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retinaface tvm

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retinaface-tvm's Issues

build, run successed.. but result not correct

Hi, I have successfully completed the build and run.

The problem is that the results are not correct..

I converted the model at https://github.com/Charrin/RetinaFace-Cpp/blob/master/convert_models/mnet/mnet.25-symbol.json

A warning appears when running from_mxnet.py

[13:57:08] src/nnvm/legacy_json_util.cc:209: Loading symbol saved by previous version v1.3.0. Attempting to upgrade...
[13:57:08] src/nnvm/legacy_json_util.cc:217: Symbol successfully upgraded!
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 3, 640, 480), 'float32'), ('TENSOR', (8, 3, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('depthwise_conv2d_NCHWc.x86', ('TENSOR', (1, 8, 320, 240), 'float32'), ('TENSOR', (8, 1, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 8, 320, 240), 'float32'), ('TENSOR', (16, 8, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('depthwise_conv2d_NCHWc.x86', ('TENSOR', (1, 16, 320, 240), 'float32'), ('TENSOR', (16, 1, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 16, 160, 120), 'float32'), ('TENSOR', (32, 16, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('depthwise_conv2d_NCHWc.x86', ('TENSOR', (1, 32, 160, 120), 'float32'), ('TENSOR', (32, 1, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 32, 160, 120), 'float32'), ('TENSOR', (32, 32, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('depthwise_conv2d_NCHWc.x86', ('TENSOR', (1, 32, 160, 120), 'float32'), ('TENSOR', (32, 1, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 32, 80, 60), 'float32'), ('TENSOR', (64, 32, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('depthwise_conv2d_NCHWc.x86', ('TENSOR', (1, 64, 80, 60), 'float32'), ('TENSOR', (64, 1, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 80, 60), 'float32'), ('TENSOR', (64, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('depthwise_conv2d_NCHWc.x86', ('TENSOR', (1, 64, 80, 60), 'float32'), ('TENSOR', (64, 1, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 40, 30), 'float32'), ('TENSOR', (128, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('depthwise_conv2d_NCHWc.x86', ('TENSOR', (1, 128, 40, 30), 'float32'), ('TENSOR', (128, 1, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 128, 40, 30), 'float32'), ('TENSOR', (128, 128, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('depthwise_conv2d_NCHWc.x86', ('TENSOR', (1, 128, 40, 30), 'float32'), ('TENSOR', (128, 1, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 128, 20, 15), 'float32'), ('TENSOR', (256, 128, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('depthwise_conv2d_NCHWc.x86', ('TENSOR', (1, 256, 20, 15), 'float32'), ('TENSOR', (256, 1, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 20, 15), 'float32'), ('TENSOR', (256, 256, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 20, 15), 'float32'), ('TENSOR', (64, 256, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 20, 15), 'float32'), ('TENSOR', (32, 64, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 20, 15), 'float32'), ('TENSOR', (16, 64, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 16, 20, 15), 'float32'), ('TENSOR', (16, 16, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 20, 15), 'float32'), ('TENSOR', (4, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 20, 15), 'float32'), ('TENSOR', (8, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 20, 15), 'float32'), ('TENSOR', (20, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 128, 40, 30), 'float32'), ('TENSOR', (64, 128, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 40, 30), 'float32'), ('TENSOR', (64, 64, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 40, 30), 'float32'), ('TENSOR', (32, 64, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 40, 30), 'float32'), ('TENSOR', (16, 64, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 16, 40, 30), 'float32'), ('TENSOR', (16, 16, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 40, 30), 'float32'), ('TENSOR', (4, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 40, 30), 'float32'), ('TENSOR', (8, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 40, 30), 'float32'), ('TENSOR', (20, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 80, 60), 'float32'), ('TENSOR', (64, 64, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 80, 60), 'float32'), ('TENSOR', (32, 64, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 80, 60), 'float32'), ('TENSOR', (16, 64, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 16, 80, 60), 'float32'), ('TENSOR', (16, 16, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 80, 60), 'float32'), ('TENSOR', (4, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 80, 60), 'float32'), ('TENSOR', (8, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 80, 60), 'float32'), ('TENSOR', (20, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Compile...
------convert done!!!------

