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pretrained_example.py
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pretrained_example.py
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# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
"""Minimal script for generating an image using pre-trained StyleGAN generator."""
import os
import pickle
import numpy as np
import PIL.Image
from PIL import Image
import dnnlib
import dnnlib.tflib as tflib
import config
from training import misc
fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
rnd = np.random.RandomState(5)
num_classes = 10
def main():
# Initialize TensorFlow.
tflib.init_tf()
# Load pre-trained network.
url = 'https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ' # karras2019stylegan-ffhq-1024x1024.pkl
with dnnlib.util.open_url(url, cache_dir=config.cache_dir) as f:
_G, _D, Gs = pickle.load(f)
# _G = Instantaneous snapshot of the generator. Mainly useful for resuming a previous training run.
# _D = Instantaneous snapshot of the discriminator. Mainly useful for resuming a previous training run.
# Gs = Long-term average of the generator. Yields higher-quality results than the instantaneous snapshot.
# Print network details.
Gs.print_layers()
# Pick latent vector.
latents = rnd.randn(1, Gs.input_shape[1])
# Generate image.
images = Gs.run(latents, None, truncation_psi=0.7, randomize_noise=True, output_transform=fmt)
# Save image.
os.makedirs(config.result_dir, exist_ok=True)
png_filename = os.path.join(config.result_dir, 'example.png')
PIL.Image.fromarray(images[0], 'RGB').save(png_filename)
def main_conditional():
# Initialize TensorFlow
tflib.init_tf()
# Load pre-trained network
dir = 'results/00004-sgan-cifar10-1gpu-cond/'
fn = 'network-snapshot-010372.pkl'
_G, _D, Gs = pickle.load(open(os.path.join(dir,fn), 'rb'))
# Print network details
Gs.print_layers()
# rnd = np.random.RandomState(10)
# Initialize conditioning
conditioning = np.eye(num_classes)
for i, rnd in enumerate([np.random.RandomState(i) for i in np.arange(20)]):
# Pick latent vector.
latents = rnd.randn(num_classes, Gs.input_shape[1])
# Generate image.
images = Gs.run(latents, conditioning, truncation_psi=0.7, randomize_noise=True, output_transform=fmt)
images = images.reshape(32*10, 32, 3)
# Save image.
png_filename = os.path.join(dir, 'example_{}.png'.format(i))
PIL.Image.fromarray(images, 'RGB').save(png_filename)
def main_binary():
# Initialize Tensorflow
tflib.init_tf()
# Load pre-trained network
dir = 'results/00005-sgancelebahq-binary-1gpu-cond-wgangp/'
dir = 'results/00006-sgancelebahq-binary-1gpu-cond-wgangp/'
fn = 'network-snapshot-006926.pkl'
_, _, Gs = pickle.load(open(os.path.join(dir,fn), 'rb'))
# Print network details
Gs.print_layers()
# Create binary attributes
# eyeglasses, male, black_hair, smiling, young
classes = {
'5_o_Clock_Shadow': 0,
'Arched_Eyebrows': 0,
'Attractive': 1,
'Bags_Under_Eyes': 0,
'Bald': 0,
'Bangs': 0,
'Big_Lips': 0,
'Big_Nose': 0,
'Black_Hair': 0,
'Blond_Hair': 0,
'Blurry': 0,
'Brown_Hair': 1,
'Bushy_Eyebrows': 0,
'Chubby': 0,
'Double_Chin': 0,
'Eyeglasses': 0,
'Goatee': 0,
'Gray_Hair': 0,
'Heavy_Makeup': 1,
'High_Cheekbones': 1,
'Male': 0,
'Mouth_Slightly_Open': 1,
'Mustache': 0,
'Narrow_Eyes': 0,
'No_Beard': 0,
'Oval_Face': 1,
'Pale_Skin': 0,
'Pointy_Nose': 0,
'Receding_Hairline': 0,
'Rosy_Cheeks': 0,
'Sideburns': 0,
'Smiling': 0,
'Straight_Hair': 0,
'Wavy_Hair': 1,
'Wearing_Earrings': 0,
'Wearing_Hat': 0,
'Wearing_Lipstick': 1,
'Wearing_Necklace': 0,
'Wearing_Necktie': 0,
'Young': 1
}
print([attr for (attr,key) in classes.items() if key==1])
binary = np.array(list(classes.values())).reshape(1,-1)
for i, rnd in enumerate([np.random.RandomState(i) for i in np.arange(20)]):
latent = rnd.randn(1, Gs.input_shape[1])
image = Gs.run(latent, binary, truncation_psi=0.7, randomize_noise=True, output_transform=fmt)
image = image.reshape(256,256,3)
png_filename = os.path.join(dir, 'examples/example{}.png'.format(i))
PIL.Image.fromarray(image, 'RGB').save(png_filename)
def main_textual():
# Initialize Tensorflow
tflib.init_tf()
dir = 'results/00015-sgancoco_train-1gpu-cond'
fn = 'network-snapshot-025000.pkl'
_, _, Gs = pickle.load(open(os.path.join(dir,fn), 'rb'))
# Print network details
Gs.print_layers()
embeddings = np.load('datasets/coco_test/coco_test-rxx.labels')
fns=np.load('datasets/coco_test/fns.npy')
# Use only 1 description (instead of all 5, to compare to attnGAN)
embeddings = embeddings[0::5]
fns = fns[0::5]
for i, rnd in enumerate([np.random.RandomState(i) for i in np.arange(embeddings.shape[0])]):
latent = rnd.randn(1, Gs.input_shape[1])
emb = embeddings[i].reshape(1,-1)
image = Gs.run(latent, emb, truncation_psi=0.8, randomize_noise=True, output_transform=fmt)
image = image.reshape(256,256,3)
png_filename = os.path.join(dir, 'examples/{}.png'.format(fns[i]))
image = Image.fromarray(image)
image.save(png_filename)
if __name__ == "__main__":
# main()
# main_conditional()
# main_binary()
main_textual()