import os
import time
import cv2
import matplotlib.pyplot as plt
import numpy as np
import pycuda.driver as cuda
import tensorrt as trt
from PIL import Image
class HostDeviceMem(object):
def __init__(self, host_mem, device_mem):
self.host = host_mem
self.device = device_mem
def __str__(self):
return "Host:\n" + str(self.host) + "\nDevice:\n" + str(self.device)
def __repr__(self):
return self.__str__()
def load_engine(trt_runtime, engine_path):
with open(engine_path, "rb") as f:
engine_data = f.read()
engine = trt_runtime.deserialize_cuda_engine(engine_data)
return engine
def allocate_buffers(engine, batch_size=1):
"""Allocates host and device buffer for TRT engine inference.
This function is similair to the one in common.py, but
converts network outputs (which are np.float32) appropriately
before writing them to Python buffer. This is needed, since
TensorRT plugins doesn't support output type description, and
in our particular case, we use NMS plugin as network output.
Args:
engine (trt.ICudaEngine): TensorRT engine
Returns:
inputs [HostDeviceMem]: engine input memory
outputs [HostDeviceMem]: engine output memory
bindings [int]: buffer to device bindings
stream (cuda.Stream): cuda stream for engine inference synchronization
"""
inputs = []
outputs = []
bindings = []
stream = cuda.Stream()
# Current NMS implementation in TRT only supports DataType.FLOAT but
# it may change in the future, which could brake this sample here
# when using lower precision [e.g. NMS output would not be np.float32
# anymore, even though this is assumed in binding_to_type]
for i in range(engine.num_bindings):
binding = engine.get_tensor_name(i)
size = trt.volume(engine.get_tensor_shape(binding))
dtype = trt.nptype(engine.get_tensor_dtype(binding))
# Allocate host and device buffers
host_mem = cuda.pagelocked_empty(size, dtype)
device_mem = cuda.mem_alloc(host_mem.nbytes)
# Append the device buffer to device bindings.
bindings.append(int(device_mem))
# Append to the appropriate list.
mode = engine.get_tensor_mode(binding)
if str(mode) == 'TensorIOMode.INPUT':
inputs.append(HostDeviceMem(host_mem, device_mem))
else:
outputs.append(HostDeviceMem(host_mem, device_mem))
return inputs, outputs, bindings, stream
# This function is generalized for multiple inputs/outputs.
# inputs and outputs are expected to be lists of HostDeviceMem objects.
def do_inference(context, bindings, inputs, outputs, stream, batch_size=1):
# Transfer input data to the GPU.
[cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs]
# Run inference.
context.execute_async(
batch_size=batch_size, bindings=bindings, stream_handle=stream.handle
)
# Transfer predictions back from the GPU.
[cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs]
# Synchronize the stream
stream.synchronize()
# Return only the host outputs.
return [out.host for out in outputs]
def process_image(arr, w, h):
image = Image.fromarray(np.uint8(arr))
image_resized = image.resize(size=(w, h), resample=Image.BILINEAR)
img_np = np.array(image_resized)
# HWC -> CHW
img_np = img_np.transpose((2, 0, 1))
# Normalize to [0.0, 1.0] interval (expected by model)
img_np = (1.0 / 255.0) * img_np
print("Process image: ", img_np.shape)
img_np = img_np.ravel()
return img_np
def predict(image, model_w, model_h):
"""Infers model on batch of same sized images resized to fit the model.
