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 create_pretrained_model(job_id,username,epoch):
job = scheduler.get_job(job_id)
if job is None:
raise werkzeug.exceptions.NotFound('Job not found')
# 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
mean_path = None
resize_mode = None
if "labels file" in info:
labels_path = os.path.join(task.dataset.dir(), info["labels file"])
if "mean file" in info:
mean_path = os.path.join(task.dataset.dir(),info["mean file"])
if "image resize mode" in info:
resize_mode = info["image resize mode"]
model_file = os.path.join(job.dir(),str(task.model_file))
# If jobs don't container model_file (too old), raise exception:
if not os.path.isfile(model_file):
raise werkzeug.exceptions.BadRequest('Model file not found in job dir. Job may be too old for conversion.')
job = PretrainedModelJob(
snapshot_filename,
model_file,
labels_path,
mean_path,
info["framework"],
info["image dimensions"][2],
resize_mode,
info["image dimensions"][0],
info["image dimensions"][1],
username = username,
name = info["name"]
)
scheduler.add_job(job)
return job