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Posted to github@beam.apache.org by GitBox <gi...@apache.org> on 2022/07/12 15:35:09 UTC
[GitHub] [beam] ryanthompson591 commented on a diff in pull request #22131: TensorRT Initial commit
ryanthompson591 commented on code in PR #22131:
URL: https://github.com/apache/beam/pull/22131#discussion_r918961572
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sdks/python/apache_beam/examples/inference/tensorrt_object_detection.py:
##########
@@ -0,0 +1,238 @@
+#
+# 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 numpy as np
+import os
+from PIL import Image
+from typing import Iterable, Optional, Tuple
+
+import apache_beam as beam
+from apache_beam.io.filesystems import FileSystems
+from apache_beam.ml.inference.base import (KeyedModelHandler, PredictionResult,
+ RunInference)
+from apache_beam.ml.inference.tensorrt_inference import \
+ TensorRTEngineHandlerNumPy
+from apache_beam.options.pipeline_options import PipelineOptions, SetupOptions
+
+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(x):
+ width, height = x[1].size
+ return ((x[0], width, height), x[1])
+
+
+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):
+ def process(self, element: Tuple[str, PredictionResult]) -> Iterable[str]:
Review Comment:
since this is sample code it would be nice to have a short description of what post processing is done and why.
Something like:
Converts a list of prediction results into a human readable string with the filename, prediction box location, score of the predicted objects, and the name of the predicted object.
##########
sdks/python/apache_beam/ml/inference/tensorrt_inference.py:
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@@ -0,0 +1,213 @@
+#
+# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # pylint: disable=line-too-long
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed 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.
+#
+
+# pytype: skip-file
+
+import numpy as np
+import pycuda.driver as cuda
+import sys
+import tensorrt as trt
+from typing import Any, Dict, Iterable, Optional, Sequence
+
+from apache_beam.io.filesystems import FileSystems
+from apache_beam.ml.inference.base import ModelHandler, PredictionResult
+
+LOGGER = trt.Logger(trt.Logger.INFO)
+
+
+def _load_engine(engine_path):
+ file = FileSystems.open(engine_path, 'rb')
+ runtime = trt.Runtime(LOGGER)
+ engine = runtime.deserialize_cuda_engine(file.read())
+ assert engine
+ return engine
+
+
+def _load_onnx(onnx_path):
+ builder = trt.Builder(LOGGER)
+ network = builder.create_network(
+ flags=1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
+ parser = trt.OnnxParser(network, LOGGER)
+ with FileSystems.open(onnx_path) as f:
+ if not parser.parse(f.read()):
+ print("Failed to load ONNX file: {}".format(onnx_path))
+ for error in range(parser.num_errors):
+ print(parser.get_error(error))
+ sys.exit(1)
+ builder.reset()
+ return network
+
+
+def _build_engine(network):
+ builder = trt.Builder(LOGGER)
+ config = builder.create_builder_config()
+ runtime = trt.Runtime(LOGGER)
+ plan = builder.build_serialized_network(network, config)
+ engine = runtime.deserialize_cuda_engine(plan)
+ builder.reset()
+ return engine
+
+
+def _validate_inference_args(inference_args):
+ """Confirms that inference_args is None.
+
+ TensorRT engines do not need extra arguments in their execute_v2() call.
+ However, since inference_args is an argument in the RunInference interface,
+ we want to make sure it is not passed here in TensorRT's implementation of
+ RunInference.
+ """
+ if inference_args:
+ raise ValueError(
+ 'inference_args were provided, but should be None because TensorRT '
+ 'engines do not need extra arguments in their execute_v2() call.')
+
+
+class TensorRTEngineHandlerNumPy(ModelHandler[np.ndarray,
+ PredictionResult,
+ trt.ICudaEngine]):
+ def __init__(self, min_batch_size: int, max_batch_size: int, **kwargs):
+ """Implementation of the ModelHandler interface for TensorRT.
+
+ Example Usage:
+ pcoll | RunInference(
+ TensorRTEngineHandlerNumPy(
+ min_batch_size=1,
+ max_batch_size=1,
+ engine_path="my_uri"))
+
+ Args:
+ min_batch_size: minimum accepted batch size.
+ max_batch_size: maximum accepted batch size.
+ kwargs: Additional arguments like 'engine_path' and 'onnx_path' are
+ currently supported.
+
+ See https://docs.nvidia.com/deeplearning/tensorrt/api/python_api/
+ for details
+ """
+ self.min_batch_size = min_batch_size
+ self.max_batch_size = max_batch_size
+ if 'engine_path' in kwargs:
+ self.engine_path = kwargs.get('engine_path')
+ elif 'onnx_path' in kwargs:
+ self.onnx_path = kwargs.get('onnx_path')
+
+ trt.init_libnvinfer_plugins(LOGGER, namespace="")
+
+ def batch_elements_kwargs(self):
+ """Sets min_batch_size and max_batch_size of a TensorRT engine."""
+ return {
+ 'min_batch_size': self.min_batch_size,
+ 'max_batch_size': self.max_batch_size
+ }
+
+ def load_model(self) -> trt.ICudaEngine:
+ """Loads and initializes a TensorRT engine for processing."""
+ return _load_engine(self.engine_path)
+
+ def load_onnx(self) -> trt.INetworkDefinition:
Review Comment:
I'm not really familiar with what an onnx model is. Is this only used for tests?
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sdks/python/apache_beam/ml/inference/tensorrt_inference.py:
##########
@@ -0,0 +1,213 @@
+#
+# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # pylint: disable=line-too-long
Review Comment:
@kennknowles Nvidia wants to add a copyright here. Personally I think it's fine, but just wanted to double check that Apache is fine.
##########
sdks/python/apache_beam/ml/inference/tensorrt_inference.py:
##########
@@ -0,0 +1,213 @@
+#
+# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # pylint: disable=line-too-long
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed 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.
+#
+
+# pytype: skip-file
+
+import numpy as np
+import pycuda.driver as cuda
+import sys
+import tensorrt as trt
+from typing import Any, Dict, Iterable, Optional, Sequence
+
+from apache_beam.io.filesystems import FileSystems
+from apache_beam.ml.inference.base import ModelHandler, PredictionResult
+
+LOGGER = trt.Logger(trt.Logger.INFO)
+
+
+def _load_engine(engine_path):
+ file = FileSystems.open(engine_path, 'rb')
+ runtime = trt.Runtime(LOGGER)
+ engine = runtime.deserialize_cuda_engine(file.read())
+ assert engine
+ return engine
+
+
+def _load_onnx(onnx_path):
+ builder = trt.Builder(LOGGER)
+ network = builder.create_network(
+ flags=1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
+ parser = trt.OnnxParser(network, LOGGER)
+ with FileSystems.open(onnx_path) as f:
+ if not parser.parse(f.read()):
+ print("Failed to load ONNX file: {}".format(onnx_path))
Review Comment:
Is it possible to log instead of print errors? I'm just thinking that if this is done on workers users might not see printed messages.
##########
sdks/python/apache_beam/ml/inference/tensorrt_inference.py:
##########
@@ -0,0 +1,213 @@
+#
+# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # pylint: disable=line-too-long
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed 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.
