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predict-with-pretrained-model.ipynb unable to run #2718

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taoari opened this issue Jul 15, 2016 · 15 comments
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predict-with-pretrained-model.ipynb unable to run #2718

taoari opened this issue Jul 15, 2016 · 15 comments

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@taoari
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taoari commented Jul 15, 2016

The notebook predict-with-pretrained-model.ipynb is no longer able to run correctly with the latest version. I am getting the following error:

MXNetError: InferShape Error in ch_concat_3c_chconcat: [16:47:40] mxnet\src\operator\./concat-inl.h:152: Check failed: (dshape[j]) == (tmp[j]) Incorrect shape[2]: (1,320,13,13). (first input shape: (1,576,14,14))

The problem should be caused the change of the behavior of the pooling layer.

Originally, for an image of shape (32,32), with 3x3/2 pooling, it will generate an image of shape (16,16). But for current version, it generates an image of shape (15,15). Will you change this behavior back?
@taoari taoari mentioned this issue Jul 16, 2016
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@taoari
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taoari commented Jul 17, 2016

@antinucleon The pull request #2585 actually breaks the behavior of the Pooling operations.

Originally for Convolution, the shape is calculated with floor((H+2_pad-kernel)/stride+1), for Pooling, it is calculated as ceil((H+2_pad-kernel)/stride+1). Now the PR #2585 unifies both of them to floor((H+2*pad-kernel)/stride+1).

I do advocate for this change. However, this behvior breaks the compatibility with existing models such as Inception-BN and many other Caffe models using 3x3/2 Pooling structures (e.g. GoogLeNet, ResNet).

I think the ability to use existing pre-trained models is more important, so will the DMLC community revert this behavior back or provide a script to upgrade the models?

@Godricly Godricly mentioned this issue Jul 18, 2016
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@erogol
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erogol commented Jul 27, 2016

I also suffer from the same issue.

1 similar comment
@Lorrainexun
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I also suffer from the same issue.

@antinucleon
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I will update the notebook. This is because we update pooling to standard pooling shape, which is different to old shape.

@wangg12
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wangg12 commented Aug 12, 2016

@antinucleon Has this problem been fixed? I suffered the same problem when I use the pre-trained inception-bn model.

MXNetError: InferShape Error in ch_concat_3c_chconcat: [11:26:18] d:\chhong\mxne
t\src\operator\./concat-inl.h:152: Check failed: (dshape[j]) == (tmp[j]) Incorre
ct shape[2]: (1,320,15,15). (first input shape: (1,576,14,14))

@taoari
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taoari commented Aug 12, 2016

I believe the easiest way is to search in Inception_BN-symbol.json with "Pooling", and replace pad from (0,0) to (1,1) for those whose kernel is (3,3) and stride is (2,2).

The following is a modified one:

{
  "nodes": [
    {
      "op": "null", 
      "param": {}, 
      "name": "data", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "conv_1_weight", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "conv_1_bias", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "Convolution", 
      "param": {
        "kernel": "(7, 7)", 
        "no_bias": "False", 
        "num_filter": "64", 
        "num_group": "1", 
        "pad": "(3, 3)", 
        "stride": "(2, 2)", 
        "workspace": "512"
      }, 
      "name": "conv_1", 
      "inputs": [[0, 0], [1, 0], [2, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "bn_1_gamma", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "bn_1_beta", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "BatchNorm", 
      "param": {
        "eps": "1e-10", 
        "momentum": "0.1"
      }, 
      "name": "bn_1", 
      "inputs": [[3, 0], [4, 0], [5, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "Activation", 
      "param": {"act_type": "relu"}, 
      "name": "relu_1", 
      "inputs": [[6, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "Pooling", 
      "param": {
        "kernel": "(3, 3)", 
        "pad": "(1, 1)", 
        "pool_type": "max", 
        "stride": "(2, 2)"
      }, 
      "name": "max_pool_1", 
      "inputs": [[7, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "conv_2_reduce_weight", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "conv_2_reduce_bias", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "Convolution", 
      "param": {
        "kernel": "(1, 1)", 
        "no_bias": "False", 
        "num_filter": "64", 
        "num_group": "1", 
        "pad": "(0, 0)", 
        "stride": "(1, 1)", 
        "workspace": "512"
      }, 
      "name": "conv_2_reduce", 
      "inputs": [[8, 0], [9, 0], [10, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "bn_2_1_gamma", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "bn_2_1_beta", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "BatchNorm", 
      "param": {
        "eps": "1e-10", 
        "momentum": "0.1"
      }, 
      "name": "bn_2_1", 
      "inputs": [[11, 0], [12, 0], [13, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "Activation", 
      "param": {"act_type": "relu"}, 
      "name": "relu_2_1", 
      "inputs": [[14, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "conv_2_weight", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "conv_2_bias", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "Convolution", 
      "param": {
        "kernel": "(3, 3)", 
        "no_bias": "False", 
        "num_filter": "192", 
        "num_group": "1", 
        "pad": "(1, 1)", 
        "stride": "(1, 1)", 
        "workspace": "512"
      }, 
      "name": "conv_2", 
      "inputs": [[15, 0], [16, 0], [17, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "bn_2_gamma", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "bn_2_beta", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "BatchNorm", 
      "param": {
        "eps": "1e-10", 
        "momentum": "0.1"
      }, 
      "name": "bn_2", 
      "inputs": [[18, 0], [19, 0], [20, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "Activation", 
      "param": {"act_type": "relu"}, 
      "name": "relu_2", 
      "inputs": [[21, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "Pooling", 
      "param": {
        "kernel": "(3, 3)", 
        "pad": "(1, 1)", 
        "pool_type": "max", 
        "stride": "(2, 2)"
      }, 
      "name": "max_pool_2", 
      "inputs": [[22, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "conv_3a_1x1_weight", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "conv_3a_1x1_bias", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "Convolution", 
      "param": {
        "kernel": "(1, 1)", 
        "no_bias": "False", 
        "num_filter": "64", 
        "num_group": "1", 
        "pad": "(0, 0)", 
        "stride": "(1, 1)", 
        "workspace": "512"
      }, 
      "name": "conv_3a_1x1", 
      "inputs": [[23, 0], [24, 0], [25, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "bn_3a_1x1_gamma", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "bn_3a_1x1_beta", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "BatchNorm", 
      "param": {
        "eps": "1e-10", 
        "momentum": "0.1"
      }, 
      "name": "bn_3a_1x1", 
      "inputs": [[26, 0], [27, 0], [28, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "Activation", 
      "param": {"act_type": "relu"}, 
      "name": "relu_3a_1x1", 
      "inputs": [[29, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "conv_3a_3x3_reduce_weight", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "conv_3a_3x3_reduce_bias", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "Convolution", 
      "param": {
        "kernel": "(1, 1)", 
        "no_bias": "False", 
        "num_filter": "64", 
        "num_group": "1", 
        "pad": "(0, 0)", 
        "stride": "(1, 1)", 
        "workspace": "512"
      }, 
      "name": "conv_3a_3x3_reduce", 
      "inputs": [[23, 0], [31, 0], [32, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "bn_3a_3x3_reduce_gamma", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "bn_3a_3x3_reduce_beta", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "BatchNorm", 
      "param": {
        "eps": "1e-10", 
        "momentum": "0.1"
      }, 
      "name": "bn_3a_3x3_reduce", 
      "inputs": [[33, 0], [34, 0], [35, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "Activation", 
      "param": {"act_type": "relu"}, 
      "name": "relu_3a_3x3_reduce", 
      "inputs": [[36, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "conv_3a_3x3_weight", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "conv_3a_3x3_bias", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "Convolution", 
      "param": {
        "kernel": "(3, 3)", 
        "no_bias": "False", 
        "num_filter": "64", 
        "num_group": "1", 
        "pad": "(1, 1)", 
        "stride": "(1, 1)", 
        "workspace": "512"
      }, 
      "name": "conv_3a_3x3", 
      "inputs": [[37, 0], [38, 0], [39, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "bn_3a_3x3_gamma", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "bn_3a_3x3_beta", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "BatchNorm", 
      "param": {
        "eps": "1e-10", 
        "momentum": "0.1"
      }, 
      "name": "bn_3a_3x3", 
      "inputs": [[40, 0], [41, 0], [42, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "Activation", 
      "param": {"act_type": "relu"}, 
      "name": "relu_3a_3x3", 
