wehub-resource-sync
539 lines
16 KiB
Python
539 lines
16 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from get_test_cover_info import (
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XPUOpTestWrapper,
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create_test_class,
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get_xpu_op_support_types,
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)
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from op_test_xpu import XPUOpTest
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import paddle
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paddle.enable_static()
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def adaptive_start_index(index, input_size, output_size):
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return int(np.floor(index * input_size / output_size))
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def adaptive_end_index(index, input_size, output_size):
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return int(np.ceil((index + 1) * input_size / output_size))
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def pool3D_forward_naive(
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x,
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ksize,
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strides,
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paddings,
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global_pool=0,
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ceil_mode=False,
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exclusive=True,
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adaptive=False,
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data_format='NCDHW',
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pool_type='max',
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padding_algorithm="EXPLICIT",
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):
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# update paddings
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def _get_padding_with_SAME(input_shape, pool_size, pool_stride):
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padding = []
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for input_size, filter_size, stride_size in zip(
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input_shape, pool_size, pool_stride
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):
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out_size = int((input_size + stride_size - 1) / stride_size)
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pad_sum = np.max(
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((out_size - 1) * stride_size + filter_size - input_size, 0)
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)
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pad_0 = int(pad_sum / 2)
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pad_1 = int(pad_sum - pad_0)
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padding.append(pad_0)
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padding.append(pad_1)
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return padding
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if isinstance(padding_algorithm, str):
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padding_algorithm = padding_algorithm.upper()
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if padding_algorithm not in ["SAME", "VALID", "EXPLICIT"]:
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raise ValueError(
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f"Unknown Attr(padding_algorithm): '{padding_algorithm}'. "
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"It can only be 'SAME' or 'VALID'."
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)
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if padding_algorithm == "VALID":
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paddings = [0, 0, 0, 0, 0, 0]
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if ceil_mode is not False:
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raise ValueError(
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'When Attr(pool_padding) is "VALID", Attr(ceil_mode)'
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" must be False. "
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"Received ceil_mode: True."
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)
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elif padding_algorithm == "SAME":
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input_data_shape = []
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if data_format == "NCDHW":
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input_data_shape = x.shape[2:5]
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elif data_format == "NDHWC":
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input_data_shape = x.shape[1:4]
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paddings = _get_padding_with_SAME(input_data_shape, ksize, strides)
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assert len(paddings) == 3 or len(paddings) == 6
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is_sys = True if len(paddings) == 3 else False
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N = x.shape[0]
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C, D, H, W = (
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[x.shape[1], x.shape[2], x.shape[3], x.shape[4]]
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if data_format == 'NCDHW'
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else [x.shape[4], x.shape[1], x.shape[2], x.shape[3]]
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)
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if global_pool == 1:
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ksize = [D, H, W]
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paddings = [0 for _ in range(len(paddings))]
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pad_d_forth = paddings[0] if is_sys else paddings[0]
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pad_d_back = paddings[0] if is_sys else paddings[1]
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pad_h_up = paddings[1] if is_sys else paddings[2]
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pad_h_down = paddings[1] if is_sys else paddings[3]
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pad_w_left = paddings[2] if is_sys else paddings[4]
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pad_w_right = paddings[2] if is_sys else paddings[5]
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if adaptive:
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D_out, H_out, W_out = ksize
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else:
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D_out = (
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(D - ksize[0] + pad_d_forth + pad_d_back + strides[0] - 1)
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// strides[0]
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+ 1
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if ceil_mode
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else (D - ksize[0] + pad_d_forth + pad_d_back) // strides[0] + 1
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)
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H_out = (
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(H - ksize[1] + pad_h_up + pad_h_down + strides[1] - 1)
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// strides[1]
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+ 1
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if ceil_mode
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else (H - ksize[1] + pad_h_up + pad_h_down) // strides[1] + 1
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)
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W_out = (
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(W - ksize[2] + pad_w_left + pad_w_right + strides[2] - 1)
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// strides[2]
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+ 1
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if ceil_mode
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else (W - ksize[2] + pad_w_left + pad_w_right) // strides[2] + 1
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)
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out = (
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np.zeros((N, C, D_out, H_out, W_out))
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if data_format == 'NCDHW'
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else np.zeros((N, D_out, H_out, W_out, C))
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)
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for k in range(D_out):
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if adaptive:
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d_start = adaptive_start_index(k, D, ksize[0])
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d_end = adaptive_end_index(k, D, ksize[0])
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for i in range(H_out):
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if adaptive:
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h_start = adaptive_start_index(i, H, ksize[1])
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h_end = adaptive_end_index(i, H, ksize[1])
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for j in range(W_out):
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if adaptive:
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w_start = adaptive_start_index(j, W, ksize[2])
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w_end = adaptive_end_index(j, W, ksize[2])
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else:
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d_start = k * strides[0] - pad_d_forth
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d_end = np.min(
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(
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k * strides[0] + ksize[0] - pad_d_forth,
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D + pad_d_back,
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)
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)
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h_start = i * strides[1] - pad_h_up
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h_end = np.min(
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(i * strides[1] + ksize[1] - pad_h_up, H + pad_h_down)
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)
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w_start = j * strides[2] - pad_w_left
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w_end = np.min(
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(
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j * strides[2] + ksize[2] - pad_w_left,
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W + pad_w_right,
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)
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)
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field_size = (
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(d_end - d_start)
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* (h_end - h_start)
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* (w_end - w_start)
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)
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w_start = np.max((w_start, 0))
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d_start = np.max((d_start, 0))
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h_start = np.max((h_start, 0))
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w_end = np.min((w_end, W))
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d_end = np.min((d_end, D))
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h_end = np.min((h_end, H))
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if data_format == 'NCDHW':
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x_masked = x[
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:, :, d_start:d_end, h_start:h_end, w_start:w_end
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]
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if pool_type == 'avg':
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if exclusive or adaptive:
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field_size = (
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(d_end - d_start)
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* (h_end - h_start)
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* (w_end - w_start)
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)
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out[:, :, k, i, j] = (
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np.sum(x_masked, axis=(2, 3, 4)) / field_size
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)
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elif pool_type == 'max':
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out[:, :, k, i, j] = np.max(x_masked, axis=(2, 3, 4))
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elif data_format == 'NDHWC':
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x_masked = x[
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:, d_start:d_end, h_start:h_end, w_start:w_end, :
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]
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if pool_type == 'avg':
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if exclusive or adaptive:
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field_size = (
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(d_end - d_start)
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* (h_end - h_start)
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* (w_end - w_start)
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)
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out[:, k, i, j, :] = (
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np.sum(x_masked, axis=(1, 2, 3)) / field_size
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)
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elif pool_type == 'max':
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out[:, k, i, j, :] = np.max(x_masked, axis=(1, 2, 3))
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return out
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def max_pool3D_forward_naive(
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x,
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ksize,
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strides,
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paddings,
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global_pool=0,
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ceil_mode=False,
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exclusive=True,
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adaptive=False,
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):
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out = pool3D_forward_naive(
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x=x,
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ksize=ksize,
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strides=strides,
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paddings=paddings,
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global_pool=global_pool,
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ceil_mode=ceil_mode,
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exclusive=exclusive,
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adaptive=adaptive,
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data_format='NCDHW',
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pool_type="max",
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)
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return out
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def avg_pool3D_forward_naive(
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x,
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ksize,
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strides,
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paddings,
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global_pool=0,
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ceil_mode=False,
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exclusive=True,
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adaptive=False,
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):
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out = pool3D_forward_naive(
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x=x,
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ksize=ksize,
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strides=strides,
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paddings=paddings,
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global_pool=global_pool,
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ceil_mode=ceil_mode,
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exclusive=exclusive,
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adaptive=adaptive,
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data_format='NCDHW',
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pool_type="avg",
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)
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return out
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class XPUTestPool3DOp(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = 'pool3d'
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self.use_dynamic_create_class = False
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class TestPool3D_Op(XPUOpTest):
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def setUp(self):
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self.op_type = "pool3d"
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self.init_kernel_type()
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self.dtype = self.in_type
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self.init_test_case()
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self.padding_algorithm = "EXPLICIT"
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self.init_paddings()
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self.init_global_pool()
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self.init_kernel_type()
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self.init_pool_type()
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self.init_ceil_mode()
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self.init_exclusive()
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self.init_adaptive()
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self.init_data_format()
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self.init_shape()
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paddle.enable_static()
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input = np.random.random(self.shape).astype(self.dtype)
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output = pool3D_forward_naive(
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input,
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self.ksize,
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self.strides,
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self.paddings,
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self.global_pool,
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self.ceil_mode,
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self.exclusive,
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self.adaptive,
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self.data_format,
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self.pool_type,
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self.padding_algorithm,
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).astype(self.dtype)
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self.inputs = {'X': XPUOpTest.np_dtype_to_base_dtype(input)}
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self.attrs = {
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'strides': self.strides,
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'paddings': self.paddings,
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'ksize': self.ksize,
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'pooling_type': self.pool_type,
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'global_pooling': self.global_pool,
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'ceil_mode': self.ceil_mode,
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'data_format': self.data_format,
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'exclusive': self.exclusive,
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'adaptive': self.adaptive,
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"padding_algorithm": self.padding_algorithm,
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}
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self.outputs = {'Out': output}
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def test_check_output(self):
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place = paddle.XPUPlace(0)
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self.check_output_with_place(place)
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def test_check_grad(self):
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if self.dtype == np.float16:
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return
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place = paddle.XPUPlace(0)
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self.check_grad_with_place(place, {'X'}, 'Out')
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def init_data_format(self):
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self.data_format = "NCDHW"
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def init_shape(self):
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self.shape = [1, 3, 5, 6, 5]
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def init_test_case(self):
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self.ksize = [2, 3, 1]
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self.strides = [2, 2, 3]
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def init_paddings(self):
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self.paddings = [0, 0, 0]
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self.padding_algorithm = "EXPLICIT"
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def init_kernel_type(self):
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self.use_cudnn = False
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def init_pool_type(self):
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self.pool_type = "avg"
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def init_global_pool(self):
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self.global_pool = True
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def init_ceil_mode(self):
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self.ceil_mode = False
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def init_exclusive(self):
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self.exclusive = True
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def init_adaptive(self):
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self.adaptive = False
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class TestCase1(TestPool3D_Op):
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def init_shape(self):
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self.shape = [1, 3, 7, 7, 7]
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def init_test_case(self):
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self.ksize = [3, 3, 3]
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self.strides = [1, 1, 1]
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def init_paddings(self):
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self.paddings = [0, 0, 0]
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def init_pool_type(self):
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self.pool_type = "avg"
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def init_global_pool(self):
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self.global_pool = False
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class TestCase2(TestPool3D_Op):
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def init_shape(self):
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self.shape = [1, 3, 6, 7, 7]
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def init_test_case(self):
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self.ksize = [3, 3, 4]
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self.strides = [1, 3, 2]
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def init_paddings(self):
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self.paddings = [1, 1, 1]
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def init_pool_type(self):
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self.pool_type = "avg"
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def init_global_pool(self):
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self.global_pool = False
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class TestCase3(TestPool3D_Op):
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def init_pool_type(self):
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self.pool_type = "max"
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class TestCase4(TestCase1):
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def init_pool_type(self):
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self.pool_type = "max"
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class TestCase5(TestCase2):
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def init_pool_type(self):
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self.pool_type = "max"
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class TestAvgInclude(TestCase2):
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def init_exclusive(self):
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self.exclusive = False
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class TestAvgPoolAdaptive(TestCase1):
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def init_adaptive(self):
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self.adaptive = True
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class TestAvgPoolAdaptiveAsyOutSize(TestCase1):
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def init_adaptive(self):
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self.adaptive = True
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def init_shape(self):
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self.shape = [1, 3, 3, 4, 4]
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def init_test_case(self):
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self.ksize = [2, 2, 3]
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self.strides = [1, 1, 1]
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# -------test pool3d with asymmetric padding------
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class TestPool3D_Op_AsyPadding(TestPool3D_Op):
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def init_test_case(self):
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self.ksize = [3, 4, 3]
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self.strides = [1, 1, 2]
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def init_paddings(self):
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self.paddings = [0, 0, 0, 2, 3, 0]
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def init_shape(self):
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self.shape = [1, 3, 5, 5, 6]
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class TestCase1_AsyPadding(TestCase1):
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def init_test_case(self):
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self.ksize = [3, 3, 4]
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self.strides = [1, 1, 2]
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def init_paddings(self):
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self.paddings = [1, 0, 2, 1, 2, 1]
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def init_shape(self):
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self.shape = [1, 3, 7, 7, 6]
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class TestCase2_AsyPadding(TestCase2):
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def init_test_case(self):
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self.ksize = [3, 3, 3]
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self.strides = [1, 1, 1]
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def init_paddings(self):
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self.paddings = [1, 2, 1, 1, 1, 0]
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def init_shape(self):
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self.shape = [1, 3, 7, 7, 7]
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class TestCase3_AsyPadding(TestCase3):
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def init_test_case(self):
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self.ksize = [3, 3, 3]
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self.strides = [1, 1, 1]
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def init_paddings(self):
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self.paddings = [1, 0, 0, 0, 1, 0]
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def init_shape(self):
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self.shape = [1, 3, 5, 5, 5]
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class TestCase4_AsyPadding(TestCase4):
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def init_test_case(self):
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self.ksize = [3, 3, 3]
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self.strides = [1, 1, 1]
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def init_paddings(self):
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self.paddings = [1, 0, 2, 1, 2, 1]
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def init_shape(self):
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self.shape = [1, 3, 7, 7, 7]
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class TestCase5_AsyPadding(TestCase5):
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def init_test_case(self):
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self.ksize = [3, 3, 3]
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self.strides = [1, 1, 1]
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def init_paddings(self):
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self.paddings = [1, 2, 1, 1, 1, 0]
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def init_shape(self):
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self.shape = [1, 3, 7, 7, 7]
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class TestAvgInclude_AsyPadding(TestCase2):
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def init_exclusive(self):
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self.exclusive = False
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def init_paddings(self):
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self.paddings = [2, 2, 1, 1, 0, 0]
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class TestAvgPoolAdaptive_AsyPadding(TestCase1):
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def init_adaptive(self):
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self.adaptive = True
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def init_paddings(self):
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self.paddings = [1, 0, 2, 1, 2, 1]
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|
|
|
class TestCase5_Max(TestCase2):
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def init_pool_type(self):
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|
self.pool_type = "max"
|
|
|
|
def test_check_grad(self):
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|
if self.dtype == np.float16:
|
|
return
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|
place = paddle.XPUPlace(0)
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|
self.check_grad_with_place(place, {'X'}, 'Out')
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|
|
|
|
|
support_types = get_xpu_op_support_types('pool3d')
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|
for stype in ["float32"]:
|
|
create_test_class(globals(), XPUTestPool3DOp, stype)
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|