TensorRT Engine Model is not working correctly

Description

I am trying to convert and load my custom Yolov5 pytorch model from the memory. Also, I am encrypting/ decrypting the model for some security reasons.
Is there anything wrong? I am out of the idea at this point.
Here is the steps:
1-Load the pt model
2-Convert it to onnx.
3-Convert to the engine.
4- Write serialized engine to an bytesIO object.
5- Encrypt the bytesIO object with using a secret-key and Fernet module.
6- Write encrypted model to the disk.
7- Read encrypted model, decrypt it with using the same secret-key
8- Load the decrypted bytes with using trt
9- inference

When I try encrypt/decrypt and inference with .pt model, there is no problems.
And Also I
When I print the predictions after loading the engine, there is boxes, but after applying the NMS, there is nothing left.

Here is my test.py:

import sys
sys.path.append("repos/yolov5/")

from importlib import import_module
import torch
from torch import nn
from model_files.object_detection.repos.yolov5.utils.general import check_version
class Detect(nn.Module):
    # YOLOv5 Detect head for detection models
    stride = None  # strides computed during build
    dynamic = False  # force grid reconstruction
    export = False  # export mode

    def __init__(self, nc=80, anchors=(), ch=(), inplace=True):  # detection layer
        super().__init__()
        self.nc = nc  # number of classes
        self.no = nc + 5  # number of outputs per anchor
        self.nl = len(anchors)  # number of detection layers
        self.na = len(anchors[0]) // 2  # number of anchors
        self.grid = [torch.empty(0) for _ in range(self.nl)]  # init grid
        self.anchor_grid = [torch.empty(0) for _ in range(self.nl)]  # init anchor grid
        self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2))  # shape(nl,na,2)
        self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv
        self.inplace = inplace  # use inplace ops (e.g. slice assignment)

    def forward(self, x):
        z = []  # inference output
        for i in range(self.nl):
            x[i] = self.m[i](x[i])  # conv
            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
            x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()

            if not self.training:  # inference
                if self.dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
                    self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)

                if isinstance(self, Segment):  # (boxes + masks)
                    xy, wh, conf, mask = x[i].split((2, 2, self.nc + 1, self.no - self.nc - 5), 4)
                    xy = (xy.sigmoid() * 2 + self.grid[i]) * self.stride[i]  # xy
                    wh = (wh.sigmoid() * 2) ** 2 * self.anchor_grid[i]  # wh
                    y = torch.cat((xy, wh, conf.sigmoid(), mask), 4)
                else:  # Detect (boxes only)
                    xy, wh, conf = x[i].sigmoid().split((2, 2, self.nc + 1), 4)
                    xy = (xy * 2 + self.grid[i]) * self.stride[i]  # xy
                    wh = (wh * 2) ** 2 * self.anchor_grid[i]  # wh
                    y = torch.cat((xy, wh, conf), 4)
                z.append(y.view(bs, self.na * nx * ny, self.no))

        return x if self.training else (torch.cat(z, 1), ) if self.export else (torch.cat(z, 1), x)

    def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, '1.10.0')):
        d = self.anchors[i].device
        t = self.anchors[i].dtype
        shape = 1, self.na, ny, nx, 2  # grid shape
        y, x = torch.arange(ny, device=d, dtype=t), torch.arange(nx, device=d, dtype=t)
        yv, xv = torch.meshgrid(y, x, indexing='ij') if torch_1_10 else torch.meshgrid(y, x)  # torch>=0.7 compatibility
        grid = torch.stack((xv, yv), 2).expand(shape) - 0.5  # add grid offset, i.e. y = 2.0 * x - 0.5
        anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape)
        return grid, anchor_grid

def import_via_name(name):
    try:
        module_path, class_name = name.rsplit('.', 1)
        module = import_module(module_path)
        return getattr(module, class_name)
    except (ImportError, AttributeError) as e:
        raise ImportError(name)


def yolov5_inference_test():
    import cv2
    import time

    class DataLoader:
        length = 1
    #
    class_str: str = "model_files.object_detection.models.YoloV5"

    object_detector = import_via_name(class_str)(
        weights="../../model_weights/human.pt",
        half=True,
        device="cuda:0",
        data="model_files/object_detection/repos/yolov5/data/coco128.yaml",
        imgsz=(640, 640),
        dataloader=DataLoader(),
        conf_thres=0.25,
        repo_address="https://github.com/ultralytics/yolov5.git",
        repo_branch="master"
    )

    import torch
    
    # I am using following while exporting
    '''object_detector.model.eval()
    for k, m in object_detector.model.named_modules():
        if isinstance(m, Detect):
            m.inplace = inplace
            m.dynamic = dynamic
            m.export = True
    object_detector.export_engine(object_detector.model.half(), torch.zeros(1, 3, *(640, 640)).to(object_detector.device).half(),
                                  "../../model_weights/human.pt",
                                  True, False, False, workspace=4, verbose=False,
                          prefix='TensorRT:')'''
    im = cv2.imread("im.jpg")
    while 1:
        t = time.time()
        # will be used at Dataloader following
        for_batching = object_detector.preprocess(im)
        batch = object_detector.batching_process([for_batching])

