3dAFNIto3D.c 3dAFNItoANALYZE.c 3dAFNItoMINC.c 3dAFNItoNIFTI.c 3dAFNItoNIML.c 3dAFNItoRaw.c 3dANALYZEtoAFNI.c 3dANOVA.c 3dANOVA.h 3dANOVA.lib 3dANOVA2.c 3dANOVA3.c 3dAcost.c 3dAllineate.c 3dAnatNudge.c 3dAnhist.c 3dAttribute.c 3dAutoTcorrelate.c 3dAutobox.c 3dAutomask.c ...
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+ DYNSYM (isl_union_map_is_equal); \ + DYNSYM (isl_union_access_info_compute_flow); \ + DYNSYM (isl_union_access_info_from_sink); \ + DYNSYM (isl_union_access_info_set_may_source); \ + DYNSYM (isl_union_access_info_set_must_source); \ + DYNSYM (isl_union_access_info...
ReduceMax(keep_dims=False) self.input_shape = Tensor(tuple(config.img_shape[::-1]), ms.float32) def construct(self, grid, prediction, pred_xy, pred_wh, y_true, gt_box): object_mask = y_true[:, :, :, :, 4:5] class_probs = y_true[:, :, :, :, 5:] grid_...
$class . '"'; } // If size is defined and not equal to wpex_custom if ($size && 'wpex_custom' != $size) { $dims = wpex_get_thumbnail_sizes($size); $width = $dims['width']; $height = $dims['height']; $crop = !empty($dims['crop']) ? $dims['crop'] : $crop; } ...
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The first dimension of filter must be equal to the // second dimension of bias for (size_t i = 0; i < ndims; ++i) { for (int i = 0; i < ndims; ++i) { dims_[io::bia][i] = 1; strides_[io::bia][i] = (format != CUDNN_TENSOR_NHWC ? 1 : bias_dim); }...
"sameCompare": "The comparison operator must be unique.", "unreasonable": "The logic is improper.", "info": "Information", "isDelete": "Are you sure you want to delete the current threshold?", "noLog":"There is no logarithm of 0 and negative numbers" }, "images...
self.ftr_dims, self.hidden_units[0], self.num_heads[0], self.ftr_drop, self.attn_drop, self.activation, residual=False)) # intermediate layer for i in range(1, len(self.hidden_units)): self.layers.append(AttentionAggregator( self.hidden_units[i-1]*self.num_heads[i-...
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