/third_party/ffmpeg/libavcodec/ |
D | aptx.c | 424 static int32_t *aptx_reconstructed_differences_update(Prediction *prediction, in aptx_reconstructed_differences_update() argument 428 int32_t *rd1 = prediction->reconstructed_differences, *rd2 = rd1 + order; in aptx_reconstructed_differences_update() 429 int p = prediction->pos; in aptx_reconstructed_differences_update() 432 prediction->pos = p = (p + 1) % order; in aptx_reconstructed_differences_update() 437 static void aptx_prediction_filtering(Prediction *prediction, in aptx_prediction_filtering() argument 446 … reconstructed_sample = av_clip_intp2(reconstructed_difference + prediction->predicted_sample, 23); in aptx_prediction_filtering() 447 …predictor = av_clip_intp2((MUL64(prediction->s_weight[0], prediction->previous_reconstructed_sampl… in aptx_prediction_filtering() 448 + MUL64(prediction->s_weight[1], reconstructed_sample)) >> 22, 23); in aptx_prediction_filtering() 449 prediction->previous_reconstructed_sample = reconstructed_sample; in aptx_prediction_filtering() 451 …reconstructed_differences = aptx_reconstructed_differences_update(prediction, reconstructed_differ… in aptx_prediction_filtering() [all …]
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D | pixlet.c | 59 int16_t *prediction; member 78 av_freep(&ctx->prediction); in free_buffers() 97 ctx->prediction = av_malloc_array((ctx->w >> NB_LEVELS), sizeof(int16_t)); in init_decoder() 98 if (!ctx->filter[0] || !ctx->filter[1] || !ctx->prediction) in init_decoder() 593 lowpass_prediction(dst, ctx->prediction, ctx->band[plane][0].width, in decode_plane()
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D | aptx.h | 91 Prediction prediction[NB_SUBBANDS]; member
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/third_party/mindspore/mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/ |
D | smooth_l1_loss_impl.cu | 21 __global__ void SmoothL1LossKernel(const int input_size, const float beta, const T *prediction, con… in SmoothL1LossKernel() argument 24 T value = fabsf(prediction[i] - target[i]); in SmoothL1LossKernel() 34 void SmoothL1Loss(const int &input_size, const float &beta, const T *prediction, const T *target, T… in SmoothL1Loss() argument 36 …el<<<GET_BLOCKS(input_size), GET_THREADS, 0, stream>>>(input_size, beta, prediction, target, loss); in SmoothL1Loss() 40 __global__ void SmoothL1LossGradKernel(const int input_size, const float beta, const T *prediction,… in SmoothL1LossGradKernel() argument 43 T value = prediction[i] - target[i]; in SmoothL1LossGradKernel() 55 void SmoothL1LossGrad(const int &input_size, const float &beta, const T *prediction, const T *targe… in SmoothL1LossGrad() argument 57 …radKernel<<<GET_BLOCKS(input_size), GET_THREADS, 0, stream>>>(input_size, beta, prediction, target, in SmoothL1LossGrad() 61 template void SmoothL1Loss<float>(const int &input_size, const float &beta, const float *prediction, 63 …ate void SmoothL1LossGrad<float>(const int &input_size, const float &beta, const float *prediction,
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D | smooth_l1_loss_impl.cuh | 20 void SmoothL1Loss(const int &input_size, const float &beta, const T *prediction, const T *target, T… 23 void SmoothL1LossGrad(const int &input_size, const float &beta, const T *prediction, const T *targe…
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/third_party/mindspore/tests/st/ops/cpu/ |
D | test_binary_cross_entropy_op.py | 42 prediction = np.random.rand(20).astype(np.float32) 47 loss = net(Tensor(prediction), Tensor(target), Tensor(weight)) 57 prediction = np.random.rand(20).astype(np.float32) 62 loss = net(Tensor(prediction), Tensor(target), Tensor(weight)) 69 prediction = np.random.rand(20).astype(np.float32) 74 loss = net(Tensor(prediction), Tensor(target), Tensor(weight)) 81 prediction = np.random.rand(20).astype(np.float32) 85 loss = net(Tensor(prediction), Tensor(target)) 92 prediction = np.random.rand(20).astype(np.float16) 97 loss = net(Tensor(prediction), Tensor(target), Tensor(weight)) [all …]
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D | test_smoothl1loss_op.py | 26 prediction = np.random.randn(20).astype(np.float32) 30 return net(Tensor(prediction), Tensor(target)) 72 prediction = np.random.randn(20).astype(np.float32) 78 return grad(Tensor(prediction), Tensor(target), Tensor(sens))
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/third_party/mindspore/tests/st/ops/gpu/ |
D | test_binary_cross_entropy_op.py | 42 prediction = np.random.rand(20).astype(np.float32) 46 loss = net(Tensor(prediction), Tensor(target), Tensor(weight)) 56 prediction = np.random.rand(20).astype(np.float32) 60 loss = net(Tensor(prediction), Tensor(target)) 81 prediction = np.random.rand(20).astype(np.float32) 86 dx = grad(Tensor(prediction), Tensor(target), Tensor(sens), Tensor(weight))
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D | test_kl_div_op.py | 42 prediction = np.random.rand(20).astype(np.float32) 45 loss = net(Tensor(prediction), Tensor(target)) 69 prediction = np.random.rand(20).astype(np.float32) 73 dx = grad(Tensor(prediction), Tensor(target), Tensor(sens))
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D | test_smoothl1loss_op.py | 26 prediction = np.random.randn(20).astype(np.float32) 30 return net(Tensor(prediction), Tensor(target)) 72 prediction = np.random.randn(20).astype(np.float32) 78 return grad(Tensor(prediction), Tensor(target), Tensor(sens))
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/third_party/mindspore/tests/st/model_zoo_tests/yolov3_darknet53/src/ |
D | yolo.py | 204 prediction = P.Reshape()(x, (num_batch, 209 prediction = P.Transpose()(prediction, (0, 3, 4, 1, 2)) 222 box_xy = prediction[:, :, :, :, :2] 223 box_wh = prediction[:, :, :, :, 2:4] 224 box_confidence = prediction[:, :, :, :, 4:5] 225 box_probs = prediction[:, :, :, :, 5:] 236 return grid, prediction, box_xy, box_wh 300 def construct(self, grid, prediction, pred_xy, pred_wh, y_true, gt_box, input_shape): argument 310 grid_shape = P.Shape()(prediction)[1:3] 342 xy_loss = self.xy_loss(object_mask, box_loss_scale, prediction[:, :, :, :, :2], true_xy) [all …]
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/third_party/mindspore/mindspore/core/ops/grad/ |
D | smooth_l1_loss_grad.cc | 46 …auto prediction = CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[kInputIndex0]->BuildS… in SmoothL1LossGradInfer() local 49 …CheckAndConvertUtils::Check("prediction shape", prediction, kEqual, "target shape", target, prim_n… in SmoothL1LossGradInfer() 50 …CheckAndConvertUtils::Check("prediction shape", prediction, kEqual, "dloss", dloss, prim_name, Typ… in SmoothL1LossGradInfer() 61 return std::make_shared<abstract::AbstractTensor>(dloss_type, prediction); in SmoothL1LossGradInfer()
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/third_party/mindspore/mindspore/core/ops/ |
D | smooth_l1_loss.cc | 44 …auto prediction = CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[0]->BuildShape())[kSh… in SmoothL1LossInfer() local 46 …CheckAndConvertUtils::Check("prediction shape", prediction, kEqual, "target shape", target, prim_n… in SmoothL1LossInfer() 55 return std::make_shared<abstract::AbstractTensor>(prediction_type, prediction); in SmoothL1LossInfer()
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/third_party/mindspore/tests/st/model_zoo_tests/yolov3/src/ |
D | yolov3.py | 423 prediction = P.Reshape()(x, (num_batch, 428 prediction = P.Transpose()(prediction, (0, 3, 4, 1, 2)) 440 box_xy = prediction[:, :, :, :, :2] 441 box_wh = prediction[:, :, :, :, 2:4] 442 box_confidence = prediction[:, :, :, :, 4:5] 443 box_probs = prediction[:, :, :, :, 5:] 451 return grid, prediction, box_xy, box_wh 521 def construct(self, grid, prediction, pred_xy, pred_wh, y_true, gt_box): argument 526 grid_shape = P.Shape()(prediction)[1:3] 548 … xy_loss = object_mask * box_loss_scale * self.cross_entropy(prediction[:, :, :, :, :2], true_xy) [all …]
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/third_party/mindspore/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/ |
D | smooth_l1_loss_grad_gpu_kernel.h | 41 T *prediction = GetDeviceAddress<T>(inputs, 0); in Launch() local 46 …SmoothL1LossGrad(input_size_, beta_, prediction, target, dloss, dx, reinterpret_cast<cudaStream_t>… in Launch()
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D | smooth_l1_loss_gpu_kernel.h | 41 T *prediction = GetDeviceAddress<T>(inputs, 0); in Launch() local 45 …SmoothL1Loss(input_size_, beta_, prediction, target, loss, reinterpret_cast<cudaStream_t>(stream_p… in Launch()
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/third_party/openssl/doc/man3/ |
D | RAND_DRBG_generate.pod | 61 A request for prediction resistance can only be satisfied by pulling fresh 65 entropy source, a request for prediction resistance will always fail. 66 In other words, prediction resistance is currently not supported yet by the DRBG.
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D | RAND_DRBG_set_callbacks.pod | 67 triggered by a prediction resistance request. 106 A request for prediction resistance can only be satisfied by pulling fresh 110 to such an approved entropy source, a request for prediction resistance will 112 In other words, prediction resistance is currently not supported yet by the DRBG.
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/third_party/openssl/doc/man7/ |
D | RAND_DRBG.pod | 190 I<prediction resistance> parameter to 1 when calling L<RAND_DRBG_generate(3)>. 192 The document [NIST SP 800-90C] describes prediction resistance requests 194 approved for providing prediction resistance. 196 entropy source, a request for prediction resistance will currently always fail. 197 In other words, prediction resistance is currently not supported yet by the DRBG.
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/third_party/ffmpeg/doc/ |
D | snow.txt | 39 prediction 96 prediction: 304 | | LL0 subband prediction 387 motion vector prediction 505 LL band prediction: 509 be 0. To reverse this prediction in the decoder apply the following.
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/third_party/mbedtls/tests/suites/ |
D | test_suite_ctr_drbg.function | 12 RESEED_ALWAYS /* prediction resistance, no explicit reseed */ 68 /* Then reseed if prediction resistance is enabled. */ 85 /* Then reseed if prediction resistance is enabled. */
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/third_party/openh264/codec/build/windowsphone/all/CodecApp/ |
D | welsenc.cfg | 58 # Can be disabled when no inter spatial layer prediction in case of its value as 0
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/third_party/boost/libs/poly_collection/ |
D | README.md | 15 problems related to CPU caching and branch prediction. Boost.PolyCollection
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/third_party/openh264/testbin/ |
D | welsenc_vd_1d.cfg | 66 … # Can be disabled when no inter spatial layer prediction in case of its value as 0
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D | welsenc_arbitrary_res.cfg | 66 … # Can be disabled when no inter spatial layer prediction in case of its value as 0
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