Neural Networks
A neural network object describes how input data should be preprocessed to be able to perform a simple inference and get raw output features from any layer.
Parameter | Type | Attributes | Description |
---|---|---|---|
id | int | read-only | The ID of the neural network. |
name | string | | A short name for your network. |
description | string | | A longer description of your network. |
update_date | string | read-only | Date time (ISO 8601 format) of the last update of the network. |
metadata | object | | A JSON field containing any kind of information that you may find interesting to store. |
framework | string | immutable | A string describing which framework to use for your network. Possible values are:
|
preprocessing | immutable | A pre-processing object to describe how input data should be pre-processed. | |
postprocessings | immutable | An array of post-processing object to list output tensors. | |
task_id | int | read-only | ID of the task containing the deployment status of the network. |
Once the network has been created, you cannot modify the
preprocessing
field anymore.This object describes how data should be preprocessed for each input of the network.
Parameter | Type | Description |
---|---|---|
inputs | A list of Input Preprocessing Object. The order matters as input data will be fed in the same order at inference time. | |
batched_output | bool | [deprecated] Set this value to True |
Parameter | Type | Description |
---|---|---|
tensor_name | string | The name of the input tensor that this input will feed. |
image | An Image Preprocessing Object. Currently, the only supported input type. |
Parameter | Type | Description |
---|---|---|
dimension_order | string | A value describing the order of the dimensions in a batch N = batch_size, C = Channel, H = Height, W = Width Possible values are:
|
resize_type | string | Possible values are:
|
target_size | string | Target size of the input image. It might have multiple formats. In the following W , H and N denote integer numbers, W (and H ) being used specifically for width (and height), respectively:
|
color_channels | string | Might be RGB , BGR or L (for gray levels). |
pixel_scaling | float | Pixel values will be normalized between 0 and pixel_scaling before mean subtraction. |
This object maps output tensors to a specific function in order to interpret them thanks to recognition versions.
You must specify exactly one of the
tensors_output
or standard_output
fields. When we specify an expected tensor size in the description of those fields, we omit the first dimension of the tensor (i.e. the batch size).Attribute | Type | Description |
---|---|---|
tensors | array(string) | A simple ordered list of tensor names. |
Attribute | Type | Description |
---|---|---|
boxes_tensor | string | The name of the output tensor containing the boxes. It must be of size N x 4 where N is the number of detected regions and the 4 columns correspond to xmin , ymin , xmax and ymax coordinates of the bounding boxes, xmin and ymin being the coordinates of the upper-left corner. |
scores_tensor | string | The name of the output tensor containing the scores for each label and box. It must be of size N x K where K is the number of labels in the recognition specification. |
Creates a new custom network after you have trained a model of your own on your infrastructure.
cURL
Python
POST https://api.deepomatic.com/v0.7/networks
import os
from deepomatic.api.client import Client
client = Client(api_key=os.getenv('DEEPOMATIC_API_KEY'))
client.Network.create(...)
Parameter | Type | Default | Description |
---|---|---|---|
name | string | | A short name for your network. |
description | string | | A longer description of your network. |
metadata | object | {} | A JSON field containing any kind of information that you may find interesting to store. |
framework | string | | A string describing which framework to use for your network. Possible values are:
|
preprocessing | | A preprocessing object to describe how input data should be pre-processed. | |
<additionnal-files> | file | | Extra files for network graph and weights, as well as mean files needed by the preprocessing. See below. |
Once the network has been created, you cannot modify the
preprocessing
field anymore.cURL
Python
# We download the Caffe a GoogleNet pre-trained network
curl -o /tmp/deploy.prototxt https://raw.githubusercontent.com/BVLC/caffe/master/models/bvlc_googlenet/deploy.prototxt
curl -o /tmp/snapshot.caffemodel http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel
curl -o /tmp/caffe_ilsvrc12.tar.gz http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz
tar -zxvf /tmp/caffe_ilsvrc12.tar.gz -C /tmp
# Now proceed to upload
curl https://api.deepomatic.com/v0.7/networks \
-H "X-API-KEY: ${DEEPOMATIC_API_KEY}" \
-F name='my new network' \
-F description='trained with more images' \
-F metadata='{"author": "me", "project": "Go to mars"}' \
-F framework='nv-caffe-0.x-mod' \
-F preprocessing='{"inputs": [{"tensor_name": "data","image": {"dimension_order":"NCHW", "target_size":"224x224", "resize_type":"SQUASH", "mean_file": "mean_file_1.binaryproto", "color_channels": "BGR", "pixel_scaling": 255.0, "data_type": "FLOAT32"}}], "batched_output": true}' \
-F deploy.prototxt=@/tmp/deploy.prototxt \
-F snapshot.caffemodel=@/tmp/snapshot.caffemodel \
-F mean_file_1.binaryproto=@/tmp/imagenet_mean.binaryproto
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))
You will need to provide several additional files to create the network.
You need to specify at least one of those files for the tensorflow-1.x framework:
saved_model.pb
: the file that specifies the the network architecture.saved_model.pbtxt
: same as above but serialised in it text format.
If the saved model does not embed the variables weights, you will need to specify additional files:
variables.data-00000-of-00001
: the file that specifies variables' weights. It is usually located in a variables directory. Numbers can change but must respect those of your original file.variables.index
: the index of variables. It is usually located in a variables directory.
