Skip to content

Split adaptive_pool2d_avg into sum and div #4186

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
tqchen merged 1 commit into from
Oct 24, 2019
Merged

Split adaptive_pool2d_avg into sum and div #4186

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
18 changes: 15 additions & 3 deletions topi/include/topi/nn/pooling.h
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -492,7 +492,7 @@ inline Tensor adaptive_pool_impl(const Tensor& x,
return tvm::max(x(indices), { dheight, dwidth }); // NOLINT(*)
}, "tensor", "adaptive_pool_max");
} else if (pool_type == kAvgPool) {
return tvm::compute(out_shape, [&](const Array<Var>& output) {
auto pool_sum = tvm::compute(out_shape, [&](const Array<Var>& output) {
Array<Expr> indices;
for (const Var& var : output) indices.push_back(var);
auto i_start_h = start_index(output[height_axis], out_height, height);
Expand All @@ -505,8 +505,20 @@ inline Tensor adaptive_pool_impl(const Tensor& x,
auto dwidth = tvm::reduce_axis(Range(0, i_end_w - i_start_w), "rv2");
indices.Set(height_axis, i_start_h + dheight);
indices.Set(width_axis, i_start_w + dwidth);
return tvm::sum(div(x(indices), divide_factor), { dheight, dwidth });
}, "tensor", "adaptive_pool_avg");
return tvm::sum(x(indices), { dheight, dwidth });
}, "tensor", "adaptive_pool_sum");

return tvm::compute(out_shape, [&](const Array<Var>& output) {
Array<Expr> indices;
for (const Var& var : output) indices.push_back(var);
auto i_start_h = start_index(output[height_axis], out_height, height);
auto i_end_h = end_index(output[height_axis], out_height, height);
auto i_start_w = start_index(output[width_axis], out_width, width);
auto i_end_w = end_index(output[width_axis], out_width, width);
auto divide_factor = tvm::cast(x->dtype, (i_end_h - i_start_h)
* (i_end_w - i_start_w));
return div(pool_sum(indices), divide_factor);
}, "tensor", kElementWise);
} else {
LOG(ERROR) << "Unrecognized pool_type: " << pool_type;
return x;
Expand Down
5 changes: 5 additions & 0 deletions topi/python/topi/x86/pooling.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -147,6 +147,11 @@ def traverse(OP):
traverse(tensor.op)
# schedule pool
elif OP.tag.startswith('adaptive_pool'):
if OP != outs[0].op:
output = outs[0]
output_fused = s[output].fuse(output.op.axis[0], output.op.axis[1])
s[output].parallel(output_fused)

Pool = OP.output(0)
_parallel_sch(s[Pool], outs[0].shape)
else:
Expand Down