My device is RK3399.I fllow this tutorial:http://nnvm.tvmlang.org/tutorials/from_coreml.html#sphx-glr-tutorials-from-coreml-py.
When I let target = “llvm”, ctx = tvm.cpu(0),the result is ok:(‘Top-1 id’, 287, ‘class name’, ‘lynx, catamount’).
When I let target = tvm.target.mali(), ctx=tvm.cl(0),the result is wrong:(‘Top-1 id’, 0, ‘class name’, ‘‘tench, Tinca tinca’’).
Does anyone have this problem?
Thank you very much!
Can you inspect the output and check the disparity between versions?
e.g., np.allclose(output_cpu.asnumpy(), output_maliclasnumpy())?
My output is:
output_shape = (1000,)
tvm_output = m.get_output(0, tvm.nd.empty(output_shape, dtype)).asnumpy()
Is not ok?
How can I check the disparity between versions?
.asnumpy() will give you numpy array versions of the output so you are free to check however you wish; numpy.allclose is one method.
I check it. When I let target = tvm.target.mali(), ctx=tvm.cl(0), all elements in the tvm_output array are equal to zero.
When I let target = “llvm”, ctx=tvm.cpu(0), all elements in the tvm_output array are not equal to zero.
Ok, that’s interesting. Can you try using the time evaluator to check the run time of the cl version?
The run time of the cl version is 0.117s.
The run time of the cpu version is 1.008s.
This is my coreml.py
import nnvm
import tvm
import coremltools as cm
import numpy as np
from PIL import Image
model_file = ‘mobilenet.mlmodel’
mlmodel = cm.models.MLModel(model_file)
we can load the graph as NNVM compatible model
sym, params = nnvm.frontend.from_coreml(mlmodel)
######################################################################
Load a test image
from PIL import Image
img = Image.open(‘cat.png’).resize((224, 224))
#x = np.transpose(img, (2, 0, 1))[np.newaxis, :]
image = np.asarray(img)
image = image.transpose((2, 0, 1))
x = image[np.newaxis, :]
######################################################################
Compile the model on NNVM
---------------------------
We should be familiar with the process right now.
import nnvm.compiler
#target = ‘llvm’
target_host = “llvm -target=aarch64-linux-gnu -mattr=+neon”
shape_dict = {‘image’: x.shape}
graph, lib, params = nnvm.compiler.build(sym, target=tvm.target.mali(), shape=shape_dict, params=params,target_host=target_host);
print (“build ok”)
######################################################################
Execute on TVM
-------------------
The process is no different from other example
from tvm.contrib import graph_runtime
#ctx = tvm.cpu(0)
ctx = tvm.cl(0)
dtype = ‘float32’
m = graph_runtime.create(graph, lib, ctx)
set inputs
m.set_input(‘image’, tvm.nd.array(x.astype(dtype)))
m.set_input(**params)
execute
m.run()
test
num_test=1
num_warmup=1
print(“warm up…”)
warm_up_timer = m.module.time_evaluator(“run”, ctx, num_warmup)
warm = warm_up_timer()
print warm.mean
print(“test…”)
ftimer = m.module.time_evaluator(“run”, ctx, num_test)
prof_res = ftimer()
print(“cost per image: %.4fs” % prof_res.mean)
print(“backend: TVM-mali\tmodel: %s\tdtype: %s\tcost:%.4fs” % (mlmodel, dtype, prof_res.mean))
get outputs
output_shape = (1000,)
tvm_output = m.get_output(0, tvm.nd.empty(output_shape, dtype)).asnumpy()
print tvm_output
top1 = np.argmax(tvm_output)
synset_name = ‘synset.txt’
with open(synset_name) as f:
synset = eval(f.read())
print(‘Top-1 id’, top1, ‘class name’, synset[top1])
Can you try specifying the target slightly differently; e.g., instead of tvm.target.mali() try opencl -device=mali -model=Mali-T860MP4@800Mhz
I try it:target=“opencl -device=mali -model=Mali-T860MP4@800Mhz”
But all elements in the tvm_output array are also equal to zero.
Looking at the code posted, are you doing module.run()?
Yes,i’m donging module.run().
When I run the program I will uncomment it “m.run()”
Does your mali device run this expamle ok?