You may try this initial version of changes where I could compile Resnet_v2 via tensorflow frontend.
I am planning to PR it soon.
Yes set the --labels_offset=1 flag when exporting inference graph solves this problem. Thanks.
Thanks for the quick commit!
When I tried tf slim models of resnet 50 v1 and v2 (https://github.com/tensorflow/models/tree/master/research/slim), I got NotImplementedError: Please freeze the graph with add_shapes=True. I use the freeze script from https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py, and it does not have an add_shapes option. Is there any other freeze_graph I should use?
Sorry for bothering you so much 
freeze_graph.py --input_saved_model_dir=20180601_resnet_v2_imagenet_savedmodel/1527888387/ --output_graph=frozen_model-v2-fp16.pb --output_node_names=ArgMax --clear_devices
I use this command to freeze the model.
Ref.
helper function as shown below can be used to add shapes.
graph_def = nnvm.testing.tf.AddShapesToGraphDef(‘softmax’)
This is the official model I first used. The input node and output node of this model (inspected by https://github.com/tf-coreml/tf-coreml/blob/master/utils/inspect_pb.py) are:
0: op name = import/input_tensor, op type = ( Placeholder ), inputs = , outputs = import/input_tensor:0
@input shapes:
@output shapes:
name = import/input_tensor:0 : (128, 224, 224, 3)
......
702: op name = import/ArgMax, op type = ( ArgMax ), inputs = import/resnet_model/final_dense:0, import/ArgMax/dimension:0, outputs = import/ArgMax:0
@input shapes:
name = import/resnet_model/final_dense:0 : (128, 1001)
name = import/ArgMax/dimension:0 : ()
@output shapes:
name = import/ArgMax:0 : (128,)
Since NNVM/TVM does not support batch_size > 1, so I set batch_size to 1.
With you patch, it compiled to nnvm successfully 
.
But I have questions of inference. The output of the graph is ArgMax, so it is the class number of the classification. And if batch_size is 1, the output shape should be (1,). I used the code below to inference:
......
out = module.get_output(0)
tvm_out = out.asnumpy()
print(tvm_out)
It printed out [766 774 457 766 729 701 824]
while I expected 230. And I don’t understand why it is a 7-element vector.
Below is one picture I used for inference. It is from the ImageNet dataset, classified as 230: ‘Shetland sheepdog, Shetland sheep dog, Shetland’.
Do you have any insights how I should do the inference? Thanks a lot.
For the research slim model,
graph_def = nnvm.testing.tf.AddShapesToGraphDef('resnet_v2_50/predictions/Reshape_1')
does solve the add shape error. But during conversion,
File "from_tensorflow_slim_v2.py", line 124, in <module>
graph, lib, params = nnvm.compiler.build(sym, target, shape_dict, params=params)
File "/tvm/nnvm/python/nnvm/compiler/build_module.py", line 270, in build
ishape, _ = graph_util.infer_shape(graph, **shape)
File "/tvm/nnvm/python/nnvm/compiler/graph_util.py", line 31, in infer_shape
graph = graph.apply("InferShape")
File "/tvm/nnvm/python/nnvm/graph.py", line 234, in apply
check_call(_LIB.NNGraphApplyPasses(self.handle, npass, cpass, ctypes.byref(ghandle)))
File "/tvm/nnvm/python/nnvm/_base.py", line 75, in check_call
raise NNVMError(py_str(_LIB.NNGetLastError()))
nnvm._base.NNVMError: Error in operator resnet_v2_50/SpatialSqueeze: [17:58:16] /tvm/nnvm/src/top/tensor/transform.cc:693: Check failed: shp[i] == 1 (7 vs. 1) The squeezed axis must have shape 1!Want to squeeze 2, which has shape7
The input, output and the resnet_v2_50/SpatialSqueeze nodes are as below:
0: op name = import/input, op type = ( Placeholder ), inputs = , outputs = import/input:0
@input shapes:
@output shapes:
name = import/input:0 : (?, 224, 224, 3)
......
1762: op name = import/resnet_v2_50/SpatialSqueeze, op type = ( Squeeze ), inputs = import/resnet_v2_50/logits/BiasAdd:0, outputs = import/resnet_v2_50/SpatialSqueeze:0
@input shapes:
name = import/resnet_v2_50/logits/BiasAdd:0 : (?, 1, 1, 1001)
@output shapes:
name = import/resnet_v2_50/SpatialSqueeze:0 : (?, 1001)
......
1767: op name = import/resnet_v2_50/predictions/Reshape_1, op type = ( Reshape ), inputs = import/resnet_v2_50/predictions/Softmax:0, import/resnet_v2_50/predictions/Shape:0, outputs = import/resnet_v2_50/predictions/Reshape_1:0
@input shapes:
name = import/resnet_v2_50/predictions/Softmax:0 : (?, 1001)
name = import/resnet_v2_50/predictions/Shape:0 : (2,)
@output shapes:
name = import/resnet_v2_50/predictions/Reshape_1:0 : (?, 1001)
Did you have
File "/tvm/nnvm/python/nnvm/frontend/coreml.py", line 182, in PoolingLayerParams
raise NotImplementedError("Other convolution padding not implemented")
NotImplementedError: Other convolution padding not implemented
when converting coreml to nnvm? (The coreml model is converted from research slim resnet50 v2 tf model.)
I have done many things for CoreML. For convolution, I have support SAME / VALID using 4-D padding (haven’t contributed back to community, will do soon) And for pooling, also support its padding completely too. So, I really cam not figure out the detail error only having this information. I suggest converting .mlmodel to Text format, you could Google it how to do it and then check what is this layer detail information.
I know the shape operator issue above resnet_v2_50, I will try sharing the fix for it soon.
With this tensorflow frontend could support all models(Inception, Resnet, MobilenetV1/V2, Vgg) from research/slim.
As all models can’t be integrated into TVM test cases. I have added some utils to validate https://github.com/srkreddy1238/dmlc_data/tree/master/work/tf/samples for reference.