mirror of https://github.com/JDAI-CV/fast-reid.git
support repvgg (#429)
Summary: * support repvgg backbone, and verify the consistency of train mode and eval mode * onnx export logger style modificationpull/440/head
Xingyu Liao
GitHub
parent
cb7a1cb3e1
commit
9b5af4166e
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@ -13,3 +13,4 @@ from .resnext import build_resnext_backbone
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from .regnet import build_regnet_backbone, build_effnet_backbone
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from .shufflenet import build_shufflenetv2_backbone
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from .mobilenet import build_mobilenetv2_backbone
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from .repvgg import build_repvgg_backbone
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@ -549,6 +549,10 @@ def build_regnet_backbone(cfg):
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# fmt: on
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cfg_files = {
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'200x': 'fastreid/modeling/backbones/regnet/regnetx/RegNetX-200MF_dds_8gpu.yaml',
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'200y': 'fastreid/modeling/backbones/regnet/regnety/RegNetY-200MF_dds_8gpu.yaml',
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'400x': 'fastreid/modeling/backbones/regnet/regnetx/RegNetX-400MF_dds_8gpu.yaml',
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'400y': 'fastreid/modeling/backbones/regnet/regnety/RegNetY-400MF_dds_8gpu.yaml',
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'800x': 'fastreid/modeling/backbones/regnet/regnetx/RegNetX-800MF_dds_8gpu.yaml',
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'800y': 'fastreid/modeling/backbones/regnet/regnety/RegNetY-800MF_dds_8gpu.yaml',
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'1600x': 'fastreid/modeling/backbones/regnet/regnetx/RegNetX-1.6GF_dds_8gpu.yaml',
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@ -0,0 +1,309 @@
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# encoding: utf-8
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# ref: https://github.com/CaoWGG/RepVGG/blob/develop/repvgg.py
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import logging
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import numpy as np
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import torch
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import torch.nn as nn
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from fastreid.layers import *
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from fastreid.utils.checkpoint import get_missing_parameters_message, get_unexpected_parameters_message
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from .build import BACKBONE_REGISTRY
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logger = logging.getLogger(__name__)
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def deploy(self, mode=False):
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self.deploying = mode
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for module in self.children():
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if hasattr(module, 'deploying'):
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module.deploy(mode)
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nn.Sequential.deploying = False
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nn.Sequential.deploy = deploy
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def conv_bn(norm_type, in_channels, out_channels, kernel_size, stride, padding, groups=1):
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result = nn.Sequential()
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result.add_module('conv', nn.Conv2d(in_channels=in_channels, out_channels=out_channels,
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kernel_size=kernel_size, stride=stride, padding=padding, groups=groups,
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bias=False))
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result.add_module('bn', get_norm(norm_type, out_channels))
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return result
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class RepVGGBlock(nn.Module):
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def __init__(self, in_channels, out_channels, norm_type, kernel_size,
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stride=1, padding=0, groups=1):
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super(RepVGGBlock, self).__init__()
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self.deploying = False
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self.groups = groups
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self.in_channels = in_channels
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assert kernel_size == 3
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assert padding == 1
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padding_11 = padding - kernel_size // 2
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self.nonlinearity = nn.ReLU()
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self.in_channels = in_channels
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self.in_channels = in_channels
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self.kernel_size = kernel_size
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self.stride = stride
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self.padding = padding
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self.groups = groups
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self.register_parameter('fused_weight', None)
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self.register_parameter('fused_bias', None)
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self.rbr_identity = get_norm(norm_type, in_channels) if out_channels == in_channels and stride == 1 else None
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self.rbr_dense = conv_bn(norm_type, in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
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stride=stride, padding=padding, groups=groups)
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self.rbr_1x1 = conv_bn(norm_type, in_channels=in_channels, out_channels=out_channels, kernel_size=1,
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stride=stride, padding=padding_11, groups=groups)
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def forward(self, inputs):
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if self.deploying:
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assert self.fused_weight is not None and self.fused_bias is not None, \
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"Make deploy mode=True to generate fused weight and fused bias first"
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fused_out = self.nonlinearity(torch.nn.functional.conv2d(
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inputs, self.fused_weight, self.fused_bias, self.stride, self.padding, 1, self.groups))
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return fused_out
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if self.rbr_identity is None:
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id_out = 0
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else:
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id_out = self.rbr_identity(inputs)
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out = self.nonlinearity(self.rbr_dense(inputs) + self.rbr_1x1(inputs) + id_out)
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return out
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def get_equivalent_kernel_bias(self):
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kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense)
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kernel1x1, bias1x1 = self._fuse_bn_tensor(self.rbr_1x1)
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kernelid, biasid = self._fuse_bn_tensor(self.rbr_identity)
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return kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, bias3x3 + bias1x1 + biasid
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def _pad_1x1_to_3x3_tensor(self, kernel1x1):
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if kernel1x1 is None:
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return 0
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else:
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return torch.nn.functional.pad(kernel1x1, [1, 1, 1, 1])
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def _fuse_bn_tensor(self, branch):
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if branch is None:
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return 0, 0
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if isinstance(branch, nn.Sequential):
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kernel = branch.conv.weight
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running_mean = branch.bn.running_mean
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running_var = branch.bn.running_var
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gamma = branch.bn.weight
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beta = branch.bn.bias
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eps = branch.bn.eps
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else:
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assert branch.__class__.__name__.find('BatchNorm') != -1
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if not hasattr(self, 'id_tensor'):
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input_dim = self.in_channels // self.groups
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kernel_value = np.zeros((self.in_channels, input_dim, 3, 3), dtype=np.float32)
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for i in range(self.in_channels):
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kernel_value[i, i % input_dim, 1, 1] = 1
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self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device)
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kernel = self.id_tensor
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running_mean = branch.running_mean
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running_var = branch.running_var
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gamma = branch.weight
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beta = branch.bias
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eps = branch.eps
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std = (running_var + eps).sqrt()
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t = (gamma / std).reshape(-1, 1, 1, 1)
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return kernel * t, beta - running_mean * gamma / std
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def deploy(self, mode=False):
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self.deploying = mode
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if mode:
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fused_weight, fused_bias = self.get_equivalent_kernel_bias()
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self.register_parameter('fused_weight', nn.Parameter(fused_weight))
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self.register_parameter('fused_bias', nn.Parameter(fused_bias))
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del self.rbr_identity, self.rbr_1x1, self.rbr_dense
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class RepVGG(nn.Module):
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def __init__(self, last_stride, norm_type, num_blocks, width_multiplier=None, override_groups_map=None):
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super(RepVGG, self).__init__()
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assert len(width_multiplier) == 4
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self.deploying = False
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self.override_groups_map = override_groups_map or dict()
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assert 0 not in self.override_groups_map
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self.in_planes = min(64, int(64 * width_multiplier[0]))
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self.stage0 = RepVGGBlock(in_channels=3, out_channels=self.in_planes, norm_type=norm_type,
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kernel_size=3, stride=2, padding=1)
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self.cur_layer_idx = 1
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self.stage1 = self._make_stage(int(64 * width_multiplier[0]), norm_type, num_blocks[0], stride=2)
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self.stage2 = self._make_stage(int(128 * width_multiplier[1]), norm_type, num_blocks[1], stride=2)
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self.stage3 = self._make_stage(int(256 * width_multiplier[2]), norm_type, num_blocks[2], stride=2)
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self.stage4 = self._make_stage(int(512 * width_multiplier[3]), norm_type, num_blocks[3], stride=last_stride)
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def _make_stage(self, planes, norm_type, num_blocks, stride):
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strides = [stride] + [1] * (num_blocks - 1)
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blocks = []
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for stride in strides:
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cur_groups = self.override_groups_map.get(self.cur_layer_idx, 1)
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blocks.append(RepVGGBlock(in_channels=self.in_planes, out_channels=planes, norm_type=norm_type,
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kernel_size=3, stride=stride, padding=1, groups=cur_groups))
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self.in_planes = planes
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self.cur_layer_idx += 1
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return nn.Sequential(*blocks)
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def deploy(self, mode=False):
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self.deploying = mode
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for module in self.children():
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if hasattr(module, 'deploying'):
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module.deploy(mode)
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def forward(self, x):
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out = self.stage0(x)
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out = self.stage1(out)
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out = self.stage2(out)
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out = self.stage3(out)
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out = self.stage4(out)
