示例#1
0
def get_super_resolution():
    factor = 3
    size = 224
    data = sym.Variable(name='9')
    conv1 = sym.conv2d(data,
                       channels=64,
                       kernel_size=(5, 5),
                       padding=(2, 2),
                       use_bias=False)
    relu1 = sym.relu(conv1 + sym.Variable(name='2'))
    conv2 = sym.conv2d(relu1,
                       channels=64,
                       kernel_size=(3, 3),
                       padding=(1, 1),
                       use_bias=False)
    relu2 = sym.relu(conv2 + sym.Variable(name='4'))
    conv3 = sym.conv2d(relu2,
                       channels=32,
                       kernel_size=(3, 3),
                       padding=(1, 1),
                       use_bias=False)
    relu3 = sym.relu(conv3 + sym.Variable(name='6'))
    conv4 = sym.conv2d(relu3,
                       channels=factor**2,
                       kernel_size=(3, 3),
                       padding=(1, 1),
                       use_bias=False)
    conv4 = conv4 + sym.Variable(name='8')
    # TODO(zhreshold): allow shape inference for batch size > 1
    r1 = sym.reshape(conv4, shape=(1, 1, factor, factor, size, size))
    t1 = sym.transpose(r1, axes=(0, 1, 4, 2, 5, 3))
    r2 = sym.reshape(t1, shape=(1, 1, size * factor, size * factor))
    return r2
示例#2
0
def basic_block(data,
                num_filter,
                stride=1,
                downsample=None,
                dilation=(1, 1),
                residual=True):
    out = data
    out = conv3x3(out,
                  num_filter,
                  stride,
                  padding=dilation[0],
                  dilation=dilation[0])
    out = int8_wrapper(sym.batch_norm, data=out)
    out = sym.relu(data=out)

    out = conv3x3(out, num_filter, padding=dilation[1], dilation=dilation[1])
    out = int8_wrapper(sym.batch_norm, data=out)

    if residual:
        _residual = data
        if downsample is not None:
            _residual = downsample(data=_residual)
        out = _residual + out

    out = sym.relu(data=out)

    return out
示例#3
0
def get_classifier(input_data, num_classes):
    """Get VGG classifier layers as fc layers."""
    flatten = sym.flatten(data=input_data, name="flatten")
    fc6 = sym.dense(data=flatten, units=4096, name="fc6")
    relu6 = sym.relu(data=fc6, name="relu6")
    drop6 = sym.dropout(data=relu6, rate=0.5, name="drop6")
    fc7 = sym.dense(data=drop6, units=4096, name="fc7")
    relu7 = sym.relu(data=fc7, name="relu7")
    drop7 = sym.dropout(data=relu7, rate=0.5, name="drop7")
    fc8 = sym.dense(data=drop7, units=num_classes, name="fc8")
    return fc8
示例#4
0
def get_super_resolution_deprecated():
    factor = 3
    size = 224
    data = sym.Variable(name='9')
    conv1 = sym.conv2d(data, channels=64, kernel_size=(5, 5), padding=(2, 2))
    relu1 = sym.relu(conv1)
    conv2 = sym.conv2d(relu1, channels=64, kernel_size=(3, 3), padding=(1, 1))
    relu2 = sym.relu(conv2)
    conv3 = sym.conv2d(relu2, channels=32, kernel_size=(3, 3), padding=(1, 1))
    relu3 = sym.relu(conv3)
    conv4 = sym.conv2d(relu3,
                       channels=factor**2,
                       kernel_size=(3, 3),
                       padding=(1, 1))
    r1 = sym.reshape(conv4, shape=(0, 1, factor, factor, size, size))
    t1 = sym.transpose(r1, axes=(0, 1, 4, 2, 5, 3))
    r2 = sym.reshape(t1, shape=(0, 1, size * factor, size * factor))
    return r2
示例#5
0
def Conv(data,
         num_filter,
         kernel=(1, 1),
         stride=(1, 1),
         pad=(0, 0),
         name=None,
         suffix=''):
    conv = sym.conv2d(data=data,
                      channels=num_filter,
                      kernel_size=kernel,
                      strides=stride,
                      padding=pad,
                      use_bias=False,
                      name='%s%s_conv2d' % (name, suffix))
    bn = int8_wrapper(sym.batch_norm,
                      data=conv,
                      name='%s%s_bn' % (name, suffix),
                      epsilon=2e-5)
    act = sym.relu(data=bn, name='%s%s_relu' % (name, suffix))
    return act
示例#6
0
def block1_block_nnvm(sym_149080784, sym_consts):
    # Begin of Cell 1
    cs = sym_consts
    sym_75044384 = cs["sym_75044384"]
    sym_73427696 = cs["sym_73427696"]
    sym_223382672 = cs["sym_223382672"]
    sym_356827536 = cs["sym_356827536"]
    sym_451228704 = cs["sym_451228704"]
    sym_88828560 = cs["sym_88828560"]
    sym_379167584 = cs["sym_379167584"]
    sym_492256464 = cs["sym_492256464"]
    sym_104779696 = cs["sym_104779696"]
    sym_378983504 = cs["sym_378983504"]
    sym_73418512 = cs["sym_73418512"]
    sym_134609696 = cs["sym_134609696"]
    sym_131967104 = cs["sym_131967104"]
    sym_473740336 = cs["sym_473740336"]
    sym_112766576 = cs["sym_112766576"]
    sym_105635760 = cs["sym_105635760"]

    sym_457698256 = _sym.pad(sym_149080784,
                             pad_width=((0, 0), (0, 0), (0, 0), (0, 0)))
    sym_382705136 = _sym.conv2d(
        sym_457698256,
        sym_75044384,
        padding=[0, 0],
        dilation=(1, 1),
        layout="NHWC",
        strides=(1, 1),
        kernel_size=(1, 1),
        channels=64,
        kernel_layout="HWIO",
        name="Rcnn_ctcV3/expand_conv1/conv2d_4/convolution",
        use_bias=False)
    sym_457698256 = _sym.broadcast_add(sym_382705136, sym_73427696)
    sym_394053216 = _sym.pad(sym_149080784,
                             pad_width=((0, 0), (1, 1), (1, 1), (0, 0)))
    sym_140340480 = _sym.conv2d(
        sym_394053216,
        sym_451228704,
        dilation=(1, 1),
        layout="NHWC",
        strides=(1, 1),
        padding=[0, 0],
        kernel_size=(3, 3),
        channels=64,
        kernel_layout="HWIO",
        name="Rcnn_ctcV3/expand_conv1/conv2d_5/convolution",
        use_bias=False)
    sym_394053216 = _sym.broadcast_add(sym_140340480, sym_88828560)
    sym_88729488 = _sym.broadcast_add(
        sym_378983504,
        sym_73418512,
        name=
        "Rcnn_ctcV3/expand_conv1/static_batch_normalization_3/batchnorm/add")
    sym_104808848 = _sym.__pow_scalar__(
        sym_88729488,
        name=
        "Rcnn_ctcV3/expand_conv1/static_batch_normalization_3/batchnorm/Rsqrt",
        scalar=-0.5)
    sym_80975232 = _sym.broadcast_mul(
        sym_104808848,
        sym_379167584,
        name=
        "Rcnn_ctcV3/expand_conv1/static_batch_normalization_3/batchnorm/mul")
    sym_86811088 = _sym.broadcast_mul(
        sym_394053216,
        sym_80975232,
        name=
        "Rcnn_ctcV3/expand_conv1/static_batch_normalization_3/batchnorm/mul_1")
    sym_382126160 = _sym.broadcast_mul(
        sym_104779696,
        sym_80975232,
        name=
        "Rcnn_ctcV3/expand_conv1/static_batch_normalization_3/batchnorm/mul_2")
    sym_104808912 = _sym.broadcast_sub(
        sym_492256464,
        sym_382126160,
        name=
        "Rcnn_ctcV3/expand_conv1/static_batch_normalization_3/batchnorm/sub")
    sym_114622080 = _sym.broadcast_add(
        sym_86811088,
        sym_104808912,
        name=
        "Rcnn_ctcV3/expand_conv1/static_batch_normalization_3/batchnorm/add_1")
    sym_382620512 = _sym.pad(sym_114622080,
                             pad_width=((0, 0), (0, 0), (0, 0), (0, 0)))
    sym_457310224 = _sym.conv2d(
        sym_382620512,
        sym_223382672,
        layout="NHWC",
        strides=(1, 1),
        padding=[0, 0],
        dilation=(1, 1),
        kernel_size=(1, 1),
        channels=64,
        kernel_layout="HWIO",
        name="Rcnn_ctcV3/expand_conv1/activation/conv2d_6/convolution",
        use_bias=False)
    sym_382620512 = _sym.broadcast_add(sym_457310224, sym_356827536)
    sym_74589536 = _sym.pad(sym_114622080,
                            pad_width=((0, 0), (0, 0), (0, 0), (0, 0)))
    sym_73915248 = _sym.conv2d(
        sym_74589536,
        sym_134609696,
        layout="NHWC",
        strides=(1, 1),
        padding=[0, 0],
        dilation=(1, 1),
        kernel_size=(1, 1),
        channels=64,
        kernel_layout="HWIO",
        name="Rcnn_ctcV3/expand_conv1/activation/conv2d_7/convolution",
        use_bias=False)
    sym_74589536 = _sym.broadcast_add(sym_73915248, sym_131967104)
    sym_73915280 = _sym.broadcast_add(
        sym_382620512,
        sym_74589536,
        name="Rcnn_ctcV3/expand_conv1/activation/max_2/add")
    sym_73550304 = _sym.broadcast_sub(
        sym_382620512,
        sym_74589536,
        name="Rcnn_ctcV3/expand_conv1/activation/max_2/sub")
    sym_457788432 = _sym.relu(
        sym_73550304, name="Rcnn_ctcV3/expand_conv1/activation/max_2/Relu")
    sym_457788880 = _sym.broadcast_add(
        sym_73915280,
        sym_457788432,
        name="Rcnn_ctcV3/expand_conv1/activation/max_2/add_1")
    sym_105411888 = _sym.broadcast_sub(
        sym_74589536,
        sym_382620512,
        name="Rcnn_ctcV3/expand_conv1/activation/max_2/sub_1")
    sym_76993232 = _sym.relu(
        sym_105411888, name="Rcnn_ctcV3/expand_conv1/activation/max_2/Relu_1")
    sym_223578592 = _sym.broadcast_add(
        sym_457788880,
        sym_76993232,
        name="Rcnn_ctcV3/expand_conv1/activation/max_2/add_2")
    sym_152477424 = _sym.broadcast_mul(
        sym_473740336,
        sym_223578592,
        name="Rcnn_ctcV3/expand_conv1/activation/max_2/mul")
    sym_393365024 = _sym.pad(sym_152477424,
                             pad_width=((0, 0), (1, 1), (1, 1), (0, 0)))
    sym_79064400 = _sym.conv2d(
        sym_393365024,
        sym_112766576,
        dilation=(1, 1),
        layout="NHWC",
        strides=(1, 1),
        padding=[0, 0],
        kernel_size=(3, 3),
        channels=64,
        kernel_layout="HWIO",
        name="Rcnn_ctcV3/expand_conv1/conv2d_8/convolution",
        use_bias=False)
    sym_393365024 = _sym.broadcast_add(sym_79064400, sym_105635760)
    sym_118484944 = _sym.broadcast_add(
        sym_393365024, sym_457698256, name="Rcnn_ctcV3/expand_conv1/add_1/add")
    # End of Cell 1
    return sym_118484944
示例#7
0
def demo_softmax_debug():
    x = np.array([[0, 0], [0, 1], [1, 0], [1, 1]]).astype(np.float32)
    print(with_nnvm(0, 1, [x], lambda a: sym.relu(sym.softmax(a)), debug=True))
示例#8
0
def residual_unit(data,
                  num_filter,
                  stride,
                  dim_match,
                  name,
                  bottle_neck=True,
                  last_filter=16):
    """Return ResNet Unit symbol for building ResNet
    Parameters
    ----------
    data : str
        Input data
    num_filter : int
        Number of output channels
    bnf : int
        Bottle neck channels factor with regard to num_filter
    stride : tuple
        Stride used in convolution
    dim_match : Boolean
        True means channel number between input and output is the same,
        otherwise means differ
    name : str
        Base name of the operators
    """
    if bottle_neck:
        bn1 = sym.batch_norm(data=data,
                             epsilon=2e-5,
                             name=name + '_bn1',
                             axis=3)
        act1 = sym.relu(data=bn1, name=name + '_relu1')
        conv1 = sym.conv2d(data=act1,
                           channels=int(num_filter * 0.25),
                           kernel_size=(1, 1),
                           strides=stride,
                           padding=(0, 0),
                           use_bias=False,
                           name=name + '_conv1')
        if last_filter <= 129:
            conv1 = sym.conv2d(data=conv1,channels=int(num_filter*0.25),kernel_size=(1, 1),\
                strides=(1,1),padding=(0,0),dilation=(0,0),use_bias=False,name=name + '_conv1b')
        bn2 = sym.batch_norm(data=conv1,
                             epsilon=2e-5,
                             name=name + '_bn2',
                             axis=3)
        act2 = sym.relu(data=bn2, name=name + '_relu2')