        # use at main proc
        res = object_detector.detect(batch)

        # it is not completely true way for calculating the inference time on gpu due to sync. problems between gpu&cpu
        print(res, time.time() - t)

yolov5_inference_test()

my inferencing function:

    def detect(self, img_batch: np.ndarray) -> Union[np.ndarray, List]:
          print(self.model.fp16, "IS FP16")
          with torch.no_grad():
              with self.dt[0]:
                  model_ts = torch.from_numpy(img_batch.copy()).to(self.model.device)
                  model_ts = model_ts.half() if self.model.fp16 else model_ts.float()  # uint8 to fp16/32
                  model_ts /= 255  # 0 - 255 to 0.0 - 1.0
                  if len(model_ts.shape) == 3:
                      model_ts = model_ts[None]  # expand for batch dim
  
              # Inference
              with self.dt[1]:
                  pred = self.model(model_ts, augment=False, visualize=False)
              # NMS
              with self.dt[2]:
                  pred = self.non_max_suppression(pred, self.conf_thres,
                                                  self.iou_thres, self.classes,
                                                  self.agnostic_nms, max_det=self.max_det)
  
              return pred

    def preprocess(self, img: Any) -> Any:
        return self.letterbox(img, new_shape=self.imgsz, stride=self.stride, auto=False)[0][:]

    @staticmethod
    def batching_process(images: Any) -> Any:
        im = np.stack([x for x in images])  # resize
        im = im[..., ::-1].transpose((0, 3, 1, 2))  # BGR to RGB, BHWC to BCHW
        im = np.ascontiguousarray(im)  # contiguous

        return im

my exporting functions:

def export_onnx(self, model, im, file, opset, dynamic, simplify, prefix='ONNX:'):
    # YOLOv5 ONNX export
    self.check_requirements('onnx>=1.12.0')
    import onnx
    import io

    io_file = io.BytesIO()

    print(f'\n{prefix} starting export with onnx {onnx.__version__}...', model)
    file = file.split(".")[0] + '.nz.onnx'

    print(file)
    output_names = ['output0', 'output1'] if isinstance(model, self.SegmentationModel) else ['output0']
    if dynamic:
        dynamic = {'images': {0: 'batch', 2: 'height', 3: 'width'}}  # shape(1,3,640,640)
        if isinstance(model, self.SegmentationModel):
            dynamic['output0'] = {0: 'batch', 1: 'anchors'}  # shape(1,25200,85)
            dynamic['output1'] = {0: 'batch', 2: 'mask_height', 3: 'mask_width'}  # shape(1,32,160,160)
        elif isinstance(model, self.DetectionModel):
            dynamic['output0'] = {0: 'batch', 1: 'anchors'}  # shape(1,25200,85)

    torch.onnx.export(
        model.cpu() if dynamic else model,  # --dynamic only compatible with cpu
        im.cpu() if dynamic else im,
        io_file,
        verbose=True,
        opset_version=12,
        do_constant_folding=True,  # WARNING: DNN inference with torch>=1.12 may require do_constant_folding=False
        input_names=['images'],
        output_names=output_names,
        dynamic_axes=dynamic or None)

    # Checks
    model_onnx = onnx.load_model_from_string(io_file.getvalue())  # load onnx model
    print(onnx.checker.check_model(model_onnx), "MODEL ONNX")  # check onnx model

    # Metadata
    d = {'stride': int(model.stride), 'names': model.names}
    for k, v in d.items():
        meta = model_onnx.metadata_props.add()
        meta.key, meta.value = k, str(v)
    io_file2 = io.BytesIO()
    onnx.save(model_onnx, io_file2)

    return file, model_onnx, io_file2


def export_engine(self, model, im, file, half, dynamic, simplify, workspace=4, verbose=False,
                  prefix='TensorRT:'):
    # YOLOv5 TensorRT export https://developer.nvidia.com/tensorrt
    assert im.device.type != 'cpu', 'export running on CPU but must be on GPU, i.e. `python export.py --device 0`'
    import tensorrt as trt
    import io
    import onnx
    if trt.__version__[0] == '7':  # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012
        grid = model.model[-1].anchor_grid
        model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid]
        _, _l, io_file2 = self.export_onnx(model, im, file, 12, dynamic, simplify)  # opset 12
        model.model[-1].anchor_grid = grid
    else:  # TensorRT >= 8
        _, _l, io_file2 = self.export_onnx(model, im, file, 12, dynamic, simplify)  # opset 12
    # onnx = file.split(".")[0]+'.nz.onnx'

    print(f'\n{prefix} starting export with TensorRT {trt.__version__}...')

