def shp_apply(self, inputs):
info = self.info
batch_size, channels, height, width = cgt.shape(inputs[0])
pooled_height = cgt.ceil_divide(height + 2*info.pad_h - info.kernel_h, info.stride_h)
pooled_width = cgt.ceil_divide(width + 2*info.pad_w - info.kernel_w, info.stride_w)
outshape = [batch_size , channels, pooled_height, pooled_width]
return outshape
def shp_apply(self, inputs):
X, W, _b = inputs
h = cgt.ceil_divide(
cgt.size(X, 2) + self.ph * 2 - cgt.size(W, 2) + 1, self.sv)
w = cgt.ceil_divide(
cgt.size(X, 3) + self.pw * 2 - cgt.size(W, 3) + 1, self.sh)
return [cgt.size(X, 0), cgt.size(W, 0), h, w]
def shp_apply(self, inputs):
X, W, _b = inputs
h = cgt.ceil_divide(
cgt.size(X, 2) + self.ph * 2 - cgt.size(W, 2) + 1, self.sv)
w = cgt.ceil_divide(
cgt.size(X, 3) + self.pw * 2 - cgt.size(W, 3) + 1, self.sh)
return [cgt.size(X, 0), cgt.size(W, 0), h, w]
def shp_apply(self, inputs):
# 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;
info = self.info
batch_size, channels, height, width = cgt.shape(inputs[0])
pooled_height = cgt.ceil_divide(height + 2*info.pad_h - info.kernel_h, info.stride_h)
pooled_width = cgt.ceil_divide(width + 2*info.pad_w - info.kernel_w, info.stride_w)
outshape = [batch_size , channels, pooled_height, pooled_width]
return (outshape, outshape)
def shp_apply(self, inputs):
info = self.info
batch_size, channels, height, width = cgt.shape(inputs[0])
pooled_height = cgt.ceil_divide(
height + 2 * info.pad_h - info.kernel_h, info.stride_h)
pooled_width = cgt.ceil_divide(width + 2 * info.pad_w - info.kernel_w,
info.stride_w)
outshape = [batch_size, channels, pooled_height, pooled_width]
return outshape
def shp_apply(self, inputs):
# 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;
info = self.info
batch_size, channels, height, width = cgt.shape(inputs[0])
pooled_height = cgt.ceil_divide(
height + 2 * info.pad_h - info.kernel_h, info.stride_h)
pooled_width = cgt.ceil_divide(width + 2 * info.pad_w - info.kernel_w,
info.stride_w)
outshape = [batch_size, channels, pooled_height, pooled_width]
return (outshape, outshape)
