@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
时间: 2023-12-06 14:02:46 浏览: 110
Python @函数装饰器及@staticmethod,@classmethod.docx
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这段代码是一个静态方法,用于对输入的图像进行手部分割,返回一个列表,包含了手部分割的分数图。其中,输入参数包括一个形状为[B, H, W, 3]的tf.float32类型的图像张量,train参数用于指定权重是否可训练。输出为一个形状为[B, 256, 256, 2]的tf.float32类型的列表,其中每个元素是手部分割的分数图。这段代码的实现主要是通过卷积和池化操作来学习图像的特征表示,并利用学到的特征表示来进行手部分割。具体实现包括了5个卷积层和1个上采样层,其中前4个卷积层包含了不同的卷积核数目和卷积层数,最后一个卷积层和上采样层用于生成手部分割的分数图。
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