def _check_sum_1(self, name): """Check that an array describes one or more distributions.""" s = getattr(self, name).sum(axis=-1) if not np.allclose(s, 1): raise ValueError( f"{name} must sum to 1 (got {s:.4f})" if s.ndim == 0 else f"{name} rows must sum to 1 (got {s})" if s.ndim == 1 else "Expected 1D or 2D array")

时间: 2024-02-14 21:16:53 浏览: 18
这段代码的作用是检查一个数组是否描述了一个或多个分布。它首先从对象中获取指定名称的数组,然后沿着最后一个维度计算该数组的总和。如果总和不等于1,则抛出ValueError异常,指示该数组必须总和为1。异常消息根据维度数的不同而有所不同。如果总和是标量,则消息将指示该数组必须总和为1。如果总和是一个一维数组,则消息将指示数组的每一行必须总和为1。如果总和是一个二维数组,则消息将指示期望1D或2D数组。
相关问题

python def data_process(raw_text_iter: dataset.IterableDataset) -> Tensor:

This is an example of a Python function definition that takes an iterable dataset (`raw_text_iter`) as input and returns a `Tensor` object. The general syntax of the function definition is as follows: ``` def function_name(input_argument: input_type) -> output_type: """ Docstring: description of the function """ # Function body return output_value ``` In this case, the function is named `data_process` and takes a single input argument called `raw_text_iter`. The input argument is annotated with the `dataset.IterableDataset` type, indicating the expected type of the argument. The `-> Tensor` annotation specifies that the function returns a `Tensor` object. The function body, which is not shown here, would contain the code that processes the input data and returns a `Tensor` object. The docstring is a string literal that describes what the function does, and it is enclosed in triple quotes. It is optional, but it is good practice to include informative docstrings in your code to help others understand how to use your function.

def __str__

__str__ is a special method in Python that is used to return a string representation of an object. It is invoked when an object is passed to the built-in str() function or when the object is used in a string context (such as when it is printed). For example, suppose we have a class called Person with instance variables name and age: ``` class Person: def __init__(self, name, age): self.name = name self.age = age def __str__(self): return f"{self.name} is {self.age} years old" ``` The __str__ method is defined to return a string that describes the object. In this case, it returns a string that concatenates the person's name and age. When we create a Person object and print it: ``` p = Person("Alice", 30) print(p) ``` The __str__ method is invoked automatically, and the output will be: ``` Alice is 30 years old ```

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@staticmethod def inference_detection(image, train=False): """ HandSegNet: Detects the hand in the input image by segmenting it. Inputs: image: [B, H, W, 3] tf.float32 tensor, Image with mean subtracted train: bool, True in case weights should be trainable Outputs: scoremap_list_large: list of [B, 256, 256, 2] tf.float32 tensor, Scores for the hand segmentation classes """ with tf.compat.v1.variable_scope('HandSegNet'): scoremap_list = list() layers_per_block = [2, 2, 4, 4] out_chan_list = [64, 128, 256, 512] pool_list = [True, True, True, False] # learn some feature representation, that describes the image content well x = image for block_id, (layer_num, chan_num, pool) in enumerate(zip(layers_per_block, out_chan_list, pool_list), 1): for layer_id in range(layer_num): x = ops.conv_relu(x, 'conv%d_%d' % (block_id, layer_id+1), kernel_size=3, stride=1, out_chan=chan_num, trainable=train) if pool: x = ops.max_pool(x, 'pool%d' % block_id) x = ops.conv_relu(x, 'conv5_1', kernel_size=3, stride=1, out_chan=512, trainable=train) encoding = ops.conv_relu(x, 'conv5_2', kernel_size=3, stride=1, out_chan=128, trainable=train) # use encoding to detect initial scoremap x = ops.conv_relu(encoding, 'conv6_1', kernel_size=1, stride=1, out_chan=512, trainable=train) scoremap = ops.conv(x, 'conv6_2', kernel_size=1, stride=1, out_chan=2, trainable=train) scoremap_list.append(scoremap) # upsample to full size s = image.get_shape().as_list() scoremap_list_large = [tf.image.resize_images(x, (s[1], s[2])) for x in scoremap_list] return scoremap_list_large详细注释

Calling tool in ralgen.py: /hpc/simulation/jzhou/awakening_soc/infra/flow/dv/tools/ralgen/../../../../util/regtool.py -s -t /tmp/mct_dv_bb_env-ral_0.1cvwdpui1 /hpc/simulation/jzhou/awakening_soc/design/bb/dv/env/../../data/bb.hjson RAL pkg for bb written to /tmp/mct_dv_bb_env-ral_0.1cvwdpui1. INFO: Wrote dependency graph to /hpc/simulation/jzhou/awakening_soc/scratch/default/gnss_top-sim-vcs/default/sim-vcs/mct_dv_bb_sim_0.1.deps-after-generators.dot WARNING: The file ../../include/yuu_ahb_interface.svi in /hpc/simulation/jzhou/awakening_soc/infra/verif/uvc/yuu_ahb/src/sv/ahb_env.core is not within the directory containing the core file. This is deprecated and will be an error in a future FuseSoC version. A typical solution is to move core file into the root directory of the IP block it describes WARNING: The file ../../include/yuu_ahb_pkg.sv in /hpc/simulation/jzhou/awakening_soc/infra/verif/uvc/yuu_ahb/src/sv/ahb_env.core is not within the directory containing the core file. This is deprecated and will be an error in a future FuseSoC version. A typical solution is to move core file into the root directory of the IP block it describes. WARNING: The file ../../test/ahb_base_seq.sv in /hpc/simulation/jzhou/awakening_soc/infra/verif/uvc/yuu_ahb/src/sv/ahb_env.core is not within the directory containing the core file. This is deprecated and will be an error in a future FuseSoC version. A typical solution is to move core file into the root directory of the IP block it describes. ERROR: Setup failed : Cannot find ../../test/ahb_base_seq.sv in : /hpc/simulation/jzhou/awakening_soc/infra/verif/uvc/yuu_ahb/src/sv

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