Skip to content

Instantly share code, notes, and snippets.

View cvalenzuela's full-sized avatar
🦌

Cristóbal Valenzuela cvalenzuela

🦌
View GitHub Profile
import runway
from runway.data_types import number, text, image
import numpy as np
from scipy import ndimage
import time
@runway.command(name='convert',
inputs={ 'image': image },
outputs={ 'image': image })
def generate(model, inputs):
import runway
import torch
from transformers import BertTokenizer
from transformers import BertForNextSentencePrediction
from runway.data_types import array, text, number, boolean
# Setup block copy-pasted from Cris's tutorial
@runway.setup(options={"checkpoint": runway.category(description="Pretrained checkpoints to use.",
choices=['celebAHQ-512', 'celebAHQ-256', 'celeba'],
entrypoint: python runway_model.py
python: 3.6
cuda: 9.0
spec:
gpu: True
cpu: True
build_steps:
- pip install torch==1.1.0 runway-python numpy==1.16.4
import torchvision
import runway
import numpy as np


@runway.setup(options={"checkpoint": runway.category(description="Pretrained checkpoints to use.",
                                       choices=['celebAHQ-512', 'celebAHQ-256', 'celeba'],
                                       default='celebAHQ-512')})
def setup(opts):
import torch
import runway
import numpy as np


@runway.setup(options={"checkpoint": runway.category(description="Pretrained checkpoints to use.",
                                       choices=['celebAHQ-512', 'celebAHQ-256', 'celeba'],
                                       default='celebAHQ-512')})
def setup(opts):
import torch
import runway
import numpy as np


+ @runway.setup(options={"checkpoint": runway.category(description="Pretrained checkpoints to use.",
+                                      choices=['celebAHQ-512', 'celebAHQ-256', 'celeba'],
+                                      default='celebAHQ-512')})
+ def setup(opts):
import torch
+ import runway
+ import numpy as np

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='celebAHQ-512',
## Sample code to generate an image using the
## pre-trained PGAN celebAHQ-512 checkpoint from the Pytorch Hub
import torch
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='celebAHQ-512',
pretrained=True, useGPU=use_gpu)
We can make this file beautiful and searchable if this error is corrected: No commas found in this CSV file in line 0.
AD 42.546245 1.601554 Andorra
AE 23.424076 53.847818 United Arab Emirates
AF 33.93911 67.709953 Afghanistan
AG 17.060816 -61.796428 Antigua and Barbuda
AI 18.220554 -63.068615 Anguilla
AL 41.153332 20.168331 Albania
AM 40.069099 45.038189 Armenia
AN 12.226079 -69.060087 Netherlands Antilles
AO -11.202692 17.873887 Angola
AQ -75.250973 -0.071389 Antarctica
classifier.classify(someImage, gotResult);
function gotResult(labels) {
console.log(label);
}