wehub-resource-sync
560 lines
18 KiB
Python
560 lines
18 KiB
Python
# Copyright (c) 2020 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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from paddle.base import core
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def conv2d_forward_naive(
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input,
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filter,
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group,
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conv_param,
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padding_algorithm='EXPLICIT',
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data_format='NCHW',
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):
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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 data_format not in ["NCHW", "NHWC"]:
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raise ValueError(
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f"Unknown Attr(data_format): '{data_format}' ."
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"It can only be 'NCHW' or 'NHWC'."
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)
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channel_last = data_format == "NHWC"
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if channel_last:
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input = np.transpose(input, [0, 3, 1, 2])
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in_n, in_c, in_h, in_w = input.shape
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f_n, f_c, f_h, f_w = filter.shape
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out_n = in_n
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out_c = f_n
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assert f_c * group == in_c
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assert np.mod(out_c, group) == 0
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sub_out_c = out_c // group
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sub_f_n = f_n // group
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stride, pad, dilation = (
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conv_param['stride'],
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conv_param['pad'],
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conv_param['dilation'],
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)
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# update pad and dilation
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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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ksize = filter.shape[2:4]
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if padding_algorithm == "VALID":
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pad = [0, 0, 0, 0]
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elif padding_algorithm == "SAME":
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dilation = [1, 1]
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input_data_shape = input.shape[2:4]
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pad = _get_padding_with_SAME(input_data_shape, ksize, stride)
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pad_h_0, pad_h_1 = pad[0], pad[0]
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pad_w_0, pad_w_1 = pad[1], pad[1]
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if len(pad) == 4:
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pad_h_0, pad_h_1 = pad[0], pad[1]
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pad_w_0, pad_w_1 = pad[2], pad[3]
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out_h = (
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1
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+ (in_h + pad_h_0 + pad_h_1 - (dilation[0] * (f_h - 1) + 1))
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// stride[0]
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)
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out_w = (
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1
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+ (in_w + pad_w_0 + pad_w_1 - (dilation[1] * (f_w - 1) + 1))
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// stride[1]
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)
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out = np.zeros((out_n, out_c, out_h, out_w))
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d_block_h = dilation[0] * (f_h - 1) + 1
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d_block_w = dilation[1] * (f_w - 1) + 1
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input_pad = np.pad(
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input,
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((0, 0), (0, 0), (pad_h_0, pad_h_1), (pad_w_0, pad_w_1)),
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mode='constant',
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constant_values=0,
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)
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filter_dilation = np.zeros((f_n, f_c, d_block_h, d_block_w))
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filter_dilation[
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:, :, 0 : d_block_h : dilation[0], 0 : d_block_w : dilation[1]
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] = filter
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for i in range(out_h):
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for j in range(out_w):
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for g in range(group):
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input_pad_masked = input_pad[
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:,
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g * f_c : (g + 1) * f_c,
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i * stride[0] : i * stride[0] + d_block_h,
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j * stride[1] : j * stride[1] + d_block_w,
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]
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f_sub = filter_dilation[
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g * sub_f_n : (g + 1) * sub_f_n, :, :, :
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]
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# sub_f_n == sub_out_c
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for k in range(sub_out_c):
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# Multiplication of Corresponding Elements, then sum all
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out[:, g * sub_out_c + k, i, j] = np.sum(
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input_pad_masked * f_sub[k, :, :, :], axis=(1, 2, 3)
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)
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if channel_last:
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out = np.transpose(out, [0, 2, 3, 1])
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return out, in_n, out_h, out_w, out_c
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def create_test_channel_last_class(parent):
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class TestChannelLastCase(parent):
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def init_data_format(self):
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self.data_format = "NHWC"
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def init_test_case_2(self):
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N, C, H, W = self.input_size
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self.input_size = [N, H, W, C]
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cls_name = "{}_{}".format(parent.__name__, "ChannelLast")
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TestChannelLastCase.__name__ = cls_name
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globals()[cls_name] = TestChannelLastCase
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def create_test_padding_SAME_class(parent):
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class TestPaddingSAMECase(parent):
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def init_paddings(self):
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self.pad = [0, 0]