      "inputs": [[43, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "conv_3a_double_3x3_reduce_weight", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "conv_3a_double_3x3_reduce_bias", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "Convolution", 
      "param": {
        "kernel": "(1, 1)", 
        "no_bias": "False", 
        "num_filter": "64", 
        "num_group": "1", 
        "pad": "(0, 0)", 
        "stride": "(1, 1)", 
        "workspace": "512"
      }, 
      "name": "conv_3a_double_3x3_reduce", 
      "inputs": [[23, 0], [45, 0], [46, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "bn_3a_double_3x3_reduce_gamma", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "bn_3a_double_3x3_reduce_beta", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "BatchNorm", 
      "param": {
        "eps": "1e-10", 
        "momentum": "0.1"
      }, 
      "name": "bn_3a_double_3x3_reduce", 
      "inputs": [[47, 0], [48, 0], [49, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "Activation", 
      "param": {"act_type": "relu"}, 
      "name": "relu_3a_double_3x3_reduce", 
      "inputs": [[50, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "conv_3a_double_3x3_0_weight", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "conv_3a_double_3x3_0_bias", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "Convolution", 
      "param": {
        "kernel": "(3, 3)", 
        "no_bias": "False", 
        "num_filter": "96", 
        "num_group": "1", 
        "pad": "(1, 1)", 
        "stride": "(1, 1)", 
        "workspace": "512"
      }, 
      "name": "conv_3a_double_3x3_0", 
      "inputs": [[51, 0], [52, 0], [53, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "bn_3a_double_3x3_0_gamma", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "bn_3a_double_3x3_0_beta", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "BatchNorm", 
      "param": {
        "eps": "1e-10", 
        "momentum": "0.1"
      }, 
      "name": "bn_3a_double_3x3_0", 
      "inputs": [[54, 0], [55, 0], [56, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "Activation", 
      "param": {"act_type": "relu"}, 
      "name": "relu_3a_double_3x3_0", 
      "inputs": [[57, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "conv_3a_double_3x3_1_weight", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "conv_3a_double_3x3_1_bias", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "Convolution", 
      "param": {
        "kernel": "(3, 3)", 
        "no_bias": "False", 
        "num_filter": "96", 
        "num_group": "1", 
        "pad": "(1, 1)", 
        "stride": "(1, 1)", 
        "workspace": "512"
      }, 
      "name": "conv_3a_double_3x3_1", 
      "inputs": [[58, 0], [59, 0], [60, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "bn_3a_double_3x3_1_gamma", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "bn_3a_double_3x3_1_beta", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "BatchNorm", 
      "param": {
        "eps": "1e-10", 
        "momentum": "0.1"
      }, 
      "name": "bn_3a_double_3x3_1", 
      "inputs": [[61, 0], [62, 0], [63, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "Activation", 
      "param": {"act_type": "relu"}, 
      "name": "relu_3a_double_3x3_1", 
      "inputs": [[64, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "Pooling", 
      "param": {
        "kernel": "(3, 3)", 
        "pad": "(1, 1)", 
        "pool_type": "avg", 
        "stride": "(1, 1)"
      }, 
      "name": "avg_pool_3a_pool", 
      "inputs": [[23, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "conv_3a_proj_weight", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "conv_3a_proj_bias", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "Convolution", 
      "param": {
        "kernel": "(1, 1)", 
        "no_bias": "False", 
        "num_filter": "32", 
        "num_group": "1", 
        "pad": "(0, 0)", 
        "stride": "(1, 1)", 
        "workspace": "512"
      }, 
      "name": "conv_3a_proj", 
      "inputs": [[66, 0], [67, 0], [68, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "bn_3a_proj_gamma", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "bn_3a_proj_beta", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "BatchNorm", 
      "param": {
        "eps": "1e-10", 
        "momentum": "0.1"
      }, 
      "name": "bn_3a_proj", 
      "inputs": [[69, 0], [70, 0], [71, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "Activation", 
      "param": {"act_type": "relu"}, 
      "name": "relu_3a_proj", 
      "inputs": [[72, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "Concat", 
      "param": {"num_args": "4"}, 
      "name": "ch_concat_3a_chconcat", 
      "inputs": [[30, 0], [44, 0], [65, 0], [73, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "conv_3b_1x1_weight", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "conv_3b_1x1_bias", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "Convolution", 
      "param": {
        "kernel": "(1, 1)", 
        "no_bias": "False", 
        "num_filter": "64", 
        "num_group": "1", 
        "pad": "(0, 0)", 
        "stride": "(1, 1)", 
        "workspace": "512"
      }, 
      "name": "conv_3b_1x1", 
      "inputs": [[74, 0], [75, 0], [76, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "bn_3b_1x1_gamma", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "bn_3b_1x1_beta", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "BatchNorm", 
      "param": {
        "eps": "1e-10", 
        "momentum": "0.1"
      }, 
      "name": "bn_3b_1x1", 
      "inputs": [[77, 0], [78, 0], [79, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "Activation", 
      "param": {"act_type": "relu"}, 
      "name": "relu_3b_1x1", 
      "inputs": [[80, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "conv_3b_3x3_reduce_weight", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "conv_3b_3x3_reduce_bias", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "Convolution", 
      "param": {
        "kernel": "(1, 1)", 
        "no_bias": "False", 
        "num_filter": "64", 
        "num_group": "1", 
        "pad": "(0, 0)", 
        "stride": "(1, 1)", 
        "workspace": "512"
      }, 
      "name": "conv_3b_3x3_reduce", 
      "inputs": [[74, 0], [82, 0], [83, 0]], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "bn_3b_3x3_reduce_gamma", 
      "inputs": [], 
      "backward_source_id": -1
    }, 
    {
      "op": "null", 
      "param": {}, 
      "name": "bn_3b_3x3_reduce_beta", 
      "inputs": [], 
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@shesung
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shesung commented Aug 15, 2016 •
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@taoari Only need to modify -symbol.json ? What about .params ? Is it ok to run old .params with modified -symbol.json?

@taoari
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taoari commented Aug 15, 2016

@shesung To make it run, yes. Of course, it will no longer be compatible with the old model, but I think CNN is robust enough to tackle with these small variances for different pooling methods.

@shesung
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shesung commented Aug 15, 2016

@taori Thanks! I'll have a try.

@taori
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taori commented Aug 17, 2016

@shesung you meant to tell that to taoari ;)

@GeorgeXia1828
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yeah, I noticed the only difference is code in the InceptionFactoryB

pooling = mx.symbol.Pooling(data=data, kernel=(3, 3), stride=(2, 2), pad=(1, 1), pool_type="max", name=('max_pool_%s_pool' % name))

while the old version mxnet the code here, pad is not set (1, 1).

@tengfly
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tengfly commented Nov 25, 2016 •
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Thanks, the "Incorrect shape" error fixed by adding pad=(1,1) in the InceptionFactoryB, and to search in Inception_BN-symbol.json with "Pooling", and replace pad from (0,0) to (1,1) for those whose kernel is (3,3) and stride is (2,2).

@pyd1data
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pyd1data commented Jan 31, 2017 •
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Hello everyone, I had the same problem and did the correction using pad(0,0) to pad(1,1), everything works fine in the prediction except the fact that layer global_pool has a shape (1024L,2L,2L) instead of (1024L)... I don't understand why...
I use Inception 21k pre-trained network.

@phunterlau
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This issue is closed due to lack of activity in the last 90 days. Feel free to reopen if this is still an active issue. Thanks!

@sufeidechabei
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I also meet the same error

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