Add Ghostnet && Fix object destruction order in APIToModel function to avoid undefined behavior (#1581)
* add ghostnet * add ghostnet * add ghostnetV1&ghostnetV2 * Fix object destruction order in APIToModel function to avoid undefined behavior * Fix pre-commit errors in ghostnet/README.md * Fix pre-commit errors * Fix pre-commit errors in mobilenetV3 * Add a noqa marker in mobilenet py files
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82
ghostnet/README.md
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ghostnet/README.md
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# GhostNet
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GhostNetv1 architecture is from the paper "GhostNet: More Features from Cheap Operations" [(https://arxiv.org/abs/1911.11907)](https://arxiv.org/abs/1911.11907).
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GhostNetv2 architecture is from the paper "GhostNetV2: Enhance Cheap Operation with Long-Range Attention" [(https://arxiv.org/abs/2211.12905)](https://arxiv.org/abs/2211.12905).
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For the PyTorch implementations, you can refer to [huawei-noah/ghostnet](https://github.com/huawei-noah/ghostnet).
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Both versions use the following techniques in their TensorRT implementations:
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- **BatchNorm** layer is implemented by TensorRT's **Scale** layer.
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- **Ghost Modules** are used to generate more features from cheap operations, as described in the paper.
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- Replacing `IPoolingLayer` with `IReduceLayer` in TensorRT for Global Average Pooling. The `IReduceLayer` allows you to perform reduction operations (such as sum, average, max) over specified dimensions without being constrained by the kernel size limitations of pooling layers.
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## Project Structure
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```plaintext
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ghostnet
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│
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├── ghostnetv1
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│ ├── CMakeLists.txt
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│ ├── gen_wts.py
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│ ├── ghostnetv1.cpp
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│ └── logging.h
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│
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├── ghostnetv2
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│ ├── CMakeLists.txt
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│ ├── gen_wts.py
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│ ├── ghostnetv2.cpp
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│ └── logging.h
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│
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└── README.md
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```
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## Steps to use GhostNet in TensorRT
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### 1. Generate `.wts` files for both GhostNetv1 and GhostNetv2
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```bash
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# For ghostnetv1
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python ghostnetv1/gen_wts.py
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# For ghostnetv2
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python ghostnetv2/gen_wts.py
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```
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### 2. Build the project
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```bash
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cd tensorrtx/ghostnet
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mkdir build
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cd build
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cmake ..
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make
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```
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### 3. Serialize the models to engine files
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Use the following commands to serialize the PyTorch models into TensorRT engine files (`ghostnetv1.engine` and `ghostnetv2.engine`):
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```bash
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# For ghostnetv1
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sudo ./ghostnetv1 -s
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# For ghostnetv2
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sudo ./ghostnetv2 -s
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```
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### 4. Run inference using the engine files
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Once the engine files are generated, you can run inference with the following commands:
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```bash
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# For ghostnetv1
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sudo ./ghostnetv1 -d
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# For ghostnetv2
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sudo ./ghostnetv2 -d
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```
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### 5. Verify output
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Compare the output with the PyTorch implementation from [huawei-noah/ghostnet](https://github.com/huawei-noah/ghostnet) to ensure that the TensorRT results are consistent with the PyTorch model.
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24
ghostnet/ghostnetv1/CMakeLists.txt
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ghostnet/ghostnetv1/CMakeLists.txt
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cmake_minimum_required(VERSION 2.6)
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project(ghostnetv1)
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add_definitions(-std=c++11)
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option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
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set(CMAKE_CXX_STANDARD 11)
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set(CMAKE_BUILD_TYPE Debug)
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include_directories(${PROJECT_SOURCE_DIR}/include)
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# include and link dirs of cuda and tensorrt, you need adapt them if yours are different
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# cuda
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include_directories(/usr/local/cuda/include)
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link_directories(/usr/local/cuda/lib64)
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# tensorrt
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include_directories(/usr/include/x86_64-linux-gnu/)
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link_directories(/usr/lib/x86_64-linux-gnu/)
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add_executable(ghostnetv1 ${PROJECT_SOURCE_DIR}/ghostnetv1.cpp)
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target_link_libraries(ghostnetv1 nvinfer)
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target_link_libraries(ghostnetv1 cudart)
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add_definitions(-O2 -pthread)
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292
ghostnet/ghostnetv1/gen_wts.py
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ghostnet/ghostnetv1/gen_wts.py
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"""
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Creates a GhostNet Model as defined in:
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GhostNet: More Features from Cheap Operations By Kai Han, Yunhe Wang, Qi Tian, Jianyuan Guo, Chunjing Xu, Chang Xu.
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https://arxiv.org/abs/1911.11907
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Modified from https://github.com/d-li14/mobilenetv3.pytorch and https://github.com/rwightman/pytorch-image-models
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"""
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import torch
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import torch.nn as nn
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import torch.onnx
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import struct
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import torch
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import torch.nn.functional as F
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import math
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def _make_divisible(v, divisor, min_value=None):
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"""
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This function is taken from the original tf repo.
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It ensures that all layers have a channel number that is divisible by 8
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It can be seen here:
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https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
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"""
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if min_value is None:
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min_value = divisor
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new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
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# Make sure that round down does not go down by more than 10%.
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if new_v < 0.9 * v:
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new_v += divisor
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return new_v
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def hard_sigmoid(x, inplace: bool = False):
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if inplace:
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return x.add_(3.).clamp_(0., 6.).div_(6.)
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else:
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return F.relu6(x + 3.) / 6.
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class SqueezeExcite(nn.Module):
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def __init__(self, in_chs, se_ratio=0.25, reduced_base_chs=None,
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act_layer=nn.ReLU, gate_fn=hard_sigmoid, divisor=4, **_):
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super(SqueezeExcite, self).__init__()
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self.gate_fn = gate_fn
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reduced_chs = _make_divisible((reduced_base_chs or in_chs) * se_ratio, divisor)
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self.avg_pool = nn.AdaptiveAvgPool2d(1)
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self.conv_reduce = nn.Conv2d(in_chs, reduced_chs, 1, bias=True)
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self.act1 = act_layer(inplace=True)
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self.conv_expand = nn.Conv2d(reduced_chs, in_chs, 1, bias=True)
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def forward(self, x):
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x_se = self.avg_pool(x)
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x_se = self.conv_reduce(x_se)
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x_se = self.act1(x_se)
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x_se = self.conv_expand(x_se)
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x = x * self.gate_fn(x_se)
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return x
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class ConvBnAct(nn.Module):
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def __init__(self, in_chs, out_chs, kernel_size,
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stride=1, act_layer=nn.ReLU):
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super(ConvBnAct, self).__init__()
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self.conv = nn.Conv2d(in_chs, out_chs, kernel_size, stride, kernel_size//2, bias=False)
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self.bn1 = nn.BatchNorm2d(out_chs)
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self.act1 = act_layer(inplace=True)
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def forward(self, x):
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x = self.conv(x)
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x = self.bn1(x)
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x = self.act1(x)
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return x
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class GhostModule(nn.Module):
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def __init__(self, inp, oup, kernel_size=1, ratio=2, dw_size=3, stride=1, relu=True):
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super(GhostModule, self).__init__()
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self.oup = oup
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init_channels = math.ceil(oup / ratio)
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new_channels = init_channels*(ratio-1)
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self.primary_conv = nn.Sequential(
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nn.Conv2d(inp, init_channels, kernel_size, stride, kernel_size//2, bias=False),
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nn.BatchNorm2d(init_channels),
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nn.ReLU(inplace=True) if relu else nn.Sequential(),
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)
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self.cheap_operation = nn.Sequential(
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nn.Conv2d(init_channels, new_channels, dw_size, 1, dw_size//2, groups=init_channels, bias=False),
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nn.BatchNorm2d(new_channels),
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nn.ReLU(inplace=True) if relu else nn.Sequential(),
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)
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def forward(self, x):
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x1 = self.primary_conv(x)
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x2 = self.cheap_operation(x1)
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out = torch.cat([x1, x2], dim=1)
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return out[:, :self.oup, :, :]
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class GhostBottleneck(nn.Module):
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""" Ghost bottleneck w/ optional SE"""
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def __init__(self, in_chs, mid_chs, out_chs, dw_kernel_size=3,
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stride=1, act_layer=nn.ReLU, se_ratio=0.):
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super(GhostBottleneck, self).__init__()
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has_se = se_ratio is not None and se_ratio > 0.
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self.stride = stride
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# Point-wise expansion
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self.ghost1 = GhostModule(in_chs, mid_chs, relu=True)
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# Depth-wise convolution
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if self.stride > 1:
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self.conv_dw = nn.Conv2d(mid_chs, mid_chs, dw_kernel_size, stride=stride,
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padding=(dw_kernel_size-1)//2, groups=mid_chs, bias=False)
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self.bn_dw = nn.BatchNorm2d(mid_chs)
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# Squeeze-and-excitation
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if has_se:
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self.se = SqueezeExcite(mid_chs, se_ratio=se_ratio)
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else:
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self.se = None
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# Point-wise linear projection
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self.ghost2 = GhostModule(mid_chs, out_chs, relu=False)
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# shortcut
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if (in_chs == out_chs and self.stride == 1):
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self.shortcut = nn.Sequential()
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else:
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self.shortcut = nn.Sequential(
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nn.Conv2d(in_chs, in_chs, dw_kernel_size, stride=stride,
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padding=(dw_kernel_size-1)//2, groups=in_chs, bias=False),
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nn.BatchNorm2d(in_chs),
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nn.Conv2d(in_chs, out_chs, 1, stride=1, padding=0, bias=False),
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nn.BatchNorm2d(out_chs),
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)
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def forward(self, x):
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residual = x
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# 1st ghost bottleneck
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x = self.ghost1(x)
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# Depth-wise convolution
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if self.stride > 1:
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x = self.conv_dw(x)
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x = self.bn_dw(x)
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# Squeeze-and-excitation
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if self.se is not None:
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x = self.se(x)
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# 2nd ghost bottleneck
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x = self.ghost2(x)
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x += self.shortcut(residual)
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return x
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class GhostNet(nn.Module):
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def __init__(self, cfgs, num_classes=1000, width=1.0, dropout=0.2):
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super(GhostNet, self).__init__()
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# setting of inverted residual blocks
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self.cfgs = cfgs
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self.dropout = dropout
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# building first layer
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output_channel = _make_divisible(16 * width, 4)
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self.conv_stem = nn.Conv2d(3, output_channel, 3, 2, 1, bias=False)
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self.bn1 = nn.BatchNorm2d(output_channel)
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self.act1 = nn.ReLU(inplace=True)
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input_channel = output_channel
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# building inverted residual blocks
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stages = []
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block = GhostBottleneck
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for cfg in self.cfgs:
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layers = []
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for k, exp_size, c, se_ratio, s in cfg:
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output_channel = _make_divisible(c * width, 4)
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hidden_channel = _make_divisible(exp_size * width, 4)
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layers.append(block(input_channel, hidden_channel, output_channel, k, s,
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se_ratio=se_ratio))
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input_channel = output_channel
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stages.append(nn.Sequential(*layers))
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output_channel = _make_divisible(exp_size * width, 4)
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stages.append(nn.Sequential(ConvBnAct(input_channel, output_channel, 1)))
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input_channel = output_channel
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self.blocks = nn.Sequential(*stages)
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# building last several layers
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output_channel = 1280
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self.global_pool = nn.AdaptiveAvgPool2d((1, 1))
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self.conv_head = nn.Conv2d(input_channel, output_channel, 1, 1, 0, bias=True)
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self.act2 = nn.ReLU(inplace=True)
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self.classifier = nn.Linear(output_channel, num_classes)
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def forward(self, x):
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x = self.conv_stem(x)
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x = self.bn1(x)
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x = self.act1(x)
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x = self.blocks(x)
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x = self.global_pool(x)
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x = self.conv_head(x)
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x = self.act2(x)
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x = x.view(x.size(0), -1)
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if self.dropout > 0.:
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x = F.dropout(x, p=self.dropout, training=self.training)
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x = self.classifier(x)
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return x
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def ghostnet(**kwargs):
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"""
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Constructs a GhostNet model
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"""
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cfgs = [
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# k, t, c, SE, s
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# stage1
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[[3, 16, 16, 0, 1]],
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# stage2
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[[3, 48, 24, 0, 2]],
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[[3, 72, 24, 0, 1]],
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# stage3
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[[5, 72, 40, 0.25, 2]],
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[[5, 120, 40, 0.25, 1]],
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# stage4
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[[3, 240, 80, 0, 2]],
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[[3, 200, 80, 0, 1],
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[3, 184, 80, 0, 1],
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[3, 184, 80, 0, 1],
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[3, 480, 112, 0.25, 1],
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[3, 672, 112, 0.25, 1]],
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# stage5
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[[5, 672, 160, 0.25, 2]],
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[[5, 960, 160, 0, 1],
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[5, 960, 160, 0.25, 1],
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[5, 960, 160, 0, 1],
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[5, 960, 160, 0.25, 1]]
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]
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return GhostNet(cfgs, **kwargs)
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def setup_seed(seed):
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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torch.backends.cudnn.deterministic = True
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# Function to export weights in the specified format
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def export_weight(model):
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f = open("ghostnetv1.weights", 'w')
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f.write("{}\n".format(len(model.state_dict().keys())))
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# Convert weights to hexadecimal format
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for k, v in model.state_dict().items():
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print('exporting ... {}: {}'.format(k, v.shape))
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# Reshape the weights to 1D
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vr = v.reshape(-1).cpu().numpy()
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f.write("{} {}".format(k, len(vr)))
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for vv in vr:
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f.write(" ")
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f.write(struct.pack(">f", float(vv)).hex())
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f.write("\n")
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f.close()
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# Function to evaluate the model (optional)
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def eval_model(input, model):
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output = model(input)
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print("------from inference------")
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print(input)
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print(output)
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if __name__ == "__main__":
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setup_seed(1)
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model = ghostnet(num_classes=1000, width=1.0, dropout=0.2)
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model.eval()
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input = torch.full((32, 3, 320, 256), 10.0)
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export_weight(model)
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eval_model(input, model)
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516
ghostnet/ghostnetv1/ghostnetv1.cpp
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516
ghostnet/ghostnetv1/ghostnetv1.cpp
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#include <chrono>
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#include <cmath>
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#include <fstream>
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#include <iostream>
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#include <map>
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#include <sstream>
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#include <vector>
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#include "NvInfer.h"
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#include "cuda_runtime_api.h"
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#include "logging.h"
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using namespace std;
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#define CHECK(status) \
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do { \
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auto ret = (status); \
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if (ret != 0) { \
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std::cerr << "Cuda failure: " << ret << std::endl; \
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abort(); \
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} \
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} while (0)
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// stuff we know about the network and the input/output blobs
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static const int INPUT_H = 256;
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static const int INPUT_W = 320;
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static const int OUTPUT_SIZE = 1000;
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static const int batchSize = 32;
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const char* INPUT_BLOB_NAME = "data";
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const char* OUTPUT_BLOB_NAME = "prob";
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using namespace nvinfer1;
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static Logger gLogger;
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// Load weights from files shared with TensorRT samples.
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// TensorRT weight files have a simple space delimited format:
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// [type] [size] <data x size in hex>
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std::map<std::string, Weights> loadWeights(const std::string file) {
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std::cout << "Loading weights: " << file << std::endl;
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std::map<std::string, Weights> weightMap;
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// Open weights file
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std::ifstream input(file);
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if (!input.is_open()) {
|
||||
std::cerr << "Unable to load weight file." << std::endl;
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
|
||||
// Read number of weight blobs
|
||||
int32_t count;
|
||||
input >> count;
|
||||
if (count <= 0) {
|
||||
std::cerr << "Invalid weight map file." << std::endl;
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
|
||||
while (count--) {
|
||||
Weights wt{DataType::kFLOAT, nullptr, 0};
|
||||
uint32_t size;
|
||||
|
||||
// Read name and type of blob
|
||||
std::string name;
|
||||
input >> name >> std::dec >> size;
|
||||
wt.type = DataType::kFLOAT;
|
||||
|
||||
// Load blob
|
||||
uint32_t* val = reinterpret_cast<uint32_t*>(malloc(sizeof(uint32_t) * size));
|
||||
for (uint32_t x = 0, y = size; x < y; ++x) {
|
||||
input >> std::hex >> val[x];
|
||||
}
|
||||
wt.values = val;
|
||||
|
||||
wt.count = size;
|
||||
weightMap[name] = wt;
|
||||
}
|
||||
|
||||
return weightMap;
|
||||
}
|
||||
|
||||
int _make_divisible(int v, int divisor, int min_value = -1) {
|
||||
if (min_value == -1) {
|
||||
min_value = divisor;
|
||||
}
|
||||
|
||||
int new_v = std::max(min_value, (v + divisor / 2) / divisor * divisor);
|
||||
|
||||
if (new_v < static_cast<int>(0.9 * v)) {
|
||||
new_v += divisor;
|
||||
}
|
||||
|
||||
return new_v;
|
||||
}
|
||||
|
||||
ILayer* hardSigmoid(INetworkDefinition* network, ITensor& input) {
|
||||
|
||||
IActivationLayer* scale_layer = network->addActivation(input, ActivationType::kHARD_SIGMOID);
|
||||
|
||||
return scale_layer;
|
||||
}
|
||||
|
||||
IScaleLayer* addBatchNorm2d(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input,
|
||||
std::string lname, float eps) {
|
||||
float* gamma = (float*)weightMap[lname + ".weight"].values;
|
||||
float* beta = (float*)weightMap[lname + ".bias"].values;
|
||||
float* mean = (float*)weightMap[lname + ".running_mean"].values;
|
||||
float* var = (float*)weightMap[lname + ".running_var"].values;
|
||||
int len = weightMap[lname + ".running_var"].count;
|
||||
|
||||
float* scval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
|
||||
for (int i = 0; i < len; i++) {
|
||||
scval[i] = gamma[i] / sqrt(var[i] + eps);
|
||||
}
|
||||
Weights scale{DataType::kFLOAT, scval, len};
|
||||
|
||||
float* shval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
|
||||
for (int i = 0; i < len; i++) {
|
||||
shval[i] = beta[i] - mean[i] * gamma[i] / sqrt(var[i] + eps);
|
||||
}
|
||||
Weights shift{DataType::kFLOAT, shval, len};
|
||||
|
||||
float* pval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
|
||||
for (int i = 0; i < len; i++) {
|
||||
pval[i] = 1.0;
|
||||
}
|
||||
Weights power{DataType::kFLOAT, pval, len};
|
||||
|
||||
weightMap[lname + ".scale"] = scale;
|
||||
weightMap[lname + ".shift"] = shift;
|
||||
weightMap[lname + ".power"] = power;
|
||||
IScaleLayer* scale_1 = network->addScale(input, ScaleMode::kCHANNEL, shift, scale, power);
|
||||
assert(scale_1);
|
||||
return scale_1;
|
||||
}
|
||||
|
||||
IActivationLayer* convBnReluStem(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input,
|
||||
int outch, std::string lname) {
|
||||
Weights emptywts{DataType::kFLOAT, nullptr, 0};
|
||||
|
||||
IConvolutionLayer* conv1 =
|
||||
network->addConvolutionNd(input, outch, DimsHW{3, 3}, weightMap[lname + ".weight"], emptywts);
|
||||
assert(conv1);
|
||||
conv1->setStrideNd(DimsHW{2, 2}); // Stride = 2
|
||||
conv1->setPaddingNd(DimsHW{1, 1}); // Padding = 1
|
||||
|
||||
IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), "bn1", 1e-5);
|
||||
|
||||
IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
|
||||
assert(relu1);
|
||||
|
||||
return relu1;
|
||||
}
|
||||
|
||||
ILayer* convBnAct(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input,
|
||||
int out_channels, std::string lname, ActivationType actType = ActivationType::kRELU) {
|
||||
Weights emptywts{DataType::kFLOAT, nullptr, 0};
|
||||
|
||||
IConvolutionLayer* conv =
|
||||
network->addConvolutionNd(input, out_channels, DimsHW{1, 1}, weightMap[lname + ".conv.weight"], emptywts);
|
||||
assert(conv);
|
||||
conv->setStrideNd(DimsHW{1, 1});
|
||||
|
||||
IScaleLayer* bn = addBatchNorm2d(network, weightMap, *conv->getOutput(0), lname + ".bn1", 1e-5);
|
||||
|
||||
IActivationLayer* act = network->addActivation(*bn->getOutput(0), actType);
|
||||
assert(act);
|
||||
|
||||
return act;
|
||||
}
|
||||
|
||||
ILayer* squeezeExcite(INetworkDefinition* network, ITensor& input, std::map<std::string, Weights>& weightMap,
|
||||
int in_chs, float se_ratio = 0.25, std::string lname = "", float eps = 1e-5) {
|
||||
|
||||
IReduceLayer* avg_pool = network->addReduce(input, ReduceOperation::kAVG, 1 << 2 | 1 << 3, true);
|
||||
assert(avg_pool);
|
||||
|
||||
// Reduce channels with 1x1 convolution
|
||||
int reduced_chs = _make_divisible(static_cast<int>(in_chs * se_ratio), 4);
|
||||
IConvolutionLayer* conv_reduce =
|
||||
network->addConvolutionNd(*avg_pool->getOutput(0), reduced_chs, DimsHW{1, 1},
|
||||
weightMap[lname + ".conv_reduce.weight"], weightMap[lname + ".conv_reduce.bias"]);
|
||||
assert(conv_reduce);
|
||||
|
||||
IActivationLayer* relu1 = network->addActivation(*conv_reduce->getOutput(0), ActivationType::kRELU);
|
||||
assert(relu1);
|
||||
|
||||
// Expand channels back with another 1x1 convolution
|
||||
IConvolutionLayer* conv_expand =
|
||||
network->addConvolutionNd(*relu1->getOutput(0), in_chs, DimsHW{1, 1},
|
||||
weightMap[lname + ".conv_expand.weight"], weightMap[lname + ".conv_expand.bias"]);
|
||||
assert(conv_expand);
|
||||
cout << "SE conv_expand -> " << printTensorShape(conv_expand->getOutput(0)) << endl;
|
||||
|
||||
// Apply hardSigmoid function
|
||||
ILayer* hard_sigmoid = hardSigmoid(network, *conv_expand->getOutput(0));
|
||||
cout << "hard_sigmoid conv_expand -> " << printTensorShape(hard_sigmoid->getOutput(0)) << endl;
|
||||
|
||||
// Elementwise multiplication of input and gated SE output
|
||||
IElementWiseLayer* scale = network->addElementWise(input, *hard_sigmoid->getOutput(0), ElementWiseOperation::kPROD);
|
||||
assert(scale);
|
||||
|
||||
return scale;
|
||||
}
|
||||
|
||||
ILayer* ghostModule(INetworkDefinition* network, ITensor& input, std::map<std::string, Weights>& weightMap, int inp,
|
||||
int oup, int kernel_size = 1, int ratio = 2, int dw_size = 3, int stride = 1, bool relu = true,
|
||||
std::string lname = "") {
|
||||
int init_channels = std::ceil(oup / ratio);
|
||||
int new_channels = init_channels * (ratio - 1);
|
||||
|
||||
// Primary convolution
|
||||
IConvolutionLayer* primary_conv = network->addConvolutionNd(input, init_channels, DimsHW{kernel_size, kernel_size},
|
||||
weightMap[lname + ".primary_conv.0.weight"], Weights{});
|
||||
primary_conv->setStrideNd(DimsHW{stride, stride});
|
||||
primary_conv->setPaddingNd(DimsHW{kernel_size / 2, kernel_size / 2});
|
||||
IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *primary_conv->getOutput(0), lname + ".primary_conv.1", 1e-5);
|
||||
|
||||
// Cheap operation (Depthwise Convolution)
|
||||
IConvolutionLayer* cheap_conv =
|
||||
network->addConvolutionNd(*bn1->getOutput(0), new_channels, DimsHW{dw_size, dw_size},
|
||||
weightMap[lname + ".cheap_operation.0.weight"], Weights{});
|
||||
cheap_conv->setStrideNd(DimsHW{1, 1});
|
||||
cheap_conv->setPaddingNd(DimsHW{dw_size / 2, dw_size / 2});
|
||||
cheap_conv->setNbGroups(init_channels);
|
||||
IScaleLayer* bn2 =
|
||||
addBatchNorm2d(network, weightMap, *cheap_conv->getOutput(0), lname + ".cheap_operation.1", 1e-5);
|
||||
|
||||
// Define relu1 and relu2
|
||||
IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
|
||||
IActivationLayer* relu2 = network->addActivation(*bn2->getOutput(0), ActivationType::kRELU);
|
||||
|
||||
// Initialize inputs array based on the `relu` flag
|
||||
std::vector<ITensor*> inputs_vec;
|
||||
if (relu) {
|
||||
inputs_vec = {relu1->getOutput(0), relu2->getOutput(0)};
|
||||
} else {
|
||||
inputs_vec = {bn1->getOutput(0), bn2->getOutput(0)};
|
||||
}
|
||||
|
||||
ITensor* inputs[] = {inputs_vec[0], inputs_vec[1]};
|
||||
IConcatenationLayer* concat = network->addConcatenation(inputs, 2);
|
||||
std::cout << printTensorShape(concat->getOutput(0)) << std::endl;
|
||||
|
||||
// Slice the output to keep only the first `oup` channels
|
||||
Dims start{4, {0, 0, 0, 0}}; // Starting from batch=0, channel=0, height=0, width=0
|
||||
Dims size{4,
|
||||
{concat->getOutput(0)->getDimensions().d[0], oup, concat->getOutput(0)->getDimensions().d[2],
|
||||
concat->getOutput(0)
|
||||
->getDimensions()
|
||||
.d[3]}}; // Keep all batches, first `oup` channels, all heights and widths
|
||||
Dims stride_{4, {1, 1, 1, 1}}; // Stride is 1 for all dimensions
|
||||
|
||||
ISliceLayer* slice = network->addSlice(*concat->getOutput(0), start, size, stride_);
|
||||
cout << "slice" << printTensorShape(slice->getOutput(0)) << endl;
|
||||
|
||||
return slice;
|
||||
}
|
||||
|
||||
ILayer* ghostBottleneck(INetworkDefinition* network, ITensor& input, std::map<std::string, Weights>& weightMap,
|
||||
int in_chs, int mid_chs, int out_chs, int dw_kernel_size = 3, int stride = 1,
|
||||
float se_ratio = 0.0f, std::string lname = "") {
|
||||
ILayer* ghost1 = ghostModule(network, input, weightMap, in_chs, mid_chs, 1, 2, 3, 1, true, lname + ".ghost1");
|
||||
|
||||
ILayer* depthwise_conv = ghost1;
|
||||
if (stride > 1) {
|
||||
IConvolutionLayer* conv_dw =
|
||||
network->addConvolutionNd(*ghost1->getOutput(0), mid_chs, DimsHW{dw_kernel_size, dw_kernel_size},
|
||||
weightMap[lname + ".conv_dw.weight"], Weights{});
|
||||
conv_dw->setStrideNd(DimsHW{stride, stride});
|
||||
conv_dw->setPaddingNd(DimsHW{(dw_kernel_size - 1) / 2, (dw_kernel_size - 1) / 2});
|
||||
conv_dw->setNbGroups(mid_chs); // Depth-wise convolution
|
||||
IScaleLayer* bn_dw = addBatchNorm2d(network, weightMap, *conv_dw->getOutput(0), lname + ".bn_dw", 1e-5);
|
||||
depthwise_conv = bn_dw;
|
||||
}
|
||||
|
||||
ILayer* se_layer = depthwise_conv;
|
||||
if (se_ratio > 0.0f) {
|
||||
se_layer = squeezeExcite(network, *depthwise_conv->getOutput(0), weightMap, mid_chs, se_ratio, lname + ".se");
|
||||
}
|
||||
|
||||
ILayer* ghost2 = ghostModule(network, *se_layer->getOutput(0), weightMap, mid_chs, out_chs, 1, 2, 3, 1, false,
|
||||
lname + ".ghost2");
|
||||
|
||||
ILayer* shortcut_layer = nullptr;
|
||||
if (in_chs == out_chs && stride == 1) {
|
||||
shortcut_layer = network->addIdentity(input);
|
||||
} else {
|
||||
IConvolutionLayer* conv_shortcut_dw =
|
||||
network->addConvolutionNd(input, in_chs, DimsHW{dw_kernel_size, dw_kernel_size},
|
||||
weightMap[lname + ".shortcut.0.weight"], Weights{});
|
||||
|
||||
conv_shortcut_dw->setStrideNd(DimsHW{stride, stride});
|
||||
conv_shortcut_dw->setPaddingNd(DimsHW{(dw_kernel_size - 1) / 2, (dw_kernel_size - 1) / 2});
|
||||
conv_shortcut_dw->setNbGroups(in_chs); // Depth-wise convolution
|
||||
IScaleLayer* bn_shortcut_dw =
|
||||
addBatchNorm2d(network, weightMap, *conv_shortcut_dw->getOutput(0), lname + ".shortcut.1", 1e-5);
|
||||
|
||||
IConvolutionLayer* conv_shortcut_pw =
|
||||
network->addConvolutionNd(*bn_shortcut_dw->getOutput(0), out_chs, DimsHW{1, 1},
|
||||
weightMap[lname + ".shortcut.2.weight"], Weights{});
|
||||
IScaleLayer* bn_shortcut_pw =
|
||||
addBatchNorm2d(network, weightMap, *conv_shortcut_pw->getOutput(0), lname + ".shortcut.3", 1e-5);
|
||||
shortcut_layer = bn_shortcut_pw;
|
||||
}
|
||||
|
||||
IElementWiseLayer* ew_sum =
|
||||
network->addElementWise(*ghost2->getOutput(0), *shortcut_layer->getOutput(0), ElementWiseOperation::kSUM);
|
||||
|
||||
return ew_sum;
|
||||
}
|
||||
|
||||
ICudaEngine* createEngine(IBuilder* builder, IBuilderConfig* config, DataType dt) {
|
||||
|
||||
INetworkDefinition* network =
|
||||
builder->createNetworkV2(1U << static_cast<uint32_t>(NetworkDefinitionCreationFlag::kEXPLICIT_BATCH));
|
||||
|
||||
// Create input tensor of shape {batchSize, 3, INPUT_H, INPUT_W} with name INPUT_BLOB_NAME
|
||||
ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims4{batchSize, 3, INPUT_H, INPUT_W});
|
||||
assert(data);
|
||||
|
||||
std::map<std::string, Weights> weightMap = loadWeights("../ghostnetv1.weights");
|
||||
Weights emptywts{DataType::kFLOAT, nullptr, 0};
|
||||
|
||||
// Conv Stem
|
||||
IActivationLayer* conv_stem = convBnReluStem(network, weightMap, *data, 16, "conv_stem");
|
||||
|
||||
ILayer* current_layer = conv_stem;
|
||||
current_layer =
|
||||
ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 16, 16, 16, 3, 1, 0, "blocks.0.0");
|
||||
current_layer =
|
||||
ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 16, 48, 24, 3, 2, 0, "blocks.1.0");
|
||||
current_layer =
|
||||
ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 24, 72, 24, 3, 1, 0, "blocks.2.0");
|
||||
current_layer =
|
||||
ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 24, 72, 40, 5, 2, 0.25, "blocks.3.0");
|
||||
current_layer =
|
||||
ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 40, 120, 40, 5, 1, 0.25, "blocks.4.0");
|
||||
current_layer =
|
||||
ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 40, 240, 80, 3, 2, 0, "blocks.5.0");
|
||||
current_layer =
|
||||
ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 80, 200, 80, 3, 1, 0, "blocks.6.0");
|
||||
current_layer =
|
||||
ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 80, 184, 80, 3, 1, 0, "blocks.6.1");
|
||||
current_layer =
|
||||
ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 80, 184, 80, 3, 1, 0, "blocks.6.2");
|
||||
current_layer =
|
||||
ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 80, 480, 112, 3, 1, 0.25, "blocks.6.3");
|
||||
current_layer =
|
||||
ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 112, 672, 112, 3, 1, 0.25, "blocks.6.4");
|
||||
current_layer =
|
||||
ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 112, 672, 160, 5, 2, 0.25, "blocks.7.0");
|
||||
current_layer =
|
||||
ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 160, 960, 160, 5, 1, 0, "blocks.8.0");
|
||||
current_layer =
|
||||
ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 160, 960, 160, 5, 1, 0.25, "blocks.8.1");
|
||||
current_layer =
|
||||
ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 160, 960, 160, 5, 1, 0, "blocks.8.2");
|
||||
current_layer =
|
||||
ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 160, 960, 160, 5, 1, 0.25, "blocks.8.3");
|
||||
|
||||
// Apply ConvBnAct
|
||||
current_layer = convBnAct(network, weightMap, *current_layer->getOutput(0), 960, "blocks.9.0");
|
||||
// Global Average Pooling
|
||||
IReduceLayer* global_pool =
|
||||
network->addReduce(*current_layer->getOutput(0), ReduceOperation::kAVG, 1 << 2 | 1 << 3, true);
|
||||
assert(global_pool);
|
||||
|
||||
// Conv Head
|
||||
IConvolutionLayer* conv_head = network->addConvolutionNd(
|
||||
*global_pool->getOutput(0), 1280, DimsHW{1, 1}, weightMap["conv_head.weight"], weightMap["conv_head.bias"]);
|
||||
IActivationLayer* act2 = network->addActivation(*conv_head->getOutput(0), ActivationType::kRELU);
|
||||
|
||||
// Fully Connected Layer (Classifier)
|
||||
IFullyConnectedLayer* classifier = network->addFullyConnected(
|
||||
*act2->getOutput(0), 1000, weightMap["classifier.weight"], weightMap["classifier.bias"]);
|
||||
classifier->getOutput(0)->setName(OUTPUT_BLOB_NAME);
|
||||
network->markOutput(*classifier->getOutput(0));
|
||||
|
||||
// Build engine
|
||||
config->setMaxWorkspaceSize(1 << 24);
|
||||
ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
|
||||
|
||||
// Don't need the network any more
|
||||
network->destroy();
|
||||
|
||||
// Release host memory
|
||||
for (auto& mem : weightMap) {
|
||||
free((void*)(mem.second.values));
|
||||
}
|
||||
|
||||
return engine;
|
||||
}
|
||||
|
||||
void APIToModel(IHostMemory** modelStream) {
|
||||
// Create builder
|
||||
IBuilder* builder = createInferBuilder(gLogger);
|
||||
IBuilderConfig* config = builder->createBuilderConfig();
|
||||
|
||||
// Create model to populate the network, then set the outputs and create an engine
|
||||
ICudaEngine* engine = createEngine(builder, config, DataType::kFLOAT);
|
||||
assert(engine != nullptr);
|
||||
|
||||
// Serialize the engine
|
||||
(*modelStream) = engine->serialize();
|
||||
|
||||
// Close everything down
|
||||
engine->destroy();
|
||||
config->destroy();
|
||||
builder->destroy();
|
||||
}
|
||||
|
||||
void doInference(IExecutionContext& context, float* input, float* output, int batchSize) {
|
||||
const ICudaEngine& engine = context.getEngine();
|
||||
|
||||
const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);
|
||||
const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);
|
||||
|
||||
// Pointers to input and output device buffers to pass to engine.
|
||||
void* buffers[2];
|
||||
|
||||
// Create GPU buffers on device
|
||||
CHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * INPUT_H * INPUT_W * sizeof(float)));
|
||||
CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float)));
|
||||
|
||||
// Create stream
|
||||
cudaStream_t stream;
|
||||
CHECK(cudaStreamCreate(&stream));
|
||||
|
||||
// DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
|
||||
CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float),
|
||||
cudaMemcpyHostToDevice, stream));
|
||||
context.enqueueV2(buffers, stream, nullptr);
|
||||
CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost,
|
||||
stream));
|
||||
cudaStreamSynchronize(stream);
|
||||
|
||||
// Release stream and buffers
|
||||
cudaStreamDestroy(stream);
|
||||
CHECK(cudaFree(buffers[inputIndex]));
|
||||
CHECK(cudaFree(buffers[outputIndex]));
|
||||
}
|
||||
|
||||
int main(int argc, char** argv) {
|
||||
if (argc != 2) {
|
||||
std::cerr << "arguments not right!" << std::endl;
|
||||
std::cerr << "./ghostnetv1 -s // serialize model to plan file" << std::endl;
|
||||
std::cerr << "./ghostnetv1 -d // deserialize plan file and run inference" << std::endl;
|
||||
return -1;
|
||||
}
|
||||
|
||||
// create a model using the API directly and serialize it to a stream
|
||||
char* trtModelStream{nullptr};
|
||||
size_t size{0};
|
||||
|
||||
if (std::string(argv[1]) == "-s") {
|
||||
IHostMemory* modelStream{nullptr};
|
||||
APIToModel(&modelStream);
|
||||
assert(modelStream != nullptr);
|
||||
|
||||
std::ofstream p("ghostnetv1.engine", std::ios::binary);
|
||||
if (!p) {
|
||||
std::cerr << "could not open plan output file" << std::endl;
|
||||
return -1;
|
||||
}
|
||||
p.write(reinterpret_cast<const char*>(modelStream->data()), modelStream->size());
|
||||
modelStream->destroy();
|
||||
return 0;
|
||||
} else if (std::string(argv[1]) == "-d") {
|
||||
std::ifstream file("ghostnetv1.engine", std::ios::binary);
|
||||
if (file.good()) {
|
||||
file.seekg(0, file.end);
|
||||
size = file.tellg();
|
||||
file.seekg(0, file.beg);
|
||||
trtModelStream = new char[size];
|
||||
assert(trtModelStream);
|
||||
file.read(trtModelStream, size);
|
||||
file.close();
|
||||
}
|
||||
} else {
|
||||
return -1;
|
||||
}
|
||||
|
||||
float* data = new float[batchSize * 3 * INPUT_H * INPUT_W];
|
||||
for (int i = 0; i < batchSize * 3 * INPUT_H * INPUT_W; i++)
|
||||
data[i] = 10.0;
|
||||
|
||||
float* prob = new float[batchSize * OUTPUT_SIZE];
|
||||
|
||||
IRuntime* runtime = createInferRuntime(gLogger);
|
||||
assert(runtime != nullptr);
|
||||
ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size, nullptr);
|
||||
assert(engine != nullptr);
|
||||
IExecutionContext* context = engine->createExecutionContext();
|
||||
assert(context != nullptr);
|
||||
delete[] trtModelStream;
|
||||
|
||||
doInference(*context, data, prob, batchSize);
|
||||
|
||||
std::cout << "\nOutput:\n\n";
|
||||
for (int i = 0; i < batchSize; i++) {
|
||||
std::cout << "Batch " << i << ":\n";
|
||||
for (unsigned int j = 0; j < OUTPUT_SIZE; j++) {
|
||||
std::cout << prob[i * OUTPUT_SIZE + j] << ", ";
|
||||
if (j % 10 == 0)
|
||||
std::cout << j / 10 << std::endl;
|
||||
}
|
||||
std::cout << "\n";
|
||||
}
|
||||
|
||||
context->destroy();
|
||||
engine->destroy();
|
||||
runtime->destroy();
|
||||
delete[] data;
|
||||
delete[] prob;
|
||||
|
||||
return 0;
|
||||
}
|
||||
455
ghostnet/ghostnetv1/logging.h
Normal file
455
ghostnet/ghostnetv1/logging.h
Normal file
@ -0,0 +1,455 @@
|
||||
/*
|
||||
* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
#ifndef TENSORRT_LOGGING_H
|
||||
#define TENSORRT_LOGGING_H
|
||||
|
||||
#include <cassert>
|
||||
#include <ctime>
|
||||
#include <iomanip>
|
||||
#include <iostream>
|
||||
#include <ostream>
|
||||
#include <sstream>
|
||||
#include <string>
|
||||
#include "NvInferRuntimeCommon.h"
|
||||
|
||||
using Severity = nvinfer1::ILogger::Severity;
|
||||
|
||||
class LogStreamConsumerBuffer : public std::stringbuf {
|
||||
public:
|
||||
LogStreamConsumerBuffer(std::ostream& stream, const std::string& prefix, bool shouldLog)
|
||||
: mOutput(stream), mPrefix(prefix), mShouldLog(shouldLog) {}
|
||||
|
||||
LogStreamConsumerBuffer(LogStreamConsumerBuffer&& other) : mOutput(other.mOutput) {}
|
||||
|
||||
~LogStreamConsumerBuffer() {
|
||||
// std::streambuf::pbase() gives a pointer to the beginning of the buffered part of the output sequence
|
||||
// std::streambuf::pptr() gives a pointer to the current position of the output sequence
|
||||
// if the pointer to the beginning is not equal to the pointer to the current position,
|
||||
// call putOutput() to log the output to the stream
|
||||
if (pbase() != pptr()) {
|
||||
putOutput();
|
||||
}
|
||||
}
|
||||
|
||||
// synchronizes the stream buffer and returns 0 on success
|
||||
// synchronizing the stream buffer consists of inserting the buffer contents into the stream,
|
||||
// resetting the buffer and flushing the stream
|
||||
virtual int sync() {
|
||||
putOutput();
|
||||
return 0;
|
||||
}
|
||||
|
||||
void putOutput() {
|
||||
if (mShouldLog) {
|
||||
// prepend timestamp
|
||||
std::time_t timestamp = std::time(nullptr);
|
||||
tm* tm_local = std::localtime(×tamp);
|
||||
std::cout << "[";
|
||||
std::cout << std::setw(2) << std::setfill('0') << 1 + tm_local->tm_mon << "/";
|
||||
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_mday << "/";
|
||||
std::cout << std::setw(4) << std::setfill('0') << 1900 + tm_local->tm_year << "-";
|
||||
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_hour << ":";
|
||||
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_min << ":";
|
||||
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_sec << "] ";
|
||||
// std::stringbuf::str() gets the string contents of the buffer
|
||||
// insert the buffer contents pre-appended by the appropriate prefix into the stream
|
||||
mOutput << mPrefix << str();
|
||||
// set the buffer to empty
|
||||
str("");
|
||||
// flush the stream
|
||||
mOutput.flush();
|
||||
}
|
||||
}
|
||||
|
||||
void setShouldLog(bool shouldLog) { mShouldLog = shouldLog; }
|
||||
|
||||
private:
|
||||
std::ostream& mOutput;
|
||||
std::string mPrefix;
|
||||
bool mShouldLog;
|
||||
};
|
||||
|
||||
//!
|
||||
//! \class LogStreamConsumerBase
|
||||
//! \brief Convenience object used to initialize LogStreamConsumerBuffer before std::ostream in LogStreamConsumer
|
||||
//!
|
||||
class LogStreamConsumerBase {
|
||||
public:
|
||||
LogStreamConsumerBase(std::ostream& stream, const std::string& prefix, bool shouldLog)
|
||||
: mBuffer(stream, prefix, shouldLog) {}
|
||||
|
||||
protected:
|
||||
LogStreamConsumerBuffer mBuffer;
|
||||
};
|
||||
|
||||
//!
|
||||
//! \class LogStreamConsumer
|
||||
//! \brief Convenience object used to facilitate use of C++ stream syntax when logging messages.
|
||||
//! Order of base classes is LogStreamConsumerBase and then std::ostream.
|
||||
//! This is because the LogStreamConsumerBase class is used to initialize the LogStreamConsumerBuffer member field
|
||||
//! in LogStreamConsumer and then the address of the buffer is passed to std::ostream.
|
||||
//! This is necessary to prevent the address of an uninitialized buffer from being passed to std::ostream.
|
||||
//! Please do not change the order of the parent classes.
|
||||
//!
|
||||
class LogStreamConsumer : protected LogStreamConsumerBase, public std::ostream {
|
||||
public:
|
||||
//! \brief Creates a LogStreamConsumer which logs messages with level severity.
|
||||
//! Reportable severity determines if the messages are severe enough to be logged.
|
||||
LogStreamConsumer(Severity reportableSeverity, Severity severity)
|
||||
: LogStreamConsumerBase(severityOstream(severity), severityPrefix(severity), severity <= reportableSeverity),
|
||||
std::ostream(&mBuffer) // links the stream buffer with the stream
|
||||
,
|
||||
mShouldLog(severity <= reportableSeverity),
|
||||
mSeverity(severity) {}
|
||||
|
||||
LogStreamConsumer(LogStreamConsumer&& other)
|
||||
: LogStreamConsumerBase(severityOstream(other.mSeverity), severityPrefix(other.mSeverity), other.mShouldLog),
|
||||
std::ostream(&mBuffer) // links the stream buffer with the stream
|
||||
,
|
||||
mShouldLog(other.mShouldLog),
|
||||
mSeverity(other.mSeverity) {}
|
||||
|
||||
void setReportableSeverity(Severity reportableSeverity) {
|
||||
mShouldLog = mSeverity <= reportableSeverity;
|
||||
mBuffer.setShouldLog(mShouldLog);
|
||||
}
|
||||
|
||||
private:
|
||||
static std::ostream& severityOstream(Severity severity) {
|
||||
return severity >= Severity::kINFO ? std::cout : std::cerr;
|
||||
}
|
||||
|
||||
static std::string severityPrefix(Severity severity) {
|
||||
switch (severity) {
|
||||
case Severity::kINTERNAL_ERROR:
|
||||
return "[F] ";
|
||||
case Severity::kERROR:
|
||||
return "[E] ";
|
||||
case Severity::kWARNING:
|
||||
return "[W] ";
|
||||
case Severity::kINFO:
|
||||
return "[I] ";
|
||||
case Severity::kVERBOSE:
|
||||
return "[V] ";
|
||||
default:
|
||||
assert(0);
|
||||
return "";
|
||||
}
|
||||
}
|
||||
|
||||
bool mShouldLog;
|
||||
Severity mSeverity;
|
||||
};
|
||||
|
||||
//! \class Logger
|
||||
//!
|
||||
//! \brief Class which manages logging of TensorRT tools and samples
|
||||
//!
|
||||
//! \details This class provides a common interface for TensorRT tools and samples to log information to the console,
|
||||
//! and supports logging two types of messages:
|
||||
//!
|
||||
//! - Debugging messages with an associated severity (info, warning, error, or internal error/fatal)
|
||||
//! - Test pass/fail messages
|
||||
//!
|
||||
//! The advantage of having all samples use this class for logging as opposed to emitting directly to stdout/stderr is
|
||||
//! that the logic for controlling the verbosity and formatting of sample output is centralized in one location.
|
||||
//!
|
||||
//! In the future, this class could be extended to support dumping test results to a file in some standard format
|
||||
//! (for example, JUnit XML), and providing additional metadata (e.g. timing the duration of a test run).
|
||||
//!
|
||||
//! TODO: For backwards compatibility with existing samples, this class inherits directly from the nvinfer1::ILogger
|
||||
//! interface, which is problematic since there isn't a clean separation between messages coming from the TensorRT
|
||||
//! library and messages coming from the sample.
|
||||
//!
|
||||
//! In the future (once all samples are updated to use Logger::getTRTLogger() to access the ILogger) we can refactor the
|
||||
//! class to eliminate the inheritance and instead make the nvinfer1::ILogger implementation a member of the Logger
|
||||
//! object.
|
||||
|
||||
class Logger : public nvinfer1::ILogger {
|
||||
public:
|
||||
Logger(Severity severity = Severity::kWARNING) : mReportableSeverity(severity) {}
|
||||
|
||||
//!
|
||||
//! \enum TestResult
|
||||
//! \brief Represents the state of a given test
|
||||
//!
|
||||
enum class TestResult {
|
||||
kRUNNING, //!< The test is running
|
||||
kPASSED, //!< The test passed
|
||||
kFAILED, //!< The test failed
|
||||
kWAIVED //!< The test was waived
|
||||
};
|
||||
|
||||
//!
|
||||
//! \brief Forward-compatible method for retrieving the nvinfer::ILogger associated with this Logger
|
||||
//! \return The nvinfer1::ILogger associated with this Logger
|
||||
//!
|
||||
//! TODO Once all samples are updated to use this method to register the logger with TensorRT,
|
||||
//! we can eliminate the inheritance of Logger from ILogger
|
||||
//!
|
||||
nvinfer1::ILogger& getTRTLogger() { return *this; }
|
||||
|
||||
//!
|
||||
//! \brief Implementation of the nvinfer1::ILogger::log() virtual method
|
||||
//!
|
||||
//! Note samples should not be calling this function directly; it will eventually go away once we eliminate the
|
||||
//! inheritance from nvinfer1::ILogger
|
||||
//!
|
||||
void log(Severity severity, const char* msg) noexcept override {
|
||||
LogStreamConsumer(mReportableSeverity, severity) << "[TRT] " << std::string(msg) << std::endl;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief Method for controlling the verbosity of logging output
|
||||
//!
|
||||
//! \param severity The logger will only emit messages that have severity of this level or higher.
|
||||
//!
|
||||
void setReportableSeverity(Severity severity) { mReportableSeverity = severity; }
|
||||
|
||||
//!
|
||||
//! \brief Opaque handle that holds logging information for a particular test
|
||||
//!
|
||||
//! This object is an opaque handle to information used by the Logger to print test results.
|
||||
//! The sample must call Logger::defineTest() in order to obtain a TestAtom that can be used
|
||||
//! with Logger::reportTest{Start,End}().
|
||||
//!
|
||||
class TestAtom {
|
||||
public:
|
||||
TestAtom(TestAtom&&) = default;
|
||||
|
||||
private:
|
||||
friend class Logger;
|
||||
|
||||
TestAtom(bool started, const std::string& name, const std::string& cmdline)
|
||||
: mStarted(started), mName(name), mCmdline(cmdline) {}
|
||||
|
||||
bool mStarted;
|
||||
std::string mName;
|
||||
std::string mCmdline;
|
||||
};
|
||||
|
||||
//!
|
||||
//! \brief Define a test for logging
|
||||
//!
|
||||
//! \param[in] name The name of the test. This should be a string starting with
|
||||
//! "TensorRT" and containing dot-separated strings containing
|
||||
//! the characters [A-Za-z0-9_].
|
||||
//! For example, "TensorRT.sample_googlenet"
|
||||
//! \param[in] cmdline The command line used to reproduce the test
|
||||
//
|
||||
//! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
|
||||
//!
|
||||
static TestAtom defineTest(const std::string& name, const std::string& cmdline) {
|
||||
return TestAtom(false, name, cmdline);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief A convenience overloaded version of defineTest() that accepts an array of command-line arguments
|
||||
//! as input
|
||||
//!
|
||||
//! \param[in] name The name of the test
|
||||
//! \param[in] argc The number of command-line arguments
|
||||
//! \param[in] argv The array of command-line arguments (given as C strings)
|
||||
//!
|
||||
//! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
|
||||
static TestAtom defineTest(const std::string& name, int argc, char const* const* argv) {
|
||||
auto cmdline = genCmdlineString(argc, argv);
|
||||
return defineTest(name, cmdline);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief Report that a test has started.
|
||||
//!
|
||||
//! \pre reportTestStart() has not been called yet for the given testAtom
|
||||
//!
|
||||
//! \param[in] testAtom The handle to the test that has started
|
||||
//!
|
||||
static void reportTestStart(TestAtom& testAtom) {
|
||||
reportTestResult(testAtom, TestResult::kRUNNING);
|
||||
assert(!testAtom.mStarted);
|
||||
testAtom.mStarted = true;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief Report that a test has ended.
|
||||
//!
|
||||
//! \pre reportTestStart() has been called for the given testAtom
|
||||
//!
|
||||
//! \param[in] testAtom The handle to the test that has ended
|
||||
//! \param[in] result The result of the test. Should be one of TestResult::kPASSED,
|
||||
//! TestResult::kFAILED, TestResult::kWAIVED
|
||||
//!
|
||||
static void reportTestEnd(const TestAtom& testAtom, TestResult result) {
|
||||
assert(result != TestResult::kRUNNING);
|
||||
assert(testAtom.mStarted);
|
||||
reportTestResult(testAtom, result);
|
||||
}
|
||||
|
||||
static int reportPass(const TestAtom& testAtom) {
|
||||
reportTestEnd(testAtom, TestResult::kPASSED);
|
||||
return EXIT_SUCCESS;
|
||||
}
|
||||
|
||||
static int reportFail(const TestAtom& testAtom) {
|
||||
reportTestEnd(testAtom, TestResult::kFAILED);
|
||||
return EXIT_FAILURE;
|
||||
}
|
||||
|
||||
static int reportWaive(const TestAtom& testAtom) {
|
||||
reportTestEnd(testAtom, TestResult::kWAIVED);
|
||||
return EXIT_SUCCESS;
|
||||
}
|
||||
|
||||
static int reportTest(const TestAtom& testAtom, bool pass) {
|
||||
return pass ? reportPass(testAtom) : reportFail(testAtom);
|
||||
}
|
||||
|
||||
Severity getReportableSeverity() const { return mReportableSeverity; }
|
||||
|
||||
private:
|
||||
//!
|
||||
//! \brief returns an appropriate string for prefixing a log message with the given severity
|
||||
//!
|
||||
static const char* severityPrefix(Severity severity) {
|
||||
switch (severity) {
|
||||
case Severity::kINTERNAL_ERROR:
|
||||
return "[F] ";
|
||||
case Severity::kERROR:
|
||||
return "[E] ";
|
||||
case Severity::kWARNING:
|
||||
return "[W] ";
|
||||
case Severity::kINFO:
|
||||
return "[I] ";
|
||||
case Severity::kVERBOSE:
|
||||
return "[V] ";
|
||||
default:
|
||||
assert(0);
|
||||
return "";
|
||||
}
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief returns an appropriate string for prefixing a test result message with the given result
|
||||
//!
|
||||
static const char* testResultString(TestResult result) {
|
||||
switch (result) {
|
||||
case TestResult::kRUNNING:
|
||||
return "RUNNING";
|
||||
case TestResult::kPASSED:
|
||||
return "PASSED";
|
||||
case TestResult::kFAILED:
|
||||
return "FAILED";
|
||||
case TestResult::kWAIVED:
|
||||
return "WAIVED";
|
||||
default:
|
||||
assert(0);
|
||||
return "";
|
||||
}
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief returns an appropriate output stream (cout or cerr) to use with the given severity
|
||||
//!
|
||||
static std::ostream& severityOstream(Severity severity) {
|
||||
return severity >= Severity::kINFO ? std::cout : std::cerr;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief method that implements logging test results
|
||||
//!
|
||||
static void reportTestResult(const TestAtom& testAtom, TestResult result) {
|
||||
severityOstream(Severity::kINFO) << "&&&& " << testResultString(result) << " " << testAtom.mName << " # "
|
||||
<< testAtom.mCmdline << std::endl;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief generate a command line string from the given (argc, argv) values
|
||||
//!
|
||||
static std::string genCmdlineString(int argc, char const* const* argv) {
|
||||
std::stringstream ss;
|
||||
for (int i = 0; i < argc; i++) {
|
||||
if (i > 0)
|
||||
ss << " ";
|
||||
ss << argv[i];
|
||||
}
|
||||
return ss.str();
|
||||
}
|
||||
|
||||
Severity mReportableSeverity;
|
||||
};
|
||||
|
||||
namespace {
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kVERBOSE
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_VERBOSE(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_VERBOSE(const Logger& logger) {
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kVERBOSE);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINFO
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_INFO(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_INFO(const Logger& logger) {
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINFO);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kWARNING
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_WARN(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_WARN(const Logger& logger) {
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kWARNING);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kERROR
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_ERROR(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_ERROR(const Logger& logger) {
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kERROR);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINTERNAL_ERROR
|
||||
// ("fatal" severity)
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_FATAL(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_FATAL(const Logger& logger) {
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINTERNAL_ERROR);
|
||||
}
|
||||
|
||||
} // anonymous namespace
|
||||
|
||||
#endif // TENSORRT_LOGGING_H
|
||||
24
ghostnet/ghostnetv2/CMakeLists.txt
Normal file
24
ghostnet/ghostnetv2/CMakeLists.txt
Normal file
@ -0,0 +1,24 @@
|
||||
cmake_minimum_required(VERSION 2.6)
|
||||
|
||||
project(ghostnetv2)
|
||||
|
||||
add_definitions(-std=c++11)
|
||||
|
||||
option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
|
||||
set(CMAKE_CXX_STANDARD 11)
|
||||
set(CMAKE_BUILD_TYPE Debug)
|
||||
|
||||
include_directories(${PROJECT_SOURCE_DIR}/include)
|
||||
# include and link dirs of cuda and tensorrt, you need adapt them if yours are different
|
||||
# cuda
|
||||
include_directories(/usr/local/cuda/include)
|
||||
link_directories(/usr/local/cuda/lib64)
|
||||
# tensorrt
|
||||
include_directories(/usr/include/x86_64-linux-gnu/)
|
||||
link_directories(/usr/lib/x86_64-linux-gnu/)
|
||||
|
||||
add_executable(ghostnetv2 ${PROJECT_SOURCE_DIR}/ghostnetv2.cpp)
|
||||
target_link_libraries(ghostnetv2 nvinfer)
|
||||
target_link_libraries(ghostnetv2 cudart)
|
||||
|
||||
add_definitions(-O2 -pthread)
|
||||
312
ghostnet/ghostnetv2/gen_wts.py
Normal file
312
ghostnet/ghostnetv2/gen_wts.py
Normal file
@ -0,0 +1,312 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.onnx
|
||||
import struct
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import math
|
||||
|
||||
from timm.models.registry import register_model
|
||||
|
||||
|
||||
def _make_divisible(v, divisor, min_value=None):
|
||||
"""
|
||||
This function is taken from the original tf repo.
|
||||
It ensures that all layers have a channel number that is divisible by 8
|
||||
It can be seen here:
|
||||
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
|
||||
"""
|
||||
if min_value is None:
|
||||
min_value = divisor
|
||||
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
|
||||
# Make sure that round down does not go down by more than 10%.
|
||||
if new_v < 0.9 * v:
|
||||
new_v += divisor
|
||||
return new_v
|
||||
|
||||
|
||||
def hard_sigmoid(x, inplace: bool = False):
|
||||
if inplace:
|
||||
return x.add_(3.).clamp_(0., 6.).div_(6.)
|
||||
else:
|
||||
return F.relu6(x + 3.) / 6.
|
||||
|
||||
|
||||
class SqueezeExcite(nn.Module):
|
||||
def __init__(self, in_chs, se_ratio=0.25, reduced_base_chs=None,
|
||||
act_layer=nn.ReLU, gate_fn=hard_sigmoid, divisor=4, **_):
|
||||
super(SqueezeExcite, self).__init__()
|
||||
self.gate_fn = gate_fn
|
||||
reduced_chs = _make_divisible((reduced_base_chs or in_chs) * se_ratio, divisor)
|
||||
self.avg_pool = nn.AdaptiveAvgPool2d(1)
|
||||
self.conv_reduce = nn.Conv2d(in_chs, reduced_chs, 1, bias=True)
|
||||
self.act1 = act_layer(inplace=True)
|
||||
self.conv_expand = nn.Conv2d(reduced_chs, in_chs, 1, bias=True)
|
||||
|
||||
def forward(self, x):
|
||||
x_se = self.avg_pool(x)
|
||||
x_se = self.conv_reduce(x_se)
|
||||
x_se = self.act1(x_se)
|
||||
x_se = self.conv_expand(x_se)
|
||||
x = x * self.gate_fn(x_se)
|
||||
return x
|
||||
|
||||
|
||||
class ConvBnAct(nn.Module):
|
||||
def __init__(self, in_chs, out_chs, kernel_size,
|
||||
stride=1, act_layer=nn.ReLU):
|
||||
super(ConvBnAct, self).__init__()
|
||||
self.conv = nn.Conv2d(in_chs, out_chs, kernel_size, stride, kernel_size//2, bias=False)
|
||||
self.bn1 = nn.BatchNorm2d(out_chs)
|
||||
self.act1 = act_layer(inplace=True)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv(x)
|
||||
x = self.bn1(x)
|
||||
x = self.act1(x)
|
||||
return x
|
||||
|
||||
|
||||
class GhostModuleV2(nn.Module):
|
||||
def __init__(self, inp, oup, kernel_size=1, ratio=2, dw_size=3, stride=1, relu=True, mode=None, args=None):
|
||||
super(GhostModuleV2, self).__init__()
|
||||
self.mode = mode
|
||||
self.gate_fn = nn.Sigmoid()
|
||||
|
||||
if self.mode in ['original']:
|
||||
self.oup = oup
|
||||
init_channels = math.ceil(oup / ratio)
|
||||
new_channels = init_channels*(ratio-1)
|
||||
self.primary_conv = nn.Sequential(
|
||||
nn.Conv2d(inp, init_channels, kernel_size, stride, kernel_size//2, bias=False),
|
||||
nn.BatchNorm2d(init_channels),
|
||||
nn.ReLU(inplace=True) if relu else nn.Sequential(),
|
||||
)
|
||||
self.cheap_operation = nn.Sequential(
|
||||
nn.Conv2d(init_channels, new_channels, dw_size, 1, dw_size//2, groups=init_channels, bias=False),
|
||||
nn.BatchNorm2d(new_channels),
|
||||
nn.ReLU(inplace=True) if relu else nn.Sequential(),
|
||||
)
|
||||
elif self.mode in ['attn']:
|
||||
self.oup = oup
|
||||
init_channels = math.ceil(oup / ratio)
|
||||
new_channels = init_channels*(ratio-1)
|
||||
self.primary_conv = nn.Sequential(
|
||||
nn.Conv2d(inp, init_channels, kernel_size, stride, kernel_size//2, bias=False),
|
||||
nn.BatchNorm2d(init_channels),
|
||||
nn.ReLU(inplace=True) if relu else nn.Sequential(),
|
||||
)
|
||||
self.cheap_operation = nn.Sequential(
|
||||
nn.Conv2d(init_channels, new_channels, dw_size, 1, dw_size//2, groups=init_channels, bias=False),
|
||||
nn.BatchNorm2d(new_channels),
|
||||
nn.ReLU(inplace=True) if relu else nn.Sequential(),
|
||||
)
|
||||
self.short_conv = nn.Sequential(
|
||||
nn.Conv2d(inp, oup, kernel_size, stride, kernel_size//2, bias=False),
|
||||
nn.BatchNorm2d(oup),
|
||||
nn.Conv2d(oup, oup, kernel_size=(1, 5), stride=1, padding=(0, 2), groups=oup, bias=False),
|
||||
nn.BatchNorm2d(oup),
|
||||
nn.Conv2d(oup, oup, kernel_size=(5, 1), stride=1, padding=(2, 0), groups=oup, bias=False),
|
||||
nn.BatchNorm2d(oup),
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
if self.mode in ['original']:
|
||||
x1 = self.primary_conv(x)
|
||||
x2 = self.cheap_operation(x1)
|
||||
out = torch.cat([x1, x2], dim=1)
|
||||
return out[:, :self.oup, :, :]
|
||||
elif self.mode in ['attn']:
|
||||
res = self.short_conv(F.avg_pool2d(x, kernel_size=2, stride=2))
|
||||
x1 = self.primary_conv(x)
|
||||
x2 = self.cheap_operation(x1)
|
||||
out = torch.cat([x1, x2], dim=1)
|
||||
return out[:, :self.oup, :, :]*F.interpolate(self.gate_fn(res),
|
||||
size=(out.shape[-2], out.shape[-1]), mode='nearest')
|
||||
|
||||
|
||||
class GhostBottleneckV2(nn.Module):
|
||||
|
||||
def __init__(self, in_chs, mid_chs, out_chs, dw_kernel_size=3,
|
||||
stride=1, act_layer=nn.ReLU, se_ratio=0., layer_id=None, args=None):
|
||||
super(GhostBottleneckV2, self).__init__()
|
||||
has_se = se_ratio is not None and se_ratio > 0.
|
||||
self.stride = stride
|
||||
|
||||
# Point-wise expansion
|
||||
if layer_id <= 1:
|
||||
self.ghost1 = GhostModuleV2(in_chs, mid_chs, relu=True, mode='original', args=args)
|
||||
else:
|
||||
self.ghost1 = GhostModuleV2(in_chs, mid_chs, relu=True, mode='attn', args=args)
|
||||
|
||||
# Depth-wise convolution
|
||||
if self.stride > 1:
|
||||
self.conv_dw = nn.Conv2d(mid_chs, mid_chs, dw_kernel_size, stride=stride,
|
||||
padding=(dw_kernel_size-1)//2, groups=mid_chs, bias=False)
|
||||
self.bn_dw = nn.BatchNorm2d(mid_chs)
|
||||
|
||||
# Squeeze-and-excitation
|
||||
if has_se:
|
||||
self.se = SqueezeExcite(mid_chs, se_ratio=se_ratio)
|
||||
else:
|
||||
self.se = None
|
||||
|
||||
self.ghost2 = GhostModuleV2(mid_chs, out_chs, relu=False, mode='original', args=args)
|
||||
|
||||
# shortcut
|
||||
if (in_chs == out_chs and self.stride == 1):
|
||||
self.shortcut = nn.Sequential()
|
||||
else:
|
||||
self.shortcut = nn.Sequential(
|
||||
nn.Conv2d(in_chs, in_chs, dw_kernel_size, stride=stride,
|
||||
padding=(dw_kernel_size-1)//2, groups=in_chs, bias=False),
|
||||
nn.BatchNorm2d(in_chs),
|
||||
nn.Conv2d(in_chs, out_chs, 1, stride=1, padding=0, bias=False),
|
||||
nn.BatchNorm2d(out_chs),
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
residual = x
|
||||
x = self.ghost1(x)
|
||||
if self.stride > 1:
|
||||
x = self.conv_dw(x)
|
||||
x = self.bn_dw(x)
|
||||
if self.se is not None:
|
||||
x = self.se(x)
|
||||
x = self.ghost2(x)
|
||||
x += self.shortcut(residual)
|
||||
return x
|
||||
|
||||
|
||||
class GhostNetV2(nn.Module):
|
||||
def __init__(self, cfgs, num_classes=1000, width=1.0, dropout=0.2, block=GhostBottleneckV2, args=None):
|
||||
super(GhostNetV2, self).__init__()
|
||||
self.cfgs = cfgs
|
||||
self.dropout = dropout
|
||||
|
||||
# building first layer
|
||||
output_channel = _make_divisible(16 * width, 4)
|
||||
self.conv_stem = nn.Conv2d(3, output_channel, 3, 2, 1, bias=False)
|
||||
self.bn1 = nn.BatchNorm2d(output_channel)
|
||||
self.act1 = nn.ReLU(inplace=True)
|
||||
input_channel = output_channel
|
||||
|
||||
# building inverted residual blocks
|
||||
stages = []
|
||||
layer_id = 0
|
||||
for cfg in self.cfgs:
|
||||
layers = []
|
||||
for k, exp_size, c, se_ratio, s in cfg:
|
||||
output_channel = _make_divisible(c * width, 4)
|
||||
hidden_channel = _make_divisible(exp_size * width, 4)
|
||||
layers.append(block(input_channel, hidden_channel, output_channel, k, s,
|
||||
se_ratio=se_ratio, layer_id=layer_id, args=args))
|
||||
input_channel = output_channel
|
||||
layer_id += 1
|
||||
stages.append(nn.Sequential(*layers))
|
||||
|
||||
output_channel = _make_divisible(exp_size * width, 4)
|
||||
stages.append(nn.Sequential(ConvBnAct(input_channel, output_channel, 1)))
|
||||
input_channel = output_channel
|
||||
|
||||
self.blocks = nn.Sequential(*stages)
|
||||
|
||||
# building last several layers
|
||||
output_channel = 1280
|
||||
self.global_pool = nn.AdaptiveAvgPool2d((1, 1))
|
||||
self.conv_head = nn.Conv2d(input_channel, output_channel, 1, 1, 0, bias=True)
|
||||
self.act2 = nn.ReLU(inplace=True)
|
||||
self.classifier = nn.Linear(output_channel, num_classes)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv_stem(x)
|
||||
x = self.bn1(x)
|
||||
x = self.act1(x)
|
||||
x = self.blocks(x)
|
||||
x = self.global_pool(x)
|
||||
x = self.conv_head(x)
|
||||
x = self.act2(x)
|
||||
x = x.view(x.size(0), -1)
|
||||
if self.dropout > 0.:
|
||||
x = F.dropout(x, p=self.dropout, training=self.training)
|
||||
x = self.classifier(x)
|
||||
return x
|
||||
|
||||
|
||||
@register_model
|
||||
def ghostnetv2(**kwargs):
|
||||
cfgs = [
|
||||
# k, t, c, SE, s
|
||||
[[3, 16, 16, 0, 1]],
|
||||
[[3, 48, 24, 0, 2]],
|
||||
[[3, 72, 24, 0, 1]],
|
||||
[[5, 72, 40, 0.25, 2]],
|
||||
[[5, 120, 40, 0.25, 1]],
|
||||
[[3, 240, 80, 0, 2]],
|
||||
[[3, 200, 80, 0, 1],
|
||||
[3, 184, 80, 0, 1],
|
||||
[3, 184, 80, 0, 1],
|
||||
[3, 480, 112, 0.25, 1],
|
||||
[3, 672, 112, 0.25, 1]],
|
||||
[[5, 672, 160, 0.25, 2]],
|
||||
[[5, 960, 160, 0, 1],
|
||||
[5, 960, 160, 0.25, 1],
|
||||
[5, 960, 160, 0, 1],
|
||||
[5, 960, 160, 0.25, 1]]
|
||||
]
|
||||
return GhostNetV2(cfgs, num_classes=kwargs['num_classes'],
|
||||
width=kwargs['width'],
|
||||
dropout=kwargs['dropout'],
|
||||
args=kwargs['args'])
|
||||
|
||||
|
||||
def setup_seed(seed):
|
||||
torch.manual_seed(seed)
|
||||
torch.cuda.manual_seed_all(seed)
|
||||
torch.backends.cudnn.deterministic = True
|
||||
|
||||
|
||||
# Function to export weights in the specified format
|
||||
def export_weight(model):
|
||||
f = open("ghostnetv2.weights", 'w')
|
||||
f.write("{}\n".format(len(model.state_dict().keys())))
|
||||
|
||||
# Convert weights to hexadecimal format
|
||||
for k, v in model.state_dict().items():
|
||||
print('exporting ... {}: {}'.format(k, v.shape))
|
||||
|
||||
# Reshape the weights to 1D
|
||||
vr = v.reshape(-1).cpu().numpy()
|
||||
f.write("{} {}".format(k, len(vr)))
|
||||
for vv in vr:
|
||||
f.write(" ")
|
||||
f.write(struct.pack(">f", float(vv)).hex())
|
||||
f.write("\n")
|
||||
|
||||
f.close()
|
||||
|
||||
|
||||
# Function to evaluate the model (optional)
|
||||
def eval_model(input, model):
|
||||
output = model(input)
|
||||
print("------from inference------")
|
||||
print(input)
|
||||
print(output)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
setup_seed(1)
|
||||
|
||||
# Create an instance of GhostNetV2
|
||||
model = ghostnetv2(width=1.0, num_classes=1000, dropout=0.2, args=None)
|
||||
model.eval()
|
||||
|
||||
# Dummy input tensor (adjust the shape as per your requirement)
|
||||
input = torch.full((32, 3, 320, 256), 10.0)
|
||||
|
||||
# Export the model weights
|
||||
export_weight(model)
|
||||
|
||||
# Evaluate the model
|
||||
eval_model(input, model)
|
||||
591
ghostnet/ghostnetv2/ghostnetv2.cpp
Normal file
591
ghostnet/ghostnetv2/ghostnetv2.cpp
Normal file
@ -0,0 +1,591 @@
|
||||
#include <chrono>
|
||||
#include <cmath>
|
||||
#include <fstream>
|
||||
#include <iostream>
|
||||
#include <map>
|
||||
#include <sstream>
|
||||
#include <vector>
|
||||
#include "NvInfer.h"
|
||||
#include "cuda_runtime_api.h"
|
||||
#include "logging.h"
|
||||
|
||||
using namespace std;
|
||||
|
||||
#define CHECK(status) \
|
||||
do { \
|
||||
auto ret = (status); \
|
||||
if (ret != 0) { \
|
||||
std::cerr << "Cuda failure: " << ret << std::endl; \
|
||||
abort(); \
|
||||
} \
|
||||
} while (0)
|
||||
|
||||
// Define input/output parameters
|
||||
static const int INPUT_H = 256;
|
||||
static const int INPUT_W = 320;
|
||||
static const int OUTPUT_SIZE = 1000;
|
||||
static const int batchSize = 32;
|
||||
|
||||
const char* INPUT_BLOB_NAME = "data";
|
||||
const char* OUTPUT_BLOB_NAME = "prob";
|
||||
using namespace nvinfer1;
|
||||
|
||||
static Logger gLogger;
|
||||
|
||||
// Load weight file
|
||||
std::map<std::string, Weights> loadWeights(const std::string file) {
|
||||
std::cout << "Loading weights: " << file << std::endl;
|
||||
std::map<std::string, Weights> weightMap;
|
||||
|
||||
// Open the weight file
|
||||
std::ifstream input(file);
|
||||
if (!input.is_open()) {
|
||||
std::cerr << "Unable to load weight file." << std::endl;
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
|
||||
// Read the number of weights
|
||||
int32_t count;
|
||||
input >> count;
|
||||
if (count <= 0) {
|
||||
std::cerr << "Invalid weight map file." << std::endl;
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
|
||||
while (count--) {
|
||||
Weights wt{DataType::kFLOAT, nullptr, 0};
|
||||
uint32_t size;
|
||||
|
||||
// Read the name and size
|
||||
std::string name;
|
||||
input >> name >> std::dec >> size;
|
||||
wt.type = DataType::kFLOAT;
|
||||
|
||||
// Load weight data
|
||||
uint32_t* val = reinterpret_cast<uint32_t*>(malloc(sizeof(uint32_t) * size));
|
||||
for (uint32_t x = 0, y = size; x < y; ++x) {
|
||||
input >> std::hex >> val[x];
|
||||
}
|
||||
wt.values = val;
|
||||
|
||||
wt.count = size;
|
||||
weightMap[name] = wt;
|
||||
}
|
||||
|
||||
return weightMap;
|
||||
}
|
||||
|
||||
int _make_divisible(int v, int divisor, int min_value = -1) {
|
||||
// If min_value is not specified, set it to divisor
|
||||
if (min_value == -1) {
|
||||
min_value = divisor;
|
||||
}
|
||||
|
||||
// Calculate new channel size to be divisible by divisor
|
||||
int new_v = std::max(min_value, (v + divisor / 2) / divisor * divisor);
|
||||
|
||||
// Ensure rounding down does not reduce by more than 10%
|
||||
if (new_v < static_cast<int>(0.9 * v)) {
|
||||
new_v += divisor;
|
||||
}
|
||||
|
||||
return new_v;
|
||||
}
|
||||
|
||||
ILayer* hardSigmoid(INetworkDefinition* network, ITensor& input) {
|
||||
// Apply Hard Sigmoid activation function
|
||||
IActivationLayer* scale_layer = network->addActivation(input, ActivationType::kHARD_SIGMOID);
|
||||
|
||||
// Return the output after activation
|
||||
return scale_layer;
|
||||
}
|
||||
|
||||
IScaleLayer* addBatchNorm2d(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input,
|
||||
std::string lname, float eps) {
|
||||
float* gamma = (float*)weightMap[lname + ".weight"].values;
|
||||
float* beta = (float*)weightMap[lname + ".bias"].values;
|
||||
float* mean = (float*)weightMap[lname + ".running_mean"].values;
|
||||
float* var = (float*)weightMap[lname + ".running_var"].values;
|
||||
int len = weightMap[lname + ".running_var"].count;
|
||||
|
||||
float* scval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
|
||||
for (int i = 0; i < len; i++) {
|
||||
scval[i] = gamma[i] / sqrt(var[i] + eps);
|
||||
}
|
||||
Weights scale{DataType::kFLOAT, scval, len};
|
||||
|
||||
float* shval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
|
||||
for (int i = 0; i < len; i++) {
|
||||
shval[i] = beta[i] - mean[i] * gamma[i] / sqrt(var[i] + eps);
|
||||
}
|
||||
Weights shift{DataType::kFLOAT, shval, len};
|
||||
|
||||
float* pval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
|
||||
for (int i = 0; i < len; i++) {
|
||||
pval[i] = 1.0;
|
||||
}
|
||||
