def setup(opts):
checkpoint = opts['checkpoint'
use_gpu = True if torch.cuda.is_available() else False
# Load the model from the Pytorch Hub
model = torch.hub.load('facebookresearch/pytorch_GAN_zoo:hub',
'PGAN', model_name=checkpoint,
pretrained=True, useGPU=use_gpu)
return model
@runway.command('generate',
inputs={ 'z': runway.vector(length=512, sampling_std=0.5)},
outputs={ 'image': runway.image })
def generate(model, inputs):
# Generate ♾ infinite ♾ images
z = inputs['z']
latents = z.reshape((1, 559))
latents = torch.from_numpy(latents)
with torch.no_grad():
generated_image = model.test(latents.float())
generated_image = generated_image.clamp(min=-1, max=1)
generated_image = ((generated_image + 1.0) * 255 / 2.0)
# Now generated_image contains our generated image!
return generated_image[0].permute(1, 2, 0).numpy().astype(np.uint8)
if __name__ == '__main__':
runway.run(port=5232)
import numpy as np
from scipy.misc import toimage
from tqdm import tqdm
from pytorch_pretrained_biggan import (BigGAN, one_hot_from_names, truncated_noise_sample,
save_as_images, display_in_terminal)
@runway.setup(options={"checkpoint": runway.category(description="Pretrained checkpoints to use.",
choices=['celebAHQ-512', 'celebAHQ-256', 'celeba'],
default='celebAHQ-512')})
def setup(opts):
checkpoint = opts['checkpoint']
'PGAN', model_name=checkpoint,
return model
@runway.command('generate',
inputs={ 'z': runway.vector(length=512, sampling_std=0.5)},
outputs={ 'image': runway.image })
def generate(model, inputs):
# Generate ♾ infinite ♾ images
z = inputs['z']
latents = z.reshape((1, 559))
latents = torch.from_numpy(latents)
- noise, _ = model.buildNoiseData(1)
with torch.no_grad():
generated_image = model.test(latents.float())
generated_image = generated_image.clamp(min=-1, max=1)
generated_image = ((generated_image + 1.0) * 255 / 2.0)
# Now generated_image contains our generated image! 🌞
return generated_image[0].permute(1, 2, 0).numpy().astype(np.uint8)
if __name__ == '__main__':
from keras.preprocessing import image
from keras.applications.vgg16 import VGG16
from keras.applications.vgg16 import preprocess_input
from keras.models import Model
import numpy as np
import runway
@runway.setup
def setup():
base_model = VGG16(weights='imagenet')
model = Model(inputs=base_model.input, outputs=base_model.get_layer('fc1').output)
return model
@runway.command('extract_features', inputs={'image': runway.image}, outputs={'features': runway.vector(4096)})
def extract_features(model, inputs):
x = np.array(inputs['image'].resize((224, 224)))
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
features = model.predict(x)[0]
return features.flatten()
if __name__ == "__main__":
runway.run()
import lucid.optvis.param as param
import lucid.optvis.render as render
import lucid.optvis.transform as transform
import time
import runway
import random
@runway.setup(options={})
def setup(opts):
model = models.InceptionV1()
model.load_graphdef()
return model
generate_inputs = {
'z': runway.vector(length=1, sampling_std=0.5),
'layer': runway.category(choices=["Mixed3a", "Mixed4a", "Mixed5a"]
, default="Mixed4a"),
'steps': runway.number(min=64, max=512, step=10, default=128)
}
@runway.command('generate', inputs=generate_inputs, outputs={'image': runway.image})
def convert(model, inputs):
print(inputs['z'])
print(inputs['layer'])
num_neurons = {'mixed3a':255, 'mixed4a':507, 'mixed5a':831}
start = time.time()
#set up parameters
layer = inputs['layer'].lower()
neuron = int(np.clip(np.abs(np.float(inputs['z'])) * 1000, 0, num_neurons[layer]))
tflib.init_tf()
with open(opts['checkpoint'], 'rb') as file:
_G, _D, Gs = pickle.load(file, encoding='latin1')
noise_vars = [
var for name, var in Gs.components.synthesis.vars.items()
if name.startswith('noise')
]
rnd = np.random.RandomState()
tflib.set_vars(
{var: rnd.randn(*var.shape.as_list())
for var in noise_vars})
return Gs
generate_inputs = {
'z': runway.vector(512, sampling_std=0.5),
'truncation': runway.number(min=0, max=1, default=0.8, step=0.01)
}
@runway.command('generate',
inputs=generate_inputs,
outputs={'image': runway.image})
def convert(model, inputs):
