| /third_party/mindspore/mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/ |
| D | resize_bilinear_impl.cu | 23 …const int output_h, const int output_w, const int nchw, const int chw, const int hw, const float h… in ResizeBilinear() argument
28 const int posh = pos / output_w % output_h; in ResizeBilinear()
53 …const int output_h, const int output_w, const int nchw, const int chw, const int hw, const float h… in ResizeBilinearGrad() argument
65 const int h_high = min(static_cast<int>(ceilf(posh_scaled)), output_h - 1); // NOLINT in ResizeBilinearGrad()
75 const int output_start = output_h * output_w * (posn * c + posc); in ResizeBilinearGrad()
86 …const int output_h, const int output_w, const int nchw, const int chw, const int hw, const float h… in ResizeBilinearGrad() argument
98 const int h_high = min(static_cast<int>(ceilf(posh_scaled)), output_h - 1); // NOLINT in ResizeBilinearGrad()
108 const int output_start = output_h * output_w * (posn * c + posc); in ResizeBilinearGrad()
126 const int output_h, const int output_w, const float h_scale, const float w_scale, T *output, in CalResizeBilinear() argument
128 const int nchw = n * c * output_h * output_w; in CalResizeBilinear()
[all …]
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| /third_party/mindspore/mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/ |
| D | conv2d_infer.c | 19 int ConvInferShape(int input_h, int input_w, int *output_h, int *output_w, ConvParameter *param) { in ConvInferShape() argument
35 *output_h = ceil((float)(input_h) / (float)(stride_h)); in ConvInferShape()
36 int pad_h_all = ((*output_h - 1) * stride_h + (kernel_h - 1) * dilate_h + 1 - input_h); in ConvInferShape()
53 …*output_h = ceil(((float)(input_h) + param->pad_u_ + param->pad_d_ - ((float)(kernel_h)-1) * (floa… in ConvInferShape()
59 *output_h = ((input_h) + param->pad_u_ + param->pad_d_ - kernel_height) / stride_h + 1; in ConvInferShape()
100 int output_w = 0, output_h = 0; in Conv2dInferShape() local
115 int ret = ConvInferShape(input_h, input_w, &output_h, &output_w, param); in Conv2dInferShape()
123 out_shape[1] = output_h >= 0 ? output_h : 1; in Conv2dInferShape()
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| D | pooling_infer.c | 50 int output_h = 0; in PoolingInferShape() local
57 output_h = ceil((float)(input_h) / (float)(param->stride_h_)); in PoolingInferShape()
58 int pad_h_all = ((output_h - 1) * param->stride_h_ + (window_h - 1) + 1 - input_h); in PoolingInferShape()
75 …output_h = floor((float)(input_h + param->pad_u_ + param->pad_d_ - window_h) / param->stride_h_) +… in PoolingInferShape()
78 …output_h = ceil((float)(input_h + param->pad_u_ + param->pad_d_ - window_h) / param->stride_h_) + … in PoolingInferShape()
87 input_shape[1] = output_h > 0 ? output_h : 1; in PoolingInferShape()
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| D | deconv2d_infer.c | 47 int32_t output_h = 0; in Deconv2dInferShape() local
72 …output_h = (input_h - 1) * stride_h + ((kernel_h - 1) * dilate_h + 1) - param->pad_u_ - param->pad… in Deconv2dInferShape()
75 output_h = input_h * stride_h; in Deconv2dInferShape()
78 output_h = (input_h - 1) * stride_h + kernel_h; in Deconv2dInferShape()
84 output_h += param->output_padding_h_; in Deconv2dInferShape()
89 output->shape_[1] = output_h; in Deconv2dInferShape()
94 param->pad_u_ = ((input_h - 1) * stride_h + (kernel_h - 1) * dilate_h + 1 - output_h) / 2; in Deconv2dInferShape()
107 param->output_h_ = output_h; in Deconv2dInferShape()
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| D | depthwise_conv2d_infer.c | 40 int output_w = 0, output_h = 0; in DepthwiseConv2dInferShape() local
49 output_h = ceil((float)(input_h) / (float)(param->stride_h_)); in DepthwiseConv2dInferShape()
51 …int pad_h_all = ((output_h - 1) * param->stride_h_ + (param->kernel_h_ - 1) * param->dilation_h_ +… in DepthwiseConv2dInferShape()
62 output_h = ceil(((float)(input_h) + param->pad_u_ + param->pad_d_ - in DepthwiseConv2dInferShape()
72 out_shape[1] = output_h; in DepthwiseConv2dInferShape()
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| D | pooling_grad_infer.c | 52 int output_h = ceil((float)(input_h) / (float)(param->stride_h_)); in PoolingGradInferShape() local
