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

Hi @rferrandis and @Morganh

I tried with the python3.7 env and TAO==5.0.0 but getting below issue.

!tao model re_identification export \
                   -e $SPECS_DIR/experiment_market1501.yaml \
                   -r $RESULTS_DIR/market1501 \
                   -k $KEY \
                   export.checkpoint=$RESULTS_DIR/market1501/resnet50_market1501_model.tlt \
                   export.onnx_file=$RESULTS_DIR/market1501/export/resnet50_market1501_model.onnx




2023-10-05 18:29:30,616 [TAO Toolkit] [INFO] root 160: Registry: ['nvcr.io']
2023-10-05 18:29:30,660 [TAO Toolkit] [INFO] nvidia_tao_cli.components.instance_handler.local_instance 361: Running command in container: nvcr.io/nvidia/tao/tao-toolkit:5.0.0-pyt
2023-10-05 18:29:30,679 [TAO Toolkit] [WARNING] nvidia_tao_cli.components.docker_handler.docker_handler 267: 
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.
2023-10-05 18:29:30,679 [TAO Toolkit] [INFO] nvidia_tao_cli.components.docker_handler.docker_handler 275: Printing tty value True
sys:1: 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.
<frozen core.hydra.hydra_runner>:107: 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.
/usr/local/lib/python3.8/dist-packages/hydra/_internal/hydra.py:119: UserWarning: Future Hydra versions will no longer change working directory at job runtime by default.
See https://hydra.cc/docs/next/upgrades/1.1_to_1.2/changes_to_job_working_dir/ for more information.
  ret = run_job(
Export results will be saved at: /results/market1501/export
<frozen core.loggers.api_logging>:245: UserWarning: Log file already exists at /results/market1501/export/status.json
Starting Re-identification export
module 'nvidia_tao_pytorch.cv.re_identification.config.default_config' has no attribute 'ReIDTrainConfig'
Error executing job with overrides: ['encryption_key=nvidia_tao', 'export.checkpoint=/results/market1501/resnet50_market1501_model.tlt', 'export.onnx_file=/results/market1501/export/resnet50_market1501_model.onnx']
An error occurred during Hydra's exception formatting:
AssertionError()
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 254, in run_and_report
    assert mdl is not None
AssertionError

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "</usr/local/lib/python3.8/dist-packages/nvidia_tao_pytorch/cv/re_identification/scripts/export.py>", line 3, in <module>
  File "<frozen cv.re_identification.scripts.export>", line 150, in <module>
  File "<frozen core.hydra.hydra_runner>", line 107, in wrapper
  File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 389, in _run_hydra
    _run_app(
  File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 452, in _run_app
    run_and_report(
  File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 296, in run_and_report
    raise ex
  File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 213, in run_and_report
    return func()
  File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 453, in <lambda>
    lambda: hydra.run(
  File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/hydra.py", line 132, in run
    _ = ret.return_value
  File "/usr/local/lib/python3.8/dist-packages/hydra/core/utils.py", line 260, in return_value
    raise self._return_value
  File "/usr/local/lib/python3.8/dist-packages/hydra/core/utils.py", line 186, in run_job
    ret.return_value = task_function(task_cfg)
  File "<frozen cv.re_identification.scripts.export>", line 71, in main
  File "<frozen cv.re_identification.scripts.export>", line 60, in main
  File "<frozen cv.re_identification.scripts.export>", line 123, in run_export
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/core/saving.py", line 137, in load_from_checkpoint
    return _load_from_checkpoint(
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/core/saving.py", line 158, in _load_from_checkpoint
    checkpoint = pl_load(checkpoint_path, map_location=map_location)
  File "/usr/local/lib/python3.8/dist-packages/lightning_lite/utilities/cloud_io.py", line 48, in _load
    return torch.load(f, map_location=map_location)
  File "/usr/local/lib/python3.8/dist-packages/torch/serialization.py", line 804, in load
    return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
  File "/usr/local/lib/python3.8/dist-packages/torch/serialization.py", line 1151, in _load
    result = unpickler.load()
  File "/usr/lib/python3.8/pickle.py", line 1212, in load
    dispatch[key[0]](self)
  File "/usr/lib/python3.8/pickle.py", line 1528, in load_global
    klass = self.find_class(module, name)
  File "/usr/local/lib/python3.8/dist-packages/torch/serialization.py", line 1144, in find_class
    return super().find_class(mod_name, name)
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/_graveyard/legacy_import_unpickler.py", line 24, in find_class
    return super().find_class(new_module, name)
  File "/usr/lib/python3.8/pickle.py", line 1583, in find_class
    return getattr(sys.modules[module], name)
AttributeError: module 'nvidia_tao_pytorch.cv.re_identification.config.default_config' has no attribute 'ReIDTrainConfig'
Execution status: FAIL
2023-10-05 18:29:44,714 [TAO Toolkit] [INFO] nvidia_tao_cli.components.docker_handler.docker_handler 337: Stopping container.


Thanks.

For TAO 5.0, please use TAO 5.0 spec file.
Please refer to the example spec file: https://github.com/NVIDIA/tao_tutorials/blob/main/notebooks/tao_launcher_starter_kit/re_identification_net/specs/experiment_market1501.yaml

Hi @Morganh

I have used the same file but still same issue.

!tao model re_identification export \
                   -e $SPECS_DIR/experiment_market1501.yaml \
                   -r $RESULTS_DIR/market1501 \
                   -k $KEY \
                   export.checkpoint=$RESULTS_DIR/market1501/resnet50_market1501_model.tlt \
                   export.onnx_file=$RESULTS_DIR/market1501/export/resnet50_market1501_model.onnx


2023-10-06 10:55:03,216 [TAO Toolkit] [INFO] root 160: Registry: ['nvcr.io']
2023-10-06 10:55:03,448 [TAO Toolkit] [INFO] nvidia_tao_cli.components.instance_handler.local_instance 361: Running command in container: nvcr.io/nvidia/tao/tao-toolkit:5.0.0-pyt
2023-10-06 10:55:03,597 [TAO Toolkit] [WARNING] nvidia_tao_cli.components.docker_handler.docker_handler 267: 
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.
2023-10-06 10:55:03,597 [TAO Toolkit] [INFO] nvidia_tao_cli.components.docker_handler.docker_handler 275: Printing tty value True
sys:1: 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.
<frozen core.hydra.hydra_runner>:107: 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.
/usr/local/lib/python3.8/dist-packages/hydra/_internal/hydra.py:119: UserWarning: Future Hydra versions will no longer change working directory at job runtime by default.
See https://hydra.cc/docs/next/upgrades/1.1_to_1.2/changes_to_job_working_dir/ for more information.
  ret = run_job(
Export results will be saved at: /results/market1501/export
<frozen core.loggers.api_logging>:245: UserWarning: Log file already exists at /results/market1501/export/status.json
Starting Re-identification export
module 'nvidia_tao_pytorch.cv.re_identification.config.default_config' has no attribute 'ReIDTrainConfig'
Error executing job with overrides: ['encryption_key=nvidia_tao', 'export.checkpoint=/results/market1501/resnet50_market1501_model.tlt', 'export.onnx_file=/results/market1501/export/resnet50_market1501_model.onnx']
An error occurred during Hydra's exception formatting:
AssertionError()
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 254, in run_and_report
    assert mdl is not None
AssertionError

