We read every piece of feedback, and take your input very seriously.
To see all available qualifiers, see our documentation.
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
使用deit_tiny_patch16_224训练出来的pth模型转成onnx模型再用pnnx和ncnn转换都报错
pnnx报错
./pnnx model.onnx inputshape=[1,3,224,224] 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 = [1,3,224,224]f32 inputshape2 = customop = moduleop = ############# pass_level0 onnx inline_containers ... 0.00ms eliminate_noop ... 0.29ms fold_constants ... 0.12ms canonicalize ... 0.57ms shape_inference ... 77.85ms fold_constants_dynamic_shape ... 0.11ms inline_if_graph ... 0.01ms fuse_constant_as_attribute ... 0.18ms eliminate_noop_with_shape ... 0.14ms ┌──────────────────┬──────────┬──────────┐ │ │ orig │ opt │ ├──────────────────┼──────────┼──────────┤ │ node │ 580 │ 580 │ │ initializer │ 164 │ 159 │ │ functions │ 0 │ 0 │ ├──────────────────┼──────────┼──────────┤ │ nn module op │ 0 │ 0 │ │ custom module op │ 0 │ 0 │ │ aten op │ 0 │ 0 │ │ prims op │ 0 │ 0 │ │ onnx native op │ 580 │ 580 │ ├──────────────────┼──────────┼──────────┤ │ Add │ 135 │ 135 │ │ Concat │ 1 │ 1 │ │ Conv │ 1 │ 1 │ │ Div │ 37 │ 37 │ │ Erf │ 12 │ 12 │ │ Gather │ 1 │ 1 │ │ Gemm │ 1 │ 1 │ │ MatMul │ 72 │ 72 │ │ Mul │ 73 │ 73 │ │ Pow │ 25 │ 25 │ │ ReduceMean │ 50 │ 50 │ │ Reshape │ 25 │ 25 │ │ Softmax │ 12 │ 12 │ │ Split │ 12 │ 12 │ │ Sqrt │ 25 │ 25 │ │ Squeeze │ 36 │ 36 │ │ Sub │ 25 │ 25 │ │ Transpose │ 37 │ 37 │ └──────────────────┴──────────┴──────────┘ ############# pass_level1 onnx ############# pass_level2 ############# pass_level3 open failed ############# pass_level4 ############# pass_level5 ############# pass_ncnn ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_158 param dropout_p=0.000000e+00 ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_158 param is_causal=False ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_159 param dropout_p=0.000000e+00 ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_159 param is_causal=False ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_160 param dropout_p=0.000000e+00 ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_160 param is_causal=False ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_161 param dropout_p=0.000000e+00 ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_161 param is_causal=False ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_162 param dropout_p=0.000000e+00 ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_162 param is_causal=False ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_163 param dropout_p=0.000000e+00 ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_163 param is_causal=False ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_164 param dropout_p=0.000000e+00 ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_164 param is_causal=False ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_165 param dropout_p=0.000000e+00 ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_165 param is_causal=False ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_166 param dropout_p=0.000000e+00 ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_166 param is_causal=False ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_167 param dropout_p=0.000000e+00 ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_167 param is_causal=False ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_168 param dropout_p=0.000000e+00 ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_168 param is_causal=False ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_169 param dropout_p=0.000000e+00 ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_169 param is_causal=False
使用模型执行脚本也报错 import numpy as np import ncnn import torch
def test_inference(): torch.manual_seed(0) in0 = torch.rand(1, 3, 224, 224, dtype=torch.float) out = []
with ncnn.Net() as net: net.load_param("model.ncnn.param") net.load_model("model.ncnn.bin") with net.create_extractor() as ex: ex.input("in0", ncnn.Mat(in0.squeeze(0).numpy()).clone()) _, out0 = ex.extract("out0") out.append(torch.from_numpy(np.array(out0)).unsqueeze(0)) if len(out) == 1: return out[0] else: return tuple(out)
if name == "main": print(test_inference())
layer F.scaled_dot_product_attention not exists or registered
The text was updated successfully, but these errors were encountered:
你还可以试试先把pth导成torchscript,然后再用pnnx直接转到ncnn
Sorry, something went wrong.
