def GetPrediction_frame(input_to_model):
''' Accepts dicom, return prediction from model '''
# # intialize variables:
# input_to_model = []
# prediction = []
# # convert frames to grayscale and resize:
# for frame in dicom['pixel_data']:
# # convert frame to grayscale:
# grayscale_image = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
# # reduce frame size:
# reduced_image = cv2.resize(grayscale_image,(96*2,64*2))
# # append to list:
# input_to_model.append(reduced_image)
# # convert data to numpy array for further processing:
# input_to_model = np.array(input_to_model)
# # reshape video to fit model requirements:
# number_of_frames = input_to_model.shape[0]
# means = input_to_model.reshape(number_of_frames, -1).mean(-1)
# stds = input_to_model.reshape(number_of_frames, -1).std(-1)
# input_to_model = input_to_model - means[:, np.newaxis, np.newaxis]
# input_to_model = input_to_model / stds[:, np.newaxis, np.newaxis]
# input_to_model = input_to_model.reshape(input_to_model.shape+(1,))
# input_to_model = pickle.dumps(input_to_model.astype('float16'))
if configuration['view']['binary_model_type'] == 'None':
# get endpoint of model:
views_predictor = Predictor('tf-multi-model-endpoint',
model_name='views_model',
content_type='application/npy',
serializer=None)
elif configuration['view']['binary_model_type'] == 'frame':
# get endpoint of model:
views_predictor = Predictor('tf-multi-model-endpoint',
model_name='master_model',
content_type='application/npy',
serializer=None)
# contact endpoint for prediction:
result = np.array(views_predictor.predict(input_to_model)['predictions'])
# build prediction object:
prediction = {
'model_name': model_name,
'predictions': result,
}
return prediction
def test_predictor_csv(sagemaker_session):
predictor = Predictor('endpoint', sagemaker_session, serializer=csv_serializer)
mock_response(json.dumps(PREDICT_RESPONSE).encode('utf-8'), sagemaker_session)
result = predictor.predict([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
assert_invoked(sagemaker_session,
EndpointName='endpoint',
ContentType=CSV_CONTENT_TYPE,
Accept=JSON_CONTENT_TYPE,
Body='1.0,2.0,3.0\n4.0,5.0,6.0')
assert PREDICT_RESPONSE == result
def test_predictor(sagemaker_session):
predictor = Predictor('endpoint', sagemaker_session)
mock_response(json.dumps(PREDICT_RESPONSE).encode('utf-8'), sagemaker_session)
result = predictor.predict(PREDICT_INPUT)
assert_invoked(sagemaker_session,
EndpointName='endpoint',
ContentType=JSON_CONTENT_TYPE,
Accept=JSON_CONTENT_TYPE,
Body=json.dumps(PREDICT_INPUT))
assert PREDICT_RESPONSE == result
def test_predictor_regress(sagemaker_session):
predictor = Predictor('endpoint', sagemaker_session, model_name='model', model_version='123')
mock_response(json.dumps(REGRESS_RESPONSE).encode('utf-8'), sagemaker_session)
result = predictor.regress(REGRESS_INPUT)
assert_invoked_with_body_dict(sagemaker_session,
EndpointName='endpoint',
ContentType=JSON_CONTENT_TYPE,
Accept=JSON_CONTENT_TYPE,
CustomAttributes='tfs-method=regress,tfs-model-name=model,tfs-model-version=123',
Body=json.dumps(REGRESS_INPUT))
assert REGRESS_RESPONSE == result
def test_predictor_classify(sagemaker_session):
predictor = Predictor('endpoint', sagemaker_session)
mock_response(json.dumps(CLASSIFY_RESPONSE).encode('utf-8'), sagemaker_session)
result = predictor.classify(CLASSIFY_INPUT)
assert_invoked_with_body_dict(sagemaker_session,
EndpointName='endpoint',
ContentType=JSON_CONTENT_TYPE,
Accept=JSON_CONTENT_TYPE,
