Issue while running train_id.sh in Person Re-Identification code

Accelerated Computing Intelligent Video Analytics TAO Toolkit
, ,
1

Hi Team,

I want to test the Person Re-Identification and I am using below code link.

I did the complete setup as mentioned in the README.

I am facing issue while running bash train_id.sh

I am using dGPU (2080TI) machine.

Below is the error.

root@smarg:~/data/Pritam/PersonREID/reid# bash train_id.sh
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
2023-09-19 06:01:56.078355: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2023-09-19 06:01:56.103458: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3000000000 Hz
2023-09-19 06:01:56.104983: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x326c010 executing computations on platform Host. Devices:
2023-09-19 06:01:56.105003: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/control_flow_ops.py:3632: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
n_labels:  702
False
excluding block block1
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
Initializing new variables..
[<tf.Variable 'resnet_v1_50/conv1/weights:0' shape=(7, 7, 3, 64) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block1/unit_1/bottleneck_v1/shortcut/weights:0' shape=(1, 1, 64, 256) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block1/unit_1/bottleneck_v1/conv1/weights:0' shape=(1, 1, 64, 64) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block1/unit_1/bottleneck_v1/conv2/weights:0' shape=(3, 3, 64, 64) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block1/unit_1/bottleneck_v1/conv3/weights:0' shape=(1, 1, 64, 256) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block1/unit_2/bottleneck_v1/conv1/weights:0' shape=(1, 1, 256, 64) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block1/unit_2/bottleneck_v1/conv2/weights:0' shape=(3, 3, 64, 64) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block1/unit_2/bottleneck_v1/conv3/weights:0' shape=(1, 1, 64, 256) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block1/unit_3/bottleneck_v1/conv1/weights:0' shape=(1, 1, 256, 64) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block1/unit_3/bottleneck_v1/conv2/weights:0' shape=(3, 3, 64, 64) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block1/unit_3/bottleneck_v1/conv3/weights:0' shape=(1, 1, 64, 256) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block2/unit_1/bottleneck_v1/shortcut/weights:0' shape=(1, 1, 256, 512) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block2/unit_1/bottleneck_v1/conv1/weights:0' shape=(1, 1, 256, 128) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block2/unit_1/bottleneck_v1/conv2/weights:0' shape=(3, 3, 128, 128) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block2/unit_1/bottleneck_v1/conv3/weights:0' shape=(1, 1, 128, 512) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block2/unit_2/bottleneck_v1/conv1/weights:0' shape=(1, 1, 512, 128) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block2/unit_2/bottleneck_v1/conv2/weights:0' shape=(3, 3, 128, 128) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block2/unit_2/bottleneck_v1/conv3/weights:0' shape=(1, 1, 128, 512) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block2/unit_3/bottleneck_v1/conv1/weights:0' shape=(1, 1, 512, 128) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block2/unit_3/bottleneck_v1/conv2/weights:0' shape=(3, 3, 128, 128) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block2/unit_3/bottleneck_v1/conv3/weights:0' shape=(1, 1, 128, 512) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block2/unit_4/bottleneck_v1/conv1/weights:0' shape=(1, 1, 512, 128) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block2/unit_4/bottleneck_v1/conv2/weights:0' shape=(3, 3, 128, 128) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block2/unit_4/bottleneck_v1/conv3/weights:0' shape=(1, 1, 128, 512) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block3/unit_1/bottleneck_v1/shortcut/weights:0' shape=(1, 1, 512, 1024) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block3/unit_1/bottleneck_v1/conv1/weights:0' shape=(1, 1, 512, 256) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block3/unit_1/bottleneck_v1/conv2/weights:0' shape=(3, 3, 256, 256) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block3/unit_1/bottleneck_v1/conv3/weights:0' shape=(1, 1, 256, 1024) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block3/unit_2/bottleneck_v1/conv1/weights:0' shape=(1, 1, 1024, 256) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block3/unit_2/bottleneck_v1/conv2/weights:0' shape=(3, 