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "</usr/local/lib/python3.8/dist-packages/nvidia_tao_pytorch/cv/re_identification/scripts/export.py>", line 3, in <module>
  File "<frozen cv.re_identification.scripts.export>", line 150, in <module>
  File "<frozen core.hydra.hydra_runner>", line 107, in wrapper
  File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 389, in _run_hydra
    _run_app(
  File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 452, in _run_app
    run_and_report(
  File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 296, in run_and_report
    raise ex
  File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 213, in run_and_report
    return func()
  File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 453, in <lambda>
    lambda: hydra.run(
  File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/hydra.py", line 132, in run
    _ = ret.return_value
  File "/usr/local/lib/python3.8/dist-packages/hydra/core/utils.py", line 260, in return_value
    raise self._return_value
  File "/usr/local/lib/python3.8/dist-packages/hydra/core/utils.py", line 186, in run_job
    ret.return_value = task_function(task_cfg)
  File "<frozen cv.re_identification.scripts.export>", line 71, in main
  File "<frozen cv.re_identification.scripts.export>", line 60, in main
  File "<frozen cv.re_identification.scripts.export>", line 123, in run_export
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/core/saving.py", line 137, in load_from_checkpoint
    return _load_from_checkpoint(
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/core/saving.py", line 158, in _load_from_checkpoint
    checkpoint = pl_load(checkpoint_path, map_location=map_location)
  File "/usr/local/lib/python3.8/dist-packages/lightning_lite/utilities/cloud_io.py", line 48, in _load
    return torch.load(f, map_location=map_location)
  File "/usr/local/lib/python3.8/dist-packages/torch/serialization.py", line 804, in load
    return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
  File "/usr/local/lib/python3.8/dist-packages/torch/serialization.py", line 1151, in _load
    result = unpickler.load()
  File "/usr/lib/python3.8/pickle.py", line 1212, in load
    dispatch[key[0]](self)
  File "/usr/lib/python3.8/pickle.py", line 1528, in load_global
    klass = self.find_class(module, name)
  File "/usr/local/lib/python3.8/dist-packages/torch/serialization.py", line 1144, in find_class
    return super().find_class(mod_name, name)
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/_graveyard/legacy_import_unpickler.py", line 24, in find_class
    return super().find_class(new_module, name)
  File "/usr/lib/python3.8/pickle.py", line 1583, in find_class
    return getattr(sys.modules[module], name)
AttributeError: module 'nvidia_tao_pytorch.cv.re_identification.config.default_config' has no attribute 'ReIDTrainConfig'
Execution status: FAIL
2023-10-06 10:55:21,558 [TAO Toolkit] [INFO] nvidia_tao_cli.components.docker_handler.docker_handler 337: Stopping container.

Thanks.

Please share the yaml file.

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

experiment_market1501.yml.txt (1.2 KB)

Hi @Morganh

Issue is resolved.

Issue was because I was using tlt file trained on TAO==4.0.1 inside TAO==5.0.
I have trained model on TAO==5.0 again, use new tlt file and generate onnx file and this time the file is generated successfully.

Thanks.

1 Like

Thanks for the info.

Hi @Morganh,

I have modified one script to get the embedding vector of person from reid model but I am getting wrong output. The vector has only 0 value.

Please find the script and output below.

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 glob

#import pycuda.autoinit 


fire_engine_file_path = './REID_MODELS/resnet50_market1501_model.plan'
# image_path = './images/image.jpg'
image_folder_path = "./images/Kuldeep/Security1/"


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()
   
   # pics_1 = cv2.cvtColor(pics_1, cv2.COLOR_BGR2RGB)

   width = int(width)
   height = int(height)

   pics_1 = cv2.resize(pics_1,(height,width))

   image = np.asarray(pics_1)

   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)



for image_path in glob.glob(image_folder_path+"*.jpg"):
    print("image_path : ",image_path)
    opencv_image = cv2.imread(image_path)
    embedding_vec = do_inference(engine, opencv_image, h_input, d_input, h_output, d_output, stream, 1, 256, 128)

    print(embedding_vec)

Output

root@smarg:~/data/Pritam/Script# python3 PersonReID_InferScript.py 
PersonReID_InferScript.py:1: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses
  import imp
image_path :  ./images/Kuldeep/Security1/Male_IN_36-55_999336_Basement E- Lobby Entry__289.9431686401367.jpg
256 128
PersonReID_InferScript.py:66: DeprecationWarning: Use execute_v2 instead.
  context.execute(batch_size=1, bindings=[int(d_input_1), int(d_output)])
[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]
image_path :  ./images/Kuldeep/Security1/Male_IN_36-55_865159_P2 Lobby-2 Customer Entry__268.4658966064453.jpg
256 128
[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]
image_path :  ./images/Kuldeep/Security1/Male_IN_36-55_1347350_L2 Customer Entry__196.3636245727539.jpg
256 128
[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]
image_path :  ./images/Kuldeep/Security1/Male_IN_36-55_1006623_Shoppers Stop Entry__279.2045478820801.jpg
256 128
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image_path :  ./images/Kuldeep/Security1/Male_IN_36-55_581276_P2 Lobby-2 Customer Entry__275.3693084716797.jpg
256 128
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image_path :  ./images/Kuldeep/Security1/Male_IN_36-55_617083_Shoppers Stop Entry__273.06817626953125.jpg
256 128
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image_path :  ./images/Kuldeep/Security1/Male_IN_36-55_1614886_L2 Customer Entry__205.56818008422852.jpg
256 128
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Please help where I am making mistake. Why the vector has only 0 value.

Thanks.

Please create a new topic since original issue is fixed. Thanks.

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