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 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 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 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 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 infer_one():
"""
Infer one image
"""
model_job = job_from_request()
image = None
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='.bin')
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 = ImageInferenceJob(
username = utils.auth.get_username(),
name = "Infer 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()
# delete job folder and remove from scheduler list
scheduler.delete_job(inference_job)
# remove file (fails if a URL was provided)
try:
os.remove(image_path)
except:
pass
image = None
if inputs is not None and len(inputs) == 1:
image = utils.image.embed_image_html(inputs[0])
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',
model_job = model_job,
job = inference_job,
image_src = image,
network_outputs = outputs,
visualizations = visualizations,
total_parameters= sum(v['param_count'] for v in visualizations if v['vis_type'] == 'Weights'),
)
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 = []
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
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)
ground_truths.append(ground_truth)
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 = "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 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 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 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 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 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_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'])
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 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 = ImageInferenceJob(
username=utils.auth.get_username(),
name="TopN Image Classification",
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)
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 occurred 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))
# Can't have more categories than the number of labels or the number of outputs
n_categories = min(indices.shape[1], len(labels))
for i in xrange(n_categories):
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('models/images/classification/top_n.html',
model_job=model_job,
job=inference_job,
results=results,
)
def infer_one():
"""
Infer one image
"""
model_job = job_from_request()
image = None
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='.bin')
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 = ImageInferenceJob(username=utils.auth.get_username(),
name="Infer 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()
# delete job folder and remove from scheduler list
scheduler.delete_job(inference_job)
# remove file (fails if a URL was provided)
try:
os.remove(image_path)
except:
pass
image = None
if inputs is not None and len(inputs['data']) == 1:
image = utils.image.embed_image_html(inputs['data'][0])
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',
model_job=model_job,
job=inference_job,
image_src=image,
network_outputs=outputs,
visualizations=visualizations,
total_parameters=sum(v['param_count'] for v in visualizations
if v['vis_type'] == 'Weights'),
)
