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EfficientPhys onnx转ncnn模型转换报错
error log | 日志或报错信息 | ログ
model | 模型 | モデル
- original model
"""EfficientPhys: Enabling Simple, Fast and Accurate Camera-Based Vitals Measurement
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2023)
Xin Liu, Brial Hill, Ziheng Jiang, Shwetak Patel, Daniel McDuff
"""
import torch
import torch.nn as nn
class Attention_mask(nn.Module):
def __init__(self):
super(Attention_mask, self).__init__()
def forward(self, x):
xsum = torch.sum(x, dim=2, keepdim=True)
xsum = torch.sum(xsum, dim=3, keepdim=True)
xshape = tuple(x.size())
return x / xsum * xshape[2] * xshape[3] * 0.5
def get_config(self):
"""May be generated manually. """
config = super(Attention_mask, self).get_config()
return config
class TSM(nn.Module):
def __init__(self, n_segment=10, fold_div=3):
super(TSM, self).__init__()
self.n_segment = n_segment
self.fold_div = fold_div
def forward(self, x):
nt, c, h, w = x.size()
n_batch = nt // self.n_segment
x = x.view(n_batch, self.n_segment, c, h, w)
fold = c // self.fold_div
out = torch.zeros_like(x)
out[:, :-1, :fold] = x[:, 1:, :fold] # shift left
out[:, 1:, fold: 2 * fold] = x[:, :-1, fold: 2 * fold] # shift right
out[:, :, 2 * fold:] = x[:, :, 2 * fold:] # not shift
return out.view(nt, c, h, w)
class EfficientPhys(nn.Module):
def __init__(self, in_channels=3, nb_filters1=32, nb_filters2=64, kernel_size=3, dropout_rate1=0.25,
dropout_rate2=0.5, pool_size=(2, 2), nb_dense=128, frame_depth=20, img_size=36, channel='raw'):
super(EfficientPhys, self).__init__()
self.in_channels = in_channels
self.kernel_size = kernel_size
self.dropout_rate1 = dropout_rate1
self.dropout_rate2 = dropout_rate2
self.pool_size = pool_size
self.nb_filters1 = nb_filters1
self.nb_filters2 = nb_filters2
self.nb_dense = nb_dense
# TSM layers
self.TSM_1 = TSM(n_segment=frame_depth)
self.TSM_2 = TSM(n_segment=frame_depth)
self.TSM_3 = TSM(n_segment=frame_depth)
self.TSM_4 = TSM(n_segment=frame_depth)
# Motion branch convs
self.motion_conv1 = nn.Conv2d(self.in_channels, self.nb_filters1, kernel_size=self.kernel_size, padding=(1, 1),
bias=True)
self.motion_conv2 = nn.Conv2d(self.nb_filters1, self.nb_filters1, kernel_size=self.kernel_size, bias=True)
self.motion_conv3 = nn.Conv2d(self.nb_filters1, self.nb_filters2, kernel_size=self.kernel_size, padding=(1, 1),
bias=True)
self.motion_conv4 = nn.Conv2d(self.nb_filters2, self.nb_filters2, kernel_size=self.kernel_size, bias=True)
# Attention layers
self.apperance_att_conv1 = nn.Conv2d(self.nb_filters1, 1, kernel_size=1, padding=(0, 0), bias=True)
self.attn_mask_1 = Attention_mask()
self.apperance_att_conv2 = nn.Conv2d(self.nb_filters2, 1, kernel_size=1, padding=(0, 0), bias=True)
self.attn_mask_2 = Attention_mask()
# Avg pooling
self.avg_pooling_1 = nn.AvgPool2d(self.pool_size)
self.avg_pooling_2 = nn.AvgPool2d(self.pool_size)
self.avg_pooling_3 = nn.AvgPool2d(self.pool_size)
# Dropout layers
self.dropout_1 = nn.Dropout(self.dropout_rate1)
self.dropout_2 = nn.Dropout(self.dropout_rate1)
self.dropout_3 = nn.Dropout(self.dropout_rate1)
self.dropout_4 = nn.Dropout(self.dropout_rate2)
# Dense layers
if img_size == 36:
self.final_dense_1 = nn.Linear(3136, self.nb_dense, bias=True)
elif img_size == 72:
self.final_dense_1 = nn.Linear(16384, self.nb_dense, bias=True)
elif img_size == 96:
self.final_dense_1 = nn.Linear(30976, self.nb_dense, bias=True)
else:
raise Exception('Unsupported image size')
self.final_dense_2 = nn.Linear(self.nb_dense, 1, bias=True)
self.batch_norm = nn.BatchNorm2d(3)
self.channel = channel
def forward(self, inputs, params=None):
#inputs = torch.diff(inputs, dim=0)
inputs = self.batch_norm(inputs)
network_input = self.TSM_1(inputs)
d1 = torch.tanh(self.motion_conv1(network_input))
d1 = self.TSM_2(d1)
d2 = torch.tanh(self.motion_conv2(d1))
g1 = torch.sigmoid(self.apperance_att_conv1(d2))
g1 = self.attn_mask_1(g1)
gated1 = d2 * g1
d3 = self.avg_pooling_1(gated1)
d4 = self.dropout_1(d3)
d4 = self.TSM_3(d4)
d5 = torch.tanh(self.motion_conv3(d4))
d5 = self.TSM_4(d5)
d6 = torch.tanh(self.motion_conv4(d5))
g2 = torch.sigmoid(self.apperance_att_conv2(d6))
g2 = self.attn_mask_2(g2)
gated2 = d6 * g2
d7 = self.avg_pooling_3(gated2)
d8 = self.dropout_3(d7)
d9 = d8.view(d8.size(0), -1)
d10 = torch.tanh(self.final_dense_1(d9))
d11 = self.dropout_4(d10)
out = self.final_dense_2(d11)
return out
how to reproduce | 复现步骤 | 再現方法
转onnx时无异常，在onnx转ncnn时报如下错：
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直接使用pnnx也会报错： pnnxparam = ./model.pnnx.param pnnxbin = ./model.pnnx.bin pnnxpy = ./model_pnnx.py pnnxonnx = ./model.pnnx.onnx ncnnparam = ./model.ncnn.param ncnnbin = ./model.ncnn.bin ncnnpy = ./model_ncnn.py fp16 = 1 optlevel = 2 device = cpu inputshape = [180,3,72,72]f32 inputshape2 = customop = moduleop = ############# pass_level0 inline module = EfficientPhys.Attention_mask inline module = EfficientPhys.TSM inline module = EfficientPhys.Attention_mask inline module = EfficientPhys.TSM
############# pass_level1 ############# pass_level2 ############# pass_level3 ############# pass_level4 ############# pass_level5 ############# pass_ncnn fallback batch axis 233 for operand 0 生成的1kb的bin和param
针对onnx模型转换的各种问题，推荐使用最新的pnnx工具转换到ncnn In view of various problems in onnx model conversion, it is recommended to use the latest pnnx tool to convert your model to ncnn
pip install pnnx
pnnx model.onnx inputshape=[1,3,224,224]
详细参考文档 Detailed reference documentation https://github.com/pnnx/pnnx https://github.com/Tencent/ncnn/wiki/use-ncnn-with-pytorch-or-onnx#how-to-use-pnnx