def show_classify_one(inference_job):
# retrieve inference parameters
model_job, paths, _ = inference_job.get_parameters()
if inference_job.status.is_running():
# the inference job is still running
if request_wants_json():
return flask.jsonify(inference_job.json_dict())
else:
return flask.render_template('inference/images/classification/classify_one.html',
model_job = model_job,
job = inference_job,
running = True,
)
else:
# the inference job has completed
# retrieve inference data
inputs, outputs, visualizations = inference_job.get_data()
# delete job
scheduler.delete_job(inference_job)
# remove file (fails silently if a URL was provided)
try:
os.remove(paths[0])
except:
pass
image = None
predictions = []
if inputs is not None and len(inputs['data']) == 1:
image = utils.image.embed_image_html(inputs['data'][0])
# convert to class probabilities for viewing
last_output_name, last_output_data = outputs.items()[-1]
if len(last_output_data) == 1:
scores = last_output_data[0].flatten()
indices = (-scores).argsort()
labels = model_job.train_task().get_labels()
predictions = []
for i in indices:
predictions.append( (labels[i], scores[i]) )
predictions = [(p[0], round(100.0*p[1],2)) for p in predictions[:5]]
if request_wants_json():
return flask.jsonify({'predictions': predictions})
else:
return flask.render_template('inference/images/classification/classify_one.html',
model_job = model_job,
job = inference_job,
image_src = image,
predictions = predictions,
visualizations = visualizations,
total_parameters= sum(v['param_count'] for v in visualizations if v['vis_type'] == 'Weights'),
running = False,
)
def image_classification_dataset_create():
"""
Creates a new ImageClassificationDatasetJob
Returns JSON when requested: {job_id,name,status} or {errors:[]}
"""
form = ImageClassificationDatasetForm()
## Is there a request to clone a job with ?clone=<job_id>
fill_form_if_cloned(form)
if not form.validate_on_submit():
if request_wants_json():
return flask.jsonify({'errors': form.errors}), 400
else:
return flask.render_template(
'datasets/images/classification/new.html', form=form), 400
job = None
try:
job = ImageClassificationDatasetJob(
name=form.dataset_name.data,
image_dims=(
int(form.resize_height.data),
int(form.resize_width.data),
int(form.resize_channels.data),
),
resize_mode=form.resize_mode.data,
bbox_mode=int(form.bbox_mode.data),
scale_factor=float(form.scale_factor.data),
)
if form.method.data == 'folder':
from_folders(job, form)
elif form.method.data == 'textfile':
from_files(job, form)
elif form.method.data == 'jsonfile':
from_json(job, form)
else:
raise ValueError('method not supported')
## Save form data with the job so we can easily clone it later.
save_form_to_job(job, form)
scheduler.add_job(job)
if request_wants_json():
return flask.jsonify(job.json_dict())
else:
return flask.redirect(
flask.url_for('datasets_show', job_id=job.id()))
except:
if job:
scheduler.delete_job(job)
raise
def create():
"""
Creates a new ImageClassificationDatasetJob
Returns JSON when requested: {job_id,name,status} or {errors:[]}
"""
form = ImageClassificationDatasetForm()
# Is there a request to clone a job with ?clone=<job_id>
fill_form_if_cloned(form)
if not form.validate_on_submit():
if request_wants_json():
return flask.jsonify({'errors': form.errors}), 400
else:
return flask.render_template('datasets/images/classification/new.html', form=form), 400
job = None
try:
job = ImageClassificationDatasetJob(
username=utils.auth.get_username(),
name=form.dataset_name.data,
group=form.group_name.data,
image_dims=(
int(form.resize_height.data),
int(form.resize_width.data),
int(form.resize_channels.data),
),
resize_mode=form.resize_mode.data
)
if form.method.data == 'folder':
from_folders(job, form)
elif form.method.data == 'textfile':
from_files(job, form)
elif form.method.data == 's3':
from_s3(job, form)
else:
raise ValueError('method not supported')
# Save form data with the job so we can easily clone it later.
save_form_to_job(job, form)
scheduler.add_job(job)
if request_wants_json():
return flask.jsonify(job.json_dict())
else:
return flask.redirect(flask.url_for('digits.dataset.views.show', job_id=job.id()))
except:
if job:
scheduler.delete_job(job)
raise
def image_classification_dataset_create():
"""
Creates a new ImageClassificationDatasetJob
Returns JSON when requested: {job_id,name,status} or {errors:[]}
"""
form = ImageClassificationDatasetForm()
## Is there a request to clone a job with ?clone=<job_id>
fill_form_if_cloned(form)
if not form.validate_on_submit():
if request_wants_json():
return flask.jsonify({"errors": form.errors}), 400
else:
return flask.render_template("datasets/images/classification/new.html", form=form), 400
job = None
try:
job = ImageClassificationDatasetJob(
name=form.dataset_name.data,
image_dims=(int(form.resize_height.data), int(form.resize_width.data), int(form.resize_channels.data)),
resize_mode=form.resize_mode.data,
bbox_mode=int(form.bbox_mode.data),
scale_factor=float(form.scale_factor.data),
)
if form.method.data == "folder":
from_folders(job, form)
elif form.method.data == "textfile":
from_files(job, form)
elif form.method.data == "jsonfile":
from_json(job, form)
else:
raise ValueError("method not supported")
## Save form data with the job so we can easily clone it later.
save_form_to_job(job, form)
scheduler.add_job(job)
if request_wants_json():
return flask.jsonify(job.json_dict())
else:
return flask.redirect(flask.url_for("datasets_show", job_id=job.id()))
except:
if job:
scheduler.delete_job(job)
raise
def image_classification_model_classify_one():
"""
Classify one image and return the top 5 classifications
Returns JSON when requested: {predictions: {category: confidence,...}}
"""
job = job_from_request()
image = None
if "image_url" in flask.request.form and flask.request.form["image_url"]:
image = utils.image.load_image(flask.request.form["image_url"])
elif "image_file" in flask.request.files and flask.request.files["image_file"]:
outfile = tempfile.mkstemp(suffix=".bin")
flask.request.files["image_file"].save(outfile[1])
image = utils.image.load_image(outfile[1])
os.close(outfile[0])
os.remove(outfile[1])
else:
raise werkzeug.exceptions.BadRequest("must provide image_url or image_file")
# resize image
db_task = job.train_task().dataset.train_db_task()
height = db_task.image_dims[0]
width = db_task.image_dims[1]
if job.train_task().crop_size:
height = job.train_task().crop_size
width = job.train_task().crop_size
image = utils.image.resize_image(
image, height, width, channels=db_task.image_dims[2], resize_mode=db_task.resize_mode
)
epoch = None
if "snapshot_epoch" in flask.request.form:
epoch = float(flask.request.form["snapshot_epoch"])
layers = "none"
if "show_visualizations" in flask.request.form and flask.request.form["show_visualizations"]:
layers = "all"
predictions, visualizations = None, None
try:
predictions, visualizations = job.train_task().infer_one(image, snapshot_epoch=epoch, layers=layers)
except frameworks.errors.InferenceError as e:
return e.__str__(), 403
# take top 5
if predictions:
predictions = [(p[0], round(100.0 * p[1], 2)) for p in predictions[:5]]
if request_wants_json():
return flask.jsonify({"predictions": predictions})
else:
return flask.render_template(
"models/images/classification/classify_one.html",
job=job,
image_src=utils.image.embed_image_html(image),
predictions=predictions,
visualizations=visualizations,
total_parameters=sum(v["param_count"] for v in visualizations if v["vis_type"] == "Weights"),
)
def handle_error(e):
"""
Handle errors, formatting them as JSON if requested
"""
error_type = type(e).__name__
message = str(e)
trace = None
description = None
status_code = 500
if isinstance(e, werkzeug.exceptions.HTTPException):
status_code = e.code
description = e.description
if app.debug:
trace = traceback.format_exc()
if request_wants_json():
details = {"message": message, "type": error_type}
if description is not None:
details["description"] = description
if trace is not None:
details["trace"] = trace.split("\n")
return flask.jsonify({"error": details}), status_code
else:
return (
flask.render_template(
"error.html", title=error_type, message=message, description=description, trace=trace
),
status_code,
)
def handle_error(e):
"""
Handle errors, formatting them as JSON if requested
"""
error_type = type(e).__name__
message = str(e)
trace = None
description = None
status_code = 500
if isinstance(e, werkzeug.exceptions.HTTPException):
status_code = e.code
description = e.description
if app.debug:
trace = traceback.format_exc()
if request_wants_json():
details = {
'message': message,
'type': error_type,
}
if description is not None:
details['description'] = description
if trace is not None:
details['trace'] = trace.split('\n')
return flask.jsonify({'error': details}), status_code
else:
return flask.render_template(
'error.html',
title=error_type,
message=message,
description=description,
trace=trace,
), status_code
def handle_error(e):
"""
Handle errors, formatting them as JSON if requested
"""
error_type = type(e).__name__
message = str(e)
trace = None
description = None
status_code = 500
if isinstance(e, werkzeug.exceptions.HTTPException):
status_code = e.code
description = e.description
if app.debug:
trace = traceback.format_exc()
if request_wants_json():
details = {
'message': message,
'type': error_type,
}
if description is not None:
details['description'] = description
if trace is not None:
details['trace'] = trace.split('\n')
return flask.jsonify({'error': details}), status_code
else:
return flask.render_template('error.html',
title = error_type,
message = message,
description = description,
trace = trace,
), status_code
def generic_image_model_infer_one():
"""
Infer one image
"""
job = job_from_request()
image = None
if 'image_url' in flask.request.form and flask.request.form['image_url']:
image = utils.image.load_image(flask.request.form['image_url'])
elif 'image_file' in flask.request.files and flask.request.files[
'image_file']:
outfile = tempfile.mkstemp(suffix='.bin')
flask.request.files['image_file'].save(outfile[1])
image = utils.image.load_image(outfile[1])
os.close(outfile[0])
os.remove(outfile[1])
else:
raise werkzeug.exceptions.BadRequest(
'must provide image_url or image_file')
# resize image
db_task = job.train_task().dataset.analyze_db_tasks()[0]
height = db_task.image_height
width = db_task.image_width
if job.train_task().crop_size:
height = job.train_task().crop_size
width = job.train_task().crop_size
image = utils.image.resize_image(
image,
height,
width,
channels=db_task.image_channels,
resize_mode='squash',
)
epoch = None
if 'snapshot_epoch' in flask.request.form:
epoch = float(flask.request.form['snapshot_epoch'])
layers = 'none'
if 'show_visualizations' in flask.request.form and flask.request.form[
'show_visualizations']:
layers = 'all'
outputs, visualizations = job.train_task().infer_one(image,
snapshot_epoch=epoch,
layers=layers)
if request_wants_json():
return flask.jsonify({
'outputs':
dict((name, blob.tolist()) for name, blob in outputs.iteritems())
})
else:
return flask.render_template(
'models/images/generic/infer_one.html',
image_src=utils.image.embed_image_html(image),
network_outputs=outputs,
visualizations=visualizations,
)
def show(job_id):
"""
Show a DatasetJob
Returns JSON when requested:
{id, name, directory, status}
"""
job = scheduler.get_job(job_id)
if job is None:
raise werkzeug.exceptions.NotFound('Job not found')
related_jobs = scheduler.get_related_jobs(job)
if request_wants_json():
return flask.jsonify(job.json_dict(True))
else:
if isinstance(job, dataset_images.ImageClassificationDatasetJob):
return dataset_images.classification.views.show(
job, related_jobs=related_jobs)
elif isinstance(job, dataset_images.GenericImageDatasetJob):
return dataset_images.generic.views.show(job,
related_jobs=related_jobs)
elif isinstance(job, generic.GenericDatasetJob):
return generic.views.show(job, related_jobs=related_jobs)
else:
raise werkzeug.exceptions.BadRequest('Invalid job type')
def image_classification_model_classify_one():
"""
Classify one image and return the top 5 classifications
Returns JSON when requested: {predictions: {category: confidence,...}}
"""
job = job_from_request()
image = None
if 'image_url' in flask.request.form and flask.request.form['image_url']:
image = utils.image.load_image(flask.request.form['image_url'])
elif 'image_file' in flask.request.files and flask.request.files[
'image_file']:
outfile = tempfile.mkstemp(suffix='.bin')
flask.request.files['image_file'].save(outfile[1])
image = utils.image.load_image(outfile[1])
os.close(outfile[0])
os.remove(outfile[1])
else:
raise werkzeug.exceptions.BadRequest(
'must provide image_url or image_file')
# resize image
db_task = job.train_task().dataset.train_db_task()
height = db_task.image_dims[0]
width = db_task.image_dims[1]
if job.train_task().crop_size:
height = job.train_task().crop_size
width = job.train_task().crop_size
image = utils.image.resize_image(
image,
height,
width,
channels=db_task.image_dims[2],
resize_mode=db_task.resize_mode,
)
epoch = None
if 'snapshot_epoch' in flask.request.form:
epoch = float(flask.request.form['snapshot_epoch'])
layers = 'none'
if 'show_visualizations' in flask.request.form and flask.request.form[
'show_visualizations']:
layers = 'all'
predictions, visualizations = job.train_task().infer_one(
image, snapshot_epoch=epoch, layers=layers)
# take top 5
predictions = [(p[0], round(100.0 * p[1], 2)) for p in predictions[:5]]
if request_wants_json():
return flask.jsonify({'predictions': predictions})
else:
return flask.render_template(
'models/images/classification/classify_one.html',
image_src=utils.image.embed_image_html(image),
predictions=predictions,
visualizations=visualizations,
)
def generic_image_model_infer_many():
"""
Infer many images
"""
job = job_from_request()
image_list = flask.request.files.get("image_list")
if not image_list:
raise werkzeug.exceptions.BadRequest("image_list is a required field")
epoch = None
if "snapshot_epoch" in flask.request.form:
epoch = float(flask.request.form["snapshot_epoch"])
paths = []
images = []
db_task = job.train_task().dataset.analyze_db_tasks()[0]
height = db_task.image_height
width = db_task.image_width
channels = db_task.image_channels
for line in image_list.readlines():
line = line.strip()
if not line:
continue
path = None
# might contain a numerical label at the end
match = re.match(r"(.*\S)\s+\d+$", line)
