Searched refs:shape_1 (Results 1 – 8 of 8) sorted by relevance
/third_party/mindspore/tests/ut/python/nn/ |
D | test_psnr.py | 71 shape_1 = (8, 3, 16, 16) 73 img1 = Tensor(np.random.random(shape_1)) 91 shape_1 = (8, 3, 16, 16) 94 img1 = Tensor(np.random.random(shape_1))
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D | test_ssim.py | 82 shape_1 = (8, 3, 16, 16) 84 img1 = Tensor(np.random.random(shape_1)) 102 shape_1 = (8, 3, 16, 16) 105 img1 = Tensor(np.random.random(shape_1))
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D | test_msssim.py | 98 shape_1 = (8, 3, 128, 128) 101 img1 = Tensor(np.random.random(shape_1)) 120 shape_1 = (8, 3, 128, 128) 124 img1 = Tensor(np.random.random(shape_1))
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/third_party/mindspore/mindspore/_extends/graph_kernel/model/ |
D | op_infer.py | 376 shape_1 = list(self.inputs[1].shape) 378 check_nd(shape_1, 4) 383 n, h, w, out_channel = shape_0[0], shape_0[1], shape_0[2], shape_1[0] 415 shape_1 = list(self.inputs[1].shape) 416 if len(shape_0) != 2 or len(shape_1) != 2: 417 … GKException("MatMul's inputs shape must be 2D, but got {}, {}".format(len(shape_0), len(shape_1))) 421 k2, n = (shape_1[-1], shape_1[-2]) if transpose_b else (shape_1[-2], shape_1[-1])
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/third_party/mindspore/mindspore/ccsrc/minddata/dataset/audio/kernels/ |
D | audio_utils.h | 201 auto shape_1 = input->shape()[1]; in LFilter() local 204 std::vector<T> out_vect(shape_0 * shape_1); in LFilter() 222 while (x_idx < shape_1 * channel_idx) { in LFilter() 235 for (size_t i = x_idx - shape_1; i < x_idx; i++) { in LFilter() 262 if (x_idx % shape_1 == 0) { in LFilter()
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/third_party/mindspore/mindspore/_extends/graph_kernel/expanders/ |
D | conv2d.py | 98 shape_1 = self.inputs[1]['shape'] 101 check_nd(shape_1, 4) 104 n1, h1, w1, c1 = shape_1
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/third_party/mindspore/tests/st/fl/cross_silo_faster_rcnn/src/FasterRcnn/ |
D | rcnn.py | 92 shape_1 = (self.rcnn_fc_out_channels, self.rcnn_fc_out_channels) 93 … weights_1 = initializer("XavierUniform", shape=shape_1[::-1], dtype=self.ms_type).to_tensor()
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/third_party/mindspore/mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/ |
D | rec_generate_strategy.cc | 85 auto shape_1 = ops[iter_ops]->inputs_tensor_info()[0].shape()[0]; in PrepareMatMul() local 87 shape_1 = ops[iter_ops]->inputs_tensor_info()[0].shape()[1]; in PrepareMatMul() 95 if (shape_1 >= shape_4) { in PrepareMatMul() 96 if (LongToSize(shape_1) % g_device_manager->DeviceNum() == 0) { in PrepareMatMul() 112 if (!already_cut && LongToSize(shape_1) % g_device_manager->DeviceNum() == 0) { in PrepareMatMul()
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