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/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/python/tvm/topi/nn/
H A Dpooling.py60 data, kernel, stride, padding, pool_type, ceil_mode=False, layout="NCHW", count_include_pad=True argument
115 count_include_pad,
128 count_include_pad=True, argument
187 count_include_pad,
238 data, kernel, stride, padding, pool_type, ceil_mode=False, layout="NCW", count_include_pad=True argument
301 count_include_pad,
313 count_include_pad=True, argument
368 count_include_pad,
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/src/operator/nn/
H A Dpooling-inl.h54 dmlc::optional<bool> count_include_pad; member
90 DMLC_DECLARE_FIELD(count_include_pad).set_default(dmlc::optional<bool>()) in DMLC_DECLARE_PARAMETER()
117 this->count_include_pad == other.count_include_pad &&
158 ret = dmlc::HashCombine(ret, val.count_include_pad);
210 const bool count_include_pad = (param_.count_include_pad.has_value()) ?
211 param_.count_include_pad.value() : true;
218 param_.pool_type, req, out_data.dptr<DType>(), count_include_pad, layout);
225 param_.pool_type, req, out_data.dptr<DType>(), count_include_pad, layout);
232 param_.pool_type, req, out_data.dptr<DType>(), count_include_pad, layout);
268 const bool count_include_pad = (param_.count_include_pad.has_value()) ?
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H A Dpool.cuh222 if (get_avg && !count_include_pad) { in pool_sum_1d_gpu_kernel()
267 if (get_avg && !count_include_pad) { in pool_sum_2d_gpu_kernel()
320 if (get_avg && !count_include_pad) { in pool_sum_3d_gpu_kernel()
550 if (is_avg && !count_include_pad) { in unpool_sum_1d_gpu_kernel()
608 if (is_avg && !count_include_pad) { in unpool_sum_2d_gpu_kernel()
678 if (is_avg && !count_include_pad) { in unpool_sum_3d_gpu_kernel()
734 true, count_include_pad); in pool()
769 true, count_include_pad); in pool()
918 const bool count_include_pad) { in unpool() argument
978 true, count_include_pad); in unpool()
[all …]
/dports/misc/mxnet/incubator-mxnet-1.9.0/src/operator/nn/
H A Dpooling-inl.h54 dmlc::optional<bool> count_include_pad; member
90 DMLC_DECLARE_FIELD(count_include_pad).set_default(dmlc::optional<bool>()) in DMLC_DECLARE_PARAMETER()
117 this->count_include_pad == other.count_include_pad &&
158 ret = dmlc::HashCombine(ret, val.count_include_pad);
210 const bool count_include_pad = (param_.count_include_pad.has_value()) ?
211 param_.count_include_pad.value() : true;
218 param_.pool_type, req, out_data.dptr<DType>(), count_include_pad, layout);
225 param_.pool_type, req, out_data.dptr<DType>(), count_include_pad, layout);
232 param_.pool_type, req, out_data.dptr<DType>(), count_include_pad, layout);
268 const bool count_include_pad = (param_.count_include_pad.has_value()) ?