Weights power{DataType::kFLOAT, pval, len};
|
||||
|
||||
weightMap[lname + ".scale"] = scale;
|
||||
weightMap[lname + ".shift"] = shift;
|
||||
weightMap[lname + ".power"] = power;
|
||||
IScaleLayer* scale_1 = network->addScale(input, ScaleMode::kCHANNEL, shift, scale, power);
|
||||
assert(scale_1);
|
||||
return scale_1;
|
||||
}
|
||||
|
||||
IActivationLayer* convBnReluStem(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input,
|
||||
int outch, std::string lname) {
|
||||
Weights emptywts{DataType::kFLOAT, nullptr, 0};
|
||||
|
||||
// Step 1: Convolution layer
|
||||
IConvolutionLayer* conv1 =
|
||||
network->addConvolutionNd(input, outch, DimsHW{3, 3}, weightMap[lname + ".weight"], emptywts);
|
||||
assert(conv1);
|
||||
conv1->setStrideNd(DimsHW{2, 2}); // Stride of 2
|
||||
conv1->setPaddingNd(DimsHW{1, 1}); // Padding of 1
|
||||
|
||||
// Step 2: Batch normalization layer
|
||||
IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), "bn1", 1e-5);
|
||||
|
||||
// Step 3: ReLU activation
|
||||
IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
|
||||
assert(relu1);
|
||||
|
||||
return relu1; // Return the result after activation
|
||||
}
|
||||
|
||||
ILayer* convBnAct(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input,
|
||||
int out_channels, std::string lname, ActivationType actType = ActivationType::kRELU) {
|
||||
Weights emptywts{DataType::kFLOAT, nullptr, 0};
|
||||
|
||||
// Add convolution layer
|
||||
IConvolutionLayer* conv =
|
||||
network->addConvolutionNd(input, out_channels, DimsHW{1, 1}, weightMap[lname + ".conv.weight"], emptywts);
|
||||
assert(conv);
|
||||
conv->setStrideNd(DimsHW{1, 1});
|
||||
|
||||
// Add batch normalization layer
|
||||
IScaleLayer* bn = addBatchNorm2d(network, weightMap, *conv->getOutput(0), lname + ".bn1", 1e-5);
|
||||
|
||||
// Add activation layer (default is ReLU)
|
||||
IActivationLayer* act = network->addActivation(*bn->getOutput(0), actType);
|
||||
assert(act);
|
||||
|
||||
return act;
|
||||
}
|
||||
|
||||
ILayer* squeezeExcite(INetworkDefinition* network, ITensor& input, std::map<std::string, Weights>& weightMap,
|
||||
int in_chs, float se_ratio = 0.25, std::string lname = "", float eps = 1e-5) {
|
||||
// Step 1: Global average pooling
|
||||
IReduceLayer* avg_pool = network->addReduce(input, ReduceOperation::kAVG, 1 << 2 | 1 << 3, true);
|
||||
assert(avg_pool);
|
||||
|
||||
// Step 2: 1x1 convolution for dimension reduction
|
||||
int reduced_chs = _make_divisible(static_cast<int>(in_chs * se_ratio), 4);
|
||||
IConvolutionLayer* conv_reduce =
|
||||
network->addConvolutionNd(*avg_pool->getOutput(0), reduced_chs, DimsHW{1, 1},
|
||||
weightMap[lname + ".conv_reduce.weight"], weightMap[lname + ".conv_reduce.bias"]);
|
||||
assert(conv_reduce);
|
||||
|
||||
// Step 3: ReLU activation
|
||||
IActivationLayer* relu1 = network->addActivation(*conv_reduce->getOutput(0), ActivationType::kRELU);
|
||||
assert(relu1);
|
||||
|
||||
// Step 4: 1x1 convolution for dimension expansion
|
||||
IConvolutionLayer* conv_expand =
|
||||
network->addConvolutionNd(*relu1->getOutput(0), in_chs, DimsHW{1, 1},
|
||||
weightMap[lname + ".conv_expand.weight"], weightMap[lname + ".conv_expand.bias"]);
|
||||
assert(conv_expand);
|
||||
|
||||
// Step 5: Hard Sigmoid activation
|
||||
ILayer* hard_sigmoid = hardSigmoid(network, *conv_expand->getOutput(0));
|
||||
|
||||
// Step 6: Multiply input by the output of SE module
|
||||
IElementWiseLayer* scale = network->addElementWise(input, *hard_sigmoid->getOutput(0), ElementWiseOperation::kPROD);
|
||||
assert(scale);
|
||||
|
||||
return scale;
|
||||
}
|
||||
|
||||
ILayer* ghostModuleV2(INetworkDefinition* network, ITensor& input, std::map<std::string, Weights>& weightMap, int inp,
|
||||
int oup, int kernel_size = 1, int ratio = 2, int dw_size = 3, int stride = 1, bool relu = true,
|
||||
std::string lname = "", std::string mode = "original") {
|
||||
int init_channels = std::ceil(oup / ratio);
|
||||
int new_channels = init_channels * (ratio - 1);
|
||||
|
||||
// Primary convolution
|
||||
IConvolutionLayer* primary_conv = network->addConvolutionNd(input, init_channels, DimsHW{kernel_size, kernel_size},
|
||||
weightMap[lname + ".primary_conv.0.weight"], Weights{});
|
||||
primary_conv->setStrideNd(DimsHW{stride, stride});
|
||||
primary_conv->setPaddingNd(DimsHW{kernel_size / 2, kernel_size / 2});
|
||||
|
||||
IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *primary_conv->getOutput(0), lname + ".primary_conv.1", 1e-5);
|
||||
|
||||
ITensor* act1_output = bn1->getOutput(0);
|
||||
if (relu) {
|
||||
IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
|
||||
act1_output = relu1->getOutput(0);
|
||||
}
|
||||
|
||||
// Cheap operation
|
||||
IConvolutionLayer* cheap_conv =
|
||||
network->addConvolutionNd(*act1_output, new_channels, DimsHW{dw_size, dw_size},
|
||||
weightMap[lname + ".cheap_operation.0.weight"], Weights{});
|
||||
cheap_conv->setStrideNd(DimsHW{1, 1});
|
||||
cheap_conv->setPaddingNd(DimsHW{dw_size / 2, dw_size / 2});
|
||||
cheap_conv->setNbGroups(init_channels);
|
||||
|
||||
IScaleLayer* bn2 =
|
||||
addBatchNorm2d(network, weightMap, *cheap_conv->getOutput(0), lname + ".cheap_operation.1", 1e-5);
|
||||
|
||||
ITensor* act2_output = bn2->getOutput(0);
|
||||
if (relu) {
|
||||
IActivationLayer* relu2 = network->addActivation(*bn2->getOutput(0), ActivationType::kRELU);
|
||||
act2_output = relu2->getOutput(0);
|
||||
}
|
||||
|
||||
// Concatenate
|
||||
ITensor* concat_inputs[] = {act1_output, act2_output};
|
||||
IConcatenationLayer* concat = network->addConcatenation(concat_inputs, 2);
|
||||
|
||||
// Slice to oup channels
|
||||
Dims start{4, {0, 0, 0, 0}};
|
||||
Dims size = concat->getOutput(0)->getDimensions();
|
||||
size.d[1] = oup;
|
||||
Dims stride_{4, {1, 1, 1, 1}};
|
||||
|
||||
ISliceLayer* slice = network->addSlice(*concat->getOutput(0), start, size, stride_);
|
||||
|
||||
ITensor* out = slice->getOutput(0);
|
||||
|
||||
if (mode == "original") {
|
||||
return slice;
|
||||
} else if (mode == "attn") {
|
||||
// Attention mechanism
|
||||
// Average pooling
|
||||
IPoolingLayer* avg_pool = network->addPoolingNd(input, PoolingType::kAVERAGE, DimsHW{2, 2});
|
||||
avg_pool->setStrideNd(DimsHW{2, 2});
|
||||
|
||||
ITensor* avg_pooled = avg_pool->getOutput(0);
|
||||
|
||||
// Short convolution branch
|
||||
IConvolutionLayer* short_conv1 =
|
||||
network->addConvolutionNd(*avg_pooled, oup, DimsHW{kernel_size, kernel_size},
|
||||
weightMap[lname + ".short_conv.0.weight"], Weights{});
|
||||
short_conv1->setStrideNd(DimsHW{1, 1});
|
||||
short_conv1->setPaddingNd(DimsHW{kernel_size / 2, kernel_size / 2});
|
||||
IScaleLayer* short_bn1 =
|
||||
addBatchNorm2d(network, weightMap, *short_conv1->getOutput(0), lname + ".short_conv.1", 1e-5);
|
||||
|
||||
// Conv with kernel size (1,5)
|
||||
IConvolutionLayer* short_conv2 = network->addConvolutionNd(
|
||||
*short_bn1->getOutput(0), oup, DimsHW{1, 5}, weightMap[lname + ".short_conv.2.weight"], Weights{});
|
||||
short_conv2->setStrideNd(DimsHW{1, 1});
|
||||
short_conv2->setPaddingNd(DimsHW{0, 2});
|
||||
short_conv2->setNbGroups(oup);
|
||||
IScaleLayer* short_bn2 =
|
||||
addBatchNorm2d(network, weightMap, *short_conv2->getOutput(0), lname + ".short_conv.3", 1e-5);
|
||||
|
||||
// Conv with kernel size (5,1)
|
||||
IConvolutionLayer* short_conv3 = network->addConvolutionNd(
|
||||
*short_bn2->getOutput(0), oup, DimsHW{5, 1}, weightMap[lname + ".short_conv.4.weight"], Weights{});
|
||||
short_conv3->setStrideNd(DimsHW{1, 1});
|
||||
short_conv3->setPaddingNd(DimsHW{2, 0});
|
||||
short_conv3->setNbGroups(oup);
|
||||
IScaleLayer* short_bn3 =
|
||||
addBatchNorm2d(network, weightMap, *short_conv3->getOutput(0), lname + ".short_conv.5", 1e-5);
|
||||
|
||||
ITensor* res = short_bn3->getOutput(0);
|
||||
|
||||
// Sigmoid activation
|
||||
IActivationLayer* gate = network->addActivation(*res, ActivationType::kSIGMOID);
|
||||
|
||||
// Upsample to the same size as out
|
||||
IResizeLayer* gate_upsampled = network->addResize(*gate->getOutput(0));
|
||||
gate_upsampled->setResizeMode(ResizeMode::kNEAREST);
|
||||
Dims out_dims = out->getDimensions();
|
||||
gate_upsampled->setOutputDimensions(out_dims);
|
||||
|
||||
// Element-wise multiplication
|
||||
IElementWiseLayer* scaled_out =
|
||||
network->addElementWise(*out, *gate_upsampled->getOutput(0), ElementWiseOperation::kPROD);
|
||||
|
||||
return scaled_out;
|
||||
} else {
|
||||
std::cerr << "Invalid mode: " << mode << " in ghostModuleV2" << std::endl;
|
||||
return nullptr;
|
||||
}
|
||||
}
|
||||
|
||||
ILayer* ghostBottleneck(INetworkDefinition* network, ITensor& input, std::map<std::string, Weights>& weightMap,
|
||||
int in_chs, int mid_chs, int out_chs, int dw_kernel_size = 3, int stride = 1,
|
||||
float se_ratio = 0.0f, std::string lname = "", int layer_id = 0) {
|
||||
// Determine mode based on layer_id
|
||||
std::string mode = (layer_id <= 1) ? "original" : "attn";
|
||||
|
||||
// ghost1
|
||||
ILayer* ghost1 =
|
||||
ghostModuleV2(network, input, weightMap, in_chs, mid_chs, 1, 2, 3, 1, true, lname + ".ghost1", mode);
|
||||
|
||||
ILayer* depthwise_conv = ghost1;
|
||||
if (stride > 1) {
|
||||
IConvolutionLayer* conv_dw =
|
||||
network->addConvolutionNd(*ghost1->getOutput(0), mid_chs, DimsHW{dw_kernel_size, dw_kernel_size},
|
||||
weightMap[lname + ".conv_dw.weight"], Weights{});
|
||||
conv_dw->setStrideNd(DimsHW{stride, stride});
|
||||
conv_dw->setPaddingNd(DimsHW{(dw_kernel_size - 1) / 2, (dw_kernel_size - 1) / 2});
|
||||
conv_dw->setNbGroups(mid_chs);
|
||||
IScaleLayer* bn_dw = addBatchNorm2d(network, weightMap, *conv_dw->getOutput(0), lname + ".bn_dw", 1e-5);
|
||||
depthwise_conv = bn_dw;
|
||||
}
|
||||
|
||||
ILayer* se_layer = depthwise_conv;
|
||||
if (se_ratio > 0.0f) {
|
||||
se_layer = squeezeExcite(network, *depthwise_conv->getOutput(0), weightMap, mid_chs, se_ratio, lname + ".se");
|
||||
}
|
||||
|
||||
// ghost2 uses original mode
|
||||
ILayer* ghost2 = ghostModuleV2(network, *se_layer->getOutput(0), weightMap, mid_chs, out_chs, 1, 2, 3, 1, false,
|
||||
lname + ".ghost2", "original");
|
||||
|
||||
ILayer* shortcut_layer = nullptr;
|
||||
if (in_chs == out_chs && stride == 1) {
|
||||
shortcut_layer = network->addIdentity(input);
|
||||
} else {
|
||||
IConvolutionLayer* conv_shortcut_dw =
|
||||
network->addConvolutionNd(input, in_chs, DimsHW{dw_kernel_size, dw_kernel_size},
|
||||
weightMap[lname + ".shortcut.0.weight"], Weights{});
|
||||
conv_shortcut_dw->setStrideNd(DimsHW{stride, stride});
|
||||
conv_shortcut_dw->setPaddingNd(DimsHW{(dw_kernel_size - 1) / 2, (dw_kernel_size - 1) / 2});
|
||||
conv_shortcut_dw->setNbGroups(in_chs);
|
||||
IScaleLayer* bn_shortcut_dw =
|
||||
addBatchNorm2d(network, weightMap, *conv_shortcut_dw->getOutput(0), lname + ".shortcut.1", 1e-5);
|
||||
|
||||
IConvolutionLayer* conv_shortcut_pw =
|
||||
network->addConvolutionNd(*bn_shortcut_dw->getOutput(0), out_chs, DimsHW{1, 1},
|
||||
weightMap[lname + ".shortcut.2.weight"], Weights{});
|
||||
IScaleLayer* bn_shortcut_pw =
|
||||
addBatchNorm2d(network, weightMap, *conv_shortcut_pw->getOutput(0), lname + ".shortcut.3", 1e-5);
|
||||
shortcut_layer = bn_shortcut_pw;
|
||||
}
|
||||
|
||||
IElementWiseLayer* ew_sum =
|
||||
network->addElementWise(*ghost2->getOutput(0), *shortcut_layer->getOutput(0), ElementWiseOperation::kSUM);
|
||||
|
||||
return ew_sum;
|
||||
}
|
||||
|
||||
ICudaEngine* createEngine(IBuilder* builder, IBuilderConfig* config, DataType dt) {
|
||||
// Use explicit batch mode
|
||||
INetworkDefinition* network =
|
||||
builder->createNetworkV2(1U << static_cast<uint32_t>(NetworkDefinitionCreationFlag::kEXPLICIT_BATCH));
|
||||
|
||||
// Create input tensor
|
||||
ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims4{batchSize, 3, INPUT_H, INPUT_W});
|
||||
assert(data);
|
||||
|
||||
// Load weights
|
||||
std::map<std::string, Weights> weightMap = loadWeights("../ghostnetv2.weights");
|
||||
Weights emptywts{DataType::kFLOAT, nullptr, 0};
|
||||
|
||||
// Step 1: Conv Stem
|
||||
IActivationLayer* conv_stem = convBnReluStem(network, weightMap, *data, 16, "conv_stem");
|
||||
|
||||
ILayer* current_layer = conv_stem;
|
||||
|
||||
current_layer =
|
||||
ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 16, 16, 16, 3, 1, 0.0f, "blocks.0.0", 0);
|
||||
current_layer =
|
||||
ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 16, 48, 24, 3, 2, 0.0f, "blocks.1.0", 1);
|
||||
current_layer =
|
||||
ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 24, 72, 24, 3, 1, 0.0f, "blocks.2.0", 2);
|
||||
current_layer =
|
||||
ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 24, 72, 40, 5, 2, 0.25f, "blocks.3.0", 3);
|
||||
current_layer = ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 40, 120, 40, 5, 1, 0.25f,
|
||||
"blocks.4.0", 4);
|
||||
current_layer =
|
||||
ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 40, 240, 80, 3, 2, 0.0f, "blocks.5.0", 5);
|
||||
current_layer =
|
||||
ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 80, 200, 80, 3, 1, 0.0f, "blocks.6.0", 6);
|
||||
current_layer =
|
||||
ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 80, 184, 80, 3, 1, 0.0f, "blocks.6.1", 7);
|
||||
current_layer =
|
||||
ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 80, 184, 80, 3, 1, 0.0f, "blocks.6.2", 8);
|
||||
current_layer = ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 80, 480, 112, 3, 1, 0.25f,
|
||||
"blocks.6.3", 9);
|
||||
current_layer = ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 112, 672, 112, 3, 1, 0.25f,
|
||||
"blocks.6.4", 10);
|
||||
current_layer = ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 112, 672, 160, 5, 2, 0.25f,
|
||||
"blocks.7.0", 11);
|
||||
current_layer = ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 160, 960, 160, 5, 1, 0.0f,
|
||||
"blocks.8.0", 12);
|
||||
current_layer = ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 160, 960, 160, 5, 1, 0.25f,
|
||||
"blocks.8.1", 13);
|
||||
current_layer = ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 160, 960, 160, 5, 1, 0.0f,
|
||||
"blocks.8.2", 14);
|
||||
current_layer = ghostBottleneck(network, *current_layer->getOutput(0), weightMap, 160, 960, 160, 5, 1, 0.25f,
|
||||
"blocks.8.3", 15);
|
||||
|
||||
// Apply ConvBnAct
|
||||
current_layer = convBnAct(network, weightMap, *current_layer->getOutput(0), 960, "blocks.9.0");
|
||||
|
||||
// Global average pooling
|
||||
IReduceLayer* global_pool =
|
||||
network->addReduce(*current_layer->getOutput(0), ReduceOperation::kAVG, 1 << 2 | 1 << 3, true);
|
||||
assert(global_pool);
|
||||
|
||||
// Conv Head
|
||||
IConvolutionLayer* conv_head = network->addConvolutionNd(
|
||||
*global_pool->getOutput(0), 1280, DimsHW{1, 1}, weightMap["conv_head.weight"], weightMap["conv_head.bias"]);
|
||||
IActivationLayer* act2 = network->addActivation(*conv_head->getOutput(0), ActivationType::kRELU);
|
||||
|
||||
// Fully connected layer (classifier)
|
||||
IFullyConnectedLayer* classifier = network->addFullyConnected(
|
||||
*act2->getOutput(0), 1000, weightMap["classifier.weight"], weightMap["classifier.bias"]);
|
||||
classifier->getOutput(0)->setName(OUTPUT_BLOB_NAME);
|
||||
network->markOutput(*classifier->getOutput(0));
|
||||
|
||||
// Build the engine
|
||||
config->setMaxWorkspaceSize(1 << 24);
|
||||
ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
|
||||
|
||||
// Destroy the network
|
||||
network->destroy();
|
||||
|
||||
// Free memory
|
||||
for (auto& mem : weightMap) {
|
||||
free((void*)(mem.second.values));
|
||||
}
|
||||
|
||||
return engine;
|
||||
}
|
||||
|
||||
void APIToModel(IHostMemory** modelStream) {
|
||||
// Create builder
|
||||
IBuilder* builder = createInferBuilder(gLogger);
|
||||
IBuilderConfig* config = builder->createBuilderConfig();
|
||||
|
||||
// Create model and serialize
|
||||
ICudaEngine* engine = createEngine(builder, config, DataType::kFLOAT);
|
||||
assert(engine != nullptr);
|
||||
|
||||
// Serialize the engine
|
||||
(*modelStream) = engine->serialize();
|
||||
|
||||
// Release resources
|
||||
engine->destroy();
|
||||
config->destroy();
|
||||
builder->destroy();
|
||||
}
|
||||
|
||||
void doInference(IExecutionContext& context, float* input, float* output, int batchSize) {
|
||||
const ICudaEngine& engine = context.getEngine();
|
||||
|
||||
const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);
|
||||
const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);
|
||||
|
||||
// Input and output buffers
|
||||
void* buffers[2];
|
||||
|
||||
// Create buffers on device
|
||||
CHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * INPUT_H * INPUT_W * sizeof(float)));
|
||||
CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float)));
|
||||
|
||||
// Create stream
|
||||
cudaStream_t stream;
|
||||
CHECK(cudaStreamCreate(&stream));
|
||||
|
||||
// Copy input data to device, execute inference, and copy output back to host
|
||||
CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float),
|
||||
cudaMemcpyHostToDevice, stream));
|
||||
context.enqueueV2(buffers, stream, nullptr);
|
||||
CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost,
|
||||
stream));
|
||||
cudaStreamSynchronize(stream);
|
||||
|
||||
// Release stream and buffers
|
||||
cudaStreamDestroy(stream);
|
||||
CHECK(cudaFree(buffers[inputIndex]));
|
||||
CHECK(cudaFree(buffers[outputIndex]));
|
||||
}
|
||||
|
||||
int main(int argc, char** argv) {
|
||||
if (argc != 2) {
|
||||
std::cerr << "arguments not right!" << std::endl;
|
||||
std::cerr << "./ghostnetv2 -s // serialize model to plan file" << std::endl;
|
||||
std::cerr << "./ghostnetv2 -d // deserialize plan file and run inference" << std::endl;
|
||||
return -1;
|
||||
}
|
||||
|
||||
// Create model and serialize
|
||||
char* trtModelStream{nullptr};
|
||||
size_t size{0};
|
||||
|
||||
if (std::string(argv[1]) == "-s") {
|
||||
IHostMemory* modelStream{nullptr};
|
||||
APIToModel(&modelStream);
|
||||
assert(modelStream != nullptr);
|
||||
|
||||
std::ofstream p("ghostnetv2.engine", std::ios::binary);
|
||||
if (!p) {
|
||||
std::cerr << "could not open plan output file" << std::endl;
|
||||
return -1;
|
||||
}
|
||||
p.write(reinterpret_cast<const char*>(modelStream->data()), modelStream->size());
|
||||
modelStream->destroy();
|
||||
return 0;
|
||||
} else if (std::string(argv[1]) == "-d") {
|
||||
std::ifstream file("ghostnetv2.engine", std::ios::binary);
|
||||
if (file.good()) {
|
||||
file.seekg(0, file.end);
|
||||
size = file.tellg();
|
||||
file.seekg(0, file.beg);
|
||||
trtModelStream = new char[size];
|
||||
assert(trtModelStream);
|
||||
file.read(trtModelStream, size);
|
||||
file.close();
|
||||
}
|
||||
} else {
|
||||
return -1;
|
||||
}
|
||||
|
||||
// Allocate input and output data
|
||||
float* data = new float[batchSize * 3 * INPUT_H * INPUT_W];
|
||||
for (int i = 0; i < batchSize * 3 * INPUT_H * INPUT_W; i++)
|
||||
data[i] = 10.0;
|
||||
|
||||
float* prob = new float[batchSize * OUTPUT_SIZE];
|
||||
|
||||
IRuntime* runtime = createInferRuntime(gLogger);
|
||||
assert(runtime != nullptr);
|
||||
ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size, nullptr);
|
||||
assert(engine != nullptr);
|
||||
IExecutionContext* context = engine->createExecutionContext();
|
||||
assert(context != nullptr);
|
||||
delete[] trtModelStream;
|
||||
|
||||
// Execute inference
|
||||
doInference(*context, data, prob, batchSize);
|
||||
|
||||
// Print output results
|
||||
std::cout << "\nOutput:\n\n";
|
||||
for (int i = 0; i < batchSize; i++) {
|
||||
std::cout << "Batch " << i << ":\n";
|
||||
for (unsigned int j = 0; j < OUTPUT_SIZE; j++) {
|
||||
std::cout << prob[i * OUTPUT_SIZE + j] << ", ";
|
||||
if (j % 10 == 0)
|
||||
std::cout << j / 10 << std::endl;
|
||||
}
|
||||
std::cout << "\n";
|
||||
}
|
||||
|
||||
// Release resources
|
||||
context->destroy();
|
||||
engine->destroy();
|
||||
runtime->destroy();
|
||||
delete[] data;
|
||||
delete[] prob;
|
||||
|
||||
return 0;
|
||||
}
|
||||
455
ghostnet/ghostnetv2/logging.h
Normal file
455
ghostnet/ghostnetv2/logging.h
Normal file
@ -0,0 +1,455 @@
|
||||
/*
|
||||
* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
#ifndef TENSORRT_LOGGING_H
|
||||
#define TENSORRT_LOGGING_H
|
||||
|
||||
#include <cassert>
|
||||
#include <ctime>
|
||||
#include <iomanip>
|
||||
#include <iostream>
|
||||
#include <ostream>
|
||||
#include <sstream>
|
||||
#include <string>
|
||||
#include "NvInferRuntimeCommon.h"
|
||||
|
||||
using Severity = nvinfer1::ILogger::Severity;
|
||||
|
||||
class LogStreamConsumerBuffer : public std::stringbuf {
|
||||
public:
|
||||
LogStreamConsumerBuffer(std::ostream& stream, const std::string& prefix, bool shouldLog)
|
||||
: mOutput(stream), mPrefix(prefix), mShouldLog(shouldLog) {}
|
||||
|
||||
LogStreamConsumerBuffer(LogStreamConsumerBuffer&& other) : mOutput(other.mOutput) {}
|
||||
|
||||
~LogStreamConsumerBuffer() {
|
||||
// std::streambuf::pbase() gives a pointer to the beginning of the buffered part of the output sequence
|
||||
// std::streambuf::pptr() gives a pointer to the current position of the output sequence
|
||||
// if the pointer to the beginning is not equal to the pointer to the current position,
|
||||
// call putOutput() to log the output to the stream
|
||||
if (pbase() != pptr()) {
|
||||
putOutput();
|
||||
}
|
||||
}
|
||||
|
||||
// synchronizes the stream buffer and returns 0 on success
|
||||
// synchronizing the stream buffer consists of inserting the buffer contents into the stream,
|
||||
// resetting the buffer and flushing the stream
|
||||
virtual int sync() {
|
||||
putOutput();
|
||||
return 0;
|
||||
}
|
||||
|
||||
void putOutput() {
|
||||
if (mShouldLog) {
|
||||
// prepend timestamp
|
||||
std::time_t timestamp = std::time(nullptr);
|
||||
tm* tm_local = std::localtime(×tamp);
|
||||
std::cout << "[";
|
||||
std::cout << std::setw(2) << std::setfill('0') << 1 + tm_local->tm_mon << "/";
|
||||
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_mday << "/";
|
||||
std::cout << std::setw(4) << std::setfill('0') << 1900 + tm_local->tm_year << "-";
|
||||
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_hour << ":";
|
||||
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_min << ":";
|
||||
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_sec << "] ";
|
||||
// std::stringbuf::str() gets the string contents of the buffer
|
||||
// insert the buffer contents pre-appended by the appropriate prefix into the stream
|
||||
mOutput << mPrefix << str();
|
||||
// set the buffer to empty
|
||||
str("");
|
||||
// flush the stream
|
||||
mOutput.flush();
|
||||
}
|
||||
}
|
||||
|
||||
void setShouldLog(bool shouldLog) { mShouldLog = shouldLog; }
|
||||
|
||||
private:
|
||||
std::ostream& mOutput;
|
||||
std::string mPrefix;
|
||||
bool mShouldLog;
|
||||
};
|
||||
|
||||
//!