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return out
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optional_groupwise_layers = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26]
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g2_map = {l: 2 for l in optional_groupwise_layers}
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g4_map = {l: 4 for l in optional_groupwise_layers}
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def create_RepVGG_A0(last_stride, norm_type):
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return RepVGG(last_stride, norm_type, num_blocks=[2, 4, 14, 1],
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width_multiplier=[0.75, 0.75, 0.75, 2.5], override_groups_map=None)
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def create_RepVGG_A1(last_stride, norm_type):
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return RepVGG(last_stride, norm_type, num_blocks=[2, 4, 14, 1],
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width_multiplier=[1, 1, 1, 2.5], override_groups_map=None)
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def create_RepVGG_A2(last_stride, norm_type):
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return RepVGG(last_stride, norm_type, num_blocks=[2, 4, 14, 1],
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width_multiplier=[1.5, 1.5, 1.5, 2.75], override_groups_map=None)
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def create_RepVGG_B0(last_stride, norm_type):
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return RepVGG(last_stride, norm_type, num_blocks=[4, 6, 16, 1],
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width_multiplier=[1, 1, 1, 2.5], override_groups_map=None)
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def create_RepVGG_B1(last_stride, norm_type):
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return RepVGG(last_stride, norm_type, num_blocks=[4, 6, 16, 1],
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width_multiplier=[2, 2, 2, 4], override_groups_map=None)
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def create_RepVGG_B1g2(last_stride, norm_type):
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return RepVGG(last_stride, norm_type, num_blocks=[4, 6, 16, 1],
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width_multiplier=[2, 2, 2, 4], override_groups_map=g2_map)
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def create_RepVGG_B1g4(last_stride, norm_type):
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return RepVGG(last_stride, norm_type, num_blocks=[4, 6, 16, 1],
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width_multiplier=[2, 2, 2, 4], override_groups_map=g4_map)
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def create_RepVGG_B2(last_stride, norm_type):
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return RepVGG(last_stride, norm_type, num_blocks=[4, 6, 16, 1],
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width_multiplier=[2.5, 2.5, 2.5, 5], override_groups_map=None)
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def create_RepVGG_B2g2(last_stride, norm_type):
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return RepVGG(last_stride, norm_type, num_blocks=[4, 6, 16, 1],
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width_multiplier=[2.5, 2.5, 2.5, 5], override_groups_map=g2_map)
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def create_RepVGG_B2g4(last_stride, norm_type):
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return RepVGG(last_stride, norm_type, num_blocks=[4, 6, 16, 1],
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width_multiplier=[2.5, 2.5, 2.5, 5], override_groups_map=g4_map)
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def create_RepVGG_B3(last_stride, norm_type):
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return RepVGG(last_stride, norm_type, num_blocks=[4, 6, 16, 1],
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width_multiplier=[3, 3, 3, 5], override_groups_map=None)
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def create_RepVGG_B3g2(last_stride, norm_type):
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return RepVGG(last_stride, norm_type, num_blocks=[4, 6, 16, 1],
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width_multiplier=[3, 3, 3, 5], override_groups_map=g2_map)
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def create_RepVGG_B3g4(last_stride, norm_type):
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return RepVGG(last_stride, norm_type, num_blocks=[4, 6, 16, 1],
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width_multiplier=[3, 3, 3, 5], override_groups_map=g4_map)
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@BACKBONE_REGISTRY.register()
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def build_repvgg_backbone(cfg):
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"""
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Create a RepVGG instance from config.
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Returns:
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RepVGG: a :class: `RepVGG` instance.
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"""
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# fmt: off
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pretrain = cfg.MODEL.BACKBONE.PRETRAIN
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pretrain_path = cfg.MODEL.BACKBONE.PRETRAIN_PATH
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last_stride = cfg.MODEL.BACKBONE.LAST_STRIDE
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bn_norm = cfg.MODEL.BACKBONE.NORM
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depth = cfg.MODEL.BACKBONE.DEPTH
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# fmt: on
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func_dict = {
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'A0': create_RepVGG_A0,
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'A1': create_RepVGG_A1,
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'A2': create_RepVGG_A2,
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'B0': create_RepVGG_B0,
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'B1': create_RepVGG_B1,
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'B1g2': create_RepVGG_B1g2,
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'B1g4': create_RepVGG_B1g4,
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'B2': create_RepVGG_B2,
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'B2g2': create_RepVGG_B2g2,
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'B2g4': create_RepVGG_B2g4,
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'B3': create_RepVGG_B3,
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'B3g2': create_RepVGG_B3g2,
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'B3g4': create_RepVGG_B3g4,
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}
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model = func_dict[depth](last_stride, bn_norm)
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if pretrain:
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try:
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state_dict = torch.load(pretrain_path, map_location=torch.device("cpu"))
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logger.info(f"Loading pretrained model from {pretrain_path}")
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except FileNotFoundError as e:
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logger.info(f'{pretrain_path} is not found! Please check this path.')
