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/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/tests/python/contrib/test_arm_compute_lib/
H A Dtest_dense.py71 weight_shape, argument
192 for dtype, (shape, weight_shape, units), composite in trials:
203 "weight_shape": weight_shape,
222 for dtype, (shape, weight_shape, units), composite in trials:
225 args = (shape, weight_shape, units, dtype)
246 for dtype, (shape, weight_shape, units), composite in trials:
254 input_zp, input_sc, kernel_zp, kernel_sc, weight_shape[0], weight_shape[1]
259 weight_shape,
277 "weight_shape": weight_shape,
304 args = (shape, weight_shape, units, dtype)
[all …]
H A Dtest_conv2d.py60 weight_shape = (kernel_h, kernel_w, shape[3] // groups, channels)
61 w = tvm.nd.array(np.random.uniform(-128, 127, weight_shape).astype(dtype))
78 b = tvm.nd.array(np.random.uniform(-128, 127, weight_shape[3]).astype(dtype))
137 weight_shape = (kernel_h, kernel_w, shape[3] // groups, channels)
138 w = tvm.nd.array(np.random.uniform(0, 255, weight_shape).astype(dtype))
159 b = tvm.nd.array(np.random.uniform(0, 255, weight_shape[3]).astype("int32"))
191 weight_shape = (channels, kernel_h, kernel_w, shape[3] // groups)
226 "attrs": {"shape": [[list(weight_shape)]], "dtype": [[str(dtype)]]},
249 "attrs": {"shape": [[[weight_shape[0]]]], "dtype": [[bias_dtype]]},
/dports/lang/halide/Halide-release_2019_08_27-2654-g664dc4993/apps/resnet_50/
H A DResnet50Generator.cpp254 int p = weight_shape.pad; in conv2D()
262 RDom r(0, input.shape[0], 0, weight_shape.w, 0, weight_shape.h); in conv2D()
264 …(c, i, j) += weights(c, r.y, r.z, r.x) * padded(r.x, weight_shape.stride * i + r.y - p, weight_sha… in conv2D()
269 output.shape = compute_shape(input, weight_shape); in conv2D()
299 int p = weight_shape.pad; in max_pool_layer()
306 RDom r(0, weight_shape.w, 0, weight_shape.h); in max_pool_layer()
308 …pool(c, i, j) = maximum(padded(c, weight_shape.stride * i + r.x - p, weight_shape.stride * j + r.y… in max_pool_layer()
318 int p = weight_shape.pad; in avg_pool_layer()
325 RDom r(0, weight_shape.w, 0, weight_shape.h); in avg_pool_layer()
326 float scale = weight_shape.w * weight_shape.h; in avg_pool_layer()
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/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/src/relay/transforms/
H A Dconvert_sparse_dense.cc76 const Array<Array<PrimExpr> >& weight_shape) in DenseToSparseDenseMutator()
78 CHECK_EQ(weight_name.size(), weight_shape.size()); in DenseToSparseDenseMutator()
82 const auto& ws = weight_shape[i]; in DenseToSparseDenseMutator()
122 const Array<Array<PrimExpr> >& weight_shape) { in DenseToSparse()
123 auto rewriter = DenseToSparseDenseMutator(weight_name, weight_shape); in DenseToSparse()
130 const Array<Array<PrimExpr> >& weight_shape) { in DenseToSparse()
134 auto f0 = Downcast<Function>(DenseToSparse(f, weight_name, weight_shape)); in DenseToSparse()
/dports/misc/glow/glow-f24d960e3cc80db95ac0bc17b1900dbf60ca044a/thirdparty/onnx/onnx/optimizer/passes/
H A Dfuse_add_bias_into_conv.h55 auto weight_shape = orig_conv->node()->inputs()[1]->sizes(); in runTransform() local
64 if (weight_shape.size() > 0 && weight_shape[0].is_int) { in runTransform()
65 ONNX_ASSERT(M == -1 || M == weight_shape[0].dim); in runTransform()
66 M = weight_shape[0].dim; in runTransform()
68 rank == -1 || rank == static_cast<int64_t>(weight_shape.size())); in runTransform()
69 rank = weight_shape.size(); in runTransform()