3, 256, 256) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block3/unit_2/bottleneck_v1/conv3/weights:0' shape=(1, 1, 256, 1024) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block3/unit_3/bottleneck_v1/conv1/weights:0' shape=(1, 1, 1024, 256) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block3/unit_3/bottleneck_v1/conv2/weights:0' shape=(3, 3, 256, 256) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block3/unit_3/bottleneck_v1/conv3/weights:0' shape=(1, 1, 256, 1024) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block3/unit_4/bottleneck_v1/conv1/weights:0' shape=(1, 1, 1024, 256) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block3/unit_4/bottleneck_v1/conv2/weights:0' shape=(3, 3, 256, 256) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block3/unit_4/bottleneck_v1/conv3/weights:0' shape=(1, 1, 256, 1024) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block3/unit_5/bottleneck_v1/conv1/weights:0' shape=(1, 1, 1024, 256) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block3/unit_5/bottleneck_v1/conv2/weights:0' shape=(3, 3, 256, 256) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block3/unit_5/bottleneck_v1/conv3/weights:0' shape=(1, 1, 256, 1024) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block3/unit_6/bottleneck_v1/conv1/weights:0' shape=(1, 1, 1024, 256) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block3/unit_6/bottleneck_v1/conv2/weights:0' shape=(3, 3, 256, 256) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block3/unit_6/bottleneck_v1/conv3/weights:0' shape=(1, 1, 256, 1024) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block4/unit_1/bottleneck_v1/shortcut/weights:0' shape=(1, 1, 1024, 2048) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block4/unit_1/bottleneck_v1/conv1/weights:0' shape=(1, 1, 1024, 512) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block4/unit_1/bottleneck_v1/conv2/weights:0' shape=(3, 3, 512, 512) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block4/unit_1/bottleneck_v1/conv3/weights:0' shape=(1, 1, 512, 2048) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block4/unit_2/bottleneck_v1/conv1/weights:0' shape=(1, 1, 2048, 512) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block4/unit_2/bottleneck_v1/conv2/weights:0' shape=(3, 3, 512, 512) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block4/unit_2/bottleneck_v1/conv3/weights:0' shape=(1, 1, 512, 2048) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block4/unit_3/bottleneck_v1/conv1/weights:0' shape=(1, 1, 2048, 512) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block4/unit_3/bottleneck_v1/conv2/weights:0' shape=(3, 3, 512, 512) dtype=float32_ref>,
 <tf.Variable 'resnet_v1_50/block4/unit_3/bottleneck_v1/conv3/weights:0' shape=(1, 1, 512, 2048) dtype=float32_ref>]
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to check for files with this prefix.
Starting training....
2023-09-19 06:02:02.065943: W tensorflow/core/framework/allocator.cc:124] Allocation of 67108864 exceeds 10% of system memory.
Traceback (most recent call last):
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1334, in _do_call
    return fn(*args)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1319, in _run_fn
    options, feed_dict, fetch_list, target_list, run_metadata)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1407, in _call_tf_sessionrun
    run_metadata)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Input to reshape is a tensor with 96128 values, but the requested shape requires a multiple of 702
	 [[{{node Reshape}}]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "main_id.py", line 55, in <module>
    tf.app.run()
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/platform/app.py", line 125, in run
    _sys.exit(main(argv))
  File "main_id.py", line 51, in main
    num_epochs=FLAGS.num_epochs)
  File "/root/data/Pritam/PersonREID/reid/model_id.py", line 139, in train
    {self.is_train: True})
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 929, in run
    run_metadata_ptr)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1152, in _run
    feed_dict_tensor, options, run_metadata)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1328, in _do_run
    run_metadata)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1348, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Input to reshape is a tensor with 96128 values, but the requested shape requires a multiple of 702
	 [[node Reshape (defined at /root/data/Pritam/PersonREID/reid/model_id.py:29) ]]