if match:
path = match.group(1)
else:
path = line
try:
image = utils.image.load_image(path)
image = utils.image.resize_image(image, height, width, channels=channels, resize_mode="squash")
paths.append(path)
images.append(image)
except utils.errors.LoadImageError as e:
print e
if not len(images):
raise werkzeug.exceptions.BadRequest("Unable to load any images from the file")
outputs = job.train_task().infer_many(images, snapshot_epoch=epoch)
if outputs is None:
raise RuntimeError("An error occured while processing the images")
if request_wants_json():
result = {}
for i, path in enumerate(paths):
result[path] = dict((name, blob[i].tolist()) for name, blob in outputs.iteritems())
return flask.jsonify({"outputs": result})
else:
return flask.render_template(
"models/images/generic/infer_many.html", job=job, paths=paths, network_outputs=outputs
)
def image_classification_model_classify_one():
"""
Classify one image and return the top 5 classifications
Returns JSON when requested: {predictions: {category: confidence,...}}
"""
job = job_from_request()
image = None
if 'image_url' in flask.request.form and flask.request.form['image_url']:
image = utils.image.load_image(flask.request.form['image_url'])
elif 'image_file' in flask.request.files and flask.request.files['image_file']:
outfile = tempfile.mkstemp(suffix='.bin')
flask.request.files['image_file'].save(outfile[1])
image = utils.image.load_image(outfile[1])
os.close(outfile[0])
os.remove(outfile[1])
else:
raise werkzeug.exceptions.BadRequest('must provide image_url or image_file')
# resize image
db_task = job.train_task().dataset.train_db_task()
height = db_task.image_dims[0]
width = db_task.image_dims[1]
if job.train_task().crop_size:
height = job.train_task().crop_size
width = job.train_task().crop_size
image = utils.image.resize_image(image, height, width,
channels = db_task.image_dims[2],
resize_mode = db_task.resize_mode,
)
epoch = None
if 'snapshot_epoch' in flask.request.form:
epoch = float(flask.request.form['snapshot_epoch'])
layers = 'none'
if 'show_visualizations' in flask.request.form and flask.request.form['show_visualizations']:
layers = 'all'
predictions, visualizations = None, None
predictions, visualizations = job.train_task().infer_one(image, snapshot_epoch=epoch, layers=layers)
# take top 5
if predictions:
predictions = [(p[0], round(100.0*p[1],2)) for p in predictions[:5]]
if request_wants_json():
return flask.jsonify({'predictions': predictions})
else:
return flask.render_template('models/images/classification/classify_one.html',
job = job,
image_src = utils.image.embed_image_html(image),
predictions = predictions,
visualizations = visualizations,
total_parameters= sum(v['param_count'] for v in visualizations if v['vis_type'] == 'Weights'),
)
def image_classification_dataset_create():
"""
Creates a new ImageClassificationDatasetJob
Returns JSON when requested: {job_id,name,status} or {errors:[]}
"""
form = ImageClassificationDatasetForm()
if not form.validate_on_submit():
if request_wants_json():
return flask.jsonify({'errors': form.errors}), 400
else:
return flask.render_template('datasets/images/classification/new.html', form=form), 400
job = None
try:
job = ImageClassificationDatasetJob(
name = form.dataset_name.data,
image_dims = (
int(form.resize_height.data),
int(form.resize_width.data),
int(form.resize_channels.data),
),
resize_mode = form.resize_mode.data
)
if form.method.data == 'folder':
from_folders(job, form)
elif form.method.data == 'textfile':
from_files(job, form)
else:
raise ValueError('method not supported')
scheduler.add_job(job)
if request_wants_json():
return flask.jsonify(job.json_dict())
else:
return flask.redirect(flask.url_for('datasets_show', job_id=job.id()))
except:
if job:
scheduler.delete_job(job)
raise
def image_classification_dataset_create():
"""
Creates a new ImageClassificationDatasetJob
Returns JSON when requested: {job_id,name,status} or {errors:[]}
"""
form = ImageClassificationDatasetForm()
if not form.validate_on_submit():
if request_wants_json():
return flask.jsonify({'errors': form.errors}), 400
else:
return flask.render_template('datasets/images/classification/new.html', form=form), 400
job = None
try:
job = ImageClassificationDatasetJob(
name = form.dataset_name.data,
image_dims = (
int(form.resize_height.data),
int(form.resize_width.data),
int(form.resize_channels.data),
),
resize_mode = form.resize_mode.data
)
if form.method.data == 'folder':
from_folders(job, form)
elif form.method.data == 'textfile':
from_files(job, form)
else:
raise ValueError('method not supported')
scheduler.add_job(job)
if request_wants_json():
return flask.jsonify(job.json_dict())
else:
return flask.redirect(flask.url_for('datasets_show', job_id=job.id()))
except:
if job:
scheduler.delete_job(job)
raise
def show_top_n(inference_job):
# retrieve inference parameters
model_job, _, _, top_n = inference_job.get_parameters()
if inference_job.status.is_running():
# the inference job is still running
if request_wants_json():
return flask.jsonify(inference_job.json_dict())
else:
return flask.render_template('inference/images/classification/top_n.html',
model_job = model_job,
job = inference_job,
running = True,
)
else:
# retrieve inference data
inputs, outputs, _ = inference_job.get_data()
# delete job
scheduler.delete_job(inference_job)
results = None
if outputs is not None and len(outputs) > 0:
# convert to class probabilities for viewing
last_output_name, last_output_data = outputs.items()[-1]
scores = last_output_data
if scores is None:
raise RuntimeError('An error occured while processing the images')
labels = model_job.train_task().get_labels()
images = inputs['data']
indices = (-scores).argsort(axis=0)[:top_n]
results = []
# Can't have more images per category than the number of images
images_per_category = min(top_n, len(images))
for i in xrange(indices.shape[1]):
result_images = []
for j in xrange(images_per_category):
result_images.append(images[indices[j][i]])
results.append((
labels[i],
utils.image.embed_image_html(
utils.image.vis_square(np.array(result_images),
colormap='white')
)
))
return flask.render_template('inference/images/classification/top_n.html',
model_job = model_job,
job = inference_job,
results = results,
running = False,
)
def feature_extraction_dataset_create():
"""
Creates a new FeatureExtractionDatasetJob
Returns JSON when requested: {job_id,name,status} or {errors:[]}
"""
workspace = get_workspace_details(flask.request.url)
form = FeatureExtractionDatasetForm()
if not form.validate_on_submit():
if request_wants_json():
return flask.jsonify({'errors': form.errors}), 400
else:
return flask.render_template('datasets/images/extraction/new.html', form=form, workspace = workspace), 400
job = None
try:
job = FeatureExtractionDatasetJob(
name = form.dataset_name.data,
image_dims = (
int(form.resize_height.data),
int(form.resize_width.data),
int(form.resize_channels.data),
),
resize_mode = form.resize_mode.data,
workspace = workspace,
)
#if form.method.data == 'folder':
# from_folders(job, form)
#elif form.method.data == 'textfile':
from_files(job, form)
scheduler.add_job(job)
if request_wants_json():
return flask.jsonify(job.json_dict())
else:
return flask.redirect(flask.url_for('datasets_show', job_id=job.id())+'?workspace='+workspace['workspace_hash'])
except:
if job:
scheduler.delete_job(job)
raise
def image_classification_model_classify_one():
"""
Classify one image and return the top 5 classifications
Returns JSON when requested: {predictions: {category: confidence,...}}
"""
job = scheduler.get_job(flask.request.args['job_id'])
if job is None:
raise werkzeug.exceptions.NotFound('Job not found')
image = None
if 'image_url' in flask.request.form and flask.request.form['image_url']:
image = utils.image.load_image(flask.request.form['image_url'])
elif 'image_file' in flask.request.files and flask.request.files['image_file']:
with tempfile.NamedTemporaryFile() as outfile:
flask.request.files['image_file'].save(outfile.name)
image = utils.image.load_image(outfile.name)
else:
raise werkzeug.exceptions.BadRequest('No image given')
# resize image
db_task = job.train_task().dataset.train_db_task()
height = db_task.image_dims[0]
width = db_task.image_dims[1]
if job.train_task().crop_size:
height = job.train_task().crop_size
width = job.train_task().crop_size
image = utils.image.resize_image(image, height, width,
channels = db_task.image_dims[2],
resize_mode = db_task.resize_mode,
)
epoch = None
if 'snapshot_epoch' in flask.request.form:
epoch = float(flask.request.form['snapshot_epoch'])
layers = 'none'
if 'show_visualizations' in flask.request.form and flask.request.form['show_visualizations']:
layers = 'all'
predictions, visualizations = job.train_task().infer_one(image, snapshot_epoch=epoch, layers=layers)
# take top 5
predictions = [(p[0], round(100.0*p[1],2)) for p in predictions[:5]]
if request_wants_json():
return flask.jsonify({'predictions': predictions})
else:
return flask.render_template('models/images/classification/classify_one.html',
image_src = utils.image.embed_image_html(image),
predictions = predictions,
visualizations = visualizations,
)
def home():
"""
DIGITS home page
Returns information about each job on the server
Returns JSON when requested:
{
datasets: [{id, name, status},...],
models: [{id, name, status},...]
}
"""
running_datasets = get_job_list(dataset.DatasetJob, True)
completed_datasets = get_job_list(dataset.DatasetJob, False)
running_models = get_job_list(model.ModelJob, True)
completed_models = get_job_list(model.ModelJob, False)
if request_wants_json():
return flask.jsonify({
'datasets': [j.json_dict()
for j in running_datasets + completed_datasets],
'models': [j.json_dict()
for j in running_models + completed_models],
})
else:
new_dataset_options = [
('Images', [
{
'title': 'Classification',
'id': 'image-classification',
'url': flask.url_for('image_classification_dataset_new'),
},
])
]
new_model_options = [
('Images', [
{
'title': 'Classification',
'id': 'image-classification',
'url': flask.url_for('image_classification_model_new'),
},
])
]
return flask.render_template('home.html',
new_dataset_options = new_dataset_options,
running_datasets = running_datasets,
completed_datasets = completed_datasets,
new_model_options = new_model_options,
running_models = running_models,
completed_models = completed_models,
)
def home():
"""
DIGITS home page
Returns information about each job on the server
Returns JSON when requested:
{
datasets: [{id, name, status},...],
models: [{id, name, status},...]
}
"""
running_datasets = get_job_list(dataset.DatasetJob, True)
completed_datasets = get_job_list(dataset.DatasetJob, False)
running_models = get_job_list(model.ModelJob, True)
completed_models = get_job_list(model.ModelJob, False)
if request_wants_json():
return flask.jsonify({
'datasets': [j.json_dict()
for j in running_datasets + completed_datasets],
'models': [j.json_dict()
for j in running_models + completed_models],
})
else:
new_dataset_options = [
('Images', [
{
'title': 'Classification',
'id': 'image-classification',
'url': flask.url_for('image_classification_dataset_new'),
},
])
]
new_model_options = [
('Images', [
{
'title': 'Classification',
'id': 'image-classification',
'url': flask.url_for('image_classification_model_new'),
},
])
]
return flask.render_template('home.html',
new_dataset_options = new_dataset_options,
running_datasets = running_datasets,
completed_datasets = completed_datasets,
new_model_options = new_model_options,
running_models = running_models,
completed_models = completed_models,
)
def top_n():
"""
Classify many images and show the top N images per category by confidence
"""
model_job = job_from_request()
image_list = flask.request.files['image_list']
if not image_list:
raise werkzeug.exceptions.BadRequest('File upload not found')
epoch = None
if 'snapshot_epoch' in flask.request.form:
epoch = float(flask.request.form['snapshot_epoch'])
if 'top_n' in flask.request.form and flask.request.form['top_n'].strip():
top_n = int(flask.request.form['top_n'])
else:
top_n = 9
if 'image_folder' in flask.request.form and flask.request.form['image_folder'].strip():
image_folder = flask.request.form['image_folder']
if not os.path.exists(image_folder):
raise werkzeug.exceptions.BadRequest('image_folder "%s" does not exit' % image_folder)
else:
image_folder = None
if 'num_test_images' in flask.request.form and flask.request.form['num_test_images'].strip():
num_test_images = int(flask.request.form['num_test_images'])
else:
num_test_images = None
paths, _ = read_image_list(image_list, image_folder, num_test_images)
# create inference job
inference_job = ImageInferenceTopNJob(
username = utils.auth.get_username(),
name = "TopN Image Classification",
model = model_job,
images = paths,
epoch = epoch,
layers = 'none',
top_n = top_n,
)
# schedule tasks
scheduler.add_job(inference_job)
if request_wants_json():
return flask.jsonify(inference_job.json_dict())
else:
return flask.redirect(flask.url_for('digits.inference.views.show', job_id=inference_job.id()))
def generic_image_model_infer_one():
"""
Infer one image
"""
job = job_from_request()
image = None
if 'image_url' in flask.request.form and flask.request.form['image_url']:
image = utils.image.load_image(flask.request.form['image_url'])
elif 'image_file' in flask.request.files and flask.request.files['image_file']:
outfile = tempfile.mkstemp(suffix='.bin')
flask.request.files['image_file'].save(outfile[1])
image = utils.image.load_image(outfile[1])
os.close(outfile[0])
os.remove(outfile[1])
else:
raise werkzeug.exceptions.BadRequest('must provide image_url or image_file')
# resize image
db_task = job.train_task().dataset.analyze_db_tasks()[0]
height = db_task.image_height
width = db_task.image_width
if job.train_task().crop_size:
height = job.train_task().crop_size
width = job.train_task().crop_size
image = utils.image.resize_image(image, height, width,
channels = db_task.image_channels,
resize_mode = 'squash',
)
epoch = None
if 'snapshot_epoch' in flask.request.form:
epoch = float(flask.request.form['snapshot_epoch'])
layers = 'none'
if 'show_visualizations' in flask.request.form and flask.request.form['show_visualizations']:
layers = 'all'
outputs, visualizations = job.train_task().infer_one(image, snapshot_epoch=epoch, layers=layers)
if request_wants_json():
return flask.jsonify({'outputs': dict((name, blob.tolist()) for name,blob in outputs.iteritems())})
else:
return flask.render_template('models/images/generic/infer_one.html',
job = job,
image_src = utils.image.embed_image_html(image),
network_outputs = outputs,
visualizations = visualizations,
total_parameters= sum(v['param_count'] for v in visualizations if v['vis_type'] == 'Weights'),
)
def image_classification_model_classify_one():
"""
Classify one image and return the top 5 classifications
Returns JSON when requested: {predictions: {category: confidence,...}}
"""
job = job_from_request()
image = None
if "image_url" in flask.request.form and flask.request.form["image_url"]:
image = utils.image.load_image(flask.request.form["image_url"])
elif "image_file" in flask.request.files and flask.request.files["image_file"]:
with tempfile.NamedTemporaryFile() as outfile:
flask.request.files["image_file"].save(outfile.name)
image = utils.image.load_image(outfile.name)
else:
raise werkzeug.exceptions.BadRequest("must provide image_url or image_file")
# resize image
db_task = job.train_task().dataset.train_db_task()
height = db_task.image_dims[0]
width = db_task.image_dims[1]
if job.train_task().crop_size:
height = job.train_task().crop_size
width = job.train_task().crop_size
image = utils.image.resize_image(
image, height, width, channels=db_task.image_dims[2], resize_mode=db_task.resize_mode
)
epoch = None
if "snapshot_epoch" in flask.request.form:
epoch = float(flask.request.form["snapshot_epoch"])
layers = "none"
if "show_visualizations" in flask.request.form and flask.request.form["show_visualizations"]:
layers = "all"
predictions, visualizations = job.train_task().infer_one(image, snapshot_epoch=epoch, layers=layers)
# take top 5
predictions = [(p[0], round(100.0 * p[1], 2)) for p in predictions[:5]]
if request_wants_json():
return flask.jsonify({"predictions": predictions})
else:
return flask.render_template(
"models/images/classification/classify_one.html",
image_src=utils.image.embed_image_html(image),
predictions=predictions,
visualizations=visualizations,
)
def classify_many():
"""
Start a new classify_may job
"""
# kicking off a new inference job
model_job = job_from_request()
image_list = flask.request.files.get('image_list')
if not image_list:
raise werkzeug.exceptions.BadRequest('image_list is a required field')
if 'image_folder' in flask.request.form and flask.request.form['image_folder'].strip():
image_folder = flask.request.form['image_folder']
if not os.path.exists(image_folder):
raise werkzeug.exceptions.BadRequest('image_folder "%s" does not exit' % image_folder)
else:
image_folder = None
if 'num_test_images' in flask.request.form and flask.request.form['num_test_images'].strip():
num_test_images = int(flask.request.form['num_test_images'])
else:
num_test_images = None
epoch = None
if 'snapshot_epoch' in flask.request.form:
epoch = float(flask.request.form['snapshot_epoch'])
paths, ground_truths = read_image_list(image_list, image_folder, num_test_images)
# create inference job
inference_job = ImageInferenceClassifyManyJob(
username = utils.auth.get_username(),
name = "Classify Many Images",
model = model_job,
images = paths,
epoch = epoch,
layers = 'none',
ground_truths = ground_truths,
)
# schedule tasks
scheduler.add_job(inference_job)
if request_wants_json():
return flask.jsonify(inference_job.json_dict())
else:
return flask.redirect(flask.url_for('digits.inference.views.show', job_id=inference_job.id()))
def generic_image_model_infer_one():
"""
Infer one image
"""
job = job_from_request()
image = None
if 'image_url' in flask.request.form and flask.request.form['image_url']:
image = utils.image.load_image(flask.request.form['image_url'])
elif 'image_file' in flask.request.files and flask.request.files['image_file']:
with tempfile.NamedTemporaryFile() as outfile:
flask.request.files['image_file'].save(outfile.name)
image = utils.image.load_image(outfile.name)
else:
raise werkzeug.exceptions.BadRequest('must provide image_url or image_file')
# resize image
db_task = job.train_task().dataset.analyze_db_tasks()[0]
height = db_task.image_height
width = db_task.image_width
if job.train_task().crop_size:
height = job.train_task().crop_size
width = job.train_task().crop_size
image = utils.image.resize_image(image, height, width,
channels = db_task.image_channels,
resize_mode = 'squash',
)
epoch = None
if 'snapshot_epoch' in flask.request.form:
epoch = float(flask.request.form['snapshot_epoch'])
layers = 'none'
if 'show_visualizations' in flask.request.form and flask.request.form['show_visualizations']:
layers = 'all'
outputs, visualizations = job.train_task().infer_one(image, snapshot_epoch=epoch, layers=layers)
if request_wants_json():
return flask.jsonify({'outputs': dict((name, blob.tolist()) for name,blob in outputs.iteritems())})
else:
return flask.render_template('models/images/generic/infer_one.html',
image_src = utils.image.embed_image_html(image),
network_outputs = outputs,
visualizations = visualizations,
)
def generic_image_model_infer_one():
"""
Infer one image
"""
job = job_from_request()
image = None
if "image_url" in flask.request.form and flask.request.form["image_url"]:
image = utils.image.load_image(flask.request.form["image_url"])
elif "image_file" in flask.request.files and flask.request.files["image_file"]:
outfile = tempfile.mkstemp(suffix=".bin")
flask.request.files["image_file"].save(outfile[1])
image = utils.image.load_image(outfile[1])
os.close(outfile[0])
os.remove(outfile[1])
else:
raise werkzeug.exceptions.BadRequest("must provide image_url or image_file")
# resize image
db_task = job.train_task().dataset.analyze_db_tasks()[0]
height = db_task.image_height
width = db_task.image_width
image = utils.image.resize_image(image, height, width, channels=db_task.image_channels, resize_mode="squash")
epoch = None
if "snapshot_epoch" in flask.request.form:
epoch = float(flask.request.form["snapshot_epoch"])
layers = "none"
if "show_visualizations" in flask.request.form and flask.request.form["show_visualizations"]:
layers = "all"
outputs, visualizations = job.train_task().infer_one(image, snapshot_epoch=epoch, layers=layers)
if request_wants_json():
return flask.jsonify({"outputs": dict((name, blob.tolist()) for name, blob in outputs.iteritems())})
else:
return flask.render_template(
"models/images/generic/infer_one.html",
job=job,
image_src=utils.image.embed_image_html(image),
network_outputs=outputs,
visualizations=visualizations,
total_parameters=sum(v["param_count"] for v in visualizations if v["vis_type"] == "Weights"),
)
def models_show(job_id):
"""
Show a ModelJob
Returns JSON when requested:
{id, name, directory, status, snapshots: [epoch,epoch,...]}
"""
job = scheduler.get_job(job_id)
if job is None:
raise werkzeug.exceptions.NotFound('Job not found')
if request_wants_json():
return flask.jsonify(job.json_dict(True))
else:
if isinstance(job, model_images.ImageClassificationModelJob):
return model_images.classification.views.show(job)
else:
raise werkzeug.exceptions.BadRequest('Invalid job type')
def datasets_show(job_id):
"""
Show a DatasetJob
Returns JSON when requested:
{id, name, directory, status}
"""
job = scheduler.get_job(job_id)
if job is None:
raise werkzeug.exceptions.NotFound('Job not found')
if request_wants_json():
return flask.jsonify(job.json_dict(True))
else:
if isinstance(job, dataset_images.ImageClassificationDatasetJob):
return dataset_images.classification.views.show(job)
else:
raise werkzeug.exceptions.BadRequest('Invalid job type')
def classify_one():
"""
Classify one image and return the top 5 classifications
Returns JSON when requested: {predictions: {category: confidence,...}}
"""
model_job = job_from_request()
if 'image_url' in flask.request.form and flask.request.form['image_url']:
image_path = flask.request.form['image_url']
elif 'image_file' in flask.request.files and flask.request.files['image_file']:
outfile = tempfile.mkstemp(suffix='.png')
flask.request.files['image_file'].save(outfile[1])
image_path = outfile[1]
os.close(outfile[0])
else:
raise werkzeug.exceptions.BadRequest('must provide image_url or image_file')
epoch = None
if 'snapshot_epoch' in flask.request.form:
epoch = float(flask.request.form['snapshot_epoch'])
layers = 'none'
if 'show_visualizations' in flask.request.form and flask.request.form['show_visualizations']:
layers = 'all'
# create inference job
inference_job = ImageInferenceClassifyOneJob(
username = utils.auth.get_username(),
name = "Classify One Image",
model = model_job,
images = [image_path],
epoch = epoch,
layers = layers
)
# schedule tasks
scheduler.add_job(inference_job)
if request_wants_json():
return flask.jsonify(inference_job.json_dict())
else:
return flask.redirect(flask.url_for('digits.inference.views.show', job_id=inference_job.id()))
def models_show(job_id):
"""
Show a ModelJob
Returns JSON when requested:
{id, name, directory, status, snapshots: [epoch,epoch,...]}
"""
job = scheduler.get_job(job_id)
if job is None:
raise werkzeug.exceptions.NotFound("Job not found")
if request_wants_json():
return flask.jsonify(job.json_dict(True))
else:
if isinstance(job, model_images.ImageClassificationModelJob):
return model_images.classification.views.show(job)
elif isinstance(job, model_images.GenericImageModelJob):
return model_images.generic.views.show(job)
else:
raise werkzeug.exceptions.BadRequest("Invalid job type")
def handle_exception(e, status_code=500):
if 'DIGITS_MODE_TEST' in os.environ:
raise
error_type = type(e).__name__
message = str(e)
trace = None
if app.debug:
trace = traceback.format_exc()
if request_wants_json():
details = {
'message': message,
'type': error_type,
}
if trace is not None:
details['trace'] = trace.split('\n')
return flask.jsonify({'error': details}), status_code
else:
return flask.render_template('500.html',
title = error_type,
message = message,
trace = trace,
), status_code
def home():
"""
DIGITS home page
Returns information about each job on the server
Returns JSON when requested:
{
datasets: [{id, name, status},...],
models: [{id, name, status},...]
}
"""
running_datasets = get_job_list(dataset.DatasetJob, True)
completed_datasets = get_job_list(dataset.DatasetJob, False)
running_models = get_job_list(model.ModelJob, True)
completed_models = get_job_list(model.ModelJob, False)
if request_wants_json():
data = {
'version': digits.__version__,
'jobs_dir': config_value('jobs_dir'),
'datasets': [j.json_dict()
for j in running_datasets + completed_datasets],
'models': [j.json_dict()
for j in running_models + completed_models],
}
if config_value('server_name'):
data['server_name'] = config_value('server_name')
return flask.jsonify(data)
else:
new_dataset_options = [
('Images', [
{
'title': 'Classification',
'id': 'image-classification',
'url': flask.url_for('image_classification_dataset_new'),
},
{
'title': 'Other',
'id': 'image-generic',
'url': flask.url_for('generic_image_dataset_new'),
},
])
]
new_model_options = [
('Images', [
{
'title': 'Classification',
'id': 'image-classification',
'url': flask.url_for('image_classification_model_new'),
},
{
'title': 'Other',
'id': 'image-generic',
'url': flask.url_for('generic_image_model_new'),
},
])
]
return flask.render_template('home.html',
new_dataset_options = new_dataset_options,
running_datasets = running_datasets,
completed_datasets = completed_datasets,
new_model_options = new_model_options,
running_models = running_models,
completed_models = completed_models,
total_gpu_count = len(scheduler.resources['gpus']),
remaining_gpu_count = sum(r.remaining() for r in scheduler.resources['gpus']),
)
def create():
"""
Create a new GenericImageModelJob
Returns JSON when requested: {job_id,name,status} or {errors:[]}
"""
form = GenericImageModelForm()
form.dataset.choices = get_datasets()
form.standard_networks.choices = []
form.previous_networks.choices = get_previous_networks()
prev_network_snapshots = get_previous_network_snapshots()
## Is there a request to clone a job with ?clone=<job_id>
fill_form_if_cloned(form)
if not form.validate_on_submit():
if request_wants_json():
return flask.jsonify({'errors': form.errors}), 400
else:
return flask.render_template('models/images/generic/new.html',
form = form,
frameworks = frameworks.get_frameworks(),
previous_network_snapshots = prev_network_snapshots,
previous_networks_fullinfo = get_previous_networks_fulldetails(),
multi_gpu = config_value('caffe_root')['multi_gpu'],
), 400
datasetJob = scheduler.get_job(form.dataset.data)
if not datasetJob:
raise werkzeug.exceptions.BadRequest(
'Unknown dataset job_id "%s"' % form.dataset.data)
# sweeps will be a list of the the permutations of swept fields
# Get swept learning_rate
sweeps = [{'learning_rate': v} for v in form.learning_rate.data]
add_learning_rate = len(form.learning_rate.data) > 1
# Add swept batch_size
sweeps = [dict(s.items() + [('batch_size', bs)]) for bs in form.batch_size.data for s in sweeps[:]]
add_batch_size = len(form.batch_size.data) > 1
n_jobs = len(sweeps)
jobs = []
for sweep in sweeps:
# Populate the form with swept data to be used in saving and
# launching jobs.
form.learning_rate.data = sweep['learning_rate']
form.batch_size.data = sweep['batch_size']
# Augment Job Name
extra = ''
if add_learning_rate:
extra += ' learning_rate:%s' % str(form.learning_rate.data[0])
if add_batch_size:
extra += ' batch_size:%d' % form.batch_size.data[0]
job = None
try:
job = GenericImageModelJob(
username = utils.auth.get_username(),
name = form.model_name.data + extra,
dataset_id = datasetJob.id(),
)
# get framework (hard-coded to caffe for now)
fw = frameworks.get_framework_by_id(form.framework.data)
pretrained_model = None
#if form.method.data == 'standard':
if form.method.data == 'previous':
old_job = scheduler.get_job(form.previous_networks.data)
if not old_job:
raise werkzeug.exceptions.BadRequest(
'Job not found: %s' % form.previous_networks.data)
use_same_dataset = (old_job.dataset_id == job.dataset_id)
network = fw.get_network_from_previous(old_job.train_task().network, use_same_dataset)
for choice in form.previous_networks.choices:
if choice[0] == form.previous_networks.data:
epoch = float(flask.request.form['%s-snapshot' % form.previous_networks.data])
if epoch == 0:
pass
elif epoch == -1:
pretrained_model = old_job.train_task().pretrained_model
else:
for filename, e in old_job.train_task().snapshots:
if e == epoch:
pretrained_model = filename
break
if pretrained_model is None:
raise werkzeug.exceptions.BadRequest(
"For the job %s, selected pretrained_model for epoch %d is invalid!"