|
||||
//! \class LogStreamConsumerBase
|
||||
//! \brief Convenience object used to initialize LogStreamConsumerBuffer before std::ostream in LogStreamConsumer
|
||||
//!
|
||||
class LogStreamConsumerBase {
|
||||
public:
|
||||
LogStreamConsumerBase(std::ostream& stream, const std::string& prefix, bool shouldLog)
|
||||
: mBuffer(stream, prefix, shouldLog) {}
|
||||
|
||||
protected:
|
||||
LogStreamConsumerBuffer mBuffer;
|
||||
};
|
||||
|
||||
//!
|
||||
//! \class LogStreamConsumer
|
||||
//! \brief Convenience object used to facilitate use of C++ stream syntax when logging messages.
|
||||
//! Order of base classes is LogStreamConsumerBase and then std::ostream.
|
||||
//! This is because the LogStreamConsumerBase class is used to initialize the LogStreamConsumerBuffer member field
|
||||
//! in LogStreamConsumer and then the address of the buffer is passed to std::ostream.
|
||||
//! This is necessary to prevent the address of an uninitialized buffer from being passed to std::ostream.
|
||||
//! Please do not change the order of the parent classes.
|
||||
//!
|
||||
class LogStreamConsumer : protected LogStreamConsumerBase, public std::ostream {
|
||||
public:
|
||||
//! \brief Creates a LogStreamConsumer which logs messages with level severity.
|
||||
//! Reportable severity determines if the messages are severe enough to be logged.
|
||||
LogStreamConsumer(Severity reportableSeverity, Severity severity)
|
||||
: LogStreamConsumerBase(severityOstream(severity), severityPrefix(severity), severity <= reportableSeverity),
|
||||
std::ostream(&mBuffer) // links the stream buffer with the stream
|
||||
,
|
||||
mShouldLog(severity <= reportableSeverity),
|
||||
mSeverity(severity) {}
|
||||
|
||||
LogStreamConsumer(LogStreamConsumer&& other)
|
||||
: LogStreamConsumerBase(severityOstream(other.mSeverity), severityPrefix(other.mSeverity), other.mShouldLog),
|
||||
std::ostream(&mBuffer) // links the stream buffer with the stream
|
||||
,
|
||||
mShouldLog(other.mShouldLog),
|
||||
mSeverity(other.mSeverity) {}
|
||||
|
||||
void setReportableSeverity(Severity reportableSeverity) {
|
||||
mShouldLog = mSeverity <= reportableSeverity;
|
||||
mBuffer.setShouldLog(mShouldLog);
|
||||
}
|
||||
|
||||
private:
|
||||
static std::ostream& severityOstream(Severity severity) {
|
||||
return severity >= Severity::kINFO ? std::cout : std::cerr;
|
||||
}
|
||||
|
||||
static std::string severityPrefix(Severity severity) {
|
||||
switch (severity) {
|
||||
case Severity::kINTERNAL_ERROR:
|
||||
return "[F] ";
|
||||
case Severity::kERROR:
|
||||
return "[E] ";
|
||||
case Severity::kWARNING:
|
||||
return "[W] ";
|
||||
case Severity::kINFO:
|
||||
return "[I] ";
|
||||
case Severity::kVERBOSE:
|
||||
return "[V] ";
|
||||
default:
|
||||
assert(0);
|
||||
return "";
|
||||
}
|
||||
}
|
||||
|
||||
bool mShouldLog;
|
||||
Severity mSeverity;
|
||||
};
|
||||
|
||||
//! \class Logger
|
||||
//!
|
||||
//! \brief Class which manages logging of TensorRT tools and samples
|
||||
//!
|
||||
//! \details This class provides a common interface for TensorRT tools and samples to log information to the console,
|
||||
//! and supports logging two types of messages:
|
||||
//!
|
||||
//! - Debugging messages with an associated severity (info, warning, error, or internal error/fatal)
|
||||
//! - Test pass/fail messages
|
||||
//!
|
||||
//! The advantage of having all samples use this class for logging as opposed to emitting directly to stdout/stderr is
|
||||
//! that the logic for controlling the verbosity and formatting of sample output is centralized in one location.
|
||||
//!
|
||||
//! In the future, this class could be extended to support dumping test results to a file in some standard format
|
||||
//! (for example, JUnit XML), and providing additional metadata (e.g. timing the duration of a test run).
|
||||
//!
|
||||
//! TODO: For backwards compatibility with existing samples, this class inherits directly from the nvinfer1::ILogger
|
||||
//! interface, which is problematic since there isn't a clean separation between messages coming from the TensorRT
|
||||
//! library and messages coming from the sample.
|
||||
//!
|
||||
//! In the future (once all samples are updated to use Logger::getTRTLogger() to access the ILogger) we can refactor the
|
||||
//! class to eliminate the inheritance and instead make the nvinfer1::ILogger implementation a member of the Logger
|
||||
//! object.
|
||||
|
||||
class Logger : public nvinfer1::ILogger {
|
||||
public:
|
||||
Logger(Severity severity = Severity::kWARNING) : mReportableSeverity(severity) {}
|
||||
|
||||
//!
|
||||
//! \enum TestResult
|
||||
//! \brief Represents the state of a given test
|
||||
//!
|
||||
enum class TestResult {
|
||||
kRUNNING, //!< The test is running
|
||||
kPASSED, //!< The test passed
|
||||
kFAILED, //!< The test failed
|
||||
kWAIVED //!< The test was waived
|
||||
};
|
||||
|
||||
//!
|
||||
//! \brief Forward-compatible method for retrieving the nvinfer::ILogger associated with this Logger
|
||||
//! \return The nvinfer1::ILogger associated with this Logger
|
||||
//!
|
||||
//! TODO Once all samples are updated to use this method to register the logger with TensorRT,
|
||||
//! we can eliminate the inheritance of Logger from ILogger
|
||||
//!
|
||||
nvinfer1::ILogger& getTRTLogger() { return *this; }
|
||||
|
||||
//!
|
||||
//! \brief Implementation of the nvinfer1::ILogger::log() virtual method
|
||||
//!
|
||||
//! Note samples should not be calling this function directly; it will eventually go away once we eliminate the
|
||||
//! inheritance from nvinfer1::ILogger
|
||||
//!
|
||||
void log(Severity severity, const char* msg) noexcept override {
|
||||
LogStreamConsumer(mReportableSeverity, severity) << "[TRT] " << std::string(msg) << std::endl;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief Method for controlling the verbosity of logging output
|
||||
//!
|
||||
//! \param severity The logger will only emit messages that have severity of this level or higher.
|
||||
//!
|
||||
void setReportableSeverity(Severity severity) { mReportableSeverity = severity; }
|
||||
|
||||
//!
|
||||
//! \brief Opaque handle that holds logging information for a particular test
|
||||
//!
|
||||
//! This object is an opaque handle to information used by the Logger to print test results.
|
||||
//! The sample must call Logger::defineTest() in order to obtain a TestAtom that can be used
|
||||
//! with Logger::reportTest{Start,End}().
|
||||
//!
|
||||
class TestAtom {
|
||||
public:
|
||||
TestAtom(TestAtom&&) = default;
|
||||
|
||||
private:
|
||||
friend class Logger;
|
||||
|
||||
TestAtom(bool started, const std::string& name, const std::string& cmdline)
|
||||
: mStarted(started), mName(name), mCmdline(cmdline) {}
|
||||
|
||||
bool mStarted;
|
||||
std::string mName;
|
||||
std::string mCmdline;
|
||||
};
|
||||
|
||||
//!
|
||||
//! \brief Define a test for logging
|
||||
//!
|
||||
//! \param[in] name The name of the test. This should be a string starting with
|
||||
//! "TensorRT" and containing dot-separated strings containing
|
||||
//! the characters [A-Za-z0-9_].
|
||||
//! For example, "TensorRT.sample_googlenet"
|
||||
//! \param[in] cmdline The command line used to reproduce the test
|
||||
//
|
||||
//! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
|
||||
//!
|
||||
static TestAtom defineTest(const std::string& name, const std::string& cmdline) {
|
||||
return TestAtom(false, name, cmdline);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief A convenience overloaded version of defineTest() that accepts an array of command-line arguments
|
||||
//! as input
|
||||
//!
|
||||
//! \param[in] name The name of the test
|
||||
//! \param[in] argc The number of command-line arguments
|
||||
//! \param[in] argv The array of command-line arguments (given as C strings)
|
||||
//!
|
||||
//! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
|
||||
static TestAtom defineTest(const std::string& name, int argc, char const* const* argv) {
|
||||
auto cmdline = genCmdlineString(argc, argv);
|
||||
return defineTest(name, cmdline);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief Report that a test has started.
|
||||
//!
|
||||
//! \pre reportTestStart() has not been called yet for the given testAtom
|
||||
//!
|
||||
//! \param[in] testAtom The handle to the test that has started
|
||||
//!
|
||||
static void reportTestStart(TestAtom& testAtom) {
|
||||
reportTestResult(testAtom, TestResult::kRUNNING);
|
||||
assert(!testAtom.mStarted);
|
||||
testAtom.mStarted = true;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief Report that a test has ended.
|
||||
//!
|
||||
//! \pre reportTestStart() has been called for the given testAtom
|
||||
//!
|
||||
//! \param[in] testAtom The handle to the test that has ended
|
||||
//! \param[in] result The result of the test. Should be one of TestResult::kPASSED,
|
||||
//! TestResult::kFAILED, TestResult::kWAIVED
|
||||
//!
|
||||
static void reportTestEnd(const TestAtom& testAtom, TestResult result) {
|
||||
assert(result != TestResult::kRUNNING);
|
||||
assert(testAtom.mStarted);
|
||||
reportTestResult(testAtom, result);
|
||||
}
|
||||
|
||||
static int reportPass(const TestAtom& testAtom) {
|
||||
reportTestEnd(testAtom, TestResult::kPASSED);
|
||||
return EXIT_SUCCESS;
|
||||
}
|
||||
|
||||
static int reportFail(const TestAtom& testAtom) {
|
||||
reportTestEnd(testAtom, TestResult::kFAILED);
|
||||
return EXIT_FAILURE;
|
||||
}
|
||||
|
||||
static int reportWaive(const TestAtom& testAtom) {
|
||||
reportTestEnd(testAtom, TestResult::kWAIVED);
|
||||
return EXIT_SUCCESS;
|
||||
}
|
||||
|
||||
static int reportTest(const TestAtom& testAtom, bool pass) {
|
||||
return pass ? reportPass(testAtom) : reportFail(testAtom);
|
||||
}
|
||||
|
||||
Severity getReportableSeverity() const { return mReportableSeverity; }
|
||||
|
||||
private:
|
||||
//!
|
||||
//! \brief returns an appropriate string for prefixing a log message with the given severity
|
||||
//!
|
||||
static const char* severityPrefix(Severity severity) {
|
||||
switch (severity) {
|
||||
case Severity::kINTERNAL_ERROR:
|
||||
return "[F] ";
|
||||
case Severity::kERROR:
|
||||
return "[E] ";
|
||||
case Severity::kWARNING:
|
||||
return "[W] ";
|
||||
case Severity::kINFO:
|
||||
return "[I] ";
|
||||
case Severity::kVERBOSE:
|
||||
return "[V] ";
|
||||
default:
|
||||
assert(0);
|
||||
return "";
|
||||
}
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief returns an appropriate string for prefixing a test result message with the given result
|
||||
//!
|
||||
static const char* testResultString(TestResult result) {
|
||||
switch (result) {
|
||||
case TestResult::kRUNNING:
|
||||
return "RUNNING";
|
||||
case TestResult::kPASSED:
|
||||
return "PASSED";
|
||||
case TestResult::kFAILED:
|
||||
return "FAILED";
|
||||
case TestResult::kWAIVED:
|
||||
return "WAIVED";
|
||||
default:
|
||||
assert(0);
|
||||
return "";
|
||||
}
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief returns an appropriate output stream (cout or cerr) to use with the given severity
|
||||
//!
|
||||
static std::ostream& severityOstream(Severity severity) {
|
||||
return severity >= Severity::kINFO ? std::cout : std::cerr;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief method that implements logging test results
|
||||
//!
|
||||
static void reportTestResult(const TestAtom& testAtom, TestResult result) {
|
||||
severityOstream(Severity::kINFO) << "&&&& " << testResultString(result) << " " << testAtom.mName << " # "
|
||||
<< testAtom.mCmdline << std::endl;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief generate a command line string from the given (argc, argv) values
|
||||
//!
|
||||
static std::string genCmdlineString(int argc, char const* const* argv) {
|
||||
std::stringstream ss;
|
||||
for (int i = 0; i < argc; i++) {
|
||||
if (i > 0)
|
||||
ss << " ";
|
||||
ss << argv[i];
|
||||
}
|
||||
return ss.str();
|
||||
}
|
||||
|
||||
Severity mReportableSeverity;
|
||||
};
|
||||
|
||||
namespace {
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kVERBOSE
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_VERBOSE(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_VERBOSE(const Logger& logger) {
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kVERBOSE);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINFO
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_INFO(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_INFO(const Logger& logger) {
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINFO);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kWARNING
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_WARN(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_WARN(const Logger& logger) {
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kWARNING);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kERROR
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_ERROR(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_ERROR(const Logger& logger) {
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kERROR);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINTERNAL_ERROR
|
||||
// ("fatal" severity)
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_FATAL(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_FATAL(const Logger& logger) {
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINTERNAL_ERROR);
|
||||
}
|
||||
|
||||
} // anonymous namespace
|
||||
|
||||
#endif // TENSORRT_LOGGING_H
|
||||
@ -22,4 +22,3 @@ target_link_libraries(mobilenet nvinfer)
|
||||
target_link_libraries(mobilenet cudart)
|
||||
|
||||
add_definitions(-O2 -pthread)
|
||||
|
||||
|
||||
@ -17,7 +17,6 @@
|
||||
#ifndef TENSORRT_LOGGING_H
|
||||
#define TENSORRT_LOGGING_H
|
||||
|
||||
#include "NvInferRuntimeCommon.h"
|
||||
#include <cassert>
|
||||
#include <ctime>
|
||||
#include <iomanip>
|
||||
@ -25,32 +24,23 @@
|
||||
#include <ostream>
|
||||
#include <sstream>
|
||||
#include <string>
|
||||
#include "NvInferRuntimeCommon.h"
|
||||
|
||||
using Severity = nvinfer1::ILogger::Severity;
|
||||
|
||||
class LogStreamConsumerBuffer : public std::stringbuf
|
||||
{
|
||||
public:
|
||||
class LogStreamConsumerBuffer : public std::stringbuf {
|
||||
public:
|
||||
LogStreamConsumerBuffer(std::ostream& stream, const std::string& prefix, bool shouldLog)
|
||||
: mOutput(stream)
|
||||
, mPrefix(prefix)
|
||||
, mShouldLog(shouldLog)
|
||||
{
|
||||
}
|
||||
: mOutput(stream), mPrefix(prefix), mShouldLog(shouldLog) {}
|
||||
|
||||
LogStreamConsumerBuffer(LogStreamConsumerBuffer&& other)
|
||||
: mOutput(other.mOutput)
|
||||
{
|
||||
}
|
||||
LogStreamConsumerBuffer(LogStreamConsumerBuffer&& other) : mOutput(other.mOutput) {}
|
||||
|
||||
~LogStreamConsumerBuffer()
|
||||
{
|
||||
~LogStreamConsumerBuffer() {
|
||||
// std::streambuf::pbase() gives a pointer to the beginning of the buffered part of the output sequence
|
||||
// std::streambuf::pptr() gives a pointer to the current position of the output sequence
|
||||
// if the pointer to the beginning is not equal to the pointer to the current position,
|
||||
// call putOutput() to log the output to the stream
|
||||
if (pbase() != pptr())
|
||||
{
|
||||
if (pbase() != pptr()) {
|
||||
putOutput();
|
||||
}
|
||||
}
|
||||
@ -58,16 +48,13 @@ public:
|
||||
// synchronizes the stream buffer and returns 0 on success
|
||||
// synchronizing the stream buffer consists of inserting the buffer contents into the stream,
|
||||
// resetting the buffer and flushing the stream
|
||||
virtual int sync()
|
||||
{
|
||||
virtual int sync() {
|
||||
putOutput();
|
||||
return 0;
|
||||
}
|
||||
|
||||
void putOutput()
|
||||
{
|
||||
if (mShouldLog)
|
||||
{
|
||||
void putOutput() {
|
||||
if (mShouldLog) {
|
||||
// prepend timestamp
|
||||
std::time_t timestamp = std::time(nullptr);
|
||||
tm* tm_local = std::localtime(×tamp);
|
||||
@ -88,12 +75,9 @@ public:
|
||||
}
|
||||
}
|
||||
|
||||
void setShouldLog(bool shouldLog)
|
||||
{
|
||||
mShouldLog = shouldLog;
|
||||
}
|
||||
void setShouldLog(bool shouldLog) { mShouldLog = shouldLog; }
|
||||
|
||||
private:
|
||||
private:
|
||||
std::ostream& mOutput;
|
||||
std::string mPrefix;
|
||||
bool mShouldLog;
|
||||
@ -103,15 +87,12 @@ private:
|
||||
//! \class LogStreamConsumerBase
|
||||
//! \brief Convenience object used to initialize LogStreamConsumerBuffer before std::ostream in LogStreamConsumer
|
||||
//!
|
||||
class LogStreamConsumerBase
|
||||
{
|
||||
public:
|
||||
class LogStreamConsumerBase {
|
||||
public:
|
||||
LogStreamConsumerBase(std::ostream& stream, const std::string& prefix, bool shouldLog)
|
||||
: mBuffer(stream, prefix, shouldLog)
|
||||
{
|
||||
}
|
||||
: mBuffer(stream, prefix, shouldLog) {}
|
||||
|
||||
protected:
|
||||
protected:
|
||||
LogStreamConsumerBuffer mBuffer;
|
||||
};
|
||||
|
||||
@ -124,49 +105,49 @@ protected:
|
||||
//! This is necessary to prevent the address of an uninitialized buffer from being passed to std::ostream.
|
||||
//! Please do not change the order of the parent classes.
|
||||
//!
|
||||
class LogStreamConsumer : protected LogStreamConsumerBase, public std::ostream
|
||||
{
|
||||
public:
|
||||
class LogStreamConsumer : protected LogStreamConsumerBase, public std::ostream {
|
||||
public:
|
||||
//! \brief Creates a LogStreamConsumer which logs messages with level severity.
|
||||
//! Reportable severity determines if the messages are severe enough to be logged.
