AttributeError: 'Parameter' object has no attribute '_trt'

[maronuu]

Environment

Ubuntu 20.04 Python 3.8.8 torch 1.8.1 torch2trt 0.2.0 cuda 11.1 cudnn 8.1.1 TensorRT 7.2.2

Issue

I'm trying to convert YOLOR(https://github.com/WongKinYiu/yolor) implemented in PyTorch into TensorRT.

There are two kinds of layers used in YOLOR but not supported by torch2trt now.

• torch.nn.functional.silu
• torch.Tensor.expand_as

silu

Thanks to #527, there is no problem here.

expand_as

Thanks to #487, a converter for torch.Tensor.expand is provided. Since torch.Tensor.expand(other.size()) equals to torch.Tensor.expand_as(other) (https://pytorch.org/docs/stable/tensors.html), I replaced all expand_as(other) with expand(other.size()) in yolor/utils/layers.py.

I made a script for conversion:

yolor/torch2trt_conversion.py

import argparse
import os
import sys
import time

import torch
import torch.jit
from torch2trt import torch2trt

from models.models import Darknet
from utils.torch_utils import select_device

def main():
    out_path = opt.output
    weights = opt.weights
    img_size = opt.img_size
    cfg = opt.cfg
    device = select_device(opt.device)
    # str2prec = {'int8': torch.int8, 'fp16': torch.float16, 'fp32': torch.float32}
    precision = opt.precision

    # load model
    print('BEGIN loading weights')
    model = Darknet(cfg, img_size).to(device)
    model.load_state_dict(torch.load(weights, map_location=device)['model'])
    model = model.eval()
    print('END loading weights')
    print('BEGIN conversion')
    input_data = torch.randn((1, 3, img_size, img_size)).to(device)
    if precision == 'int8':
        model_trt = torch2trt(model, [input_data], int8_mode=True)
    elif precision == 'fp16':
        model_trt = torch2trt(model, [input_data], fp16_mode=True)
    elif precision == 'fp32':
        model_trt = torch2trt(model, [input_data])
    else:
        raise ValueError("Invalid precision")

    print('END conversion')

    # save
    input_data = torch.empty([1, 3, img_size, img_size], dtype=precision).to(device)
    result = model_trt(input_data)
    torch.save(model_trt.state_dict(), out_path)
    print('Successfully saved')

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--output', type=str, default='trt_darknet.pth', help='path to converted model')
    parser.add_argument('--weights', nargs='+', type=str, default='yolor_p6.pt', help='model.pt path(s)')
    parser.add_argument('--img-size', type=int, default=1280, help='inference size (pixels)')
    parser.add_argument('--cfg', type=str, default='cfg/yolor_p6.cfg', help='*.cfg path')
    parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--precision', type=str, default='fp16', 
        help='precision of inference for the output model [int8, fp16, fp32]')
    opt = parser.parse_args()
    print(opt)

    main()

In the end, I got

/workspace/yolor# python torch2trt_conversion.py --img-size 640
Namespace(cfg='cfg/yolor_p6.cfg', device='0', img_size=640, output='trt_darknet.pth', precision='fp16', weights='yolor_p6.pt')
BEGIN loading weights
END loading weights
BEGIN conversion
Traceback (most recent call last):
  File "torch2trt_conversion.py", line 62, in <module>
    main()
  File "torch2trt_conversion.py", line 34, in main
    model_trt = torch2trt(model, [input_data], fp16_mode=True)
  File "/opt/conda/lib/python3.8/site-packages/torch2trt-0.2.0-py3.8-linux-x86_64.egg/torch2trt/torch2trt.py", line 542, in torch2trt
    outputs = module(*inputs)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/workspace/yolor/models/models.py", line 550, in forward
    return self.forward_once(x)
  File "/workspace/yolor/models/models.py", line 601, in forward_once
    x = module(x, out)  # WeightedFeatureFusion(), FeatureConcat()
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/workspace/yolor/utils/layers.py", line 384, in forward
    return a.expand(x.size()) + x
  File "/opt/conda/lib/python3.8/site-packages/torch2trt-0.2.0-py3.8-linux-x86_64.egg/torch2trt/torch2trt.py", line 289, in wrapper
    converter["converter"](ctx)
  File "/opt/conda/lib/python3.8/site-packages/torch2trt-0.2.0-py3.8-linux-x86_64.egg/torch2trt/converters/expand.py", line 17, in convert_expand
    layer = ctx.network.add_slice(input._trt, start, shape, stride)
AttributeError: 'Parameter' object has no attribute '_trt'

'Parameter' object means 'torch.nn.parameter.Parameter'. This object has attribute data (torch.Tensor). So, I replaced input with input.data, but it did not work.

What is the problem?