% (form.previous_networks.data, epoch))
if not (os.path.exists(pretrained_model)):
raise werkzeug.exceptions.BadRequest(
"Pretrained_model for the selected epoch doesn't exists. May be deleted by another user/process. Please restart the server to load the correct pretrained_model details")
break
elif form.method.data == 'custom':
network = fw.get_network_from_desc(form.custom_network.data)
pretrained_model = form.custom_network_snapshot.data.strip()
else:
raise werkzeug.exceptions.BadRequest(
'Unrecognized method: "%s"' % form.method.data)
policy = {'policy': form.lr_policy.data}
if form.lr_policy.data == 'fixed':
pass
elif form.lr_policy.data == 'step':
policy['stepsize'] = form.lr_step_size.data
policy['gamma'] = form.lr_step_gamma.data
elif form.lr_policy.data == 'multistep':
policy['stepvalue'] = form.lr_multistep_values.data
policy['gamma'] = form.lr_multistep_gamma.data
elif form.lr_policy.data == 'exp':
policy['gamma'] = form.lr_exp_gamma.data
elif form.lr_policy.data == 'inv':
policy['gamma'] = form.lr_inv_gamma.data
policy['power'] = form.lr_inv_power.data
elif form.lr_policy.data == 'poly':
policy['power'] = form.lr_poly_power.data
elif form.lr_policy.data == 'sigmoid':
policy['stepsize'] = form.lr_sigmoid_step.data
policy['gamma'] = form.lr_sigmoid_gamma.data
else:
raise werkzeug.exceptions.BadRequest(
'Invalid learning rate policy')
if config_value('caffe_root')['multi_gpu']:
if form.select_gpu_count.data:
gpu_count = form.select_gpu_count.data
selected_gpus = None
else:
selected_gpus = [str(gpu) for gpu in form.select_gpus.data]
gpu_count = None
else:
if form.select_gpu.data == 'next':
gpu_count = 1
selected_gpus = None
else:
selected_gpus = [str(form.select_gpu.data)]
gpu_count = None
# Python Layer File may be on the server or copied from the client.
fs.copy_python_layer_file(
bool(form.python_layer_from_client.data),
job.dir(),
(flask.request.files[form.python_layer_client_file.name]
if form.python_layer_client_file.name in flask.request.files
else ''), form.python_layer_server_file.data)
job.tasks.append(fw.create_train_task(
job = job,
dataset = datasetJob,
train_epochs = form.train_epochs.data,
snapshot_interval = form.snapshot_interval.data,
learning_rate = form.learning_rate.data[0],
lr_policy = policy,
gpu_count = gpu_count,
selected_gpus = selected_gpus,
batch_size = form.batch_size.data[0],
batch_accumulation = form.batch_accumulation.data,
val_interval = form.val_interval.data,
pretrained_model = pretrained_model,
crop_size = form.crop_size.data,
use_mean = form.use_mean.data,
network = network,
random_seed = form.random_seed.data,
solver_type = form.solver_type.data,
shuffle = form.shuffle.data,
)
)
## Save form data with the job so we can easily clone it later.
save_form_to_job(job, form)
jobs.append(job)
scheduler.add_job(job)
if n_jobs == 1:
if request_wants_json():
return flask.jsonify(job.json_dict())
else:
return flask.redirect(flask.url_for('digits.model.views.show', job_id=job.id()))
except:
if job:
scheduler.delete_job(job)
raise
if request_wants_json():
return flask.jsonify(jobs=[job.json_dict() for job in jobs])
# If there are multiple jobs launched, go to the home page.
return flask.redirect('/')
def classify_many():
"""
Classify many images and return the top 5 classifications for each
Returns JSON when requested: {classifications: {filename: [[category,confidence],...],...}}
"""
model_job = job_from_request()
image_list = flask.request.files.get('image_list')
if not image_list:
raise werkzeug.exceptions.BadRequest('image_list is a required field')
if 'image_folder' in flask.request.form and flask.request.form['image_folder'].strip():
image_folder = flask.request.form['image_folder']
if not os.path.exists(image_folder):
raise werkzeug.exceptions.BadRequest('image_folder "%s" does not exit' % image_folder)
else:
image_folder = None
if 'num_test_images' in flask.request.form and flask.request.form['num_test_images'].strip():
num_test_images = int(flask.request.form['num_test_images'])
else:
num_test_images = None
epoch = None
if 'snapshot_epoch' in flask.request.form:
epoch = float(flask.request.form['snapshot_epoch'])
paths, ground_truths = read_image_list(image_list, image_folder, num_test_images)
# create inference job
inference_job = ImageInferenceJob(
username=utils.auth.get_username(),
name="Classify Many Images",
model=model_job,
images=paths,
epoch=epoch,
layers='none'
)
# schedule tasks
scheduler.add_job(inference_job)
# wait for job to complete
inference_job.wait_completion()
# retrieve inference data
inputs, outputs, _ = inference_job.get_data()
# set return status code
status_code = 500 if inference_job.status == 'E' else 200
# delete job
scheduler.delete_job(inference_job)
if outputs is not None and len(outputs) < 1:
# an error occurred
outputs = None
if inputs is not None:
# retrieve path and ground truth of images that were successfully processed
paths = [paths[idx] for idx in inputs['ids']]
ground_truths = [ground_truths[idx] for idx in inputs['ids']]
# defaults
classifications = None
show_ground_truth = None
top1_accuracy = None
top5_accuracy = None
confusion_matrix = None
per_class_accuracy = None
labels = None
if outputs is not None:
# convert to class probabilities for viewing
last_output_name, last_output_data = outputs.items()[-1]
if len(last_output_data) < 1:
raise werkzeug.exceptions.BadRequest(
'Unable to classify any image from the file')
scores = last_output_data
# take top 5
indices = (-scores).argsort()[:, :5]
labels = model_job.train_task().get_labels()
n_labels = len(labels)
# remove invalid ground truth
ground_truths = [x if x is not None and (0 <= x < n_labels) else None for x in ground_truths]
# how many pieces of ground truth to we have?
n_ground_truth = len([1 for x in ground_truths if x is not None])
show_ground_truth = n_ground_truth > 0
# compute classifications and statistics
classifications = []
n_top1_accurate = 0
n_top5_accurate = 0
confusion_matrix = np.zeros((n_labels, n_labels), dtype=np.dtype(int))
for image_index, index_list in enumerate(indices):
result = []
if ground_truths[image_index] is not None:
if ground_truths[image_index] == index_list[0]:
n_top1_accurate += 1
if ground_truths[image_index] in index_list:
n_top5_accurate += 1
if (0 <= ground_truths[image_index] < n_labels) and (0 <= index_list[0] < n_labels):
confusion_matrix[ground_truths[image_index], index_list[0]] += 1
for i in index_list:
# `i` is a category in labels and also an index into scores
# ignore prediction if we don't have a label for the corresponding class
# the user might have set the final fully-connected layer's num_output to
# too high a value
if i < len(labels):
result.append((labels[i], round(100.0 * scores[image_index, i], 2)))
classifications.append(result)
# accuracy
if show_ground_truth:
top1_accuracy = round(100.0 * n_top1_accurate / n_ground_truth, 2)
top5_accuracy = round(100.0 * n_top5_accurate / n_ground_truth, 2)
per_class_accuracy = []
for x in xrange(n_labels):
n_examples = sum(confusion_matrix[x])
per_class_accuracy.append(
round(100.0 * confusion_matrix[x, x] / n_examples, 2) if n_examples > 0 else None)
else:
top1_accuracy = None
top5_accuracy = None
per_class_accuracy = None
# replace ground truth indices with labels
ground_truths = [labels[x] if x is not None and (0 <= x < n_labels) else None for x in ground_truths]
if request_wants_json():
joined = dict(zip(paths, classifications))
return flask.jsonify({'classifications': joined}), status_code
else:
return flask.render_template('models/images/classification/classify_many.html',
model_job=model_job,
job=inference_job,
paths=paths,
classifications=classifications,
show_ground_truth=show_ground_truth,
ground_truths=ground_truths,
top1_accuracy=top1_accuracy,
top5_accuracy=top5_accuracy,
confusion_matrix=confusion_matrix,
per_class_accuracy=per_class_accuracy,
labels=labels,
), status_code
def infer_many():
"""
Infer many images
"""
model_job = job_from_request()
image_list = flask.request.files.get('image_list')
if not image_list:
raise werkzeug.exceptions.BadRequest('image_list is a required field')
if 'image_folder' in flask.request.form and flask.request.form[
'image_folder'].strip():
image_folder = flask.request.form['image_folder']
if not os.path.exists(image_folder):
raise werkzeug.exceptions.BadRequest(
'image_folder "%s" does not exit' % image_folder)
else:
image_folder = None
if 'num_test_images' in flask.request.form and flask.request.form[
'num_test_images'].strip():
num_test_images = int(flask.request.form['num_test_images'])
else:
num_test_images = None
epoch = None
if 'snapshot_epoch' in flask.request.form:
epoch = float(flask.request.form['snapshot_epoch'])
if 'dont_resize' in flask.request.form and flask.request.form[
'dont_resize']:
resize = False
else:
resize = True
paths = []
for line in image_list.readlines():
line = line.strip()
if not line:
continue
path = None
# might contain a numerical label at the end
match = re.match(r'(.*\S)\s+\d+$', line)
if match:
path = match.group(1)
else:
path = line
if not utils.is_url(path) and image_folder and not os.path.isabs(path):
path = os.path.join(image_folder, path)
paths.append(path)
if num_test_images is not None and len(paths) >= num_test_images:
break
# create inference job
inference_job = ImageInferenceJob(
username=utils.auth.get_username(),
name="Infer Many Images",
model=model_job,
images=paths,
epoch=epoch,
layers='none',
resize=resize,
)
# schedule tasks
scheduler.add_job(inference_job)
# wait for job to complete
inference_job.wait_completion()
# retrieve inference data
inputs, outputs, _ = inference_job.get_data()
# set return status code
status_code = 500 if inference_job.status == 'E' else 200
# delete job folder and remove from scheduler list
scheduler.delete_job(inference_job)
if outputs is not None and len(outputs) < 1:
# an error occurred
outputs = None
if inputs is not None:
paths = [paths[idx] for idx in inputs['ids']]
inference_views_html, header_html, app_begin_html, app_end_html = get_inference_visualizations(
model_job.dataset, inputs, outputs)
else:
inference_views_html = None
header_html = None
app_begin_html = None
app_end_html = None
if request_wants_json():
result = {}
for i, path in enumerate(paths):
result[path] = dict(
(name, blob[i].tolist()) for name, blob in outputs.iteritems())
return flask.jsonify({'outputs': result}), status_code
else:
return flask.render_template(
'models/images/generic/infer_many.html',
model_job=model_job,
job=inference_job,
paths=paths,
inference_views_html=inference_views_html,
header_html=header_html,
app_begin_html=app_begin_html,
app_end_html=app_end_html,
), status_code
def infer_extension():
"""
Perform inference using the data from an extension inference form
"""
model_job = job_from_request()
inference_db_job = None
try:
# create an inference database
inference_db_job = create_inference_db(model_job)
db_path = inference_db_job.get_feature_db_path(constants.TEST_DB)
# create database creation job
epoch = None
if 'snapshot_epoch' in flask.request.form:
epoch = float(flask.request.form['snapshot_epoch'])
layers = 'none'
if 'show_visualizations' in flask.request.form and flask.request.form[
'show_visualizations']:
layers = 'all'
# create inference job
inference_job = ImageInferenceJob(
username=utils.auth.get_username(),
name="Inference",
model=model_job,
images=db_path,
epoch=epoch,
layers=layers,
resize=False,
)
# schedule tasks
scheduler.add_job(inference_job)
# wait for job to complete
inference_job.wait_completion()
finally:
if inference_db_job:
scheduler.delete_job(inference_db_job)
# retrieve inference data
inputs, outputs, model_visualization = inference_job.get_data()
# set return status code
status_code = 500 if inference_job.status == 'E' else 200
# delete job folder and remove from scheduler list
scheduler.delete_job(inference_job)
if outputs is not None and len(outputs) < 1:
# an error occurred
outputs = None
if inputs is not None:
keys = [str(idx) for idx in inputs['ids']]
inference_views_html, header_html, app_begin_html, app_end_html = get_inference_visualizations(
model_job.dataset, inputs, outputs)
else:
inference_views_html = None
header_html = None
keys = None
app_begin_html = None
app_end_html = None
if request_wants_json():
result = {}
for i, key in enumerate(keys):
result[key] = dict(
(name, blob[i].tolist()) for name, blob in outputs.iteritems())
return flask.jsonify({'outputs': result}), status_code
else:
return flask.render_template(
'models/images/generic/infer_extension.html',
model_job=model_job,
job=inference_job,
keys=keys,
inference_views_html=inference_views_html,
header_html=header_html,
app_begin_html=app_begin_html,
app_end_html=app_end_html,
visualizations=model_visualization,
total_parameters=sum(v['param_count'] for v in model_visualization
if v['vis_type'] == 'Weights'),
), status_code
def generic_image_model_create():
"""
Create a new GenericImageModelJob
Returns JSON when requested: {job_id,name,status} or {errors:[]}
"""
form = GenericImageModelForm()
form.dataset.choices = get_datasets()
form.standard_networks.choices = []
form.previous_networks.choices = get_previous_networks()
prev_network_snapshots = get_previous_network_snapshots()
if not form.validate_on_submit():
if request_wants_json():
return flask.jsonify({'errors': form.errors}), 400
else:
return flask.render_template('models/images/generic/new.html',
form = form,
previous_network_snapshots = prev_network_snapshots,
previous_networks_fullinfo = get_previous_networks_fulldetails(),
multi_gpu = config_value('caffe_root')['multi_gpu'],
), 400
datasetJob = scheduler.get_job(form.dataset.data)
if not datasetJob:
raise werkzeug.exceptions.BadRequest(
'Unknown dataset job_id "%s"' % form.dataset.data)
job = None
try:
job = GenericImageModelJob(
name = form.model_name.data,
dataset_id = datasetJob.id(),
)
# get framework (hard-coded to caffe for now)
fw = frameworks.get_framework_by_id('caffe')
pretrained_model = None
#if form.method.data == 'standard':
if form.method.data == 'previous':
old_job = scheduler.get_job(form.previous_networks.data)
if not old_job:
raise werkzeug.exceptions.BadRequest(
'Job not found: %s' % form.previous_networks.data)
network = fw.get_network_from_previous(old_job.train_task().network)
for choice in form.previous_networks.choices:
if choice[0] == form.previous_networks.data:
epoch = float(flask.request.form['%s-snapshot' % form.previous_networks.data])
if epoch == 0:
pass
elif epoch == -1:
pretrained_model = old_job.train_task().pretrained_model
else:
for filename, e in old_job.train_task().snapshots:
if e == epoch:
pretrained_model = filename
break
if pretrained_model is None:
raise werkzeug.exceptions.BadRequest(
"For the job %s, selected pretrained_model for epoch %d is invalid!"