试过一样报错
No branches or pull requests
使用deit_tiny_patch16_224训练出来的pth模型转成onnx模型再用pnnx和ncnn转换都报错
pnnx报错
./pnnx model.onnx inputshape=[1,3,224,224]
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 = [1,3,224,224]f32
inputshape2 =
customop =
moduleop =
############# pass_level0 onnx
inline_containers ... 0.00ms
eliminate_noop ... 0.29ms
fold_constants ... 0.12ms
canonicalize ... 0.57ms
shape_inference ... 77.85ms
fold_constants_dynamic_shape ... 0.11ms
inline_if_graph ... 0.01ms
fuse_constant_as_attribute ... 0.18ms
eliminate_noop_with_shape ... 0.14ms
┌──────────────────┬──────────┬──────────┐
│ │ orig │ opt │
├──────────────────┼──────────┼──────────┤
│ node │ 580 │ 580 │
│ initializer │ 164 │ 159 │
│ functions │ 0 │ 0 │
├──────────────────┼──────────┼──────────┤
│ nn module op │ 0 │ 0 │
│ custom module op │ 0 │ 0 │
│ aten op │ 0 │ 0 │
│ prims op │ 0 │ 0 │
│ onnx native op │ 580 │ 580 │
├──────────────────┼──────────┼──────────┤
│ Add │ 135 │ 135 │
│ Concat │ 1 │ 1 │
│ Conv │ 1 │ 1 │
│ Div │ 37 │ 37 │
│ Erf │ 12 │ 12 │
│ Gather │ 1 │ 1 │
│ Gemm │ 1 │ 1 │
│ MatMul │ 72 │ 72 │
│ Mul │ 73 │ 73 │
│ Pow │ 25 │ 25 │
│ ReduceMean │ 50 │ 50 │
│ Reshape │ 25 │ 25 │
│ Softmax │ 12 │ 12 │
│ Split │ 12 │ 12 │
│ Sqrt │ 25 │ 25 │
│ Squeeze │ 36 │ 36 │
│ Sub │ 25 │ 25 │
│ Transpose │ 37 │ 37 │
└──────────────────┴──────────┴──────────┘
############# pass_level1 onnx
############# pass_level2
############# pass_level3
open failed
############# pass_level4
############# pass_level5
############# pass_ncnn
ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_158 param dropout_p=0.000000e+00
ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_158 param is_causal=False
ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_159 param dropout_p=0.000000e+00
ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_159 param is_causal=False
ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_160 param dropout_p=0.000000e+00
ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_160 param is_causal=False
ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_161 param dropout_p=0.000000e+00
ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_161 param is_causal=False
ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_162 param dropout_p=0.000000e+00
ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_162 param is_causal=False
ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_163 param dropout_p=0.000000e+00
ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_163 param is_causal=False
ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_164 param dropout_p=0.000000e+00
ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_164 param is_causal=False
ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_165 param dropout_p=0.000000e+00
ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_165 param is_causal=False
ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_166 param dropout_p=0.000000e+00
ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_166 param is_causal=False
ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_167 param dropout_p=0.000000e+00
ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_167 param is_causal=False
ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_168 param dropout_p=0.000000e+00
ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_168 param is_causal=False
ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_169 param dropout_p=0.000000e+00
ignore F.scaled_dot_product_attention F.scaled_dot_product_attention_169 param is_causal=False
使用模型执行脚本也报错
import numpy as np
import ncnn
import torch
def test_inference():
torch.manual_seed(0)
in0 = torch.rand(1, 3, 224, 224, dtype=torch.float)
out = []
if name == "main":
print(test_inference())
layer F.scaled_dot_product_attention not exists or registered
The text was updated successfully, but these errors were encountered: