Neural Networks

import os
from deepomatic.api.client import Client
client = Client(api_key=os.getenv('DEEPOMATIC_API_KEY'))

client.Network.create(...)

import os
import sys
import tempfile
import shutil
import hashlib
import requests
import zipfile

from deepomatic.api.client import Client

if sys.version_info >= (3, 0):
    from urllib.request import urlretrieve
else:
    from urllib import urlretrieve

# Initialize the client
client = Client(api_key=os.getenv('DEEPOMATIC_API_KEY'))

# Helper function to download demo resources for the Caffe pre-trained networks
def download_file(url):
    _, ext = os.path.splitext(url)
    filename = os.path.join(tempfile.gettempdir(),
                            hashlib.sha1(url.encode()).hexdigest() + ext)
    print("Downloading {} to {}".format(url, filename))
    if os.path.exists(filename):  # avoid redownloading
        return filename
    r = requests.get(url, stream=True)
    r.raise_for_status()
    with open(filename, 'wb') as f:
        r.raw.decode_content = True
        shutil.copyfileobj(r.raw, f)
    return filename


# We download the Tensorflow Inception v3 pre-trained network
extract_dir = tempfile.gettempdir()
net_zip = download_file('https://s3-eu-west-1.amazonaws.com/deepo-public/run-demo-networks/imagenet-inception-v3/network.zip')
preproc_zip = download_file('https://s3-eu-west-1.amazonaws.com/deepo-public/run-demo-networks/imagenet-inception-v3/preprocessing.zip')

model_file_name = 'saved_model.pb'
variables_file_name = 'variables.index'
variables_data_file_name = 'variables.data-00000-of-00001'
mean_file_name = 'mean.proto.bin'

model_file = os.path.join(extract_dir, model_file_name)
mean_file = os.path.join(extract_dir, mean_file_name)
variables_file = os.path.join(extract_dir + '/variables/', variables_file_name)
variables_data_file = os.path.join(extract_dir + '/variables/', variables_data_file_name)

print("Extracting archive...")
if not os.path.exists(model_file):
    with zipfile.ZipFile(net_zip) as f:
        f.extractall(extract_dir)
if not os.path.exists(mean_file):
    with zipfile.ZipFile(preproc_zip) as f:
        f.extractall(extract_dir)

"""
Here, we specify the network preprocessing. Please refer to the documentation to see what each
field is used for.
"""
preprocessing = {
    "inputs": [
        {
            "tensor_name": "map/TensorArrayStack/TensorArrayGatherV3:0",
            "image": {
                "dimension_order": "NHWC",
                "target_size": "299x299",
                "resize_type": "CROP",
                "mean_file": mean_file_name,
                "color_channels": "BGR",
                "pixel_scaling": 2.0,
                "data_type": "FLOAT32"
            }
        }
    ],
    "batched_output": True
}

"""
We now register the three files needed by our network
"""
files = {
    model_file_name: open(model_file, 'rb'),
    variables_file_name: open(variables_file, 'rb'),
    variables_data_file_name: open(variables_data_file, 'rb'),
    mean_file_name: open(mean_file, 'rb')
}

"""
We now upload our new network via the 'client.Network().create(...)' network.
Please refere to the documentation for a description of each parameter.
"""
print("Uploading model to API...")
network = client.Network.create(name="My first network",
                                framework='tensorflow-1.x',
                                preprocessing=preprocessing,
                                files=files)
network_id = network['id']
print("Network ID = {}".format(network_id))

import os
from deepomatic.api.client import Client
client = Client(api_key=os.getenv('DEEPOMATIC_API_KEY'))

# For public networks:
for network in client.Network.list(public=True):
    print(network)

# For private networks:
for network in client.Network.list():
    print(network)

import os
from deepomatic.api.client import Client
client = Client(api_key=os.getenv('DEEPOMATIC_API_KEY'))

# {NETWORK_ID} may be a string for a public
# network or an integer for your own network.
client.Network.retrieve({NETWORK_ID})

import os
from deepomatic.api.client import Client
client = Client(api_key=os.getenv('DEEPOMATIC_API_KEY'))

# For a public network:
client.Network.retrieve("imagenet-inception-v1")

# For a private network:
client.Network.retrieve(42)

import os
from deepomatic.api.client import Client
client = Client(api_key=os.getenv('DEEPOMATIC_API_KEY'))

network = client.Network.retrieve({NETWORK_ID})
network.update(...)

import os
from deepomatic.api.client import Client
client = Client(api_key=os.getenv('DEEPOMATIC_API_KEY'))

network = client.Network.retrieve(42)
network.update(
    name="new name",
    description="new description"
)

import os
from deepomatic.api.client import Client
client = Client(api_key=os.getenv('DEEPOMATIC_API_KEY'))

network = client.Network.retrieve({NETWORK_ID})
network.delete()

import os
from deepomatic.api.client import Client
client = Client(api_key=os.getenv('DEEPOMATIC_API_KEY'))

network = client.Network.retrieve(42)
network.delete()