        conv2 = sym.conv2d(data=act2,
                           channels=int(num_filter * 0.25),
                           kernel_size=(3, 3),
                           strides=(1, 1),
                           padding=(1, 1),
                           use_bias=False,
                           name=name + '_conv2')

        if num_filter * 0.25 <= 129:
            conv2 = sym.conv2d(data=conv2,channels=int(num_filter*0.25),kernel_size=(1, 1),\
                strides=(1,1),padding=(0,0),dilation=(0,0),use_bias=False,name=name + '_conv2b')

        bn3 = sym.batch_norm(data=conv2,
                             epsilon=2e-5,
                             name=name + '_bn3',
                             axis=3)
        act3 = sym.relu(data=bn3, name=name + '_relu3')

        conv3 = sym.conv2d(data=act3,
                           channels=num_filter,
                           kernel_size=(1, 1),
                           strides=(1, 1),
                           padding=(0, 0),
                           use_bias=False,
                           name=name + '_conv3')

        if num_filter * 0.25 <= 129:
            conv3 = sym.conv2d(data=conv3,channels=num_filter,kernel_size=(1, 1),\
                strides=(1,1),padding=(0,0),dilation=(0,0),use_bias=False,name=name + '_conv3b')
        if dim_match:
            shortcut = data
        else:
            shortcut = sym.conv2d(data=act1,
                                  channels=num_filter,
                                  kernel_size=(1, 1),
                                  strides=stride,
                                  use_bias=False,
                                  name=name + '_sc')
        return sym.elemwise_add(conv3, shortcut)
    else:
        bn1 = sym.batch_norm(data=data,
                             epsilon=2e-5,
                             name=name + '_bn1',
                             axis=3)
        act1 = sym.relu(data=bn1, name=name + '_relu1')
        conv1 = sym.conv2d(data=act1,
                           channels=num_filter,
                           kernel_size=(3, 3),
                           strides=stride,
                           padding=(1, 1),
                           use_bias=False,
                           name=name + '_conv1')
        if last_filter <= 129:
            conv1 = sym.conv2d(data=conv1,channels=num_filter,kernel_size=(1, 1),\
                strides=(1,1),padding=(0,0),dilation=(0,0),use_bias=False,name=name + '_conv1b')

        bn2 = sym.batch_norm(data=conv1,
                             epsilon=2e-5,
                             name=name + '_bn2',
                             axis=3)
        act2 = sym.relu(data=bn2, name=name + '_relu2')

        conv2 = sym.conv2d(data=act2,
                           channels=num_filter,
                           kernel_size=(3, 3),
                           strides=(1, 1),
                           padding=(1, 1),
                           use_bias=False,
                           name=name + '_conv2')

        if num_filter <= 129:
            conv2 = sym.conv2d(data=conv2,channels=num_filter,kernel_size=(1, 1),\
                strides=(1,1),padding=(0,0),dilation=(0,0),use_bias=False,name=name + '_conv2b')

        if dim_match:
            shortcut = data
        else:
            shortcut = sym.conv2d(data=act1,
                                  channels=num_filter,
                                  kernel_size=(1, 1),
                                  strides=stride,
                                  use_bias=False,
                                  name=name + '_sc')
        return sym.elemwise_add(conv2, shortcut)
示例#9
0
def resnet(units,
           num_stages,
           filter_list,
           num_classes,
           image_shape,
           bottle_neck=True):
    """Return ResNet symbol of
    Parameters
    ----------
    units : list
        Number of units in each stage
    num_stages : int
        Number of stage
    filter_list : list
        Channel size of each stage
    num_classes : int
        Ouput size of symbol
    dataset : str
        Dataset type, only cifar10 and imagenet supports
    """
    num_unit = len(units)
    assert num_unit == num_stages
    data = sym.Variable(name='data')

    #data = sym.batch_norm(data=data, epsilon=2e-5, scale=False, name='bn_data',axis=3)
    (_, height, _) = image_shape
    if height <= 32:  # such as cifar10
        body = sym.conv2d(data=data,
                          channels=filter_list[0],
                          kernel_size=(3, 3),
                          strides=(1, 1),
                          padding=(1, 1),
                          use_bias=False,
                          name="conv0")
    else:  # often expected to be 224 such as imagenet
        body = sym.conv2d(data=data,
                          channels=filter_list[0],
                          kernel_size=(7, 7),
                          strides=(2, 2),
                          padding=(3, 3),
                          use_bias=False,
                          name="conv0")
        body = sym.batch_norm(data=body, epsilon=2e-5, name='bn0', axis=3)
        body = sym.relu(data=body, name='relu0')
        body = sym.max_pool2d(data=body,
                              pool_size=(3, 3),
                              strides=(2, 2),
                              padding=(1, 1),
                              layout="NHWC")
    #body = residual_unit2(
    #        body, filter_list[1], (1,1),
    #        False, name='stage%d_unit%d' % (1, 1), bottle_neck=bottle_neck,filter_last=filter_list[0])

    for i in range(num_stages):
        body = residual_unit(body,
                             filter_list[i + 1],
                             (1 if i == 0 else 2, 1 if i == 0 else 2),
                             False,
                             name='stage%d_unit%d' % (i + 1, 1),
                             bottle_neck=bottle_neck,
                             last_filter=filter_list[i])
        for j in range(units[i] - 1):
            body = residual_unit(body,
                                 filter_list[i + 1], (1, 1),
                                 True,
                                 name='stage%d_unit%d' % (i + 1, j + 2),
                                 bottle_neck=bottle_neck,
                                 last_filter=filter_list[i + 1])

    bn1 = sym.batch_norm(data=body, epsilon=2e-5, name='bn1', axis=3)
    relu1 = sym.relu(data=bn1, name='relu1')

    return relu1
示例#10
0
文件: block2.py 项目: ryansoq/nixtvm
def block2_block_nnvm(nblocks:int,sym_480391360):
  varnames = {}

  def addvar(name,shape):
    nonlocal varnames
    s = _sym.Variable(name=name,shape=shape)
    varnames.update({name:s})
    return s

