def new():
"""
Upload a pretrained model
"""
labels_path = None
mean_path = None
framework = None
form = flask.request.form
files = flask.request.files
if 'framework' not in form:
framework = "caffe"
else:
framework = form['framework']
if 'job_name' not in flask.request.form:
raise werkzeug.exceptions.BadRequest('Missing job name')
elif str(flask.request.form['job_name']) is '':
raise werkzeug.exceptions.BadRequest('Missing job name')
if framework == "caffe":
weights_path, model_def_path = validate_caffe_files(files)
else:
weights_path, model_def_path = validate_torch_files(files)
if str(flask.request.files['labels_file'].filename) is not '':
labels_path = get_tempfile(flask.request.files['labels_file'], ".txt")
if str(flask.request.files['mean_file'].filename) is not '':
mean_path = get_tempfile(flask.request.files['mean_file'], ".prototxt")
job = PretrainedModelJob(weights_path,
model_def_path,
labels_path,
mean_path,
framework,
form["image_type"],
form["resize_mode"],
form["height"],
form["width"],
username=utils.auth.get_username(),
name=flask.request.form['job_name'])
scheduler.add_job(job)
job.wait_completion()
weights_job = WeightsJob(job,
name=flask.request.form['job_name'],
username=utils.auth.get_username())
scheduler.add_job(weights_job)
return flask.redirect(flask.url_for('digits.views.home', tab=3)), 302
def upload_archive():
"""
Upload archive
"""
files = flask.request.files
archive_file = get_tempfile(files["archive"], ".archive")
if tarfile.is_tarfile(archive_file):
archive = tarfile.open(archive_file, 'r')
names = archive.getnames()
elif zipfile.is_zipfile(archive_file):
archive = zipfile.ZipFile(archive_file, 'r')
names = archive.namelist()
else:
return flask.jsonify({"status": "Incorrect Archive Type"}), 500
if "info.json" in names:
# Create a temp directory to storce archive
tempdir = tempfile.mkdtemp()
archive.extractall(path=tempdir)
with open(os.path.join(tempdir, "info.json")) as data_file:
info = json.load(data_file)
valid, key = validate_archive_keys(info)
if valid is False:
return flask.jsonify(
{"status": "Missing Key '" + key + "' in info.json"}), 500
# Get path to files needed to be uploaded in directory
weights_file = os.path.join(tempdir, info["snapshot file"])
if "model file" in info:
model_file = os.path.join(tempdir, info["model file"])
elif "network file" in info:
model_file = os.path.join(tempdir, info["network file"])
else:
return flask.jsonify(
{"status": "Missing model definition in info.json"}), 500
labels_file = os.path.join(tempdir, info["labels file"])
# Upload the Model:
job = PretrainedModelJob(weights_file,
model_file,
labels_file,
info["framework"],
username=utils.auth.get_username(),
name=info["name"])
scheduler.add_job(job)
job.wait_completion()
# Delete temp directory
shutil.rmtree(tempdir, ignore_errors=True)
return flask.jsonify({"status": "success"}), 200
else:
return flask.jsonify({"status": "Missing or Incorrect json file"}), 500
def upload_archive():
"""
Upload archive
"""
files = flask.request.files
archive_file = get_tempfile(files["archive"],".archive");
if tarfile.is_tarfile(archive_file):
archive = tarfile.open(archive_file,'r')
names = archive.getnames()
elif zipfile.is_zipfile(archive_file):
archive = zipfile.ZipFile(archive_file, 'r')
names = archive.namelist()
else:
return flask.jsonify({"status": "Incorrect Archive Type"}), 500
if "info.json" in names:
# Create a temp directory to storce archive
tempdir = tempfile.mkdtemp()
archive.extractall(path=tempdir)
with open(os.path.join(tempdir, "info.json")) as data_file:
info = json.load(data_file)
valid, key = validate_archive_keys(info)
if valid is False:
return flask.jsonify({"status": "Missing Key '"+ key +"' in info.json"}), 500
# Get path to files needed to be uploaded in directory
weights_file = os.path.join(tempdir, info["snapshot file"])
if "model file" in info:
model_file = os.path.join(tempdir, info["model file"])
elif "network file" in info:
model_file = os.path.join(tempdir, info["network file"])
else:
return flask.jsonify({"status": "Missing model definition in info.json"}), 500
labels_file = os.path.join(tempdir, info["labels file"])
# Upload the Model:
job = PretrainedModelJob(
weights_file,
model_file ,
labels_file,
info["framework"],
username = utils.auth.get_username(),
name = info["name"]
)
scheduler.add_job(job)
job.wait_completion()
# Delete temp directory
shutil.rmtree(tempdir, ignore_errors=True)
return flask.jsonify({"status": "success"}), 200
else:
return flask.jsonify({"status": "Missing or Incorrect json file"}), 500