[all …]
H A Dpool.cuh222 if (get_avg && !count_include_pad) { in pool_sum_1d_gpu_kernel()
267 if (get_avg && !count_include_pad) { in pool_sum_2d_gpu_kernel()
320 if (get_avg && !count_include_pad) { in pool_sum_3d_gpu_kernel()
550 if (is_avg && !count_include_pad) { in unpool_sum_1d_gpu_kernel()
608 if (is_avg && !count_include_pad) { in unpool_sum_2d_gpu_kernel()
678 if (is_avg && !count_include_pad) { in unpool_sum_3d_gpu_kernel()
734 true, count_include_pad); in pool()
769 true, count_include_pad); in pool()
918 const bool count_include_pad) { in unpool() argument
978 true, count_include_pad); in unpool()
[all …]
/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/tests/python/contrib/test_arm_compute_lib/
H A Dtest_pooling.py42 shape, dtype, typef, sizes, strides, padding, ceil_mode, count_include_pad, var_names argument
67 count_include_pad=count_include_pad,
80 count_include_pad=count_include_pad,
109 shape, dtype, typef, sizes, strides, padding, ceil_mode, count_include_pad argument
133 node["attrs"]["count_include_pad"] = [["1" if count_include_pad else "0"]]
192 count_include_pad,
202 shape, dtype, typef, size, stride, pad, ceil_mode, count_include_pad, iter(inputs)
213 "count_include_pad": count_include_pad,
304 count_include_pad,
/dports/misc/ncnn/ncnn-20211208/tools/pnnx/tests/
H A Dtest_F_avg_pool1d.py26 … x = F.avg_pool1d(x, kernel_size=3, stride=1, padding=(0), ceil_mode=False, count_include_pad=True)
27 … x = F.avg_pool1d(x, kernel_size=5, stride=2, padding=(2), ceil_mode=True, count_include_pad=False)
28 … x = F.avg_pool1d(x, kernel_size=3, stride=2, padding=1, ceil_mode=False, count_include_pad=True)
29 … x = F.avg_pool1d(x, kernel_size=2, stride=1, padding=0, ceil_mode=True, count_include_pad=True)
30 … x = F.avg_pool1d(x, kernel_size=4, stride=1, padding=2, ceil_mode=False, count_include_pad=False)
H A Dtest_F_avg_pool2d.py26 ….avg_pool2d(x, kernel_size=(1,3), stride=1, padding=(0,1), ceil_mode=False, count_include_pad=True)
27 …_pool2d(x, kernel_size=(4,5), stride=(1,2), padding=(1,2), ceil_mode=True, count_include_pad=False)
28 ….avg_pool2d(x, kernel_size=(5,3), stride=(2,1), padding=1, ceil_mode=False, count_include_pad=True)
29 … x = F.avg_pool2d(x, kernel_size=2, stride=1, padding=0, ceil_mode=True, count_include_pad=True)
30 …_pool2d(x, kernel_size=(5,4), stride=1, padding=2, ceil_mode=False, count_include_pad=False, divis…
H A Dtest_F_avg_pool3d.py26 …_pool3d(x, kernel_size=(1,2,3), stride=1, padding=(0,1,1), ceil_mode=False, count_include_pad=True)
27 …d(x, kernel_size=(3,4,5), stride=(1,2,2), padding=(1,1,2), ceil_mode=True, count_include_pad=False)
28 …_pool3d(x, kernel_size=(5,4,3), stride=(2,1,1), padding=1, ceil_mode=False, count_include_pad=True)
29 … x = F.avg_pool3d(x, kernel_size=2, stride=1, padding=0, ceil_mode=True, count_include_pad=True)
30 …ool3d(x, kernel_size=(5,4,4), stride=1, padding=2, ceil_mode=False, count_include_pad=False, divis…
H A Dtest_nn_AvgPool1d.py25 …ool_2 = nn.AvgPool1d(kernel_size=3, stride=1, padding=(0), ceil_mode=False, count_include_pad=True)
26 …ool_3 = nn.AvgPool1d(kernel_size=5, stride=2, padding=(2), ceil_mode=True, count_include_pad=False)
27 ….pool_4 = nn.AvgPool1d(kernel_size=3, stride=2, padding=1, ceil_mode=False, count_include_pad=True)
28 …f.pool_5 = nn.AvgPool1d(kernel_size=2, stride=1, padding=0, ceil_mode=True, count_include_pad=True)
29 …pool_6 = nn.AvgPool1d(kernel_size=4, stride=1, padding=2, ceil_mode=False, count_include_pad=False)
H A Dtest_nn_AvgPool2d.py25 …= nn.AvgPool2d(kernel_size=(1,3), stride=1, padding=(0,1), ceil_mode=False, count_include_pad=True)
26 ….AvgPool2d(kernel_size=(4,5), stride=(1,2), padding=(1,2), ceil_mode=True, count_include_pad=False)