/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/python/tvm/relay/analysis/
H A Dsparse_dense.py76 memo = SparseAnalysisResult(weight_name=[], weight_shape=[])
87 memo.weight_shape.append(
97 weight_shape=tvm.runtime.convert(memo.weight_shape),
/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/tests/python/contrib/test_ethosn/
H A Dtest_fullyconnected.py28 shape, weight_shape, input_zp, input_sc, kernel_zp, kernel_sc, output_zp, output_sc, dtype argument
32 w = tvm.nd.array(np.ones(weight_shape, dtype))
41 units=weight_shape[0],
129 weight_shape,
144 weight_shape,
H A Dtest_conv2d.py75 weight_shape = (kernel_h, kernel_w, shape[3] // groups, out_channels)
77 weight_shape = (kernel_h, kernel_w, out_channels, 1)
80 np.iinfo(dtype).min, high=np.iinfo(dtype).max, size=weight_shape, dtype=dtype
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/src/operator/nn/cudnn/
H A Dcudnn_algoreg-inl.h129 mxnet::TShape data_shape, weight_shape, out_shape; member
139 this->weight_shape == other.weight_shape &&
154 ret = dmlc::HashCombine(ret, key.weight_shape); in operator()
/dports/misc/mxnet/incubator-mxnet-1.9.0/src/operator/nn/cudnn/
H A Dcudnn_algoreg-inl.h129 mxnet::TShape data_shape, weight_shape, out_shape; member
139 this->weight_shape == other.weight_shape &&
154 ret = dmlc::HashCombine(ret, key.weight_shape); in operator()
/dports/www/chromium-legacy/chromium-88.0.4324.182/chrome/browser/resource_coordinator/tab_ranker/
H A Dnative_inference.cc32 const int32_t* __restrict weight_shape, in FullyConnected() argument
38 const int32_t num_inputs = weight_shape[0]; in FullyConnected()
39 const int32_t num_outputs = weight_shape[1]; in FullyConnected()
H A Dpairwise_inference.cc32 const int32_t* __restrict weight_shape, in FullyConnected() argument
38 const int32_t num_inputs = weight_shape[0]; in FullyConnected()
39 const int32_t num_outputs = weight_shape[1]; in FullyConnected()
/dports/misc/tvm/incubator-tvm-0.6.1/src/relay/pass/
H A Dmac_count.cc139 Array<IndexExpr> weight_shape = weight_type->shape; in DenseMacCount() local
140 CHECK(data_shape.size() == 2 && weight_shape.size() == 2) in DenseMacCount()
144 int64_t d3 = static_cast<int64_t>(weight_shape[0].as<IntImm>()->value); in DenseMacCount()
145 int64_t d4 = static_cast<int64_t>(weight_shape[1].as<IntImm>()->value); in DenseMacCount()
/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/src/relay/analysis/
H A Dmac_count.cc132 Array<IndexExpr> weight_shape = weight_type->shape; in DenseMacCount() local
133 CHECK(data_shape.size() == 2 && weight_shape.size() == 2) in DenseMacCount()
137 int64_t d3 = static_cast<int64_t>(weight_shape[0].as<IntImmNode>()->value); in DenseMacCount()
138 int64_t d4 = static_cast<int64_t>(weight_shape[1].as<IntImmNode>()->value); in DenseMacCount()
/dports/misc/py-tvm/incubator-tvm-0.6.1/src/relay/pass/
H A Dmac_count.cc139 Array<IndexExpr> weight_shape = weight_type->shape; in DenseMacCount() local
140 CHECK(data_shape.size() == 2 && weight_shape.size() == 2) in DenseMacCount()
144 int64_t d3 = static_cast<int64_t>(weight_shape[0].as<IntImm>()->value); in DenseMacCount()
145 int64_t d4 = static_cast<int64_t>(weight_shape[1].as<IntImm>()->value); in DenseMacCount()
/dports/www/qt5-webengine/qtwebengine-everywhere-src-5.15.2/src/3rdparty/chromium/ui/events/ozone/evdev/touch_filter/palm_model/
H A Donedevice_train_palm_detection_filter_inference.cc178 const int32_t* __restrict weight_shape, in MatMul() argument
