Question

我正在使用 GPU 从 COLAB 中的图像运行文本识别系统。如果没有 GPU ，它可以正常工作。但是使用GPU时，它会以 InvalidArgumentError 终止。我的模型如下。

class Model:
    # Model Constants
    batchSize = 10
    imgSize = (800, 64)
    maxTextLen = 100

    def __init__(self, charList, decoderType=DecoderType.BestPath, mustRestore=False):
        self.charList = charList
        self.decoderType = decoderType
        self.mustRestore = mustRestore
        self.snapID = 0

        tf.keras.backend.clear_session()



        with(tf.device('/gpu:0')):
            # CNN
            with tf.name_scope('CNN'):
                with tf.name_scope('Input'):
                    self.inputImgs = tf.placeholder(tf.float32, shape=(
                        Model.batchSize, Model.imgSize[0], Model.imgSize[1]))
                cnnOut4d = self.setupCNN(self.inputImgs)

            # RNN
            with tf.name_scope('RNN'):
                rnnOut3d = self.setupRNN(cnnOut4d)

            # # Debuging CTC
            # self.rnnOutput = tf.transpose(rnnOut3d, [1, 0, 2])

            # CTC
            with tf.name_scope('CTC'):
                (self.loss, self.decoder) = self.setupCTC(rnnOut3d)
                self.training_loss_summary = tf.summary.scalar(
                    'loss', self.loss)  # Tensorboard: Track loss

            # Optimize NN parameters
            with tf.name_scope('Optimizer'):
                self.batchesTrained = 0
                self.learningRate = tf.placeholder(tf.float32, shape=[])
                self.optimizer = tf.train.RMSPropOptimizer(
                    self.learningRate).minimize(self.loss)

            # Initialize TensorFlow
            (self.sess, self.saver) = self.setupTF()

            self.writer = tf.summary.FileWriter(
                './logs', self.sess.graph)  # Tensorboard: Create writer
            self.merge = tf.summary.merge(
                [self.training_loss_summary])  # Tensorboard: Merge

但是在运行时会显示类似错误

InvalidArgumentError：无法分配设备进行操作   CTC / CTC_Loss / CTCLoss：无法满足明确的设备规范   '/ device：GPU：0'，因为没有GPU设备支持的内核   可用。托管调试信息：托管组具有以下内容   类型和支持的设备：根   成员（assigned_device_name_index _ =-1   required_device_name _ ='/ device：GPU：0'signed_device_name_ =''   resource_device_name _ =“ supported_device_types _ = [CPU]   Possible_devices _ = [] CTCLoss：CPU

托管成员，用户请求的设备和分配的框架   设备（如果有）：CTC / CTC_Loss / CTCLoss（CTCLoss）/ device：GPU：0

操作：CTCLoss节点属性：ignore_longer_outputs_than_inputs = true，   preprocess_collapse_repeated = false，ctc_merge_repeated = true，   T = DT_FLOAT注册的内核：device ='CPU'; [DT_DOUBLE]中的T
  设备='CPU'; [DT_FLOAT]中的T

[[{{node CTC / CTC_Loss / CTCLoss}}]]

在处理上述异常期间，发生了另一个异常：

InvalidArgumentError跟踪（最近的调用）   持续）   /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py   在_do_call（self，fn，* args）1382
  '\ nsession_config.graph_options.rewrite_options。' 1383
  'disable_meta_optimizer = True'）   -> 1384提高类型（e）（node_def，op，消息）1385 1386 def _extend_graph（自身）：

InvalidArgumentError：无法分配设备进行操作   CTC / CTC_Loss / CTCLoss：无法满足明确的设备规范   '/ device：GPU：0'，因为没有GPU设备支持的内核   可用。托管调试信息：托管组具有以下内容   类型和支持的设备：根   成员（assigned_device_name_index _ =-1   required_device_name _ ='/ device：GPU：0'signed_device_name_ =''   resource_device_name _ =“ supported_device_types _ = [CPU]   Possible_devices _ = [] CTCLoss：CPU

托管成员，用户请求的设备和分配的框架   设备（如果有）：CTC / CTC_Loss / CTCLoss（CTCLoss）/ device：GPU：0

我的设置CTC功能如下

    def setupCTC(self, ctcIn3d):
        """ Create CTC loss and decoder and return them """
        # BxTxC -> TxBxC
        ctcIn3dTBC = tf.transpose(ctcIn3d, [1, 0, 2])

        # Ground truth text as sparse tensor
        with tf.name_scope('CTC_Loss'):
            self.gtTexts = tf.SparseTensor(tf.placeholder(tf.int64, shape=[
                                           None, 2]), tf.placeholder(tf.int32, [None]), tf.placeholder(tf.int64, [2]))
            # Calculate loss for batch
            self.seqLen = tf.placeholder(tf.int32, [None])
            loss = tf.nn.ctc_loss(labels=self.gtTexts, inputs=ctcIn3dTBC, sequence_length=self.seqLen,
                                  ctc_merge_repeated=True, ignore_longer_outputs_than_inputs=True)
        with tf.name_scope('CTC_Decoder'):
            # Decoder: Best path decoding or Word beam search decoding
            if self.decoderType == DecoderType.BestPath:
                decoder = tf.nn.ctc_greedy_decoder(
                    inputs=ctcIn3dTBC, sequence_length=self.seqLen)
            elif self.decoderType == DecoderType.WordBeamSearch:
                # Import compiled word beam search operation (see https://github.com/githubharald/CTCWordBeamSearch)
                word_beam_search_module = tf.load_op_library(
                    './TFWordBeamSearch.so')

                # Prepare: dictionary, characters in dataset, characters forming words
                chars = codecs.open(FilePaths.fnCharList, 'r', 'utf8').read()
                wordChars = codecs.open(
                    FilePaths.fnWordCharList, 'r', 'utf8').read()
                corpus = codecs.open(FilePaths.fnCorpus, 'r', 'utf8').read()

                # # Decoder using the "NGramsForecastAndSample": restrict number of (possible) next words to at most 20 words: O(W) mode of word beam search
                # decoder = word_beam_search_module.word_beam_search(tf.nn.softmax(ctcIn3dTBC, dim=2), 25, 'NGramsForecastAndSample', 0.0, corpus.encode('utf8'), chars.encode('utf8'), wordChars.encode('utf8'))

                # Decoder using the "Words": only use dictionary, no scoring: O(1) mode of word beam search
                decoder = word_beam_search_module.word_beam_search(tf.nn.softmax(
                    ctcIn3dTBC, dim=2), 25, 'Words', 0.0, corpus.encode('utf8'), chars.encode('utf8'), wordChars.encode('utf8'))

        # Return a CTC operation to compute the loss and CTC operation to decode the RNN output
        return (tf.reduce_mean(loss), decoder)

InvalidArgumentError：在COLAB中使用GPU运行代码时

0 个答案: