image_classification/symbols/resnet.py [107:128]:
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    num_unit = len(units)
    assert(num_unit == num_stages)
    data = mx.sym.Variable(name='data')
    if dtype == 'float32':
        data = mx.sym.identity(data=data, name='id')
    else:
        if dtype == 'float16':
            data = mx.sym.Cast(data=data, dtype=np.float16)
    data = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='bn_data')
    (nchannel, height, width) = image_shape
    if height <= 32:            # such as cifar10
        body = mx.sym.Convolution(data=data, num_filter=filter_list[0], kernel=(3, 3), stride=(1,1), pad=(1, 1),
                                  no_bias=True, name="conv0", workspace=workspace)
    else:                       # often expected to be 224 such as imagenet
        body = mx.sym.Convolution(data=data, num_filter=filter_list[0], kernel=(7, 7), stride=(2,2), pad=(3, 3),
                                  no_bias=True, name="conv0", workspace=workspace)
        body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0')
        body = mx.sym.Activation(data=body, act_type='relu', name='relu0')
        body = mx.sym.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max')

    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,
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image_classification/symbols/resnext.py [122:143]:
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    num_unit = len(units)
    assert(num_unit == num_stages)
    data = mx.sym.Variable(name='data')
    if dtype == 'float32':
        data = mx.sym.identity(data=data, name='id')
    else:
        if dtype == 'float16':
            data = mx.sym.Cast(data=data, dtype=np.float16)
    data = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='bn_data')
    (nchannel, height, width) = image_shape
    if height <= 32:            # such as cifar10
        body = mx.sym.Convolution(data=data, num_filter=filter_list[0], kernel=(3, 3), stride=(1,1), pad=(1, 1),
                                  no_bias=True, name="conv0", workspace=workspace)
    else:                       # often expected to be 224 such as imagenet
        body = mx.sym.Convolution(data=data, num_filter=filter_list[0], kernel=(7, 7), stride=(2,2), pad=(3, 3),
                                  no_bias=True, name="conv0", workspace=workspace)
        body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0')
        body = mx.sym.Activation(data=body, act_type='relu', name='relu0')
        body = mx.sym.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max')

    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,
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