CustomAttributes='tfs-method=classify',
Body=json.dumps(CLASSIFY_INPUT))
assert CLASSIFY_RESPONSE == result
def test_predictor_jsons(sagemaker_session):
predictor = Predictor('endpoint', sagemaker_session, serializer=None,
content_type='application/jsons')
mock_response(json.dumps(PREDICT_RESPONSE).encode('utf-8'), sagemaker_session)
result = predictor.predict('[1.0, 2.0, 3.0]\n[4.0, 5.0, 6.0]')
assert_invoked(sagemaker_session,
EndpointName='endpoint',
ContentType='application/jsons',
Accept=JSON_CONTENT_TYPE,
Body='[1.0, 2.0, 3.0]\n[4.0, 5.0, 6.0]')
assert PREDICT_RESPONSE == result
def test_predictor_regress(sagemaker_session):
predictor = Predictor("endpoint", sagemaker_session, model_name="model", model_version="123")
mock_response(json.dumps(REGRESS_RESPONSE).encode("utf-8"), sagemaker_session)
result = predictor.regress(REGRESS_INPUT)
assert_invoked_with_body_dict(
sagemaker_session,
EndpointName="endpoint",
ContentType=JSON_CONTENT_TYPE,
Accept=JSON_CONTENT_TYPE,
CustomAttributes="tfs-method=regress,tfs-model-name=model,tfs-model-version=123",
Body=json.dumps(REGRESS_INPUT),
)
assert REGRESS_RESPONSE == result
def test_predictor_jsons(sagemaker_session):
predictor = Predictor(
"endpoint", sagemaker_session, serializer=None, content_type="application/jsons"
)
mock_response(json.dumps(PREDICT_RESPONSE).encode("utf-8"), sagemaker_session)
result = predictor.predict("[1.0, 2.0, 3.0]\n[4.0, 5.0, 6.0]")
assert_invoked(
sagemaker_session,
EndpointName="endpoint",
ContentType="application/jsons",
Accept=JSON_CONTENT_TYPE,
Body="[1.0, 2.0, 3.0]\n[4.0, 5.0, 6.0]",
)
assert PREDICT_RESPONSE == result
def GetPrediction_video(input_to_model):
''' Accepts input_to_model, returns prediction from model '''
# intitalize variables:
preprocessing = configuration['view']['preprocessing']
# get endpoint of model:
if configuration['view']['binary_model_type'] == 'none':
# models:
model_name = 'ResNet50V2_views_model_vid_spline'
#model_name = 'ResNet50V2_views_model_vid_spline_noweights'
# get endpoint of model:
views_predictor = Predictor('tf-multi-model-endpoint',
model_name=model_name,
content_type='application/npy',
serializer=None)
elif configuration['view']['binary_model_type'] == 'video':
# models:
if preprocessing == 'downsample':
model_name = 'ResNet50V2_master_model_vid_dows'
#model_name = 'ResNet50V2_time_distributed_master_20200126-augoff_epoch20_downs_nonorm_vid'
elif preprocessing == 'zoom_cv2' or preprocessing == 'zoom':
model_name = 'ResNet50V2_master_model_vid_spline'
# get endpoint of model:
views_predictor = Predictor('tf-multi-model-endpoint',
model_name=model_name,
content_type='application/npy',
serializer=None)
# contact endpoint for prediction:
result = np.array(views_predictor.predict(input_to_model)['predictions'])
# build prediction object:
prediction = {
'model_name': model_name,
'predictions': result,
}
return prediction
def test_predict_csv(tfs_predictor):
input_data = '1.0,2.0,5.0\n1.0,2.0,5.0'
expected_result = {'predictions': [[3.5, 4.0, 5.5], [3.5, 4.0, 5.5]]}
predictor = Predictor(tfs_predictor.endpoint,
tfs_predictor.sagemaker_session,
serializer=sagemaker.predictor.csv_serializer)
result = predictor.predict(input_data)
assert expected_result == result
def test_predictor_classify_bad_content_type(sagemaker_session):
predictor = Predictor("endpoint", sagemaker_session, csv_serializer)
with pytest.raises(ValueError):
predictor.classify(CLASSIFY_INPUT)
def test_predictor_regress_bad_content_type(sagemaker_session):