[TheConstant3]

Hi, do you solve this problem?? I have same issue

[maronuu]

Unfortunately, the problem is not solved yet... The issue posted on /yolor you tell me may be helpful for me. I'll do a further investigation. Thanks.

[rodja]

Here is a patch for the yolor paper branch which uses torch2trt in detect.py:

yolor.patch

```patch diff --git a/detect.py b/detect.py index f2d9f36..21a4f84 100644 --- a/detect.py +++ b/detect.py @@ -7,6 +7,10 @@ import torch import torch.backends.cudnn as cudnn from numpy import random +import logging + +from torch2trt import torch2trt, tensorrt_converter, get_arg, trt, add_missing_trt_tensors + from models.experimental import attempt_load from utils.datasets import LoadStreams, LoadImages from utils.general import check_img_size, non_max_suppression, apply_classifier, scale_coords, xyxy2xywh, \ @@ -14,6 +18,20 @@ from utils.general import check_img_size, non_max_suppression, apply_classifier, from utils.plots import plot_one_box from utils.torch_utils import select_device, load_classifier, time_synchronized +# REGISTER NEW CONVERTERS + +@tensorrt_converter('torch.nn.functional.silu') +def convert_silu(ctx): + input = get_arg(ctx, 'input', pos=0, default=None) + output = ctx.method_return + input_trt = add_missing_trt_tensors(ctx.network, [input])[0] + + layer = ctx.network.add_activation(input_trt, trt.ActivationType.SIGMOID) + layer = ctx.network.add_elementwise(input_trt, layer.get_output(0), trt.ElementWiseOperation.PROD) + + output._trt = layer.get_output(0) + +# missing: @tensorrt_converter('torch.nn.parameter.Parameter') def detect(save_img=False): source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size @@ -32,6 +50,14 @@ def detect(save_img=False): # Load model model = attempt_load(weights, map_location=device) # load FP32 model imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size + + x = torch.ones((1, 3, imgsz, imgsz), device=device) + try: + model_trt = torch2trt(model, [x], fp16_mode=True) + except: + logging.exception('could not create tensorRT model') + exit() + if half: model.half() # to FP16 @@ -59,6 +85,7 @@ def detect(save_img=False): t0 = time.time() img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img _ = model(img.half() if half else img) if device.type != 'cpu' else None # run once + _ = model_trt(img) if device.type != 'cpu' else None # run once for path, img, im0s, vid_cap in dataset: img = torch.from_numpy(img).to(device) img = img.half() if half else img.float() # uint8 to fp16/32 @@ -67,12 +94,17 @@ def detect(save_img=False): img = img.unsqueeze(0) # Inference + t_trt = time_synchronized() + pred = model_trt(img)[0] + print('trt pred (%.3fs)' % (time_synchronized() - t_trt)) + t1 = time_synchronized() pred = model(img, augment=opt.augment)[0] + t2 = time_synchronized() + print('torch pred. (%.3fs)' % (t2 - t1)) # Apply NMS pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) - t2 = time_synchronized() # Apply Classifier if classify: diff --git a/models/common.py b/models/common.py index 0a1e7e3..d991fcf 100644 --- a/models/common.py +++ b/models/common.py @@ -41,7 +41,7 @@ class ImplicitA(nn.Module): nn.init.normal_(self.implicit, std=.02) def forward(self, x): - return self.implicit.expand_as(x) + x + return self.implicit.expand(x.size()) + x class ImplicitM(nn.Module): @@ -52,7 +52,7 @@ class ImplicitM(nn.Module): nn.init.normal_(self.implicit, mean=1., std=.02) def forward(self, x): - return self.implicit.expand_as(x) * x + return self.implicit.expand(x.size()) * x class ReOrg(nn.Module): @@ -236,7 +236,7 @@ class BottleneckCSPSE(nn.Module): self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) def forward(self, x): - x = x * self.cvsig(self.cs(self.avg_pool(x))).expand_as(x) + x = x * self.cvsig(self.cs(self.avg_pool(x))).expand(x.size()) y1 = self.cv3(self.m(self.cv1(x))) y2 = self.cv2(x) return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1)))) @@ -259,7 +259,7 @@ class BottleneckCSPSEA(nn.Module): self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) def forward(self, x): - x = x + x * self.cvsig(self.cs(self.avg_pool(x))).expand_as(x) + x = x + x * self.cvsig(self.cs(self.avg_pool(x))).expand(x.size()) y1 = self.cv3(self.m(self.cv1(x))) y2 = self.cv2(x) return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1)))) \```

When executing

python3 detect.py --weights model.pt --source /tmp/example_images  --conf 0.25 --img-size 800 --device 0

I'm getting the same AttributeError: 'Parameter' object has no attribute '_trt' as @maronuu.

[X-WhyY]

Hi, have you solved this problem? I meet the same problem too.