MNIST computation do not give the good result.

[owulveryck]

MNIST computation do not give the good result.

[owulveryck]

This may be related to the implementation of the Reshape Operator, see issue 2

[owulveryck]

I have tried the mnist example with caffe2 from the docker image:

docker run -it --rm onnx/onnx-docker:cpu

then run this script:

import numpy as np
import onnx
import os
import glob
import caffe2.python.onnx.backend as backend

from onnx import numpy_helper

model = onnx.load('model.onnx')
test_data_dir = 'test_data_set_0'

# Load inputs
inputs = []
inputs_num = len(glob.glob(os.path.join(test_data_dir, 'input_*.pb')))
for i in range(inputs_num):
    input_file = os.path.join(test_data_dir, 'input_{}.pb'.format(i))
    tensor = onnx.TensorProto()
    with open(input_file, 'rb') as f:
        tensor.ParseFromString(f.read())
    inputs.append(numpy_helper.to_array(tensor))

# Load reference outputs
ref_outputs = []
ref_outputs_num = len(glob.glob(os.path.join(test_data_dir, 'output_*.pb')))
for i in range(ref_outputs_num):
    output_file = os.path.join(test_data_dir, 'output_{}.pb'.format(i))
    tensor = onnx.TensorProto()
    with open(output_file, 'rb') as f:
        tensor.ParseFromString(f.read())
    ref_outputs.append(numpy_helper.to_array(tensor))

# Run the model on the backend
outputs = list(backend.run_model(model, inputs))

# Compare the results with reference outputs.
for ref_o, o in zip(ref_outputs, outputs):
    np.testing.assert_almost_equal(ref_o, o)

which gives this result:

Traceback (most recent call last):
  File "onnx_caffe2.py", line 33, in <module>
    outputs = list(backend.run_model(model, inputs))
  File "/root/programs/onnx/onnx/backend/base.py", line 86, in run_model
    return backend.run(inputs)
  File "/root/programs/pytorch/caffe2/python/onnx/backend_rep.py", line 57, in run
    self.workspace.RunNet(self.predict_net.name)
  File "/root/programs/pytorch/caffe2/python/onnx/workspace.py", line 63, in f
    return getattr(workspace, attr)(*args, **kwargs)
  File "/root/programs/pytorch/caffe2/python/workspace.py", line 219, in RunNet
    StringifyNetName(name), num_iter, allow_fail,
  File "/root/programs/pytorch/caffe2/python/workspace.py", line 180, in CallWithExceptionIntercept
    return func(*args, **kwargs)
RuntimeError: [enforce fail at reshape_op.h:110] total_size == size. 64 vs 256. Argument `shape` does not agree with the input data. (64 != 256)Error from operator:
input: "Pooling160_Output_0" input: "Pooling160_Output_0_reshape0_shape" output: "Pooling160_Output_0_reshape0" output: "OC2_DUMMY_1" name: "Times212_reshape0" type: "Reshape" device_option { device_type: 0 cuda_gpu_id: 0 }

I think that I will raise an issue within the onnx model zoo

[owulveryck]

I can now analyze the graph a bit better and it looks that a stupid Add operation is not working as expected. Here is a way to reproduce the behavior:

First checkout gorgonnx and the version that holds the tracer:

go get github.com/owulveryck/gorgonnx
git checkout 6027f688f41fb9ee7be8d66a218c396832704545

Then download the MNIST example:

cd $TMPDIR && curl -s -o - https://onnxzoo.blob.core.windows.net/models/opset_8/mnist/mnist.tar.gz | tar xvzf -

And then launch the graph:
go run $GOPATH/src/github.com/owulveryck/gorgonnx/cmd/main.go -model $TMPDIR/mnist/model.onnx -input $TMPDIR/mnist/test_data_set_0/input_0.pb

and point your browser to http://localhost:8080.

On the graph, click on the nodes Plus30_Output0 and Convolution28_0; they should be different, but are the same.
Which means that the broadcasting is ok, but the Add operation do not perform anything.

[owulveryck]

Bad news: I have tried to rewrite the parser and Gorgonia (see the branch "directed-graph").
But I still have the same result.
[55.41009 984.514 -1191.4886 -652.1293 802.4857 497.57553 -303.6763 952.77106 -233.73296 -672.92255]

So the problem does not come from the graph definition. It may be related to the computation phase of Gorgonia.