% (form.previous_networks.data, epoch))
if not (os.path.exists(pretrained_model)):
raise werkzeug.exceptions.BadRequest(
"Pretrained_model for the selected epoch doesn't exists. May be deleted by another user/process. Please restart the server to load the correct pretrained_model details")
break
elif form.method.data == 'custom':
network = fw.get_network_from_desc(form.custom_network.data)
pretrained_model = form.custom_network_snapshot.data.strip()
else:
raise werkzeug.exceptions.BadRequest(
'Unrecognized method: "%s"' % form.method.data)
policy = {'policy': form.lr_policy.data}
if form.lr_policy.data == 'fixed':
pass
elif form.lr_policy.data == 'step':
policy['stepsize'] = form.lr_step_size.data
policy['gamma'] = form.lr_step_gamma.data
elif form.lr_policy.data == 'multistep':
policy['stepvalue'] = form.lr_multistep_values.data
policy['gamma'] = form.lr_multistep_gamma.data
elif form.lr_policy.data == 'exp':
policy['gamma'] = form.lr_exp_gamma.data
elif form.lr_policy.data == 'inv':
policy['gamma'] = form.lr_inv_gamma.data
policy['power'] = form.lr_inv_power.data
elif form.lr_policy.data == 'poly':
policy['power'] = form.lr_poly_power.data
elif form.lr_policy.data == 'sigmoid':
policy['stepsize'] = form.lr_sigmoid_step.data
policy['gamma'] = form.lr_sigmoid_gamma.data
else:
raise werkzeug.exceptions.BadRequest(
'Invalid learning rate policy')
if config_value('caffe_root')['multi_gpu']:
if form.select_gpu_count.data:
gpu_count = form.select_gpu_count.data
selected_gpus = None
else:
selected_gpus = [str(gpu) for gpu in form.select_gpus.data]
gpu_count = None
else:
if form.select_gpu.data == 'next':
gpu_count = 1
selected_gpus = None
else:
selected_gpus = [str(form.select_gpu.data)]
gpu_count = None
job.tasks.append(fw.create_train_task(
job_dir = job.dir(),
dataset = datasetJob,
train_epochs = form.train_epochs.data,
snapshot_interval = form.snapshot_interval.data,
learning_rate = form.learning_rate.data,
lr_policy = policy,
gpu_count = gpu_count,
selected_gpus = selected_gpus,
batch_size = form.batch_size.data,
val_interval = form.val_interval.data,
pretrained_model= pretrained_model,
crop_size = form.crop_size.data,
use_mean = bool(form.use_mean.data),
network = network,
random_seed = form.random_seed.data,
solver_type = form.solver_type.data,
)
)
scheduler.add_job(job)
if request_wants_json():
return flask.jsonify(job.json_dict())
else:
return flask.redirect(flask.url_for('models_show', job_id=job.id()))
except:
if job:
scheduler.delete_job(job)
raise
def create():
"""
Creates a new GenericImageDatasetJob
Returns JSON when requested: {job_id,name,status} or {errors:[]}
"""
form = GenericImageDatasetForm()
## Is there a request to clone a job with ?clone=<job_id>
fill_form_if_cloned(form)
if not form.validate_on_submit():
if request_wants_json():
return flask.jsonify({'errors': form.errors}), 400
else:
return flask.render_template('datasets/images/generic/new.html', form=form), 400
job = None
try:
job = GenericImageDatasetJob(
username = utils.auth.get_username(),
name = form.dataset_name.data,
mean_file = form.prebuilt_mean_file.data.strip(),
)
if form.method.data == 'prebuilt':
pass
else:
raise ValueError('method not supported')
force_same_shape = form.force_same_shape.data
job.tasks.append(
tasks.AnalyzeDbTask(
job_dir = job.dir(),
database = form.prebuilt_train_images.data,
purpose = form.prebuilt_train_images.label.text,
force_same_shape = force_same_shape,
)
)
if form.prebuilt_train_labels.data:
job.tasks.append(
tasks.AnalyzeDbTask(
job_dir = job.dir(),
database = form.prebuilt_train_labels.data,
purpose = form.prebuilt_train_labels.label.text,
force_same_shape = force_same_shape,
)
)
if form.prebuilt_val_images.data:
job.tasks.append(
tasks.AnalyzeDbTask(
job_dir = job.dir(),
database = form.prebuilt_val_images.data,
purpose = form.prebuilt_val_images.label.text,
force_same_shape = force_same_shape,
)
)
if form.prebuilt_val_labels.data:
job.tasks.append(
tasks.AnalyzeDbTask(
job_dir = job.dir(),
database = form.prebuilt_val_labels.data,
purpose = form.prebuilt_val_labels.label.text,
force_same_shape = force_same_shape,
)
)
## Save form data with the job so we can easily clone it later.
save_form_to_job(job, form)
scheduler.add_job(job)
if request_wants_json():
return flask.jsonify(job.json_dict())
else:
return flask.redirect(flask.url_for('digits.dataset.views.show', job_id=job.id()))
except:
if job:
scheduler.delete_job(job)
raise
def create():
"""
Create a new ImageClassificationModelJob
Returns JSON when requested: {job_id,name,status} or {errors:[]}
"""
form = ImageClassificationModelForm()
form.dataset.choices = get_datasets()
form.standard_networks.choices = get_standard_networks()
form.standard_networks.default = get_default_standard_network()
form.previous_networks.choices = get_previous_networks()
prev_network_snapshots = get_previous_network_snapshots()
## Is there a request to clone a job with ?clone=<job_id>
fill_form_if_cloned(form)
if not form.validate_on_submit():
if request_wants_json():
return flask.jsonify({'errors': form.errors}), 400
else:
return flask.render_template('models/images/classification/new.html',
form = form,
frameworks = frameworks.get_frameworks(),
previous_network_snapshots = prev_network_snapshots,
previous_networks_fullinfo = get_previous_networks_fulldetails(),
multi_gpu = config_value('caffe_root')['multi_gpu'],
), 400
datasetJob = scheduler.get_job(form.dataset.data)
if not datasetJob:
raise werkzeug.exceptions.BadRequest(
'Unknown dataset job_id "%s"' % form.dataset.data)
job = None
try:
job = ImageClassificationModelJob(
username = utils.auth.get_username(),
name = form.model_name.data,
dataset_id = datasetJob.id(),
)
# get handle to framework object
fw = frameworks.get_framework_by_id(form.framework.data)
pretrained_model = None
if form.method.data == 'standard':
found = False
# can we find it in standard networks?
network_desc = fw.get_standard_network_desc(form.standard_networks.data)
if network_desc:
found = True
network = fw.get_network_from_desc(network_desc)
if not found:
raise werkzeug.exceptions.BadRequest(
'Unknown standard model "%s"' % form.standard_networks.data)
elif form.method.data == 'previous':
old_job = scheduler.get_job(form.previous_networks.data)
if not old_job:
raise werkzeug.exceptions.BadRequest(
'Job not found: %s' % form.previous_networks.data)
use_same_dataset = (old_job.dataset_id == job.dataset_id)
network = fw.get_network_from_previous(old_job.train_task().network, use_same_dataset)
for choice in form.previous_networks.choices:
if choice[0] == form.previous_networks.data:
epoch = float(flask.request.form['%s-snapshot' % form.previous_networks.data])
if epoch == 0:
pass
elif epoch == -1:
pretrained_model = old_job.train_task().pretrained_model
else:
for filename, e in old_job.train_task().snapshots:
if e == epoch:
pretrained_model = filename
break
if pretrained_model is None:
raise werkzeug.exceptions.BadRequest(
"For the job %s, selected pretrained_model for epoch %d is invalid!"
% (form.previous_networks.data, epoch))
if not (os.path.exists(pretrained_model)):
raise werkzeug.exceptions.BadRequest(
"Pretrained_model for the selected epoch doesn't exists. May be deleted by another user/process. Please restart the server to load the correct pretrained_model details")
break
elif form.method.data == 'custom':
network = fw.get_network_from_desc(form.custom_network.data)
pretrained_model = form.custom_network_snapshot.data.strip()
else:
raise werkzeug.exceptions.BadRequest(
'Unrecognized method: "%s"' % form.method.data)
policy = {'policy': form.lr_policy.data}
if form.lr_policy.data == 'fixed':
pass
elif form.lr_policy.data == 'step':
policy['stepsize'] = form.lr_step_size.data
policy['gamma'] = form.lr_step_gamma.data
elif form.lr_policy.data == 'multistep':
policy['stepvalue'] = form.lr_multistep_values.data
policy['gamma'] = form.lr_multistep_gamma.data
elif form.lr_policy.data == 'exp':
policy['gamma'] = form.lr_exp_gamma.data
elif form.lr_policy.data == 'inv':
policy['gamma'] = form.lr_inv_gamma.data
policy['power'] = form.lr_inv_power.data
elif form.lr_policy.data == 'poly':
policy['power'] = form.lr_poly_power.data
elif form.lr_policy.data == 'sigmoid':
policy['stepsize'] = form.lr_sigmoid_step.data
policy['gamma'] = form.lr_sigmoid_gamma.data
else:
raise werkzeug.exceptions.BadRequest(
'Invalid learning rate policy')
if config_value('caffe_root')['multi_gpu']:
if form.select_gpus.data:
selected_gpus = [str(gpu) for gpu in form.select_gpus.data]
gpu_count = None
elif form.select_gpu_count.data:
gpu_count = form.select_gpu_count.data
selected_gpus = None
else:
gpu_count = 1
selected_gpus = None
else:
if form.select_gpu.data == 'next':
gpu_count = 1
selected_gpus = None
else:
selected_gpus = [str(form.select_gpu.data)]
gpu_count = None
# Python Layer File may be on the server or copied from the client.
fs.copy_python_layer_file(
bool(form.python_layer_from_client.data),
job.dir(),
(flask.request.files[form.python_layer_client_file.name]
if form.python_layer_client_file.name in flask.request.files
else ''), form.python_layer_server_file.data)
job.tasks.append(fw.create_train_task(
job_dir = job.dir(),
dataset = datasetJob,
train_epochs = form.train_epochs.data,
snapshot_interval = form.snapshot_interval.data,
learning_rate = form.learning_rate.data,
lr_policy = policy,
gpu_count = gpu_count,
selected_gpus = selected_gpus,
batch_size = form.batch_size.data,
val_interval = form.val_interval.data,
pretrained_model= pretrained_model,
crop_size = form.crop_size.data,
use_mean = form.use_mean.data,
network = network,
random_seed = form.random_seed.data,
solver_type = form.solver_type.data,
shuffle = form.shuffle.data,
)
)
## Save form data with the job so we can easily clone it later.
save_form_to_job(job, form)
scheduler.add_job(job)
if request_wants_json():
return flask.jsonify(job.json_dict())
else:
return flask.redirect(flask.url_for('digits.model.views.show', job_id=job.id()))
except:
if job:
scheduler.delete_job(job)
raise
def classify_many():
"""
Classify many images and return the top 5 classifications for each
Returns JSON when requested: {classifications: {filename: [[category,confidence],...],...}}
"""
model_job = job_from_request()
image_list = flask.request.files.get('image_list')
if not image_list:
raise werkzeug.exceptions.BadRequest('image_list is a required field')
if 'image_folder' in flask.request.form and flask.request.form['image_folder'].strip():
image_folder = flask.request.form['image_folder']
if not os.path.exists(image_folder):
raise werkzeug.exceptions.BadRequest('image_folder "%s" does not exit' % image_folder)
else:
image_folder = None
if 'num_test_images' in flask.request.form and flask.request.form['num_test_images'].strip():
num_test_images = int(flask.request.form['num_test_images'])
else:
num_test_images = None
epoch = None
if 'snapshot_epoch' in flask.request.form:
epoch = float(flask.request.form['snapshot_epoch'])
paths, ground_truths = read_image_list(image_list, image_folder, num_test_images)
# create inference job
inference_job = ImageInferenceJob(
username = utils.auth.get_username(),
name = "Classify Many Images",
model = model_job,
images = paths,
epoch = epoch,
layers = 'none'
)
# schedule tasks
scheduler.add_job(inference_job)
# wait for job to complete
inference_job.wait_completion()
# retrieve inference data
inputs, outputs, _ = inference_job.get_data()
# delete job
scheduler.delete_job(inference_job)
if outputs is not None and len(outputs) < 1:
# an error occurred
outputs = None
if inputs is not None:
# retrieve path and ground truth of images that were successfully processed
paths = [paths[idx] for idx in inputs['ids']]
ground_truths = [ground_truths[idx] for idx in inputs['ids']]
classifications = None
if outputs is not None:
# convert to class probabilities for viewing
last_output_name, last_output_data = outputs.items()[-1]
if len(last_output_data) < 1:
raise werkzeug.exceptions.BadRequest(
'Unable to classify any image from the file')
scores = last_output_data
# take top 5
indices = (-scores).argsort()[:, :5]
labels = model_job.train_task().get_labels()
classifications = []
for image_index, index_list in enumerate(indices):
result = []
for i in index_list:
# `i` is a category in labels and also an index into scores
result.append((labels[i], round(100.0*scores[image_index, i],2)))
classifications.append(result)
# replace ground truth indices with labels
ground_truths = [labels[x] if x is not None and (0 <= x < len(labels)) else None for x in ground_truths]
if request_wants_json():
joined = dict(zip(paths, classifications))
return flask.jsonify({'classifications': joined})
else:
return flask.render_template('models/images/classification/classify_many.html',
model_job = model_job,
job = inference_job,
paths = paths,
classifications = classifications,
show_ground_truth= not(ground_truths == [None]*len(ground_truths)),
ground_truths = ground_truths
)
def image_classification_model_create():
"""
Create a new ImageClassificationModelJob
Returns JSON when requested: {job_id,name,status} or {errors:[]}
"""
form = ImageClassificationModelForm()
form.dataset.choices = get_datasets()
form.standard_networks.choices = get_standard_networks()
form.standard_networks.default = get_default_standard_network()
form.previous_networks.choices = get_previous_networks()
prev_network_snapshots = get_previous_network_snapshots()
if not form.validate_on_submit():
if request_wants_json():
return flask.jsonify({'errors': form.errors}), 400
else:
return flask.render_template(
'models/images/classification/new.html',
form=form,
previous_network_snapshots=prev_network_snapshots,
multi_gpu=config_value('caffe_root')['multi_gpu'],
), 400
datasetJob = scheduler.get_job(form.dataset.data)
if not datasetJob:
raise werkzeug.exceptions.BadRequest('Unknown dataset job_id "%s"' %
form.dataset.data)
job = None
try:
job = ImageClassificationModelJob(
name=form.model_name.data,
dataset_id=datasetJob.id(),
)
network = caffe_pb2.NetParameter()
pretrained_model = None
if form.method.data == 'standard':
found = False
networks_dir = os.path.join(os.path.dirname(digits.__file__),
'standard-networks')
for filename in os.listdir(networks_dir):
path = os.path.join(networks_dir, filename)
if os.path.isfile(path):
match = re.match(
r'%s.prototxt' % form.standard_networks.data, filename)
if match:
with open(path) as infile:
text_format.Merge(infile.read(), network)
found = True
break
if not found:
raise werkzeug.exceptions.BadRequest(
'Unknown standard model "%s"' %
form.standard_networks.data)
elif form.method.data == 'previous':
old_job = scheduler.get_job(form.previous_networks.data)
if not old_job:
raise werkzeug.exceptions.BadRequest(
'Job not found: %s' % form.previous_networks.data)
network.CopyFrom(old_job.train_task().network)
# Rename the final layer
# XXX making some assumptions about network architecture here
ip_layers = [l for l in network.layer if l.type == 'InnerProduct']
if len(ip_layers) > 0:
ip_layers[-1].name = '%s_retrain' % ip_layers[-1].name
for choice in form.previous_networks.choices:
if choice[0] == form.previous_networks.data:
epoch = float(
flask.request.form['%s-snapshot' %
form.previous_networks.data])
if epoch != 0:
for filename, e in old_job.train_task().snapshots:
if e == epoch:
pretrained_model = filename
break
if pretrained_model is None:
raise werkzeug.exceptions.BadRequest(
"For the job %s, selected pretrained_model for epoch %d is invalid!"