+#
+
+# pytype: skip-file
+
+import numpy as np
+import pycuda.driver as cuda
+import sys
+import tensorrt as trt
+from typing import Any, Dict, Iterable, Optional, Sequence
+
+from apache_beam.io.filesystems import FileSystems
+from apache_beam.ml.inference.base import ModelHandler, PredictionResult
+
+LOGGER = trt.Logger(trt.Logger.INFO)
+
+
+def _load_engine(engine_path):
+ file = FileSystems.open(engine_path, 'rb')
+ runtime = trt.Runtime(LOGGER)
+ engine = runtime.deserialize_cuda_engine(file.read())
+ assert engine
+ return engine
+
+
+def _load_onnx(onnx_path):
+ builder = trt.Builder(LOGGER)
+ network = builder.create_network(
+ flags=1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
+ parser = trt.OnnxParser(network, LOGGER)
+ with FileSystems.open(onnx_path) as f:
+ if not parser.parse(f.read()):
+ print("Failed to load ONNX file: {}".format(onnx_path))
+ for error in range(parser.num_errors):
+ print(parser.get_error(error))
+ sys.exit(1)
+ builder.reset()
+ return network
+
+
+def _build_engine(network):
+ builder = trt.Builder(LOGGER)
+ config = builder.create_builder_config()
+ runtime = trt.Runtime(LOGGER)
+ plan = builder.build_serialized_network(network, config)
+ engine = runtime.deserialize_cuda_engine(plan)
+ builder.reset()
+ return engine
+
+
+def _validate_inference_args(inference_args):
+ """Confirms that inference_args is None.
+
+ TensorRT engines do not need extra arguments in their execute_v2() call.
+ However, since inference_args is an argument in the RunInference interface,
+ we want to make sure it is not passed here in TensorRT's implementation of
+ RunInference.
+ """
+ if inference_args:
+ raise ValueError(
+ 'inference_args were provided, but should be None because TensorRT '
+ 'engines do not need extra arguments in their execute_v2() call.')
+
+
+class TensorRTEngineHandlerNumPy(ModelHandler[np.ndarray,
+ PredictionResult,
+ trt.ICudaEngine]):
+ def __init__(self, min_batch_size: int, max_batch_size: int, **kwargs):
+ """Implementation of the ModelHandler interface for TensorRT.
+
+ Example Usage:
Review Comment:
I find the documentation generates better if you do this:
```
Example Usage::
pcoll | RunInference(
TensorRTEngineHandlerNumPy(
min_batch_size=1,
max_batch_size=1,
engine_path="my_uri"))
```
Like how it's done in sklearn_inference.
##########
sdks/python/apache_beam/ml/inference/tensorrt_inference.py:
##########
@@ -0,0 +1,213 @@
+#
+# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # pylint: disable=line-too-long
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed 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.
+#
+
+# pytype: skip-file
+
+import numpy as np
+import pycuda.driver as cuda
+import sys
+import tensorrt as trt
+from typing import Any, Dict, Iterable, Optional, Sequence
+
+from apache_beam.io.filesystems import FileSystems
+from apache_beam.ml.inference.base import ModelHandler, PredictionResult
+
+LOGGER = trt.Logger(trt.Logger.INFO)
+
+
+def _load_engine(engine_path):
+ file = FileSystems.open(engine_path, 'rb')
+ runtime = trt.Runtime(LOGGER)
+ engine = runtime.deserialize_cuda_engine(file.read())
+ assert engine
+ return engine
+
+
+def _load_onnx(onnx_path):
+ builder = trt.Builder(LOGGER)
+ network = builder.create_network(
+ flags=1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
+ parser = trt.OnnxParser(network, LOGGER)
+ with FileSystems.open(onnx_path) as f:
+ if not parser.parse(f.read()):
+ print("Failed to load ONNX file: {}".format(onnx_path))
+ for error in range(parser.num_errors):
+ print(parser.get_error(error))
+ sys.exit(1)
+ builder.reset()
+ return network
+
+
+def _build_engine(network):
+ builder = trt.Builder(LOGGER)
+ config = builder.create_builder_config()
+ runtime = trt.Runtime(LOGGER)
+ plan = builder.build_serialized_network(network, config)
+ engine = runtime.deserialize_cuda_engine(plan)
+ builder.reset()
+ return engine
+
+
+def _validate_inference_args(inference_args):
+ """Confirms that inference_args is None.
+
+ TensorRT engines do not need extra arguments in their execute_v2() call.
