text: # Copyright (c) 2016-present, Facebook, Inc.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  ##############################################################################
  
  ## @package conv
  # Module caffe2.python.layers.conv
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  
  from caffe2.python import schema
  from caffe2.python.layers.layers import (
      ModelLayer,
  )
  import numpy as np
  
  
  class Conv(ModelLayer):
      """
          Convolutional layer
          Input:
          - input_record: at least has the shape info of C (num_channels)
          - output_dim: number of convolutional filters
          - kernel_h, kernel_w: kernel size for h and w
          - stride_h, stride_w: stride for h and w
          - pad_b, pad_l, pad_r, pad_t: padding sizes, if stride == 1,
                                        'None' value will do auto padding
          - order: either 'NHWC' or 'NCHW'
      """
  
      def __init__(self, model, input_record, output_dim, kernel_h, kernel_w,
                   stride_h, stride_w, pad_b=None, pad_l=None, pad_r=None,
                   pad_t=None, order='NHWC', kernel_init=None, bias_init=None,
                   kernel_optim=None, bias_optim=None,
                   name='conv', **kwargs):
  
          super(Conv, self).__init__(model, name, input_record, **kwargs)
          assert isinstance(input_record, schema.Scalar), "Incorrect input type"
          # input num_channels (C) is needed
          input_dims = input_record.field_type().shape
  
          assert (kernel_h > 0 and isinstance(kernel_h, int)), (
              "kernel_h should be positive integer")
          assert (kernel_w > 0 and isinstance(kernel_w, int)), (
              "kernel_w should be positive integer")
          self.kernel_h = kernel_h
          self.kernel_w = kernel_w
  
          assert (stride_h > 0 and isinstance(stride_h, int)), (
              "stride_h should be positive integer")
          assert (stride_w > 0 and isinstance(stride_w, int)), (
              "stride_w should be positive integer")
          self.stride_h = stride_h
          self.stride_w = stride_w
  
          # output_dim calculation (http://cs231n.github.io/convolutional-networks/)
          # output_dim_w = (input_dim_w - kernel_w + pad_r + pad_l) / stride_w + 1
          # so, do auto_padding requires
          # pad_r, pad_l = [(input_dim_w - 1) * stride_w - input_dim_w + kernel_w] / 2
          # similair for pad_t and pad_b to auto pad kernel_h
          # here we only do auto padding for stride = 1 case
          if stride_h == 1:
              pad_t = int((kernel_h - 1) / 2) if pad_t is None else pad_t
              pad_b = int((kernel_h - 1) / 2) if pad_b is None else pad_b
          else:
              pad_t = 0 if pad_t is None else pad_t
              pad_b = 0 if pad_b is None else pad_b
  
          if stride_w == 1:
              pad_r = int((kernel_w - 1) / 2) if pad_r is None else pad_r
              pad_l = int((kernel_w - 1) / 2) if pad_l is None else pad_l
          else:
              pad_r = 0 if pad_r is None else pad_r
              pad_l = 0 if pad_l is None else pad_l
  
          assert (pad_t >= 0 and isinstance(pad_t, int)), "pad_t should be int >= 0"
          assert (pad_b >= 0 and isinstance(pad_b, int)), "pad_b should be int >= 0"
          assert (pad_r >= 0 and isinstance(pad_r, int)), "pad_r should be int >= 0"
          assert (pad_l >= 0 and isinstance(pad_l, int)), "pad_l should be int >= 0"
          self.pad_t = pad_t
          self.pad_b = pad_b
          self.pad_r = pad_r
          self.pad_l = pad_l
  
          assert order in ['NHWC', 'NCHW'], "order should either 'NHWC' or 'NCHW'"
          self.order = order
  
          if order == 'NHWC':
              input_c = input_dims[-1]
              kernel_shape = [output_dim, kernel_h, kernel_w, input_c]
          elif order == 'NCHW':
              input_c = input_dims[0]
              kernel_shape = [output_dim, input_c, kernel_h, kernel_w]
          assert input_c > 0, (
              "Number of input channels in conv parameters should be positive")
  
          kernel_init = kernel_init if kernel_init else (
              'XavierFill', {}
          )
          bias_init = bias_init if bias_init else (
              'ConstantFill', {'value': 0.0}
          )
  
          self.kernel = self.create_param(
              param_name='conv_kernel',
              shape=kernel_shape,
              initializer=kernel_init,
              optimizer=kernel_optim,
          )
  
          self.bias = self.create_param(
              param_name='conv_bias',
              shape=[output_dim],
              initializer=bias_init,
              optimizer=bias_optim,
          )
  
          # the output_schema only has the num of output channels
          # output_h and output_w would be inferred internally
          self.output_schema = schema.Scalar(
              (np.float32, (output_dim,)),
              self.get_next_blob_reference('output')
          )
  
      def add_ops(self, net):
          net.Conv(
              self.input_record.field_blobs() + [self.kernel, self.bias],
              self.output_schema.field_blobs(),
              kernel_h=self.kernel_h,
              kernel_w=self.kernel_w,
              stride_h=self.stride_h,
              stride_w=self.stride_w,
              pad_t=self.pad_t,
              pad_l=self.pad_l,
              pad_b=self.pad_b,
              pad_r=self.pad_r,
              order=self.order
          )
  