% (form.previous_networks.data, epoch))
if not (os.path.exists(pretrained_model)):
raise werkzeug.exceptions.BadRequest(
"Pretrained_model for the selected epoch doesn't exists. May be deleted by another user/process. Please restart the server to load the correct pretrained_model details"
)
break
elif form.method.data == 'custom':
text_format.Merge(form.custom_network.data, network)
pretrained_model = form.custom_network_snapshot.data.strip()
else:
raise werkzeug.exceptions.BadRequest('Unrecognized method: "%s"' %
form.method.data)
policy = {'policy': form.lr_policy.data}
if form.lr_policy.data == 'fixed':
pass
elif form.lr_policy.data == 'step':
policy['stepsize'] = form.lr_step_size.data
policy['gamma'] = form.lr_step_gamma.data
elif form.lr_policy.data == 'multistep':
policy['stepvalue'] = form.lr_multistep_values.data
policy['gamma'] = form.lr_multistep_gamma.data
elif form.lr_policy.data == 'exp':
policy['gamma'] = form.lr_exp_gamma.data
elif form.lr_policy.data == 'inv':
policy['gamma'] = form.lr_inv_gamma.data
policy['power'] = form.lr_inv_power.data
elif form.lr_policy.data == 'poly':
policy['power'] = form.lr_poly_power.data
elif form.lr_policy.data == 'sigmoid':
policy['stepsize'] = form.lr_sigmoid_step.data
policy['gamma'] = form.lr_sigmoid_gamma.data
else:
raise werkzeug.exceptions.BadRequest(
'Invalid learning rate policy')
if config_value('caffe_root')['multi_gpu']:
if form.select_gpu_count.data:
gpu_count = form.select_gpu_count.data
selected_gpus = None
else:
selected_gpus = [str(gpu) for gpu in form.select_gpus.data]
gpu_count = None
else:
if form.select_gpu.data == 'next':
gpu_count = 1
selected_gpus = None
else:
selected_gpus = [str(form.select_gpu.data)]
gpu_count = None
job.tasks.append(
tasks.CaffeTrainTask(
job_dir=job.dir(),
dataset=datasetJob,
train_epochs=form.train_epochs.data,
snapshot_interval=form.snapshot_interval.data,
learning_rate=form.learning_rate.data,
lr_policy=policy,
gpu_count=gpu_count,
selected_gpus=selected_gpus,
batch_size=form.batch_size.data,
val_interval=form.val_interval.data,
pretrained_model=pretrained_model,
crop_size=form.crop_size.data,
use_mean=bool(form.use_mean.data),
network=network,
random_seed=form.random_seed.data,
solver_type=form.solver_type.data,
))
scheduler.add_job(job)
if request_wants_json():
return flask.jsonify(job.json_dict())
else:
return flask.redirect(flask.url_for('models_show',
job_id=job.id()))
except:
if job:
scheduler.delete_job(job)
raise
def image_classification_model_classify_many():
"""
Classify many images and return the top 5 classifications for each
Returns JSON when requested: {classifications: {filename: [[category,confidence],...],...}}
"""
job = job_from_request()
image_list = flask.request.files.get('image_list')
if not image_list:
raise werkzeug.exceptions.BadRequest('image_list is a required field')
epoch = None
if 'snapshot_epoch' in flask.request.form:
epoch = float(flask.request.form['snapshot_epoch'])
paths = []
images = []
dataset = job.train_task().dataset
for line in image_list.readlines():
line = line.strip()
if not line:
continue
path = None
# might contain a numerical label at the end
match = re.match(r'(.*\S)\s+\d+$', line)
if match:
path = match.group(1)
else:
path = line
try:
image = utils.image.load_image(path)
image = utils.image.resize_image(
image,
dataset.image_dims[0],
dataset.image_dims[1],
channels=dataset.image_dims[2],
resize_mode=dataset.resize_mode,
)
paths.append(path)
images.append(image)
except utils.errors.LoadImageError as e:
print e
if not len(images):
raise werkzeug.exceptions.BadRequest(
'Unable to load any images from the file')
labels, scores = job.train_task().infer_many(images, snapshot_epoch=epoch)
if scores is None:
raise RuntimeError('An error occured while processing the images')
# take top 5
indices = (-scores).argsort()[:, :5]
classifications = []
for image_index, index_list in enumerate(indices):
result = []
for i in index_list:
# `i` is a category in labels and also an index into scores
result.append((labels[i], round(100.0 * scores[image_index, i],
2)))
classifications.append(result)
if request_wants_json():
joined = dict(zip(paths, classifications))
return flask.jsonify({'classifications': joined})
else:
return flask.render_template(
'models/images/classification/classify_many.html',
paths=paths,
classifications=classifications,
)
def home(dataset_id=None):
"""
DIGITS home page
Returns information about each job on the server
Returns JSON when requested:
{
datasets: [{id, name, status},...],
models: [{id, name, status},...]
}
"""
# Dataset Job
if dataset_id is None:
job_type = dataset.DatasetJob
name = 'Dataset'
# Model Job
else:
job_type = model.ModelJob
name = 'Model'
running_jobs = get_job_list(job_type, True, dataset_id)
completed_jobs = get_job_list(job_type, False, dataset_id)
if request_wants_json():
data = {
'version': digits.__version__,
'jobs_dir': config_value('jobs_dir'),
'job_type': name,
'jobs': [j.json_dict() for j in running_jobs + completed_jobs],
}
if config_value('server_name'):
data['server_name'] = config_value('server_name')
return flask.jsonify(data)
else:
if dataset_id is None:
name = 'Dataset'
dataset_name = None
options = [('New Dataset', [
{
'title': 'Images',
'id': 'images',
'url': flask.url_for('image_classification_dataset_new'),
},
{
'title': 'Generic',
'id': 'generic',
'url': flask.url_for('generic_image_dataset_new'),
},
])]
else:
dataset_name, dataset_type = get_dataset_name(dataset_id)
if dataset_type == 'Image Classification Dataset':
options = [('New Model', [
{
'title':
'Classification',
'id':
'classification',
'url':
flask.url_for('image_classification_model_new',
dataset_id=dataset_id),
},
])]
elif dataset_type == 'Generic Image Dataset':
options = [('New Model', [
{
'title':
'Generic',
'id':
'generic-classification',
'url':
flask.url_for('generic_image_model_new',
dataset_id=dataset_id),
},
])]
return flask.render_template(
'home.html',
name=name,
dataset_name=dataset_name,
dataset_id=dataset_id,
options=options,
running_jobs=running_jobs,
completed_jobs=completed_jobs,
total_gpu_count=len(scheduler.resources['gpus']),
remaining_gpu_count=sum(r.remaining()
for r in scheduler.resources['gpus']),
)
def infer_db():
"""
Infer a database
"""
model_job = job_from_request()
if not 'db_path' in flask.request.form or flask.request.form['db_path'] is None:
raise werkzeug.exceptions.BadRequest('db_path is a required field')
db_path = flask.request.form['db_path']
if not os.path.exists(db_path):
raise werkzeug.exceptions.BadRequest('DB "%s" does not exit' % db_path)
epoch = None
if 'snapshot_epoch' in flask.request.form:
epoch = float(flask.request.form['snapshot_epoch'])
# create inference job
inference_job = ImageInferenceJob(
username = utils.auth.get_username(),
name = "Infer Many Images",
model = model_job,
images = db_path,
epoch = epoch,
layers = 'none',
)
# schedule tasks
scheduler.add_job(inference_job)
# wait for job to complete
inference_job.wait_completion()
# retrieve inference data
inputs, outputs, _ = inference_job.get_data()
# delete job folder and remove from scheduler list
scheduler.delete_job(inference_job)
if outputs is not None and len(outputs) < 1:
# an error occurred
outputs = None
if inputs is not None:
keys = [str(idx) for idx in inputs['ids']]
else:
keys = None
if request_wants_json():
result = {}
for i, key in enumerate(keys):
result[key] = dict((name, blob[i].tolist()) for name,blob in outputs.iteritems())
return flask.jsonify({'outputs': result})
else:
return flask.render_template('models/images/generic/infer_db.html',
model_job = model_job,
job = inference_job,
keys = keys,
network_outputs = outputs,
)
def create():
"""
Creates a new GenericImageDatasetJob
Returns JSON when requested: {job_id,name,status} or {errors:[]}
"""
form = GenericImageDatasetForm()
# Is there a request to clone a job with ?clone=<job_id>
fill_form_if_cloned(form)
if not form.validate_on_submit():
if request_wants_json():
return flask.jsonify({'errors': form.errors}), 400
else:
return flask.render_template('datasets/images/generic/new.html',
form=form), 400
job = None
try:
job = GenericImageDatasetJob(
username=utils.auth.get_username(),
name=form.dataset_name.data,
group=form.group_name.data,
mean_file=form.prebuilt_mean_file.data.strip(),
)
if form.method.data == 'prebuilt':
pass
else:
raise ValueError('method not supported')
force_same_shape = form.force_same_shape.data
job.tasks.append(
tasks.AnalyzeDbTask(
job_dir=job.dir(),
database=form.prebuilt_train_images.data,
purpose=form.prebuilt_train_images.label.text,
force_same_shape=force_same_shape,
))
if form.prebuilt_train_labels.data:
job.tasks.append(
tasks.AnalyzeDbTask(
job_dir=job.dir(),
database=form.prebuilt_train_labels.data,
purpose=form.prebuilt_train_labels.label.text,
force_same_shape=force_same_shape,
))
if form.prebuilt_val_images.data:
job.tasks.append(
tasks.AnalyzeDbTask(
job_dir=job.dir(),
database=form.prebuilt_val_images.data,
purpose=form.prebuilt_val_images.label.text,
force_same_shape=force_same_shape,
))
if form.prebuilt_val_labels.data:
job.tasks.append(
tasks.AnalyzeDbTask(
job_dir=job.dir(),
database=form.prebuilt_val_labels.data,
purpose=form.prebuilt_val_labels.label.text,
force_same_shape=force_same_shape,
))
# Save form data with the job so we can easily clone it later.
save_form_to_job(job, form)
scheduler.add_job(job)
if request_wants_json():
return flask.jsonify(job.json_dict())
else:
return flask.redirect(
flask.url_for('digits.dataset.views.show', job_id=job.id()))
except:
if job:
scheduler.delete_job(job)
raise
def create(extension_id):
"""
Creates a new GenericDatasetJob
Returns JSON when requested: {job_id,name,status} or {errors:[]}
"""
form = GenericDatasetForm()
form_valid = form.validate_on_submit()
extension_class = extensions.data.get_extension(extension_id)
extension_form = extension_class.get_dataset_form()
extension_form_valid = extension_form.validate_on_submit()
if not (extension_form_valid and form_valid):
# merge errors
errors = form.errors.copy()
errors.update(extension_form.errors)
template, context = extension_class.get_dataset_template(
extension_form)
rendered_extension = flask.render_template_string(template, **context)
if request_wants_json():
return flask.jsonify({'errors': errors}), 400
else:
return flask.render_template(
'datasets/generic/new.html',
extension_title=extension_class.get_title(),
extension_id=extension_id,
extension_html=rendered_extension,
form=form,
errors=errors), 400
# create instance of extension class
extension = extension_class(**extension_form.data)
job = None
try:
# create job
job = GenericDatasetJob(
username=utils.auth.get_username(),
name=form.dataset_name.data,
group=form.group_name.data,
backend=form.dsopts_backend.data,
feature_encoding=form.dsopts_feature_encoding.data,
label_encoding=form.dsopts_label_encoding.data,
batch_size=int(form.dsopts_batch_size.data),
num_threads=int(form.dsopts_num_threads.data),
force_same_shape=form.dsopts_force_same_shape.data,
extension_id=extension_id,
extension_userdata=extension.get_user_data(),
)
## Save form data with the job so we can easily clone it later.
utils.forms.save_form_to_job(job, form)
utils.forms.save_form_to_job(job, extension_form)
# schedule tasks
scheduler.add_job(job)
if request_wants_json():
return flask.jsonify(job.json_dict())
else:
return flask.redirect(
flask.url_for('digits.dataset.views.show', job_id=job.id()))
except:
if job:
scheduler.delete_job(job)
raise
def home(tab=2):
"""
DIGITS home page
Returns information about each job on the server
Returns JSON when requested:
{
datasets: [{id, name, status},...],
models: [{id, name, status},...]
