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/OK3568_Linux_fs/external/rknn-toolkit2/examples/onnx/yolov5/
H A Dtest.py56 col = np.tile(np.arange(0, grid_w), grid_w).reshape(-1, grid_w)
57 row = np.tile(np.arange(0, grid_h).reshape(-1, 1), grid_h)
58 col = col.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
59 row = row.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
85 boxes = boxes.reshape(-1, 4)
86 box_confidences = box_confidences.reshape(-1)
87 box_class_probs = box_class_probs.reshape(-1, box_class_probs.shape[-1])
298 input0_data = input0_data.reshape([3, -1]+list(input0_data.shape[-2:]))
299 input1_data = input1_data.reshape([3, -1]+list(input1_data.shape[-2:]))
300 input2_data = input2_data.reshape([3, -1]+list(input2_data.shape[-2:]))
/OK3568_Linux_fs/prebuilts/gcc/linux-x86/arm/gcc-arm-10.3-2021.07-x86_64-arm-none-linux-gnueabihf/lib/gcc/arm-none-linux-gnueabihf/10.3.1/plugin/include/
H A Dvector-builder.h137 void reshape (unsigned int, unsigned int);
385 vector_builder<T, Shape, Derived>::reshape (unsigned int npatterns, in reshape() function
464 reshape (npatterns, 1); in try_npatterns()
480 reshape (npatterns, 2); in try_npatterns()
496 reshape (npatterns, 3); in try_npatterns()
539 reshape (m_npatterns, m_nelts_per_pattern - 1); in finalize()
600 reshape (m_npatterns / 4, 3); in finalize()
/OK3568_Linux_fs/prebuilts/gcc/linux-x86/aarch64/gcc-arm-10.3-2021.07-x86_64-aarch64-none-linux-gnu/lib/gcc/aarch64-none-linux-gnu/10.3.1/plugin/include/
H A Dvector-builder.h137 void reshape (unsigned int, unsigned int);
385 vector_builder<T, Shape, Derived>::reshape (unsigned int npatterns, in reshape() function
464 reshape (npatterns, 1); in try_npatterns()
480 reshape (npatterns, 2); in try_npatterns()
496 reshape (npatterns, 3); in try_npatterns()
539 reshape (m_npatterns, m_nelts_per_pattern - 1); in finalize()
600 reshape (m_npatterns / 4, 3); in finalize()
/OK3568_Linux_fs/external/rknn-toolkit2/examples/darknet/yolov3_416x416/
H A Dtest.py80 input0_data = input0_data.reshape(SPAN, LISTSIZE, GRID0, GRID0)
81 input1_data = input1_data.reshape(SPAN, LISTSIZE, GRID1, GRID1)
82 input2_data = input2_data.reshape(SPAN, LISTSIZE, GRID2, GRID2)
H A Dyolov3_utils.py36 col = np.tile(np.arange(0, grid_w), grid_w).reshape(-1, grid_w)
37 row = np.tile(np.arange(0, grid_h).reshape(-1, 1), grid_h)
39 col = col.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
40 row = row.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
/OK3568_Linux_fs/external/rknn-toolkit2/examples/functions/hybrid_quant/
H A Dstep2.py54 …ssd_post_process(np.reshape(outputs[0], (-1)), np.reshape(outputs[1], (-1)), './dog_bike_car_300x3…
H A Dssd_post_process.py74 prior_bboxes = prior_bboxes.reshape(4, prior_num)
76 conf_data = conf_data.reshape(-1, NUM_CLS)
/OK3568_Linux_fs/external/rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/
H A Dtest.py53 box_priors = box_priors.reshape(4, NUM_RESULTS)
113 predictions = outputs[0].reshape((1, NUM_RESULTS, 4))
115 outputClasses = outputs[1].reshape((1, NUM_RESULTS, NUM_CLASSES))
/OK3568_Linux_fs/external/rknn-toolkit2/examples/caffe/vgg-ssd/
H A Dtest.py53 conf_data = conf_data.reshape(-1, 21)
208 outputs[0] = outputs[0].reshape((-1, 1))
209 outputs[1] = outputs[1].reshape((-1, 1))
/OK3568_Linux_fs/kernel/Documentation/admin-guide/device-mapper/
H A Ddm-raid.rst175 value) to any reshape supporting raid levels 4/5/6 and 10.
207 Takeover/reshape is not possible with a raid4/5/6 journal device;
312 reshape
313 - The array is undergoing a reshape.
398 with a reshape in progress.
399 1.9.0 Add support for RAID level takeover/reshape/region size
407 1.10.1 Fix data corruption on reshape request
416 1.14.0 Fix reshape race on small devices. Fix stripe adding reshape
/OK3568_Linux_fs/external/rknpu2/examples/rknn_yolov5_android_apk_demo/tools/
H A Dsave_capture_img.py14 data = np.reshape(data, (640, 640, 3)).astype(np.uint8)
/OK3568_Linux_fs/external/rknn-toolkit2/examples/functions/multi_input_test/
H A Dtest.py67 outputs[0] = outputs[0].reshape((1, -1))
/OK3568_Linux_fs/external/rknn-toolkit2/examples/caffe/mobilenet_v2/
H A Dtest.py8 output = outputs[0].reshape(-1)
/OK3568_Linux_fs/external/rknn-toolkit2/examples/functions/model_pruning/
H A Dtest.py8 output = outputs[0].reshape(-1)
/OK3568_Linux_fs/external/rknn-toolkit2/examples/functions/batch_size/
H A Dtest.py7 output_ = outputs[0].reshape((-1, 1000))
/OK3568_Linux_fs/external/rknn-toolkit2/doc/
H A Dchangelog-1.5.0.txt57 1. [RK3562] 优化Transformer模型中的transpose/reshape多算子级联的性能
452 1) 最后一层是reshape转换bug；
/OK3568_Linux_fs/external/rknn-toolkit2/examples/functions/board_test/
H A Dtest.py7 output = outputs[0].reshape(-1)
/OK3568_Linux_fs/external/rknn-toolkit2/rknn_toolkit_lite2/examples/inference_with_lite/
H A Dtest.py39 output = result[0].reshape(-1)
/OK3568_Linux_fs/external/rknn-toolkit2/examples/functions/dynamic_input/
H A Dtest.py7 output_ = outputs[0].reshape((-1, 1000))
/OK3568_Linux_fs/buildroot/dl/unixbench/git/UnixBench/src/
H A Dubgears.c314 reshape(int width, int height) in reshape() function
469 reshape(event.xconfigure.width, event.xconfigure.height); in event_loop()
629 reshape(winrect.width, winrect.height); in main()
/OK3568_Linux_fs/external/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/
H A Dcuda.hpp272 GpuMat reshape(int cn, int rows = 0) const;
557 HostMem reshape(int cn, int rows = 0) const;
/OK3568_Linux_fs/external/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/
H A Dcuda.hpp272 GpuMat reshape(int cn, int rows = 0) const;
557 HostMem reshape(int cn, int rows = 0) const;
/OK3568_Linux_fs/external/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/
H A Dcuda.hpp272 GpuMat reshape(int cn, int rows = 0) const;
557 HostMem reshape(int cn, int rows = 0) const;
/OK3568_Linux_fs/docs/en/Common/NPU/rknn-toolkit2/
H A Dchangelog-1.4.0.txt118 1) 最后一层是reshape转换bug；
/OK3568_Linux_fs/docs/cn/Common/NPU/rknn-toolkit2/
H A Dchangelog-1.4.0.txt118 1) 最后一层是reshape转换bug；

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