caffe-tensorflow-darknet-pytorch关于pad的比较

Tnesrflow：

https://www.imooc.com/article/details/id/29525

https://blog.youkuaiyun.com/wuzqChom/article/details/74785643

https://www.cnblogs.com/White-xzx/p/9497029.html

3.卷积padding的实战分析

接下来我们通过在TensorFlow中使用卷积和池化函数来分析padding参数在实际中的应用，代码如下：

# -*- coding: utf-8 -*-
import tensorflow as tf

# 首先，模拟输入一个图像矩阵，大小为5*5
# 输入图像矩阵的shape为[批次大小，图像的高度，图像的宽度，图像的通道数]
input = tf.Variable(tf.constant(1.0, shape=[1, 5, 5, 1]))

# 定义卷积核，大小为2*2，输入和输出都是单通道
# 卷积核的shape为[卷积核的高度，卷积核的宽度，图像通道数，卷积核的个数]
filter1 = tf.Variable(tf.constant([-1.0, 0, 0, -1], shape=[2, 2, 1, 1]))

# 卷积操作 strides为[批次大小，高度方向的移动步长，宽度方向的移动步长，通道数]
# SAME
op1_conv_same = tf.nn.conv2d(input, filter1, strides=[1,2,2,1],padding='SAME')
# VALID
op2_conv_valid = tf.nn.conv2d(input, filter1, strides=[1,2,2,1],padding='VALID')

init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    print("op1_conv_same:\n", sess.run(op1_conv_same))
    print("op2_conv_valid:\n", sess.run(op2_conv_valid))

VALID模式的分析：

SAME模式分析：
 

o_w = i_w  / s_w = 5/2 = 3
o_h = i_h / s_h = 5/2 = 3

pad_w = max ( (o_w - 1 ) × s_w + f_w -  i_w , 0 )
      =  max ( (3 - 1 ) × 2 + 2 - 5 , 0 )  = 1
pad_left = 1 / 2 =0
pad_right = 1 - 0 =0
# 同理
pad_top = 0
pad_bottom = 1

 计算avg_pool时，每次的计算是除以图像被filter框出的非零元素的个数，而不是filter元素的个数，如下图，第一行第三列我们计算出的结果是除以2而非4

 

caffe：

template <typename Dtype>
void ConvolutionLayer<Dtype>::compute_output_shape() {
  const int* kernel_shape_data = this->kernel_shape_.cpu_data();
  const int* stride_data = this->stride_.cpu_data();
  const int* pad_data = this->pad_.cpu_data();
  const int* dilation_data = this->dilation_.cpu_data();
  this->output_shape_.clear();
  for (int i = 0; i < this->num_spatial_axes_; ++i) {
    // i + 1 to skip channel axis
    const int input_dim = this->input_shape(i + 1);
    const int kernel_extent = dilation_data[i] * (kernel_shape_data[i] - 1) + 1;
    const int output_dim = (input_dim + 2 * pad_data[i] - kernel_extent)
        / stride_data[i] + 1;
    this->output_shape_.push_back(output_dim);
  }
}

template <typename Dtype>
void PoolingLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top) {
  CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, "
      << "corresponding to (num, channels, height, width)";
  channels_ = bottom[0]->channels();
  height_ = bottom[0]->height();
  width_ = bottom[0]->width();
  if (global_pooling_) {
    kernel_h_ = bottom[0]->height();
    kernel_w_ = bottom[0]->width();
  }
  pooled_height_ = static_cast<int>(ceil(static_cast<float>(
      height_ + 2 * pad_h_ - kernel_h_) / stride_h_)) + 1;
  pooled_width_ = static_cast<int>(ceil(static_cast<float>(
      width_ + 2 * pad_w_ - kernel_w_) / stride_w_)) + 1;
  if (pad_h_ || pad_w_) {
    // If we have padding, ensure that the last pooling starts strictly
    // inside the image (instead of at the padding); otherwise clip the last.
    if ((pooled_height_ - 1) * stride_h_ >= height_ + pad_h_) {
      --pooled_height_;
    }
    if ((pooled_width_ - 1) * stride_w_ >= width_ + pad_w_) {
      --pooled_width_;
    }
    CHECK_LT((pooled_height_ - 1) * stride_h_, height_ + pad_h_);
    CHECK_LT((pooled_width_ - 1) * stride_w_, width_ + pad_w_);
  }
  top[0]->Reshape(bottom[0]->num(), channels_, pooled_height_,
      pooled_width_);
  if (top.size() > 1) {
    top[1]->ReshapeLike(*top[0]);
  }
  // If max pooling, we will initialize the vector index part.
  if (this->layer_param_.pooling_param().pool() ==
      PoolingParameter_PoolMethod_MAX && top.size() == 1) {
    max_idx_.Reshape(bottom[0]->num(), channels_, pooled_height_,
        pooled_width_);
  }
  // If stochastic pooling, we will initialize the random index part.
  if (this->layer_param_.pooling_param().pool() ==
      PoolingParameter_PoolMethod_STOCHASTIC) {
    rand_idx_.Reshape(bottom[0]->num(), channels_, pooled_height_,
      pooled_width_);
  }
}