}
"""
running_datasets = get_job_list(dataset.DatasetJob, True)
completed_datasets = get_job_list(dataset.DatasetJob, False)
running_models = get_job_list(model.ModelJob, True)
completed_models = get_job_list(model.ModelJob, False)
if request_wants_json():
data = {
'version':
digits.__version__,
'jobs_dir':
config_value('jobs_dir'),
'datasets':
[j.json_dict() for j in running_datasets + completed_datasets],
'models':
[j.json_dict() for j in running_models + completed_models],
}
if config_value('server_name'):
data['server_name'] = config_value('server_name')
return flask.jsonify(data)
else:
new_dataset_options = {
'Images': {
'image-classification': {
'title':
'Classification',
'url':
flask.url_for(
'digits.dataset.images.classification.views.new'),
},
'image-other': {
'title':
'Other',
'url':
flask.url_for('digits.dataset.images.generic.views.new'),
},
},
}
new_model_options = {
'Images': {
'image-classification': {
'title':
'Classification',
'url':
flask.url_for(
'digits.model.images.classification.views.new'),
},
'image-other': {
'title': 'Other',
'url':
flask.url_for('digits.model.images.generic.views.new'),
},
},
}
load_model_options = {
'Images': {
'pretrained-model': {
'title': 'Upload Pretrained Model',
'id': 'uploadPretrainedModel',
'url': flask.url_for('digits.pretrained_model.views.new'),
},
'access-model-store': {
'title': 'Retrieve from Model Store',
'id': 'retrieveModelStore',
'url': flask.url_for('digits.store.views.store'),
}
},
}
# add dataset options for known dataset extensions
data_extensions = extensions.data.get_extensions()
for extension in data_extensions:
ext_category = extension.get_category()
ext_title = extension.get_title()
ext_title = ext_title[:21] + ' ..' if len(
ext_title) > 21 else ext_title
ext_id = extension.get_id()
if ext_category not in new_dataset_options:
new_dataset_options[ext_category] = {}
new_dataset_options[ext_category][ext_id] = {
'title':
ext_title,
'url':
flask.url_for('digits.dataset.generic.views.new',
extension_id=ext_id),
}
if ext_category not in new_model_options:
new_model_options[ext_category] = {}
new_model_options[ext_category][ext_id] = {
'title':
ext_title,
'url':
flask.url_for('digits.model.images.generic.views.new',
extension_id=ext_id),
}
return flask.render_template(
'home.html',
tab=tab,
new_dataset_options=new_dataset_options,
running_datasets=running_datasets,
completed_datasets=completed_datasets,
new_model_options=new_model_options,
running_models=running_models,
completed_models=completed_models,
load_model_options=load_model_options,
total_gpu_count=len(scheduler.resources['gpus']),
remaining_gpu_count=sum(r.remaining()
for r in scheduler.resources['gpus']),
)
def classify_many():
"""
Classify many images and return the top 5 classifications for each
Returns JSON when requested: {classifications: {filename: [[category,confidence],...],...}}
"""
job = job_from_request()
image_list = flask.request.files.get('image_list')
if not image_list:
raise werkzeug.exceptions.BadRequest('image_list is a required field')
if 'image_folder' in flask.request.form and flask.request.form[
'image_folder'].strip():
image_folder = flask.request.form['image_folder']
if not os.path.exists(image_folder):
raise werkzeug.exceptions.BadRequest(
'image_folder "%s" does not exit' % image_folder)
else:
image_folder = None
epoch = None
if 'snapshot_epoch' in flask.request.form:
epoch = float(flask.request.form['snapshot_epoch'])
paths = []
images = []
ground_truths = []
dataset = job.train_task().dataset
for line in image_list.readlines():
line = line.strip()
if not line:
continue
path = None
# might contain a numerical label at the end
match = re.match(r'(.*\S)\s+(\d+)$', line)
if match:
path = match.group(1)
ground_truth = int(match.group(2))
else:
path = line
ground_truth = None
try:
if not utils.is_url(path) and image_folder and not os.path.isabs(
path):
path = os.path.join(image_folder, path)
image = utils.image.load_image(path)
image = utils.image.resize_image(
image,
dataset.image_dims[0],
dataset.image_dims[1],
channels=dataset.image_dims[2],
resize_mode=dataset.resize_mode,
)
paths.append(path)
images.append(image)
ground_truths.append(ground_truth)
except utils.errors.LoadImageError as e:
print e
if not len(images):
raise werkzeug.exceptions.BadRequest(
'Unable to load any images from the file')
labels, scores = job.train_task().infer_many(images, snapshot_epoch=epoch)
if scores is None:
raise RuntimeError('An error occured while processing the images')
# take top 5
indices = (-scores).argsort()[:, :5]
classifications = []
for image_index, index_list in enumerate(indices):
result = []
for i in index_list:
# `i` is a category in labels and also an index into scores
result.append((labels[i], round(100.0 * scores[image_index, i],
2)))
classifications.append(result)
# replace ground truth indices with labels
ground_truths = [
labels[x] if x is not None and (0 <= x < len(labels)) else None
for x in ground_truths
]
if request_wants_json():
joined = dict(zip(paths, classifications))
return flask.jsonify({'classifications': joined})
else:
return flask.render_template(
'models/images/classification/classify_many.html',
job=job,
paths=paths,
classifications=classifications,
show_ground_truth=not (ground_truths
== [None] * len(ground_truths)),
ground_truths=ground_truths)
def generic_image_dataset_create():
"""
Creates a new GenericImageDatasetJob
Returns JSON when requested: {job_id,name,status} or {errors:[]}
"""
form = GenericImageDatasetForm()
if not form.validate_on_submit():
if request_wants_json():
return flask.jsonify({'errors': form.errors}), 400
else:
return flask.render_template('datasets/images/generic/new.html',
form=form), 400
job = None
try:
job = GenericImageDatasetJob(
name=form.dataset_name.data,
mean_file=form.prebuilt_mean_file.data.strip(),
)
if form.method.data == 'prebuilt':
pass
else:
raise ValueError('method not supported')
job.tasks.append(
tasks.AnalyzeDbTask(
job_dir=job.dir(),
database=form.prebuilt_train_images.data,
purpose=form.prebuilt_train_images.label.text,
))
if form.prebuilt_train_labels.data:
job.tasks.append(
tasks.AnalyzeDbTask(
job_dir=job.dir(),
database=form.prebuilt_train_labels.data,
purpose=form.prebuilt_train_labels.label.text,
))
if form.prebuilt_val_images.data:
job.tasks.append(
tasks.AnalyzeDbTask(
job_dir=job.dir(),
database=form.prebuilt_val_images.data,
purpose=form.prebuilt_val_images.label.text,
))
if form.prebuilt_val_labels.data:
job.tasks.append(
tasks.AnalyzeDbTask(
job_dir=job.dir(),
database=form.prebuilt_val_labels.data,
purpose=form.prebuilt_val_labels.label.text,
))
scheduler.add_job(job)
if request_wants_json():
return flask.jsonify(job.json_dict())
else:
return flask.redirect(
flask.url_for('datasets_show', job_id=job.id()))
except:
if job:
scheduler.delete_job(job)
raise
def infer_one():
"""
Infer one image
"""
model_job = job_from_request()
remove_image_path = False
if 'image_path' in flask.request.form and flask.request.form['image_path']:
image_path = flask.request.form['image_path']
elif 'image_file' in flask.request.files and flask.request.files[
'image_file']:
outfile = tempfile.mkstemp(suffix='.bin')
flask.request.files['image_file'].save(outfile[1])
image_path = outfile[1]
os.close(outfile[0])
remove_image_path = True
else:
raise werkzeug.exceptions.BadRequest(
'must provide image_path or image_file')
epoch = None
if 'snapshot_epoch' in flask.request.form:
epoch = float(flask.request.form['snapshot_epoch'])
layers = 'none'
if 'show_visualizations' in flask.request.form and flask.request.form[
'show_visualizations']:
layers = 'all'
if 'dont_resize' in flask.request.form and flask.request.form[
'dont_resize']:
resize = False
else:
resize = True
# create inference job
inference_job = ImageInferenceJob(
username=utils.auth.get_username(),
name="Infer One Image",
model=model_job,
images=[image_path],
epoch=epoch,
layers=layers,
resize=resize,
)
# schedule tasks
scheduler.add_job(inference_job)
# wait for job to complete
inference_job.wait_completion()
# retrieve inference data
inputs, outputs, model_visualization = inference_job.get_data()
# set return status code
status_code = 500 if inference_job.status == 'E' else 200
# delete job folder and remove from scheduler list
scheduler.delete_job(inference_job)
if remove_image_path:
os.remove(image_path)
if inputs is not None and len(inputs['data']) == 1:
image = utils.image.embed_image_html(inputs['data'][0])
visualizations, header_html, app_begin_html, app_end_html = get_inference_visualizations(
model_job.dataset, inputs, outputs)
inference_view_html = visualizations[0]
else:
image = None
inference_view_html = None
header_html = None
app_begin_html = None
app_end_html = None
if request_wants_json():
return flask.jsonify({
'outputs':
dict((name, blob.tolist()) for name, blob in outputs.iteritems())
}), status_code
else:
return flask.render_template(
'models/images/generic/infer_one.html',
model_job=model_job,
job=inference_job,
image_src=image,
inference_view_html=inference_view_html,
header_html=header_html,
app_begin_html=app_begin_html,
app_end_html=app_end_html,
visualizations=model_visualization,
total_parameters=sum(v['param_count'] for v in model_visualization
if v['vis_type'] == 'Weights'),
), status_code
def home():
"""
DIGITS home page
Returns information about each job on the server
Returns JSON when requested:
{
datasets: [{id, name, status},...],
models: [{id, name, status},...]
}
"""
running_datasets = get_job_list(dataset.DatasetJob, True)
completed_datasets = get_job_list(dataset.DatasetJob, False)
running_models = get_job_list(model.ModelJob, True)
completed_models = get_job_list(model.ModelJob, False)
if request_wants_json():
data = {
'version':
digits.__version__,
'jobs_dir':
config_value('jobs_dir'),
'datasets':
[j.json_dict() for j in running_datasets + completed_datasets],
'models':
[j.json_dict() for j in running_models + completed_models],
}
if config_value('server_name'):
data['server_name'] = config_value('server_name')
return flask.jsonify(data)
else:
new_dataset_options = [('Images', [
{
'title': 'Classification',
'id': 'image-classification',
'url': flask.url_for('image_classification_dataset_new'),
},
{
'title': 'Other',
'id': 'image-generic',
'url': flask.url_for('generic_image_dataset_new'),
},
])]
new_model_options = [('Images', [
{
'title': 'Classification',
'id': 'image-classification',
'url': flask.url_for('image_classification_model_new'),
},
{
'title': 'Other',
'id': 'image-generic',
'url': flask.url_for('generic_image_model_new'),
},
])]
return flask.render_template(
'home.html',
new_dataset_options=new_dataset_options,
running_datasets=running_datasets,
completed_datasets=completed_datasets,
new_model_options=new_model_options,
running_models=running_models,
completed_models=completed_models,
total_gpu_count=len(scheduler.resources['gpus']),
remaining_gpu_count=sum(r.remaining()
for r in scheduler.resources['gpus']),
)
def infer_db():
"""
Infer a database
"""
model_job = job_from_request()
if 'db_path' not in flask.request.form or flask.request.form[
'db_path'] is None:
raise werkzeug.exceptions.BadRequest('db_path is a required field')
db_path = flask.request.form['db_path']
if not os.path.exists(db_path):
raise werkzeug.exceptions.BadRequest('DB "%s" does not exit' % db_path)
epoch = None
if 'snapshot_epoch' in flask.request.form:
epoch = float(flask.request.form['snapshot_epoch'])
if 'dont_resize' in flask.request.form and flask.request.form[
'dont_resize']:
resize = False
else:
resize = True
# create inference job
inference_job = ImageInferenceJob(
username=utils.auth.get_username(),
name="Infer Many Images",
model=model_job,
images=db_path,
epoch=epoch,
layers='none',
resize=resize,
)
# schedule tasks
scheduler.add_job(inference_job)
# wait for job to complete
inference_job.wait_completion()
# retrieve inference data
inputs, outputs, _ = inference_job.get_data()
# set return status code
status_code = 500 if inference_job.status == 'E' else 200
# delete job folder and remove from scheduler list
scheduler.delete_job(inference_job)
if outputs is not None and len(outputs) < 1:
# an error occurred
outputs = None
if inputs is not None:
keys = [str(idx) for idx in inputs['ids']]
inference_views_html, header_html, app_begin_html, app_end_html = get_inference_visualizations(
model_job.dataset, inputs, outputs)
else:
inference_views_html = None
header_html = None
keys = None
app_begin_html = None
app_end_html = None
if request_wants_json():
result = {}
for i, key in enumerate(keys):
result[key] = dict(
(name, blob[i].tolist()) for name, blob in outputs.iteritems())
return flask.jsonify({'outputs': result}), status_code
else:
return flask.render_template(
'models/images/generic/infer_db.html',
model_job=model_job,
job=inference_job,
keys=keys,
inference_views_html=inference_views_html,
header_html=header_html,
app_begin_html=app_begin_html,
app_end_html=app_end_html,
), status_code
def infer_many():
"""
Infer many images
"""
model_job = job_from_request()
image_list = flask.request.files.get('image_list')
if not image_list:
raise werkzeug.exceptions.BadRequest('image_list is a required field')
if 'image_folder' in flask.request.form and flask.request.form['image_folder'].strip():
image_folder = flask.request.form['image_folder']
if not os.path.exists(image_folder):
raise werkzeug.exceptions.BadRequest('image_folder "%s" does not exit' % image_folder)
else:
image_folder = None
if 'num_test_images' in flask.request.form and flask.request.form['num_test_images'].strip():
num_test_images = int(flask.request.form['num_test_images'])
else:
num_test_images = None
epoch = None
if 'snapshot_epoch' in flask.request.form:
epoch = float(flask.request.form['snapshot_epoch'])
if 'dont_resize' in flask.request.form and flask.request.form['dont_resize']:
resize = False
else:
resize = True
paths = []
for line in image_list.readlines():
line = line.strip()
if not line:
continue
path = None
# might contain a numerical label at the end
match = re.match(r'(.*\S)\s+\d+$', line)
if match:
path = match.group(1)
else:
path = line
if not utils.is_url(path) and image_folder and not os.path.isabs(path):
path = os.path.join(image_folder, path)
paths.append(path)
if num_test_images is not None and len(paths) >= num_test_images:
break
# create inference job
inference_job = ImageInferenceJob(
username=utils.auth.get_username(),
name="Infer Many Images",
model=model_job,
images=paths,
epoch=epoch,
layers='none',
resize=resize,
)
# schedule tasks
scheduler.add_job(inference_job)
# wait for job to complete
inference_job.wait_completion()
# retrieve inference data
inputs, outputs, _ = inference_job.get_data()
# set return status code
status_code = 500 if inference_job.status == 'E' else 200
# delete job folder and remove from scheduler list
scheduler.delete_job(inference_job)
if outputs is not None and len(outputs) < 1:
# an error occurred
outputs = None
if inputs is not None:
paths = [paths[idx] for idx in inputs['ids']]
inference_views_html, header_html, app_begin_html, app_end_html = get_inference_visualizations(
model_job.dataset,
inputs,
outputs)
else:
inference_views_html = None
header_html = None
app_begin_html = None
app_end_html = None
if request_wants_json():
result = {}
for i, path in enumerate(paths):
result[path] = dict((name, blob[i].tolist()) for name, blob in outputs.iteritems())
return flask.jsonify({'outputs': result}), status_code
else:
return flask.render_template(
'models/images/generic/infer_many.html',
model_job=model_job,
job=inference_job,
paths=paths,
inference_views_html=inference_views_html,
header_html=header_html,
app_begin_html=app_begin_html,
app_end_html=app_end_html,
), status_code
def classify_one():
"""
Classify one image and return the top 5 classifications
Returns JSON when requested: {predictions: {category: confidence,...}}
"""
model_job = job_from_request()
remove_image_path = False
if 'image_path' in flask.request.form and flask.request.form['image_path']:
image_path = flask.request.form['image_path']
elif 'image_file' in flask.request.files and flask.request.files['image_file']:
outfile = tempfile.mkstemp(suffix='.png')
flask.request.files['image_file'].save(outfile[1])
image_path = outfile[1]
os.close(outfile[0])
remove_image_path = True
else:
raise werkzeug.exceptions.BadRequest('must provide image_path or image_file')
epoch = None
if 'snapshot_epoch' in flask.request.form:
epoch = float(flask.request.form['snapshot_epoch'])
layers = 'none'
if 'show_visualizations' in flask.request.form and flask.request.form['show_visualizations']:
layers = 'all'
# create inference job
inference_job = ImageInferenceJob(
username=utils.auth.get_username(),
name="Classify One Image",
model=model_job,
images=[image_path],
epoch=epoch,
layers=layers
)
# schedule tasks
scheduler.add_job(inference_job)
# wait for job to complete
inference_job.wait_completion()
# retrieve inference data
inputs, outputs, visualizations = inference_job.get_data()
# set return status code
status_code = 500 if inference_job.status == 'E' else 200
# delete job
scheduler.delete_job(inference_job)
if remove_image_path:
os.remove(image_path)
image = None
predictions = []
if inputs is not None and len(inputs['data']) == 1:
image = utils.image.embed_image_html(inputs['data'][0])
# convert to class probabilities for viewing
last_output_name, last_output_data = outputs.items()[-1]
if len(last_output_data) == 1:
scores = last_output_data[0].flatten()
indices = (-scores).argsort()
labels = model_job.train_task().get_labels()
predictions = []
for i in indices:
# ignore prediction if we don't have a label for the corresponding class
# the user might have set the final fully-connected layer's num_output to
# too high a value
if i < len(labels):
predictions.append((labels[i], scores[i]))
predictions = [(p[0], round(100.0 * p[1], 2)) for p in predictions[:5]]
if request_wants_json():
return flask.jsonify({'predictions': predictions}), status_code
else:
return flask.render_template('models/images/classification/classify_one.html',
model_job=model_job,
job=inference_job,
image_src=image,
predictions=predictions,
visualizations=visualizations,
total_parameters=sum(v['param_count']
for v in visualizations if v['vis_type'] == 'Weights'),
), status_code
def generic_image_model_create():
"""
Create a new GenericImageModelJob
Returns JSON when requested: {job_id,name,status} or {errors:[]}
"""
form = GenericImageModelForm()
form.dataset.choices = get_datasets()
form.standard_networks.choices = []
form.previous_networks.choices = get_previous_networks()
prev_network_snapshots = get_previous_network_snapshots()
if not form.validate_on_submit():
if request_wants_json():
return flask.jsonify({'errors': form.errors}), 400
else:
return flask.render_template(
'models/images/generic/new.html',
form=form,
previous_network_snapshots=prev_network_snapshots,
previous_networks_fullinfo=get_previous_networks_fulldetails(),
multi_gpu=config_value('caffe_root')['multi_gpu'],
), 400
datasetJob = scheduler.get_job(form.dataset.data)
if not datasetJob:
raise werkzeug.exceptions.BadRequest('Unknown dataset job_id "%s"' %
form.dataset.data)
job = None
try:
job = GenericImageModelJob(
name=form.model_name.data,
dataset_id=datasetJob.id(),
)
# get framework (hard-coded to caffe for now)
fw = frameworks.get_framework_by_id('caffe')
pretrained_model = None
#if form.method.data == 'standard':
if form.method.data == 'previous':
old_job = scheduler.get_job(form.previous_networks.data)
if not old_job:
raise werkzeug.exceptions.BadRequest(
'Job not found: %s' % form.previous_networks.data)
network = fw.get_network_from_previous(
old_job.train_task().network)
for choice in form.previous_networks.choices:
if choice[0] == form.previous_networks.data:
epoch = float(
flask.request.form['%s-snapshot' %
form.previous_networks.data])
if epoch != 0:
for filename, e in old_job.train_task().snapshots:
if e == epoch:
pretrained_model = filename
break
if pretrained_model is None:
raise werkzeug.exceptions.BadRequest(
"For the job %s, selected pretrained_model for epoch %d is invalid!"