|
||||
LogStreamConsumer(Severity reportableSeverity, Severity severity)
|
||||
: LogStreamConsumerBase(severityOstream(severity), severityPrefix(severity), severity <= reportableSeverity)
|
||||
, std::ostream(&mBuffer) // links the stream buffer with the stream
|
||||
, mShouldLog(severity <= reportableSeverity)
|
||||
, mSeverity(severity)
|
||||
{
|
||||
}
|
||||
: LogStreamConsumerBase(severityOstream(severity), severityPrefix(severity), severity <= reportableSeverity),
|
||||
std::ostream(&mBuffer) // links the stream buffer with the stream
|
||||
,
|
||||
mShouldLog(severity <= reportableSeverity),
|
||||
mSeverity(severity) {}
|
||||
|
||||
LogStreamConsumer(LogStreamConsumer&& other)
|
||||
: LogStreamConsumerBase(severityOstream(other.mSeverity), severityPrefix(other.mSeverity), other.mShouldLog)
|
||||
, std::ostream(&mBuffer) // links the stream buffer with the stream
|
||||
, mShouldLog(other.mShouldLog)
|
||||
, mSeverity(other.mSeverity)
|
||||
{
|
||||
}
|
||||
: LogStreamConsumerBase(severityOstream(other.mSeverity), severityPrefix(other.mSeverity), other.mShouldLog),
|
||||
std::ostream(&mBuffer) // links the stream buffer with the stream
|
||||
,
|
||||
mShouldLog(other.mShouldLog),
|
||||
mSeverity(other.mSeverity) {}
|
||||
|
||||
void setReportableSeverity(Severity reportableSeverity)
|
||||
{
|
||||
void setReportableSeverity(Severity reportableSeverity) {
|
||||
mShouldLog = mSeverity <= reportableSeverity;
|
||||
mBuffer.setShouldLog(mShouldLog);
|
||||
}
|
||||
|
||||
private:
|
||||
static std::ostream& severityOstream(Severity severity)
|
||||
{
|
||||
private:
|
||||
static std::ostream& severityOstream(Severity severity) {
|
||||
return severity >= Severity::kINFO ? std::cout : std::cerr;
|
||||
}
|
||||
|
||||
static std::string severityPrefix(Severity severity)
|
||||
{
|
||||
switch (severity)
|
||||
{
|
||||
case Severity::kINTERNAL_ERROR: return "[F] ";
|
||||
case Severity::kERROR: return "[E] ";
|
||||
case Severity::kWARNING: return "[W] ";
|
||||
case Severity::kINFO: return "[I] ";
|
||||
case Severity::kVERBOSE: return "[V] ";
|
||||
default: assert(0); return "";
|
||||
static std::string severityPrefix(Severity severity) {
|
||||
switch (severity) {
|
||||
case Severity::kINTERNAL_ERROR:
|
||||
return "[F] ";
|
||||
case Severity::kERROR:
|
||||
return "[E] ";
|
||||
case Severity::kWARNING:
|
||||
return "[W] ";
|
||||
case Severity::kINFO:
|
||||
return "[I] ";
|
||||
case Severity::kVERBOSE:
|
||||
return "[V] ";
|
||||
default:
|
||||
assert(0);
|
||||
return "";
|
||||
}
|
||||
}
|
||||
|
||||
@ -198,24 +179,19 @@ private:
|
||||
//! class to eliminate the inheritance and instead make the nvinfer1::ILogger implementation a member of the Logger
|
||||
//! object.
|
||||
|
||||
class Logger : public nvinfer1::ILogger
|
||||
{
|
||||
public:
|
||||
Logger(Severity severity = Severity::kWARNING)
|
||||
: mReportableSeverity(severity)
|
||||
{
|
||||
}
|
||||
class Logger : public nvinfer1::ILogger {
|
||||
public:
|
||||
Logger(Severity severity = Severity::kWARNING) : mReportableSeverity(severity) {}
|
||||
|
||||
//!
|
||||
//! \enum TestResult
|
||||
//! \brief Represents the state of a given test
|
||||
//!
|
||||
enum class TestResult
|
||||
{
|
||||
kRUNNING, //!< The test is running
|
||||
kPASSED, //!< The test passed
|
||||
kFAILED, //!< The test failed
|
||||
kWAIVED //!< The test was waived
|
||||
enum class TestResult {
|
||||
kRUNNING, //!< The test is running
|
||||
kPASSED, //!< The test passed
|
||||
kFAILED, //!< The test failed
|
||||
kWAIVED //!< The test was waived
|
||||
};
|
||||
|
||||
//!
|
||||
@ -225,10 +201,7 @@ public:
|
||||
//! TODO Once all samples are updated to use this method to register the logger with TensorRT,
|
||||
//! we can eliminate the inheritance of Logger from ILogger
|
||||
//!
|
||||
nvinfer1::ILogger& getTRTLogger()
|
||||
{
|
||||
return *this;
|
||||
}
|
||||
nvinfer1::ILogger& getTRTLogger() { return *this; }
|
||||
|
||||
//!
|
||||
//! \brief Implementation of the nvinfer1::ILogger::log() virtual method
|
||||
@ -236,8 +209,7 @@ public:
|
||||
//! Note samples should not be calling this function directly; it will eventually go away once we eliminate the
|
||||
//! inheritance from nvinfer1::ILogger
|
||||
//!
|
||||
void log(Severity severity, const char* msg) override
|
||||
{
|
||||
void log(Severity severity, const char* msg) override {
|
||||
LogStreamConsumer(mReportableSeverity, severity) << "[TRT] " << std::string(msg) << std::endl;
|
||||
}
|
||||
|
||||
@ -246,10 +218,7 @@ public:
|
||||
//!
|
||||
//! \param severity The logger will only emit messages that have severity of this level or higher.
|
||||
//!
|
||||
void setReportableSeverity(Severity severity)
|
||||
{
|
||||
mReportableSeverity = severity;
|
||||
}
|
||||
void setReportableSeverity(Severity severity) { mReportableSeverity = severity; }
|
||||
|
||||
//!
|
||||
//! \brief Opaque handle that holds logging information for a particular test
|
||||
@ -258,20 +227,15 @@ public:
|
||||
//! The sample must call Logger::defineTest() in order to obtain a TestAtom that can be used
|
||||
//! with Logger::reportTest{Start,End}().
|
||||
//!
|
||||
class TestAtom
|
||||
{
|
||||
public:
|
||||
class TestAtom {
|
||||
public:
|
||||
TestAtom(TestAtom&&) = default;
|
||||
|
||||
private:
|
||||
private:
|
||||
friend class Logger;
|
||||
|
||||
TestAtom(bool started, const std::string& name, const std::string& cmdline)
|
||||
: mStarted(started)
|
||||
, mName(name)
|
||||
, mCmdline(cmdline)
|
||||
{
|
||||
}
|
||||
: mStarted(started), mName(name), mCmdline(cmdline) {}
|
||||
|
||||
bool mStarted;
|
||||
std::string mName;
|
||||
@ -289,8 +253,7 @@ public:
|
||||
//
|
||||
//! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
|
||||
//!
|
||||
static TestAtom defineTest(const std::string& name, const std::string& cmdline)
|
||||
{
|
||||
static TestAtom defineTest(const std::string& name, const std::string& cmdline) {
|
||||
return TestAtom(false, name, cmdline);
|
||||
}
|
||||
|
||||
@ -303,8 +266,7 @@ public:
|
||||
//! \param[in] argv The array of command-line arguments (given as C strings)
|
||||
//!
|
||||
//! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
|
||||
static TestAtom defineTest(const std::string& name, int argc, char const* const* argv)
|
||||
{
|
||||
static TestAtom defineTest(const std::string& name, int argc, char const* const* argv) {
|
||||
auto cmdline = genCmdlineString(argc, argv);
|
||||
return defineTest(name, cmdline);
|
||||
}
|
||||
@ -316,8 +278,7 @@ public:
|
||||
//!
|
||||
//! \param[in] testAtom The handle to the test that has started
|
||||
//!
|
||||
static void reportTestStart(TestAtom& testAtom)
|
||||
{
|
||||
static void reportTestStart(TestAtom& testAtom) {
|
||||
reportTestResult(testAtom, TestResult::kRUNNING);
|
||||
assert(!testAtom.mStarted);
|
||||
testAtom.mStarted = true;
|
||||
@ -332,86 +293,85 @@ public:
|
||||
//! \param[in] result The result of the test. Should be one of TestResult::kPASSED,
|
||||
//! TestResult::kFAILED, TestResult::kWAIVED
|
||||
//!
|
||||
static void reportTestEnd(const TestAtom& testAtom, TestResult result)
|
||||
{
|
||||
static void reportTestEnd(const TestAtom& testAtom, TestResult result) {
|
||||
assert(result != TestResult::kRUNNING);
|
||||
assert(testAtom.mStarted);
|
||||
reportTestResult(testAtom, result);
|
||||
}
|
||||
|
||||
static int reportPass(const TestAtom& testAtom)
|
||||
{
|
||||
static int reportPass(const TestAtom& testAtom) {
|
||||
reportTestEnd(testAtom, TestResult::kPASSED);
|
||||
return EXIT_SUCCESS;
|
||||
}
|
||||
|
||||
static int reportFail(const TestAtom& testAtom)
|
||||
{
|
||||
static int reportFail(const TestAtom& testAtom) {
|
||||
reportTestEnd(testAtom, TestResult::kFAILED);
|
||||
return EXIT_FAILURE;
|
||||
}
|
||||
|
||||
static int reportWaive(const TestAtom& testAtom)
|
||||
{
|
||||
static int reportWaive(const TestAtom& testAtom) {
|
||||
reportTestEnd(testAtom, TestResult::kWAIVED);
|
||||
return EXIT_SUCCESS;
|
||||
}
|
||||
|
||||
static int reportTest(const TestAtom& testAtom, bool pass)
|
||||
{
|
||||
static int reportTest(const TestAtom& testAtom, bool pass) {
|
||||
return pass ? reportPass(testAtom) : reportFail(testAtom);
|
||||
}
|
||||
|
||||
Severity getReportableSeverity() const
|
||||
{
|
||||
return mReportableSeverity;
|
||||
}
|
||||
Severity getReportableSeverity() const { return mReportableSeverity; }
|
||||
|
||||
private:
|
||||
private:
|
||||
//!
|
||||
//! \brief returns an appropriate string for prefixing a log message with the given severity
|
||||
//!
|
||||
static const char* severityPrefix(Severity severity)
|
||||
{
|
||||
switch (severity)
|
||||
{
|
||||
case Severity::kINTERNAL_ERROR: return "[F] ";
|
||||
case Severity::kERROR: return "[E] ";
|
||||
case Severity::kWARNING: return "[W] ";
|
||||
case Severity::kINFO: return "[I] ";
|
||||
case Severity::kVERBOSE: return "[V] ";
|
||||
default: assert(0); return "";
|
||||
static const char* severityPrefix(Severity severity) {
|
||||
switch (severity) {
|
||||
case Severity::kINTERNAL_ERROR:
|
||||
return "[F] ";
|
||||
case Severity::kERROR:
|
||||
return "[E] ";
|
||||
case Severity::kWARNING:
|
||||
return "[W] ";
|
||||
case Severity::kINFO:
|
||||
return "[I] ";
|
||||
case Severity::kVERBOSE:
|
||||
return "[V] ";
|
||||
default:
|
||||
assert(0);
|
||||
return "";
|
||||
}
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief returns an appropriate string for prefixing a test result message with the given result
|
||||
//!
|
||||
static const char* testResultString(TestResult result)
|
||||
{
|
||||
switch (result)
|
||||
{
|
||||
case TestResult::kRUNNING: return "RUNNING";
|
||||
case TestResult::kPASSED: return "PASSED";
|
||||
case TestResult::kFAILED: return "FAILED";
|
||||
case TestResult::kWAIVED: return "WAIVED";
|
||||
default: assert(0); return "";
|
||||
static const char* testResultString(TestResult result) {
|
||||
switch (result) {
|
||||
case TestResult::kRUNNING:
|
||||
return "RUNNING";
|
||||
case TestResult::kPASSED:
|
||||
return "PASSED";
|
||||
case TestResult::kFAILED:
|
||||
return "FAILED";
|
||||
case TestResult::kWAIVED:
|
||||
return "WAIVED";
|
||||
default:
|
||||
assert(0);
|
||||
return "";
|
||||
}
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief returns an appropriate output stream (cout or cerr) to use with the given severity
|
||||
//!
|
||||
static std::ostream& severityOstream(Severity severity)
|
||||
{
|
||||
static std::ostream& severityOstream(Severity severity) {
|
||||
return severity >= Severity::kINFO ? std::cout : std::cerr;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief method that implements logging test results
|
||||
//!
|
||||
static void reportTestResult(const TestAtom& testAtom, TestResult result)
|
||||
{
|
||||
static void reportTestResult(const TestAtom& testAtom, TestResult result) {
|
||||
severityOstream(Severity::kINFO) << "&&&& " << testResultString(result) << " " << testAtom.mName << " # "
|
||||
<< testAtom.mCmdline << std::endl;
|
||||
}
|
||||
@ -419,11 +379,9 @@ private:
|
||||
//!
|
||||
//! \brief generate a command line string from the given (argc, argv) values
|
||||
//!
|
||||
static std::string genCmdlineString(int argc, char const* const* argv)
|
||||
{
|
||||
static std::string genCmdlineString(int argc, char const* const* argv) {
|
||||
std::stringstream ss;
|
||||
for (int i = 0; i < argc; i++)
|
||||
{
|
||||
for (int i = 0; i < argc; i++) {
|
||||
if (i > 0)
|
||||
ss << " ";
|
||||
ss << argv[i];
|
||||
@ -434,8 +392,7 @@ private:
|
||||
Severity mReportableSeverity;
|
||||
};
|
||||
|
||||
namespace
|
||||
{
|
||||
namespace {
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kVERBOSE
|
||||
@ -444,8 +401,7 @@ namespace
|
||||
//!
|
||||
//! LOG_VERBOSE(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_VERBOSE(const Logger& logger)
|
||||
{
|
||||
inline LogStreamConsumer LOG_VERBOSE(const Logger& logger) {
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kVERBOSE);
|
||||
}
|
||||
|
||||
@ -456,8 +412,7 @@ inline LogStreamConsumer LOG_VERBOSE(const Logger& logger)
|
||||
//!
|
||||
//! LOG_INFO(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_INFO(const Logger& logger)
|
||||
{
|
||||
inline LogStreamConsumer LOG_INFO(const Logger& logger) {
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINFO);
|
||||
}
|
||||
|
||||
@ -468,8 +423,7 @@ inline LogStreamConsumer LOG_INFO(const Logger& logger)
|
||||
//!
|
||||
//! LOG_WARN(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_WARN(const Logger& logger)
|
||||
{
|
||||
inline LogStreamConsumer LOG_WARN(const Logger& logger) {
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kWARNING);
|
||||
}
|
||||
|
||||
@ -480,8 +434,7 @@ inline LogStreamConsumer LOG_WARN(const Logger& logger)
|
||||
//!
|
||||
//! LOG_ERROR(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_ERROR(const Logger& logger)
|
||||
{
|
||||
inline LogStreamConsumer LOG_ERROR(const Logger& logger) {
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kERROR);
|
||||
}
|
||||
|
||||
@ -493,11 +446,10 @@ inline LogStreamConsumer LOG_ERROR(const Logger& logger)
|
||||
//!
|
||||
//! LOG_FATAL(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_FATAL(const Logger& logger)
|
||||
{
|
||||
inline LogStreamConsumer LOG_FATAL(const Logger& logger) {
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINTERNAL_ERROR);
|
||||
}
|
||||
|
||||
} // anonymous namespace
|
||||
} // anonymous namespace
|
||||
|
||||
#endif // TENSORRT_LOGGING_H
|
||||
#endif // TENSORRT_LOGGING_H
|
||||
|
||||
@ -1,23 +1,21 @@
|
||||
#include "NvInfer.h"
|
||||
#include "cuda_runtime_api.h"
|
||||
#include "logging.h"
|
||||
#include <chrono>
|
||||
#include <cmath>
|
||||
#include <fstream>
|
||||
#include <iostream>
|
||||
#include <map>
|
||||
#include <sstream>
|
||||
#include <vector>
|
||||
#include <chrono>
|
||||
#include <cmath>
|
||||
#include "NvInfer.h"
|
||||
#include "cuda_runtime_api.h"
|
||||
#include "logging.h"
|
||||
|
||||
#define CHECK(status) \
|
||||
do\
|
||||
{\
|
||||
auto ret = (status);\
|
||||
if (ret != 0)\
|
||||
{\
|
||||
std::cerr << "Cuda failure: " << ret << std::endl;\
|
||||
abort();\
|
||||
}\
|
||||
#define CHECK(status) \
|
||||
do { \
|
||||
auto ret = (status); \
|
||||
if (ret != 0) { \
|
||||
std::cerr << "Cuda failure: " << ret << std::endl; \
|
||||
abort(); \
|
||||
} \
|
||||
} while (0)
|
||||
|
||||
// stuff we know about the network and the input/output blobs
|
||||
@ -35,8 +33,7 @@ static Logger gLogger;
|
||||
// Load weights from files shared with TensorRT samples.
|
||||
// TensorRT weight files have a simple space delimited format:
|
||||
// [type] [size] <data x size in hex>
|
||||
std::map<std::string, Weights> loadWeights(const std::string file)
|
||||
{
|
||||
std::map<std::string, Weights> loadWeights(const std::string file) {
|
||||
std::cout << "Loading weights: " << file << std::endl;
|
||||
std::map<std::string, Weights> weightMap;
|
||||
|
||||
@ -49,8 +46,7 @@ std::map<std::string, Weights> loadWeights(const std::string file)
|
||||
input >> count;
|
||||
assert(count > 0 && "Invalid weight map file.");
|
||||
|
||||
while (count--)
|
||||
{
|
||||
while (count--) {
|
||||
Weights wt{DataType::kFLOAT, nullptr, 0};
|
||||
uint32_t size;
|
||||
|
||||
@ -61,12 +57,11 @@ std::map<std::string, Weights> loadWeights(const std::string file)
|
||||
|
||||
// Load blob
|
||||
uint32_t* val = reinterpret_cast<uint32_t*>(malloc(sizeof(val) * size));
|
||||
for (uint32_t x = 0, y = size; x < y; ++x)
|
||||
{
|
||||
for (uint32_t x = 0, y = size; x < y; ++x) {
|
||||
input >> std::hex >> val[x];
|
||||
}
|
||||
wt.values = val;
|
||||
|
||||
|
||||
wt.count = size;
|
||||
weightMap[name] = wt;
|
||||
}
|
||||
@ -74,27 +69,28 @@ std::map<std::string, Weights> loadWeights(const std::string file)
|
||||
return weightMap;
|
||||
}
|
||||
|
||||
IScaleLayer* addBatchNorm2d(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname, float eps) {
|
||||
float *gamma = (float*)weightMap[lname + ".weight"].values;
|
||||
float *beta = (float*)weightMap[lname + ".bias"].values;
|
||||
float *mean = (float*)weightMap[lname + ".running_mean"].values;
|
||||
float *var = (float*)weightMap[lname + ".running_var"].values;
|
||||
IScaleLayer* addBatchNorm2d(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input,
|
||||
std::string lname, float eps) {
|
||||
float* gamma = (float*)weightMap[lname + ".weight"].values;
|
||||
float* beta = (float*)weightMap[lname + ".bias"].values;
|
||||
float* mean = (float*)weightMap[lname + ".running_mean"].values;
|
||||
float* var = (float*)weightMap[lname + ".running_var"].values;
|
||||
int len = weightMap[lname + ".running_var"].count;
|
||||
std::cout << "len " << len << std::endl;
|
||||
|
||||
float *scval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
|
||||
float* scval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
|
||||
for (int i = 0; i < len; i++) {
|
||||
scval[i] = gamma[i] / sqrt(var[i] + eps);
|
||||
}
|
||||
Weights scale{DataType::kFLOAT, scval, len};
|
||||
|
||||
float *shval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
|
||||
|
||||
float* shval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
|
||||
for (int i = 0; i < len; i++) {
|
||||
shval[i] = beta[i] - mean[i] * gamma[i] / sqrt(var[i] + eps);
|
||||
}
|
||||
Weights shift{DataType::kFLOAT, shval, len};
|
||||
|
||||
float *pval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
|
||||
float* pval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
|
||||
for (int i = 0; i < len; i++) {
|
||||
pval[i] = 1.0;
|
||||
}
|
||||
@ -108,10 +104,12 @@ IScaleLayer* addBatchNorm2d(INetworkDefinition *network, std::map<std::string, W
|
||||
return scale_1;
|
||||
}
|
||||
|
||||
IElementWiseLayer* convBnRelu(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int outch, int ksize, int s, int g, std::string lname) {
|
||||
IElementWiseLayer* convBnRelu(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input,
|
||||
int outch, int ksize, int s, int g, std::string lname) {
|
||||
Weights emptywts{DataType::kFLOAT, nullptr, 0};
|
||||
int p = (ksize - 1) / 2;
|
||||
IConvolutionLayer* conv1 = network->addConvolutionNd(input, outch, DimsHW{ksize, ksize}, weightMap[lname + "0.weight"], emptywts);
|
||||
IConvolutionLayer* conv1 =
|
||||
network->addConvolutionNd(input, outch, DimsHW{ksize, ksize}, weightMap[lname + "0.weight"], emptywts);
|
||||
assert(conv1);
|
||||
conv1->setStrideNd(DimsHW{s, s});
|
||||
conv1->setPaddingNd(DimsHW{p, p});
|
||||
@ -122,9 +120,9 @@ IElementWiseLayer* convBnRelu(INetworkDefinition *network, std::map<std::string,
|
||||
IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
|
||||
assert(relu1);
|
||||
|
||||
float *shval = reinterpret_cast<float*>(malloc(sizeof(float) * 1));
|
||||
float *scval = reinterpret_cast<float*>(malloc(sizeof(float) * 1));
|
||||
float *pval = reinterpret_cast<float*>(malloc(sizeof(float) * 1));
|
||||
float* shval = reinterpret_cast<float*>(malloc(sizeof(float) * 1));
|
||||
float* scval = reinterpret_cast<float*>(malloc(sizeof(float) * 1));
|
||||
float* pval = reinterpret_cast<float*>(malloc(sizeof(float) * 1));
|
||||
shval[0] = -6.0;
|
||||
scval[0] = 1.0;
|
||||
pval[0] = 1.0;
|
||||
@ -140,39 +138,43 @@ IElementWiseLayer* convBnRelu(INetworkDefinition *network, std::map<std::string,
|
||||
IActivationLayer* relu2 = network->addActivation(*scale1->getOutput(0), ActivationType::kRELU);
|
||||
assert(relu2);
|
||||
|
||||
IElementWiseLayer* ew1 = network->addElementWise(*relu1->getOutput(0), *relu2->getOutput(0), ElementWiseOperation::kSUB);
|
||||
IElementWiseLayer* ew1 =
|
||||
network->addElementWise(*relu1->getOutput(0), *relu2->getOutput(0), ElementWiseOperation::kSUB);
|
||||
assert(ew1);
|
||||
return ew1;
|
||||
}
|
||||
|
||||
ILayer* invertedRes(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname,
|
||||
int inch, int outch, int s, int exp) {
|
||||
ILayer* invertedRes(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input,
|
||||
std::string lname, int inch, int outch, int s, int exp) {
|
||||
Weights emptywts{DataType::kFLOAT, nullptr, 0};
|
||||
int hidden = inch * exp;
|
||||
bool use_res_connect = (s == 1 && inch == outch);
|
||||
|
||||
IScaleLayer *bn1 = nullptr;
|
||||
IScaleLayer* bn1 = nullptr;
|
||||
if (exp != 1) {
|
||||
IElementWiseLayer* ew1 = convBnRelu(network, weightMap, input, hidden, 1, 1, 1, lname + "conv.0.");
|
||||
IElementWiseLayer* ew2 = convBnRelu(network, weightMap, *ew1->getOutput(0), hidden, 3, s, hidden, lname + "conv.1.");
|
||||
IConvolutionLayer* conv1 = network->addConvolutionNd(*ew2->getOutput(0), outch, DimsHW{1, 1}, weightMap[lname + "conv.2.weight"], emptywts);
|
||||
IElementWiseLayer* ew2 =
|
||||
convBnRelu(network, weightMap, *ew1->getOutput(0), hidden, 3, s, hidden, lname + "conv.1.");
|
||||
IConvolutionLayer* conv1 = network->addConvolutionNd(*ew2->getOutput(0), outch, DimsHW{1, 1},
|
||||
weightMap[lname + "conv.2.weight"], emptywts);
|
||||
assert(conv1);
|
||||
bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + "conv.3", 1e-5);
|
||||
} else {
|
||||
IElementWiseLayer* ew1 = convBnRelu(network, weightMap, input, hidden, 3, s, hidden, lname + "conv.0.");
|
||||
IConvolutionLayer* conv1 = network->addConvolutionNd(*ew1->getOutput(0), outch, DimsHW{1, 1}, weightMap[lname + "conv.1.weight"], emptywts);
|
||||
IConvolutionLayer* conv1 = network->addConvolutionNd(*ew1->getOutput(0), outch, DimsHW{1, 1},
|
||||
weightMap[lname + "conv.1.weight"], emptywts);
|
||||
assert(conv1);
|
||||
bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + "conv.2", 1e-5);
|
||||
}
|
||||
if (!use_res_connect) return bn1;
|
||||
if (!use_res_connect)
|
||||
return bn1;
|
||||
IElementWiseLayer* ew3 = network->addElementWise(input, *bn1->getOutput(0), ElementWiseOperation::kSUM);
|
||||
assert(ew3);
|
||||
return ew3;
|
||||
}
|
||||
|
||||
// Creat the engine using only the API and not any parser.
