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1447 lines
46 KiB
1447 lines
46 KiB
# Copyright (c) 2018 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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from __future__ import print_function
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from __future__ import division
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import unittest
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import numpy as np
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import paddle.fluid.core as core
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from op_test import OpTest
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import paddle.fluid as fluid
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from paddle.fluid import Program, program_guard
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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 max_pool2D_forward_naive(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_type=np.float64):
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N, C, H, W = x.shape
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if global_pool == 1:
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ksize = [H, W]
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if adaptive:
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H_out, W_out = ksize
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else:
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H_out = (H - ksize[0] + 2 * paddings[0] + strides[0] - 1
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) // strides[0] + 1 if ceil_mode else (
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H - ksize[0] + 2 * paddings[0]) // strides[0] + 1
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W_out = (W - ksize[1] + 2 * paddings[1] + strides[1] - 1
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) // strides[1] + 1 if ceil_mode else (
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W - ksize[1] + 2 * paddings[1]) // strides[1] + 1
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out = np.zeros((N, C, H_out, W_out))
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for i in range(H_out):
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for j in range(W_out):
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if adaptive:
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r_start = adaptive_start_index(i, H, ksize[0])
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r_end = adaptive_end_index(i, H, ksize[0])
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c_start = adaptive_start_index(j, W, ksize[1])
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c_end = adaptive_end_index(j, W, ksize[1])
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else:
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r_start = np.max((i * strides[0] - paddings[0], 0))
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r_end = np.min((i * strides[0] + ksize[0] - paddings[0], H))
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c_start = np.max((j * strides[1] - paddings[1], 0))
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c_end = np.min((j * strides[1] + ksize[1] - paddings[1], W))
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x_masked = x[:, :, r_start:r_end, c_start:c_end]
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out[:, :, i, j] = np.max(x_masked, axis=(2, 3))
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return out
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def avg_pool2D_forward_naive(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_type=np.float64):
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N, C, H, W = x.shape
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if global_pool == 1:
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ksize = [H, W]
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if adaptive:
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H_out, W_out = ksize
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else:
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H_out = (H - ksize[0] + 2 * paddings[0] + strides[0] - 1
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) // strides[0] + 1 if ceil_mode else (
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H - ksize[0] + 2 * paddings[0]) // strides[0] + 1
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W_out = (W - ksize[1] + 2 * paddings[1] + strides[1] - 1
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) // strides[1] + 1 if ceil_mode else (
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W - ksize[1] + 2 * paddings[1]) // strides[1] + 1
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out = np.zeros((N, C, H_out, W_out))
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for i in range(H_out):
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for j in range(W_out):
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if adaptive:
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r_start = adaptive_start_index(i, H, ksize[0])
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r_end = adaptive_end_index(i, H, ksize[0])
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c_start = adaptive_start_index(j, W, ksize[1])
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c_end = adaptive_end_index(j, W, ksize[1])
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else:
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r_start = i * strides[0] - paddings[0]
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r_end = i * strides[0] + ksize[0] - paddings[0]
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c_start = j * strides[1] - paddings[1]
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c_end = j * strides[1] + ksize[1] - paddings[1]
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field_size = (r_end - r_start) * (c_end - c_start)
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r_start = np.max((r_start, 0))
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r_end = np.min((r_end, H))
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c_start = np.max((c_start, 0))
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c_end = np.min((c_end, W))
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x_masked = x[:, :, r_start:r_end, c_start:c_end]
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if (exclusive or adaptive):
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field_size = (r_end - r_start) * (c_end - c_start)
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if data_type == np.int8 or data_type == np.uint8:
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out[:, :, i, j] = (np.rint(
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np.sum(x_masked, axis=(2, 3)) /
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field_size)).astype(data_type)
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else:
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out[:, :, i, j] = (np.sum(x_masked, axis=(2, 3)) /
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field_size).astype(data_type)
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return out
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def pool2D_forward_naive(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='NCHW',
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pool_type="max",
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padding_algorithm="EXPLICIT"):
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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(input_shape, pool_size,
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pool_stride):
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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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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("Unknown Attr(padding_algorithm): '%s'. "
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"It can only be 'SAME' or 'VALID'." %
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str(padding_algorithm))
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if padding_algorithm == "VALID":
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paddings = [0, 0, 0, 0]
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if ceil_mode != 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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elif padding_algorithm == "SAME":
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input_data_shape = []
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if data_format == "NCHW":
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input_data_shape = x.shape[2:4]
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elif data_format == "NHWC":
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input_data_shape = x.shape[1:3]
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paddings = _get_padding_with_SAME(input_data_shape, ksize, strides)
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assert len(paddings) == 2 or len(paddings) == 4
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is_sys = True if len(paddings) == 2 else False
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N = x.shape[0]
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C, H, W = [x.shape[1], x.shape[2], x.shape[3]] if data_format == 'NCHW' \
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else [x.shape[3], x.shape[1], x.shape[2]]
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if global_pool == 1:
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ksize = [H, W]
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paddings = [0 for _ in range(len(paddings))]
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pad_h_up = paddings[0] if is_sys else paddings[0]
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pad_h_down = paddings[0] if is_sys else paddings[1]
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pad_w_left = paddings[1] if is_sys else paddings[2]
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pad_w_right = paddings[1] if is_sys else paddings[3]
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if adaptive:
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H_out, W_out = ksize
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else:
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H_out = (H - ksize[0] + pad_h_up + pad_h_down + strides[0] - 1) // strides[0] + 1 \
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if ceil_mode else (H - ksize[0] + pad_h_up + pad_h_down) // strides[0] + 1
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W_out = (W - ksize[1] + pad_w_left + pad_w_right + strides[1] - 1) // strides[1] + 1 \
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if ceil_mode else (W - ksize[1] + pad_w_left + pad_w_right) // strides[1] + 1
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out = np.zeros((N, C, H_out, W_out)) if data_format=='NCHW' \
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else np.zeros((N, H_out, W_out, C))
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for i in range(H_out):
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if adaptive:
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in_h_start = adaptive_start_index(i, H, ksize[0])
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in_h_end = adaptive_end_index(i, H, ksize[0])
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else:
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in_h_start = np.max((i * strides[0] - pad_h_up, 0))
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in_h_end = np.min((i * strides[0] + ksize[0] - pad_h_up, H))
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for j in range(W_out):
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if adaptive:
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in_w_start = adaptive_start_index(j, W, ksize[1])
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in_w_end = adaptive_end_index(j, W, ksize[1])
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else:
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in_h_start = i * strides[0] - pad_h_up
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in_w_start = j * strides[1] - pad_w_left
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in_h_end = i * strides[0] + ksize[0] - pad_h_up
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in_w_end = j * strides[1] + ksize[1] - pad_w_left
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field_size = (in_h_end - in_h_start) * (in_w_end - in_w_start)
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in_h_start = np.max((in_h_start, 0))
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in_w_start = np.max((in_w_start, 0))
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in_h_end = np.min((in_h_end, H))
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in_w_end = np.min((in_w_end, W))
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if data_format == 'NCHW':
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x_masked = x[:, :, in_h_start:in_h_end, in_w_start:in_w_end]
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if pool_type == 'avg':
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if (exclusive or adaptive):
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field_size = (in_h_end - in_h_start) * (
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in_w_end - in_w_start)
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# if (exclusive or adaptive) else (ksize[0] * ksize[1])
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out[:, :, i, j] = np.sum(x_masked, axis=(2, 3)) / field_size
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elif pool_type == 'max':
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out[:, :, i, j] = np.max(x_masked, axis=(2, 3))
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elif data_format == 'NHWC':
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x_masked = x[:, in_h_start:in_h_end, in_w_start:in_w_end, :]
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if pool_type == 'avg':
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if (exclusive or adaptive):
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field_size = (in_h_end - in_h_start) * (
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in_w_end - in_w_start)
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out[:, i, j, :] = np.sum(x_masked, axis=(1, 2)) / field_size
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elif pool_type == 'max':
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out[:, i, j, :] = np.max(x_masked, axis=(1, 2))
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return out
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class TestPool2D_Op(OpTest):
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def setUp(self):
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self.op_type = "pool2d"
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self.use_cudnn = False
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self.init_kernel_type()
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self.use_mkldnn = False
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self.init_data_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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input = np.random.random(self.shape).astype(self.dtype)
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output = pool2D_forward_naive(
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input, self.ksize, self.strides, self.paddings, self.global_pool,
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self.ceil_mode, self.exclusive, self.adaptive, self.data_format,
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self.pool_type, self.padding_algorithm).astype(self.dtype)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_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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'use_cudnn': self.use_cudnn,
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'use_mkldnn': self.use_mkldnn,
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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 has_cudnn(self):
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return core.is_compiled_with_cuda() and self.use_cudnn
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def test_check_output(self):
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# TODO(wangzhongpu): support mkldnn op in dygraph mode
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if self.has_cudnn():
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place = core.CUDAPlace(0)
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self.check_output_with_place(
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place, atol=1e-5, check_dygraph=(self.use_mkldnn == False))
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else:
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self.check_output(check_dygraph=(self.use_mkldnn == False))
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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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# TODO(wangzhongpu): support mkldnn op in dygraph mode
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if self.has_cudnn() and self.pool_type != "max":
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place = core.CUDAPlace(0)
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self.check_grad_with_place(
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place,
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set(['X']),
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'Out',
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max_relative_error=0.07,
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check_dygraph=(self.use_mkldnn == False))
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elif self.pool_type != "max":
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self.check_grad(
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set(['X']),
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'Out',
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max_relative_error=0.07,
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check_dygraph=(self.use_mkldnn == False))
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def init_data_format(self):
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self.data_format = "NCHW"
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def init_shape(self):
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self.shape = [2, 3, 5, 5]
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def init_test_case(self):
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self.ksize = [3, 3]
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self.strides = [1, 1]
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def init_paddings(self):
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self.paddings = [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_data_type(self):
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self.dtype = np.float64
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def init_pool_type(self):
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self.pool_type = "avg"
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self.pool2D_forward_naive = avg_pool2D_forward_naive
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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(TestPool2D_Op):
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def init_test_case(self):
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self.ksize = [3, 3]
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self.strides = [1, 1]
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def init_paddings(self):
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self.paddings = [0, 0]
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def init_pool_type(self):
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self.pool_type = "avg"
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self.pool2D_forward_naive = avg_pool2D_forward_naive
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def init_global_pool(self):
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self.global_pool = False
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def init_shape(self):
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self.shape = [2, 3, 7, 7]
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class TestCase2(TestPool2D_Op):
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def init_test_case(self):
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self.ksize = [3, 3]
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self.strides = [1, 1]
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def init_paddings(self):
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self.paddings = [1, 1]
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def init_pool_type(self):
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self.pool_type = "avg"
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self.pool2D_forward_naive = avg_pool2D_forward_naive
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def init_global_pool(self):
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self.global_pool = False
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def init_shape(self):
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self.shape = [2, 3, 7, 7]
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class TestCase3(TestPool2D_Op):
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def init_pool_type(self):
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self.pool_type = "max"
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self.pool2D_forward_naive = max_pool2D_forward_naive
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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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self.pool2D_forward_naive = max_pool2D_forward_naive
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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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self.pool2D_forward_naive = max_pool2D_forward_naive
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#--------------------test pool2d cudnn--------------------
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def create_test_cudnn_class(parent):
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestCUDNNCase(parent):
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def init_kernel_type(self):
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self.use_cudnn = True
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cls_name = "{0}_{1}".format(parent.__name__, "CUDNNOp")
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TestCUDNNCase.__name__ = cls_name
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globals()[cls_name] = TestCUDNNCase
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create_test_cudnn_class(TestPool2D_Op)
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create_test_cudnn_class(TestCase1)
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create_test_cudnn_class(TestCase2)
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create_test_cudnn_class(TestCase3)
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create_test_cudnn_class(TestCase4)
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create_test_cudnn_class(TestCase5)
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#--------------------test pool2d cudnn_fp16--------------------
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def create_test_cudnn_fp16_class(parent, check_grad=True):
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestCUDNNFp16Case(parent):
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def init_kernel_type(self):
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self.use_cudnn = True
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self.dtype = np.float16
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def test_check_output(self):
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# TODO(wangzhongpu): support mkldnn op in dygraph mode
|
|
if core.is_compiled_with_cuda():
|
|
place = core.CUDAPlace(0)
|
|
if core.is_float16_supported(place):
|
|
self.check_output_with_place(
|
|
place,
|
|
atol=1e-3,
|
|
check_dygraph=(self.use_mkldnn == False))
|
|
|
|
def test_check_grad(self):
|
|
# TODO(wangzhongpu): support mkldnn op in dygraph mode
|
|
place = core.CUDAPlace(0)
|
|
if core.is_float16_supported(
|
|
place) and self.pool_type != "max" and check_grad:
|
|
self.check_grad_with_place(
|
|
place,
|
|
set(['X']),
|
|
'Out',
|
|
max_relative_error=0.07,
|
|
check_dygraph=(self.use_mkldnn == False))
|
|
|
|
cls_name = "{0}_{1}".format(parent.__name__, "CUDNNFp16Op")
|
|
TestCUDNNFp16Case.__name__ = cls_name
|
|
globals()[cls_name] = TestCUDNNFp16Case
|
|
|
|
|
|
def create_test_fp16_class(parent, check_grad=True):
|
|
@unittest.skipIf(not core.is_compiled_with_cuda(),
|
|
"core is not compiled with CUDA")
|
|
class TestFp16Case(parent):
|
|
def init_kernel_type(self):
|
|
self.use_cudnn = False
|
|
self.dtype = np.float16
|
|
|
|
def test_check_output(self):
|
|
# TODO(wangzhongpu): support mkldnn op in dygraph mode
|
|
if core.is_compiled_with_cuda():
|
|
place = core.CUDAPlace(0)
|
|
if core.is_float16_supported(place):
|
|
self.check_output_with_place(
|
|
place,
|
|
atol=1e-3,
|
|
check_dygraph=(self.use_mkldnn == False))
|
|
|
|
def test_check_grad(self):
|
|
# TODO(wangzhongpu): support mkldnn op in dygraph mode
|
|
place = core.CUDAPlace(0)
|
|
if core.is_float16_supported(
|
|
place) and self.pool_type != "max" and check_grad:
|
|
self.check_grad_with_place(
|
|
place,
|
|
set(['X']),
|
|
'Out',
|
|
max_relative_error=0.07,
|
|
check_dygraph=(self.use_mkldnn == False))
|
|
|
|
cls_name = "{0}_{1}".format(parent.__name__, "Fp16Op")
|
|
TestFp16Case.__name__ = cls_name
|
|
globals()[cls_name] = TestFp16Case
|
|
|
|
|
|
create_test_cudnn_fp16_class(TestPool2D_Op)
|
|
create_test_cudnn_fp16_class(TestCase1, check_grad=False)
|
|
create_test_cudnn_fp16_class(TestCase2)
|
|
create_test_cudnn_fp16_class(TestCase3)
|
|
create_test_cudnn_fp16_class(TestCase4)
|
|
create_test_cudnn_fp16_class(TestCase5)
|
|
|
|
create_test_fp16_class(TestPool2D_Op)
|
|
create_test_fp16_class(TestCase1, check_grad=False)
|
|
create_test_fp16_class(TestCase2)
|
|
create_test_fp16_class(TestCase3)
|
|
create_test_fp16_class(TestCase4)
|
|
create_test_fp16_class(TestCase5)
|
|
|
|
#--------------------test pool2d use ceil mode--------------------
|
|
|
|
|
|
def create_test_cudnn_use_ceil_class(parent):
|
|
@unittest.skipIf(not core.is_compiled_with_cuda(),
|
|
"core is not compiled with CUDA")
|
|
class TestPool2DUseCeilCase(parent):
|
|
def init_kernel_type(self):
|
|
self.use_cudnn = True
|
|
|
|
def init_ceil_mode(self):
|
|
self.ceil_mode = True
|
|
|
|
cls_name = "{0}_{1}".format(parent.__name__, "CUDNNOpCeilMode")
|
|
TestPool2DUseCeilCase.__name__ = cls_name
|
|
globals()[cls_name] = TestPool2DUseCeilCase
|
|
|
|
|
|
create_test_cudnn_use_ceil_class(TestPool2D_Op)
|
|
create_test_cudnn_use_ceil_class(TestCase1)
|
|
|
|
|
|
def create_test_use_ceil_class(parent):
|
|
class TestPool2DUseCeilCase(parent):
|
|
def init_ceil_mode(self):
|
|
self.ceil_mode = True
|
|
|
|
cls_name = "{0}_{1}".format(parent.__name__, "CeilModeCast")
|
|
TestPool2DUseCeilCase.__name__ = cls_name
|
|
globals()[cls_name] = TestPool2DUseCeilCase
|
|
|
|
|
|
create_test_use_ceil_class(TestCase1)
|
|
create_test_use_ceil_class(TestCase2)
|
|
|
|
|
|
class TestAvgInclude(TestCase2):
|
|
def init_exclusive(self):
|
|
self.exclusive = False
|
|
|
|
|
|
class TestCUDNNAvgInclude(TestCase2):
|
|
def init_kernel_type(self):
|
|
self.use_cudnn = True
|
|
|
|
def init_exclusive(self):
|
|
self.exclusive = False
|
|
|
|
|
|
class TestAvgPoolAdaptive(TestCase1):
|
|
def init_adaptive(self):
|
|
self.adaptive = True
|
|
|
|
|
|
class TestAvgPoolAdaptiveAsyOutSize(TestCase1):
|
|
def init_adaptive(self):
|
|
self.adaptive = True
|
|
|
|
def init_shape(self):
|
|
self.shape = [8, 3, 6, 6]
|
|
|
|
def init_test_case(self):
|
|
self.ksize = [2, 3]
|
|
self.strides = [1, 1]
|
|
self.paddings = [0, 0, 0, 0]
|
|
|
|
|
|
#-------test pool2d with asymmetric padding-----
|
|
|
|
|
|
class TestPool2D_AsyPadding(TestPool2D_Op):
|
|
def init_test_case(self):
|
|
self.ksize = [3, 3]
|
|
self.strides = [1, 1]
|
|
self.paddings = [1, 0, 1, 2]
|
|
|
|
def init_shape(self):
|
|
self.shape = [2, 3, 5, 5]
|
|
|
|
|
|
class TestCase1_AsyPadding(TestCase1):
|
|
def init_test_case(self):
|
|
self.ksize = [3, 3]
|
|
self.strides = [1, 1]
|
|
self.paddings = [1, 0, 1, 0]
|
|
|
|
def init_shape(self):
|
|
self.shape = [2, 3, 7, 7]
|
|
|
|
|
|
class TestCase2_AsyPadding(TestCase2):
|
|
def init_test_case(self):
|
|
self.ksize = [3, 3]
|
|
self.strides = [1, 1]
|
|
self.paddings = [1, 2, 1, 2]
|
|
|
|
def init_shape(self):
|
|
self.shape = [2, 3, 7, 7]
|
|
|
|
|
|
class TestCase3_AsyPadding(TestCase3):
|
|
def init_test_case(self):
|
|
self.ksize = [3, 3]
|
|
self.strides = [1, 1]
|
|
self.paddings = [1, 0, 1, 2]
|
|
|
|
def init_shape(self):
|
|
self.shape = [2, 3, 5, 5]
|
|
|
|
|
|
class TestCase4_AsyPadding(TestCase4):
|
|
def init_test_case(self):
|
|
self.ksize = [3, 3]
|
|
self.strides = [1, 1]
|
|
self.paddings = [1, 0, 1, 0]
|
|
|
|
def init_shape(self):
|
|
self.shape = [2, 3, 7, 7]
|
|
|
|
|
|
class TestCase5_AsyPadding((TestCase5)):
|
|
def init_test_case(self):
|
|
self.ksize = [3, 3]
|
|
self.strides = [1, 1]
|
|
self.paddings = [2, 2, 1, 2]
|
|
|
|
def init_shape(self):
|
|
self.shape = [2, 3, 7, 7]
|
|
|
|
|
|
create_test_cudnn_class(TestPool2D_AsyPadding)
|
|
create_test_cudnn_class(TestCase1_AsyPadding)
|
|
create_test_cudnn_class(TestCase2_AsyPadding)
|
|
create_test_cudnn_class(TestCase3_AsyPadding)
|
|
create_test_cudnn_class(TestCase4_AsyPadding)
|
|
create_test_cudnn_class(TestCase5_AsyPadding)
|
|
|
|
create_test_cudnn_fp16_class(TestPool2D_AsyPadding)
|
|
create_test_cudnn_fp16_class(TestCase1_AsyPadding, check_grad=False)
|
|
create_test_cudnn_fp16_class(TestCase2_AsyPadding)
|
|
create_test_cudnn_fp16_class(TestCase3_AsyPadding)
|
|
create_test_cudnn_fp16_class(TestCase4_AsyPadding)
|
|
create_test_cudnn_fp16_class(TestCase5_AsyPadding)
|
|
|
|
create_test_cudnn_use_ceil_class(TestPool2D_AsyPadding)
|
|
create_test_cudnn_use_ceil_class(TestCase1_AsyPadding)
|
|
|
|
create_test_use_ceil_class(TestCase1_AsyPadding)
|
|
create_test_use_ceil_class(TestCase2_AsyPadding)
|
|
|
|
|
|
class TestAvgInclude_AsyPadding(TestCase2):
|
|
def init_exclusive(self):
|
|
self.exclusive = False
|
|
|
|
def init_test_case(self):
|
|
self.ksize = [3, 3]
|
|
self.strides = [1, 1]
|
|
self.paddings = [1, 2, 1, 2]
|
|
|
|
def init_shape(self):
|
|
self.shape = [2, 3, 7, 7]
|
|
|
|
|
|
class TestCUDNNAvgInclude_AsyPadding(TestCase2):
|
|
def init_kernel_type(self):
|
|
self.use_cudnn = True
|
|
|
|
def init_exclusive(self):
|
|
self.exclusive = False
|
|
|
|
def init_test_case(self):
|
|
self.ksize = [3, 3]
|
|
self.strides = [1, 1]
|
|
self.paddings = [2, 1, 1, 1]
|
|
|
|
def init_shape(self):
|
|
self.shape = [2, 3, 7, 7]
|
|
|
|
|
|
class TestAvgPoolAdaptive_AsyPadding(TestCase1):
|
|
def init_adaptive(self):
|
|
self.adaptive = True
|
|
|
|
def init_test_case(self):
|
|
self.ksize = [3, 3]
|
|
self.strides = [1, 1]
|
|
self.paddings = [1, 1, 0, 2]
|
|
|
|
def init_shape(self):
|
|
self.shape = [2, 3, 7, 7]
|
|
|
|
|
|
#----------- test channel_last --------------
|
|
class TestPool2D_channel_last(TestPool2D_Op):
|
|
def init_data_format(self):
|
|
self.data_format = "NHWC"
|
|
|
|
def init_shape(self):
|
|
self.shape = [2, 5, 5, 3]
|
|
|
|
|
|
class TestCase1_channel_last(TestCase1):
|
|
def init_data_format(self):
|
|
self.data_format = "NHWC"
|
|
|
|
def init_shape(self):
|
|
self.shape = [2, 7, 7, 3]
|
|
|
|
|
|
class TestCase2_channel_last(TestCase2):
|
|
def init_data_format(self):
|
|
self.data_format = "NHWC"
|
|
|
|
def init_shape(self):
|
|
self.shape = [2, 7, 7, 3]
|
|
|
|
|
|
class TestCase3_channel_last(TestCase3):
|
|
def init_data_format(self):
|
|
self.data_format = "NHWC"
|
|
|
|
def init_shape(self):
|
|
self.shape = [2, 5, 5, 3]
|
|
|
|
|
|
class TestCase4_channel_last(TestCase4):
|
|
def init_data_format(self):
|
|
self.data_format = "NHWC"
|
|
|
|
def init_shape(self):
|
|
self.shape = [2, 7, 7, 3]
|
|
|
|
|
|
class TestCase5_channel_last(TestCase5):
|
|
def init_data_format(self):
|
|
self.data_format = "NHWC"
|
|
|
|
def init_shape(self):
|
|
self.shape = [2, 7, 7, 3]
|
|
|
|
|
|
create_test_cudnn_class(TestPool2D_channel_last)
|
|
create_test_cudnn_class(TestCase1_channel_last)
|
|
create_test_cudnn_class(TestCase2_channel_last)
|
|
create_test_cudnn_class(TestCase3_channel_last)
|
|
create_test_cudnn_class(TestCase4_channel_last)
|
|
create_test_cudnn_class(TestCase5_channel_last)
|
|
|
|
create_test_cudnn_fp16_class(TestPool2D_channel_last)
|
|
create_test_cudnn_fp16_class(TestCase1_channel_last, check_grad=False)
|
|
create_test_cudnn_fp16_class(TestCase2_channel_last)
|
|
create_test_cudnn_fp16_class(TestCase3_channel_last)
|
|
create_test_cudnn_fp16_class(TestCase4_channel_last)
|
|
create_test_cudnn_fp16_class(TestCase5_channel_last)
|
|
|
|
create_test_cudnn_use_ceil_class(TestPool2D_channel_last)
|
|
create_test_cudnn_use_ceil_class(TestCase1_channel_last)
|
|
|
|
create_test_use_ceil_class(TestCase1_channel_last)
|
|
create_test_use_ceil_class(TestCase2_channel_last)
|
|
|
|
|
|
class TestCase5_Max(TestCase2):
|
|
def init_pool_type(self):
|
|
self.pool_type = "max"
|
|
|
|
def test_check_grad(self):
|
|
if self.dtype == np.float16:
|
|
return
|
|
if self.has_cudnn() and self.pool_type == "max":
|
|
place = core.CUDAPlace(0)
|
|
self.check_grad_with_place(
|
|
place, set(['X']), 'Out', max_relative_error=1.00)
|
|
elif self.pool_type == "max":
|
|
self.check_grad(set(['X']), 'Out', max_relative_error=1.00)
|
|
|
|
|
|
class TestCase5_channel_last_Max(TestCase5_Max):
|
|
def init_data_format(self):
|
|
self.data_format = "NHWC"
|
|
|
|
def init_shape(self):
|
|
self.shape = [2, 7, 7, 3]
|
|
|
|
|
|
create_test_cudnn_class(TestCase5_Max)
|
|
create_test_cudnn_class(TestCase5_channel_last_Max)
|
|
|
|
|
|
class TestAvgInclude_channel_last(TestCase2_channel_last):
|
|
def init_exclusive(self):
|
|
self.exclusive = False
|
|
|
|
|
|
class TestCUDNNAvgInclude_channel_last(TestCase2_channel_last):
|
|
def init_kernel_type(self):
|
|
self.use_cudnn = True
|
|
|
|
def init_exclusive(self):
|
|
self.exclusive = False
|
|
|
|
|
|
class TestAvgPoolAdaptive_channel_last(TestCase1_channel_last):
|
|
def init_adaptive(self):
|
|
self.adaptive = True
|
|
|
|
|
|
class TestPool2D_AsyPadding_channel_last(TestPool2D_AsyPadding):
|
|
def init_data_format(self):
|
|
self.data_format = "NHWC"
|
|
|
|
def init_shape(self):
|
|
self.shape = [2, 5, 5, 3]
|
|
|
|
|
|
class TestCase1_AsyPadding_channel_last(TestCase1_AsyPadding):
|
|
def init_data_format(self):
|
|
self.data_format = "NHWC"
|
|
|
|
def init_shape(self):
|
|
self.shape = [2, 7, 7, 3]
|
|
|
|
|
|
class TestCase2_AsyPadding_channel_last(TestCase2_AsyPadding):
|
|
def init_data_format(self):
|
|
self.data_format = "NHWC"
|
|
|
|
def init_shape(self):
|
|
self.shape = [2, 7, 7, 3]
|
|
|
|
|
|
class TestCase3_AsyPadding_channel_last(TestCase3_AsyPadding):
|
|
def init_data_format(self):
|
|
self.data_format = "NHWC"
|
|
|
|
def init_shape(self):
|
|
self.shape = [2, 5, 5, 3]
|
|
|
|
|
|
class TestCase4_AsyPadding_channel_last(TestCase4_AsyPadding):
|
|
def init_data_format(self):
|
|
self.data_format = "NHWC"
|
|
|
|
def init_shape(self):
|
|
self.shape = [2, 7, 7, 3]
|
|
|
|
|
|
class TestCase5_AsyPadding_channel_last(TestCase5_AsyPadding):
|
|
def init_data_format(self):
|
|
self.data_format = "NHWC"
|
|
|
|
def init_shape(self):
|
|
self.shape = [2, 7, 7, 3]
|
|
|
|
|
|
create_test_cudnn_class(TestPool2D_AsyPadding_channel_last)
|
|
create_test_cudnn_class(TestCase1_AsyPadding_channel_last)
|
|
create_test_cudnn_class(TestCase2_AsyPadding_channel_last)
|
|
create_test_cudnn_class(TestCase3_AsyPadding_channel_last)
|
|
create_test_cudnn_class(TestCase4_AsyPadding_channel_last)
|
|
create_test_cudnn_class(TestCase5_AsyPadding_channel_last)
|
|
|
|
create_test_cudnn_fp16_class(TestPool2D_AsyPadding_channel_last)
|
|
create_test_cudnn_fp16_class(
|
|
TestCase1_AsyPadding_channel_last, check_grad=False)
|
|
create_test_cudnn_fp16_class(TestCase2_AsyPadding_channel_last)
|
|
create_test_cudnn_fp16_class(TestCase3_AsyPadding_channel_last)
|
|
create_test_cudnn_fp16_class(TestCase4_AsyPadding_channel_last)
|
|
create_test_cudnn_fp16_class(TestCase5_AsyPadding_channel_last)
|
|
|
|
create_test_cudnn_use_ceil_class(TestPool2D_AsyPadding_channel_last)
|
|
create_test_cudnn_use_ceil_class(TestCase1_AsyPadding_channel_last)
|
|
|
|
create_test_use_ceil_class(TestCase1_AsyPadding_channel_last)
|
|
create_test_use_ceil_class(TestCase2_AsyPadding_channel_last)
|
|
|
|
|
|
class TestAvgInclude_AsyPadding_channel_last(TestAvgInclude_AsyPadding):
|
|
def init_data_format(self):
|
|
self.data_format = "NHWC"
|
|
|
|
def init_shape(self):
|
|
self.shape = [2, 7, 7, 3]
|
|
|
|
|
|
class TestCUDNNAvgInclude_AsyPadding_channel_last(
|
|
TestCUDNNAvgInclude_AsyPadding):
|
|
def init_data_format(self):
|
|
self.data_format = "NHWC"
|
|
|
|
def init_shape(self):
|
|
self.shape = [2, 7, 7, 3]
|
|
|
|
|
|
class TestAvgPoolAdaptive_AsyPadding_channel_last(
|
|
TestAvgPoolAdaptive_AsyPadding):
|
|
def init_data_format(self):
|
|
self.data_format = "NHWC"
|
|
|
|
def init_shape(self):
|
|
self.shape = [2, 7, 7, 3]
|
|
|
|
|
|
# test paddings: SAME VALID
|
|
|
|
|
|
def create_test_padding_SAME_class(parent):
|
|
class TestPaddingSMAECase(parent):
|
|
def init_paddings(self):
|
|
self.paddings = [0, 0]
|
|
self.padding_algorithm = "SAME"
|
|
|
|
cls_name = "{0}_{1}".format(parent.__name__, "PaddingSAMEOp")
|
|
TestPaddingSMAECase.__name__ = cls_name
|
|
globals()[cls_name] = TestPaddingSMAECase
|
|
|
|
|
|
create_test_padding_SAME_class(TestPool2D_Op)
|
|
create_test_padding_SAME_class(TestCase1)
|
|
create_test_padding_SAME_class(TestCase2)
|
|
create_test_padding_SAME_class(TestCase3)
|
|
create_test_padding_SAME_class(TestCase4)
|
|
create_test_padding_SAME_class(TestCase5)
|
|
|
|
create_test_padding_SAME_class(TestPool2D_channel_last)
|
|
create_test_padding_SAME_class(TestCase1_channel_last)
|
|
create_test_padding_SAME_class(TestCase2_channel_last)
|
|
create_test_padding_SAME_class(TestCase3_channel_last)
|
|
create_test_padding_SAME_class(TestCase4_channel_last)
|
|
create_test_padding_SAME_class(TestCase5_channel_last)
|
|
|
|
|
|
def create_test_cudnn_padding_SAME_class(parent):
|
|
@unittest.skipIf(not core.is_compiled_with_cuda(),
|
|
"core is not compiled with CUDA")
|
|
class TestCUDNNPaddingSMAECase(parent):
|
|
def init_kernel_type(self):
|
|
self.use_cudnn = True
|
|
|
|
def init_paddings(self):
|
|
self.paddings = [1, 1]
|
|
self.padding_algorithm = "SAME"
|
|
|
|
cls_name = "{0}_{1}".format(parent.__name__, "CudnnPaddingSAMEOp")
|
|
TestCUDNNPaddingSMAECase.__name__ = cls_name
|
|
globals()[cls_name] = TestCUDNNPaddingSMAECase
|
|
|
|
|
|
create_test_cudnn_padding_SAME_class(TestPool2D_Op)
|
|
create_test_cudnn_padding_SAME_class(TestCase1)
|
|
create_test_cudnn_padding_SAME_class(TestCase2)
|
|
create_test_cudnn_padding_SAME_class(TestCase3)
|
|
create_test_cudnn_padding_SAME_class(TestCase4)
|
|
create_test_cudnn_padding_SAME_class(TestCase5)
|
|
|
|
create_test_cudnn_padding_SAME_class(TestPool2D_channel_last)
|
|
create_test_cudnn_padding_SAME_class(TestCase1_channel_last)
|
|
create_test_cudnn_padding_SAME_class(TestCase2_channel_last)
|
|
create_test_cudnn_padding_SAME_class(TestCase3_channel_last)
|
|
create_test_cudnn_padding_SAME_class(TestCase4_channel_last)
|
|
create_test_cudnn_padding_SAME_class(TestCase5_channel_last)
|
|
|
|
|
|
def create_test_padding_VALID_class(parent):
|
|
class TestPaddingVALIDCase(parent):
|
|
def init_paddings(self):
|
|
self.paddings = [1, 1]
|
|
self.padding_algorithm = "VALID"
|
|
|
|
cls_name = "{0}_{1}".format(parent.__name__, "PaddingVALIDOp")
|
|
TestPaddingVALIDCase.__name__ = cls_name
|
|
globals()[cls_name] = TestPaddingVALIDCase
|
|
|
|
|
|
create_test_padding_VALID_class(TestPool2D_Op)
|
|
create_test_padding_VALID_class(TestCase1)
|
|
create_test_padding_VALID_class(TestCase2)
|
|
create_test_padding_VALID_class(TestCase3)
|
|
create_test_padding_VALID_class(TestCase4)
|
|
create_test_padding_VALID_class(TestCase5)
|
|
|
|
create_test_padding_VALID_class(TestPool2D_channel_last)
|
|
create_test_padding_VALID_class(TestCase1_channel_last)
|
|
create_test_padding_VALID_class(TestCase2_channel_last)
|
|
create_test_padding_VALID_class(TestCase3_channel_last)
|
|
create_test_padding_VALID_class(TestCase4_channel_last)
|
|
create_test_padding_VALID_class(TestCase5_channel_last)
|
|
|
|
|
|
def create_test_cudnn_padding_VALID_class(parent):
|
|
@unittest.skipIf(not core.is_compiled_with_cuda(),
|
|
"core is not compiled with CUDA")
|
|
class TestCUDNNPaddingVALIDCase(parent):
|
|
def init_kernel_type(self):
|
|
self.use_cudnn = True
|
|
|
|
def init_paddings(self):
|
|
self.paddings = [1, 1]
|
|
self.padding_algorithm = "VALID"
|
|
|
|
cls_name = "{0}_{1}".format(parent.__name__, "CudnnPaddingVALIDOp")
|
|
TestCUDNNPaddingVALIDCase.__name__ = cls_name
|
|
globals()[cls_name] = TestCUDNNPaddingVALIDCase
|
|
|
|
|
|
create_test_cudnn_padding_VALID_class(TestPool2D_Op)
|
|
create_test_cudnn_padding_VALID_class(TestCase1)
|
|
create_test_cudnn_padding_VALID_class(TestCase2)
|
|
create_test_cudnn_padding_VALID_class(TestCase3)
|
|
create_test_cudnn_padding_VALID_class(TestCase4)
|
|
create_test_cudnn_padding_VALID_class(TestCase5)
|
|
|
|
create_test_cudnn_padding_VALID_class(TestPool2D_channel_last)
|
|
create_test_cudnn_padding_VALID_class(TestCase1_channel_last)
|
|
create_test_cudnn_padding_VALID_class(TestCase2_channel_last)
|
|
create_test_cudnn_padding_VALID_class(TestCase3_channel_last)
|
|
create_test_cudnn_padding_VALID_class(TestCase4_channel_last)
|
|
create_test_cudnn_padding_VALID_class(TestCase5_channel_last)
|
|
|
|
|
|
class TestCase1_strides(TestCase1):
|
|
def init_test_case(self):
|
|
self.ksize = [3, 3]
|
|
self.strides = [1, 2]
|
|
|
|
def init_shape(self):
|
|
self.shape = [2, 3, 4, 5]
|
|
|
|
|
|
create_test_cudnn_class(TestCase1_strides)
|
|
create_test_padding_SAME_class(TestCase1_strides)
|
|
create_test_cudnn_padding_SAME_class(TestCase1_strides)
|
|
|
|
|
|
# ----- test API
|
|
class TestPool2DAPI(unittest.TestCase):
|
|
def test_api(self):
|
|
x_NHWC = np.random.random([2, 5, 5, 3]).astype("float32")
|
|
x_NCHW = np.random.random([2, 3, 5, 5]).astype("float32")
|
|
|
|
input_NHWC = fluid.layers.data(
|
|
name="input_NHWC",
|
|
shape=[2, 5, 5, 3],
|
|
append_batch_size=False,
|
|
dtype="float32")
|
|
|
|
input_NCHW = fluid.layers.data(
|
|
name="input_NCHW",
|
|
shape=[2, 3, 5, 5],
|
|
append_batch_size=False,
|
|
dtype="float32")
|
|
|
|
input_NHWC_negetive = fluid.layers.data(
|
|
name="input_NHWC_negetive",
|
|
shape=[2, -1, 5, 3],
|
|
append_batch_size=False,
|
|
dtype="float32")
|
|
|
|
input_NCHW_negetive = fluid.layers.data(
|
|
name="input_NCHW_negetive",
|
|
shape=[2, 3, -1, -1],
|
|
append_batch_size=False,
|
|
dtype="float32")
|
|
|
|
ksize = [3, 3]
|
|
out_1 = fluid.layers.pool2d(
|
|
input=input_NHWC,
|
|
pool_size=ksize,
|
|
pool_type="max",
|
|
pool_padding=[1, 1],
|
|
use_cudnn=False,
|
|
data_format="NHWC")
|
|
|
|
out_2 = fluid.layers.pool2d(
|
|
input=input_NHWC,
|
|
pool_size=ksize,
|
|
pool_type="avg",
|
|
pool_padding=[[0, 0], [1, 1], [1, 1], [0, 0]],
|
|
use_cudnn=False,
|
|
data_format="NHWC")
|
|
|
|
out_3 = fluid.layers.pool2d(
|
|
input=input_NCHW,
|
|
pool_size=ksize,
|
|
pool_type="avg",
|
|
pool_padding=[[0, 0], [0, 0], [1, 1], [1, 1]],
|
|
use_cudnn=False,
|
|
data_format="NCHW")
|
|
|
|
out_4 = fluid.layers.pool2d(
|
|
input=input_NCHW,
|
|
pool_size=ksize,
|
|
pool_type="avg",
|
|
pool_padding=[1, 2, 1, 0],
|
|
use_cudnn=False,
|
|
data_format="NCHW")
|
|
# test VALID
|
|
out_5 = fluid.layers.pool2d(
|
|
input=input_NCHW,
|
|
pool_size=ksize,
|
|
pool_type="avg",
|
|
pool_padding="VALID",
|
|
use_cudnn=False,
|
|
data_format="NCHW")
|
|
|
|
out_6 = fluid.layers.pool2d(
|
|
input=input_NHWC,
|
|
pool_size=ksize,
|
|
pool_type="max",
|
|
pool_padding="VALID",
|
|
use_cudnn=False,
|
|
data_format="NHWC")
|
|
|
|
# test SAME
|
|
out_7 = fluid.layers.pool2d(
|
|
input=input_NCHW,
|
|
pool_size=[4, 4],
|
|
pool_type="avg",
|
|
pool_padding="SAME",
|
|
use_cudnn=False,
|
|
data_format="NCHW")
|
|
|
|
out_8 = fluid.layers.pool2d(
|
|
input=input_NHWC,
|
|
pool_size=[4, 4],
|
|
pool_type="max",
|
|
pool_padding="SAME",
|
|
use_cudnn=False,
|
|
data_format="NHWC")
|
|
|
|
# test negetive
|
|
out_9 = fluid.layers.pool2d(
|
|
input=input_NHWC_negetive,
|
|
pool_size=ksize,
|
|
pool_type="avg",
|
|
pool_padding=[0, 0],
|
|
use_cudnn=False,
|
|
data_format="NHWC")
|
|
assert out_9.shape == (2, -1, 3, 3)
|
|
|
|
out_10 = fluid.layers.pool2d(
|
|
input=input_NCHW_negetive,
|
|
pool_size=ksize,
|
|
pool_type="avg",
|
|
pool_padding=[0, 0],
|
|
use_cudnn=False,
|
|
data_format="NCHW")
|
|
assert out_10.shape == (2, 3, -1, -1)
|
|
|
|
exe = fluid.Executor(place=fluid.CPUPlace())
|
|
[res_1, res_2, res_3, res_4, res_5, res_6, res_7, res_8] = exe.run(
|
|
fluid.default_main_program(),
|
|
feed={
|
|
"input_NHWC": x_NHWC,
|
|
"input_NCHW": x_NCHW,
|
|
"input_NHWC_negetive": x_NHWC,
|
|
"input_NCHW_negetive": x_NCHW
|
|
},
|
|
fetch_list=[
|
|
out_1, out_2, out_3, out_4, out_5, out_6, out_7, out_8
|
|
])
|
|
|
|
assert np.allclose(
|
|
res_1,
|
|
pool2D_forward_naive(
|
|
x=x_NHWC,
|
|
ksize=ksize,
|
|
pool_type="max",
|
|
strides=[1, 1],
|
|
paddings=[1, 1],
|
|
data_format="NHWC"))
|
|
|
|
assert np.allclose(
|
|
res_2,
|
|
pool2D_forward_naive(
|
|
x=x_NHWC,
|
|
ksize=ksize,
|
|
pool_type="avg",
|
|
strides=[1, 1],
|
|
paddings=[1, 1, 1, 1],
|
|
data_format="NHWC"))
|
|
assert np.allclose(
|
|
res_3,
|
|
pool2D_forward_naive(
|
|
x=x_NCHW,
|
|
ksize=ksize,
|
|
pool_type="avg",
|
|
strides=[1, 1],
|
|
paddings=[1, 1, 1, 1],
|
|
data_format="NCHW"),
|
|
rtol=0.07,
|
|
atol=1e-05)
|
|
|
|
assert np.allclose(
|
|
res_4,
|
|
pool2D_forward_naive(
|
|
x=x_NCHW,
|
|
ksize=ksize,
|
|
pool_type="avg",
|
|
strides=[1, 1],
|
|
paddings=[1, 2, 1, 0],
|
|
data_format="NCHW"),
|
|
rtol=0.07,
|
|
atol=1e-05)
|
|
|
|
# VALID
|
|
assert np.allclose(
|
|
res_5,
|
|
pool2D_forward_naive(
|
|
x=x_NCHW,
|
|
ksize=ksize,
|
|
pool_type="avg",
|
|
strides=[1, 1],
|
|
paddings=[10, 20], # any ele is ok
|
|
padding_algorithm="VALID",
|
|
data_format="NCHW"),
|
|
rtol=0.07,
|
|
atol=1e-05)
|
|
assert np.allclose(
|
|
res_6,
|
|
pool2D_forward_naive(
|
|
x=x_NHWC,
|
|
ksize=ksize,
|
|
pool_type="max",
|
|
strides=[1, 1],
|
|
paddings=[10, 20],
|
|
padding_algorithm="VALID",
|
|
data_format="NHWC"))
|
|
# SAME
|
|
assert np.allclose(
|
|
res_7,
|
|
pool2D_forward_naive(
|
|
x=x_NCHW,
|
|
ksize=[4, 4],
|
|
pool_type="avg",
|
|
strides=[1, 1],
|
|
paddings=[10, 20],
|
|
padding_algorithm="SAME",
|
|
data_format="NCHW"),
|
|
rtol=0.07,
|
|
atol=1e-05)
|
|
|
|
assert np.allclose(
|
|
res_8,
|
|
pool2D_forward_naive(
|
|
x=x_NHWC,
|
|
ksize=[4, 4],
|
|
pool_type="max",
|
|
strides=[1, 1],
|
|
paddings=[10, 20],
|
|
padding_algorithm="SAME",
|
|
data_format="NHWC"))
|
|
|
|
|
|
class TestPool2DAPI_Error(unittest.TestCase):
|
|
def test_api(self):
|
|
input_NHWC = fluid.layers.data(
|
|
name="input_NHWC",
|
|
shape=[2, 5, 5, 3],
|
|
append_batch_size=False,
|
|
dtype="float32")
|
|
ksize = [3, 3]
|
|
|
|
# cudnn type error
|
|
def run_1():
|
|
out_1 = fluid.layers.pool2d(
|
|
input=input_NHWC,
|
|
pool_size=ksize,
|
|
pool_type="max",
|
|
pool_padding=[1, 1],
|
|
use_cudnn=[0],
|
|
data_format="NHWC")
|
|
|
|
self.assertRaises(TypeError, run_1)
|
|
|
|
# data_format value error
|
|
def run_2():
|
|
out_2 = fluid.layers.pool2d(
|
|
input=input_NHWC,
|
|
pool_size=ksize,
|
|
pool_type="max",
|
|
pool_padding=[1, 1],
|
|
use_cudnn=False,
|
|
data_format="NHWCC")
|
|
|
|
self.assertRaises(ValueError, run_2)
|
|
|
|
# padding str value error
|
|
def run_3():
|
|
out_3 = fluid.layers.pool2d(
|
|
input=input_NHWC,
|
|
pool_size=ksize,
|
|
pool_type="max",
|
|
pool_padding="VALIDSAME",
|
|
use_cudnn=False,
|
|
data_format="NHWC")
|
|
|
|
self.assertRaises(ValueError, run_3)
|
|
|
|
# padding str valid and ceil_mode value error
|
|
def run_4():
|
|
out_4 = fluid.layers.pool2d(
|
|
input=input_NHWC,
|
|
pool_size=ksize,
|
|
pool_type="max",
|
|
pool_padding="VALID",
|
|
use_cudnn=False,
|
|
ceil_mode=True,
|
|
data_format="NHWC")
|
|
|
|
self.assertRaises(ValueError, run_4)
|
|
|
|
# padding with 8 ele. value error
|
|
def run_5():
|
|
out_5 = fluid.layers.pool2d(
|
|
input=input_NHWC,
|
|
pool_size=ksize,
|
|
pool_type="max",
|
|
pool_padding=[[1, 1], [0, 0], [0, 0], [1, 1]],
|
|
use_cudnn=False,
|
|
data_format="NHWC")
|
|
|
|
self.assertRaises(ValueError, run_5)
|
|
|
|
|
|
class TestDygraphPool2DAPIError(unittest.TestCase):
|
|
def test_errors(self):
|
|
with program_guard(Program(), Program()):
|
|
# the input of Pool2D must be Variable.
|
|
data1 = np.random.random((3, 32, 32, 5)).astype('float32')
|
|
pool2d = fluid.dygraph.Pool2D(
|
|
pool_size=2,
|
|
pool_type='max',
|
|
pool_stride=1,
|
|
global_pooling=False)
|
|
self.assertRaises(TypeError, pool2d, data1)
|
|
|
|
# the input dtype of Pool2D must be uint8 or int8 or float16 or float32 or float64
|
|
# uint8 and int8 only can be set on mkldnn
|
|
# float16 only can be set on GPU place
|
|
data2 = fluid.layers.data(
|
|
name='x1', shape=[3, 32, 32, 5], dtype="int32")
|
|
self.assertRaises(TypeError, pool2d, data2)
|
|
|
|
def test_data_format_error(self):
|
|
with program_guard(Program(), Program()):
|
|
# the data_format must be 'NCHW' or 'NHWC'
|
|
data1 = np.random.random((3, 32, 32, 5)).astype('float32')
|
|
self.assertRaises(
|
|
ValueError,
|
|
fluid.dygraph.Pool2D,
|
|
pool_size=2,
|
|
pool_type='max',
|
|
pool_stride=1,
|
|
global_pooling=False,
|
|
data_format='NWHC')
|
|
|
|
|
|
class TestDygraphPool2DAPI(unittest.TestCase):
|
|
def test_nhwc(self):
|
|
with fluid.dygraph.guard():
|
|
data = np.random.random((3, 32, 32, 5)).astype('float32')
|
|
x = fluid.dygraph.to_variable(data)
|
|
pool2d = fluid.dygraph.Pool2D(
|
|
pool_size=2,
|
|
pool_type='max',
|
|
pool_stride=1,
|
|
pool_padding=[0, 0],
|
|
global_pooling=False,
|
|
data_format='NHWC')
|
|
out1 = pool2d(x)
|
|
out2 = pool2D_forward_naive(
|
|
data, [2, 2], [1, 1],
|
|
paddings=[0, 0],
|
|
pool_type='max',
|
|
data_format='NHWC')
|
|
self.assertTrue(np.allclose(out1.numpy(), out2))
|
|
|
|
def test_lower_case(self):
|
|
with fluid.dygraph.guard():
|
|
data = np.random.random((3, 32, 32, 5)).astype('float32')
|
|
x = fluid.dygraph.to_variable(data)
|
|
pool2d = fluid.dygraph.Pool2D(
|
|
pool_size=2,
|
|
pool_type='max',
|
|
pool_stride=1,
|
|
pool_padding=[0, 0],
|
|
global_pooling=False,
|
|
data_format='nhwc')
|
|
out1 = pool2d(x)
|
|
out2 = pool2D_forward_naive(
|
|
data, [2, 2], [1, 1],
|
|
paddings=[0, 0],
|
|
pool_type='max',
|
|
data_format='NHWC')
|
|
self.assertTrue(np.allclose(out1.numpy(), out2))
|
|
|
|
def test_upper_case(self):
|
|
with fluid.dygraph.guard():
|
|
data = np.random.random((3, 32, 32, 5)).astype('float32')
|
|
x = fluid.dygraph.to_variable(data)
|
|
pool2d = fluid.dygraph.Pool2D(
|
|
pool_size=2,
|
|
pool_type='MAX',
|
|
pool_stride=1,
|
|
pool_padding=[0, 0],
|
|
global_pooling=False,
|
|
data_format='nhwc')
|
|
out1 = pool2d(x)
|
|
out2 = pool2D_forward_naive(
|
|
data, [2, 2], [1, 1],
|
|
paddings=[0, 0],
|
|
pool_type='max',
|
|
data_format='NHWC')
|
|
self.assertTrue(np.allclose(out1.numpy(), out2))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|