% (form.previous_networks.data, epoch))
if not (os.path.exists(pretrained_model)):
raise werkzeug.exceptions.BadRequest(
"Pretrained_model for the selected epoch doesn't exists. May be deleted by another user/process. Please restart the server to load the correct pretrained_model details"
)
break
elif form.method.data == 'custom':
network = fw.get_network_from_desc(form.custom_network.data)
pretrained_model = form.custom_network_snapshot.data.strip()
else:
raise werkzeug.exceptions.BadRequest('Unrecognized method: "%s"' %
form.method.data)
policy = {'policy': form.lr_policy.data}
if form.lr_policy.data == 'fixed':
pass
elif form.lr_policy.data == 'step':
policy['stepsize'] = form.lr_step_size.data
policy['gamma'] = form.lr_step_gamma.data
elif form.lr_policy.data == 'multistep':
policy['stepvalue'] = form.lr_multistep_values.data
policy['gamma'] = form.lr_multistep_gamma.data
elif form.lr_policy.data == 'exp':
policy['gamma'] = form.lr_exp_gamma.data
elif form.lr_policy.data == 'inv':
policy['gamma'] = form.lr_inv_gamma.data
policy['power'] = form.lr_inv_power.data
elif form.lr_policy.data == 'poly':
policy['power'] = form.lr_poly_power.data
elif form.lr_policy.data == 'sigmoid':
policy['stepsize'] = form.lr_sigmoid_step.data
policy['gamma'] = form.lr_sigmoid_gamma.data
else:
raise werkzeug.exceptions.BadRequest(
'Invalid learning rate policy')
if config_value('caffe_root')['multi_gpu']:
if form.select_gpu_count.data:
gpu_count = form.select_gpu_count.data
selected_gpus = None
else:
selected_gpus = [str(gpu) for gpu in form.select_gpus.data]
gpu_count = None
else:
if form.select_gpu.data == 'next':
gpu_count = 1
selected_gpus = None
else:
selected_gpus = [str(form.select_gpu.data)]
gpu_count = None
job.tasks.append(
fw.create_train_task(
job_dir=job.dir(),
dataset=datasetJob,
train_epochs=form.train_epochs.data,
snapshot_interval=form.snapshot_interval.data,
learning_rate=form.learning_rate.data,
lr_policy=policy,
gpu_count=gpu_count,
selected_gpus=selected_gpus,
batch_size=form.batch_size.data,
val_interval=form.val_interval.data,
pretrained_model=pretrained_model,
crop_size=form.crop_size.data,
use_mean=bool(form.use_mean.data),
network=network,
random_seed=form.random_seed.data,
solver_type=form.solver_type.data,
))
scheduler.add_job(job)
if request_wants_json():
return flask.jsonify(job.json_dict())
else:
return flask.redirect(flask.url_for('models_show',
job_id=job.id()))
except:
if job:
scheduler.delete_job(job)
raise
def create():
"""
Create a new ImageClassificationModelJob
Returns JSON when requested: {job_id,name,status} or {errors:[]}
"""
form = ImageClassificationModelForm()
form.dataset.choices = get_datasets()
form.standard_networks.choices = get_standard_networks()
form.standard_networks.default = get_default_standard_network()
form.previous_networks.choices = get_previous_networks()
form.pretrained_networks.choices = get_pretrained_networks()
prev_network_snapshots = get_previous_network_snapshots()
# Is there a request to clone a job with ?clone=<job_id>
fill_form_if_cloned(form)
if not form.validate_on_submit():
if request_wants_json():
return flask.jsonify({'errors': form.errors}), 400
else:
return flask.render_template('models/images/classification/new.html',
form=form,
frameworks=frameworks.get_frameworks(),
previous_network_snapshots=prev_network_snapshots,
previous_networks_fullinfo=get_previous_networks_fulldetails(),
pretrained_networks_fullinfo=get_pretrained_networks_fulldetails(),
multi_gpu=config_value('caffe')['multi_gpu'],
), 400
datasetJob = scheduler.get_job(form.dataset.data)
if not datasetJob:
raise werkzeug.exceptions.BadRequest(
'Unknown dataset job_id "%s"' % form.dataset.data)
# sweeps will be a list of the the permutations of swept fields
# Get swept learning_rate
sweeps = [{'learning_rate': v} for v in form.learning_rate.data]
add_learning_rate = len(form.learning_rate.data) > 1
# Add swept batch_size
sweeps = [dict(s.items() + [('batch_size', bs)]) for bs in form.batch_size.data for s in sweeps[:]]
add_batch_size = len(form.batch_size.data) > 1
n_jobs = len(sweeps)
jobs = []
for sweep in sweeps:
# Populate the form with swept data to be used in saving and
# launching jobs.
form.learning_rate.data = sweep['learning_rate']
form.batch_size.data = sweep['batch_size']
# Augment Job Name
extra = ''
if add_learning_rate:
extra += ' learning_rate:%s' % str(form.learning_rate.data[0])
if add_batch_size:
extra += ' batch_size:%d' % form.batch_size.data[0]
job = None
try:
job = ImageClassificationModelJob(
username=utils.auth.get_username(),
name=form.model_name.data + extra,
group=form.group_name.data,
dataset_id=datasetJob.id(),
)
# get handle to framework object
fw = frameworks.get_framework_by_id(form.framework.data)
pretrained_model = None
if form.method.data == 'standard':
found = False
# can we find it in standard networks?
network_desc = fw.get_standard_network_desc(form.standard_networks.data)
if network_desc:
found = True
network = fw.get_network_from_desc(network_desc)
if not found:
raise werkzeug.exceptions.BadRequest(
'Unknown standard model "%s"' % form.standard_networks.data)
elif form.method.data == 'previous':
old_job = scheduler.get_job(form.previous_networks.data)
if not old_job:
raise werkzeug.exceptions.BadRequest(
'Job not found: %s' % form.previous_networks.data)
use_same_dataset = (old_job.dataset_id == job.dataset_id)
network = fw.get_network_from_previous(old_job.train_task().network, use_same_dataset)
for choice in form.previous_networks.choices:
if choice[0] == form.previous_networks.data:
epoch = float(flask.request.form['%s-snapshot' % form.previous_networks.data])
if epoch == 0:
pass
elif epoch == -1:
pretrained_model = old_job.train_task().pretrained_model
else:
# verify snapshot exists
pretrained_model = old_job.train_task().get_snapshot(epoch, download=True)
if pretrained_model is None:
raise werkzeug.exceptions.BadRequest(
"For the job %s, selected pretrained_model for epoch %d is invalid!"
% (form.previous_networks.data, epoch))
# the first is the actual file if a list is returned, other should be meta data
if isinstance(pretrained_model, list):
pretrained_model = pretrained_model[0]
if not (os.path.exists(pretrained_model)):
raise werkzeug.exceptions.BadRequest(
"Pretrained_model for the selected epoch doesn't exist. "
"May be deleted by another user/process. "
"Please restart the server to load the correct pretrained_model details.")
# get logical path
pretrained_model = old_job.train_task().get_snapshot(epoch)
break
elif form.method.data == 'pretrained':
pretrained_job = scheduler.get_job(form.pretrained_networks.data)
model_def_path = pretrained_job.get_model_def_path()
weights_path = pretrained_job.get_weights_path()
network = fw.get_network_from_path(model_def_path)
pretrained_model = weights_path
elif form.method.data == 'custom':
network = fw.get_network_from_desc(form.custom_network.data)
pretrained_model = form.custom_network_snapshot.data.strip()
else:
raise werkzeug.exceptions.BadRequest(
'Unrecognized method: "%s"' % form.method.data)
policy = {'policy': form.lr_policy.data}
if form.lr_policy.data == 'fixed':
pass
elif form.lr_policy.data == 'step':
policy['stepsize'] = form.lr_step_size.data
policy['gamma'] = form.lr_step_gamma.data
elif form.lr_policy.data == 'multistep':
policy['stepvalue'] = form.lr_multistep_values.data
policy['gamma'] = form.lr_multistep_gamma.data
elif form.lr_policy.data == 'exp':
policy['gamma'] = form.lr_exp_gamma.data
elif form.lr_policy.data == 'inv':
policy['gamma'] = form.lr_inv_gamma.data
policy['power'] = form.lr_inv_power.data
elif form.lr_policy.data == 'poly':
policy['power'] = form.lr_poly_power.data
elif form.lr_policy.data == 'sigmoid':
policy['stepsize'] = form.lr_sigmoid_step.data
policy['gamma'] = form.lr_sigmoid_gamma.data
else:
raise werkzeug.exceptions.BadRequest(
'Invalid learning rate policy')
if config_value('caffe')['multi_gpu']:
if form.select_gpus.data:
selected_gpus = [str(gpu) for gpu in form.select_gpus.data]
gpu_count = None
elif form.select_gpu_count.data:
gpu_count = form.select_gpu_count.data
selected_gpus = None
else:
gpu_count = 1
selected_gpus = None
else:
if form.select_gpu.data == 'next':
gpu_count = 1
selected_gpus = None
else:
selected_gpus = [str(form.select_gpu.data)]
gpu_count = None
# Set up data augmentation structure
data_aug = {}
data_aug['flip'] = form.aug_flip.data
data_aug['quad_rot'] = form.aug_quad_rot.data
data_aug['rot'] = form.aug_rot.data
data_aug['scale'] = form.aug_scale.data
data_aug['noise'] = form.aug_noise.data
data_aug['contrast'] = form.aug_contrast.data
data_aug['whitening'] = form.aug_whitening.data
data_aug['hsv_use'] = form.aug_hsv_use.data
data_aug['hsv_h'] = form.aug_hsv_h.data
data_aug['hsv_s'] = form.aug_hsv_s.data
data_aug['hsv_v'] = form.aug_hsv_v.data
# Python Layer File may be on the server or copied from the client.
fs.copy_python_layer_file(
bool(form.python_layer_from_client.data),
job.dir(),
(flask.request.files[form.python_layer_client_file.name]
if form.python_layer_client_file.name in flask.request.files
else ''), form.python_layer_server_file.data)
job.tasks.append(fw.create_train_task(
job=job,
dataset=datasetJob,
train_epochs=form.train_epochs.data,
snapshot_interval=form.snapshot_interval.data,
learning_rate=form.learning_rate.data[0],
lr_policy=policy,
gpu_count=gpu_count,
selected_gpus=selected_gpus,
batch_size=form.batch_size.data[0],
batch_accumulation=form.batch_accumulation.data,
val_interval=form.val_interval.data,
traces_interval=form.traces_interval.data,
pretrained_model=pretrained_model,
crop_size=form.crop_size.data,
use_mean=form.use_mean.data,
network=network,
random_seed=form.random_seed.data,
solver_type=form.solver_type.data,
rms_decay=form.rms_decay.data,
shuffle=form.shuffle.data,
data_aug=data_aug,
)
)
# Save form data with the job so we can easily clone it later.
save_form_to_job(job, form)
jobs.append(job)
scheduler.add_job(job)
if n_jobs == 1:
if request_wants_json():
return flask.jsonify(job.json_dict())
else:
return flask.redirect(flask.url_for('digits.model.views.show', job_id=job.id()))
except:
if job:
scheduler.delete_job(job)
raise
if request_wants_json():
return flask.jsonify(jobs=[j.json_dict() for j in jobs])
# If there are multiple jobs launched, go to the home page.
return flask.redirect('/')
def generic_image_model_infer_many():
"""
Infer many images
"""
job = job_from_request()
image_list = flask.request.files.get('image_list')
if not image_list:
raise werkzeug.exceptions.BadRequest('image_list is a required field')
epoch = None
if 'snapshot_epoch' in flask.request.form:
epoch = float(flask.request.form['snapshot_epoch'])
paths = []
images = []
db_task = job.train_task().dataset.analyze_db_tasks()[0]
height = db_task.image_height
width = db_task.image_width
if job.train_task().crop_size:
height = job.train_task().crop_size
width = job.train_task().crop_size
channels = db_task.image_channels
for line in image_list.readlines():
line = line.strip()
if not line:
continue
path = None
# might contain a numerical label at the end
match = re.match(r'(.*\S)\s+\d+$', line)
if match:
path = match.group(1)
else:
path = line
try:
image = utils.image.load_image(path)
image = utils.image.resize_image(image, height, width,
channels = channels,
resize_mode = 'squash',
)
paths.append(path)
images.append(image)
except utils.errors.LoadImageError as e:
print e
if not len(images):
raise werkzeug.exceptions.BadRequest(
'Unable to load any images from the file')
outputs = job.train_task().infer_many(images, snapshot_epoch=epoch)
if outputs is None:
raise RuntimeError('An error occured while processing the images')
if request_wants_json():
result = {}
for i, path in enumerate(paths):
result[path] = dict((name, blob[i].tolist()) for name,blob in outputs.iteritems())
return flask.jsonify({'outputs': result})
else:
return flask.render_template('models/images/generic/infer_many.html',
paths = paths,
network_outputs = outputs,
)