27 …= nn.AvgPool2d(kernel_size=(5,3), stride=(2,1), padding=1, ceil_mode=False, count_include_pad=True)
28 …f.pool_5 = nn.AvgPool2d(kernel_size=2, stride=1, padding=0, ceil_mode=True, count_include_pad=True)
29 ….AvgPool2d(kernel_size=(5,4), stride=1, padding=2, ceil_mode=False, count_include_pad=False, divis…
H A Dtest_nn_AvgPool3d.py25 ….AvgPool3d(kernel_size=(1,2,3), stride=1, padding=(0,1,1), ceil_mode=False, count_include_pad=True)
26 …ol3d(kernel_size=(3,4,5), stride=(1,2,2), padding=(1,1,2), ceil_mode=True, count_include_pad=False)
27 ….AvgPool3d(kernel_size=(5,4,3), stride=(2,1,1), padding=1, ceil_mode=False, count_include_pad=True)
28 …f.pool_5 = nn.AvgPool3d(kernel_size=2, stride=1, padding=0, ceil_mode=True, count_include_pad=True)
29 …vgPool3d(kernel_size=(5,4,4), stride=1, padding=2, ceil_mode=False, count_include_pad=False, divis…
/dports/misc/ncnn/ncnn-20211208/tools/pnnx/tests/ncnn/
H A Dtest_F_avg_pool1d.py26 … x = F.avg_pool1d(x, kernel_size=3, stride=1, padding=(0), ceil_mode=False, count_include_pad=True)
27 … x = F.avg_pool1d(x, kernel_size=5, stride=2, padding=(2), ceil_mode=True, count_include_pad=False)
28 … x = F.avg_pool1d(x, kernel_size=3, stride=2, padding=1, ceil_mode=False, count_include_pad=True)
29 … x = F.avg_pool1d(x, kernel_size=2, stride=1, padding=0, ceil_mode=True, count_include_pad=True)
30 … x = F.avg_pool1d(x, kernel_size=4, stride=1, padding=2, ceil_mode=False, count_include_pad=False)
H A Dtest_nn_AvgPool1d.py25 …ool_2 = nn.AvgPool1d(kernel_size=3, stride=1, padding=(0), ceil_mode=False, count_include_pad=True)
26 …ool_3 = nn.AvgPool1d(kernel_size=5, stride=2, padding=(2), ceil_mode=True, count_include_pad=False)
27 ….pool_4 = nn.AvgPool1d(kernel_size=3, stride=2, padding=1, ceil_mode=False, count_include_pad=True)
28 …f.pool_5 = nn.AvgPool1d(kernel_size=2, stride=1, padding=0, ceil_mode=True, count_include_pad=True)
29 …pool_6 = nn.AvgPool1d(kernel_size=4, stride=1, padding=2, ceil_mode=False, count_include_pad=False)
H A Dtest_F_avg_pool3d.py26 …_pool3d(x, kernel_size=(1,2,3), stride=1, padding=(0,1,1), ceil_mode=False, count_include_pad=True)
27 …d(x, kernel_size=(3,4,5), stride=(1,2,2), padding=(1,1,2), ceil_mode=True, count_include_pad=False)
28 …_pool3d(x, kernel_size=(5,4,3), stride=(2,1,1), padding=1, ceil_mode=False, count_include_pad=True)
29 … x = F.avg_pool3d(x, kernel_size=2, stride=1, padding=0, ceil_mode=True, count_include_pad=True)
H A Dtest_F_avg_pool2d.py26 ….avg_pool2d(x, kernel_size=(1,3), stride=1, padding=(0,1), ceil_mode=False, count_include_pad=True)
27 …_pool2d(x, kernel_size=(4,5), stride=(1,2), padding=(1,2), ceil_mode=True, count_include_pad=False)
28 ….avg_pool2d(x, kernel_size=(5,3), stride=(2,1), padding=1, ceil_mode=False, count_include_pad=True)
29 … x = F.avg_pool2d(x, kernel_size=2, stride=1, padding=0, ceil_mode=True, count_include_pad=True)
H A Dtest_nn_AvgPool2d.py25 …= nn.AvgPool2d(kernel_size=(1,3), stride=1, padding=(0,1), ceil_mode=False, count_include_pad=True)
26 ….AvgPool2d(kernel_size=(4,5), stride=(1,2), padding=(1,2), ceil_mode=True, count_include_pad=False)
27 …= nn.AvgPool2d(kernel_size=(5,3), stride=(2,1), padding=1, ceil_mode=False, count_include_pad=True)
28 …f.pool_5 = nn.AvgPool2d(kernel_size=2, stride=1, padding=0, ceil_mode=True, count_include_pad=True)
H A Dtest_nn_AvgPool3d.py25 ….AvgPool3d(kernel_size=(1,2,3), stride=1, padding=(0,1,1), ceil_mode=False, count_include_pad=True)
26 …ol3d(kernel_size=(3,4,5), stride=(1,2,2), padding=(1,1,2), ceil_mode=True, count_include_pad=False)
27 ….AvgPool3d(kernel_size=(5,4,3), stride=(2,1,1), padding=1, ceil_mode=False, count_include_pad=True)
