1231: g0plus dockerfile

38fb1f6 verified about 2 months ago

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	#
	# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
	# 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.
	#

	import os
	import sys
	import argparse
	import numpy as np
	import tensorrt as trt
	from cuda import cudart
	from image_batcher import ImageBatcher
	from visualize import visualize_detections

	sys.path.insert(1, os.path.join(os.path.dirname(os.path.realpath(__file__)), os.pardir))
	import common


	class TensorRTInfer:
	"""
	Implements inference for the Model TensorRT engine.
	"""

	def __init__(self, engine_path):
	"""
	:param engine_path: The path to the serialized engine to load from disk.
	"""

	# Load TRT engine
	self.logger = trt.Logger(trt.Logger.ERROR)
	trt.init_libnvinfer_plugins(self.logger, namespace="")
	with open(engine_path, "rb") as f, trt.Runtime(self.logger) as runtime:
	assert runtime
	self.engine = runtime.deserialize_cuda_engine(f.read())
	assert self.engine
	self.context = self.engine.create_execution_context()
	assert self.context

	# Setup I/O bindings
	self.inputs = []
	self.outputs = []
	self.allocations = []
	for i in range(self.engine.num_io_tensors):
	name = self.engine.get_tensor_name(i)
	is_input = False
	if self.engine.get_tensor_mode(name) == trt.TensorIOMode.INPUT:
	is_input = True
	dtype = self.engine.get_tensor_dtype(name)
	shape = self.engine.get_tensor_shape(name)
	if is_input:
	self.batch_size = shape[0]
	size = np.dtype(trt.nptype(dtype)).itemsize
	for s in shape:
	size *= s
	allocation = common.cuda_call(cudart.cudaMalloc(size))
	binding = {
	"index": i,
	"name": name,
	"dtype": np.dtype(trt.nptype(dtype)),
	"shape": list(shape),
	"allocation": allocation,
	"size": size,
	}
	self.allocations.append(allocation)
	if is_input:
	self.inputs.append(binding)
	else:
	self.outputs.append(binding)

	assert self.batch_size > 0
	assert len(self.inputs) > 0
	assert len(self.outputs) > 0
	assert len(self.allocations) > 0

	def input_spec(self):
	"""
	Get the specs for the input tensor of the network. Useful to prepare memory allocations.
	:return: Two items, the shape of the input tensor and its (numpy) datatype.
	"""
	return self.inputs[0]["shape"], self.inputs[0]["dtype"]

	def output_spec(self):
	"""
	Get the specs for the output tensors of the network. Useful to prepare memory allocations.
	:return: A list with two items per element, the shape and (numpy) datatype of each output tensor.
	"""
	specs = []
	for o in self.outputs:
	specs.append((o["shape"], o["dtype"]))
	return specs

	def infer(self, batch, scales=None, nms_threshold=None):
	"""
	Execute inference on a batch of images. The images should already be batched and preprocessed, as prepared by
	the ImageBatcher class. Memory copying to and from the GPU device will be performed here.
	:param batch: A numpy array holding the image batch.
	:param scales: The image resize scales for each image in this batch. Default: No scale postprocessing applied.
	:return: A nested list for each image in the batch and each detection in the list.
	"""

	# Prepare the output data.
	outputs = []
	for shape, dtype in self.output_spec():
	outputs.append(np.zeros(shape, dtype))

	# Process I/O and execute the network.
	common.memcpy_host_to_device(
	self.inputs[0]["allocation"], np.ascontiguousarray(batch)
	)

	self.context.execute_v2(self.allocations)
	for o in range(len(outputs)):
	common.memcpy_device_to_host(outputs[o], self.outputs[o]["allocation"])

	# Process the results.
	nums = outputs[0]
	boxes = outputs[1]
	scores = outputs[2]
	pred_classes = outputs[3]
	masks = outputs[4]

	detections = []
	for i in range(self.batch_size):
	detections.append([])
	for n in range(int(nums[i])):
	# Select a mask.
	mask = masks[i][n]