53 int pad_h_all = ((output_h - 1) * param->stride_h_ + (window_h - 1) + 1 - input_h); in PoolingGradInferShape()
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| /third_party/mindspore/mindspore/ccsrc/minddata/dataset/kernels/image/ |
| D | resize_op.cc | 33 int32_t output_h = 0; in Compute() local
40 output_h = size1_; in Compute()
41 output_w = static_cast<int>(std::lround(static_cast<float>(input_w) / input_h * output_h)); in Compute()
45 output_h = static_cast<int>(std::lround(static_cast<float>(input_h) / input_w * output_w)); in Compute()
48 output_h = size1_; in Compute()
51 return Resize(input, output, output_h, output_w, 0, 0, interpolation_); in Compute()
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| D | resize_with_bbox_op.cc | 44 int32_t output_h = (*output)[0]->shape()[0]; // output height if ResizeWithBBox in Compute() local
48 …RETURN_IF_NOT_OK(BoundingBox::UpdateBBoxesForResize((*output)[1], bboxCount, output_w, output_h, i… in Compute()
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| /third_party/grpc/third_party/upb/tools/ |
| D | amalgamate.py | 17 self.output_h = open(output_path + prefix + "upb.h", "w")
24 self.output_h.write("/* Amalgamated source file */\n")
25 self.output_h.write('#include <stdint.h>')
26 self.output_h.write(open("upb/port_def.inc").read())
33 self.output_h.write(open("upb/port_undef.inc").read())
68 self._process_file(filename, self.output_h)
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| /third_party/mindspore/mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/fp32/ |
| D | winograd_transform.c | 103 int output_h = conv_param->output_h_; in WinogradOutputNHWCTransform() local
117 int r_h = output_h - dst_y_s * output_unit; in WinogradOutputNHWCTransform()
122 dst_y_s = tmp_iy > output_h ? output_h : tmp_iy; in WinogradOutputNHWCTransform()
162 int output_h = conv_param->output_h_; in WinogradOutputNC4HW4Transform() local
163 int output_plane = output_w * output_h; in WinogradOutputNC4HW4Transform()
177 int r_h = output_h - dst_y_s * output_unit; in WinogradOutputNC4HW4Transform()
182 dst_y_s = tmp_iy > output_h ? output_h : tmp_iy; in WinogradOutputNC4HW4Transform()
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| D | pooling_fp32.c | 30 int output_h = pooling_param->output_h_; in AvgPooling() local
31 int out_plane = output_w * output_h; in AvgPooling()
47 float *dst_b_ptr = output_ptr + batch * output_h * output_w * channel; in AvgPooling()
144 int output_h = pooling_param->output_h_; in MaxPooling() local
146 int out_plane = output_w * output_h; in MaxPooling()
162 float *dst_b_ptr = output_ptr + batch * output_h * output_w * channel; in MaxPooling()
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| D | common_func_fp32.h | 81 … int row_size, int channel, int output_h, int output_w, size_t relu, size_t relu6);
84 … int row_size, int channel, int output_h, int output_w, size_t relu, size_t relu6);
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| /third_party/mindspore/mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/fp16/ |
| D | winograd_transform_fp16.c | 133 int output_h = conv_param->output_h_; in WinogradOutputNHWCTransformFp16() local
143 int r_h = output_h - dst_y_s * output_unit; in WinogradOutputNHWCTransformFp16()
148 dst_y_s = tmp_iy > output_h ? output_h : tmp_iy; in WinogradOutputNHWCTransformFp16()
172 int output_h = conv_param->output_h_; in WinogradOutputNC8HW8TransformFp16() local
173 int plane = output_w * output_h; in WinogradOutputNC8HW8TransformFp16()
183 int r_h = output_h - dst_y_s * output_unit; in WinogradOutputNC8HW8TransformFp16()
188 dst_y_s = tmp_iy > output_h ? output_h : tmp_iy; in WinogradOutputNC8HW8TransformFp16()
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| D | pooling_fp16.c | 29 int output_h = pooling_param->output_h_; in AvgPoolingFp16() local
30 int out_plane = output_w * output_h; in AvgPoolingFp16()
41 float16_t *dst_b_ptr = output_ptr + batch * output_h * output_w * channel; in AvgPoolingFp16()
265 int output_h = pooling_param->output_h_; in MaxPoolingFp16() local
267 int out_plane = output_w * output_h; in MaxPoolingFp16()
275 int out_batch_offset = batch * output_h * output_w * channel; in MaxPoolingFp16()
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| /third_party/mindspore/mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/fp32_grad/ |
| D | pooling_grad.c | 32 int output_h = pooling_param->output_h_; in AvgPoolingGrad() local
40 const float *inPtr = input_ptr + ib * output_h * output_w * channel; in AvgPoolingGrad()
42 for (int yh = 0; yh < output_h; yh++) { in AvgPoolingGrad()
115 int output_h = pooling_param->output_h_; in MaxPoolingGrad() local
119 const float *dyPtr = dy_ptr + ib * output_h * output_w * channel; in MaxPoolingGrad()
120 for (int yh = 0; yh < output_h; yh++) { in MaxPoolingGrad()
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| /third_party/mindspore/mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/fp16_grad/ |
| D | pooling_grad.c | 32 int output_h = pooling_param->output_h_; in AvgPoolingFp16Grad() local
40 const float16_t *inPtr = &input_ptr[(ib * output_h * output_w * channel)]; in AvgPoolingFp16Grad()
42 for (int yh = 0; yh < output_h; yh++) { in AvgPoolingFp16Grad()
117 int output_h = pooling_param->output_h_; in MaxPoolingFp16Grad() local
122 const float16_t *dyPtr = &dy_ptr[(ib * output_h * output_w * channel)]; in MaxPoolingFp16Grad()
123 for (int yh = 0; yh < output_h; yh++) { in MaxPoolingFp16Grad()
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| /third_party/mindspore/tests/ut/cpp/dataset/ |
| D | resize_op_test.cc | 36 int output_h = (s[0] * output_w) / s[1]; in TEST_F() local
39 std::unique_ptr<ResizeOp> op(new ResizeOp(output_h, output_w)); in TEST_F()
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| D | random_resize_op_test.cc | 35 int output_h = 0.5 * s[0]; in TEST_F() local
42 std::unique_ptr<RandomResizeOp> op(new RandomResizeOp(output_h, output_w)); in TEST_F()
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| /third_party/mindspore/mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/int8/ |
| D | pooling_int8.c | 32 int output_h = pooling_param->output_h_; in AvgPoolingInt8() local
34 int out_plane = output_w * output_h; in AvgPoolingInt8()
45 int out_batch_offset = batch * output_h * output_w * channel; in AvgPoolingInt8()
288 int output_h = pooling_param->output_h_; in MaxPoolingInt8() local
290 int out_plane = output_w * output_h; in MaxPoolingInt8()
300 int out_batch_offset = batch * output_h * output_w * channel; in MaxPoolingInt8()
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| D | common_func_int8.h | 71 … int input_col_size, int input_row_size, int channel, int output_h, int output_w, int8_t in_zp,
75 … int input_col_size, int input_row_size, int channel, int output_h, int output_w, int8_t in_zp,
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| /third_party/mindspore/mindspore/lite/micro/coder/opcoders/nnacl/int8/ |
| D | conv2d_3x3_int8_coder.cc | 87 int output_h = conv_param_->output_h_; in InitTmpBuffer() local
103 tmp_out_size_ = oc4 * C4NUM * output_batch * output_w * output_h * sizeof(uint8_t); in InitTmpBuffer()
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| /third_party/flutter/skia/third_party/externals/angle2/src/libANGLE/renderer/ |
| D | gen_angle_format_table.py | 401 output_h = template_autogen_h.format(
408 out_file.write(output_h)
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| /third_party/skia/third_party/externals/angle2/src/libANGLE/renderer/ |
| D | gen_angle_format_table.py | 437 output_h = template_autogen_h.format(
443 out_file.write(output_h)
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| /third_party/mindspore/mindspore/lite/src/runtime/kernel/arm/int8/ |
| D | convolution_3x3_int8.cc | 136 int output_h = conv_param_->output_h_; in InitTmpBuffer() local
169 size_t tmp_out_size = oc4 * C4NUM * output_batch * output_w * output_h * sizeof(uint8_t); in InitTmpBuffer()
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| /third_party/mindspore/mindspore/ccsrc/transform/graph_ir/op_declare/ |
| D | rnn_declare.cc | 77 OUTPUT_MAP(DynamicRNN) = {{0, OUTPUT_DESC(y)}, {1, OUTPUT_DESC(output_h)}, {2, OUTPUT_DESC(output_c…
119 OUTPUT_MAP(DynamicGRUV2) = {{0, OUTPUT_DESC(y)}, {1, OUTPUT_DESC(output_h)}, {2, OUTPUT_DESC(up…
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