  sym_85993200 = addvar(name="Rcnn_ctcV3/conv_block3_1/unit2/static_batch_normalization_28/gamma",shape=(192,))
  sym_367190496 = addvar(name="Rcnn_ctcV3/conv_block3_1/unit2/static_batch_normalization_28/beta",shape=(192,))
  sym_393365200 = addvar(name="Rcnn_ctcV3/conv_block3_1/unit2/static_batch_normalization_28/moving_mean",shape=(192,))
  sym_89108592 = addvar(name="Rcnn_ctcV3/conv_block3_1/unit2/static_batch_normalization_28/moving_variance",shape=(192,))
  sym_86660000 = addvar(name="Rcnn_ctcV3/conv_block3_1/unit2/static_batch_normalization_28/batchnorm/add/y",shape=(1,))
  sym_91999584 = addvar(name="Rcnn_ctcV3/conv_block3_1/unit2/activation/conv2d_85/kernel",shape=(1, 1, 192, 192))
  sym_489572528 = addvar(name="Rcnn_ctcV3/conv_block3_1/unit2/activation/conv2d_85/bias",shape=(192,))
  sym_105212592 = addvar(name="Rcnn_ctcV3/conv_block3_1/unit2/activation/conv2d_86/kernel",shape=(1, 1, 192, 192))
  sym_100075984 = addvar(name="Rcnn_ctcV3/conv_block3_1/unit2/activation/conv2d_86/bias",shape=(192,))
  sym_88942384 = addvar(name="Rcnn_ctcV3/conv_block3_1/unit2/activation/max_27/mul/x",shape=(1,))
  sym_146185312 = addvar(name="Rcnn_ctcV3/conv_block3_1/unit2/conv2d_87/kernel",shape=(3, 3, 192, 192))
  sym_123818576 = addvar(name="Rcnn_ctcV3/conv_block3_1/unit2/conv2d_87/bias",shape=(192,))
  sym_118481536 = addvar(name="Rcnn_ctcV3/conv_block3_1/unit2/static_batch_normalization_29/gamma",shape=(192,))
  sym_108661440 = addvar(name="Rcnn_ctcV3/conv_block3_1/unit2/static_batch_normalization_29/beta",shape=(192,))
  sym_83230208 = addvar(name="Rcnn_ctcV3/conv_block3_1/unit2/static_batch_normalization_29/moving_mean",shape=(192,))
  sym_75303200 = addvar(name="Rcnn_ctcV3/conv_block3_1/unit2/static_batch_normalization_29/moving_variance",shape=(192,))
  sym_223540640 = addvar(name="Rcnn_ctcV3/conv_block3_1/unit2/static_batch_normalization_29/batchnorm/add/y",shape=(1,))
  sym_149765696 = addvar(name="Rcnn_ctcV3/conv_block3_1/unit2/activation_1/conv2d_88/kernel",shape=(1, 1, 192, 192))
  sym_483354480 = addvar(name="Rcnn_ctcV3/conv_block3_1/unit2/activation_1/conv2d_88/bias",shape=(192,))
  sym_145792752 = addvar(name="Rcnn_ctcV3/conv_block3_1/unit2/activation_1/conv2d_89/kernel",shape=(1, 1, 192, 192))
  sym_457499856 = addvar(name="Rcnn_ctcV3/conv_block3_1/unit2/activation_1/conv2d_89/bias",shape=(192,))
  sym_149177840 = addvar(name="Rcnn_ctcV3/conv_block3_1/unit2/activation_1/max_28/mul/x",shape=(1,))
  sym_104094144 = addvar(name="Rcnn_ctcV3/conv_block3_1/unit2/conv2d_90/kernel",shape=(3, 3, 192, 192))
  sym_385754128 = addvar(name="Rcnn_ctcV3/conv_block3_1/unit2/conv2d_90/bias",shape=(192,))
  sym_377560640 = addvar(name="Rcnn_ctcV3/conv_block3_2/unit3/static_batch_normalization_30/gamma",shape=(192,))
  sym_105564416 = addvar(name="Rcnn_ctcV3/conv_block3_2/unit3/static_batch_normalization_30/beta",shape=(192,))
  sym_149177744 = addvar(name="Rcnn_ctcV3/conv_block3_2/unit3/static_batch_normalization_30/moving_mean",shape=(192,))
  sym_394323280 = addvar(name="Rcnn_ctcV3/conv_block3_2/unit3/static_batch_normalization_30/moving_variance",shape=(192,))
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  sym_367184720 = addvar(name="Rcnn_ctcV3/conv_block3_2/unit3/activation/conv2d_91/kernel",shape=(1, 1, 192, 192))
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  sym_223532000 = addvar(name="Rcnn_ctcV3/conv_block3_2/unit3/activation/conv2d_92/kernel",shape=(1, 1, 192, 192))
  sym_75038320 = addvar(name="Rcnn_ctcV3/conv_block3_2/unit3/activation/conv2d_92/bias",shape=(192,))
  sym_88156448 = addvar(name="Rcnn_ctcV3/conv_block3_2/unit3/activation/max_29/mul/x",shape=(1,))
  sym_238287504 = addvar(name="Rcnn_ctcV3/conv_block3_2/unit3/conv2d_93/kernel",shape=(3, 3, 192, 192))
  sym_93313120 = addvar(name="Rcnn_ctcV3/conv_block3_2/unit3/conv2d_93/bias",shape=(192,))
  sym_453626080 = addvar(name="Rcnn_ctcV3/conv_block3_2/unit3/static_batch_normalization_31/gamma",shape=(192,))
  sym_385757328 = addvar(name="Rcnn_ctcV3/conv_block3_2/unit3/static_batch_normalization_31/beta",shape=(192,))
  sym_92005968 = addvar(name="Rcnn_ctcV3/conv_block3_2/unit3/static_batch_normalization_31/moving_mean",shape=(192,))
  sym_457127312 = addvar(name="Rcnn_ctcV3/conv_block3_2/unit3/static_batch_normalization_31/moving_variance",shape=(192,))
  sym_104985248 = addvar(name="Rcnn_ctcV3/conv_block3_2/unit3/static_batch_normalization_31/batchnorm/add/y",shape=(1,))
  sym_83206384 = addvar(name="Rcnn_ctcV3/conv_block3_2/unit3/activation_1/conv2d_94/kernel",shape=(1, 1, 192, 192))
  sym_125060048 = addvar(name="Rcnn_ctcV3/conv_block3_2/unit3/activation_1/conv2d_94/bias",shape=(192,))
  sym_492264992 = addvar(name="Rcnn_ctcV3/conv_block3_2/unit3/activation_1/conv2d_95/kernel",shape=(1, 1, 192, 192))
  sym_94777856 = addvar(name="Rcnn_ctcV3/conv_block3_2/unit3/activation_1/conv2d_95/bias",shape=(192,))
  sym_120063008 = addvar(name="Rcnn_ctcV3/conv_block3_2/unit3/activation_1/max_30/mul/x",shape=(1,))
  sym_116113008 = addvar(name="Rcnn_ctcV3/conv_block3_2/unit3/conv2d_96/kernel",shape=(3, 3, 192, 192))
  sym_382719232 = addvar(name="Rcnn_ctcV3/conv_block3_2/unit3/conv2d_96/bias",shape=(192,))
  sym_489301152 = addvar(name="Rcnn_ctcV3/expand_conv4/conv2d_97/kernel",shape=(1, 1, 192, 256))
  sym_457717648 = addvar(name="Rcnn_ctcV3/expand_conv4/conv2d_97/bias",shape=(256,))
  sym_86813200 = addvar(name="Rcnn_ctcV3/expand_conv4/conv2d_98/kernel",shape=(3, 3, 192, 256))
  sym_149206176 = addvar(name="Rcnn_ctcV3/expand_conv4/conv2d_98/bias",shape=(256,))
  sym_79062816 = addvar(name="Rcnn_ctcV3/expand_conv4/static_batch_normalization_32/gamma",shape=(256,))
  sym_100932496 = addvar(name="Rcnn_ctcV3/expand_conv4/static_batch_normalization_32/beta",shape=(256,))
  sym_75061856 = addvar(name="Rcnn_ctcV3/expand_conv4/static_batch_normalization_32/moving_mean",shape=(256,))
  sym_121639120 = addvar(name="Rcnn_ctcV3/expand_conv4/static_batch_normalization_32/moving_variance",shape=(256,))
  sym_489278688 = addvar(name="Rcnn_ctcV3/expand_conv4/static_batch_normalization_32/batchnorm/add/y",shape=(1,))
  sym_86776256 = addvar(name="Rcnn_ctcV3/expand_conv4/activation/conv2d_99/kernel",shape=(1, 1, 256, 256))
  sym_379070224 = addvar(name="Rcnn_ctcV3/expand_conv4/activation/conv2d_99/bias",shape=(256,))
  sym_92742368 = addvar(name="Rcnn_ctcV3/expand_conv4/activation/conv2d_100/kernel",shape=(1, 1, 256, 256))
  sym_88574976 = addvar(name="Rcnn_ctcV3/expand_conv4/activation/conv2d_100/bias",shape=(256,))
  sym_453605008 = addvar(name="Rcnn_ctcV3/expand_conv4/activation/max_31/mul/x",shape=(1,))
  sym_234740896 = addvar(name="Rcnn_ctcV3/expand_conv4/conv2d_101/kernel",shape=(3, 3, 256, 256))
  sym_394390128 = addvar(name="Rcnn_ctcV3/expand_conv4/conv2d_101/bias",shape=(256,))
  sym_124429376 = addvar(name="Rcnn_ctcV3/conv_block4/unit1/static_batch_normalization_33/gamma",shape=(256,))
  sym_385617296 = addvar(name="Rcnn_ctcV3/conv_block4/unit1/static_batch_normalization_33/beta",shape=(256,))
  sym_75392816 = addvar(name="Rcnn_ctcV3/conv_block4/unit1/static_batch_normalization_33/moving_mean",shape=(256,))
  sym_103921888 = addvar(name="Rcnn_ctcV3/conv_block4/unit1/static_batch_normalization_33/moving_variance",shape=(256,))
  sym_394044400 = addvar(name="Rcnn_ctcV3/conv_block4/unit1/static_batch_normalization_33/batchnorm/add/y",shape=(1,))
  sym_70178352 = addvar(name="Rcnn_ctcV3/conv_block4/unit1/activation/conv2d_102/kernel",shape=(1, 1, 256, 256))
  sym_73075840 = addvar(name="Rcnn_ctcV3/conv_block4/unit1/activation/conv2d_102/bias",shape=(256,))
  sym_392605408 = addvar(name="Rcnn_ctcV3/conv_block4/unit1/activation/conv2d_103/kernel",shape=(1, 1, 256, 256))
  sym_152478752 = addvar(name="Rcnn_ctcV3/conv_block4/unit1/activation/conv2d_103/bias",shape=(256,))
  sym_392605408 = addvar(name="Rcnn_ctcV3/conv_block4/unit1/activation/conv2d_103/kernel",shape=(1, 1, 256, 256))
  sym_152478752 = addvar(name="Rcnn_ctcV3/conv_block4/unit1/activation/conv2d_103/bias",shape=(256,))
  sym_83201008 = addvar(name="Rcnn_ctcV3/conv_block4/unit1/conv2d_104/kernel",shape=(3, 3, 256, 256))
  sym_93335072 = addvar(name="Rcnn_ctcV3/conv_block4/unit1/conv2d_104/bias",shape=(256,))
  sym_81047024 = addvar(name="Rcnn_ctcV3/conv_block4/unit1/static_batch_normalization_34/gamma",shape=(256,))
  sym_79073392 = addvar(name="Rcnn_ctcV3/conv_block4/unit1/static_batch_normalization_34/beta",shape=(256,))
  sym_91927664 = addvar(name="Rcnn_ctcV3/conv_block4/unit1/static_batch_normalization_34/moving_mean",shape=(256,))
  sym_238348176 = addvar(name="Rcnn_ctcV3/conv_block4/unit1/static_batch_normalization_34/moving_variance",shape=(256,))
  sym_379840640 = addvar(name="Rcnn_ctcV3/conv_block4/unit1/static_batch_normalization_34/batchnorm/add/y",shape=(1,))
  sym_76408096 = addvar(name="Rcnn_ctcV3/conv_block4/unit1/activation_1/conv2d_105/kernel",shape=(1, 1, 256, 256))
  sym_218187488 = addvar(name="Rcnn_ctcV3/conv_block4/unit1/activation_1/conv2d_105/bias",shape=(256,))
  sym_51337392 = addvar(name="Rcnn_ctcV3/conv_block4/unit1/activation_1/conv2d_106/kernel",shape=(1, 1, 256, 256))
  sym_104880304 = addvar(name="Rcnn_ctcV3/conv_block4/unit1/activation_1/conv2d_106/bias",shape=(256,))
  sym_146204704 = addvar(name="Rcnn_ctcV3/conv_block4/unit1/activation_1/max_33/mul/x",shape=(1,))
  sym_79059936 = addvar(name="Rcnn_ctcV3/conv_block4/unit1/conv2d_107/kernel",shape=(3, 3, 256, 256))
  sym_78831712 = addvar(name="Rcnn_ctcV3/conv_block4/unit1/conv2d_107/bias",shape=(256,))
  sym_256158240 = addvar(name="Rcnn_ctcV3/conv_block4_1/unit2/static_batch_normalization_35/gamma",shape=(256,))
  sym_83430496 = addvar(name="Rcnn_ctcV3/conv_block4_1/unit2/static_batch_normalization_35/beta",shape=(256,))
  sym_329254032 = addvar(name="Rcnn_ctcV3/conv_block4_1/unit2/static_batch_normalization_35/moving_mean",shape=(256,))
  sym_378173584 = addvar(name="Rcnn_ctcV3/conv_block4_1/unit2/static_batch_normalization_35/moving_variance",shape=(256,))
  sym_457507168 = addvar(name="Rcnn_ctcV3/conv_block4_1/unit2/static_batch_normalization_35/batchnorm/add/y",shape=(1,))
  sym_378281392 = addvar(name="Rcnn_ctcV3/conv_block4_1/unit2/activation/max_34/mul/x",shape=(1,))
  sym_152788384 = addvar(name="Rcnn_ctcV3/conv_block4_1/unit2/conv2d_110/kernel",shape=(3, 3, 256, 256))
  sym_77188768 = addvar(name="Rcnn_ctcV3/conv_block4_1/unit2/conv2d_110/bias",shape=(256,))
  sym_88778896 = addvar(name="Rcnn_ctcV3/conv_block4_1/unit2/static_batch_normalization_36/gamma",shape=(256,))
  sym_238290352 = addvar(name="Rcnn_ctcV3/conv_block4_1/unit2/static_batch_normalization_36/beta",shape=(256,))
  sym_104191024 = addvar(name="Rcnn_ctcV3/conv_block4_1/unit2/static_batch_normalization_36/moving_mean",shape=(256,))
  sym_86902736 = addvar(name="Rcnn_ctcV3/conv_block4_1/unit2/static_batch_normalization_36/moving_variance",shape=(256,))
  sym_52504256 = addvar(name="Rcnn_ctcV3/conv_block4_1/unit2/static_batch_normalization_36/batchnorm/add/y",shape=(1,))
  sym_125175280 = addvar(name="Rcnn_ctcV3/conv_block4_1/unit2/activation_1/conv2d_111/kernel",shape=(1, 1, 256, 256))
  sym_366867792 = addvar(name="Rcnn_ctcV3/conv_block4_1/unit2/activation_1/conv2d_111/bias",shape=(256,))
  sym_130260432 = addvar(name="Rcnn_ctcV3/conv_block4_1/unit2/activation_1/conv2d_112/kernel",shape=(1, 1, 256, 256))
  sym_394401808 = addvar(name="Rcnn_ctcV3/conv_block4_1/unit2/activation_1/conv2d_112/bias",shape=(256,))
  sym_223649568 = addvar(name="Rcnn_ctcV3/conv_block4_1/unit2/activation_1/max_35/mul/x",shape=(1,))
  sym_394319936 = addvar(name="Rcnn_ctcV3/conv_block4_1/unit2/conv2d_113/kernel",shape=(3, 3, 256, 256))
  sym_121636512 = addvar(name="Rcnn_ctcV3/conv_block4_1/unit2/conv2d_113/bias",shape=(256,))
  sym_457721664 = addvar(name="Rcnn_ctcV3/static_batch_normalization_37/gamma",shape=(256,))
  sym_382354176 = addvar(name="Rcnn_ctcV3/static_batch_normalization_37/beta",shape=(256,))
  sym_104234656 = addvar(name="Rcnn_ctcV3/static_batch_normalization_37/moving_mean",shape=(256,))
  sym_385760896 = addvar(name="Rcnn_ctcV3/static_batch_normalization_37/moving_variance",shape=(256,))
  sym_115259392 = addvar(name="Rcnn_ctcV3/static_batch_normalization_37/batchnorm/add/y",shape=(1,))
  sym_88800208 = addvar(name="Rcnn_ctcV3/activation/conv2d_114/kernel",shape=(1, 1, 256, 256))
  sym_378170400 = addvar(name="Rcnn_ctcV3/activation/conv2d_114/bias",shape=(256,))
  sym_132599600 = addvar(name="Rcnn_ctcV3/activation/conv2d_115/kernel",shape=(1, 1, 256, 256))
  sym_394302224 = addvar(name="Rcnn_ctcV3/activation/conv2d_115/bias",shape=(256,))
  sym_100941744 = addvar(name="Rcnn_ctcV3/conv_block4_1/unit2/activation/conv2d_108/kernel",shape=(1, 1, 256, 256))
  sym_80614592 = addvar(name="Rcnn_ctcV3/conv_block4_1/unit2/activation/conv2d_108/bias",shape=(256,))
  sym_101105904 = addvar(name="Rcnn_ctcV3/conv_block4_1/unit2/activation/conv2d_109/kernel",shape=(1, 1, 256, 256))
  sym_394411888 = addvar(name="Rcnn_ctcV3/conv_block4_1/unit2/activation/conv2d_109/bias",shape=(256,))
  sym_140634816 = addvar(name="Rcnn_ctcV3/activation/max_36/mul/x",shape=(1,))
  sym_283877680 = addvar(name="Rcnn_ctcV3/conv2d_116/kernel",shape=(1, 6, 256, 1318))
  sym_105665056 = addvar(name="Rcnn_ctcV3/conv2d_116/bias",shape=(1318,))
  sym_452814752 = addvar(name="Rcnn_ctcV3/conv_block4/unit1/activation/max_32/mul/x",shape=(1,))