|
||||
ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt)
|
||||
{
|
||||
ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt) {
|
||||
INetworkDefinition* network = builder->createNetworkV2(0U);
|
||||
|
||||
// Create input tensor of shape { 3, INPUT_H, INPUT_W } with name INPUT_BLOB_NAME
|
||||
@ -205,7 +207,8 @@ ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilder
|
||||
IPoolingLayer* pool1 = network->addPoolingNd(*ew2->getOutput(0), PoolingType::kAVERAGE, DimsHW{7, 7});
|
||||
assert(pool1);
|
||||
|
||||
IFullyConnectedLayer* fc1 = network->addFullyConnected(*pool1->getOutput(0), 1000, weightMap["classifier.1.weight"], weightMap["classifier.1.bias"]);
|
||||
IFullyConnectedLayer* fc1 = network->addFullyConnected(*pool1->getOutput(0), 1000, weightMap["classifier.1.weight"],
|
||||
weightMap["classifier.1.bias"]);
|
||||
assert(fc1);
|
||||
|
||||
fc1->getOutput(0)->setName(OUTPUT_BLOB_NAME);
|
||||
@ -222,16 +225,14 @@ ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilder
|
||||
network->destroy();
|
||||
|
||||
// Release host memory
|
||||
for (auto& mem : weightMap)
|
||||
{
|
||||
free((void*) (mem.second.values));
|
||||
for (auto& mem : weightMap) {
|
||||
free((void*)(mem.second.values));
|
||||
}
|
||||
|
||||
return engine;
|
||||
}
|
||||
|
||||
void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream)
|
||||
{
|
||||
void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream) {
|
||||
// Create builder
|
||||
IBuilder* builder = createInferBuilder(gLogger);
|
||||
IBuilderConfig* config = builder->createBuilderConfig();
|
||||
@ -245,12 +246,11 @@ void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream)
|
||||
|
||||
// Close everything down
|
||||
engine->destroy();
|
||||
builder->destroy();
|
||||
config->destroy();
|
||||
builder->destroy();
|
||||
}
|
||||
|
||||
void doInference(IExecutionContext& context, float* input, float* output, int batchSize)
|
||||
{
|
||||
void doInference(IExecutionContext& context, float* input, float* output, int batchSize) {
|
||||
const ICudaEngine& engine = context.getEngine();
|
||||
|
||||
// Pointers to input and output device buffers to pass to engine.
|
||||
@ -272,9 +272,11 @@ void doInference(IExecutionContext& context, float* input, float* output, int ba
|
||||
CHECK(cudaStreamCreate(&stream));
|
||||
|
||||
// DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
|
||||
CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream));
|
||||
CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float),
|
||||
cudaMemcpyHostToDevice, stream));
|
||||
context.enqueue(batchSize, buffers, stream, nullptr);
|
||||
CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));
|
||||
CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost,
|
||||
stream));
|
||||
cudaStreamSynchronize(stream);
|
||||
|
||||
// Release stream and buffers
|
||||
@ -283,8 +285,7 @@ void doInference(IExecutionContext& context, float* input, float* output, int ba
|
||||
CHECK(cudaFree(buffers[outputIndex]));
|
||||
}
|
||||
|
||||
int main(int argc, char** argv)
|
||||
{
|
||||
int main(int argc, char** argv) {
|
||||
if (argc != 2) {
|
||||
std::cerr << "arguments not right!" << std::endl;
|
||||
std::cerr << "./mobilenet -s // serialize model to plan file" << std::endl;
|
||||
@ -293,7 +294,7 @@ int main(int argc, char** argv)
|
||||
}
|
||||
|
||||
// create a model using the API directly and serialize it to a stream
|
||||
char *trtModelStream{nullptr};
|
||||
char* trtModelStream{nullptr};
|
||||
size_t size{0};
|
||||
|
||||
if (std::string(argv[1]) == "-s") {
|
||||
@ -302,8 +303,7 @@ int main(int argc, char** argv)
|
||||
assert(modelStream != nullptr);
|
||||
|
||||
std::ofstream p("mobilenet.engine", std::ios::binary);
|
||||
if (!p)
|
||||
{
|
||||
if (!p) {
|
||||
std::cerr << "could not open plan output file" << std::endl;
|
||||
return -1;
|
||||
}
|
||||
@ -325,7 +325,6 @@ int main(int argc, char** argv)
|
||||
return -1;
|
||||
}
|
||||
|
||||
|
||||
// Subtract mean from image
|
||||
static float data[3 * INPUT_H * INPUT_W];
|
||||
for (int i = 0; i < 3 * INPUT_H * INPUT_W; i++)
|
||||
@ -355,10 +354,10 @@ int main(int argc, char** argv)
|
||||
|
||||
// Print histogram of the output distribution
|
||||
std::cout << "\nOutput:\n\n";
|
||||
for (unsigned int i = 0; i < OUTPUT_SIZE; i++)
|
||||
{
|
||||
for (unsigned int i = 0; i < OUTPUT_SIZE; i++) {
|
||||
std::cout << prob[i] << ", ";
|
||||
if (i % 10 == 0) std::cout << i / 10 << std::endl;
|
||||
if (i % 10 == 0)
|
||||
std::cout << i / 10 << std::endl;
|
||||
}
|
||||
std::cout << std::endl;
|
||||
|
||||
|
||||
@ -4,8 +4,8 @@ import struct
|
||||
import argparse
|
||||
|
||||
import numpy as np
|
||||
import pycuda.autoinit
|
||||
import pycuda.driver as cuda
|
||||
import pycuda.autoinit # noqa: F401
|
||||
import tensorrt as trt
|
||||
|
||||
BATCH_SIZE = 1
|
||||
|
||||
@ -24,4 +24,3 @@ target_link_libraries(mobilenetv3 nvinfer)
|
||||
target_link_libraries(mobilenetv3 cudart)
|
||||
|
||||
add_definitions(-O2 -pthread)
|
||||
|
||||
|
||||
@ -17,7 +17,6 @@
|
||||
#ifndef TENSORRT_LOGGING_H
|
||||
#define TENSORRT_LOGGING_H
|
||||
|
||||
#include "NvInferRuntimeCommon.h"
|
||||
#include <cassert>
|
||||
#include <ctime>
|
||||
#include <iomanip>
|
||||
@ -25,32 +24,23 @@
|
||||
#include <ostream>
|
||||
#include <sstream>
|
||||
#include <string>
|
||||
#include "NvInferRuntimeCommon.h"
|
||||
|
||||
using Severity = nvinfer1::ILogger::Severity;
|
||||
|
||||
class LogStreamConsumerBuffer : public std::stringbuf
|
||||
{
|
||||
public:
|
||||
class LogStreamConsumerBuffer : public std::stringbuf {
|
||||
public:
|
||||
LogStreamConsumerBuffer(std::ostream& stream, const std::string& prefix, bool shouldLog)
|
||||
: mOutput(stream)
|
||||
, mPrefix(prefix)
|
||||
, mShouldLog(shouldLog)
|
||||
{
|
||||
}
|
||||
: mOutput(stream), mPrefix(prefix), mShouldLog(shouldLog) {}
|
||||
|
||||
LogStreamConsumerBuffer(LogStreamConsumerBuffer&& other)
|
||||
: mOutput(other.mOutput)
|
||||
{
|
||||
}
|
||||
LogStreamConsumerBuffer(LogStreamConsumerBuffer&& other) : mOutput(other.mOutput) {}
|
||||
|
||||
~LogStreamConsumerBuffer()
|
||||
{
|
||||
~LogStreamConsumerBuffer() {
|
||||
// std::streambuf::pbase() gives a pointer to the beginning of the buffered part of the output sequence
|
||||
// std::streambuf::pptr() gives a pointer to the current position of the output sequence
|
||||
// if the pointer to the beginning is not equal to the pointer to the current position,
|
||||
// call putOutput() to log the output to the stream
|
||||
if (pbase() != pptr())
|
||||
{
|
||||
if (pbase() != pptr()) {
|
||||
putOutput();
|
||||
}
|
||||
}
|
||||
@ -58,16 +48,13 @@ public:
|
||||
// synchronizes the stream buffer and returns 0 on success
|
||||
// synchronizing the stream buffer consists of inserting the buffer contents into the stream,
|
||||
// resetting the buffer and flushing the stream
|
||||
virtual int sync()
|
||||
{
|
||||
virtual int sync() {
|
||||
putOutput();
|
||||
return 0;
|
||||
}
|
||||
|
||||
void putOutput()
|
||||
{
|
||||
if (mShouldLog)
|
||||
{
|
||||
void putOutput() {
|
||||
if (mShouldLog) {
|
||||
// prepend timestamp
|
||||
std::time_t timestamp = std::time(nullptr);
|
||||
tm* tm_local = std::localtime(×tamp);
|
||||
@ -88,12 +75,9 @@ public:
|
||||
}
|
||||
}
|
||||
|
||||
void setShouldLog(bool shouldLog)
|
||||
{
|
||||
mShouldLog = shouldLog;
|
||||
}
|
||||
void setShouldLog(bool shouldLog) { mShouldLog = shouldLog; }
|
||||
|
||||
private:
|
||||
private:
|
||||
std::ostream& mOutput;
|
||||
std::string mPrefix;
|
||||
bool mShouldLog;
|
||||
@ -103,15 +87,12 @@ private:
|
||||
//! \class LogStreamConsumerBase
|
||||
//! \brief Convenience object used to initialize LogStreamConsumerBuffer before std::ostream in LogStreamConsumer
|
||||
//!
|
||||
class LogStreamConsumerBase
|
||||
{
|
||||
public:
|
||||
class LogStreamConsumerBase {
|
||||
public:
|
||||
LogStreamConsumerBase(std::ostream& stream, const std::string& prefix, bool shouldLog)
|
||||
: mBuffer(stream, prefix, shouldLog)
|
||||
{
|
||||
}
|
||||
: mBuffer(stream, prefix, shouldLog) {}
|
||||
|
||||
protected:
|
||||
protected:
|
||||
LogStreamConsumerBuffer mBuffer;
|
||||
};
|
||||
|
||||
@ -124,49 +105,49 @@ protected:
|
||||
//! This is necessary to prevent the address of an uninitialized buffer from being passed to std::ostream.
|
||||
//! Please do not change the order of the parent classes.
|
||||
//!
|
||||
class LogStreamConsumer : protected LogStreamConsumerBase, public std::ostream
|
||||
{
|
||||
public:
|
||||
class LogStreamConsumer : protected LogStreamConsumerBase, public std::ostream {
|
||||
public:
|
||||
//! \brief Creates a LogStreamConsumer which logs messages with level severity.
|
||||
//! Reportable severity determines if the messages are severe enough to be logged.
|
||||
LogStreamConsumer(Severity reportableSeverity, Severity severity)
|
||||
: LogStreamConsumerBase(severityOstream(severity), severityPrefix(severity), severity <= reportableSeverity)
|
||||
, std::ostream(&mBuffer) // links the stream buffer with the stream
|
||||
, mShouldLog(severity <= reportableSeverity)
|
||||
, mSeverity(severity)
|
||||
{
|
||||
}
|
||||
: LogStreamConsumerBase(severityOstream(severity), severityPrefix(severity), severity <= reportableSeverity),
|
||||
std::ostream(&mBuffer) // links the stream buffer with the stream
|
||||
,
|
||||
mShouldLog(severity <= reportableSeverity),
|
||||
mSeverity(severity) {}
|
||||
|
||||
LogStreamConsumer(LogStreamConsumer&& other)
|
||||
: LogStreamConsumerBase(severityOstream(other.mSeverity), severityPrefix(other.mSeverity), other.mShouldLog)
|
||||
, std::ostream(&mBuffer) // links the stream buffer with the stream
|
||||
, mShouldLog(other.mShouldLog)
|
||||
, mSeverity(other.mSeverity)
|
||||
{
|
||||
}
|
||||
: LogStreamConsumerBase(severityOstream(other.mSeverity), severityPrefix(other.mSeverity), other.mShouldLog),
|
||||
std::ostream(&mBuffer) // links the stream buffer with the stream
|
||||
,
|
||||
mShouldLog(other.mShouldLog),
|
||||
mSeverity(other.mSeverity) {}
|
||||
|
||||
void setReportableSeverity(Severity reportableSeverity)
|
||||
{
|
||||
void setReportableSeverity(Severity reportableSeverity) {
|
||||
mShouldLog = mSeverity <= reportableSeverity;
|
||||
mBuffer.setShouldLog(mShouldLog);
|
||||
}
|
||||
|
||||
private:
|
||||
static std::ostream& severityOstream(Severity severity)
|
||||
{
|
||||
private:
|
||||
static std::ostream& severityOstream(Severity severity) {
|
||||
return severity >= Severity::kINFO ? std::cout : std::cerr;
|
||||
}
|
||||
|
||||
static std::string severityPrefix(Severity severity)
|
||||
{
|
||||
switch (severity)
|
||||
{
|
||||
case Severity::kINTERNAL_ERROR: return "[F] ";
|
||||
case Severity::kERROR: return "[E] ";
|
||||
case Severity::kWARNING: return "[W] ";
|
||||
case Severity::kINFO: return "[I] ";
|
||||
case Severity::kVERBOSE: return "[V] ";
|
||||
default: assert(0); return "";
|
||||
static std::string severityPrefix(Severity severity) {
|
||||
switch (severity) {
|
||||
case Severity::kINTERNAL_ERROR:
|
||||
return "[F] ";
|
||||
case Severity::kERROR:
|
||||
return "[E] ";
|
||||
case Severity::kWARNING:
|
||||
return "[W] ";
|
||||
case Severity::kINFO:
|
||||
return "[I] ";
|
||||
case Severity::kVERBOSE:
|
||||
return "[V] ";
|
||||
default:
|
||||
assert(0);
|
||||
return "";
|
||||
}
|
||||
}
|
||||
|
||||
@ -198,24 +179,19 @@ private:
|
||||
//! class to eliminate the inheritance and instead make the nvinfer1::ILogger implementation a member of the Logger
|
||||
//! object.
|
||||
|
||||
class Logger : public nvinfer1::ILogger
|
||||
{
|
||||
public:
|
||||
Logger(Severity severity = Severity::kWARNING)
|
||||
: mReportableSeverity(severity)
|
||||
{
|
||||
}
|
||||
class Logger : public nvinfer1::ILogger {
|
||||
public:
|
||||
Logger(Severity severity = Severity::kWARNING) : mReportableSeverity(severity) {}
|
||||
|
||||
//!
|
||||
//! \enum TestResult
|
||||
//! \brief Represents the state of a given test
|
||||
//!
|
||||
enum class TestResult
|
||||
{
|
||||
kRUNNING, //!< The test is running
|
||||
kPASSED, //!< The test passed
|
||||
kFAILED, //!< The test failed
|
||||
kWAIVED //!< The test was waived
|
||||
enum class TestResult {
|
||||
kRUNNING, //!< The test is running
|
||||
kPASSED, //!< The test passed
|
||||
kFAILED, //!< The test failed
|
||||
kWAIVED //!< The test was waived
|
||||
};
|
||||
|
||||
//!
|
||||
@ -225,10 +201,7 @@ public:
|
||||
//! TODO Once all samples are updated to use this method to register the logger with TensorRT,
|
||||
//! we can eliminate the inheritance of Logger from ILogger
|
||||
//!
|
||||
nvinfer1::ILogger& getTRTLogger()
|
||||
{
|
||||
return *this;
|
||||
}
|
||||
nvinfer1::ILogger& getTRTLogger() { return *this; }
|
||||
|
||||
//!
|
||||
//! \brief Implementation of the nvinfer1::ILogger::log() virtual method
|
||||
@ -236,8 +209,7 @@ public:
|
||||
//! Note samples should not be calling this function directly; it will eventually go away once we eliminate the
|
||||
//! inheritance from nvinfer1::ILogger
|
||||
//!
|
||||
void log(Severity severity, const char* msg) override
|
||||
{
|
||||
void log(Severity severity, const char* msg) override {
|
||||
LogStreamConsumer(mReportableSeverity, severity) << "[TRT] " << std::string(msg) << std::endl;
|
||||
}
|
||||
|
||||
@ -246,10 +218,7 @@ public:
|
||||
//!
|
||||
//! \param severity The logger will only emit messages that have severity of this level or higher.
|
||||
//!
|
||||
void setReportableSeverity(Severity severity)
|
||||
{
|
||||
mReportableSeverity = severity;
|
||||
}
|
||||
void setReportableSeverity(Severity severity) { mReportableSeverity = severity; }
|
||||
|
||||
//!
|
||||
//! \brief Opaque handle that holds logging information for a particular test
|
||||
@ -258,20 +227,15 @@ public:
|
||||
//! The sample must call Logger::defineTest() in order to obtain a TestAtom that can be used
|
||||
//! with Logger::reportTest{Start,End}().
|
||||
//!
|
||||
class TestAtom
|
||||
{
|
||||
public:
|
||||
class TestAtom {
|
||||
public:
|
||||
TestAtom(TestAtom&&) = default;
|
||||
|
||||
private:
|
||||
private:
|
||||
friend class Logger;
|
||||
|
||||
TestAtom(bool started, const std::string& name, const std::string& cmdline)
|
||||
: mStarted(started)
|
||||
, mName(name)
|
||||
, mCmdline(cmdline)
|
||||
{
|
||||
}
|
||||
: mStarted(started), mName(name), mCmdline(cmdline) {}
|
||||
|
||||
bool mStarted;
|
||||
std::string mName;
|
||||
@ -289,8 +253,7 @@ public:
|
||||
//
|
||||
//! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
|
||||
//!
|
||||
static TestAtom defineTest(const std::string& name, const std::string& cmdline)
|
||||
{
|
||||
static TestAtom defineTest(const std::string& name, const std::string& cmdline) {
|
||||
return TestAtom(false, name, cmdline);
|
||||
}
|
||||
|
||||
@ -303,8 +266,7 @@ public:
|
||||
//! \param[in] argv The array of command-line arguments (given as C strings)
|
||||
//!
|
||||
//! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
|
||||
static TestAtom defineTest(const std::string& name, int argc, char const* const* argv)
|
||||
{
|
||||
static TestAtom defineTest(const std::string& name, int argc, char const* const* argv) {
|
||||
auto cmdline = genCmdlineString(argc, argv);
|
||||
return defineTest(name, cmdline);
|
||||
}
|
||||
@ -316,8 +278,7 @@ public:
|
||||
//!
|
||||
//! \param[in] testAtom The handle to the test that has started
|
||||
//!
|
||||
static void reportTestStart(TestAtom& testAtom)
|
||||
{
|
||||
static void reportTestStart(TestAtom& testAtom) {
|
||||
reportTestResult(testAtom, TestResult::kRUNNING);
|
||||
assert(!testAtom.mStarted);
|
||||
testAtom.mStarted = true;
|
||||
@ -332,86 +293,85 @@ public:
|
||||
//! \param[in] result The result of the test. Should be one of TestResult::kPASSED,
|
||||
//! TestResult::kFAILED, TestResult::kWAIVED
|
||||
//!
|
||||
static void reportTestEnd(const TestAtom& testAtom, TestResult result)
|
||||
{
|
||||
static void reportTestEnd(const TestAtom& testAtom, TestResult result) {
|
||||
assert(result != TestResult::kRUNNING);
|
||||
assert(testAtom.mStarted);
|
||||
reportTestResult(testAtom, result);
|
||||
}
|
||||
|
||||
static int reportPass(const TestAtom& testAtom)
|
||||
{
|
||||
static int reportPass(const TestAtom& testAtom) {
|
||||
reportTestEnd(testAtom, TestResult::kPASSED);
|
||||
return EXIT_SUCCESS;
|
||||
}
|
||||
|
||||
static int reportFail(const TestAtom& testAtom)
|
||||
{
|
||||
static int reportFail(const TestAtom& testAtom) {
|
||||
reportTestEnd(testAtom, TestResult::kFAILED);
|
||||
return EXIT_FAILURE;
|
||||
}
|
||||
|
||||
static int reportWaive(const TestAtom& testAtom)
|
||||
{
|
||||
static int reportWaive(const TestAtom& testAtom) {
|
||||
reportTestEnd(testAtom, TestResult::kWAIVED);
|
||||
return EXIT_SUCCESS;
|
||||
}
|
||||
|
||||
static int reportTest(const TestAtom& testAtom, bool pass)
|
||||
{
|
||||
static int reportTest(const TestAtom& testAtom, bool pass) {
|
||||
return pass ? reportPass(testAtom) : reportFail(testAtom);
|
||||
}
|
||||
|
||||
Severity getReportableSeverity() const
|
||||
{
|
||||
return mReportableSeverity;
|
||||
}
|
||||
Severity getReportableSeverity() const { return mReportableSeverity; }
|
||||
|
||||
private:
|
||||
private:
|
||||
//!
|
||||
//! \brief returns an appropriate string for prefixing a log message with the given severity
|
||||
//!
|
||||
static const char* severityPrefix(Severity severity)
|
||||
{
|
||||
switch (severity)
|
||||
{
|
||||
case Severity::kINTERNAL_ERROR: return "[F] ";
|
||||
case Severity::kERROR: return "[E] ";
|
||||
case Severity::kWARNING: return "[W] ";
|
||||
case Severity::kINFO: return "[I] ";
|
||||
case Severity::kVERBOSE: return "[V] ";
|
||||
default: assert(0); return "";
|
||||
static const char* severityPrefix(Severity severity) {
|
||||
switch (severity) {
|
||||
case Severity::kINTERNAL_ERROR:
|
||||
return "[F] ";
|
||||
case Severity::kERROR:
|
||||
return "[E] ";
|
||||
case Severity::kWARNING:
|
||||
return "[W] ";
|
||||
case Severity::kINFO:
|
||||
return "[I] ";
|
||||
case Severity::kVERBOSE:
|
||||
return "[V] ";
|
||||
default:
|
||||
assert(0);
|
||||
return "";
|
||||
}
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief returns an appropriate string for prefixing a test result message with the given result
|
||||
//!
|
||||
static const char* testResultString(TestResult result)
|
||||
{
|
||||
switch (result)
|
||||
{
|
||||
case TestResult::kRUNNING: return "RUNNING";
|
||||
case TestResult::kPASSED: return "PASSED";
|
||||
case TestResult::kFAILED: return "FAILED";
|
||||
case TestResult::kWAIVED: return "WAIVED";
|
||||
default: assert(0); return "";
|
||||
static const char* testResultString(TestResult result) {
|
||||
switch (result) {
|
||||
case TestResult::kRUNNING:
|
||||
return "RUNNING";
|
||||
case TestResult::kPASSED:
|
||||
return "PASSED";
|
||||
case TestResult::kFAILED:
|
||||
return "FAILED";
|
||||
case TestResult::kWAIVED:
|
||||
return "WAIVED";
|
||||
default:
|
||||
assert(0);
|
||||
return "";
|
||||
}
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief returns an appropriate output stream (cout or cerr) to use with the given severity
|
||||
//!
|
||||
static std::ostream& severityOstream(Severity severity)
|
||||
{
|
||||
static std::ostream& severityOstream(Severity severity) {
|
||||
return severity >= Severity::kINFO ? std::cout : std::cerr;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief method that implements logging test results
|
||||
//!
|
||||
static void reportTestResult(const TestAtom& testAtom, TestResult result)
|
||||
{
|
||||
static void reportTestResult(const TestAtom& testAtom, TestResult result) {
|
||||
severityOstream(Severity::kINFO) << "&&&& " << testResultString(result) << " " << testAtom.mName << " # "
|
||||
<< testAtom.mCmdline << std::endl;
|
||||
}
|
||||
@ -419,11 +379,9 @@ private:
|
||||
//!
|
||||
//! \brief generate a command line string from the given (argc, argv) values
|
||||
//!
|
||||
static std::string genCmdlineString(int argc, char const* const* argv)
|
||||
{
|
||||
static std::string genCmdlineString(int argc, char const* const* argv) {
|
||||
std::stringstream ss;
|
||||
for (int i = 0; i < argc; i++)
|
||||
{
|
||||
for (int i = 0; i < argc; i++) {
|
||||
if (i > 0)
|
||||
ss << " ";
|
||||
ss << argv[i];
|
||||
@ -434,8 +392,7 @@ private:
|
||||
Severity mReportableSeverity;
|
||||
};
|
||||
|
||||
namespace
|
||||
{
|
||||
namespace {
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kVERBOSE
|
||||
@ -444,8 +401,7 @@ namespace
|
||||
//!
|
||||
//! LOG_VERBOSE(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_VERBOSE(const Logger& logger)
|
||||
{
|
||||
inline LogStreamConsumer LOG_VERBOSE(const Logger& logger) {
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kVERBOSE);
|
||||
}
|
||||
|
||||
@ -456,8 +412,7 @@ inline LogStreamConsumer LOG_VERBOSE(const Logger& logger)
|
||||
//!
|
||||
//! LOG_INFO(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_INFO(const Logger& logger)
|
||||
{
|
||||
inline LogStreamConsumer LOG_INFO(const Logger& logger) {
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINFO);
|
||||
}
|
||||
|
||||
@ -468,8 +423,7 @@ inline LogStreamConsumer LOG_INFO(const Logger& logger)
|
||||
//!
|
||||
//! LOG_WARN(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_WARN(const Logger& logger)
|
||||
{
|
||||
inline LogStreamConsumer LOG_WARN(const Logger& logger) {
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kWARNING);
|
||||
}
|
||||
|
||||
@ -480,8 +434,7 @@ inline LogStreamConsumer LOG_WARN(const Logger& logger)
|
||||
//!