+ However, since inference_args is an argument in the RunInference interface,
+ we want to make sure it is not passed here in TensorRT's implementation of
+ RunInference.
+ """
+ if inference_args:
+ raise ValueError(
+ 'inference_args were provided, but should be None because TensorRT '
+ 'engines do not need extra arguments in their execute_v2() call.')
+
+
+class TensorRTEngineHandlerNumPy(ModelHandler[np.ndarray,
+ PredictionResult,
+ trt.ICudaEngine]):
+ def __init__(self, min_batch_size: int, max_batch_size: int, **kwargs):
+ """Implementation of the ModelHandler interface for TensorRT.
+
+ Example Usage:
+ pcoll | RunInference(
+ TensorRTEngineHandlerNumPy(
+ min_batch_size=1,
+ max_batch_size=1,
+ engine_path="my_uri"))
+
+ Args:
+ min_batch_size: minimum accepted batch size.
+ max_batch_size: maximum accepted batch size.
+ kwargs: Additional arguments like 'engine_path' and 'onnx_path' are
+ currently supported.
+
+ See https://docs.nvidia.com/deeplearning/tensorrt/api/python_api/
+ for details
+ """
+ self.min_batch_size = min_batch_size
+ self.max_batch_size = max_batch_size
+ if 'engine_path' in kwargs:
+ self.engine_path = kwargs.get('engine_path')
+ elif 'onnx_path' in kwargs:
+ self.onnx_path = kwargs.get('onnx_path')
+
+ trt.init_libnvinfer_plugins(LOGGER, namespace="")
+
+ def batch_elements_kwargs(self):
+ """Sets min_batch_size and max_batch_size of a TensorRT engine."""
+ return {
+ 'min_batch_size': self.min_batch_size,
+ 'max_batch_size': self.max_batch_size
+ }
+
+ def load_model(self) -> trt.ICudaEngine:
+ """Loads and initializes a TensorRT engine for processing."""
+ return _load_engine(self.engine_path)
+
+ def load_onnx(self) -> trt.INetworkDefinition:
+ """Loads and parses an onnx model for processing."""
+ return _load_onnx(self.onnx_path)
+
+ #
+ def build_engine(self, network: trt.INetworkDefinition) -> trt.ICudaEngine:
+ """Build an engine according to parsed/created network."""
+ return _build_engine(network)
+
+ def run_inference(
+ self,
+ batch: np.ndarray,
+ engine: trt.ICudaEngine,
+ inference_args: Optional[Dict[str, Any]] = None
+ ) -> Iterable[PredictionResult]:
+ """
+ Runs inferences on a batch of Tensors and returns an Iterable of
+ TensorRT Predictions.
+
+ Args:
+ batch: A np.ndarray or a np.ndarray that represents a concatenation
+ of multiple arrays as a batch.
+ engine: A TensorRT engine.
+ inference_args: Any additional arguments for an inference
+ that are not applicable to TensorRT.
+
+ Returns:
+ An Iterable of type PredictionResult.
+ """
+ _validate_inference_args(inference_args)
+ context = engine.create_execution_context()
Review Comment:
yes, just for some context, anything that should only be done once per thread should be done in load_model if possible.
##########
sdks/python/apache_beam/examples/inference/tensorrt_object_detection.py:
##########
@@ -0,0 +1,238 @@
+#
+# 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 numpy as np
+import os
+from PIL import Image
+from typing import Iterable, Optional, Tuple
+
+import apache_beam as beam
+from apache_beam.io.filesystems import FileSystems
+from apache_beam.ml.inference.base import (KeyedModelHandler, PredictionResult,
+ RunInference)
+from apache_beam.ml.inference.tensorrt_inference import \
+ TensorRTEngineHandlerNumPy
+from apache_beam.options.pipeline_options import PipelineOptions, SetupOptions
+
+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(x):
+ width, height = x[1].size
+ return ((x[0], width, height), x[1])
+
+
+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):
+ def process(self, element: Tuple[str, PredictionResult]) -> Iterable[str]:
+ key, prediction_result = element
+ filename, im_width, im_height = key
+ nums = 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(nums[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)
+ | 'ProcessOutput' >> beam.ParDo(PostProcessor()))
+
+ if known_args.output:
+ predictions | "WriteOutputToGCS" >> beam.io.WriteToText(
+ known_args.output,
+ shard_name_template='',
+ append_trailing_newlines=True)
+
Review Comment:
you don't have to take this suggestion, but one thing you could do if there is no output directory is just print the results.