  sym_146107552 = _sym.broadcast_add(sym_89108592,sym_86660000,name="Rcnn_ctcV3/conv_block3_1/unit2/static_batch_normalization_28/batchnorm/add")
  sym_382713408 = _sym.__pow_scalar__(sym_146107552,name="Rcnn_ctcV3/conv_block3_1/unit2/static_batch_normalization_28/batchnorm/Rsqrt",scalar=-0.5)
  sym_146107280 = _sym.broadcast_mul(sym_382713408,sym_85993200,name="Rcnn_ctcV3/conv_block3_1/unit2/static_batch_normalization_28/batchnorm/mul")
  sym_146107376 = _sym.broadcast_mul(sym_480391360,sym_146107280,name="Rcnn_ctcV3/conv_block3_1/unit2/static_batch_normalization_28/batchnorm/mul_1")
  sym_100393904 = _sym.broadcast_mul(sym_393365200,sym_146107280,name="Rcnn_ctcV3/conv_block3_1/unit2/static_batch_normalization_28/batchnorm/mul_2")
  sym_100394320 = _sym.broadcast_sub(sym_367190496,sym_100393904,name="Rcnn_ctcV3/conv_block3_1/unit2/static_batch_normalization_28/batchnorm/sub")
  sym_149168256 = _sym.broadcast_add(sym_146107376,sym_100394320,name="Rcnn_ctcV3/conv_block3_1/unit2/static_batch_normalization_28/batchnorm/add_1")
  sym_452185136 = _sym.pad(sym_149168256,pad_width=((0, 0), (0, 0), (0, 0), (0, 0)))
  sym_489573008 = _sym.conv2d(sym_452185136,sym_91999584,dilation=(1, 1),layout="NHWC",strides=(1, 1),padding=[0, 0],kernel_size=(1, 1),channels=192,kernel_layout="HWIO",name="Rcnn_ctcV3/conv_block3_1/unit2/activation/conv2d_85/convolution",use_bias=False)
  sym_452185136 = _sym.broadcast_add(sym_489573008,sym_489572528)
  sym_105212304 = _sym.pad(sym_149168256,pad_width=((0, 0), (0, 0), (0, 0), (0, 0)))
  sym_455423312 = _sym.conv2d(sym_105212304,sym_105212592,layout="NHWC",strides=(1, 1),padding=[0, 0],dilation=(1, 1),kernel_size=(1, 1),channels=192,kernel_layout="HWIO",name="Rcnn_ctcV3/conv_block3_1/unit2/activation/conv2d_86/convolution",use_bias=False)
  sym_105212304 = _sym.broadcast_add(sym_455423312,sym_100075984)
  sym_455423856 = _sym.broadcast_add(sym_452185136,sym_105212304,name="Rcnn_ctcV3/conv_block3_1/unit2/activation/max_27/add")
  sym_82981456 = _sym.broadcast_sub(sym_452185136,sym_105212304,name="Rcnn_ctcV3/conv_block3_1/unit2/activation/max_27/sub")
  sym_88256992 = _sym.relu(sym_82981456,name="Rcnn_ctcV3/conv_block3_1/unit2/activation/max_27/Relu")
  sym_385748976 = _sym.broadcast_add(sym_455423856,sym_88256992,name="Rcnn_ctcV3/conv_block3_1/unit2/activation/max_27/add_1")
  sym_118633648 = _sym.broadcast_sub(sym_105212304,sym_452185136,name="Rcnn_ctcV3/conv_block3_1/unit2/activation/max_27/sub_1")
  sym_118633776 = _sym.relu(sym_118633648,name="Rcnn_ctcV3/conv_block3_1/unit2/activation/max_27/Relu_1")
  sym_83860624 = _sym.broadcast_add(sym_385748976,sym_118633776,name="Rcnn_ctcV3/conv_block3_1/unit2/activation/max_27/add_2")
  sym_83860784 = _sym.broadcast_mul(sym_88942384,sym_83860624,name="Rcnn_ctcV3/conv_block3_1/unit2/activation/max_27/mul")
  sym_51379712 = _sym.pad(sym_83860784,pad_width=((0, 0), (1, 1), (1, 1), (0, 0)))
  sym_284337840 = _sym.conv2d(sym_51379712,sym_146185312,layout="NHWC",strides=(1, 1),padding=[0, 0],dilation=(1, 1),kernel_size=(3, 3),channels=192,kernel_layout="HWIO",name="Rcnn_ctcV3/conv_block3_1/unit2/conv2d_87/convolution",use_bias=False)
  sym_51379712 = _sym.broadcast_add(sym_284337840,sym_123818576)
  sym_284338384 = _sym.broadcast_add(sym_75303200,sym_223540640,name="Rcnn_ctcV3/conv_block3_1/unit2/static_batch_normalization_29/batchnorm/add")
  sym_379070752 = _sym.__pow_scalar__(sym_284338384,name="Rcnn_ctcV3/conv_block3_1/unit2/static_batch_normalization_29/batchnorm/Rsqrt",scalar=-0.5)
  sym_457130976 = _sym.broadcast_mul(sym_379070752,sym_118481536,name="Rcnn_ctcV3/conv_block3_1/unit2/static_batch_normalization_29/batchnorm/mul")
  sym_470662800 = _sym.broadcast_mul(sym_51379712,sym_457130976,name="Rcnn_ctcV3/conv_block3_1/unit2/static_batch_normalization_29/batchnorm/mul_1")
  sym_470662864 = _sym.broadcast_mul(sym_83230208,sym_457130976,name="Rcnn_ctcV3/conv_block3_1/unit2/static_batch_normalization_29/batchnorm/mul_2")
  sym_89117184 = _sym.broadcast_sub(sym_108661440,sym_470662864,name="Rcnn_ctcV3/conv_block3_1/unit2/static_batch_normalization_29/batchnorm/sub")
  sym_89117376 = _sym.broadcast_add(sym_470662800,sym_89117184,name="Rcnn_ctcV3/conv_block3_1/unit2/static_batch_normalization_29/batchnorm/add_1")
  sym_367083280 = _sym.pad(sym_89117376,pad_width=((0, 0), (0, 0), (0, 0), (0, 0)))
  sym_483354960 = _sym.conv2d(sym_367083280,sym_149765696,dilation=(1, 1),layout="NHWC",strides=(1, 1),padding=[0, 0],kernel_size=(1, 1),channels=192,kernel_layout="HWIO",name="Rcnn_ctcV3/conv_block3_1/unit2/activation_1/conv2d_88/convolution",use_bias=False)
  sym_367083280 = _sym.broadcast_add(sym_483354960,sym_483354480)
  sym_145792464 = _sym.pad(sym_89117376,pad_width=((0, 0), (0, 0), (0, 0), (0, 0)))
  sym_149112096 = _sym.conv2d(sym_145792464,sym_145792752,padding=[0, 0],dilation=(1, 1),layout="NHWC",strides=(1, 1),kernel_size=(1, 1),channels=192,kernel_layout="HWIO",name="Rcnn_ctcV3/conv_block3_1/unit2/activation_1/conv2d_89/convolution",use_bias=False)
  sym_145792464 = _sym.broadcast_add(sym_149112096,sym_457499856)
  sym_149112640 = _sym.broadcast_add(sym_367083280,sym_145792464,name="Rcnn_ctcV3/conv_block3_1/unit2/activation_1/max_28/add")
  sym_457122720 = _sym.broadcast_sub(sym_367083280,sym_145792464,name="Rcnn_ctcV3/conv_block3_1/unit2/activation_1/max_28/sub")
  sym_457709440 = _sym.relu(sym_457122720,name="Rcnn_ctcV3/conv_block3_1/unit2/activation_1/max_28/Relu")
  sym_88086480 = _sym.broadcast_add(sym_149112640,sym_457709440,name="Rcnn_ctcV3/conv_block3_1/unit2/activation_1/max_28/add_1")
  sym_223552112 = _sym.broadcast_sub(sym_145792464,sym_367083280,name="Rcnn_ctcV3/conv_block3_1/unit2/activation_1/max_28/sub_1")
  sym_223552176 = _sym.relu(sym_223552112,name="Rcnn_ctcV3/conv_block3_1/unit2/activation_1/max_28/Relu_1")
  sym_223552560 = _sym.broadcast_add(sym_88086480,sym_223552176,name="Rcnn_ctcV3/conv_block3_1/unit2/activation_1/max_28/add_2")
  sym_394085360 = _sym.broadcast_mul(sym_149177840,sym_223552560,name="Rcnn_ctcV3/conv_block3_1/unit2/activation_1/max_28/mul")
  sym_149177968 = _sym.pad(sym_394085360,pad_width=((0, 0), (1, 1), (1, 1), (0, 0)))
  sym_359386352 = _sym.conv2d(sym_149177968,sym_104094144,dilation=(1, 1),layout="NHWC",strides=(1, 1),padding=[0, 0],kernel_size=(3, 3),channels=192,kernel_layout="HWIO",name="Rcnn_ctcV3/conv_block3_1/unit2/conv2d_90/convolution",use_bias=False)
  sym_149177968 = _sym.broadcast_add(sym_359386352,sym_385754128)
  sym_88474160 = _sym.broadcast_add(sym_149177968,sym_480391360,name="Rcnn_ctcV3/conv_block3_1/unit2/add_15/add")
  sym_149177568 = _sym.broadcast_add(sym_394323280,sym_143315712,name="Rcnn_ctcV3/conv_block3_2/unit3/static_batch_normalization_30/batchnorm/add")
  sym_223534496 = _sym.__pow_scalar__(sym_149177568,name="Rcnn_ctcV3/conv_block3_2/unit3/static_batch_normalization_30/batchnorm/Rsqrt",scalar=-0.5)
  sym_223534592 = _sym.broadcast_mul(sym_223534496,sym_377560640,name="Rcnn_ctcV3/conv_block3_2/unit3/static_batch_normalization_30/batchnorm/mul")
  sym_359373472 = _sym.broadcast_mul(sym_88474160,sym_223534592,name="Rcnn_ctcV3/conv_block3_2/unit3/static_batch_normalization_30/batchnorm/mul_1")
  sym_359373888 = _sym.broadcast_mul(sym_149177744,sym_223534592,name="Rcnn_ctcV3/conv_block3_2/unit3/static_batch_normalization_30/batchnorm/mul_2")
  sym_82826624 = _sym.broadcast_sub(sym_105564416,sym_359373888,name="Rcnn_ctcV3/conv_block3_2/unit3/static_batch_normalization_30/batchnorm/sub")
  sym_82827104 = _sym.broadcast_add(sym_359373472,sym_82826624,name="Rcnn_ctcV3/conv_block3_2/unit3/static_batch_normalization_30/batchnorm/add_1")
  sym_367184304 = _sym.pad(sym_82827104,pad_width=((0, 0), (0, 0), (0, 0), (0, 0)))
  sym_394436448 = _sym.conv2d(sym_367184304,sym_367184720,dilation=(1, 1),layout="NHWC",strides=(1, 1),padding=[0, 0],kernel_size=(1, 1),channels=192,kernel_layout="HWIO",name="Rcnn_ctcV3/conv_block3_2/unit3/activation/conv2d_91/convolution",use_bias=False)
  sym_367184304 = _sym.broadcast_add(sym_394436448,sym_284058064)
  sym_394436992 = _sym.pad(sym_82827104,pad_width=((0, 0), (0, 0), (0, 0), (0, 0)))
  sym_149198800 = _sym.conv2d(sym_394436992,sym_223532000,padding=[0, 0],dilation=(1, 1),layout="NHWC",strides=(1, 1),kernel_size=(1, 1),channels=192,kernel_layout="HWIO",name="Rcnn_ctcV3/conv_block3_2/unit3/activation/conv2d_92/convolution",use_bias=False)
