def addTensors(x):
tensors = redisAI.mgetTensorsFromKeyspace(['tensor_a'])
tensors.append(redisAI.getTensorFromKey('tensor_b'))
log(str(redisAI.tensorGetDims(tensors[0])))
log(str(redisAI.tensorGetDims(tensors[1])))
scriptRunner = redisAI.createScriptRunner('my_script', 'concat_tensors')
redisAI.scriptRunnerAddInputList(scriptRunner, tensors)
redisAI.scriptRunnerAddOutput(scriptRunner)
script_reply = redisAI.scriptRunnerRun(scriptRunner)
redisAI.setTensorInKey('script_reply', script_reply[0])
redisAI.msetTensorsInKeyspace({'script_reply_1': script_reply[0]})
def is_fraud(record):
# Retrieve tensors from keyspace
# Range query with limit 100. (Without limit it can return 100-150K results which reduce performance)
ref_data_keys = execute("ZRANGEBYSCORE", "references", record[1],
record[2], "LIMIT", "0", "100")
# Set "_tensor" suffix for every returned key
keys = [x + "_tensor" for x in ref_data_keys]
# Append the new transaction tensor key
keys.append(record[3])
# Do mgetTensors from the keyspace
tensors = redisAI.mgetTensorsFromKeyspace(keys)
# Take the reference data tensors and the sample data
ref_data = tensors[:len(tensors) - 2]
new_sample = tensors[len(tensors) - 1]
# Create a new reference tensor out the of the reference data from the keyspace, with a torch script
scriptRunner = redisAI.createScriptRunner('concat_script',
'concat_tensors')
redisAI.scriptRunnerAddInputList(scriptRunner, ref_data)
redisAI.scriptRunnerAddOutput(scriptRunner)
# Run two models over the reference data and the transaction
ref_data = redisAI.scriptRunnerRun(scriptRunner)[0]
modelRunner = redisAI.createModelRunner('model_1')
redisAI.modelRunnerAddInput(modelRunner, 'transaction', new_sample)
redisAI.modelRunnerAddInput(modelRunner, 'reference', ref_data)
redisAI.modelRunnerAddOutput(modelRunner, 'output')
output_1 = redisAI.modelRunnerRun(modelRunner)[0]
modelRunner = redisAI.createModelRunner('model_2')
redisAI.modelRunnerAddInput(modelRunner, 'transaction', new_sample)
redisAI.modelRunnerAddInput(modelRunner, 'reference', ref_data)
redisAI.modelRunnerAddOutput(modelRunner, 'output')
output_2 = redisAI.modelRunnerRun(modelRunner)[0]
# Average the results with numpy and set in keyspace
shape = redisAI.tensorGetDims(output_1)
reply_ndarray_0 = np.frombuffer(redisAI.tensorGetDataAsBlob(output_1),
dtype=np.float32).reshape(shape)
reply_ndarray_1 = np.frombuffer(redisAI.tensorGetDataAsBlob(output_2),
dtype=np.float32).reshape(shape)
res = (reply_ndarray_0 + reply_ndarray_1) / 2.0
output = redisAI.createTensorFromBlob('FLOAT', res.shape, res.tobytes())
redisAI.setTensorInKey('model_result', output)
def runYolo(x):
''' Runs the model on an input image from the stream '''
global prf
IMG_SIZE = 416 # Model's input image size
prf.start() # Start a new profiler iteration
# log('read')
# Read the image from the stream's message
buf = io.BytesIO(x['image'])
pil_image = Image.open(buf)
numpy_img = np.array(pil_image)
prf.add('read')
# log('resize')
# Resize, normalize and tensorize the image for the model (number of images, width, height, channels)
image = process_image(numpy_img, IMG_SIZE)
# log('tensor')
img_ba = bytearray(image.tobytes())
image_tensor = redisAI.createTensorFromBlob('FLOAT',
[1, IMG_SIZE, IMG_SIZE, 3],
img_ba)
prf.add('resize')
# log('model')
# Create the RedisAI model runner and run it
modelRunner = redisAI.createModelRunner('yolo:model')
redisAI.modelRunnerAddInput(modelRunner, 'input', image_tensor)
redisAI.modelRunnerAddOutput(modelRunner, 'output')
model_replies = redisAI.modelRunnerRun(modelRunner)
model_output = model_replies[0]
prf.add('model')
# log('script')
# The model's output is processed with a PyTorch script for non maxima suppression
scriptRunner = redisAI.createScriptRunner('yolo:script', 'boxes_from_tf')
redisAI.scriptRunnerAddInput(scriptRunner, model_output)
redisAI.scriptRunnerAddOutput(scriptRunner)
script_reply = redisAI.scriptRunnerRun(scriptRunner)
prf.add('script')
# log('boxes')
# The script outputs bounding boxes
shape = redisAI.tensorGetDims(script_reply)
buf = redisAI.tensorGetDataAsBlob(script_reply)
boxes = np.frombuffer(buf, dtype=np.float32).reshape(shape)
# Iterate boxes to extract the people
ratio = float(IMG_SIZE) / max(pil_image.width,
pil_image.height) # ratio = old / new
pad_x = (IMG_SIZE - pil_image.width * ratio) / 2 # Width padding
pad_y = (IMG_SIZE - pil_image.height * ratio) / 2 # Height padding
boxes_out = []
people_count = 0
for box in boxes[0]:
if box[4] == 0.0: # Remove zero-confidence detections
continue
if box[-1] != 14: # Ignore detections that aren't people
continue
people_count += 1
# Descale bounding box coordinates back to original image size
x1 = (IMG_SIZE * (box[0] - 0.5 * box[2]) - pad_x) / ratio
y1 = (IMG_SIZE * (box[1] - 0.5 * box[3]) - pad_y) / ratio
x2 = (IMG_SIZE * (box[0] + 0.5 * box[2]) - pad_x) / ratio
y2 = (IMG_SIZE * (box[1] + 0.5 * box[3]) - pad_y) / ratio
# Store boxes as a flat list
boxes_out += [x1, y1, x2, y2]
prf.add('boxes')
return x['streamId'], people_count, boxes_out