import moxel
model_vgg = moxel.Model('strin/coco-vgg:latest')
model_caption = moxel.Model('strin/coco-caption:latest')
model_story_romantic = moxel.Model('strin/story-romantic:latest')
def predict(image):
feats = model_vgg.predict(image=image)['feats']
captions = model_caption.predict(feats=feats)['sentences'].to_object()
return model_story_romantic.predict(sentences=captions)
import moxel
model = moxel.Model('moxel/awesome:latest', where='localhost')
image = moxel.space.Image.from_file('volkswagen.jpg')
result = model.predict(image=image)
print('result', result)
import moxel
model = moxel.Model('strin/coco-vgg:latest')
image = moxel.space.Image.from_file('images/ex2.jpg')
result = model.predict(image=image)['feats']
print(result.to_numpy())
import moxel
# Does it feel better to be "moxel.Image"
from moxel.space import Image
model = moxel.Model('strin/vgg19:latest', where='localhost')
# Ping the model to see if it works.
ok = model.ping()
print('ok', ok)
# Make a prediction.
img = Image.from_file('example/rock.jpg')
results = model.predict({'image': img})
print(results['feature'].to_numpy())
import moxel
model = moxel.Model('strin/colorization:latest', where='localhost')
img_in = moxel.space.Image.from_file('demo/imgs/ansel_adams3.jpg')
result = model.predict(img_in=img_in)
img_out = result['img_out'].to_PIL().save('output.png')
import moxel
model = moxel.Model('maya/acronyms:latest', where='localhost')
output = model.predict(
sentence = 'NASA is an aeronautical space company'
)
print(output['results'])
import moxel
import json
import numpy
model = moxel.Model('strin/story-romantic:latest', where='localhost')
sentences = [
u'A black and white photo of a man in suit and tie .',
u'a large clock on the wall above a radiator',
u'A young girl and a woman preparing food in a kitchen .',
u'a close up of a person grabbing a pastry in a container',
u"A woman glances at a young girl 's cooking on the stovetop"
]
passage = model.predict(sentences=sentences)['passage']
print('passage', passage)
import os
import moxel
from moxel.space import Image
model = moxel.Model('jimfan/squeezenet:latest', where='localhost')
for img in os.listdir('demo_imgs'):
img = os.path.join('demo_imgs', img)
Image.from_file(img)
output = model.predict(img=Image.from_file(img))
print('query "{}" -> {}'.format( img, output ))