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raise e
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except KeyError as e:
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logger.info("State dict keys error! Please check the state dict.")
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raise e
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incompatible = model.load_state_dict(state_dict, strict=False)
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if incompatible.missing_keys:
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logger.info(
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get_missing_parameters_message(incompatible.missing_keys)
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)
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if incompatible.unexpected_keys:
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logger.info(
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get_unexpected_parameters_message(incompatible.unexpected_keys)
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)
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return model
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@ -0,0 +1,33 @@
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import sys
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import unittest
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import torch
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sys.path.append('.')
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from fastreid.config import get_cfg
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from fastreid.modeling.backbones import build_backbone
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class MyTestCase(unittest.TestCase):
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def test_fusebn(self):
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cfg = get_cfg()
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cfg.defrost()
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cfg.MODEL.BACKBONE.NAME = 'build_repvgg_backbone'
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cfg.MODEL.BACKBONE.DEPTH = 'B1g2'
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cfg.MODEL.BACKBONE.PRETRAIN = False
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model = build_backbone(cfg)
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model.eval()
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test_inp = torch.randn((1, 3, 256, 128))
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y = model(test_inp)
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model.deploy(mode=True)
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from ipdb import set_trace; set_trace()
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fused_y = model(test_inp)
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print("final error :", torch.max(torch.abs(fused_y - y)).item())
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if __name__ == '__main__':
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unittest.main()
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@ -4,11 +4,13 @@
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@contact: sherlockliao01@gmail.com
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"""
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import os
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import argparse
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import io
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import sys
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import onnx
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import onnxoptimizer
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import torch
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from onnxsim import simplify
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from torch.onnx import OperatorExportTypes
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@ -106,6 +108,7 @@ def export_onnx_model(model, inputs):
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model.apply(_check_eval)
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logger.info("Beginning ONNX file converting")
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# Export the model to ONNX
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with torch.no_grad():
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with io.BytesIO() as f:
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@ -119,11 +122,15 @@ def export_onnx_model(model, inputs):
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)
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onnx_model = onnx.load_from_string(f.getvalue())
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logger.info("Completed convert of ONNX model")
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# Apply ONNX's Optimization
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all_passes = onnx.optimizer.get_available_passes()
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logger.info("Beginning ONNX model path optimization")
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all_passes = onnxoptimizer.get_available_passes()
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passes = ["extract_constant_to_initializer", "eliminate_unused_initializer", "fuse_bn_into_conv"]
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assert all(p in all_passes for p in passes)
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onnx_model = onnx.optimizer.optimize(onnx_model, passes)
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onnx_model = onnxoptimizer.optimize(onnx_model, passes)
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logger.info("Completed ONNX model path optimization")
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return onnx_model
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@ -137,6 +144,7 @@ if __name__ == '__main__':
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cfg.MODEL.HEADS.POOL_LAYER = 'avgpool'
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model = build_model(cfg)
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Checkpointer(model).load(cfg.MODEL.WEIGHTS)
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model.backbone.deploy(True)
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model.eval()
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logger.info(model)
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@ -151,6 +159,6 @@ if __name__ == '__main__':
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PathManager.mkdirs(args.output)
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|
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onnx.save_model(model_simp, f"{args.output}/{args.name}.onnx")
|
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|
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logger.info(f"Export onnx model in {args.output} successfully!")
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save_path = os.path.join(args.output, args.name+'.onnx')
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onnx.save_model(model_simp, save_path)
|
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logger.info("ONNX model file has already saved to {}!".format(save_path))
|
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|
|
|
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Loading…
Reference in New Issue