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Fix pooling pads issues #6650

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Merged
merged 13 commits into from
Jan 24, 2025
Merged

Fix pooling pads issues #6650

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6b8c1bc
draft
titaiwangms Jan 22, 2025
d9e1f04
op_common
titaiwangms Jan 22, 2025
53f68a8
add comment
titaiwangms Jan 22, 2025
47177b1
keep condition
titaiwangms Jan 22, 2025
42f3a65
update pool tests
titaiwangms Jan 22, 2025
82685b2
Deprecate all type casting functions (#6639)
justinchuby Jan 22, 2025
2c58a99
Clarify that FLOAT4E2M1 can be in int32_data (#6640)
justinchuby Jan 22, 2025
0595b75
add a new test
titaiwangms Jan 23, 2025
e7bb8db
Unify names of github environments (#6651)
andife Jan 23, 2025
c5ff759
update model to use count_include_pad
titaiwangms Jan 23, 2025
0913218
update mds
titaiwangms Jan 23, 2025
1ba218d
disable ort test for now
titaiwangms Jan 24, 2025
4a3a166
Merge branch 'main' into titaiwang/fix_avgpool_output_shape
titaiwangms Jan 24, 2025
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167 changes: 142 additions & 25 deletions docs/Operators.md
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Original file line number Diff line number Diff line change
Expand Up @@ -1769,6 +1769,45 @@ expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_ceil")
</details>


<details>
<summary>averagepool_2d_ceil_last_window_starts_on_pad</summary>

```python
"""input_shape: [1, 3, 2, 2]
output_shape: [1, 3, 1, 1]
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[3, 3],
strides=[3, 3],
pads=[1, 1, 1, 1],
ceil_mode=True,
count_include_pad=1,
)
x = np.array(
[
[
[[0.8580, 0.0786], [0.2692, 0.1537]],
[[0.8816, 0.4353], [0.5772, 0.6623]],
[[0.9067, 0.9483], [0.5970, 0.7630]],
]
]
).astype(np.float32)
y = np.array([[[[0.1511]], [[0.2841]], [[0.3572]]]]).astype(np.float32)

expect(
node,
inputs=[x],
outputs=[y],
name="test_averagepool_2d_ceil_last_window_starts_on_pad",
)
```

</details>


<details>
<summary>averagepool_2d_default</summary>

Expand Down Expand Up @@ -1862,16 +1901,30 @@ pad_top = 2
pad_right = 2
pad_left = 2
pads = [pad_top, pad_left, pad_bottom, pad_right]
out_shape, pads = get_output_shape_explicit_padding(
out_shape, extra_pads = get_output_shape_explicit_padding(
pads, x_shape[2:], kernel_shape, strides, ceil_mode=False
)
padded = np.pad(
x,
((0, 0), (0, 0), (pads[0], pads[2]), (pads[1], pads[3])),
(
(0, 0),
(0, 0),
(extra_pads[0], extra_pads[2]),
(extra_pads[1], extra_pads[3]),
),
mode="constant",
constant_values=np.nan,
)
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "AVG", pads)
y = pool(
padded,
x_shape,
kernel_shape,
strides,
out_shape,
"AVG",
pads_required=extra_pads,
pads=pads,
)

expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_pads")
```
Expand Down Expand Up @@ -1905,12 +1958,17 @@ pad_top = 2
pad_right = 2
pad_left = 2
pads = [pad_top, pad_left, pad_bottom, pad_right]
out_shape, pads = get_output_shape_explicit_padding(
out_shape, extra_pads = get_output_shape_explicit_padding(
pads, x_shape[2:], kernel_shape, strides, dilations, ceil_mode=False
)
padded = np.pad(
x,
((0, 0), (0, 0), (pads[0], pads[2]), (pads[1], pads[3])),
(
(0, 0),
(0, 0),
(extra_pads[0], extra_pads[2]),
(extra_pads[1], extra_pads[3]),
),
mode="constant",
constant_values=0,
)
Expand All @@ -1921,7 +1979,8 @@ y = pool(
strides,
out_shape,
"AVG",
pads,
pads_required=extra_pads,
pads=pads,
count_include_pad=1,
)

Expand Down Expand Up @@ -2160,7 +2219,16 @@ padded = np.pad(
constant_values=np.nan,
)
pads = (pad_top, pad_left, pad_bottom, pad_right)
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "AVG", pads)
y = pool(
padded,
x_shape,
kernel_shape,
strides,
out_shape,
"AVG",
pads_required=pads,
pads=pads,
)

expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_same_lower")
```
Expand Down Expand Up @@ -2204,7 +2272,16 @@ padded = np.pad(
constant_values=np.nan,
)
pads = (pad_top, pad_left, pad_bottom, pad_right)
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "AVG", pads)
y = pool(
padded,
x_shape,
kernel_shape,
strides,
out_shape,
"AVG",
pads_required=pads,
pads=pads,
)

expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_same_upper")
```
Expand Down Expand Up @@ -2234,7 +2311,16 @@ out_shape, pads = get_output_shape_explicit_padding(
None, x_shape[2:], kernel_shape, strides, ceil_mode=False
)
padded = x
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "AVG", pads)
y = pool(
padded,
x_shape,
kernel_shape,
strides,
out_shape,
"AVG",
pads_required=pads,
pads=None,
)

expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_strides")
```
Expand Down Expand Up @@ -2357,7 +2443,7 @@ for count_include_pad in (0, 1):
)

x = np.random.randn(1, 1, *x_shape).astype(np.float32)
out_shape, pads = get_output_shape_explicit_padding(
out_shape, extra_pads = get_output_shape_explicit_padding(
None,
x_shape,
kernel_shape,
Expand All @@ -2370,9 +2456,9 @@ for count_include_pad in (0, 1):
(
(0, 0),
(0, 0),
(pads[0], pads[3]),
(pads[1], pads[4]),
(pads[2], pads[5]),
(extra_pads[0], extra_pads[3]),
(extra_pads[1], extra_pads[4]),
(extra_pads[2], extra_pads[5]),
),
mode="constant",
constant_values=0 if count_include_pad == 1 else np.nan,
Expand All @@ -2384,7 +2470,8 @@ for count_include_pad in (0, 1):
strides,
out_shape,
"AVG",
pads=pads,
pads_required=extra_pads,
pads=None,
dilations=dilations,
count_include_pad=count_include_pad,
)
Expand Down Expand Up @@ -15303,16 +15390,31 @@ kernel_shape = (3, 3)
strides = (1, 1)
pad_bottom = pad_top = pad_right = pad_left = 2
pads = [pad_top, pad_left, pad_bottom, pad_right]
out_shape, pads = get_output_shape_explicit_padding(
out_shape, extra_pads = get_output_shape_explicit_padding(
pads, x_shape[2:], kernel_shape, strides
)
padded = np.pad(
x,
((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
(
(0, 0),
(0, 0),
(extra_pads[0], extra_pads[2]),
(extra_pads[1], extra_pads[3]),
),
mode="constant",
constant_values=0,
)
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", pads, p=p)
y = pool(
padded,
x_shape,
kernel_shape,
strides,
out_shape,
"LPPOOL",
pads_required=extra_pads,
pads=pads,
p=p,
)

expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_pads")
```
Expand Down Expand Up @@ -15358,7 +15460,9 @@ padded = np.pad(
constant_values=0,
)
pads = [pad_top, pad_left, pad_bottom, pad_right]
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", pads, p=p)
y = pool(
padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", pads, pads, p=p
)

expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_same_lower")
```
Expand Down Expand Up @@ -15404,7 +15508,9 @@ padded = np.pad(
constant_values=0,
)
pads = [pad_top, pad_left, pad_bottom, pad_right]
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", pads, p=p)
y = pool(
padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", pads, pads, p=p
)

expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_same_upper")
```
Expand Down Expand Up @@ -16011,7 +16117,7 @@ kernel_shape = (3, 3)
strides = (1, 1)
pad_bottom = pad_top = pad_right = pad_left = 2
pads = [pad_top, pad_left, pad_bottom, pad_right]
out_shape, pads = get_output_shape_explicit_padding(
out_shape, extra_pads = get_output_shape_explicit_padding(
pads, x_shape[2:], kernel_shape, strides
)
padded = np.pad(
Expand All @@ -16021,7 +16127,16 @@ padded = np.pad(
constant_values=np.nan,
)

y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX", pads)
y = pool(
padded,
x_shape,
kernel_shape,
strides,
out_shape,
"MAX",
pads_required=extra_pads,
pads=pads,
)

expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_pads")
```
Expand Down Expand Up @@ -16185,7 +16300,7 @@ padded = np.pad(
constant_values=np.nan,
)
pads = [pad_top, pad_left, pad_bottom, pad_right]
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX", pads)
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX", pads, pads)

expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_same_lower")
```
Expand Down Expand Up @@ -16229,7 +16344,7 @@ padded = np.pad(
constant_values=np.nan,
)
pads = [pad_top, pad_left, pad_bottom, pad_right]
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX", pads)
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX", pads, pads)

expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_same_upper")
```
Expand Down Expand Up @@ -16463,7 +16578,8 @@ y = pool(
strides,
out_shape,
"MAX",
pads,
pads_required=pads,
pads=None,
dilations=dilations,
)

Expand Down Expand Up @@ -16518,7 +16634,8 @@ y = pool(
strides,
out_shape,
"MAX",
pads,
pads_required=pads,
pads=None,
dilations=dilations,
)

Expand Down
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