  sym_394436992 = _sym.broadcast_add(sym_149198800,sym_75038320)
  sym_223512992 = _sym.broadcast_add(sym_367184304,sym_394436992,name="Rcnn_ctcV3/conv_block3_2/unit3/activation/max_29/add")
  sym_88156128 = _sym.broadcast_sub(sym_367184304,sym_394436992,name="Rcnn_ctcV3/conv_block3_2/unit3/activation/max_29/sub")
  sym_181417296 = _sym.relu(sym_88156128,name="Rcnn_ctcV3/conv_block3_2/unit3/activation/max_29/Relu")
  sym_385616848 = _sym.broadcast_add(sym_223512992,sym_181417296,name="Rcnn_ctcV3/conv_block3_2/unit3/activation/max_29/add_1")
  sym_385617264 = _sym.broadcast_sub(sym_394436992,sym_367184304,name="Rcnn_ctcV3/conv_block3_2/unit3/activation/max_29/sub_1")
  sym_52513744 = _sym.relu(sym_385617264,name="Rcnn_ctcV3/conv_block3_2/unit3/activation/max_29/Relu_1")
  sym_367099056 = _sym.broadcast_add(sym_385616848,sym_52513744,name="Rcnn_ctcV3/conv_block3_2/unit3/activation/max_29/add_2")
  sym_367099216 = _sym.broadcast_mul(sym_88156448,sym_367099056,name="Rcnn_ctcV3/conv_block3_2/unit3/activation/max_29/mul")
  sym_394312880 = _sym.pad(sym_367099216,pad_width=((0, 0), (1, 1), (1, 1), (0, 0)))
  sym_455609696 = _sym.conv2d(sym_394312880,sym_238287504,dilation=(1, 1),layout="NHWC",strides=(1, 1),padding=[0, 0],kernel_size=(3, 3),channels=192,kernel_layout="HWIO",name="Rcnn_ctcV3/conv_block3_2/unit3/conv2d_93/convolution",use_bias=False)
  sym_394312880 = _sym.broadcast_add(sym_455609696,sym_93313120)
  sym_453625568 = _sym.broadcast_add(sym_457127312,sym_104985248,name="Rcnn_ctcV3/conv_block3_2/unit3/static_batch_normalization_31/batchnorm/add")
  sym_367089856 = _sym.__pow_scalar__(sym_453625568,name="Rcnn_ctcV3/conv_block3_2/unit3/static_batch_normalization_31/batchnorm/Rsqrt",scalar=-0.5)
  sym_472030368 = _sym.broadcast_mul(sym_367089856,sym_453626080,name="Rcnn_ctcV3/conv_block3_2/unit3/static_batch_normalization_31/batchnorm/mul")
  sym_472030464 = _sym.broadcast_mul(sym_394312880,sym_472030368,name="Rcnn_ctcV3/conv_block3_2/unit3/static_batch_normalization_31/batchnorm/mul_1")
  sym_74982368 = _sym.broadcast_mul(sym_92005968,sym_472030368,name="Rcnn_ctcV3/conv_block3_2/unit3/static_batch_normalization_31/batchnorm/mul_2")
  sym_74982496 = _sym.broadcast_sub(sym_385757328,sym_74982368,name="Rcnn_ctcV3/conv_block3_2/unit3/static_batch_normalization_31/batchnorm/sub")
  sym_223613200 = _sym.broadcast_add(sym_472030464,sym_74982496,name="Rcnn_ctcV3/conv_block3_2/unit3/static_batch_normalization_31/batchnorm/add_1")
  sym_223613680 = _sym.pad(sym_223613200,pad_width=((0, 0), (0, 0), (0, 0), (0, 0)))
  sym_452030544 = _sym.conv2d(sym_223613680,sym_83206384,padding=[0, 0],dilation=(1, 1),layout="NHWC",strides=(1, 1),kernel_size=(1, 1),channels=192,kernel_layout="HWIO",name="Rcnn_ctcV3/conv_block3_2/unit3/activation_1/conv2d_94/convolution",use_bias=False)
  sym_223613680 = _sym.broadcast_add(sym_452030544,sym_125060048)
  sym_132912848 = _sym.pad(sym_223613200,pad_width=((0, 0), (0, 0), (0, 0), (0, 0)))
  sym_385631504 = _sym.conv2d(sym_132912848,sym_492264992,dilation=(1, 1),layout="NHWC",strides=(1, 1),padding=[0, 0],kernel_size=(1, 1),channels=192,kernel_layout="HWIO",name="Rcnn_ctcV3/conv_block3_2/unit3/activation_1/conv2d_95/convolution",use_bias=False)
  sym_132912848 = _sym.broadcast_add(sym_385631504,sym_94777856)
  sym_453607776 = _sym.broadcast_add(sym_223613680,sym_132912848,name="Rcnn_ctcV3/conv_block3_2/unit3/activation_1/max_30/add")
  sym_74186480 = _sym.broadcast_sub(sym_223613680,sym_132912848,name="Rcnn_ctcV3/conv_block3_2/unit3/activation_1/max_30/sub")
  sym_457775984 = _sym.relu(sym_74186480,name="Rcnn_ctcV3/conv_block3_2/unit3/activation_1/max_30/Relu")
  sym_457776176 = _sym.broadcast_add(sym_453607776,sym_457775984,name="Rcnn_ctcV3/conv_block3_2/unit3/activation_1/max_30/add_1")
  sym_216045840 = _sym.broadcast_sub(sym_132912848,sym_223613680,name="Rcnn_ctcV3/conv_block3_2/unit3/activation_1/max_30/sub_1")
  sym_299777120 = _sym.relu(sym_216045840,name="Rcnn_ctcV3/conv_block3_2/unit3/activation_1/max_30/Relu_1")
  sym_74186176 = _sym.broadcast_add(sym_457776176,sym_299777120,name="Rcnn_ctcV3/conv_block3_2/unit3/activation_1/max_30/add_2")
  sym_74186272 = _sym.broadcast_mul(sym_120063008,sym_74186176,name="Rcnn_ctcV3/conv_block3_2/unit3/activation_1/max_30/mul")
  sym_382719056 = _sym.pad(sym_74186272,pad_width=((0, 0), (1, 1), (1, 1), (0, 0)))
  sym_382112272 = _sym.conv2d(sym_382719056,sym_116113008,padding=[0, 0],dilation=(1, 1),layout="NHWC",strides=(1, 1),kernel_size=(3, 3),channels=192,kernel_layout="HWIO",name="Rcnn_ctcV3/conv_block3_2/unit3/conv2d_96/convolution",use_bias=False)
  sym_382719056 = _sym.broadcast_add(sym_382112272,sym_382719232)
  sym_74186736 = _sym.broadcast_add(sym_382719056,sym_88474160,name="Rcnn_ctcV3/conv_block3_2/unit3/add_16/add")
  sym_472042128 = _sym.pad(sym_74186736,pad_width=((0, 0), (0, 0), (0, 0), (0, 0)))
  sym_379783504 = _sym.conv2d(sym_472042128,sym_489301152,layout="NHWC",strides=(1, 1),padding=[0, 0],dilation=(1, 1),kernel_size=(1, 1),channels=256,kernel_layout="HWIO",name="Rcnn_ctcV3/expand_conv4/conv2d_97/convolution",use_bias=False)
  sym_472042128 = _sym.broadcast_add(sym_379783504,sym_457717648)
  sym_366878288 = _sym.pad(sym_74186736,pad_width=((0, 0), (1, 1), (1, 1), (0, 0)))
  sym_378188352 = _sym.conv2d(sym_366878288,sym_86813200,dilation=(1, 1),layout="NHWC",strides=(1, 1),padding=[0, 0],kernel_size=(3, 3),channels=256,kernel_layout="HWIO",name="Rcnn_ctcV3/expand_conv4/conv2d_98/convolution",use_bias=False)
  sym_366878288 = _sym.broadcast_add(sym_378188352,sym_149206176)
  sym_223609616 = _sym.broadcast_add(sym_121639120,sym_489278688,name="Rcnn_ctcV3/expand_conv4/static_batch_normalization_32/batchnorm/add")
  sym_382109104 = _sym.__pow_scalar__(sym_223609616,name="Rcnn_ctcV3/expand_conv4/static_batch_normalization_32/batchnorm/Rsqrt",scalar=-0.5)
  sym_472034112 = _sym.broadcast_mul(sym_382109104,sym_79062816,name="Rcnn_ctcV3/expand_conv4/static_batch_normalization_32/batchnorm/mul")
  sym_472034560 = _sym.broadcast_mul(sym_366878288,sym_472034112,name="Rcnn_ctcV3/expand_conv4/static_batch_normalization_32/batchnorm/mul_1")
  sym_457503264 = _sym.broadcast_mul(sym_75061856,sym_472034112,name="Rcnn_ctcV3/expand_conv4/static_batch_normalization_32/batchnorm/mul_2")
  sym_457503648 = _sym.broadcast_sub(sym_100932496,sym_457503264,name="Rcnn_ctcV3/expand_conv4/static_batch_normalization_32/batchnorm/sub")
  sym_455442096 = _sym.broadcast_add(sym_472034560,sym_457503648,name="Rcnn_ctcV3/expand_conv4/static_batch_normalization_32/batchnorm/add_1")
  sym_455442480 = _sym.pad(sym_455442096,pad_width=((0, 0), (0, 0), (0, 0), (0, 0)))
  sym_451396240 = _sym.conv2d(sym_455442480,sym_86776256,dilation=(1, 1),layout="NHWC",strides=(1, 1),padding=[0, 0],kernel_size=(1, 1),channels=256,kernel_layout="HWIO",name="Rcnn_ctcV3/expand_conv4/activation/conv2d_99/convolution",use_bias=False)
  sym_455442480 = _sym.broadcast_add(sym_451396240,sym_379070224)
  sym_223539440 = _sym.pad(sym_455442096,pad_width=((0, 0), (0, 0), (0, 0), (0, 0)))
  sym_483359056 = _sym.conv2d(sym_223539440,sym_92742368,layout="NHWC",strides=(1, 1),padding=[0, 0],dilation=(1, 1),kernel_size=(1, 1),channels=256,kernel_layout="HWIO",name="Rcnn_ctcV3/expand_conv4/activation/conv2d_100/convolution",use_bias=False)
  sym_223539440 = _sym.broadcast_add(sym_483359056,sym_88574976)
  sym_223540032 = _sym.broadcast_add(sym_455442480,sym_223539440,name="Rcnn_ctcV3/expand_conv4/activation/max_31/add")
  sym_457135056 = _sym.broadcast_sub(sym_455442480,sym_223539440,name="Rcnn_ctcV3/expand_conv4/activation/max_31/sub")
  sym_457729248 = _sym.relu(sym_457135056,name="Rcnn_ctcV3/expand_conv4/activation/max_31/Relu")
  sym_457729440 = _sym.broadcast_add(sym_223540032,sym_457729248,name="Rcnn_ctcV3/expand_conv4/activation/max_31/add_1")
  sym_457142480 = _sym.broadcast_sub(sym_223539440,sym_455442480,name="Rcnn_ctcV3/expand_conv4/activation/max_31/sub_1")
  sym_457142672 = _sym.relu(sym_457142480,name="Rcnn_ctcV3/expand_conv4/activation/max_31/Relu_1")
  sym_385623856 = _sym.broadcast_add(sym_457729440,sym_457142672,name="Rcnn_ctcV3/expand_conv4/activation/max_31/add_2")
  sym_385624016 = _sym.broadcast_mul(sym_453605008,sym_385623856,name="Rcnn_ctcV3/expand_conv4/activation/max_31/mul")
  sym_378184992 = _sym.pad(sym_385624016,pad_width=((0, 0), (1, 1), (1, 1), (0, 0)))