    f = file.split(".")[0] + 'a.nz.engine'  # TensorRT engine file

    class MyLogger(trt.ILogger):
        def __init__(self):
            trt.ILogger.__init__(self)

    logger = logger = trt.Logger(trt.Logger.INFO)
    # if verbose:
    # logger.min_severity = trt.Logger.Severity.VERBOSE

    builder = trt.Builder(logger)
    config = builder.create_builder_config()
    config.max_workspace_size = workspace * 1 << 30
    # config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30)  # fix TRT 8.4 deprecation notice

    flag = (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
    network = builder.create_network(flag)
    parser = trt.OnnxParser(network, logger)
    print(f, type(_l), network, network.num_layers)
    io_file2.seek(0)
    print(type(io_file2.getbuffer()), 9999)
    print(parser.parse(io_file2.getvalue()), "PARSER")

    inputs = [network.get_input(i) for i in range(network.num_inputs)]
    outputs = [network.get_output(i) for i in range(network.num_outputs)]
    print(f, 310000, inputs, outputs)
    print("num layers:", network.num_layers)

    for inp in inputs:
        print(f'{prefix} input "{inp.name}" with shape{inp.shape} {inp.dtype}')
    for out in outputs:
        print(f'{prefix} output "{out.name}" with shape{out.shape} {out.dtype}')

    if dynamic:
        if im.shape[0] <= 1:
            print(f'{prefix} WARNING ⚠️ --dynamic model requires maximum --batch-size argument')
        profile = builder.create_optimization_profile()
        for inp in inputs:
            profile.set_shape(inp.name, (1, *im.shape[1:]), (max(1, im.shape[0] // 2), *im.shape[1:]), im.shape)
        config.add_optimization_profile(profile)

    print(f'{prefix} building FP{16 if builder.platform_has_fast_fp16 and half else 32} engine as {f}')
    if builder.platform_has_fast_fp16 and half:
        config.set_flag(trt.BuilderFlag.FP16)
        print("FP16 TRUE")
    engine = builder.build_engine(network, config)
    print("!!!!!", engine, f)
    t = io.BytesIO()
    t.write(bytearray(engine.serialize()))
    # t.seek(0)
    crypt.save_encrypt("secret-key", t.getvalue(), f)
    return f, None

crypt functions:

from cryptography.fernet import Fernet
import io


def save_encrypt(key, io_file, saving_path):
    fernet = Fernet(key)

    encrypted = fernet.encrypt(io_file)
    with open(saving_path, 'wb') as encrypted_file:
        encrypted_file.write(encrypted)


def decrypt(key, file_name):
    fernet = Fernet(key)
    with open(file_name, 'rb') as enc_file:
        encrypted = enc_file.read()

    # decrypting the file
    decrypted = fernet.decrypt(encrypted)
    decrypted = io.BytesIO(decrypted)

    return decrypted

Loading the encrypted engine with using following code, i modified the code for decryption proccess, from the original yolov5 repo (https://github.com/ultralytics/yolov5/blob/master/models/common.py) :

elif engine:  # TensorRT
            LOGGER.info(f'Loading {w} for TensorRT inference...')
            import tensorrt as trt  # https://developer.nvidia.com/nvidia-tensorrt-download
            check_version(trt.__version__, '7.0.0', hard=True)  # require tensorrt>=7.0.0
            if device.type == 'cpu':
                device = torch.device('cuda:0')
            Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr'))
            logger = trt.Logger(trt.Logger.INFO)
            with decrypt("secret-key", w) as f, trt.Runtime(logger) as runtime:
                #f.seek(0)
                model = runtime.deserialize_cuda_engine(f.getbuffer())
            context = model.create_execution_context()
            bindings = OrderedDict()
            output_names = []
            fp16 = False  # default updated below
            dynamic = False
            for i in range(model.num_bindings):
                name = model.get_binding_name(i)
                dtype = trt.nptype(model.get_binding_dtype(i))
                print(dtype, type(dtype), "TYPE")
                if model.binding_is_input(i):
                    if -1 in tuple(model.get_binding_shape(i)):  # dynamic
                        dynamic = True
                        context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[2]))
                    if dtype == np.float16:
                        fp16 = True
                    print(fp16, "COMMON FP16")
                else:  # output
                    output_names.append(name)
                shape = tuple(context.get_binding_shape(i))
                im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device)
                bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr()))
            binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())
            batch_size = bindings['images'].shape[0]  # if dynamic, this is instead max batch size

Here is the outputs when export:
exporting_out.txt (162.9 KB)

it is the output when I try to inference with engine:

cuda:0 <function select_device at 0x7fabc75fa1f0> <class 'yolov5.models.common.DetectMultiBackend'>

YOLOv5 πŸš€ v7.0-212-g9974d51 Python-3.8.16 torch-2.0.0+cu117 CUDA:0 (NVIDIA A10G, 22564MiB)

Loading a.nz.engine for TensorRT inference...

