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out=pred.data.cpu()

时间: 2024-05-31 08:07:22 浏览: 59

基准简表-gtm264 functional analysis calculus of variations and optimal control

A.2.1 基准简表比特流若与基准简表相一致，应遵如下限制： — 只可能有I与P条带类型存在； — NAL单元流不应包含取值范围为2到4的nal_unit_type，包括2与4； — 序列参数集中frame_mbs_only_flag应为1； — 语法元素 chroma_format_idc ， bit_depth_luma_minus8, bit_depth_chroma_minus8, qpprime_y_zero_ transform_bypass_flag，seq_scaling_matrix_present_flag不应出现在序列参数集中； — 图像参数集中的参数weighted_pred_flag 与weighted_bipred_idc的取值应该为0； — 图像参数集中的参数entropy_coding_mode_flag的取值应为0； — 图像参数集中slice_groups_minus1取值应该在0到7之间，包括0与7； — 语法元素transform_8x8_mode_flag，pic_scaling_matrix_present_flag，与 second_chroma_qp_index_offset 不应在图像参数集中出现； — 语法元素level_prefix的取值不应大于15； — 应满足A.3节中对于基准简表所定义的级别规定。 profile_idc 的取值为 66 时，比特流与基准简表相一致。与基准简表某一级别相一致的解码器应可以对所有满足 profile_idc 等于 66 或 constraint_set0 等于 1，且 level_idc 和 constraint_set3_flag 所表征的级别小于或等于该级别的流进行解码。

This line of code is typically used in machine learning tasks, specifically in deep learning models that use GPUs for computations. The "out" variable is used to store the predictions made by the model. The "pred" variable is the output of the model, which is usually a tensor (a multi-dimensional array) that represents the predicted values. The ".data" attribute is used to extract the tensor from the output, as deep learning frameworks like PyTorch wrap the tensor in a special data structure that tracks the computation graph. The ".cpu()" method is used to move the tensor from the GPU to the CPU, as the tensor may have been computed on the GPU during training or inference. This is necessary because most operations in Python are faster on the CPU than on the GPU. Overall, this line of code is used to extract the predicted values from the model output and move them to the CPU for further processing or evaluation.

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