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Fix padding in pooling op #4738

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merged 1 commit into from
Jan 22, 2020
Merged

Fix padding in pooling op #4738

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24 changes: 12 additions & 12 deletions src/relay/op/nn/pooling.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -947,21 +947,21 @@ bool Pool3DRel(const Array<Type>& types,
const auto hidx = layout.IndexOf(LayoutAxis::Get('H'));
const auto widx = layout.IndexOf(LayoutAxis::Get('W'));

IndexExpr pad_d, pad_h, pad_w;
IndexExpr pad[3];
if (param->padding.size() == 1) {
pad_d = param->padding[0] * 2;
pad_h = param->padding[0] * 2;
pad_w = param->padding[0] * 2;
pad[0] = param->padding[0] * 2;
pad[1] = param->padding[0] * 2;
pad[2] = param->padding[0] * 2;
} else if (param->padding.size() == 3) {
// (front, top, left)
pad_d = param->padding[0] * 2;
pad_h = param->padding[1] * 2;
pad_w = param->padding[2] * 2;
pad[0] = param->padding[0] * 2;
pad[1] = param->padding[1] * 2;
pad[2] = param->padding[2] * 2;
} else if (param->padding.size() == 6) {
// (front, top, left, back, bottom, right)
pad_d = param->padding[0] + param->padding[3];
pad_h = param->padding[1] + param->padding[4];
pad_w = param->padding[2] + param->padding[5];
pad[0] = param->padding[0] + param->padding[3];
pad[1] = param->padding[1] + param->padding[4];
pad[2] = param->padding[2] + param->padding[5];
} else {
return false;
}
Expand All @@ -978,10 +978,10 @@ bool Pool3DRel(const Array<Type>& types,
oshape[ii] = dshape[ii];
} else {
if (param->ceil_mode) {
oshape[ii] = ((dshape[ii] + pad_d - param->pool_size[i] +
oshape[ii] = ((dshape[ii] + pad[i] - param->pool_size[i] +
param->strides[i] - 1) / param->strides[i]) + 1;
} else {
oshape[ii] = ((dshape[ii] + pad_d - param->pool_size[i]) / param->strides[i]) + 1;
oshape[ii] = ((dshape[ii] + pad[i] - param->pool_size[i]) / param->strides[i]) + 1;
}
}
}
Expand Down
24 changes: 14 additions & 10 deletions tests/python/relay/test_op_level2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,16 +21,10 @@
from tvm import autotvm
from tvm import relay
from tvm.relay import transform
from tvm.relay.testing import ctx_list
from tvm.relay.testing import ctx_list, run_infer_type
from tvm.contrib import util
import topi.testing

def run_infer_type(expr):
mod = relay.Module.from_expr(expr)
mod = transform.InferType()(mod)
entry = mod["main"]
return entry if isinstance(expr, relay.Function) else entry.body


def test_conv1d_infer_type():
# symbolic in batch dimension
Expand Down Expand Up @@ -768,7 +762,7 @@ def _test_pool1d(opfunc):

def test_pool3d():

def _test_pool3d(opfunc):
def _test_pool3d(opfunc, padding=(0, 0, 0, 0, 0, 0), out_shape=(1, 3, 16, 16, 16)):
n, c, d, h, w = tvm.size_var("n"), 10, 5, 224, 224
x = relay.var("x", relay.TensorType((n, c, d, h, w), "float32"))
y = opfunc(x, pool_size=(1, 1, 1))
Expand All @@ -780,18 +774,28 @@ def _test_pool3d(opfunc):
dshape = (1, 3, 32, 32, 32)
x = relay.var("x", shape=dshape)
pool_type = 'max' if 'max' in str(opfunc) else 'avg'
y = opfunc(x, pool_size=(2, 2, 2), strides=(2, 2, 2), padding=(0, 0, 0, 0, 0, 0))
y = opfunc(x, pool_size=(2, 2, 2), strides=(2, 2, 2), padding=padding)
func = relay.Function([x], y)
# check output shape
f_out_shape = tuple(map(lambda x: int(x), run_infer_type(func).ret_type.shape))
assert out_shape == f_out_shape, \
"Output shape mismatch. expected {}, actual {}".format(out_shape, f_out_shape)
data = np.random.uniform(size=dshape).astype(dtype)
ref_res = topi.testing.pool3d_ncdhw_python(data, (2, 2, 2), (2, 2, 2),
(0, 0, 0, 0, 0, 0), (1, 3, 16, 16, 16), pool_type, False)
padding, out_shape, pool_type, False)
for target, ctx in ctx_list():
intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
op_res1 = intrp1.evaluate(func)(data)
tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5, atol=1e-5)

_test_pool3d(relay.nn.max_pool3d)
_test_pool3d(relay.nn.max_pool3d, padding=(2, 0, 0, 2, 0, 0), out_shape=(1, 3, 18, 16, 16))
_test_pool3d(relay.nn.max_pool3d, padding=(0, 3, 0, 0, 3, 0), out_shape=(1, 3, 16, 19, 16))
_test_pool3d(relay.nn.max_pool3d, padding=(0, 0, 4, 0, 0, 4), out_shape=(1, 3, 16, 16, 20))
_test_pool3d(relay.nn.avg_pool3d)
_test_pool3d(relay.nn.avg_pool3d, padding=(2, 0, 0, 2, 0, 0), out_shape=(1, 3, 18, 16, 16))
_test_pool3d(relay.nn.avg_pool3d, padding=(0, 3, 0, 0, 3, 0), out_shape=(1, 3, 16, 19, 16))
_test_pool3d(relay.nn.avg_pool3d, padding=(0, 0, 4, 0, 0, 4), out_shape=(1, 3, 16, 16, 20))


def test_avg_pool2d_no_count_pad():
Expand Down
18 changes: 13 additions & 5 deletions topi/python/topi/testing/pool3d_python.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,7 @@
"""max_pool3d and avg_pool3d in python"""
import math
import numpy as np
import tvm

def pool3d_ncdhw_python(np_data, kernel,
strides, padding,
Expand All @@ -39,10 +40,17 @@ def pool3d_ncdhw_python(np_data, kernel,
assert out_shape[3] == int(math.floor(float(in_shape[3] - k_h + pt + pb) / s_h) + 1)
assert out_shape[4] == int(math.floor(float(in_shape[4] - k_w + pl + pr) / s_w) + 1)

pad_np = np.zeros(shape=(in_n, in_c,
in_d + pf + pk,
in_h + pt + pb,
in_w + pl + pr)).astype(dtype)
fill_value = tvm.const(0.0, dtype).value
if not(count_include_pad) and pool_type == 'max':
fill_value = tvm.min_value(dtype).value

pad_np = np.full(shape=(in_n, in_c,
in_d + pf + pk,
in_h + pt + pb,
in_w + pl + pr),
fill_value=fill_value,
dtype=dtype)

no_zero = (range(in_n),
range(in_c),
(range(pf, in_d + pf)),
Expand Down Expand Up @@ -81,5 +89,5 @@ def pool3d_ncdhw_python(np_data, kernel,
else:
raise ValueError("pool type {} is not supported".format(pool_type))

ret_np = np.maximum(ret_np, 0.0)
ret_np = np.maximum(ret_np, fill_value)
return ret_np