[10/03/2023-07:44:46] [TRT] [I] Loaded engine size: 16 MiB

[10/03/2023-07:44:46] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in engine deserialization: CPU +0, GPU +13, now: CPU 0, GPU 13 (MiB)

[10/03/2023-07:44:46] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +0, GPU +16, now: CPU 0, GPU 29 (MiB)

[10/03/2023-07:44:46] [TRT] [W] CUDA lazy loading is not enabled. Enabling it can significantly reduce device memory usage and speed up TensorRT initialization. See "Lazy Loading" section of CUDA documentation https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#lazy-loading

<class 'numpy.float16'> <class 'type'> TYPE

True COMMON FP16

<class 'numpy.float16'> <class 'type'> TYPE

<class 'numpy.float16'> <class 'type'> TYPE

<class 'numpy.float16'> <class 'type'> TYPE

<class 'numpy.float16'> <class 'type'> TYPE

True IS FP16

[tensor([], device='cuda:0', size=(0, 6))] 0.0050466060638427734

True IS FP16

[tensor([], device='cuda:0', size=(0, 6))] 0.0031447410583496094

True IS FP16

[tensor([], device='cuda:0', size=(0, 6))] 0.002671480178833008

pt model inference output:

cuda:0 <function select_device at 0x7fc212550dc0> <class 'yolov5.models.common.DetectMultiBackend'>
YOLOv5 πŸš€ v7.0-212-g9974d51 Python-3.8.16 torch-2.0.0+cu117 CUDA:0 (NVIDIA A10G, 22564MiB)

Fusing layers... 
Model summary: 157 layers, 7015519 parameters, 0 gradients, 15.8 GFLOPs
[tensor([[4.48500e+02, 1.36750e+02, 5.39500e+02, 2.60000e+02, 9.35059e-01, 1.00000e+00],
        [2.33500e+02, 1.78625e+02, 3.44000e+02, 3.15000e+02, 9.18945e-01, 1.00000e+00],
        [3.41750e+02, 1.36750e+02, 4.35250e+02, 2.58750e+02, 9.16504e-01, 1.00000e+00],
        [8.88750e+01, 1.90375e+02, 1.85875e+02, 3.12500e+02, 9.07715e-01, 1.00000e+00],
        [2.80500e+02, 1.14500e+02, 3.50000e+02, 1.96500e+02, 9.02832e-01, 1.00000e+00],
        [1.74500e+02, 1.32750e+02, 2.41500e+02, 2.27250e+02, 8.99902e-01, 1.00000e+00],
        [4.06750e+02, 1.31750e+02, 6.15000e+02, 5.29000e+02, 8.38867e-01, 0.00000e+00],
        [1.70250e+02, 2.30125e+02, 2.69750e+02, 3.46500e+02, 7.61230e-01, 1.00000e+00],
        [3.33750e+01, 1.81750e+02, 2.44375e+02, 5.30000e+02, 7.58301e-01, 0.00000e+00],
        [2.11875e+02, 1.69375e+02, 4.50500e+02, 5.31500e+02, 7.32422e-01, 0.00000e+00],
        [1.10250e+02, 1.37750e+02, 3.47500e+02, 5.30000e+02, 4.63623e-01, 0.00000e+00],
        [2.96750e+02, 1.26500e+02, 5.46000e+02, 5.33500e+02, 2.76367e-01, 0.00000e+00]], device='cuda:0')] 0.03249621391296387

Environment

TensorRT Version: 8.6.0
GPU Type: NVIDIA A10G (AWS EC2)
Nvidia Driver Version: 530.30.02
CUDA Version: 12.1
CUDNN Version:
Operating System + Version: Ubuntu 22.04.2 LTS (GNU/Linux 6.2.0-1012-aws x86_64)
Python Version (if applicable): 3.8.6
TensorFlow Version (if applicable):
PyTorch Version (if applicable): 2.0.0
Baremetal or Container (if container which image + tag):

Hi,

Apologies for the delayed response.
Are you still facing the same issue?

Thank you.

Yes, I have still facing the issue.

Be facing the issue lol no one’s gonna help

1 Like