|
||||
//! LOG_ERROR(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_ERROR(const Logger& logger)
|
||||
{
|
||||
inline LogStreamConsumer LOG_ERROR(const Logger& logger) {
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kERROR);
|
||||
}
|
||||
|
||||
@ -493,11 +446,10 @@ inline LogStreamConsumer LOG_ERROR(const Logger& logger)
|
||||
//!
|
||||
//! LOG_FATAL(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_FATAL(const Logger& logger)
|
||||
{
|
||||
inline LogStreamConsumer LOG_FATAL(const Logger& logger) {
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINTERNAL_ERROR);
|
||||
}
|
||||
|
||||
} // anonymous namespace
|
||||
} // anonymous namespace
|
||||
|
||||
#endif // TENSORRT_LOGGING_H
|
||||
#endif // TENSORRT_LOGGING_H
|
||||
|
||||
@ -1,23 +1,21 @@
|
||||
#include "NvInfer.h"
|
||||
#include "cuda_runtime_api.h"
|
||||
#include "logging.h"
|
||||
#include <chrono>
|
||||
#include <cmath>
|
||||
#include <fstream>
|
||||
#include <iostream>
|
||||
#include <map>
|
||||
#include <sstream>
|
||||
#include <vector>
|
||||
#include <chrono>
|
||||
#include <cmath>
|
||||
#include "NvInfer.h"
|
||||
#include "cuda_runtime_api.h"
|
||||
#include "logging.h"
|
||||
|
||||
#define CHECK(status) \
|
||||
do\
|
||||
{\
|
||||
auto ret = (status);\
|
||||
if (ret != 0)\
|
||||
{\
|
||||
std::cerr << "Cuda failure: " << ret << std::endl;\
|
||||
abort();\
|
||||
}\
|
||||
#define CHECK(status) \
|
||||
do { \
|
||||
auto ret = (status); \
|
||||
if (ret != 0) { \
|
||||
std::cerr << "Cuda failure: " << ret << std::endl; \
|
||||
abort(); \
|
||||
} \
|
||||
} while (0)
|
||||
|
||||
// stuff we know about the network and the input/output blobs
|
||||
@ -36,8 +34,7 @@ static Logger gLogger;
|
||||
// Load weights from files shared with TensorRT samples.
|
||||
// TensorRT weight files have a simple space delimited format:
|
||||
// [type] [size] <data x size in hex>
|
||||
std::map<std::string, Weights> loadWeights(const std::string file)
|
||||
{
|
||||
std::map<std::string, Weights> loadWeights(const std::string file) {
|
||||
std::cout << "Loading weights: " << file << std::endl;
|
||||
std::map<std::string, Weights> weightMap;
|
||||
|
||||
@ -50,8 +47,7 @@ std::map<std::string, Weights> loadWeights(const std::string file)
|
||||
input >> count;
|
||||
assert(count > 0 && "Invalid weight map file.");
|
||||
|
||||
while (count--)
|
||||
{
|
||||
while (count--) {
|
||||
Weights wt{DataType::kFLOAT, nullptr, 0};
|
||||
uint32_t size;
|
||||
|
||||
@ -62,12 +58,11 @@ std::map<std::string, Weights> loadWeights(const std::string file)
|
||||
|
||||
// Load blob
|
||||
uint32_t* val = reinterpret_cast<uint32_t*>(malloc(sizeof(val) * size));
|
||||
for (uint32_t x = 0, y = size; x < y; ++x)
|
||||
{
|
||||
for (uint32_t x = 0, y = size; x < y; ++x) {
|
||||
input >> std::hex >> val[x];
|
||||
}
|
||||
wt.values = val;
|
||||
|
||||
|
||||
wt.count = size;
|
||||
weightMap[name] = wt;
|
||||
}
|
||||
@ -75,27 +70,28 @@ std::map<std::string, Weights> loadWeights(const std::string file)
|
||||
return weightMap;
|
||||
}
|
||||
|
||||
IScaleLayer* addBatchNorm(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname, float eps) {
|
||||
float *gamma = (float*)weightMap[lname + ".weight"].values;
|
||||
float *beta = (float*)weightMap[lname + ".bias"].values;
|
||||
float *mean = (float*)weightMap[lname + ".running_mean"].values;
|
||||
float *var = (float*)weightMap[lname + ".running_var"].values;
|
||||
IScaleLayer* addBatchNorm(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input,
|
||||
std::string lname, float eps) {
|
||||
float* gamma = (float*)weightMap[lname + ".weight"].values;
|
||||
float* beta = (float*)weightMap[lname + ".bias"].values;
|
||||
float* mean = (float*)weightMap[lname + ".running_mean"].values;
|
||||
float* var = (float*)weightMap[lname + ".running_var"].values;
|
||||
int len = weightMap[lname + ".running_var"].count;
|
||||
std::cout << "len " << len << std::endl;
|
||||
|
||||
float *scval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
|
||||
float* scval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
|
||||
for (int i = 0; i < len; i++) {
|
||||
scval[i] = gamma[i] / sqrt(var[i] + eps);
|
||||
}
|
||||
Weights scale{DataType::kFLOAT, scval, len};
|
||||
|
||||
float *shval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
|
||||
|
||||
float* shval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
|
||||
for (int i = 0; i < len; i++) {
|
||||
shval[i] = beta[i] - mean[i] * gamma[i] / sqrt(var[i] + eps);
|
||||
}
|
||||
Weights shift{DataType::kFLOAT, shval, len};
|
||||
|
||||
float *pval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
|
||||
float* pval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
|
||||
for (int i = 0; i < len; i++) {
|
||||
pval[i] = 1.0;
|
||||
}
|
||||
@ -109,39 +105,44 @@ IScaleLayer* addBatchNorm(INetworkDefinition *network, std::map<std::string, Wei
|
||||
return scale_1;
|
||||
}
|
||||
|
||||
ILayer* hSwish(INetworkDefinition *network, ITensor& input, std::string name) {
|
||||
ILayer* hSwish(INetworkDefinition* network, ITensor& input, std::string name) {
|
||||
auto hsig = network->addActivation(input, ActivationType::kHARD_SIGMOID);
|
||||
assert(hsig);
|
||||
hsig->setAlpha(1.0 / 6.0);
|
||||
hsig->setBeta(0.5);
|
||||
ILayer* hsw = network->addElementWise(input, *hsig->getOutput(0),ElementWiseOperation::kPROD);
|
||||
ILayer* hsw = network->addElementWise(input, *hsig->getOutput(0), ElementWiseOperation::kPROD);
|
||||
assert(hsw);
|
||||
return hsw;
|
||||
}
|
||||
|
||||
ILayer* convBnHswish(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int outch, int ksize, int s, int g, std::string lname) {
|
||||
ILayer* convBnHswish(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input, int outch,
|
||||
int ksize, int s, int g, std::string lname) {
|
||||
Weights emptywts{DataType::kFLOAT, nullptr, 0};
|
||||
int p = (ksize - 1) / 2;
|
||||
IConvolutionLayer* conv1 = network->addConvolutionNd(input, outch, DimsHW{ksize, ksize}, weightMap[lname + "0.weight"], emptywts);
|
||||
IConvolutionLayer* conv1 =
|
||||
network->addConvolutionNd(input, outch, DimsHW{ksize, ksize}, weightMap[lname + "0.weight"], emptywts);
|
||||
assert(conv1);
|
||||
conv1->setStrideNd(DimsHW{s, s});
|
||||
conv1->setPaddingNd(DimsHW{p, p});
|
||||
conv1->setNbGroups(g);
|
||||
|
||||
IScaleLayer* bn1 = addBatchNorm(network, weightMap, *conv1->getOutput(0), lname + "1", 1e-5);
|
||||
ILayer* hsw = hSwish(network, *bn1->getOutput(0), lname+"2");
|
||||
ILayer* hsw = hSwish(network, *bn1->getOutput(0), lname + "2");
|
||||
assert(hsw);
|
||||
return hsw;
|
||||
}
|
||||
|
||||
ILayer* seLayer(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int c, int w, std::string lname) {
|
||||
ILayer* seLayer(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input, int c, int w,
|
||||
std::string lname) {
|
||||
int h = w;
|
||||
IPoolingLayer* l1 = network->addPoolingNd(input, PoolingType::kAVERAGE, DimsHW(w, h));
|
||||
assert(l1);
|
||||
l1->setStrideNd(DimsHW{w, h});
|
||||
IFullyConnectedLayer* l2 = network->addFullyConnected(*l1->getOutput(0), BS*c/4, weightMap[lname+"fc.0.weight"], weightMap[lname+"fc.0.bias"]);
|
||||
IFullyConnectedLayer* l2 = network->addFullyConnected(
|
||||
*l1->getOutput(0), BS * c / 4, weightMap[lname + "fc.0.weight"], weightMap[lname + "fc.0.bias"]);
|
||||
IActivationLayer* relu1 = network->addActivation(*l2->getOutput(0), ActivationType::kRELU);
|
||||
IFullyConnectedLayer* l4 = network->addFullyConnected(*relu1->getOutput(0), BS*c, weightMap[lname+"fc.2.weight"], weightMap[lname+"fc.2.bias"]);
|
||||
IFullyConnectedLayer* l4 = network->addFullyConnected(
|
||||
*relu1->getOutput(0), BS * c, weightMap[lname + "fc.2.weight"], weightMap[lname + "fc.2.bias"]);
|
||||
|
||||
auto hsig = network->addActivation(*l4->getOutput(0), ActivationType::kHARD_SIGMOID);
|
||||
assert(hsig);
|
||||
@ -153,10 +154,12 @@ ILayer* seLayer(INetworkDefinition *network, std::map<std::string, Weights>& wei
|
||||
return se;
|
||||
}
|
||||
|
||||
ILayer* convSeq1(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int output, int hdim, int k, int s, bool use_se, bool use_hs, int w, std::string lname) {
|
||||
ILayer* convSeq1(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input, int output,
|
||||
int hdim, int k, int s, bool use_se, bool use_hs, int w, std::string lname) {
|
||||
Weights emptywts{DataType::kFLOAT, nullptr, 0};
|
||||
int p = (k - 1) / 2;
|
||||
IConvolutionLayer* conv1 = network->addConvolutionNd(input, hdim, DimsHW{k, k}, weightMap[lname + "0.weight"], emptywts);
|
||||
IConvolutionLayer* conv1 =
|
||||
network->addConvolutionNd(input, hdim, DimsHW{k, k}, weightMap[lname + "0.weight"], emptywts);
|
||||
conv1->setStrideNd(DimsHW{s, s});
|
||||
conv1->setPaddingNd(DimsHW{p, p});
|
||||
conv1->setNbGroups(hdim);
|
||||
@ -166,28 +169,31 @@ ILayer* convSeq1(INetworkDefinition *network, std::map<std::string, Weights>& we
|
||||
tensor3 = nullptr;
|
||||
tensor4 = nullptr;
|
||||
if (use_hs) {
|
||||
ILayer* hsw = hSwish(network, *bn1->getOutput(0), lname+"2");
|
||||
ILayer* hsw = hSwish(network, *bn1->getOutput(0), lname + "2");
|
||||
tensor3 = hsw->getOutput(0);
|
||||
} else {
|
||||
IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
|
||||
tensor3 = relu1->getOutput(0);
|
||||
}
|
||||
if (use_se) {
|
||||
ILayer* se1 = seLayer(network, weightMap, *tensor3, hdim, w, lname + "3.");
|
||||
tensor4 = se1->getOutput(0);
|
||||
ILayer* se1 = seLayer(network, weightMap, *tensor3, hdim, w, lname + "3.");
|
||||
tensor4 = se1->getOutput(0);
|
||||
} else {
|
||||
tensor4 = tensor3;
|
||||
tensor4 = tensor3;
|
||||
}
|
||||
IConvolutionLayer* conv2 = network->addConvolutionNd(*tensor4, output, DimsHW{1, 1}, weightMap[lname + "4.weight"], emptywts);
|
||||
IConvolutionLayer* conv2 =
|
||||
network->addConvolutionNd(*tensor4, output, DimsHW{1, 1}, weightMap[lname + "4.weight"], emptywts);
|
||||
IScaleLayer* bn2 = addBatchNorm(network, weightMap, *conv2->getOutput(0), lname + "5", 1e-5);
|
||||
assert(bn2);
|
||||
return bn2;
|
||||
}
|
||||
|
||||
ILayer* convSeq2(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int output, int hdim, int k, int s, bool use_se, bool use_hs, int w, std::string lname) {
|
||||
ILayer* convSeq2(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input, int output,
|
||||
int hdim, int k, int s, bool use_se, bool use_hs, int w, std::string lname) {
|
||||
Weights emptywts{DataType::kFLOAT, nullptr, 0};
|
||||
int p = (k - 1) / 2;
|
||||
IConvolutionLayer* conv1 = network->addConvolutionNd(input, hdim, DimsHW{1, 1}, weightMap[lname + "0.weight"], emptywts);
|
||||
IConvolutionLayer* conv1 =
|
||||
network->addConvolutionNd(input, hdim, DimsHW{1, 1}, weightMap[lname + "0.weight"], emptywts);
|
||||
IScaleLayer* bn1 = addBatchNorm(network, weightMap, *conv1->getOutput(0), lname + "1", 1e-5);
|
||||
ITensor *tensor3, *tensor6, *tensor7;
|
||||
tensor3 = nullptr;
|
||||
@ -200,16 +206,17 @@ ILayer* convSeq2(INetworkDefinition *network, std::map<std::string, Weights>& we
|
||||
IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
|
||||
tensor3 = relu1->getOutput(0);
|
||||
}
|
||||
IConvolutionLayer* conv2 = network->addConvolutionNd(*tensor3, hdim, DimsHW{k, k}, weightMap[lname + "3.weight"], emptywts);
|
||||
IConvolutionLayer* conv2 =
|
||||
network->addConvolutionNd(*tensor3, hdim, DimsHW{k, k}, weightMap[lname + "3.weight"], emptywts);
|
||||
conv2->setStrideNd(DimsHW{s, s});
|
||||
conv2->setPaddingNd(DimsHW{p, p});
|
||||
conv2->setNbGroups(hdim);
|
||||
IScaleLayer* bn2 = addBatchNorm(network, weightMap, *conv2->getOutput(0), lname + "4", 1e-5);
|
||||
if (use_se) {
|
||||
ILayer* se1 = seLayer(network, weightMap, *bn2->getOutput(0), hdim, w, lname + "5.");
|
||||
tensor6 = se1->getOutput(0);
|
||||
ILayer* se1 = seLayer(network, weightMap, *bn2->getOutput(0), hdim, w, lname + "5.");
|
||||
tensor6 = se1->getOutput(0);
|
||||
} else {
|
||||
tensor6 = bn2->getOutput(0);
|
||||
tensor6 = bn2->getOutput(0);
|
||||
}
|
||||
if (use_hs) {
|
||||
ILayer* hsw2 = hSwish(network, *tensor6, lname + "6");
|
||||
@ -218,30 +225,32 @@ ILayer* convSeq2(INetworkDefinition *network, std::map<std::string, Weights>& we
|
||||
IActivationLayer* relu2 = network->addActivation(*tensor6, ActivationType::kRELU);
|
||||
tensor7 = relu2->getOutput(0);
|
||||
}
|
||||
IConvolutionLayer* conv3 = network->addConvolutionNd(*tensor7, output, DimsHW{1, 1}, weightMap[lname + "7.weight"], emptywts);
|
||||
IConvolutionLayer* conv3 =
|
||||
network->addConvolutionNd(*tensor7, output, DimsHW{1, 1}, weightMap[lname + "7.weight"], emptywts);
|
||||
IScaleLayer* bn3 = addBatchNorm(network, weightMap, *conv3->getOutput(0), lname + "8", 1e-5);
|
||||
assert(bn3);
|
||||
return bn3;
|
||||
}
|
||||
|
||||
ILayer* invertedRes(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname, int inch, int outch, int s, int hidden, int k, bool use_se, bool use_hs, int w) {
|
||||
ILayer* invertedRes(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input,
|
||||
std::string lname, int inch, int outch, int s, int hidden, int k, bool use_se, bool use_hs, int w) {
|
||||
bool use_res_connect = (s == 1 && inch == outch);
|
||||
ILayer *conv = nullptr;
|
||||
ILayer* conv = nullptr;
|
||||
if (inch == hidden) {
|
||||
conv = convSeq1(network, weightMap, input, outch, hidden, k, s, use_se, use_hs, w, lname + "conv.");
|
||||
} else {
|
||||
conv = convSeq2(network, weightMap, input, outch, hidden, k, s, use_se, use_hs, w, lname + "conv.");
|
||||
}
|
||||
|
||||
if (!use_res_connect) return conv;
|
||||
if (!use_res_connect)
|
||||
return conv;
|
||||
IElementWiseLayer* ew3 = network->addElementWise(input, *conv->getOutput(0), ElementWiseOperation::kSUM);
|
||||
assert(ew3);
|
||||
return ew3;
|
||||
}
|
||||
|
||||
// Creat the engine using only the API and not any parser.
|
||||
ICudaEngine* createEngineSmall(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt)
|
||||
{
|
||||
ICudaEngine* createEngineSmall(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt) {
|
||||
INetworkDefinition* network = builder->createNetworkV2(0U);
|
||||
|
||||
ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{3, INPUT_H, INPUT_W});
|
||||
@ -271,11 +280,13 @@ ICudaEngine* createEngineSmall(unsigned int maxBatchSize, IBuilder* builder, IBu
|
||||
pool1->setStrideNd(DimsHW{7, 7});
|
||||
ILayer* sw1 = hSwish(network, *pool1->getOutput(0), "hSwish.0");
|
||||
|
||||
IFullyConnectedLayer* fc1 = network->addFullyConnected(*sw1->getOutput(0), 1280, weightMap["classifier.0.weight"], weightMap["classifier.0.bias"]);
|
||||
IFullyConnectedLayer* fc1 = network->addFullyConnected(*sw1->getOutput(0), 1280, weightMap["classifier.0.weight"],
|
||||
weightMap["classifier.0.bias"]);
|
||||
assert(fc1);
|
||||
ILayer* bn1 = addBatchNorm(network, weightMap, *fc1->getOutput(0), "classifier.1", 1e-5);
|
||||
ILayer* sw2 = hSwish(network, *bn1->getOutput(0), "hSwish.1");
|
||||
IFullyConnectedLayer* fc2 = network->addFullyConnected(*sw2->getOutput(0), 1000, weightMap["classifier.3.weight"], weightMap["classifier.3.bias"]);
|
||||
IFullyConnectedLayer* fc2 = network->addFullyConnected(*sw2->getOutput(0), 1000, weightMap["classifier.3.weight"],
|
||||
weightMap["classifier.3.bias"]);
|
||||
ILayer* bn2 = addBatchNorm(network, weightMap, *fc2->getOutput(0), "classifier.4", 1e-5);
|
||||
ILayer* sw3 = hSwish(network, *bn2->getOutput(0), "hSwish.2");
|
||||
|
||||
@ -293,16 +304,14 @@ ICudaEngine* createEngineSmall(unsigned int maxBatchSize, IBuilder* builder, IBu
|
||||
network->destroy();
|
||||
|
||||
// Release host memory
|
||||
for (auto& mem : weightMap)
|
||||
{
|
||||
free((void*) (mem.second.values));
|
||||
for (auto& mem : weightMap) {
|
||||
free((void*)(mem.second.values));
|
||||
}
|
||||
|
||||
return engine;
|
||||
}
|
||||
|
||||
ICudaEngine* createEngineLarge(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt)
|
||||
{
|
||||
ICudaEngine* createEngineLarge(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt) {
|
||||
INetworkDefinition* network = builder->createNetworkV2(0U);
|
||||
|
||||
ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{3, INPUT_H, INPUT_W});
|
||||
@ -335,10 +344,12 @@ ICudaEngine* createEngineLarge(unsigned int maxBatchSize, IBuilder* builder, IBu
|
||||
pool1->setStrideNd(DimsHW{7, 7});
|
||||
ILayer* sw1 = hSwish(network, *pool1->getOutput(0), "hSwish.0");
|
||||
|
||||
IFullyConnectedLayer* fc1 = network->addFullyConnected(*sw1->getOutput(0), 1280, weightMap["classifier.0.weight"], weightMap["classifier.0.bias"]);
|
||||
IFullyConnectedLayer* fc1 = network->addFullyConnected(*sw1->getOutput(0), 1280, weightMap["classifier.0.weight"],
|
||||
weightMap["classifier.0.bias"]);
|
||||
assert(fc1);
|
||||
ILayer* sw2 = hSwish(network, *fc1->getOutput(0), "hSwish.1");
|
||||
IFullyConnectedLayer* fc2 = network->addFullyConnected(*sw2->getOutput(0), 1000, weightMap["classifier.3.weight"], weightMap["classifier.3.bias"]);
|
||||
IFullyConnectedLayer* fc2 = network->addFullyConnected(*sw2->getOutput(0), 1000, weightMap["classifier.3.weight"],
|
||||
weightMap["classifier.3.bias"]);
|
||||
|
||||
fc2->getOutput(0)->setName(OUTPUT_BLOB_NAME);
|
||||
std::cout << "set name out" << std::endl;
|
||||
@ -354,16 +365,14 @@ ICudaEngine* createEngineLarge(unsigned int maxBatchSize, IBuilder* builder, IBu
|
||||
network->destroy();
|
||||
|
||||
// Release host memory
|
||||
for (auto& mem : weightMap)
|
||||
{
|
||||
free((void*) (mem.second.values));
|
||||
for (auto& mem : weightMap) {
|
||||
free((void*)(mem.second.values));
|
||||
}
|
||||
|
||||
return engine;
|
||||
}
|
||||
|
||||
void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream, std::string mode)
|
||||
{
|
||||
void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream, std::string mode) {
|
||||
// Create builder
|
||||
IBuilder* builder = createInferBuilder(gLogger);
|
||||
IBuilderConfig* config = builder->createBuilderConfig();
|
||||
@ -384,12 +393,11 @@ void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream, std::strin
|
||||
|
||||
// Close everything down
|
||||
engine->destroy();
|
||||
builder->destroy();
|
||||
config->destroy();
|
||||
builder->destroy();
|
||||
}
|
||||
|
||||
void doInference(IExecutionContext& context, float* input, float* output, int batchSize)
|
||||
{
|
||||
void doInference(IExecutionContext& context, float* input, float* output, int batchSize) {
|
||||
const ICudaEngine& engine = context.getEngine();
|
||||
|
||||
// Pointers to input and output device buffers to pass to engine.
|
||||
@ -411,9 +419,11 @@ void doInference(IExecutionContext& context, float* input, float* output, int ba
|
||||
CHECK(cudaStreamCreate(&stream));
|
||||
|
||||
// DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
|
||||
CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream));
|
||||
CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float),
|
||||
cudaMemcpyHostToDevice, stream));
|
||||
context.enqueue(batchSize, buffers, stream, nullptr);
|
||||
CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));
|
||||
CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost,
|
||||
stream));
|
||||
cudaStreamSynchronize(stream);
|
||||
|
||||
// Release stream and buffers
|
||||
@ -422,8 +432,7 @@ void doInference(IExecutionContext& context, float* input, float* output, int ba
|
||||
CHECK(cudaFree(buffers[outputIndex]));
|
||||
}
|
||||
|
||||
int main(int argc, char** argv)
|
||||
{
|
||||
int main(int argc, char** argv) {
|
||||
if (argc != 3) {
|
||||
std::cerr << "arguments not right!" << std::endl;
|
||||
std::cerr << "./mobilenet -s small // serialize small model to plan file" << std::endl;
|
||||
@ -434,7 +443,7 @@ int main(int argc, char** argv)
|
||||
}
|
||||
|
||||
// create a model using the API directly and serialize it to a stream
|
||||
char *trtModelStream{nullptr};
|
||||
char* trtModelStream{nullptr};
|
||||
size_t size{0};
|
||||
std::string mode = std::string(argv[2]);
|
||||
std::cout << mode << std::endl;
|
||||
@ -495,8 +504,7 @@ int main(int argc, char** argv)
|
||||
|
||||
// Print histogram of the output distribution
|
||||
std::cout << "\nOutput:\n\n";
|
||||
for (unsigned int i = 0; i < OUTPUT_SIZE; i++)
|
||||
{
|
||||
for (unsigned int i = 0; i < OUTPUT_SIZE; i++) {
|
||||
std::cout << prob[i] << ", ";
|
||||
//if (i % 10 == 0) std::cout << i / 10 << std::endl;
|
||||
}
|
||||
|
||||
@ -4,8 +4,8 @@ import struct
|
||||
import argparse
|
||||
|
||||
import numpy as np
|
||||
import pycuda.autoinit
|
||||
import pycuda.driver as cuda
|
||||
import pycuda.autoinit # noqa: F401
|
||||
import tensorrt as trt
|
||||
|
||||
BATCH_SIZE = 1
|
||||
|
||||
Loading…
Reference in New Issue
Block a user