/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/python/tvm/topi/nn/ |
H A D | pooling.py | 60 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,
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/dports/misc/py-mxnet/incubator-mxnet-1.9.0/src/operator/nn/ |
H A D | pooling-inl.h | 54 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 …]
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H A D | pool.cuh | 222 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 …]
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/dports/misc/mxnet/incubator-mxnet-1.9.0/src/operator/nn/ |
H A D | pooling-inl.h | 54 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 …]
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H A D | pool.cuh | 222 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 …]
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/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/tests/python/contrib/test_arm_compute_lib/ |
H A D | test_pooling.py | 42 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,
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/dports/misc/ncnn/ncnn-20211208/tools/pnnx/tests/ |
H A D | test_F_avg_pool1d.py | 26 … 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)
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H A D | test_F_avg_pool2d.py | 26 ….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…
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H A D | test_F_avg_pool3d.py | 26 …_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…
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H A D | test_nn_AvgPool1d.py | 25 …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)
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H A D | test_nn_AvgPool2d.py | 25 …= 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…
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H A D | test_nn_AvgPool3d.py | 25 ….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…
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/dports/misc/ncnn/ncnn-20211208/tools/pnnx/tests/ncnn/ |
H A D | test_F_avg_pool1d.py | 26 … 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)
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H A D | test_nn_AvgPool1d.py | 25 …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)
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H A D | test_F_avg_pool3d.py | 26 …_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)
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H A D | test_F_avg_pool2d.py | 26 ….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)
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H A D | test_nn_AvgPool2d.py | 25 …= 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)
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H A D | test_nn_AvgPool3d.py | 25 ….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)
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/dports/misc/tvm/incubator-tvm-0.6.1/topi/python/topi/nn/ |
H A D | pooling.py | 68 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)
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/dports/misc/py-tvm/incubator-tvm-0.6.1/topi/python/topi/nn/ |
H A D | pooling.py | 68 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)
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/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/tests/python/topi/python/ |
H A D | test_topi_pooling.py | 49 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 [all …]
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/dports/misc/tvm/incubator-tvm-0.6.1/topi/tests/python/ |
H A D | test_topi_pooling.py | 27 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)
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/dports/misc/py-tvm/incubator-tvm-0.6.1/topi/tests/python/ |
H A D | test_topi_pooling.py | 27 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)
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/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/src/relay/op/nn/ |
H A D | pooling.h | 51 bool count_include_pad, String op_name) { in MakeAvgPool() argument 58 attrs->count_include_pad = count_include_pad; in MakeAvgPool()
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H A D | pooling.cc | 162 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 [all …]
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