Process finished with exit code 0

But the model is saved normally.

after build,The result of running retinaface_ncnn

Pre Forward Time: 10.3058ms
proposals:133
proposals:371
proposals:1479
Forward Time: 65.0252ms
Post Forward Time: 0.593445ms
All Time: 75.9244ms
final proposals: 92
score0: 1.000000
score1: 1.000000
score2: 1.000000
score3: 1.000000
score4: 1.000000
score5: 1.000000
score6: 1.000000
score7: 1.000000
score8: 1.000000
score9: 1.000000
score10: 1.000000
score11: 1.000000
.
.
.

What is the problem?
The converted model you attached is correct..

My environment

Ubuntu 19.10
TVM 0.7.dev1
llvm 6.0

How to forward in batches

Thank you for your work! I have some problems about batch forward.
-In from_mxnet.py, batch_size=1, input_shape=(1, 3, H, W), then the models for TVM are produced
-When inferring with TVM, the input_shape must be (1, 3, H, W)
-How to batch forward with TVM ?

TVM model

HI, Howave
This is a great work. I have a liite confuse about where did you get your mxnet model? could you please add a link? Since I am using MacOS, I have to convert it on my own env.
Thanks

Why 480x640?

The image shape of Retina face is :640x480, the aspect ratio is same with normal camera, but in your source code, it is 480x640, is it really correct? thank you

Python inference

import numpy as np
import nnvm.compiler
import nnvm.testing
import tvm
from tvm.contrib import graph_runtime
import mxnet as mx
import cv2
from mxnet import ndarray as nd

ctx = tvm.cpu()
data_shape = (640, 480, 3)
loaded_json = open("./models/480P/mnet.25.x86.cpu.json").read()
loaded_lib = tvm.module.load("./models/480P/mnet.25.x86.cpu.so")
loaded_params = bytearray(open("./models/480P/mnet.25.x86.cpu.params", "rb").read())

module = graph_runtime.create(loaded_json, loaded_lib, ctx)
module.load_params(loaded_params)

img = cv2.imread('/home/xyz/desktop/7.jpg')
resized = cv2.resize(img, (480, 640))
input_data = tvm.nd.array(resized.astype("float32"))

module.run(data=input_data)
ret = module.get_output(0).asnumpy()

I am not aware of the output shape and the way to fetch rest of the results. Can you be kind enough to help with rest of the code ?

resolved

resolved.

build successed, but run failed.

Thanks your wonderful works, I build your project successfully, but I got this error when I run the bin file, can you help me to solve the problem?

terminate called after throwing an instance of 'dmlc::Error'
what(): [09:18:21] /***/tvm/src/runtime/dso_module.cc:112: Check failed: lib_handle_ != nullptr: Failed to load dynamic shared library ./models/480P/mnet.25.x86.cpu.so /lib/x86_64-linux-gnu/libm.so.6: version `GLIBC_2.27' not found (required by ./models/480P/mnet.25.x86.cpu.so)
Stack trace:
[bt] (0) /usr/local/lib/libtvm_runtime.so(+0x5104f) [0x7f788f53804f]
[bt] (1) /usr/local/lib/libtvm_runtime.so(tvm::runtime::Module::LoadFromFile(std::__cxx11::basic_string<char, std::char_traits, std::allocator > const&, std::__cxx11::basic_string<char, std::char_traits, std::allocator > const&)+0x52c) [0x7f788f50e7ec]
[bt] (2) ./bin/retinaface_tvm(+0x4adb) [0x556739214adb]
[bt] (3) /lib/x86_64-linux-gnu/libc.so.6(__libc_start_main+0xf0) [0x7f788e89c830]
[bt] (4) ./bin/retinaface_tvm(+0x7499) [0x556739217499]

Aborted (core dumped)

detection speeds too much time

Thank you for your work! I compiled the model using TVM at Windows10 successfully and got the correct bouding box of face. But it needs 1200ms to detect one frame which is so strange. I do no know what is the problem.

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