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self.padding_algorithm = "SAME"
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cls_name = "{}_{}".format(parent.__name__, "PaddingSAMEOp")
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TestPaddingSAMECase.__name__ = cls_name
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globals()[cls_name] = TestPaddingSAMECase
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def create_test_padding_VALID_class(parent):
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class TestPaddingVALIDCase(parent):
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def init_paddings(self):
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self.pad = [1, 1]
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self.padding_algorithm = "VALID"
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cls_name = "{}_{}".format(parent.__name__, "PaddingVALIDOp")
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TestPaddingVALIDCase.__name__ = cls_name
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globals()[cls_name] = TestPaddingVALIDCase
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class XPUTestConv2DOp(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = 'conv2d'
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self.use_dynamic_create_class = False
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class TestConv2DOp(XPUOpTest):
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def setUp(self):
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self.dtype = self.in_type
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self.place = paddle.XPUPlace(0)
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self.op_type = "conv2d"
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self.use_cudnn = False
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self.exhaustive_search = False
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self.use_cuda = False
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self.use_onednn = False
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self.fuse_relu_before_depthwise_conv = False
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self.data_format = "AnyLayout"
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self.init_kernel_type()
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self.init_group()
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self.init_dilation()
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self.init_test_case()
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conv2d_param = {
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'stride': self.stride,
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'pad': self.pad,
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'dilation': self.dilations,
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}
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np.random.seed(100)
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input = np.random.random(self.input_size).astype(self.dtype)
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if not self.has_cuda():
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self.fuse_relu_before_depthwise_conv = False
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if self.fuse_relu_before_depthwise_conv:
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input = input - 0.5
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input -= (input < 0) * 0.1
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input += (input >= 0) * 0.1
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input2 = np.maximum(input, 0.0)
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else:
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input2 = input
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np.random.seed(1)
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filter = np.random.uniform(-1, 1, self.filter_size).astype(
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self.dtype
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)
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output, _, _, _, _ = conv2d_forward_naive(
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input2, filter, self.groups, conv2d_param
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)
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output = output.astype(self.dtype)
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self.inputs = {
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'Input': XPUOpTest.np_dtype_to_base_dtype(input),
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'Filter': XPUOpTest.np_dtype_to_base_dtype(filter),
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}
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self.attrs = {
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'strides': self.stride,
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'paddings': self.pad,
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'groups': self.groups,
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'dilations': self.dilations,
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'use_cudnn': self.use_cudnn,
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'use_onednn': self.use_onednn,
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'data_format': self.data_format,
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'fuse_relu_before_depthwise_conv': self.fuse_relu_before_depthwise_conv,
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'exhaustive_search': self.exhaustive_search,
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}
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self.outputs = {'Output': output}
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def has_cuda(self):
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return core.is_compiled_with_cuda() and (
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self.use_cudnn or self.use_cuda
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)
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def test_check_output(self):
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if core.is_compiled_with_xpu():
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paddle.enable_static()
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self.check_output_with_place(self.place, atol=0.005, rtol=0.005)
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def test_check_grad(self):
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if hasattr(self, "no_need_check_grad") and self.no_need_check_grad:
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return
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if core.is_compiled_with_xpu():
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paddle.enable_static()
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self.check_grad_with_place(
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self.place, {'Input', 'Filter'}, 'Output'
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)
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def test_check_grad_no_filter(self):
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if hasattr(self, "no_need_check_grad") and self.no_need_check_grad:
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return
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if core.is_compiled_with_xpu():
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paddle.enable_static()
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self.check_grad_with_place(
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self.place, ['Input'], 'Output', no_grad_set={'Filter'}
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)
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def test_check_grad_no_input(self):
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if hasattr(self, "no_need_check_grad") and self.no_need_check_grad:
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return
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if core.is_compiled_with_xpu():
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paddle.enable_static()
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self.check_grad_with_place(
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self.place, ['Filter'], 'Output', no_grad_set={'Input'}
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)
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def init_test_case(self):
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self.pad = [0, 0]
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self.stride = [1, 1]
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self.input_size = [2, 3, 5, 5] # NCHW
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assert np.mod(self.input_size[1], self.groups) == 0
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f_c = self.input_size[1] // self.groups
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self.filter_size = [6, f_c, 3, 3]
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def init_test_case_2(self):
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pass
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def init_dilation(self):
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self.dilations = [1, 1]
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def init_group(self):
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self.groups = 1
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def init_kernel_type(self):
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pass
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class TestWithPad(TestConv2DOp):
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def init_test_case(self):
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self.pad = [1, 1]
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self.stride = [1, 1]
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self.input_size = [2, 3, 5, 5] # NCHW
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assert np.mod(self.input_size[1], self.groups) == 0
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f_c = self.input_size[1] // self.groups
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self.filter_size = [6, f_c, 3, 3]
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class TestWithStride(TestConv2DOp):
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def init_test_case(self):
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self.pad = [1, 1]
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self.stride = [2, 2]
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self.input_size = [2, 3, 6, 6] # NCHW
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assert np.mod(self.input_size[1], self.groups) == 0
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f_c = self.input_size[1] // self.groups
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self.filter_size = [6, f_c, 3, 3]
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class TestWith1x1(TestConv2DOp):
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def init_test_case(self):
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self.pad = [0, 0]
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self.stride = [1, 1]
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self.input_size = [2, 3, 5, 5] # NCHW
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assert np.mod(self.input_size[1], self.groups) == 0
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f_c = self.input_size[1] // self.groups
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self.filter_size = [120, f_c, 1, 1]
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def init_group(self):
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self.groups = 1
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# ---- test asymmetric padding ----
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class XPUTestConv2DOp_v2(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = 'conv2d'
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self.use_dynamic_create_class = False
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class TestConv2DOp_v2(XPUOpTest):
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def setUp(self):
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self.dtype = self.in_type
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self.place = paddle.XPUPlace(0)
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self.op_type = "conv2d"
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self.use_cudnn = False
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self.exhaustive_search = False
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self.use_cuda = False
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self.use_onednn = False
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self.fuse_relu_before_depthwise_conv = False
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self.init_kernel_type()
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self.init_group()
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self.init_dilation()
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self.init_data_format()
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self.init_test_case()
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self.init_paddings()
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self.init_test_case_2()
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conv2d_param = {
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'stride': self.stride,
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'pad': self.pad,
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'dilation': self.dilations,
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}
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np.random.seed(100)
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input = np.random.random(self.input_size).astype(self.dtype)
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if not self.has_cuda():
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self.fuse_relu_before_depthwise_conv = False
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if self.fuse_relu_before_depthwise_conv:
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input = input - 0.5
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input -= (input < 0) * 0.1
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input += (input >= 0) * 0.1
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input2 = np.maximum(input, 0.0)
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else:
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input2 = input
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np.random.seed(8)
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filter = np.random.uniform(-1, 1, self.filter_size).astype(
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self.dtype
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)
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output, _, _, _, _ = conv2d_forward_naive(
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input2,
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filter,
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self.groups,
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conv2d_param,
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self.padding_algorithm,
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self.data_format,
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)
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output = output.astype(self.dtype)
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self.inputs = {
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'Input': XPUOpTest.np_dtype_to_base_dtype(input),
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'Filter': XPUOpTest.np_dtype_to_base_dtype(filter),
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}
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self.attrs = {
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'strides': self.stride,
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'paddings': self.pad,
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'padding_algorithm': self.padding_algorithm,
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'groups': self.groups,
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'dilations': self.dilations,
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'use_cudnn': self.use_cudnn,
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'use_onednn': self.use_onednn,
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'data_format': self.data_format,
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'fuse_relu_before_depthwise_conv': self.fuse_relu_before_depthwise_conv,
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'exhaustive_search': self.exhaustive_search,
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}
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self.outputs = {'Output': output}
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def has_cuda(self):
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return core.is_compiled_with_cuda() and (
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self.use_cudnn or self.use_cuda
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)
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def test_check_output(self):
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# TODO(wangzhongpu): support onednn op in dygraph mode
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if core.is_compiled_with_xpu():
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paddle.enable_static()
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self.check_output_with_place(
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place=self.place, atol=0.005, rtol=0.005
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)
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def test_check_grad(self):
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# TODO(wangzhongpu): support onednn op in dygraph mode
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if hasattr(self, "no_need_check_grad") and self.no_need_check_grad:
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return
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if core.is_compiled_with_xpu():
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paddle.enable_static()
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self.check_grad_with_place(
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self.place, {'Input', 'Filter'}, 'Output'
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)
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def test_check_grad_no_filter(self):
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# TODO(wangzhongpu): support onednn op in dygraph mode
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if hasattr(self, "no_need_check_grad") and self.no_need_check_grad:
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return
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if core.is_compiled_with_xpu():
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paddle.enable_static()
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self.check_grad_with_place(
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self.place, ['Input'], 'Output', no_grad_set={'Filter'}
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)
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def test_check_grad_no_input(self):
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# TODO(wangzhongpu): support onednn op in dygraph mode
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if hasattr(self, "no_need_check_grad") and self.no_need_check_grad:
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return
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if core.is_compiled_with_xpu():
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paddle.enable_static()
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self.check_grad_with_place(
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self.place, ['Filter'], 'Output', no_grad_set={'Input'}
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)
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def init_test_case(self):
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self.pad = [0, 0]
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self.stride = [1, 2]
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self.input_size = [2, 3, 5, 5] # NCHW
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assert np.mod(self.input_size[1], self.groups) == 0
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f_c = self.input_size[1] // self.groups
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self.filter_size = [6, f_c, 4, 3]
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def init_dilation(self):
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self.dilations = [1, 1]
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def init_group(self):
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self.groups = 1
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def init_kernel_type(self):
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pass
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def init_paddings(self):
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self.pad = [0, 0]
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self.padding_algorithm = "EXPLICIT"
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def init_data_format(self):
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self.data_format = "NCHW"
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def init_test_case_2(self):
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pass
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class TestConv2DOp_AsyPadding(TestConv2DOp_v2):
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def init_paddings(self):
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self.pad = [0, 0, 0, 0]
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self.padding_algorithm = "EXPLICIT"
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|
|
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class TestWithPad_AsyPadding(TestConv2DOp_v2):
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def init_test_case(self):
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self.stride = [1, 1]
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self.input_size = [2, 3, 5, 5] # NCHW
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assert np.mod(self.input_size[1], self.groups) == 0
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f_c = self.input_size[1] // self.groups
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self.filter_size = [6, f_c, 3, 3]
|
|
|
|
def init_paddings(self):
|
|
self.pad = [1, 1, 1, 1]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
class TestWithStride_AsyPadding(TestConv2DOp_v2):
|
|
def init_test_case(self):
|
|
self.stride = [2, 2]
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|
self.input_size = [2, 3, 6, 6] # NCHW
|
|
assert np.mod(self.input_size[1], self.groups) == 0
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|
f_c = self.input_size[1] // self.groups
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|
self.filter_size = [6, f_c, 3, 3]
|
|
|
|
def init_paddings(self):
|
|
self.pad = [1, 1, 1, 1]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
|
|
class XPUTestConv2DOp_NHWC(XPUOpTestWrapper):
|
|
def __init__(self):
|
|
self.op_name = 'conv2d'
|
|
self.use_dynamic_create_class = False
|
|
|
|
class TestConv2DOp_AsyPadding_NHWC(
|
|
XPUTestConv2DOp_v2.TestConv2DOp_AsyPadding
|
|
):
|
|
def init_data_format(self):
|
|
self.data_format = "NHWC"
|
|
|
|
def init_test_case_2(self):
|
|
N, C, H, W = self.input_size
|
|
self.input_size = [N, H, W, C]
|
|
|
|
class TestWithPad_AsyPadding_NHWC(
|
|
XPUTestConv2DOp_v2.TestWithPad_AsyPadding
|
|
):
|
|
def init_data_format(self):
|
|
self.data_format = "NHWC"
|
|
|
|
def init_test_case_2(self):
|
|
N, C, H, W = self.input_size
|
|
self.input_size = [N, H, W, C]
|
|
|
|
|
|
support_types = get_xpu_op_support_types('conv2d')
|
|
for stype in ['float32']:
|
|
create_test_class(globals(), XPUTestConv2DOp, stype)
|
|
create_test_class(globals(), XPUTestConv2DOp_v2, stype)
|
|
create_test_class(
|
|
globals(),
|
|
XPUTestConv2DOp_NHWC,
|
|
stype,
|
|
ignore_device_version=[core.XPUVersion.XPU1],
|
|
)
|
|
|
|
# ---------- test SAME VALID -----------
|
|
# create_test_padding_SAME_class(TestConv2DOp_AsyPadding)
|
|
# create_test_padding_SAME_class(TestWithPad_AsyPadding)
|
|
# create_test_padding_SAME_class(TestWithStride_AsyPadding)
|
|
|
|
# create_test_padding_VALID_class(TestConv2DOp_AsyPadding)
|
|
# create_test_padding_VALID_class(TestWithPad_AsyPadding)
|
|
# create_test_padding_VALID_class(TestWithStride_AsyPadding)
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|