	# Calculate scaling values for bboxes.
	scale = self.inputs[0]["shape"][2]
	scale /= scales[i]
	scale_y = scale
	scale_x = scale

	if nms_threshold and scores[i][n] < nms_threshold:
	continue
	# Append to detections
	detections[i].append(
	{
	"ymin": boxes[i][n][0] * scale_y,
	"xmin": boxes[i][n][1] * scale_x,
	"ymax": boxes[i][n][2] * scale_y,
	"xmax": boxes[i][n][3] * scale_x,
	"score": scores[i][n],
	"class": int(pred_classes[i][n]),
	"mask": mask,
	}
	)
	return detections


	def main(args):
	output_dir = os.path.realpath(args.output)
	os.makedirs(output_dir, exist_ok=True)

	labels = [
	"person",
	"bicycle",
	"car",
	"motorcycle",
	"airplane",
	"bus",
	"train",
	"truck",
	"boat",
	"traffic light",
	"fire hydrant",
	"stop sign",
	"parking meter",
	"bench",
	"bird",
	"cat",
	"dog",
	"horse",
	"sheep",
	"cow",
	"elephant",
	"bear",
	"zebra",
	"giraffe",
	"backpack",
	"umbrella",
	"handbag",
	"tie",
	"suitcase",
	"frisbee",
	"skis",
	"snowboard",
	"sports ball",
	"kite",
	"baseball bat",
	"baseball glove",
	"skateboard",
	"surfboard",
	"tennis racket",
	"bottle",
	"wine glass",
	"cup",
	"fork",
	"knife",
	"spoon",
	"bowl",
	"banana",
	"apple",
	"sandwich",
	"orange",
	"broccoli",
	"carrot",
	"hot dog",
	"pizza",
	"donut",
	"cake",
	"chair",
	"couch",
	"potted plant",
	"bed",
	"dining table",
	"toilet",
	"tv",
	"laptop",
	"mouse",
	"remote",
	"keyboard",
	"cell phone",
	"microwave",
	"oven",
	"toaster",
	"sink",
	"refrigerator",
	"book",
	"clock",
	"vase",
	"scissors",
	"teddy bear",
	"hair drier",
	"toothbrush",
	]

	trt_infer = TensorRTInfer(args.engine)
	batcher = ImageBatcher(
	args.input, *trt_infer.input_spec(), config_file=args.det2_config
	)
	for batch, images, scales in batcher.get_batch():
	print(
	"Processing Image {} / {}".format(batcher.image_index, batcher.num_images),
	end="\r",
	)
	detections = trt_infer.infer(batch, scales, args.nms_threshold)
	for i in range(len(images)):
	basename = os.path.splitext(os.path.basename(images[i]))[0]
	# Image Visualizations
	output_path = os.path.join(output_dir, "{}.png".format(basename))
	visualize_detections(
	images[i], output_path, detections[i], labels, args.iou_threshold
	)
	# Text Results
	output_results = ""
	for d in detections[i]:
	line = [
	d["xmin"],
	d["ymin"],
	d["xmax"],
	d["ymax"],
	d["score"],
	d["class"],
	]
	output_results += "\t".join([str(f) for f in line]) + "\n"
	with open(os.path.join(args.output, "{}.txt".format(basename)), "w") as f:
	f.write(output_results)
	print()
	print("Finished Processing")


	if __name__ == "__main__":
	parser = argparse.ArgumentParser()
	parser.add_argument(
	"-e", "--engine", default=None, help="The serialized TensorRT engine"
	)
	parser.add_argument(
	"-i", "--input", default=None, help="Path to the image or directory to process"
	)
	parser.add_argument(
	"-c",
	"--det2_config",
	help="The Detectron 2 config file (.yaml) for the model",
	type=str,
	)
	parser.add_argument(
	"-o",
	"--output",
	default=None,
	help="Directory where to save the visualization results",
	)
	parser.add_argument(
	"-t",
	"--nms_threshold",
	type=float,
	help="Override the score threshold for the NMS operation, if higher than the threshold in the engine.",
	)
	parser.add_argument(
	"--iou_threshold",
	default=0.5,
	type=float,
	help="Select the IoU threshold for the mask segmentation. Range is 0 to 1. Pixel values more than threshold will become 1, less 0",
	)
	args = parser.parse_args()
	if not all([args.engine, args.input, args.output, args.det2_config]):
	parser.print_help()
	print(
	"\nThese arguments are required: --engine --input --output and --det2_config"
	)
	sys.exit(1)
	main(args)