -> Tuple[float, float]:
+    '''Parse a floating point 2-vector of syntax 'a,b'.
+
+    Example:
+        '0,1' returns (0,1)
+    '''
+    if isinstance(s, tuple): return s
+    parts = s.split(',')
+    if len(parts) == 2:
+        return (float(parts[0]), float(parts[1]))
+    raise ValueError(f'cannot parse 2-vector {s}')
+
+#----------------------------------------------------------------------------
+
+def make_transform(translate: Tuple[float,float], angle: float):
+    m = np.eye(3)
+    s = np.sin(angle/360.0*np.pi*2)
+    c = np.cos(angle/360.0*np.pi*2)
+    m[0][0] = c
+    m[0][1] = s
+    m[0][2] = translate[0]
+    m[1][0] = -s
+    m[1][1] = c
+    m[1][2] = translate[1]
+    return m
+
+#----------------------------------------------------------------------------
+
+@click.command()
+@click.option('--network', 'network_pkl', help='Network pickle filename', required=True)
+@click.option('--seeds', type=parse_range, help='List of random seeds (e.g., \'0,1,4-6\')', required=True)
+@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=1, show_default=True)
+@click.option('--class', 'class_idx', type=int, help='Class label (unconditional if not specified)')
+@click.option('--noise-mode', help='Noise mode', type=click.Choice(['const', 'random', 'none']), default='const', show_default=True)
+@click.option('--translate', help='Translate XY-coordinate (e.g. \'0.3,1\')', type=parse_vec2, default='0,0', show_default=True, metavar='VEC2')
+@click.option('--rotate', help='Rotation angle in degrees', type=float, default=0, show_default=True, metavar='ANGLE')
+@click.option('--outdir', help='Where to save the output images', type=str, required=True, metavar='DIR')
+def generate_images(
+    network_pkl: str,
+    seeds: List[int],
+    truncation_psi: float,
+    noise_mode: str,
+    outdir: str,
+    translate: Tuple[float,float],
+    rotate: float,
+    class_idx: Optional[int]
+):
+    """Generate images using pretrained network pickle.
+
+    Examples:
+
+    \b
+    # Generate an image using pre-trained AFHQv2 model ("Ours" in Figure 1, left).
+    python gen_images.py --outdir=out --trunc=1 --seeds=2 \\
+        --network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-afhqv2-512x512.pkl
+
+    \b
+    # Generate uncurated images with truncation using the MetFaces-U dataset
+    python gen_images.py --outdir=out --trunc=0.7 --seeds=600-605 \\
+        --network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-t-metfacesu-1024x1024.pkl
+    """
+
+    print('Loading networks from "%s"...' % network_pkl)
+    device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+    with dnnlib.util.open_url(network_pkl) as f:
+        G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore
+
+    os.makedirs(outdhf_ShHdFYTLiCYbxIuldloQmxMXgPvuEyUPAkir, exist_ok=True)
+
+    # Labels.
+    label = torch.zeros([1, G.c_dim], device=device)
+    if G.c_dim != 0:
+        if class_idx is None:
+            raise click.ClickException('Must specify class label with --class when using a conditional network')
+        label[:, class_idx] = 1
+    else:
+        if class_idx is not None:
+            print ('warn: --class=lbl ignored when running on an unconditional network')
+
+    # Generate images.
+    for seed_idx, seed in enumerate(seeds):
+        print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds)))
+        z = torch.from_numpy(np.random.RandomState(seed).randn(1, G.z_dim)).to(device).float()
+        # Construct an inverse rotation/translation matrix and pass to the generator.  The
+        # generator expects this matrix as an inverse to avoid potentially failing numerical
+        # operations in the network.
+        if hasattr(G.synthesis, 'input'):
+            m = make_transform(translate, rotate)
+            m = np.linalg.inv(m)
+            G.synthesis.input.transform.copy_(torch.from_numpy(m))
+
+        img = G(z, label, truncation_psi=truncation_psi, noise_mode=noise_mode)
+        img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
+        PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').save(f'{outdir}/seed{seed:04d}.png')
+
+
+#----------------------------------------------------------------------------
+
+if __name__ == "__main__":
+    generate_images() # pylint: disable=no-value-for-parameter
+
+#----------------------------------------------------------------------------