  sym_51336848 = _sym.conv2d(sym_378184992,sym_234740896,padding=[0, 0],dilation=(1, 1),layout="NHWC",strides=(1, 1),kernel_size=(3, 3),channels=256,kernel_layout="HWIO",name="Rcnn_ctcV3/expand_conv4/conv2d_101/convolution",use_bias=False)
  sym_378184992 = _sym.broadcast_add(sym_51336848,sym_394390128)
  sym_477435760 = _sym.broadcast_add(sym_378184992,sym_472042128,name="Rcnn_ctcV3/expand_conv4/add_17/add")
  sym_477435888 = _sym.broadcast_add(sym_103921888,sym_394044400,name="Rcnn_ctcV3/conv_block4/unit1/static_batch_normalization_33/batchnorm/add")
  sym_385433312 = _sym.__pow_scalar__(sym_477435888,name="Rcnn_ctcV3/conv_block4/unit1/static_batch_normalization_33/batchnorm/Rsqrt",scalar=-0.5)
  sym_457495552 = _sym.broadcast_mul(sym_385433312,sym_124429376,name="Rcnn_ctcV3/conv_block4/unit1/static_batch_normalization_33/batchnorm/mul")
  sym_457496096 = _sym.broadcast_mul(sym_477435760,sym_457495552,name="Rcnn_ctcV3/conv_block4/unit1/static_batch_normalization_33/batchnorm/mul_1")
  sym_378195984 = _sym.broadcast_mul(sym_75392816,sym_457495552,name="Rcnn_ctcV3/conv_block4/unit1/static_batch_normalization_33/batchnorm/mul_2")
  sym_378196400 = _sym.broadcast_sub(sym_385617296,sym_378195984,name="Rcnn_ctcV3/conv_block4/unit1/static_batch_normalization_33/batchnorm/sub")
  sym_46963920 = _sym.broadcast_add(sym_457496096,sym_378196400,name="Rcnn_ctcV3/conv_block4/unit1/static_batch_normalization_33/batchnorm/add_1")
  sym_223558384 = _sym.pad(sym_46963920,pad_width=((0, 0), (0, 0), (0, 0), (0, 0)))
  sym_394305968 = _sym.conv2d(sym_223558384,sym_70178352,padding=[0, 0],dilation=(1, 1),layout="NHWC",strides=(1, 1),kernel_size=(1, 1),channels=256,kernel_layout="HWIO",name="Rcnn_ctcV3/conv_block4/unit1/activation/conv2d_102/convolution",use_bias=False)
  sym_223558384 = _sym.broadcast_add(sym_394305968,sym_73075840)
  sym_452990880 = _sym.pad(sym_46963920,pad_width=((0, 0), (0, 0), (0, 0), (0, 0)))
  sym_86637584 = _sym.conv2d(sym_452990880,sym_392605408,layout="NHWC",strides=(1, 1),padding=[0, 0],dilation=(1, 1),kernel_size=(1, 1),channels=256,kernel_layout="HWIO",name="Rcnn_ctcV3/conv_block4/unit1/activation/conv2d_103/convolution",use_bias=False)
  sym_452990880 = _sym.broadcast_add(sym_86637584,sym_152478752)
  sym_152480096 = _sym.broadcast_add(sym_223558384,sym_452990880,name="Rcnn_ctcV3/conv_block4/unit1/activation/max_32/add")
  sym_382186448 = _sym.broadcast_sub(sym_223558384,sym_452990880,name="Rcnn_ctcV3/conv_block4/unit1/activation/max_32/sub")
  sym_367093088 = _sym.relu(sym_382186448,name="Rcnn_ctcV3/conv_block4/unit1/activation/max_32/Relu")
  sym_367093280 = _sym.broadcast_add(sym_152480096,sym_367093088,name="Rcnn_ctcV3/conv_block4/unit1/activation/max_32/add_1")
  sym_451215696 = _sym.broadcast_sub(sym_452990880,sym_223558384,name="Rcnn_ctcV3/conv_block4/unit1/activation/max_32/sub_1")
  sym_451215888 = _sym.relu(sym_451215696,name="Rcnn_ctcV3/conv_block4/unit1/activation/max_32/Relu_1")
  sym_382119392 = _sym.broadcast_add(sym_367093280,sym_451215888,name="Rcnn_ctcV3/conv_block4/unit1/activation/max_32/add_2")
  sym_382119552 = _sym.broadcast_mul(sym_452814752,sym_382119392,name="Rcnn_ctcV3/conv_block4/unit1/activation/max_32/mul")
  sym_486331504 = _sym.pad(sym_382119552,pad_width=((0, 0), (1, 1), (1, 1), (0, 0)))
  sym_382363280 = _sym.conv2d(sym_486331504,sym_83201008,padding=[0, 0],dilation=(1, 1),layout="NHWC",strides=(1, 1),kernel_size=(3, 3),channels=256,kernel_layout="HWIO",name="Rcnn_ctcV3/conv_block4/unit1/conv2d_104/convolution",use_bias=False)
  sym_486331504 = _sym.broadcast_add(sym_382363280,sym_93335072)
  sym_394299376 = _sym.broadcast_add(sym_238348176,sym_379840640,name="Rcnn_ctcV3/conv_block4/unit1/static_batch_normalization_34/batchnorm/add")
  sym_89392304 = _sym.__pow_scalar__(sym_394299376,name="Rcnn_ctcV3/conv_block4/unit1/static_batch_normalization_34/batchnorm/Rsqrt",scalar=-0.5)
  sym_93587680 = _sym.broadcast_mul(sym_89392304,sym_81047024,name="Rcnn_ctcV3/conv_block4/unit1/static_batch_normalization_34/batchnorm/mul")
  sym_93587776 = _sym.broadcast_mul(sym_486331504,sym_93587680,name="Rcnn_ctcV3/conv_block4/unit1/static_batch_normalization_34/batchnorm/mul_1")
  sym_73448960 = _sym.broadcast_mul(sym_91927664,sym_93587680,name="Rcnn_ctcV3/conv_block4/unit1/static_batch_normalization_34/batchnorm/mul_2")
  sym_382736736 = _sym.broadcast_sub(sym_79073392,sym_73448960,name="Rcnn_ctcV3/conv_block4/unit1/static_batch_normalization_34/batchnorm/sub")
  sym_382736864 = _sym.broadcast_add(sym_93587776,sym_382736736,name="Rcnn_ctcV3/conv_block4/unit1/static_batch_normalization_34/batchnorm/add_1")
  sym_93588000 = _sym.pad(sym_382736864,pad_width=((0, 0), (0, 0), (0, 0), (0, 0)))
  sym_76671024 = _sym.conv2d(sym_93588000,sym_76408096,padding=[0, 0],dilation=(1, 1),layout="NHWC",strides=(1, 1),kernel_size=(1, 1),channels=256,kernel_layout="HWIO",name="Rcnn_ctcV3/conv_block4/unit1/activation_1/conv2d_105/convolution",use_bias=False)
  sym_93588000 = _sym.broadcast_add(sym_76671024,sym_218187488)
  sym_76671152 = _sym.pad(sym_382736864,pad_width=((0, 0), (0, 0), (0, 0), (0, 0)))
  sym_81912736 = _sym.conv2d(sym_76671152,sym_51337392,dilation=(1, 1),layout="NHWC",strides=(1, 1),padding=[0, 0],kernel_size=(1, 1),channels=256,kernel_layout="HWIO",name="Rcnn_ctcV3/conv_block4/unit1/activation_1/conv2d_106/convolution",use_bias=False)
  sym_76671152 = _sym.broadcast_add(sym_81912736,sym_104880304)
  sym_473742192 = _sym.broadcast_add(sym_93588000,sym_76671152,name="Rcnn_ctcV3/conv_block4/unit1/activation_1/max_33/add")
  sym_379159792 = _sym.broadcast_sub(sym_93588000,sym_76671152,name="Rcnn_ctcV3/conv_block4/unit1/activation_1/max_33/sub")
  sym_146204640 = _sym.relu(sym_379159792,name="Rcnn_ctcV3/conv_block4/unit1/activation_1/max_33/Relu")
  sym_366874912 = _sym.broadcast_add(sym_473742192,sym_146204640,name="Rcnn_ctcV3/conv_block4/unit1/activation_1/max_33/add_1")
  sym_366875328 = _sym.broadcast_sub(sym_76671152,sym_93588000,name="Rcnn_ctcV3/conv_block4/unit1/activation_1/max_33/sub_1")
  sym_88731184 = _sym.relu(sym_366875328,name="Rcnn_ctcV3/conv_block4/unit1/activation_1/max_33/Relu_1")
  sym_152211952 = _sym.broadcast_add(sym_366874912,sym_88731184,name="Rcnn_ctcV3/conv_block4/unit1/activation_1/max_33/add_2")
  sym_152212112 = _sym.broadcast_mul(sym_146204704,sym_152211952,name="Rcnn_ctcV3/conv_block4/unit1/activation_1/max_33/mul")
  sym_452825680 = _sym.pad(sym_152212112,pad_width=((0, 0), (1, 1), (1, 1), (0, 0)))
  sym_457779424 = _sym.conv2d(sym_452825680,sym_79059936,padding=[0, 0],dilation=(1, 1),layout="NHWC",strides=(1, 1),kernel_size=(3, 3),channels=256,kernel_layout="HWIO",name="Rcnn_ctcV3/conv_block4/unit1/conv2d_107/convolution",use_bias=False)
  sym_452825680 = _sym.broadcast_add(sym_457779424,sym_78831712)
  sym_457507664 = _sym.broadcast_add(sym_452825680,sym_477435760,name="Rcnn_ctcV3/conv_block4/unit1/add_18/add")
  sym_89401920 = _sym.broadcast_add(sym_378173584,sym_457507168,name="Rcnn_ctcV3/conv_block4_1/unit2/static_batch_normalization_35/batchnorm/add")
  sym_477439760 = _sym.__pow_scalar__(sym_89401920,name="Rcnn_ctcV3/conv_block4_1/unit2/static_batch_normalization_35/batchnorm/Rsqrt",scalar=-0.5)
  sym_477439856 = _sym.broadcast_mul(sym_477439760,sym_256158240,name="Rcnn_ctcV3/conv_block4_1/unit2/static_batch_normalization_35/batchnorm/mul")
  sym_453615040 = _sym.broadcast_mul(sym_457507664,sym_477439856,name="Rcnn_ctcV3/conv_block4_1/unit2/static_batch_normalization_35/batchnorm/mul_1")
  sym_367200976 = _sym.broadcast_mul(sym_329254032,sym_477439856,name="Rcnn_ctcV3/conv_block4_1/unit2/static_batch_normalization_35/batchnorm/mul_2")
  sym_367201104 = _sym.broadcast_sub(sym_83430496,sym_367200976,name="Rcnn_ctcV3/conv_block4_1/unit2/static_batch_normalization_35/batchnorm/sub")
  sym_101145744 = _sym.broadcast_add(sym_453615040,sym_367201104,name="Rcnn_ctcV3/conv_block4_1/unit2/static_batch_normalization_35/batchnorm/add_1")
  sym_101146224 = _sym.pad(sym_101145744,pad_width=((0, 0), (0, 0), (0, 0), (0, 0)))
  sym_143310448 = _sym.conv2d(sym_101146224,sym_100941744,padding=[0, 0],dilation=(1, 1),layout="NHWC",strides=(1, 1),kernel_size=(1, 1),channels=256,kernel_layout="HWIO",name="Rcnn_ctcV3/conv_block4_1/unit2/activation/conv2d_108/convolution",use_bias=False)
  sym_101146224 = _sym.broadcast_add(sym_143310448,sym_80614592)
  sym_104543072 = _sym.pad(sym_101145744,pad_width=((0, 0), (0, 0), (0, 0), (0, 0)))
  sym_83196848 = _sym.conv2d(sym_104543072,sym_101105904,layout="NHWC",strides=(1, 1),padding=[0, 0],dilation=(1, 1),kernel_size=(1, 1),channels=256,kernel_layout="HWIO",name="Rcnn_ctcV3/conv_block4_1/unit2/activation/conv2d_109/convolution",use_bias=False)
  sym_104543072 = _sym.broadcast_add(sym_83196848,sym_394411888)
  sym_104543664 = _sym.broadcast_add(sym_101146224,sym_104543072,name="Rcnn_ctcV3/conv_block4_1/unit2/activation/max_34/add")
  sym_117663728 = _sym.broadcast_sub(sym_101146224,sym_104543072,name="Rcnn_ctcV3/conv_block4_1/unit2/activation/max_34/sub")
  sym_105219440 = _sym.relu(sym_117663728,name="Rcnn_ctcV3/conv_block4_1/unit2/activation/max_34/Relu")
  sym_83021568 = _sym.broadcast_add(sym_104543664,sym_105219440,name="Rcnn_ctcV3/conv_block4_1/unit2/activation/max_34/add_1")
  sym_83021728 = _sym.broadcast_sub(sym_104543072,sym_101146224,name="Rcnn_ctcV3/conv_block4_1/unit2/activation/max_34/sub_1")
  sym_83407296 = _sym.relu(sym_83021728,name="Rcnn_ctcV3/conv_block4_1/unit2/activation/max_34/Relu_1")
  sym_103682960 = _sym.broadcast_add(sym_83021568,sym_83407296,name="Rcnn_ctcV3/conv_block4_1/unit2/activation/max_34/add_2")
  sym_378281200 = _sym.broadcast_mul(sym_378281392,sym_103682960,name="Rcnn_ctcV3/conv_block4_1/unit2/activation/max_34/mul")
  sym_89400064 = _sym.pad(sym_378281200,pad_width=((0, 0), (1, 1), (1, 1), (0, 0)))
  sym_77190208 = _sym.conv2d(sym_89400064,sym_152788384,dilation=(1, 1),layout="NHWC",strides=(1, 1),padding=[0, 0],kernel_size=(3, 3),channels=256,kernel_layout="HWIO",name="Rcnn_ctcV3/conv_block4_1/unit2/conv2d_110/convolution",use_bias=False)
  sym_89400064 = _sym.broadcast_add(sym_77190208,sym_77188768)
  sym_238712976 = _sym.broadcast_add(sym_86902736,sym_52504256,name="Rcnn_ctcV3/conv_block4_1/unit2/static_batch_normalization_36/batchnorm/add")
  sym_452801328 = _sym.__pow_scalar__(sym_238712976,name="Rcnn_ctcV3/conv_block4_1/unit2/static_batch_normalization_36/batchnorm/Rsqrt",scalar=-0.5)
  sym_379767200 = _sym.broadcast_mul(sym_452801328,sym_88778896,name="Rcnn_ctcV3/conv_block4_1/unit2/static_batch_normalization_36/batchnorm/mul")
  sym_379767296 = _sym.broadcast_mul(sym_89400064,sym_379767200,name="Rcnn_ctcV3/conv_block4_1/unit2/static_batch_normalization_36/batchnorm/mul_1")
  sym_149039632 = _sym.broadcast_mul(sym_104191024,sym_379767200,name="Rcnn_ctcV3/conv_block4_1/unit2/static_batch_normalization_36/batchnorm/mul_2")
  sym_149040048 = _sym.broadcast_sub(sym_238290352,sym_149039632,name="Rcnn_ctcV3/conv_block4_1/unit2/static_batch_normalization_36/batchnorm/sub")
  sym_378980928 = _sym.broadcast_add(sym_379767296,sym_149040048,name="Rcnn_ctcV3/conv_block4_1/unit2/static_batch_normalization_36/batchnorm/add_1")
  sym_366864960 = _sym.pad(sym_378980928,pad_width=((0, 0), (0, 0), (0, 0), (0, 0)))
  sym_379847360 = _sym.conv2d(sym_366864960,sym_125175280,padding=[0, 0],dilation=(1, 1),layout="NHWC",strides=(1, 1),kernel_size=(1, 1),channels=256,kernel_layout="HWIO",name="Rcnn_ctcV3/conv_block4_1/unit2/activation_1/conv2d_111/convolution",use_bias=False)
  sym_366864960 = _sym.broadcast_add(sym_379847360,sym_366867792)
  sym_378993264 = _sym.pad(sym_378980928,pad_width=((0, 0), (0, 0), (0, 0), (0, 0)))
  sym_105862560 = _sym.conv2d(sym_378993264,sym_130260432,dilation=(1, 1),layout="NHWC",strides=(1, 1),padding=[0, 0],kernel_size=(1, 1),channels=256,kernel_layout="HWIO",name="Rcnn_ctcV3/conv_block4_1/unit2/activation_1/conv2d_112/convolution",use_bias=False)
  sym_378993264 = _sym.broadcast_add(sym_105862560,sym_394401808)
  sym_83429088 = _sym.broadcast_add(sym_366864960,sym_378993264,name="Rcnn_ctcV3/conv_block4_1/unit2/activation_1/max_35/add")
  sym_118634784 = _sym.broadcast_sub(sym_366864960,sym_378993264,name="Rcnn_ctcV3/conv_block4_1/unit2/activation_1/max_35/sub")
  sym_455434320 = _sym.relu(sym_118634784,name="Rcnn_ctcV3/conv_block4_1/unit2/activation_1/max_35/Relu")
  sym_455434512 = _sym.broadcast_add(sym_83429088,sym_455434320,name="Rcnn_ctcV3/conv_block4_1/unit2/activation_1/max_35/add_1")
  sym_452016176 = _sym.broadcast_sub(sym_378993264,sym_366864960,name="Rcnn_ctcV3/conv_block4_1/unit2/activation_1/max_35/sub_1")
  sym_452016368 = _sym.relu(sym_452016176,name="Rcnn_ctcV3/conv_block4_1/unit2/activation_1/max_35/Relu_1")
  sym_149046704 = _sym.broadcast_add(sym_455434512,sym_452016368,name="Rcnn_ctcV3/conv_block4_1/unit2/activation_1/max_35/add_2")
  sym_451233824 = _sym.broadcast_mul(sym_223649568,sym_149046704,name="Rcnn_ctcV3/conv_block4_1/unit2/activation_1/max_35/mul")
  sym_149047296 = _sym.pad(sym_451233824,pad_width=((0, 0), (1, 1), (1, 1), (0, 0)))
  sym_94636928 = _sym.conv2d(sym_149047296,sym_394319936,dilation=(1, 1),layout="NHWC",strides=(1, 1),padding=[0, 0],kernel_size=(3, 3),channels=256,kernel_layout="HWIO",name="Rcnn_ctcV3/conv_block4_1/unit2/conv2d_113/convolution",use_bias=False)
  sym_149047296 = _sym.broadcast_add(sym_94636928,sym_121636512)
  sym_382095632 = _sym.broadcast_add(sym_149047296,sym_457507664,name="Rcnn_ctcV3/conv_block4_1/unit2/add_19/add")
  sym_385428032 = _sym.broadcast_add(sym_385760896,sym_115259392,name="Rcnn_ctcV3/static_batch_normalization_37/batchnorm/add")
  sym_121467136 = _sym.__pow_scalar__(sym_385428032,name="Rcnn_ctcV3/static_batch_normalization_37/batchnorm/Rsqrt",scalar=-0.5)
  sym_121467232 = _sym.broadcast_mul(sym_121467136,sym_457721664,name="Rcnn_ctcV3/static_batch_normalization_37/batchnorm/mul")
  sym_121467680 = _sym.broadcast_mul(sym_382095632,sym_121467232,name="Rcnn_ctcV3/static_batch_normalization_37/batchnorm/mul_1")
  sym_367166064 = _sym.broadcast_mul(sym_104234656,sym_121467232,name="Rcnn_ctcV3/static_batch_normalization_37/batchnorm/mul_2")
  sym_367166448 = _sym.broadcast_sub(sym_382354176,sym_367166064,name="Rcnn_ctcV3/static_batch_normalization_37/batchnorm/sub")
  sym_472209424 = _sym.broadcast_add(sym_121467680,sym_367166448,name="Rcnn_ctcV3/static_batch_normalization_37/batchnorm/add_1")
  sym_472209872 = _sym.pad(sym_472209424,pad_width=((0, 0), (0, 0), (0, 0), (0, 0)))
  sym_382373888 = _sym.conv2d(sym_472209872,sym_88800208,dilation=(1, 1),layout="NHWC",strides=(1, 1),padding=[0, 0],kernel_size=(1, 1),channels=256,kernel_layout="HWIO",name="Rcnn_ctcV3/activation/conv2d_114/convolution",use_bias=False)
  sym_472209872 = _sym.broadcast_add(sym_382373888,sym_378170400)
  sym_477431696 = _sym.pad(sym_472209424,pad_width=((0, 0), (0, 0), (0, 0), (0, 0)))
  sym_88133200 = _sym.conv2d(sym_477431696,sym_132599600,dilation=(1, 1),layout="NHWC",strides=(1, 1),padding=[0, 0],kernel_size=(1, 1),channels=256,kernel_layout="HWIO",name="Rcnn_ctcV3/activation/conv2d_115/convolution",use_bias=False)
  sym_477431696 = _sym.broadcast_add(sym_88133200,sym_394302224)
  sym_486318912 = _sym.broadcast_add(sym_472209872,sym_477431696,name="Rcnn_ctcV3/activation/max_36/add")
  sym_486319296 = _sym.broadcast_sub(sym_472209872,sym_477431696,name="Rcnn_ctcV3/activation/max_36/sub")
  sym_75084208 = _sym.relu(sym_486319296,name="Rcnn_ctcV3/activation/max_36/Relu")
  sym_75084400 = _sym.broadcast_add(sym_486318912,sym_75084208,name="Rcnn_ctcV3/activation/max_36/add_1")
  sym_75084752 = _sym.broadcast_sub(sym_477431696,sym_472209872,name="Rcnn_ctcV3/activation/max_36/sub_1")
  sym_120853792 = _sym.relu(sym_75084752,name="Rcnn_ctcV3/activation/max_36/Relu_1")
  sym_120854176 = _sym.broadcast_add(sym_75084400,sym_120853792,name="Rcnn_ctcV3/activation/max_36/add_2")
  sym_140634432 = _sym.broadcast_mul(sym_140634816,sym_120854176,name="Rcnn_ctcV3/activation/max_36/mul")
  sym_89033648 = _sym.conv2d(sym_140634432,sym_283877680,dilation=(1, 1),layout="NHWC",strides=(1, 1),padding=[0, 0],kernel_size=(1, 6),channels=1318,kernel_layout="HWIO",name="Rcnn_ctcV3/conv2d_116/convolution",use_bias=False)
  sym_375435392 = _sym.broadcast_add(sym_89033648,sym_105665056)
  return sym_375435392,varnames
示例#11
0
def drn(arch,
        block,
        layers,
        num_classes=1000,
        channels=(16, 32, 64, 128, 256, 512, 512, 512)):
    data = sym.Variable(name='data')
    if arch == 'C':
        out = data
        out = sym.conv2d(data=out,
                         channels=channels[0],
                         kernel_size=(7, 7),
                         strides=(1, 1),
                         padding=(3, 3),
                         use_bias=False)
        out = int8_wrapper(sym.batch_norm, data=out)
        out = sym.relu(data=out)

        out = drn_unit(out,
                       basic_block,
                       channels[0],
                       channels[0],
                       layers[0],
                       stride=1)
        out = drn_unit(out,
                       basic_block,
                       channels[0],
                       channels[1],
                       layers[1],
                       stride=2)
        num_channel = channels[1]
    else:
        raise NotImplementedError()

    out = drn_unit(out, block, num_channel, channels[2], layers[2], stride=2)
    out = drn_unit(out, block, channels[2], channels[3], layers[3], stride=2)
    out = drn_unit(out,
                   block,
                   channels[3],
                   channels[4],
                   layers[4],
                   dilation=2,
                   new_level=False)

    num_channel = channels[4]
    if layers[5] > 0:
        out = drn_unit(out,
                       block,
                       num_channel,
                       channels[5],
                       layers[5],
                       dilation=4,
                       new_level=False)
        num_channel = channels[5]

    if arch == 'C':
        if layers[6] > 0:
            out = drn_unit(out,
                           block,
                           num_channel,
                           channels[6],
                           layers[6],
                           dilation=2,
                           new_level=False,
                           residual=False)
            num_channel = channels[6]
        if layers[7] > 0:
            out = drn_unit(out,
                           block,
                           num_channel,
                           channels[7],
                           layers[7],
                           dilation=1,
                           new_level=False,
                           residual=False)
            num_channel = channels[7]

    return out