Hi,
I am getting the error while running inference using custom python script.
Below is the Code :
import os
import time
import cv2
#import matplotlib.pyplot as plt
import numpy as np
import pycuda.autoinit
import pycuda.driver as cuda
import tensorrt as trt
from PIL import Image
import pdb
import codecs
import glob
import datetime
import shutil
NPR_LABEL = {'0-15','16-35','36-55','55+'}
input_shape = (3,224,224)
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
# Allocates all buffers required for an engine, i.e. host/device inputs/outputs.
# def allocate_buffers(engine, batch_size=-1):
def allocate_buffers(engine, batch_size=4):
inputs = []
outputs = []
bindings = []
stream = cuda.Stream()
for binding in engine:
# pdb.set_trace()
size = trt.volume(engine.get_binding_shape(binding)) * batch_size
dtype = trt.nptype(engine.get_binding_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.
if engine.binding_is_input(binding):
inputs.append(HostDeviceMem(host_mem, device_mem))
# print(f"input: shape:{engine.get_binding_shape(binding)} dtype:{engine.get_binding_dtype(binding)}")
else:
outputs.append(HostDeviceMem(host_mem, device_mem))
# print(f"output: shape:{engine.get_binding_shape(binding)} dtype:{engine.get_binding_dtype(binding)}")
return inputs, outputs, bindings, stream
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 post_processing(label_ids):
# iterate label using label ids
number = ''
i = 0
for label in label_ids[0]:
if str(label) != '35':
number = number + NPR_LABEL[str(label)]
print("Number : {} Accuracy : {}".format(NPR_LABEL[str(label)],label_ids[1][i]))
i += 1
return number
def model_loading(trt_engine_path):
# TensorRT logger singleton
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
# trt_engine_path = "/opt/smarg/surveillance_gateway_prod/surveillance_ai_model/x86_64/Secondary_NumberPlateClassification/lpr_us_onnx_b16.engine"
trt_runtime = trt.Runtime(TRT_LOGGER)
# pdb.set_trace()
trt_engine = load_engine(trt_runtime, trt_engine_path)
# Execution context is needed for inference
context = trt_engine.create_execution_context()
# NPR input shape
# input_shape = (3,48,96)
context.set_binding_shape(0, input_shape)
# This allocates memory for network inputs/outputs on both CPU and GPU
inputs, outputs, bindings, stream = allocate_buffers(trt_engine)
return inputs, outputs, bindings, stream, context
trt_engine_path = "/home/smarg/Documents/Pritam/AGE-GROUP-MODEL-ANALYSIS/MODELS/Age_Epoch_117_b4_FP16_224_224.engine"
inputs, outputs, bindings, stream, context = model_loading(trt_engine_path)
# pdb.set_trace()
# image = [cv2.imread("/home/smarg/Downloads/Images/resized/img/IMG_20210719_160022_cropped_batch_code_image_imgGB3_BATO007_.jpg")]
# Run inference on folder
image_folder_path = "/home/smarg/Documents/Pritam/AGE-GROUP-MODEL-ANALYSIS/INPUT-IMAGES/gender_image_9Aug/gender_image/"
# output_folder_path = "/home/smarg/Documents/Pritam/TRT-INFER-NPR/OutPutImg/Cropped/"
image_count = 0
start_time = datetime.datetime.now()
for image_path in glob.glob(image_folder_path + "*.jpg"):
print("Image name :",image_path)
image = [cv2.imread(image_path)]
image = np.array([(cv2.resize(img, ( 224 , 224 )))/ 255.0 for img in image], dtype=np.float32)
image= image.transpose( 0 , 3 , 1 , 2 )
np.copyto(inputs[0].host, image.ravel())
output = do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)
# number = post_processing(output)
image_count += 1
# shutil.copy(image_path,output_folder_path+str(image_count)+"_"+number+".jpg")
end_time = datetime.datetime.now()
total_time = end_time - start_time
print("Total image processed : {} Total Time : {} ".format(image_count,total_time))
Error :
Traceback (most recent call last):
File "inference_trt_age_classification.py", line 128, in <module>
np.copyto(inputs[0].host, image.ravel())
File "<__array_function__ internals>", line 6, in copyto
ValueError: could not broadcast input array from shape (150528) into shape (602112)
Can someone please suggest where I am wrong ?
Thanks.