0h(dZddlZddlZddlZddlZddlZddlZddlZddl Z ddl Z ddl Z ddl Z ddl Z ddlZddlZddlZddlZddlmZddlmZddlZddlmcmZddlmZddlmZddlm Z ddl!m"Z"dd l#m$Z$dd l%m&Z& d3d Z'd Z)d Z*dZ+dZ,dZ-d4dZ.e$d d5dZ/dZ0d6dZ1d7dZ2d8dZ3GddZ4dZ5e$dGddZ6dZ7ia8da9ejtZ;da<e=Z>da?e jZAd ZBd!ZCd"ZDd#ZEd$ZFe$d%Gd&d'ZGe$d(Gd)d*eGZHd3d+ZId,ZJe$d-Gd.d/eGZKe$d0d1 d9d2ZLy):z(Utilities for file download and caching.N)abstractmethod)closing)urlsplit)io_utils) tf_inspect)Progbar) keras_export)urlopencdd}t||}t|d5}|||D]}|j| dddy#1swYyxYw)aReplacement for `urlretrieve` for Python 2. Under Python 2, `urlretrieve` relies on `FancyURLopener` from legacy `urllib` module, known to have issues with proxy management. Args: url: url to retrieve. filename: where to store the retrieved data locally. reporthook: a hook function that will be called once on establishment of the network connection and once after each block read thereafter. 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R#HD     s !AAc$tttttt f}t |tjtjf|zrytj|xs,t |txst |tjS)z*Check if `x` is a TF-Keras generator type.F)strlisttupledictset frozenset isinstancetfTensornpndarrayr isgeneratorSequencetypingIterator)xbuiltin_iteratorss ris_generator_or_sequencer7^sjdE4i@!bii,/@@Aq! * a " * a )cztjjtjj|SN)ospathrealpathabspath)r<s r _resolve_pathr?js$ 77  BGGOOD1 22r8crttjj||j |Sr:)r?r;r<join startswith)r<base_dirs r_is_path_in_dirrDns' h5 6 A A( KKr8cttjj|tjj |j }t |j|S)N)rC)r?r;r<rAdirnamenamerDlinkname)rbasetips r_is_link_in_dirrKrs=  T277??499+EF GC 4==3 77r8c#Ktd}|D]y}d}t|j|rd}|n2|js|j rt ||rd}||rWt jd|jd{yw)N.FTz2Skipping invalid path during archive extraction: 'z'.)r?rDrGissymislnkrKwarningswarn)membersrCfinfo valid_paths r_filter_safe_pathsrUwsS!H  5::x 0JK [[]ekkmuh/!   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Example: ```python path_to_downloaded_file = tf.keras.utils.get_file( origin="https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz", extract=True, ) ``` Args: fname: Name of the file. If an absolute path `/path/to/file.txt` is specified the file will be saved at that location. If `None`, the name of the file at `origin` will be used. origin: Original URL of the file. untar: Deprecated in favor of `extract` argument. boolean, whether the file should be decompressed md5_hash: Deprecated in favor of `file_hash` argument. md5 hash of the file for verification file_hash: The expected hash string of the file after download. The sha256 and md5 hash algorithms are both supported. cache_subdir: Subdirectory under the TF-Keras cache dir where the file is saved. If an absolute path `/path/to/folder` is specified the file will be saved at that location. hash_algorithm: Select the hash algorithm to verify the file. options are `'md5'`, `'sha256'`, and `'auto'`. The default 'auto' detects the hash algorithm in use. extract: True tries extracting the file as an Archive, like tar or zip. archive_format: Archive format to try for extracting the file. Options are `'auto'`, `'tar'`, `'zip'`, and `None`. `'tar'` includes tar, tar.gz, and tar.bz files. The default `'auto'` corresponds to `['tar', 'zip']`. None or an empty list will return no matches found. cache_dir: Location to store cached files, when None it defaults to `~/.keras/`. Returns: Path to the downloaded file. ⚠️ **Warning on malicious downloads** ⚠️ Downloading something from the Internet carries a risk. NEVER download a file/archive if you do not trust the source. We recommend that you specify the `file_hash` argument (if the hash of the source file is known) to make sure that the file you are getting is the one you expect. NzCPlease specify the "origin" argument (URL of the file to download).~z.kerasmd5z/tmpz5Can't parse the file name from the origin provided: 'z0'.Please specify the `fname` as the input param.z.tar.gzF) algorithmzNA local file was found, but it seems to be incomplete or outdated because the z0 file hash does not match the original value of z! so we will re-download the data.TzDownloading data from ceZdZdZdZdZy)get_file..DLProgbarz1Manage progress bar state for use in urlretrieve.c d|_d|_yNF)progbarfinishedselfs r__init__z$get_file..DLProgbar.__init__Ds# % r8cX|js|dk(rd}t||_||z}||jj|y||kr|jj|y|js7|jj|jjd|_yy)Nr T)ryrupdaterztarget)r| block_num block_sizercurrents r__call__z$get_file..DLProgbar.__call__Hs||!R'%) #*:#6DL#j0%LL''0+ ++G4!]] ++DLL,?,?@(, +r8N)__name__ __module__ __qualname____doc__r}rr8r DLProgbarrvAs C & -r8rz!URL fetch failure on {}: {} -- {}z+Incomplete or corrupted file detected. 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Example: ```python _hash_file('/path/to/file.zip') 'e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855' ``` Args: fpath: Path to the file being validated. algorithm: Hash algorithm, one of `'auto'`, `'sha256'`, or `'md5'`. The default `'auto'` detects the hash algorithm in use. chunk_size: Bytes to read at a time, important for large files. Returns: The file hash. rbc&jSr:)r)r fpath_filesrz_hash_file..s*//*"=r8r8N)r,r&rriterr hexdigest)rrtrhasherrrs ` @r _hash_filers&)S! + eT !j=sC !E MM%  !!     !    s 'A--Bcbt||}tt|||t|k(ryy)aValidates a file against a sha256 or md5 hash. Args: fpath: path to the file being validated file_hash: The expected hash string of the file. The sha256 and md5 hash algorithms are both supported. algorithm: Hash algorithm, one of 'auto', 'sha256', or 'md5'. The default 'auto' detects the hash algorithm in use. chunk_size: Bytes to read at a time, important for large files. 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Notes: `Sequence` is a safer way to do multiprocessing. This structure guarantees that the network will only train once on each sample per epoch, which is not the case with generators. Examples: ```python from skimage.io import imread from skimage.transform import resize import numpy as np import math # Here, `x_set` is list of path to the images # and `y_set` are the associated classes. class CIFAR10Sequence(tf.keras.utils.Sequence): def __init__(self, x_set, y_set, batch_size): self.x, self.y = x_set, y_set self.batch_size = batch_size def __len__(self): return math.ceil(len(self.x) / self.batch_size) def __getitem__(self, idx): low = idx * self.batch_size # Cap upper bound at array length; the last batch may be smaller # if the total number of items is not a multiple of batch size. high = min(low + self.batch_size, len(self.x)) batch_x = self.x[low:high] batch_y = self.y[low:high] return np.array([ resize(imread(file_name), (200, 200)) for file_name in batch_x]), np.array(batch_y) ``` ct)zGets batch at position `index`. Args: index: position of the batch in the Sequence. 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Skip the data if it is `None`. Yields: The next element in the queue, i.e. a tuple `(inputs, targets)` or `(inputs, targets, sample_weights)`. TrTr1N)rrr task_doneEmptyrr2)r|inputsrs rrzOrderedEnqueuer.getRsoo dA>BBD??$JJ((*% L oo;;    s;B9AB/B9B9B6B9B6B11B66B9)FF) rrrrr}r"rPr%r __classcell__r@s@rr<r< s!*":r8r<c|atj}d|j|_|,tj j ||jz||j|jddyy)aInitializer function for pool workers. Args: gens: State which should be made available to worker processes. random_seed: An optional value with which to seed child processes. id_queue: A multiprocessing Queue of worker ids. This is used to indicate that a worker process was created by TF-Keras and can be terminated using the cleanup_all_keras_forkpools utility. Keras_worker_NTrMr\) rrcurrent_processrGr/rUseedidentrV)gens random_seedid_queue worker_procs rrFrFjst!113K'{'7'7&89K {[%6%667 [&&dC @r8c&tt|S)a[Gets the next value from the generator `uid`. To allow multiple generators to be used at the same time, we use `uid` to get a specific one. 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The position where padding or truncation happens is determined by the arguments `padding` and `truncating`, respectively. Pre-padding or removing values from the beginning of the sequence is the default. >>> sequence = [[1], [2, 3], [4, 5, 6]] >>> tf.keras.utils.pad_sequences(sequence) array([[0, 0, 1], [0, 2, 3], [4, 5, 6]], dtype=int32) >>> tf.keras.utils.pad_sequences(sequence, value=-1) array([[-1, -1, 1], [-1, 2, 3], [ 4, 5, 6]], dtype=int32) >>> tf.keras.utils.pad_sequences(sequence, padding='post') array([[1, 0, 0], [2, 3, 0], [4, 5, 6]], dtype=int32) >>> tf.keras.utils.pad_sequences(sequence, maxlen=2) array([[0, 1], [2, 3], [5, 6]], dtype=int32) Args: sequences: List of sequences (each sequence is a list of integers). maxlen: Optional Int, maximum length of all sequences. If not provided, sequences will be padded to the length of the longest individual sequence. dtype: (Optional). Type of the output sequences. To pad sequences with variable length strings, you can use `object`. Defaults to `"int32"`. padding: String, "pre" or "post" (optional): pad either before or after each sequence. Defaults to `"pre"`. truncating: String, "pre" or "post" (optional): remove values from sequences larger than `maxlen`, either at the beginning or at the end of the sequences. Defaults to `"pre"`. value: Float or String, padding value. (Optional). Defaults to `0.`. Returns: Numpy array with shape `(len(sequences), maxlen)` Raises: ValueError: In case of invalid values for `truncating` or `padding`, or in case of invalid shape for a `sequences` entry. rz`sequences` must be iterable.rTrNFz=`sequences` must be a list of iterables. Found non-iterable: z`dtype` z( is not compatible with `value`'s type: z; You should set `dtype=object` for variable length strings.)dtypeprepostzTruncating type "z" not understoodzShape of sample z of sequence at position z" is different from expected shape zPadding type ")hasattrrrrwr/asarrayshape TypeErrorr&max issubdtypestr_r,objecttypefull enumerate) sequencesmaxlenr}padding truncatingr num_sampleslengths sample_shapeflagr5r is_dtype_stridxstruncs r pad_sequencesrsoT 9i (899i.KGL D    NN3q6 "A!zz!}22126  ~==0BMM rww5L%%6/,ugEE{m   f% 4e5IAI&IQ1v   vghKE 6 !gvJE0 rs /   !!+')4:,$ T 3L8 $5p$%     w&wt( >,  F $%KK&K\  goo e (*   %",-|"|".|"~+,[&[-[|A6 ( -.Y(Y/Yx!M    E E r8