Proposal : https://summerofcode.withgoogle.com/projects/#4979746967912448
Mentors : Liubov Batanina @l-bat, Stefano Fabri @bhack, Ilya Elizarov @ieliz
Student : Jin Yeob Chung @jinyup100
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
Code to generate ONNX Models
The code shown below to generate the ONNX models of siamrpn++ is also available from :
https://gist.github.com/jinyup100/7aa748686c5e234ed6780154141b4685
The Final Version of the Pre-Trained Weights and successfully converted ONNX format of the models using the codes are available at::
Pre-Trained Weights in pth Format
https://drive.google.com/file/d/11bwgPFVkps9AH2NOD1zBDdpF_tQghAB-/view?usp=sharing
Target Net : Import :heavy_check_mark: Export :heavy_check_mark:
https://drive.google.com/file/d/1dw_Ne3UMcCnFsaD6xkZepwE4GEpqq7U_/view?usp=sharing
Search Net : Import :heavy_check_mark: Export :heavy_check_mark:
https://drive.google.com/file/d/1Lt4oE43ZSucJvze3Y-Z87CVDreO-Afwl/view?usp=sharing
RPN_head : Import : :heavy_check_mark: Export :heavy_check_mark:
https://drive.google.com/file/d/1zT1yu12mtj3JQEkkfKFJWiZ71fJ-dQTi/view?usp=sharing
import numpy as np
import onnx
import torch
import torch.nn as nn
# Class for the Building Blocks required for ResNet
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1,
downsample=None, dilation=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
padding = 2 - stride
if downsample is not None and dilation > 1:
dilation = dilation // 2
padding = dilation
assert stride == 1 or dilation == 1, \
"stride and dilation must have one equals to zero at least"
if dilation > 1:
padding = dilation
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=padding, bias=False, dilation=dilation)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
# End of Building Blocks
# Class for ResNet - the Backbone neural network
class ResNet(nn.Module):
"ResNET"
def __init__(self, block, layers, used_layers):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=0, # 3
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.feature_size = 128 * block.expansion
self.used_layers = used_layers
layer3 = True if 3 in used_layers else False
layer4 = True if 4 in used_layers else False
if layer3:
self.layer3 = self._make_layer(block, 256, layers[2],
stride=1, dilation=2) # 15x15, 7x7
self.feature_size = (256 + 128) * block.expansion
else:
self.layer3 = lambda x: x # identity
if layer4:
self.layer4 = self._make_layer(block, 512, layers[3],
stride=1, dilation=4) # 7x7, 3x3
self.feature_size = 512 * block.expansion
else:
self.layer4 = lambda x: x # identity
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, np.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1, dilation=1):
downsample = None
dd = dilation
if stride != 1 or self.inplanes != planes * block.expansion:
if stride == 1 and dilation == 1:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
else:
if dilation > 1:
dd = dilation // 2
padding = dd
else:
dd = 1
padding = 0
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=3, stride=stride, bias=False,
padding=padding, dilation=dd),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride,
downsample, dilation=dilation))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, dilation=dilation))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x_ = self.relu(x)
x = self.maxpool(x_)
p1 = self.layer1(x)
p2 = self.layer2(p1)
p3 = self.layer3(p2)
p4 = self.layer4(p3)
out = [x_, p1, p2, p3, p4]
out = [out[i] for i in self.used_layers]
if len(out) == 1:
return out[0]
else:
return out
# End of ResNet
# Class for Adjusting the layers of the neural net
class AdjustLayer_1(nn.Module):
def __init__(self, in_channels, out_channels, center_size=7):
super(AdjustLayer_1, self).__init__()
self.downsample = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
nn.BatchNorm2d(out_channels),
)
self.center_size = center_size
def forward(self, x):
x = self.downsample(x)
l = 4
r = 11
x = x[:, :, l:r, l:r]
return x
class AdjustAllLayer_1(nn.Module):
def __init__(self, in_channels, out_channels, center_size=7):
super(AdjustAllLayer_1, self).__init__()
self.num = len(out_channels)
if self.num == 1:
self.downsample = AdjustLayer_1(in_channels[0],
out_channels[0],
center_size)
else:
for i in range(self.num):
self.add_module('downsample'+str(i+2),
AdjustLayer_1(in_channels[i],
out_channels[i],
center_size))
def forward(self, features):
if self.num == 1:
return self.downsample(features)
else:
out = []
for i in range(self.num):
adj_layer = getattr(self, 'downsample'+str(i+2))
out.append(adj_layer(features[i]))
return out
class AdjustLayer_2(nn.Module):
def __init__(self, in_channels, out_channels, center_size=7):
super(AdjustLayer_2, self).__init__()
self.downsample = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
nn.BatchNorm2d(out_channels),
)
self.center_size = center_size
def forward(self, x):
x = self.downsample(x)
return x
class AdjustAllLayer_2(nn.Module):
def __init__(self, in_channels, out_channels, center_size=7):
super(AdjustAllLayer_2, self).__init__()
self.num = len(out_channels)
if self.num == 1:
self.downsample = AdjustLayer_2(in_channels[0],
out_channels[0],
center_size)
else:
for i in range(self.num):
self.add_module('downsample'+str(i+2),
AdjustLayer_2(in_channels[i],
out_channels[i],
center_size))
def forward(self, features):
if self.num == 1:
return self.downsample(features)
else:
out = []
for i in range(self.num):
adj_layer = getattr(self, 'downsample'+str(i+2))
out.append(adj_layer(features[i]))
return out
# End of Class for Adjusting the layers of the neural net
# Class for Region Proposal Neural Network
class RPN(nn.Module):
"Region Proposal Network"
def __init__(self):
super(RPN, self).__init__()
def forward(self, z_f, x_f):
raise NotImplementedError
class DepthwiseXCorr(nn.Module):
"Depthwise Correlation Layer"
def __init__(self, in_channels, hidden, out_channels, kernel_size=3, hidden_kernel_size=5):
super(DepthwiseXCorr, self).__init__()
self.conv_kernel = nn.Sequential(
nn.Conv2d(in_channels, hidden, kernel_size=kernel_size, bias=False),
nn.BatchNorm2d(hidden),
nn.ReLU(inplace=True),
)
self.conv_search = nn.Sequential(
nn.Conv2d(in_channels, hidden, kernel_size=kernel_size, bias=False),
nn.BatchNorm2d(hidden),
nn.ReLU(inplace=True),
)
self.head = nn.Sequential(
nn.Conv2d(hidden, hidden, kernel_size=1, bias=False),
nn.BatchNorm2d(hidden),
nn.ReLU(inplace=True),
nn.Conv2d(hidden, out_channels, kernel_size=1)
)
def forward(self, kernel, search):
kernel = self.conv_kernel(kernel)
search = self.conv_search(search)
feature = xcorr_depthwise(search, kernel)
out = self.head(feature)
return out
class DepthwiseRPN(RPN):
def __init__(self, anchor_num=5, in_channels=256, out_channels=256):
super(DepthwiseRPN, self).__init__()
self.cls = DepthwiseXCorr(in_channels, out_channels, 2 * anchor_num)
self.loc = DepthwiseXCorr(in_channels, out_channels, 4 * anchor_num)
def forward(self, z_f, x_f):
cls = self.cls(z_f, x_f)
loc = self.loc(z_f, x_f)
return cls, loc
class MultiRPN(RPN):
def __init__(self, anchor_num, in_channels):
super(MultiRPN, self).__init__()
for i in range(len(in_channels)):
self.add_module('rpn'+str(i+2),
DepthwiseRPN(anchor_num, in_channels[i], in_channels[i]))
self.weight_cls = nn.Parameter(torch.Tensor([0.38156851768108546, 0.4364767608115956, 0.18195472150731892]))
self.weight_loc = nn.Parameter(torch.Tensor([0.17644893463361863, 0.16564198028417967, 0.6579090850822015]))
def forward(self, z_fs, x_fs):
cls = []
loc = []
rpn2 = self.rpn2
z_f2 = z_fs[0]
x_f2 = x_fs[0]
c2,l2 = rpn2(z_f2, x_f2)
cls.append(c2)
loc.append(l2)
rpn3 = self.rpn3
z_f3 = z_fs[1]
x_f3 = x_fs[1]
c3,l3 = rpn3(z_f3, x_f3)
cls.append(c3)
loc.append(l3)
rpn4 = self.rpn4
z_f4 = z_fs[2]
x_f4 = x_fs[2]
c4,l4 = rpn4(z_f4, x_f4)
cls.append(c4)
loc.append(l4)
def avg(lst):
return sum(lst) / len(lst)
def weighted_avg(lst, weight):
s = 0
fixed_len = 3
for i in range(3):
s += lst[i] * weight[i]
return s
return weighted_avg(cls, self.weight_cls), weighted_avg(loc, self.weight_loc)
# End of class for RPN
def conv3x3(in_planes, out_planes, stride=1, dilation=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, bias=False, dilation=dilation)
def xcorr_depthwise(x, kernel):
"""
Deptwise convolution for input and weights with different shapes
"""
batch = kernel.size(0)
channel = kernel.size(1)
x = x.view(1, batch*channel, x.size(2), x.size(3))
kernel = kernel.view(batch*channel, 1, kernel.size(2), kernel.size(3))
conv = nn.Conv2d(batch*channel, batch*channel, kernel_size=(kernel.size(2), kernel.size(3)), bias=False, groups=batch*channel)
conv.weight = nn.Parameter(kernel)
out = conv(x)
out = out.view(batch, channel, out.size(2), out.size(3))
out = out.detach()
return out
class TargetNetBuilder(nn.Module):
def __init__(self):
super(TargetNetBuilder, self).__init__()
# Build Backbone Model
self.backbone = ResNet(Bottleneck, [3,4,6,3], [2,3,4])
# Build Neck Model
self.neck = AdjustAllLayer_1([512,1024,2048], [256,256,256])
def forward(self, frame):
features = self.backbone(frame)
output = self.neck(features)
return output
class SearchNetBuilder(nn.Module):
def __init__(self):
super(SearchNetBuilder, self).__init__()
# Build Backbone Model
self.backbone = ResNet(Bottleneck, [3,4,6,3], [2,3,4])
# Build Neck Model
self.neck = AdjustAllLayer_2([512,1024,2048], [256,256,256])
def forward(self, frame):
features = self.backbone(frame)
output = self.neck(features)
return output
class RPNBuilder(nn.Module):
def __init__(self):
super(RPNBuilder, self).__init__()
# Build Adjusted Layer Builder
self.rpn_head = MultiRPN(anchor_num=5,in_channels=[256, 256, 256])
def forward(self, zf, xf):
# Get Feature
cls, loc = self.rpn_head(zf, xf)
return cls, loc
"""Load path should be the directory of the pre-trained siamrpn_r50_l234_dwxcorr.pth
The download link to siamrpn_r50_l234_dwxcorr.pth is shown in the description"""
current_path = os.getcwd()
load_path = os.path.join(current_path, "siamrpn_r50_l234_dwxcorr.pth")
pretrained_dict = torch.load(load_path,map_location=torch.device('cpu') )
pretrained_dict_backbone = pretrained_dict
pretrained_dict_neck_1 = pretrained_dict
pretrained_dict_neck_2 = pretrained_dict
pretrained_dict_head = pretrained_dict
pretrained_dict_target = pretrained_dict
pretrained_dict_search = pretrained_dict
# The shape of the inputs to the Target Network and the Search Network
target = torch.Tensor(np.random.rand(1,3,127,127))
search = torch.Tensor(np.random.rand(1,3,125,125))
# Build the torch backbone model
target_net = TargetNetBuilder()
target_net.eval()
target_net.state_dict().keys()
target_net_dict = target_net.state_dict()
# Load the pre-trained weight to the torch target net model
pretrained_dict_target = {k: v for k, v in pretrained_dict_target.items() if k in target_net_dict}
target_net_dict.update(pretrained_dict_target)
target_net.load_state_dict(target_net_dict)
# Export the torch target net model to ONNX model
torch.onnx.export(target_net, torch.Tensor(target), "target_net.onnx", export_params=True, opset_version=11,
do_constant_folding=True, input_names=['input'], output_names=['output_1,', 'output_2', 'output_3'])
# Load the saved torch target net model using ONNX
onnx_target = onnx.load("target_net.onnx")
# Check whether the ONNX target net model has been successfully imported
onnx.checker.check_model(onnx_target)
print(onnx.checker.check_model(onnx_target))
onnx.helper.printable_graph(onnx_target.graph)
print(onnx.helper.printable_graph(onnx_target.graph))
# Build the torch backbone model
search_net = SearchNetBuilder()
search_net.eval()
search_net.state_dict().keys()
search_net_dict = search_net.state_dict()
# Load the pre-trained weight to the torch target net model
pretrained_dict_search = {k: v for k, v in pretrained_dict_search.items() if k in search_net_dict}
search_net_dict.update(pretrained_dict_search)
search_net.load_state_dict(search_net_dict)
# Export the torch target net model to ONNX model
torch.onnx.export(search_net, torch.Tensor(search), "search_net.onnx", export_params=True, opset_version=11,
do_constant_folding=True, input_names=['input'], output_names=['output_1,', 'output_2', 'output_3'])
# Load the saved torch target net model using ONNX
onnx_search = onnx.load("search_net.onnx")
# Check whether the ONNX target net model has been successfully imported
onnx.checker.check_model(onnx_search)
print(onnx.checker.check_model(onnx_search))
onnx.helper.printable_graph(onnx_search.graph)
print(onnx.helper.printable_graph(onnx_search.graph))
# Outputs from the Target Net and Search Net
zfs_1, zfs_2, zfs_3 = target_net(torch.Tensor(target))
xfs_1, xfs_2, xfs_3 = search_net(torch.Tensor(search))
# Adjustments to the outputs from each of the neck models to match to input shape of the torch rpn_head model
zfs = np.stack([zfs_1.detach().numpy(), zfs_2.detach().numpy(), zfs_3.detach().numpy()])
xfs = np.stack([xfs_1.detach().numpy(), xfs_2.detach().numpy(), xfs_3.detach().numpy()])
# Build the torch rpn_head model
rpn_head = RPNBuilder()
rpn_head.eval()
rpn_head.state_dict().keys()
rpn_head_dict = rpn_head.state_dict()
# Load the pre-trained weights to the rpn_head model
pretrained_dict_head = {k: v for k, v in pretrained_dict_head.items() if k in rpn_head_dict}
pretrained_dict_head.keys()
rpn_head_dict.update(pretrained_dict_head)
rpn_head.load_state_dict(rpn_head_dict)
rpn_head.eval()
# Export the torch rpn_head model to ONNX model
torch.onnx.export(rpn_head, (torch.Tensor(np.random.rand(*zfs.shape)), torch.Tensor(np.random.rand(*xfs.shape))), "rpn_head.onnx", export_params=True, opset_version=11,
do_constant_folding=True, input_names = ['input_1', 'input_2'], output_names = ['output_1', 'output_2'])
# Load the saved rpn_head model using ONNX
onnx_rpn_head_model = onnx.load("rpn_head.onnx")
# Check whether the rpn_head model has been successfully imported
onnx.checker.check_model(onnx_rpn_head_model)
print(onnx.checker.check_model(onnx_rpn_head_model))
onnx.helper.printable_graph(onnx_rpn_head_model.graph)
print(onnx.helper.printable_graph(onnx_rpn_head_model.graph))
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