28 …f.pool_5 = nn.AvgPool3d(kernel_size=2, stride=1, padding=0, ceil_mode=True, count_include_pad=True)
/dports/misc/tvm/incubator-tvm-0.6.1/topi/python/topi/nn/
H A Dpooling.py68 count_include_pad=True): argument
115 POOL_TYPE_CODE[pool_type], ceil_mode, layout, count_include_pad)
125 count_include_pad=True): argument
176 ceil_mode, layout, count_include_pad)
/dports/misc/py-tvm/incubator-tvm-0.6.1/topi/python/topi/nn/
H A Dpooling.py68 count_include_pad=True): argument
115 POOL_TYPE_CODE[pool_type], ceil_mode, layout, count_include_pad)
125 count_include_pad=True): argument
176 ceil_mode, layout, count_include_pad)
/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/tests/python/topi/python/
H A Dtest_topi_pooling.py49 def verify_pool(n, ic, ih, kh, sh, padding, pool_type, ceil_mode, count_include_pad=True): argument
65 count_include_pad=count_include_pad,
89 if count_include_pad:
126 n, ic, ih, kh, sh, padding, pool_type, ceil_mode, count_include_pad=True, add_relu=False argument
142 count_include_pad=count_include_pad,
164 count_include_pad=count_include_pad,
179 count_include_pad=count_include_pad,
341 n, ic, ih, kh, sh, padding, pool_type, ceil_mode, count_include_pad=True, layout="NCDHW" argument
359 count_include_pad=count_include_pad,
405 n, ic, iw, kw, sw, padding, pool_type, ceil_mode, count_include_pad=True, layout="NCW" argument
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/dports/misc/tvm/incubator-tvm-0.6.1/topi/tests/python/
H A Dtest_topi_pooling.py27 def verify_pool(n, ic, ih, kh, sh, padding, pool_type, ceil_mode, count_include_pad=True): argument
36 layout="NCHW", count_include_pad=count_include_pad)
59 if count_include_pad:
89 def verify_pool_grad(n, ic, ih, kh, sh, padding, pool_type, ceil_mode, count_include_pad=True, argument
99 layout="NCHW", count_include_pad=count_include_pad)
113 layout="NCHW", count_include_pad=count_include_pad)
122 count_include_pad=count_include_pad)
/dports/misc/py-tvm/incubator-tvm-0.6.1/topi/tests/python/
H A Dtest_topi_pooling.py27 def verify_pool(n, ic, ih, kh, sh, padding, pool_type, ceil_mode, count_include_pad=True): argument
36 layout="NCHW", count_include_pad=count_include_pad)
59 if count_include_pad:
89 def verify_pool_grad(n, ic, ih, kh, sh, padding, pool_type, ceil_mode, count_include_pad=True, argument
99 layout="NCHW", count_include_pad=count_include_pad)
113 layout="NCHW", count_include_pad=count_include_pad)
122 count_include_pad=count_include_pad)
/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/src/relay/op/nn/
H A Dpooling.h51 bool count_include_pad, String op_name) { in MakeAvgPool() argument
58 attrs->count_include_pad = count_include_pad; in MakeAvgPool()
H A Dpooling.cc162 bool count_include_pad = reinterpret_cast<const AvgPool2DAttrs*>(param)->count_include_pad; in Pool2DCompute() local
211 bool count_include_pad) { in __anon3b839d080202() argument
213 count_include_pad, "nn.avg_pool2d"); in __anon3b839d080202()
716 bool count_include_pad = reinterpret_cast<const AvgPool2DAttrs*>(param)->count_include_pad; in Pool2DGradCompute() local
719 count_include_pad)}; in Pool2DGradCompute()
775 bool ceil_mode, bool count_include_pad) { in MakeAvgPool2DGrad() argument
782 attrs->count_include_pad = count_include_pad; in MakeAvgPool2DGrad()
898 bool count_include_pad = reinterpret_cast<const AvgPool1DAttrs*>(param)->count_include_pad; in Pool1DCompute() local
945 bool count_include_pad) { in __anon3b839d080402() argument
1085 bool count_include_pad = reinterpret_cast<const AvgPool3DAttrs*>(param)->count_include_pad; in Pool3DCompute() local
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