predictor = Predictor("endpoint", sagemaker_session, csv_serializer)
with pytest.raises(ValueError):
predictor.regress(REGRESS_INPUT)
def GetPrediction(prepped_data):
''' Accepts prepped data, returns prediction from segmentation model '''
# unpack prepped data:
original_video = prepped_data['original_video']
preprocess_object = prepped_data['preprocess_object']
windows = prepped_data['windows']
molded_images = prepped_data['molded_images']
image_metas = prepped_data['image_metas']
anchors = prepped_data['anchors']
# initialize variables:
BATCH_SIZE = 50
masks = []
# get endpoint of model:
predictor = Predictor('tf-multi-model-endpoint',
model_name='Mask_RCNN_a4c_seg_batch50-compact-cpu',
content_type='application/seg',
serializer=None)
# get number of frames, batches:
number_of_frames = len(molded_images)
number_of_missing_frames = 0
number_of_batches = int(np.ceil(number_of_frames / BATCH_SIZE))
# iterate over each batch:
for index in range(number_of_batches):
# build payload batches:
image_batch = molded_images[index * BATCH_SIZE:(index + 1) *
BATCH_SIZE, :, :, :]
metas_batch = image_metas[index * BATCH_SIZE:(index + 1) *
BATCH_SIZE, :]
anchor_batch = anchors[index * BATCH_SIZE:(index + 1) *
BATCH_SIZE, :, :]
# set number of missing frames to 0:
number_of_missing_frames = 0
# pad batches smaller than batch size:
if len(image_batch) < BATCH_SIZE:
# get number of missing frames:
number_of_missing_frames = BATCH_SIZE - len(image_batch)
# build pads:
extra_images = np.zeros(
(number_of_missing_frames, image_batch.shape[1],
image_batch.shape[2], image_batch.shape[3]))
extra_metas = np.zeros(
(number_of_missing_frames, metas_batch.shape[1]))
extra_anchors = np.zeros(
(number_of_missing_frames, anchor_batch.shape[1],
anchor_batch.shape[2]))
# concatenate batch and pads:
image_batch = np.concatenate((image_batch, extra_images))
metas_batch = np.concatenate((metas_batch, extra_metas))
anchor_batch = np.concatenate((anchor_batch, extra_anchors))
# compile batch payload:
batch_payload = {
"molded_images": image_batch.astype(np.float16),
"image_metas": metas_batch.astype(np.float16),
"anchors": anchor_batch.astype(np.float16),
}
# serialize input:
input_to_model = pickle.dumps(batch_payload)
# get prediction:
response = predictor.predict(input_to_model)
for index in range(BATCH_SIZE - number_of_missing_frames):
result = {
'detection':
np.array([
response['predictions'][index]
['mrcnn_detection/Reshape_50']
]),
'mask':
np.array(
[response['predictions'][index]['mrcnn_mask/Reshape_1']]),
}
result_dictionary = preprocess_object.result_to_dict(
np.expand_dims(original_video[index], axis=0), molded_images,
windows, result)[0]
if result_dictionary['mask'].size != 0:
mask = np.where(result_dictionary['mask'][:, :, 0], 255.0,
0.0).astype(np.uint8)
masks.append(mask)
else:
masks.append(np.zeros(original_video[0].shape[:-1]))
masks = np.array(masks, dtype=np.uint8)
return masks
from sagemaker.tensorflow.serving import Predictor
from tensorflow.python.keras.preprocessing.image import load_img
!wget -O /tmp/test.jpg https://YOURPath.jpeg
file_name = '/tmp/test.jpg'
# test image
from IPython.display import Image
Image(file_name)
# Resize as model was trained after resizing
test_image = load_img(file_name, target_size=(150, 150))
test_image_array = np.array(test_image).reshape((1, 150, 150, 3)).tolist()
# Predict
predictor = Predictor(endpoint_name = "my-endpointname")
print(predictor.predict({"instances": [{"inputs": test_image_array}]}))