 如果是全局池化：

template <typename Dtype>
void PoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top) {
  PoolingParameter pool_param = this->layer_param_.pooling_param();
  if (pool_param.global_pooling()) {
    CHECK(!(pool_param.has_kernel_size() ||
      pool_param.has_kernel_h() || pool_param.has_kernel_w()))
      << "With Global_pooling: true Filter size cannot specified";
  } else {
    CHECK(!pool_param.has_kernel_size() !=
      !(pool_param.has_kernel_h() && pool_param.has_kernel_w()))
      << "Filter size is kernel_size OR kernel_h and kernel_w; not both";
    CHECK(pool_param.has_kernel_size() ||
      (pool_param.has_kernel_h() && pool_param.has_kernel_w()))
      << "For non-square filters both kernel_h and kernel_w are required.";
  }
  CHECK((!pool_param.has_pad() && pool_param.has_pad_h()
      && pool_param.has_pad_w())
      || (!pool_param.has_pad_h() && !pool_param.has_pad_w()))
      << "pad is pad OR pad_h and pad_w are required.";
  CHECK((!pool_param.has_stride() && pool_param.has_stride_h()
      && pool_param.has_stride_w())
      || (!pool_param.has_stride_h() && !pool_param.has_stride_w()))
      << "Stride is stride OR stride_h and stride_w are required.";
  global_pooling_ = pool_param.global_pooling();
  if (global_pooling_) {
    kernel_h_ = bottom[0]->height();
    kernel_w_ = bottom[0]->width();
  } else {
    if (pool_param.has_kernel_size()) {
      kernel_h_ = kernel_w_ = pool_param.kernel_size();
    } else {
      kernel_h_ = pool_param.kernel_h();
      kernel_w_ = pool_param.kernel_w();
    }
  }
  CHECK_GT(kernel_h_, 0) << "Filter dimensions cannot be zero.";
  CHECK_GT(kernel_w_, 0) << "Filter dimensions cannot be zero.";
  if (!pool_param.has_pad_h()) {
    pad_h_ = pad_w_ = pool_param.pad();
  } else {
    pad_h_ = pool_param.pad_h();
    pad_w_ = pool_param.pad_w();
  }
  if (!pool_param.has_stride_h()) {
    stride_h_ = stride_w_ = pool_param.stride();
  } else {
    stride_h_ = pool_param.stride_h();
    stride_w_ = pool_param.stride_w();
  }
  if (global_pooling_) {
    CHECK(pad_h_ == 0 && pad_w_ == 0 && stride_h_ == 1 && stride_w_ == 1)
      << "With Global_pooling: true; only pad = 0 and stride = 1";
  }
  if (pad_h_ != 0 || pad_w_ != 0) {
    CHECK(this->layer_param_.pooling_param().pool()
        == PoolingParameter_PoolMethod_AVE
        || this->layer_param_.pooling_param().pool()
        == PoolingParameter_PoolMethod_MAX)
        << "Padding implemented only for average and max pooling.";
    CHECK_LT(pad_h_, kernel_h_);
    CHECK_LT(pad_w_, kernel_w_);
  }
}

平均池化：

除以所有元素

  case PoolingParameter_PoolMethod_AVE:
    for (int i = 0; i < top_count; ++i) {
      top_data[i] = 0;
    }
    // The main loop
    for (int n = 0; n < bottom[0]->num(); ++n) {
      for (int c = 0; c < channels_; ++c) {
        for (int ph = 0; ph < pooled_height_; ++ph) {
          for (int pw = 0; pw < pooled_width_; ++pw) {
            int hstart = ph * stride_h_ - pad_h_;
            int wstart = pw * stride_w_ - pad_w_;
            int hend = min(hstart + kernel_h_, height_ + pad_h_);
            int wend = min(wstart + kernel_w_, width_ + pad_w_);
            int pool_size = (hend - hstart) * (wend - wstart);
            hstart = max(hstart, 0);
            wstart = max(wstart, 0);
            hend = min(hend, height_);
            wend = min(wend, width_);
            for (int h = hstart; h < hend; ++h) {
              for (int w = wstart; w < wend; ++w) {
                top_data[ph * pooled_width_ + pw] +=
                    bottom_data[h * width_ + w];
              }
            }
            top_data[ph * pooled_width_ + pw] /= pool_size;
          }
        }
        // compute offset
        bottom_data += bottom[0]->offset(0, 1);
        top_data += top[0]->offset(0, 1);
      }
    }
    break;

pytorch：

池化是向下取整

https://blog.youkuaiyun.com/zxyhhjs2017/article/details/82850842

https://blog.youkuaiyun.com/zz2230633069/article/details/83214308

向下取整：和caffe不一样：