The Error: AttributeError: module 'common' has no attribute 'allocate_buffers'
When does it happen: I've a yolov3.onnx model, I'm trying to use TensorRT in order to run inference on the model using the trt engine. after installing the common module with pip install common (also tried pip3 install common), I receive an error: on this line: inputs, outputs, bindings, stream = common.allocate_buffers(engine)
EDIT: I've just realized that I'll just need to probably use another allocate_buffers from another onnx to trt project. that it found in /usr/src/tensorrt/samples/python/introductory_parser_samples
EDIT2: posted a solution.
Full code:
#!/usr/bin/env python2
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from __future__ import print_function
import numpy as np
import tensorrt as trt
import pycuda.driver as cuda
import pycuda.autoinit
from PIL import ImageDraw
from yolov3_to_onnx import download_file
from data_processing import PreprocessYOLO, PostprocessYOLO, ALL_CATEGORIES
import sys, os
sys.path.insert(1, os.path.join(sys.path[0], ".."))
import common
TRT_LOGGER = trt.Logger()
def draw_bboxes(image_raw, bboxes, confidences, categories, all_categories, bbox_color='blue'):
"""Draw the bounding boxes on the original input image and return it.
Keyword arguments:
image_raw -- a raw PIL Image
bboxes -- NumPy array containing the bounding box coordinates of N objects, with shape (N,4).
categories -- NumPy array containing the corresponding category for each object,
with shape (N,)
confidences -- NumPy array containing the corresponding confidence for each object,
with shape (N,)
all_categories -- a list of all categories in the correct ordered (required for looking up
the category name)
bbox_color -- an optional string specifying the color of the bounding boxes (default: 'blue')
"""
draw = ImageDraw.Draw(image_raw)
print(bboxes, confidences, categories)
for box, score, category in zip(bboxes, confidences, categories):
x_coord, y_coord, width, height = box
left = max(0, np.floor(x_coord + 0.5).astype(int))
top = max(0, np.floor(y_coord + 0.5).astype(int))
right = min(image_raw.width, np.floor(x_coord + width + 0.5).astype(int))
bottom = min(image_raw.height, np.floor(y_coord + height + 0.5).astype(int))
draw.rectangle(((left, top), (right, bottom)), outline=bbox_color)
draw.text((left, top - 12), '{0} {1:.2f}'.format(all_categories[category], score), fill=bbox_color)
return image_raw
def get_engine(onnx_file_path, engine_file_path=""):
"""Attempts to load a serialized engine if available, otherwise builds a new TensorRT engine and saves it."""
def build_engine():
"""Takes an ONNX file and creates a TensorRT engine to run inference with"""
with trt.Builder(TRT_LOGGER) as builder, builder.create_network() as network, trt.OnnxParser(network, TRT_LOGGER) as parser:
builder.max_workspace_size = 1 << 30 # 1GB
builder.max_batch_size = 1
# Parse model file
if not os.path.exists(onnx_file_path):
print('ONNX file {} not found, please run yolov3_to_onnx.py first to generate it.'.format(onnx_file_path))
exit(0)
print('Loading ONNX file from path {}...'.format(onnx_file_path))
with open(onnx_file_path, 'rb') as model:
print('Beginning ONNX file parsing')
parser.parse(model.read())
print('Completed parsing of ONNX file')
print('Building an engine from file {}; this may take a while...'.format(onnx_file_path))
engine = builder.build_cuda_engine(network)
print("Completed creating Engine")
with open(engine_file_path, "wb") as f:
f.write(engine.serialize())
return engine
if os.path.exists(engine_file_path):
# If a serialized engine exists, use it instead of building an engine.
print("Reading engine from file {}".format(engine_file_path))
with open(engine_file_path, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime:
return runtime.deserialize_cuda_engine(f.read())
else:
return build_engine()
def main():
"""Create a TensorRT engine for ONNX-based YOLOv3-608 and run inference."""
# Try to load a previously generated YOLOv3-608 network graph in ONNX format:
onnx_file_path = 'yolov3.onnx'
engine_file_path = "yolov3.trt"
# Download a dog image and save it to the following file path:
input_image_path = download_file('dog.jpg',
'https://github.com/pjreddie/darknet/raw/f86901f6177dfc6116360a13cc06ab680e0c86b0/data/dog.jpg', checksum_reference=None)
# Two-dimensional tuple with the target network's (spatial) input resolution in HW ordered
input_resolution_yolov3_HW = (608, 608)
# Create a pre-processor object by specifying the required input resolution for YOLOv3
preprocessor = PreprocessYOLO(input_resolution_yolov3_HW)
# Load an image from the specified input path, and return it together with a pre-processed version
image_raw, image = preprocessor.process(input_image_path)
# Store the shape of the original input image in WH format, we will need it for later
shape_orig_WH = image_raw.size
# Output shapes expected by the post-processor
output_shapes = [(1, 255, 19, 19), (1, 255, 38, 38), (1, 255, 76, 76)]
# Do inference with TensorRT
trt_outputs = []
with get_engine(onnx_file_path, engine_file_path) as engine, engine.create_execution_context() as context:
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
# Do inference
print('Running inference on image {}...'.format(input_image_path))
# Set host input to the image. The common.do_inference function will copy the input to the GPU before executing.
inputs[0].host = image
trt_outputs = common.do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)
# Before doing post-processing, we need to reshape the outputs as the common.do_inference will give us flat arrays.
trt_outputs = [output.reshape(shape) for output, shape in zip(trt_outputs, output_shapes)]
postprocessor_args = {"yolo_masks": [(6, 7, 8), (3, 4, 5), (0, 1, 2)], # A list of 3 three-dimensional tuples for the YOLO masks
"yolo_anchors": [(10, 13), (16, 30), (33, 23), (30, 61), (62, 45), # A list of 9 two-dimensional tuples for the YOLO anchors
(59, 119), (116, 90), (156, 198), (373, 326)],
"obj_threshold": 0.6, # Threshold for object coverage, float value between 0 and 1
"nms_threshold": 0.5, # Threshold for non-max suppression algorithm, float value between 0 and 1
"yolo_input_resolution": input_resolution_yolov3_HW}
postprocessor = PostprocessYOLO(**postprocessor_args)
# Run the post-processing algorithms on the TensorRT outputs and get the bounding box details of detected objects
boxes, classes, scores = postprocessor.process(trt_outputs, (shape_orig_WH))
# Draw the bounding boxes onto the original input image and save it as a PNG file
obj_detected_img = draw_bboxes(image_raw, boxes, scores, classes, ALL_CATEGORIES)
output_image_path = 'dog_bboxes.png'
obj_detected_img.save(output_image_path, 'PNG')
print('Saved image with bounding boxes of detected objects to {}.'.format(output_image_path))
if __name__ == '__main__':
main()
SOLUTION:
Actually the samples folder came with common.py