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squeezenet bug #6500

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fradino opened this issue Sep 30, 2018 · 5 comments

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@fradino
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commented Sep 30, 2018

Exception in thread "main" java.lang.IllegalStateException: Invalid configuration: Vertex "fire2_relu_exp1x1" has input "fire2_exp1x1" that does not exist
at org.deeplearning4j.nn.conf.ComputationGraphConfiguration.validate(ComputationGraphConfiguration.java:314)
at org.deeplearning4j.nn.conf.ComputationGraphConfiguration$GraphBuilder.build(ComputationGraphConfiguration.java:1033)
at org.deeplearning4j.zoo.model.SqueezeNet.init(SqueezeNet.java:78)
at org.deeplearning4j.zoo.model.SqueezeNet.init(SqueezeNet.java:36)

@Charele

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commented Sep 30, 2018

I check the code, some typos here:
1>.addLayer(prefix+"exp1x1", new ConvolutionLayer.Builder(1, 1).nOut(expand)
.cudnnAlgoMode(cudnnAlgoMode).build(), prefix+"_relu_sq1x1")
the "exp1x1" shoud be "_exp1x1"

2> .addLayer("conv10", new ConvolutionLayer.Builder(1,1).nOut(numClasses)
.cudnnAlgoMode(cudnnAlgoMode).build(), "input")
I think the "conv10" should connect to "drop9"?

3>
.addLayer(prefix+"exp1x1", new ConvolutionLayer.Builder(1, 1).nOut(expand)
.cudnnAlgoMode(cudnnAlgoMode).build(), prefix+"_relu_sq1x1")
.addLayer(prefix+"_relu_exp1x1", new ActivationLayer(Activation.RELU), prefix+"_exp1x1")

            .addLayer(prefix+"_exp3x3", new ConvolutionLayer.Builder(3,3).nOut(expand)
                    .cudnnAlgoMode(cudnnAlgoMode).build(), prefix+"_relu_sq1x1")
            .addLayer(prefix+"_relu_exp3x3", new ActivationLayer(Activation.RELU), prefix+"_exp3x3")

            .addVertex(prefix, new MergeVertex(), prefix+"_relu_exp1x1", prefix+"_relu_exp3x3");

The "..._relu_exp1x1" and "..._relu_exp3x3" have different shape, they cann't be merge,
I think above two new ConvolutionLayer.Builder() should have same kernelSize,,,my option

@AlexDBlack

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commented Sep 30, 2018

As per here: #6501 (comment)

Again, try on 1.0.0-beta2.
And if it still occurs, please give us some code to reproduce this. We can't do much without knowing exactly what you are doing.

@fradino

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commented Oct 1, 2018

now,I use 1.0.0-beta2
the code I write is just

ZooModel zooModel = SqueezeNet.builder().build();
ComputationGraph sqnet = zooModel.init();
log.info(sqnet.summary());

still has this problem

AlexDBlack added a commit that referenced this issue Oct 1, 2018
@AlexDBlack AlexDBlack referenced this issue Oct 1, 2018
@AlexDBlack

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commented Oct 1, 2018

Fixed here: #6504

Once it's merged, you can get the fix using snapshots: https://deeplearning4j.org/docs/latest/deeplearning4j-config-snapshots

Or, in this case, you can probably just copy the updated model code to a local project (as the changes are isolated to the zoo model class) - https://deeplearning4j.org/docs/latest/deeplearning4j-config-snapshots

AlexDBlack added a commit that referenced this issue Oct 3, 2018
Misc fixes (#6504)
* #6501 Fix multiple issues with Xception configuration

* #6500 Fix SqueezeNet (non-pretrained) config + add tests

* #6497 Fix multi-output net evaluation for ComputationGraph

* #6502 fix formatting for ComputationGraph summary

* #6502 fix formatting for MultiLayerNetwork summary

* #6489 fix JSON mapping for keras import preprocessors

* Switch default output layer activation to softmax

* Fix transposei issues with autoencoder

* Minor test fixes

* ComputationGraph: Fix possible double application of preprocessors and setting input for output layers when doing backprop

* Workspace fix for output layer dropout in compgraph
@lock

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commented Nov 2, 2018

This thread has been automatically locked since there has not been any recent activity after it was closed. Please open a new issue for related bugs.

@lock lock bot locked and limited conversation to collaborators Nov 2, 2018

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