AVhUk"dZddlZddlZddlZddlZddlZddlmZddl m Z ddl m Z ddl m Z ddl mZddl mZdd lmZdd lmZdd ldd lmZdd lmZddlmZddlmZddlmZdZ eeeddedgej@d4dZ!d5dZ"dZ#edej@dZ$e jJddZ&dZ'eddd6dZ(edd d7d!Z)edd" d7d#Z*edd$d6d%Z+edd&e jXjZfd'Z.d(Z/edd)d6d*Z0e jbd+e jbd,e jbd-e jbd.e jbd/e jbd0e jbd1e jbd2e jbd3y#e$rYwxYw)8zCLogging and Summary Operations. API docstring: tensorflow.logging N)logging) pywrap_tfe)dtypes)ops) sparse_tensor) tensor_util)gen_logging_ops) string_ops)*) tf_logging)dispatch)nest) deprecated) tf_exportc,tjyN)r%TFE_Py_EnableInteractivePythonLoggingQ/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/ops/logging_ops.pyenable_interactive_loggingr.s 224rz 2018-08-20aUse tf.print instead of tf.Print. Note that tf.print returns a no-output operator that directly prints the output. Outside of defuns or eager mode, this operator will not be executed unless it is directly specified in session.run or used as a control dependency for other operators. This is only a concern in graph mode. Below is an example of how to ensure tf.print executes in graph mode: Print)v1c6tj||||||S)aPrints a list of tensors. This is an identity op (behaves like `tf.identity`) with the side effect of printing `data` when evaluating. Note: This op prints to the standard error. It is not currently compatible with jupyter notebook (printing to the notebook *server's* output, not into the notebook). @compatibility(TF2) This API is deprecated. Use `tf.print` instead. `tf.print` does not need the `input_` argument. `tf.print` works in TF2 when executing eagerly and inside a `tf.function`. In TF1-styled sessions, an explicit control dependency declaration is needed to execute the `tf.print` operation. Refer to the documentation of `tf.print` for more details. @end_compatibility Args: input_: A tensor passed through this op. data: A list of tensors to print out when op is evaluated. message: A string, prefix of the error message. first_n: Only log `first_n` number of times. Negative numbers log always; this is the default. summarize: Only print this many entries of each tensor. If None, then a maximum of 3 elements are printed per input tensor. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type and contents as `input_`. ```python sess = tf.compat.v1.Session() with sess.as_default(): tensor = tf.range(10) print_op = tf.print(tensor) with tf.control_dependencies([print_op]): out = tf.add(tensor, tensor) sess.run(out) ``` )r _print)input_datamessagefirst_n summarizenames rrrCs l   gw 4 PPrc|}tjd}||vr#|t|jddz}||vr#|S)z9Generate and return a string that does not appear in `x`.r )randomRandomstrrandint)xdefault_placeholder placeholderrngs r_generate_placeholder_stringr-sG#+ a#qCKK1$5 66K q rcHt|txr|jdS)z-Returns True if output_stream is a file path.zfile://) isinstancer' startswith) output_streams r _is_filepathr2s M3 ' OM,D,DY,OOrprintc z |jdtj}|jdd}|jdd}|jdd}|jdtj}|rt d |zd}|r|d z}itj d tjd tjd tjd tjdtjdtjdtjdtjdtjd tjd tjd tj dtjdtjdtjdtjdtjdi}t#|r|} n0|j%|} | st dt'|zdzdzt)|dk(rxt+j,|dr`t/|dt0j2sC|dj4j6dk(r'|dj8t:j<k(r|d} ng} g} g} |D]i}t/|t>j@r<| jCt?j@tE|jGY| jC|ktIjJd| }djMd|D}tO|}|D]$}g}tIjP|D]}t/|t0j2rU| jS|jT|jV|jXg|jCdj[|||rt+j,|r#| jC||jC||jC|t/|t&r|}n)t]j^tIj`||}| jC|'|jM| }|jcd|zdz|}tejf| ||||} tijj| | ||S)aPrint the specified inputs. A TensorFlow operator that prints the specified inputs to a desired output stream or logging level. The inputs may be dense or sparse Tensors, primitive python objects, data structures that contain tensors, and printable Python objects. Printed tensors will recursively show the first and last elements of each dimension to summarize. Example: Single-input usage: ```python tensor = tf.range(10) tf.print(tensor, output_stream=sys.stderr) ``` (This prints "[0 1 2 ... 7 8 9]" to sys.stderr) Multi-input usage: ```python tensor = tf.range(10) tf.print("tensors:", tensor, {2: tensor * 2}, output_stream=sys.stdout) ``` (This prints "tensors: [0 1 2 ... 7 8 9] {2: [0 2 4 ... 14 16 18]}" to sys.stdout) Changing the input separator: ```python tensor_a = tf.range(2) tensor_b = tensor_a * 2 tf.print(tensor_a, tensor_b, output_stream=sys.stderr, sep=',') ``` (This prints "[0 1],[0 2]" to sys.stderr) Usage in a `tf.function`: ```python @tf.function def f(): tensor = tf.range(10) tf.print(tensor, output_stream=sys.stderr) return tensor range_tensor = f() ``` (This prints "[0 1 2 ... 7 8 9]" to sys.stderr) *Compatibility usage in TF 1.x graphs*: In graphs manually created outside of `tf.function`, this method returns the created TF operator that prints the data. To make sure the operator runs, users need to pass the produced op to `tf.compat.v1.Session`'s run method, or to use the op as a control dependency for executed ops by specifying `with tf.compat.v1.control_dependencies([print_op])`. ```python tf.compat.v1.disable_v2_behavior() # for TF1 compatibility only sess = tf.compat.v1.Session() with sess.as_default(): tensor = tf.range(10) print_op = tf.print("tensors:", tensor, {2: tensor * 2}, output_stream=sys.stdout) with tf.control_dependencies([print_op]): tripled_tensor = tensor * 3 sess.run(tripled_tensor) ``` (This prints "tensors: [0 1 2 ... 7 8 9] {2: [0 2 4 ... 14 16 18]}" to sys.stdout) Note: In Jupyter notebooks and colabs, `tf.print` prints to the notebook cell outputs. It will not write to the notebook kernel's console logs. Args: *inputs: Positional arguments that are the inputs to print. Inputs in the printed output will be separated by spaces. Inputs may be python primitives, tensors, data structures such as dicts and lists that may contain tensors (with the data structures possibly nested in arbitrary ways), and printable python objects. output_stream: The output stream, logging level, or file to print to. Defaults to sys.stderr, but sys.stdout, tf.compat.v1.logging.info, tf.compat.v1.logging.warning, tf.compat.v1.logging.error, absl.logging.info, absl.logging.warning and absl.logging.error are also supported. To print to a file, pass a string started with "file://" followed by the file path, e.g., "file:///tmp/foo.out". summarize: The first and last `summarize` elements within each dimension are recursively printed per Tensor. If None, then the first 3 and last 3 elements of each dimension are printed for each tensor. If set to -1, it will print all elements of every tensor. sep: The string to use to separate the inputs. Defaults to " ". end: End character that is appended at the end the printed string. Defaults to the newline character. name: A name for the operation (optional). Returns: None when executing eagerly. During graph tracing this returns a TF operator that prints the specified inputs in the specified output stream or logging level. This operator will be automatically executed except inside of `tf.compat.v1` graphs and sessions. Raises: ValueError: If an unsupported output stream is specified. r1r!Nr sep endz/Unrecognized keyword arguments for tf.print: %s_formatstdoutstderrz log(info)z log(warning)z log(error)z2Unsupported output stream, logging level, or file.zg. Supported streams are sys.stdout, sys.stderr, tf.logging.info, tf.logging.warning, tf.logging.error. z4File needs to be in the form of 'file://'.rc4tj|rdS|S)N)r is_tf_type)r)s rzprint_v2..Os ..q1"qrc3FK|]}tj|ywr)pprintpformat).0r)s r zprint_v2..Rs$:q$:s!z-SparseTensor(indices={}, values={}, shape={})')inputstemplater+r r!)r1r!r8)6popsysr;oslinesep ValueErrorr:r INFOinfoWARNwarningwarnERRORerrorrWARNINGr2getr'lenrr?r/r SparseTensorshapendimsdtyperstringpy_collections OrderedDictappendsorteditemsr map_structurejoinr-flattenextendindicesvalues dense_shapeformatrBrCpack_sequence_asreplacer string_formatr print_v2)rGkwargsr1r!r r6r8 format_nameoutput_stream_to_constantoutput_stream_stringformatted_string templatestensorsinputs_ordered_dicts_sortedrtensor_free_structuretensor_free_templater+ placeholdersr) cur_templaterHs rrmrmsj**_cjj9- FD !$jja() 5## 5"**%# FO PP+ "K jj( jj(oo{oo{  oo~  . oo~  llKllKllKoo~llNoo~ llN!"mm\#$mm\%*- (488G  K=)*@@ N N OO &kQ;11&)< fQi!;!; < ay!q 6==(HayIG #%3 FN66 7#**  & &vflln'= > @ $**62 3 !..8#%88$:#8$::./CDK$%l ||F# !! a33 4 ..!))QXXq}}= >   =DD{K9 : # #A & ..    k *   a  ! FC  ~~  ! !&, 79 |$I$%Vxx "Hk 1C 7EH!//    ! !&:3 PPrcRt|dgt|jdz zzS)Nr<)listrWrG)opgrads r _PrintGradr~s% dtfBII 23 33rcF||}|D]}tj||yr)radd_to_collection)val collectionsdefault_collectionskeys r_Collectrs,%K $c#s#$rz 2016-11-30zPlease switch to tf.summary.histogram. Note that tf.summary.histogram uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in.ctj|d||g5}tj|||}t ||tj j gddd|S#1swYSxYw)aOutputs a `Summary` protocol buffer with a histogram. This ops is deprecated. Please switch to tf.summary.histogram. For an explanation of why this op was deprecated, and information on how to migrate, look ['here'](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/deprecated/__init__.py) The generated [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) has one summary value containing a histogram for `values`. This op reports an `InvalidArgument` error if any value is not finite. Args: tag: A `string` `Tensor`. 0-D. Tag to use for the summary value. values: A real numeric `Tensor`. Any shape. Values to use to build the histogram. collections: Optional list of graph collections keys. The new summary op is added to these collections. Defaults to `[GraphKeys.SUMMARIES]`. name: A name for the operation (optional). Returns: A scalar `Tensor` of type `string`. The serialized `Summary` protocol buffer. HistogramSummary)tagrgr!N)r name_scoper histogram_summaryr GraphKeys SUMMARIES)rrgrr!scopers rrrsgB ~~d.f >:%  + +F OC S+ 7 789: *: * ?A##A-aPlease switch to tf.summary.image. Note that tf.summary.image uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, the max_images argument was renamed to max_outputs.ctj|d||g5}tj||||}t ||tj j gddd|S#1swYSxYw)aOutputs a `Summary` protocol buffer with images. For an explanation of why this op was deprecated, and information on how to migrate, look ['here'](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/deprecated/__init__.py) The summary has up to `max_images` summary values containing images. The images are built from `tensor` which must be 4-D with shape `[batch_size, height, width, channels]` and where `channels` can be: * 1: `tensor` is interpreted as Grayscale. * 3: `tensor` is interpreted as RGB. * 4: `tensor` is interpreted as RGBA. The images have the same number of channels as the input tensor. For float input, the values are normalized one image at a time to fit in the range `[0, 255]`. `uint8` values are unchanged. The op uses two different normalization algorithms: * If the input values are all positive, they are rescaled so the largest one is 255. * If any input value is negative, the values are shifted so input value 0.0 is at 127. They are then rescaled so that either the smallest value is 0, or the largest one is 255. The `tag` argument is a scalar `Tensor` of type `string`. It is used to build the `tag` of the summary values: * If `max_images` is 1, the summary value tag is '*tag*/image'. * If `max_images` is greater than 1, the summary value tags are generated sequentially as '*tag*/image/0', '*tag*/image/1', etc. Args: tag: A scalar `Tensor` of type `string`. Used to build the `tag` of the summary values. tensor: A 4-D `uint8` or `float32` `Tensor` of shape `[batch_size, height, width, channels]` where `channels` is 1, 3, or 4. max_images: Max number of batch elements to generate images for. collections: Optional list of ops.GraphKeys. The collections to add the summary to. Defaults to [ops.GraphKeys.SUMMARIES] name: A name for the operation (optional). Returns: A scalar `Tensor` of type `string`. The serialized `Summary` protocol buffer. ImageSummary)rtensor max_imagesr!N)rrr image_summaryrrr)rrrrr!rrs rrrsln ~~dNS&M::e  ' ' :E CC S+ 7 789: * : *s AA$$A.zPlease switch to tf.summary.audio. Note that tf.summary.audio uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in.c0tj|d||g5}tj|tjd}t j |||||}t||tjjgddd|S#1swYSxYw)atOutputs a `Summary` protocol buffer with audio. This op is deprecated. Please switch to tf.summary.audio. For an explanation of why this op was deprecated, and information on how to migrate, look ['here'](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/deprecated/__init__.py) The summary has up to `max_outputs` summary values containing audio. The audio is built from `tensor` which must be 3-D with shape `[batch_size, frames, channels]` or 2-D with shape `[batch_size, frames]`. The values are assumed to be in the range of `[-1.0, 1.0]` with a sample rate of `sample_rate`. The `tag` argument is a scalar `Tensor` of type `string`. It is used to build the `tag` of the summary values: * If `max_outputs` is 1, the summary value tag is '*tag*/audio'. * If `max_outputs` is greater than 1, the summary value tags are generated sequentially as '*tag*/audio/0', '*tag*/audio/1', etc. Args: tag: A scalar `Tensor` of type `string`. Used to build the `tag` of the summary values. tensor: A 3-D `float32` `Tensor` of shape `[batch_size, frames, channels]` or a 2-D `float32` `Tensor` of shape `[batch_size, frames]`. sample_rate: A Scalar `float32` `Tensor` indicating the sample rate of the signal in hertz. max_outputs: Max number of batch elements to generate audio for. collections: Optional list of ops.GraphKeys. The collections to add the summary to. Defaults to [ops.GraphKeys.SUMMARIES] name: A name for the operation (optional). Returns: A scalar `Tensor` of type `string`. The serialized `Summary` protocol buffer. AudioSummary sample_rate)r[r!)rr max_outputsrr!N) rrconvert_to_tensorrfloat32r audio_summary_v2rrr)rrrrrr!rrs r audio_summaryrs` ~~dNS&M: :e''6>> ?K  * *    C  S+ 7 789 : * : *s A'B  Bz"Please switch to tf.summary.merge.ctj|d|5tj||}t ||gddd|S#1swYSxYw)aqMerges summaries. This op is deprecated. Please switch to tf.compat.v1.summary.merge, which has identical behavior. This op creates a [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) protocol buffer that contains the union of all the values in the input summaries. When the Op is run, it reports an `InvalidArgument` error if multiple values in the summaries to merge use the same tag. Args: inputs: A list of `string` `Tensor` objects containing serialized `Summary` protocol buffers. collections: Optional list of graph collections keys. The new summary op is added to these collections. Defaults to `[GraphKeys.SUMMARIES]`. name: A name for the operation (optional). Returns: A scalar `Tensor` of type `string`. The serialized `Summary` protocol buffer resulting from the merging. MergeSummary)rGr!N)rrr merge_summaryr)rGrr!rs rrr?sP8 ~~dNF3#  ' 'vD AC S+r"# *# *s %AAz&Please switch to tf.summary.merge_all.cHtj|}|syt|S)aMerges all summaries collected in the default graph. This op is deprecated. Please switch to tf.compat.v1.summary.merge_all, which has identical behavior. Args: key: `GraphKey` used to collect the summaries. Defaults to `GraphKeys.SUMMARIES`. Returns: If no summaries were collected, returns None. Otherwise returns a scalar `Tensor` of type `string` containing the serialized `Summary` protocol buffer resulting from the merging. N)rget_collectionr)r summary_opss rmerge_all_summariesras%"""3'+    %%rctjtjj}| |r|d}nd}|:t }|.tj tjj||S)a_Returns a single Summary op that would run all summaries. Either existing one from `SUMMARY_OP` collection or merges all existing summaries. Returns: If no summaries were collected, returns None. Otherwise returns a scalar `Tensor` of type `string` containing the serialized `Summary` protocol buffer resulting from the merging. Nr)rrr SUMMARY_OPrr) summary_ops rget_summary_oprysi!!#--":":;*a=jj$&J CMM44jA ra-Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.ctj|d||g5}tj|||}t ||tj j gddd|S#1swYSxYw)aOutputs a `Summary` protocol buffer with scalar values. This ops is deprecated. Please switch to tf.summary.scalar. For an explanation of why this op was deprecated, and information on how to migrate, look ['here'](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/deprecated/__init__.py) The input `tags` and `values` must have the same shape. The generated summary has a summary value for each tag-value pair in `tags` and `values`. Args: tags: A `string` `Tensor`. Tags for the summaries. values: A real numeric Tensor. Values for the summaries. collections: Optional list of graph collections keys. The new summary op is added to these collections. Defaults to `[GraphKeys.SUMMARIES]`. name: A name for the operation (optional). Returns: A scalar `Tensor` of type `string`. The serialized `Summary` protocol buffer. ScalarSummary)tagsrgr!N)rrr scalar_summaryrrr)rrgrr!rrs rrrse< ~~dOdF^<:  ( (d6 NC S+ 7 789: *: *rrrrAudioSummaryV2rr TensorSummaryTensorSummaryV2 Timestamp)NNNN)z{})NN)r5NN)2__doc__rr]rKrBr%rJabslrtensorflow.pythonrtensorflow.python.frameworkrrrrtensorflow.python.opsr r %tensorflow.python.ops.gen_logging_opstensorflow.python.platformr tensorflow.python.utilr r"tensorflow.python.util.deprecationr tensorflow.python.util.tf_exportrr get_ipython NameErroradd_dispatch_supportrr-r2rmRegisterGradientr~rrrrrrrrrrNotDifferentiablerrrrs % (.+531,41+'965 - LBC wi ,QC,QdP 7 vPvPxg44$ 45  5  D +, 5 , 5 p 45 " 5 5 5 p L>? @ B LBCMM33&D&.0   :()n%n%&'n%o&o&'(k"Qs.HH H