z = inputs['z']
truncation = inputs['truncation']
latents = z.reshape((1, 512))
images = model.run(latents,
None,
truncation_psi=truncation,
randomize_noise=False,
use_gpu = torch.cuda.is_available()
config_file = "config.config_wake"
params = argparse.Namespace(**importlib.import_module(config_file).params)
model = BPEmbVaeSampler(lang=params.bpemb['lang'],
vs=params.bpemb['vs'],
dim=params.bpemb['dim'],
decode_from=model_path,
params=params,
cuda=use_gpu)
return model
@runway.command('generate',
inputs={
'z':
runway.vector(length=64),
'temperature':
runway.number(default=0.5, min=0.05, max=2.0, step=0.05)
},
outputs={'out': runway.text})
def generate(model, inputs):
z = torch.from_numpy(inputs['z']).float().unsqueeze(0).to(model.device)
temperature = inputs['temperature']
with torch.no_grad():
return model.sample(z, temperature)[0]
@runway.command('reconstruct',
inputs={
'in':
runway.text,
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
# init session
saver = tf.train.Saver()
sess = tf.Session(config=tfconfig)
saver.restore(sess, ckpt.model_checkpoint_path)
print('Restored from {}'.format(ckpt.model_checkpoint_path))
return sess
@runway.command('caption',
inputs={'image': runway.image},
outputs={
'captions': runway.vector(12),
'scores': runway.vector(12),
'boxes': runway.vector(12)
})
def caption(sess, inp):
img = np.array(inp['image'])
scores, boxes, captions = im_detect(sess, net, img, None, use_box_at=-1)
pos_dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32,
copy=False)
keep = nms(pos_dets, cfg.TEST.NMS)
pos_dets = pos_dets[keep, :]
pos_scores = scores[keep]
pos_captions = [sentence(vocab, captions[idx]) for idx in keep]
pos_boxes = boxes[keep, :]
return dict(captions=np.array(pos_captions),
scores=np.array(pos_scores),
from keras.applications.resnet50 import ResNet50
from keras.applications.resnet50 import preprocess_input
import numpy as np
import runway
@runway.setup
def setup():
return ResNet50(weights='imagenet', include_top=False, pooling='avg')
@runway.command('extract_features',
inputs={'image': runway.image},
outputs={'features': runway.vector(2048)})
def extract_features(model, inputs):
x = np.array(inputs['image'].resize((224, 224)))
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
features = model.predict(x)[0]
return features.flatten()
if __name__ == "__main__":
runway.run()
use_gpu = True if torch.cuda.is_available() else False
config_file = "config.config_poetry_500k_sample"
params = argparse.Namespace(**importlib.import_module(config_file).params)
model = BPEmbVaeSampler(lang='en',
vs=10000,
dim=100,
decode_from=model_path,
params=params,
cuda=use_gpu)
return model
@runway.command('generate',
inputs={
'z':
runway.vector(length=32),
'temperature':
runway.number(default=0.5, min=0.05, max=2.0, step=0.05)
},
outputs={'out': runway.text})
def generate(model, inputs):
z = torch.from_numpy(inputs['z']).float().unsqueeze(0)
temperature = inputs['temperature']
with torch.no_grad():
return model.sample(z, temperature)[0]
@runway.command('reconstruct',
inputs={
'in':
runway.text,
@runway.setup(options={'checkpoint': runway.file(extension='.pkl')})
def setup(opts):
global Gs
tflib.init_tf()
if opts['checkpoint'] is None:
opts['checkpoint'] = 'checkpoints\\network-snapshot-000600.pkl'
with open(opts['checkpoint'], 'rb') as file:
_G, _D, Gs = pickle.load(file, encoding='latin1')
noise_vars = [var for name, var in Gs.components.synthesis.vars.items() if name.startswith('noise')]
rnd = np.random.RandomState()
tflib.set_vars({var: rnd.randn(*var.shape.as_list()) for var in noise_vars})
return Gs
generate_inputs = {
'z': runway.vector(256, sampling_std=0.5),
'truncation': runway.number(min=0, max=1, default=0.8, step=0.01)
}
for i in range(4):
for j in range(4):
generate_inputs.update({'style_%i/%i' %(i, j): runway.number(min=0, max=1, default=0.5, step=0.01)})
@runway.command('generate', inputs=generate_inputs, outputs={'image': runway.image})
def convert(model, inputs):
z = inputs['z']
truncation = inputs['truncation']
latent = z.reshape((1, 256))
style_mix = np.zeros([4, 4])
for i in range(4):
for j in range(4):