186 ConstMatrixMap<T>(weight_values, weight_shape[1], weight_shape[0]); in MatMul()
191 const int32_t num_inputs = weight_shape[0]; in MatMul()
192 const int32_t num_outputs = weight_shape[1]; in MatMul()
340 ConstMatrixMap<T>(weight_values, weight_shape[1], weight_shape[0]); in FullyConnected()
346 const int32_t num_inputs = weight_shape[0]; in FullyConnected()
347 const int32_t num_outputs = weight_shape[1]; in FullyConnected()
374 const int32_t num_rows = weight_shape[1]; in SparseDenseMatmulCSR()
375 const int32_t num_cols = weight_shape[0]; in SparseDenseMatmulCSR()
396 const int32_t num_rows = weight_shape[1]; in SparseFullyConnectedCSR()
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/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/python/tvm/relay/frontend/
H A Dqnn_torch.py480 weight_shape = infer_shape(weight)
481 kernel_size = (weight_shape[2], weight_shape[3])
482 out_channels = weight_shape[0]
538 weight_shape = infer_shape(weight)
546 units=weight_shape[0],
/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/src/runtime/contrib/dnnl/
H A Ddnnl_json_runtime.cc158 dnnl::memory::dims weight_shape = nodes_[weight_entry.id_].GetOpShape()[weight_entry.index_]; in Conv2d() local
167 OC = weight_shape[0], // output channels in Conv2d()
168 KH = weight_shape[2], // weight height in Conv2d()
169 KW = weight_shape[3], // weight width in Conv2d()
253 dnnl::memory::dims weight_shape = nodes_[weight_entry.id_].GetOpShape()[weight_entry.index_]; in Dense() local
257 OC = weight_shape[0]; // output channels in Dense()
/dports/misc/tvm/incubator-tvm-0.6.1/vta/tutorials/optimize/
H A Dmatrix_multiply_opt.py111 weight_shape = (out_channels // env.BLOCK_OUT, variable
127 weight = tvm.placeholder(weight_shape, name="weight", dtype=env.wgt_dtype)
133 weight_buf = tvm.compute(weight_shape,
/dports/misc/py-tvm/incubator-tvm-0.6.1/vta/tutorials/optimize/
H A Dmatrix_multiply_opt.py111 weight_shape = (out_channels // env.BLOCK_OUT, variable
127 weight = tvm.placeholder(weight_shape, name="weight", dtype=env.wgt_dtype)
133 weight_buf = tvm.compute(weight_shape,
/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/python/tvm/relay/data_dep_optimization/
H A Dbsr_dense.py52 func, relay.transform.DenseToSparse(weight_info.weight_name, weight_info.weight_shape)
/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/vta/tutorials/optimize/
H A Dmatrix_multiply_opt.py109 weight_shape = ( variable
124 weight = te.placeholder(weight_shape, name="weight", dtype=env.wgt_dtype)
128 weight_buf = te.compute(weight_shape, lambda *i: weight(*i), "weight_buf")
/dports/misc/tvm/incubator-tvm-0.6.1/python/tvm/relay/op/nn/
H A D_nn.py165 weight_shape = get_const_tuple(inputs[1].shape)
167 return weight_shape[2] * weight_shape[3]
168 return weight_shape[0] * weight_shape[1]
930 def _dense_shape_func(data_shape, weight_shape): argument
934 out[out.shape[0] - 1] = weight_shape[0]
/dports/misc/py-tvm/incubator-tvm-0.6.1/python/tvm/relay/op/nn/
H A D_nn.py165 weight_shape = get_const_tuple(inputs[1].shape)
167 return weight_shape[2] * weight_shape[3]
168 return weight_shape[0] * weight_shape[1]
930 def _dense_shape_func(data_shape, weight_shape): argument
934 out[out.shape[0] - 1] = weight_shape[0]
/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/python/tvm/relay/transform/
H A Dtransform.py984 def DenseToSparse(weight_name, weight_shape): argument
1003 return _ffi_api.DenseToSparse(weight_name, weight_shape)

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