```
if known_args.output:
predictions | "WriteOutputToGCS" >> beam.io.WriteToText(
known_args.output,
shard_name_template='',
append_trailing_newlines=True)
else:
predictions | "PrintPredictions" >> beam.Map(print)
```
##########
sdks/python/apache_beam/ml/inference/tensorrt_inference.py:
##########
@@ -0,0 +1,213 @@
+#
+# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # pylint: disable=line-too-long
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed 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.
+#
+
+# pytype: skip-file
+
+import numpy as np
+import pycuda.driver as cuda
+import sys
+import tensorrt as trt
+from typing import Any, Dict, Iterable, Optional, Sequence
+
+from apache_beam.io.filesystems import FileSystems
+from apache_beam.ml.inference.base import ModelHandler, PredictionResult
+
+LOGGER = trt.Logger(trt.Logger.INFO)
+
+
+def _load_engine(engine_path):
+ file = FileSystems.open(engine_path, 'rb')
+ runtime = trt.Runtime(LOGGER)
+ engine = runtime.deserialize_cuda_engine(file.read())
+ assert engine
+ return engine
+
+
+def _load_onnx(onnx_path):
+ builder = trt.Builder(LOGGER)
+ network = builder.create_network(
+ flags=1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
+ parser = trt.OnnxParser(network, LOGGER)
+ with FileSystems.open(onnx_path) as f:
+ if not parser.parse(f.read()):
+ print("Failed to load ONNX file: {}".format(onnx_path))
+ for error in range(parser.num_errors):
+ print(parser.get_error(error))
Review Comment:
same here: maybe log instead of print (if possible).
##########
sdks/python/apache_beam/examples/inference/tensorrt_object_detection.py:
##########
@@ -0,0 +1,238 @@
+#
+# 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 numpy as np
+import os
+from PIL import Image
+from typing import Iterable, Optional, Tuple
+
+import apache_beam as beam
+from apache_beam.io.filesystems import FileSystems
+from apache_beam.ml.inference.base import (KeyedModelHandler, PredictionResult,
+ RunInference)
+from apache_beam.ml.inference.tensorrt_inference import \
+ TensorRTEngineHandlerNumPy
+from apache_beam.options.pipeline_options import PipelineOptions, SetupOptions
+
+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(x):
Review Comment:
what is x here, can you add a more descriptive variable name?
##########
sdks/python/apache_beam/examples/inference/tensorrt_object_detection.py:
##########
@@ -0,0 +1,238 @@
+#
+# 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 numpy as np
+import os
+from PIL import Image
+from typing import Iterable, Optional, Tuple
+
+import apache_beam as beam
+from apache_beam.io.filesystems import FileSystems
+from apache_beam.ml.inference.base import (KeyedModelHandler, PredictionResult,
+ RunInference)
+from apache_beam.ml.inference.tensorrt_inference import \
+ TensorRTEngineHandlerNumPy
+from apache_beam.options.pipeline_options import PipelineOptions, SetupOptions
+
+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(x):
+ width, height = x[1].size
+ return ((x[0], width, height), x[1])
+
+
+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):
+ def process(self, element: Tuple[str, PredictionResult]) -> Iterable[str]:
+ key, prediction_result = element
+ filename, im_width, im_height = key
+ nums = prediction_result.inference[0]
Review Comment:
can this variable be more descriptive. Is nums the number of inferences?
maybe:
num_inferences = prediction_result.inference[0][0]
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