def push():
"""
Create a pre-trained model from model store
"""
model_id = flask.request.args.get('id')
model_grand_list = app.config['store_cache'].read()
found = False
if model_grand_list is not None:
for store in model_grand_list:
for model in model_grand_list[store]['model_list']:
if model['id'] == model_id:
url = model_grand_list[store]['base_url']
directory = model['dir_name']
found = True
break
if found:
break
if not found:
return 'Unable to find requested model', 404
else:
progress = Progress(model_id)
weights, model, label, meta_data, python_layer = retrieve_files(
url, directory, progress)
job = PretrainedModelJob(weights,
model,
label,
meta_data['framework'],
username=auth.get_username(),
name=meta_data['name'])
scheduler.add_job(job)
response = flask.make_response(job.id())
return response
def push():
"""
Create a pre-trained model from model store
"""
model_id = flask.request.args.get('id')
model_grand_list = app.config['store_cache'].read()
found = False
if model_grand_list is not None:
for store in model_grand_list:
for model in model_grand_list[store]['model_list']:
if model['id'] == model_id:
url = model_grand_list[store]['base_url']
directory = model['dir_name']
found = True
break
if found:
break
if not found:
return 'Unable to find requested model', 404
else:
progress = Progress(model_id)
weights, model, label, meta_data, python_layer = retrieve_files(url, directory, progress)
job = PretrainedModelJob(
weights,
model,
label,
meta_data['framework'],
username=auth.get_username(),
name=meta_data['name']
)
scheduler.add_job(job)
response = flask.make_response(job.id())
return response
def to_pretrained(job_id):
job = scheduler.get_job(job_id)
if job is None:
raise werkzeug.exceptions.NotFound('Job not found')
epoch = -1
# GET ?epoch=n
if 'epoch' in flask.request.args:
epoch = float(flask.request.args['epoch'])
# POST ?snapshot_epoch=n (from form)
elif 'snapshot_epoch' in flask.request.form:
epoch = float(flask.request.form['snapshot_epoch'])
# Write the stats of the job to json,
# and store in tempfile (for archive)
info = job.json_dict(verbose=False, epoch=epoch)
task = job.train_task()
snapshot_filename = None
snapshot_filename = task.get_snapshot(epoch)
# Set defaults:
labels_path = None
resize_mode = None
if "labels file" in info:
labels_path = os.path.join(task.dataset.dir(), info["labels file"])
if "image resize mode" in info:
resize_mode = info["image resize mode"]
job = PretrainedModelJob(
snapshot_filename,
os.path.join(job.dir(), task.model_file),
labels_path,
info["framework"],
info["image dimensions"][2],
resize_mode,
info["image dimensions"][0],
info["image dimensions"][1],
username=auth.get_username(),
name=info["name"]
)
scheduler.add_job(job)
return flask.redirect(flask.url_for('digits.views.home', tab=3)), 302
def to_pretrained(job_id):
job = scheduler.get_job(job_id)
if job is None:
raise werkzeug.exceptions.NotFound('Job not found')
epoch = -1
# GET ?epoch=n
if 'epoch' in flask.request.args:
epoch = float(flask.request.args['epoch'])
# POST ?snapshot_epoch=n (from form)
elif 'snapshot_epoch' in flask.request.form:
epoch = float(flask.request.form['snapshot_epoch'])
# Write the stats of the job to json,
# and store in tempfile (for archive)
info = job.json_dict(verbose=False,epoch=epoch)
task = job.train_task()
snapshot_filename = None
snapshot_filename = task.get_snapshot(epoch)
# Set defaults:
labels_path = None
resize_mode = None
if "labels file" in info:
labels_path = os.path.join(task.dataset.dir(), info["labels file"])
if "image resize mode" in info:
resize_mode = info["image resize mode"]
job = PretrainedModelJob(
snapshot_filename,
os.path.join(job.dir(), task.model_file) ,
labels_path,
info["framework"],
info["image dimensions"][2],
resize_mode,
info["image dimensions"][0],
info["image dimensions"][1],
username = auth.get_username(),
name = info["name"]
)
scheduler.add_job(job)
return flask.redirect(flask.url_for('digits.views.home',tab=3)), 302
def to_pretrained(job_id):
epoch = -1
# GET ?epoch=n
if 'epoch' in flask.request.args:
epoch = float(flask.request.args['epoch'])
# POST ?snapshot_epoch=n (from form)
elif 'snapshot_epoch' in flask.request.form:
epoch = float(flask.request.form['snapshot_epoch'])
username = auth.get_username()
job = create_pretrained_model(job_id,username,epoch)
job.wait_completion()
weights_job = WeightsJob(
job,
name = info['name'],
username = username
)
scheduler.add_job(weights_job)
return flask.redirect(flask.url_for('digits.views.home', tab=3)), 302