Caused by op 'Reshape', defined at:
  File "main_id.py", line 55, in <module>
    tf.app.run()
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/platform/app.py", line 125, in run
    _sys.exit(main(argv))
  File "main_id.py", line 41, in main
    write_images=FLAGS.write_images)
  File "/root/data/Pritam/PersonREID/reid/model_id.py", line 29, in __init__
    self.labels = tf.reshape(self.labels, [-1, self.n_labels])
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 7179, in reshape
    "Reshape", tensor=tensor, shape=shape, name=name)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py", line 788, in _apply_op_helper
    op_def=op_def)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/deprecation.py", line 507, in new_func
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 3300, in create_op
    op_def=op_def)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 1801, in __init__
    self._traceback = tf_stack.extract_stack()

InvalidArgumentError (see above for traceback): Input to reshape is a tensor with 96128 values, but the requested shape requires a multiple of 702
	 [[node Reshape (defined at /root/data/Pritam/PersonREID/reid/model_id.py:29) ]]

Please help me in resolving this issue.

Thanks.

3

The github is not maintained by Nvidia. For Re-identification in TAO, please refer to Re-Identification - NVIDIA Docs. Thanks.

4

Okay Thanks.

Is there any code or script to test the re-identification model trained on TAO?

5

You can refer to ReIdentificationNet - NVIDIA Docs

Also, for inference in pytorch, you can also refer to https://github.com/NVIDIA/tao_pytorch_backend/blob/main/nvidia_tao_pytorch/cv/re_identification/scripts/inference.py

More info can be found in
Re-Identification | NVIDIA NGC.

6

Thanks @Morganh

7

Hi @Morganh

I am trying to train model Re-Identification model using TAO.

While running below command.

print("Train model")
!tao re_identification train \
                  -e $SPECS_DIR/experiment_market1501.yaml \
                  -r $RESULTS_DIR/market1501 \
                  -k $KEY

I am getting below issue.

Train model
2023-09-19 18:50:16,867 [INFO] root: Registry: ['nvcr.io']
2023-09-19 18:50:16,907 [INFO] tlt.components.instance_handler.local_instance: Running command in container: nvcr.io/nvidia/tao/tao-toolkit:4.0.0-pyt
2023-09-19 18:50:16,924 [WARNING] tlt.components.docker_handler.docker_handler: 
Docker will run the commands as root. If you would like to retain your
local host permissions, please add the "user":"UID:GID" in the
DockerOptions portion of the "/home/smarg/.tao_mounts.json" file. You can obtain your
users UID and GID by using the "id -u" and "id -g" commands on the
terminal.
ANTLR runtime and generated code versions disagree: 4.8!=4.9.3
ANTLR runtime and generated code versions disagree: 4.8!=4.9.3
[NeMo W 2023-09-19 13:20:23 nemo_logging:349] <frozen cv.re_identification.scripts.train>:91: UserWarning: 
    'experiment_market1501.yaml' is validated against ConfigStore schema with the same name.
    This behavior is deprecated in Hydra 1.1 and will be removed in Hydra 1.2.
    See https://hydra.cc/docs/next/upgrades/1.0_to_1.1/automatic_schema_matching for migration instructions.
    
Error merging 'experiment_market1501.yaml' with schema
Key 'results_dir'' not in 'ReIDTrainExpConfig'
    full_key: results_dir'
    object_type=ReIDTrainExpConfig

Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
Execution status: FAIL
2023-09-19 18:50:29,009 [INFO] tlt.components.docker_handler.docker_handler: Stopping container.

Please help.

Thanks.

8

Please refer to https://github.com/NVIDIA/tao_tutorials/blob/main/notebooks/tao_launcher_starter_kit/re_identification_net/specs/experiment_market1501.yaml.
Can you share your yaml file?

9

Hi @Morganh ,

I am using default file comes with getting_started_v5.0.0
Below is the file content.

results_dir': "/results/market1501"
encryption_key: nvidia_tao
model:
  backbone: resnet_50
  last_stride: 1
  pretrain_choice: imagenet
  pretrained_model_path: "/model/market1501/resnet50_pretrained.pth"
  input_channels: 3
  input_width: 128
  input_height: 256
  neck: bnneck
  feat_dim: 256
  neck_feat: after
  metric_loss_type: triplet
  with_center_loss: False
  with_flip_feature: False
  label_smooth: True
dataset:
  train_dataset_dir: "/data/market1501/sample_train"
  test_dataset_dir: "/data/market1501/sample_test"
  query_dataset_dir: "/data/market1501/sample_query"
  num_classes: 100
  batch_size: 64
  val_batch_size: 128
  num_workers: 1
  pixel_mean: [0.485, 0.456, 0.406]
  pixel_std: [0.226, 0.226, 0.226]
  padding: 10
  prob: 0.5
  re_prob: 0.5
  sampler: softmax_triplet
  num_instances: 4
re_ranking:
  re_ranking: True
  k1: 20
  k2: 6
  lambda_value: 0.3
train:
  optim:
    name: Adam
    steps: [40, 70]
    gamma: 0.1
    bias_lr_factor: 1
    weight_decay: 0.0005
    weight_decay_bias: 0.0005
    warmup_factor: 0.01
    warmup_iters: 10
    warmup_method: linear
    base_lr: 0.00035
    momentum: 0.9
    center_loss_weight: 0.0005
    center_lr: 0.5
    triplet_loss_margin: 0.3
  num_epochs: 120
  checkpoint_interval: 10

Thanks.