You might also include any additional file as required by you various input types, for exemple any mean file named as you like and whose name is referred by the
mean_file
parameter field of a pre-processing object as long it has one of the supported extensions, see the documentation.Please refer to the Saving mean files code sample bellow to find out how to save you mean files before sending them to the API.
In order to save numpy tensor means to files before sending them to the API, please proceed like this:
Python
import numpy as np
# example mean file when `dimension_order == "HWC"` and H = 1, W = 1 and C = 3
# typically, your mean image as been compute on the training images and you already
# have this tensor available.
example_mean_file = np.ones((1, 1, 3))
# Save this mean to 'mean.npy'
with open('mean.npy', 'wb') as f:
np.save(f, mean, allow_pickle=False)
# You can now use `"mean_file": "mean.npy"` in the preprocessing JSON
# {
# ...
# "mean_file": "mean.npy"
# ...
# }
JSON
{
"id": 42,
"name": "My first network",
"description": "A neural network trained on some data",
"task_id": 123,
"update_date": "2018-02-16T16:37:25.148189Z",
"metadata": {
"any": "value"
},
"preprocessing": {
"inputs": [
{
"tensor_name": "data",
"image": {
"dimension_order": "NCHW",
"target_size": "224x224",
"resize_type": "SQUASH",
"mean_file": "mean.proto.bin",
"color_channels": "BGR",
"pixel_scaling": 255.0,
"data_type": "FLOAT32"
}
}
],
"batched_output": true
}
}
Lists all public and private networks:
cURL
Python
# For public networks:
curl https://api.deepomatic.com/v0.7/networks/public \
-H "X-API-KEY: ${DEEPOMATIC_API_KEY}"
# For private networks:
curl https://api.deepomatic.com/v0.7/networks \
-H "X-API-KEY: ${DEEPOMATIC_API_KEY}"
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)
A paginated list of responses.
Parameter | Type | Description |
---|---|---|
count | int | The total number of results. |
next | string | The URL to the next page. |
previous | string | The URL to the previous page. |
results | A list of your neural networks objects |
Example response
JSON
{
"count": 1,
"next": null,
"previous": null,
"results": [
{
"id": 1,
"name": "Alexnet",
"description": "Alexnet",
"task_id": "123",
"update_date": "2018-02-16T13:45:36.078955Z",
"metadata": {},
"preprocessing": {
"inputs": [
{
"tensor_name": "data",
"image": {
"dimension_order": "NCHW",
"target_size": "224x224",
"resize_type": "SQUASH",
"mean_file": "data_mean.proto.bin",
"color_channels": "BGR",
"pixel_scaling": 255.0,
"data_type": "FLOAT32"
}
}
],
"batched_output": true
}
}
]
}
Retrieve a neural network by ID:
cURL
Python
# To retrieve a public network, use:
GET https://api.deepomatic.com/v0.7/networks/public/{NETWORK_ID}
# To retrieve your own network, use:
GET https://api.deepomatic.com/v0.7/networks/{NETWORK_ID}
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})
Parameter | Type | Default | Description |
---|---|---|---|
network_id | int | | The ID of the neural network to get. |
cURL
Python
# For a public network:
curl https://api.deepomatic.com/v0.7/networks/public/imagenet-inception-v1 \
-H "X-API-KEY: ${DEEPOMATIC_API_KEY}"
# For a private network:
curl https://api.deepomatic.com/v0.7/networks/42 \
-H "X-API-KEY: ${DEEPOMATIC_API_KEY}"
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)
JSON
{
"id": 1,
"name": "Alexnet",
"description": "Alexnet",
"task_id": "123",
"update_date": "2018-02-16T13:45:36.078955Z",
"metadata": {},
"preprocessing": {
"inputs": [
{
"tensor_name": "data",
"image": {
"dimension_order": "NCHW",
"target_size": "224x224",
"resize_type": "SQUASH",
"mean_file": "data_mean.proto.bin",
"color_channels": "BGR",
"pixel_scaling": 255.0,
"data_type": "FLOAT32"
}
}
],
"batched_output": true
}
}
Updates the specified network by setting the values of the parameters passed. Any parameters not provided will be left unchanged.
This request accepts only the
name
and metadata
arguments. Other values are immutable.cURL
Python
PATCH https://api.deepomatic.com/v0.7/networks/{NETWORK_ID}
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(...)
Parameter | Type | Attributes | Description |
---|---|---|---|
name | string | optionnal | A short name for your network. |
description | string | optionnal | A longer description of your network. |
metadata | object | optionnal | A JSON field containing any kind of information that you may find interesting to store. |
cURL
Python
curl https://api.deepomatic.com/v0.7/networks/42 \
-H "X-API-KEY: ${DEEPOMATIC_API_KEY}" \
-d '{"name": "new name", "description":"new description"}' \
-X PATCH
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"
)
Permanently deletes a network. This cannot be undone.
cURL
Python
DELETE https://api.deepomatic.com/v0.7/networks/{NETWORK_ID}
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()
Attached resources such as recognition versions will also be suppressed.
Parameter | Type | Default | Description |
---|---|---|---|
id | int | | The Neural Network ID to delete. |
cURL
Python
curl https://api.deepomatic.com/v0.7/networks/42 \
-H "X-API-KEY: ${DEEPOMATIC_API_KEY}" \
-X 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()
Return 204 (no content).
Last modified 8d ago