[GitHub] [beam] AnandInguva commented on a diff in pull request #22131: TensorRT Initial commit

[GitBox <gi...@apache.org>]

AnandInguva commented on code in PR #22131:
URL: https://github.com/apache/beam/pull/22131#discussion_r965260515


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sdks/python/apache_beam/examples/inference/tensorrt_object_detection.py:
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@@ -0,0 +1,258 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""A pipeline that uses RunInference API to perform object detection with
+TensorRT.
+"""
+
+import argparse
+import io
+import os
+from typing import Iterable
+from typing import Optional
+from typing import Tuple
+
+import numpy as np
+
+import apache_beam as beam
+from apache_beam.io.filesystems import FileSystems
+from apache_beam.ml.inference.base import KeyedModelHandler
+from apache_beam.ml.inference.base import PredictionResult
+from apache_beam.ml.inference.base import RunInference
+from apache_beam.ml.inference.tensorrt_inference import TensorRTEngineHandlerNumPy  # pylint: disable=line-too-long
+from apache_beam.options.pipeline_options import PipelineOptions
+from apache_beam.options.pipeline_options import SetupOptions
+from PIL import Image
+
+COCO_OBJ_DET_CLASSES = [
+    'person',
+    'bicycle',
+    'car',
+    'motorcycle',
+    'airplane',
+    'bus',
+    'train',
+    'truck',
+    'boat',
+    'traffic light',
+    'fire hydrant',
+    'street sign',
+    'stop sign',
+    'parking meter',
+    'bench',
+    'bird',
+    'cat',
+    'dog',
+    'horse',
+    'sheep',
+    'cow',
+    'elephant',
+    'bear',
+    'zebra',
+    'giraffe',
+    'hat',
+    'backpack',
+    'umbrella',
+    'shoe',
+    'eye glasses',
+    'handbag',
+    'tie',
+    'suitcase',
+    'frisbee',
+    'skis',
+    'snowboard',
+    'sports ball',
+    'kite',
+    'baseball bat',
+    'baseball glove',
+    'skateboard',
+    'surfboard',
+    'tennis racket',
+    'bottle',
+    'plate',
+    'wine glass',
+    'cup',
+    'fork',
+    'knife',
+    'spoon',
+    'bowl',
+    'banana',
+    'apple',
+    'sandwich',
+    'orange',
+    'broccoli',
+    'carrot',
+    'hot dog',
+    'pizza',
+    'donut',
+    'cake',
+    'chair',
+    'couch',
+    'potted plant',
+    'bed',
+    'mirror',
+    'dining table',
+    'window',
+    'desk',
+    'toilet',
+    'door',
+    'tv',
+    'laptop',
+    'mouse',
+    'remote',
+    'keyboard',
+    'cell phone',
+    'microwave',
+    'oven',
+    'toaster',
+    'sink',
+    'refrigerator',
+    'blender',
+    'book',
+    'clock',
+    'vase',
+    'scissors',
+    'teddy bear',
+    'hair drier',
+    'toothbrush',
+    'hair brush',
+]
+
+
+def attach_im_size_to_key(
+    data: Tuple[str, Image.Image]) -> Tuple[Tuple[str, int, int], Image.Image]:
+  filename, image = data
+  width, height = image.size
+  return ((filename, width, height), image)
+
+
+def read_image(image_file_name: str,
+               path_to_dir: Optional[str] = None) -> Tuple[str, Image.Image]:
+  if path_to_dir is not None:
+    image_file_name = os.path.join(path_to_dir, image_file_name)
+  with FileSystems().open(image_file_name, 'r') as file:
+    data = Image.open(io.BytesIO(file.read())).convert('RGB')
+    return image_file_name, data
+
+
+def preprocess_image(image: Image.Image) -> np.ndarray:
+  ssd_mobilenet_v2_320x320_input_dims = (300, 300)
+  image = image.resize(
+      ssd_mobilenet_v2_320x320_input_dims, resample=Image.Resampling.BILINEAR)
+  image = np.expand_dims(np.asarray(image, dtype=np.float32), axis=0)
+  return image
+
+
+class PostProcessor(beam.DoFn):
+  """Processes the PredictionResult that consists of
+  number of detections per image, box coordinates, scores and classes.
+
+  We loop over all detections to organize attributes on a per
+  detection basis. Box coordinates are normalized, hence we have to scale them
+  according to original image dimensions. Score is a floating point number
+  that provides probability percentage of a particular object. Class is
+  an integer that we can transform into actual string class using
+  COCO_OBJ_DET_CLASSES as reference.
+  """
+  def process(self, element: Tuple[str, PredictionResult]) -> Iterable[str]:
+    key, prediction_result = element
+    filename, im_width, im_height = key
+    num_detections = prediction_result.inference[0]
+    boxes = prediction_result.inference[1]
+    scores = prediction_result.inference[2]
+    classes = prediction_result.inference[3]
+    detections = []
+    for i in range(int(num_detections[0])):
+      detections.append({
+          'ymin': str(boxes[i][0] * im_height),
+          'xmin': str(boxes[i][1] * im_width),
+          'ymax': str(boxes[i][2] * im_height),
+          'xmax': str(boxes[i][3] * im_width),
+          'score': str(scores[i]),
+          'class': COCO_OBJ_DET_CLASSES[int(classes[i])]
+      })
+    yield filename + ',' + str(detections)
+
+
+def parse_known_args(argv):
+  """Parses args for the workflow."""
+  parser = argparse.ArgumentParser()
+  parser.add_argument(
+      '--input',
+      dest='input',
+      required=True,
+      help='Path to the text file containing image names.')
+  parser.add_argument(
+      '--output',
+      dest='output',
+      required=True,
+      help='Path where to save output predictions.'
+      ' text file.')
+  parser.add_argument(
+      '--engine_path',
+      dest='engine_path',
+      required=True,
+      help='Path to the pre-built TFOD ssd_mobilenet_v2_320x320_coco17_tpu-8'
+      'TensorRT engine.')
+  parser.add_argument(
+      '--images_dir',
+      default=None,
+      help='Path to the directory where images are stored.'
+      'Not required if image names in the input file have absolute path.')
+  return parser.parse_known_args(argv)
+
+
+def run(argv=None, save_main_session=True):
+  """
+  Args:
+    argv: Command line arguments defined for this example.
+  """
+  known_args, pipeline_args = parse_known_args(argv)
+  pipeline_options = PipelineOptions(pipeline_args)
+  pipeline_options.view_as(SetupOptions).save_main_session = save_main_session
+
+  engine_handler = KeyedModelHandler(
+      TensorRTEngineHandlerNumPy(
+          min_batch_size=1,
+          max_batch_size=1,
+          engine_path=known_args.engine_path))
+
+  with beam.Pipeline(options=pipeline_options) as p:
+    filename_value_pair = (
+        p
+        | 'ReadImageNames' >> beam.io.ReadFromText(
+            known_args.input, skip_header_lines=0)
+        | 'ReadImageData' >> beam.Map(
+            lambda image_name: read_image(
+                image_file_name=image_name, path_to_dir=known_args.images_dir))
+        | 'AttachImageSizeToKey' >> beam.Map(attach_im_size_to_key)
+        | 'PreprocessImages' >> beam.MapTuple(
+            lambda file_name, data: (file_name, preprocess_image(data))))
+    predictions = (
+        filename_value_pair
+        | 'PyTorchRunInference' >> RunInference(engine_handler)

Review Comment:
   ```suggestion
           | 'TensorRTRunInference' >> RunInference(engine_handler)
   ```



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