Args:
image_paths (str): paths to images, that will be packed into batch
and fed into model
"""
img = process_image(image, model_w, model_h)
print("Predict image shape:",img.shape)
# Copy it into appropriate place into memory
# (self.inputs was returned earlier by allocate_buffers())
np.copyto(inputs[0].host, img.ravel())
# When infering on single image, we measure inference
# time to output it to the user
inference_start_time = time.time()
# Fetch output from the model
[detection_out, keepCount_out] = do_inference(
context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream
)
# Output inference time
print(
"TensorRT inference time: {} ms".format(
int(round((time.time() - inference_start_time) * 1000))
)
)
# And return results
return detection_out, keepCount_out
# -------------- MODEL PARAMETERS FOR DETECTNET_V2 --------------------------------
model_h = 544
model_w = 960
stride = 16
box_norm = 35.0
grid_h = int(model_h / stride)
grid_w = int(model_w / stride)
grid_size = grid_h * grid_w
grid_centers_w = []
grid_centers_h = []
for i in range(grid_h):
value = (i * stride + 0.5) / box_norm
grid_centers_h.append(value)
for i in range(grid_w):
value = (i * stride + 0.5) / box_norm
grid_centers_w.append(value)
def applyBoxNorm(o1, o2, o3, o4, x, y):
"""
Applies the GridNet box normalization
Args:
o1 (float): first argument of the result
o2 (float): second argument of the result
o3 (float): third argument of the result
o4 (float): fourth argument of the result
x: row index on the grid
y: column index on the grid
Returns:
float: rescaled first argument
float: rescaled second argument
float: rescaled third argument
float: rescaled fourth argument
"""
o1 = (o1 - grid_centers_w[x]) * -box_norm
o2 = (o2 - grid_centers_h[y]) * -box_norm
o3 = (o3 + grid_centers_w[x]) * box_norm
o4 = (o4 + grid_centers_h[y]) * box_norm
return o1, o2, o3, o4
def postprocess(outputs, min_confidence, analysis_classes, wh_format=True):
"""
Postprocesses the inference output
Args:
outputs (list of float): inference output
min_confidence (float): min confidence to accept detection
analysis_classes (list of int): indices of the classes to consider
Returns: list of list tuple: each element is a two list tuple (x, y) representing the corners of a bb
"""
bbs = []
class_ids = []
scores = []
for c in analysis_classes:
x1_idx = c * 4 * grid_size
y1_idx = x1_idx + grid_size
x2_idx = y1_idx + grid_size
y2_idx = x2_idx + grid_size
boxes = outputs[0]
for h in range(grid_h):
for w in range(grid_w):
i = w + h * grid_w
score = outputs[1][c * grid_size + i]
if score >= min_confidence:
o1 = boxes[x1_idx + w + h * grid_w]
o2 = boxes[y1_idx + w + h * grid_w]
o3 = boxes[x2_idx + w + h * grid_w]
o4 = boxes[y2_idx + w + h * grid_w]
o1, o2, o3, o4 = applyBoxNorm(o1, o2, o3, o4, w, h)
xmin = int(o1)
ymin = int(o2)
xmax = int(o3)
ymax = int(o4)
if wh_format:
bbs.append([xmin, ymin, xmax - xmin, ymax - ymin])
else:
bbs.append([xmin, ymin, xmax, ymax])
class_ids.append(c)
scores.append(float(score))
return bbs, class_ids, scores
# TensorRT logger singleton
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
trt_engine_path = os.path.join("./resnet_engine.trt")
trt_runtime = trt.Runtime(TRT_LOGGER)
trt_engine = load_engine(trt_runtime, trt_engine_path)
# This allocates memory for network inputs/outputs on both CPU and GPU
inputs, outputs, bindings, stream = allocate_buffers(trt_engine)
# # Execution context is needed for inference
context = trt_engine.create_execution_context()
image = cv2.imread("sample_1.jpg")
image_cpy = cv2.resize(image.copy(), (model_w, model_h))
detection_out, keepCount_out = predict(image, model_w, model_h)
NUM_CLASSES = 2
threshold = 0.1
bboxes, class_ids, scores = postprocess(
[detection_out, keepCount_out], threshold, list(range(NUM_CLASSES))
)
image_cpy = image.copy()
image_cpy = cv2.resize(image_cpy, (model_w, model_h))
# Final bboxes only take afet NMS
indexes = cv2.dnn.NMSBoxes(bboxes, scores, threshold, 0.5)
for idx in indexes:
idx = int(idx)
xmin, ymin, w, h = bboxes[idx]
class_id = class_ids[idx]
color = [255, 0, 0] if class_id else [0, 0, 255]
cv2.rectangle(image_cpy, (xmin, ymin), (xmin + w, ymin + h), color, 2)
plt.imshow(image_cpy)
plt.show()
This the entire script. The only change that I’ve made is to add the path to the image and the trt engine