10

Which TAO version did you run?

11

Below is the TAO details.

Configuration of the TAO Toolkit Instance
dockers: [‘nvidia/tao/tao-toolkit’]
format_version: 2.0
toolkit_version: 4.0.1
published_date: 03/06/2023

12

Please update to TAO5.0. The new yaml file is compatible with TAO 5.0.

13

Or you can download 4.0.1 notebook and use its yaml file instead if you are still going to use TAO 4.0.1.

14

Hi @Morganh

Thanks for the help.

Is there any python script where I can test the generated etlt file of re-identification model?

15

There is not. You can leverage https://github.com/NVIDIA/tao_pytorch_backend/blob/main/nvidia_tao_pytorch/cv/re_identification/scripts/inference.py.

16

Hi @Morganh and Team,

How we can convert resnet50_market1501_model.etlt model file to engine file?

There is no command mention to convert reid model to engine file in tao reid sample notebook.

Please suggest how to convert reid etlt model file to engine.

or can we use etlt file directly for the inference.

below is the code

import imp
import tensorrt as trt
import pycuda.driver as cuda
import numpy as np
from PIL import Image,ImageDraw
import cv2
import numpy

#import pycuda.autoinit 


fire_engine_file_path = './REID_MODELS/resnet50_market1501_model.etlt'
image_path = './images/Img1.jpg'


TRT_LOGGER = trt.Logger(trt.Logger.INTERNAL_ERROR)
trt_runtime = trt.Runtime(TRT_LOGGER)

def allocate_buffers(engine, batch_size, data_type):
   cuda.init()
   device = cuda.Device(0)  # enter your Gpu id here
   ctx = device.make_context()
   h_input_1 = cuda.pagelocked_empty(batch_size * trt.volume(engine.get_binding_shape(0)), dtype=trt.nptype(data_type))
   h_output = cuda.pagelocked_empty(batch_size * trt.volume(engine.get_binding_shape(1)), dtype=trt.nptype(data_type))
   d_input_1 = cuda.mem_alloc(h_input_1.nbytes)
   d_output = cuda.mem_alloc(h_output.nbytes)
   stream = cuda.Stream()
   return h_input_1, d_input_1, h_output, d_output, stream

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 load_images_to_buffer(pics, pagelocked_buffer):
   preprocessed = np.asarray(pics).ravel()
   np.copyto(pagelocked_buffer, preprocessed) 

def do_inference(engine, pics_1, h_input_1, d_input_1, h_output, d_output, stream, batch_size, height, width):

   image = np.asarray(pics_1.resize((height, width), Image.ANTIALIAS)).transpose([2, 0, 1]).astype(trt.nptype(trt.float32)).ravel()
   np.copyto(h_input_1, image.ravel())

   with engine.create_execution_context() as context:
       context.debug_sync = False
       # Transfer input data to the GPU.
       cuda.memcpy_htod_async(d_input_1, h_input_1, stream)
       context.execute(batch_size=1, bindings=[int(d_input_1), int(d_output)])
       #print('Transfer predictions back from the GPU.')
       # Transfer predictions back from the GPU.
       cuda.memcpy_dtoh_async(h_output, d_output, stream)
       # Synchronize the stream
       stream.synchronize()

       out = h_output.reshape((1,-1))
       return out

engine = load_engine(trt_runtime, fire_engine_file_path)
h_input, d_input, h_output, d_output, stream = allocate_buffers(engine, 1, trt.float32)


opencv_image = cv2.imread(image_path)
embedding_vec = do_inference(engine, opencv_image, h_input, d_input, h_output, d_output, stream, 1, 224, 224)

print(embedding_vec)

Thanks

17

You can export to onnx model and then user trtexec to generate tensorrt engine.
Refer to TRTEXEC with ReIdentificationNet - NVIDIA Docs.