From 372b12a2912b514d69367e909bc2002847e66ff6 Mon Sep 17 00:00:00 2001 From: zhangmozhe Date: Tue, 1 Dec 2020 17:02:07 +0800 Subject: [PATCH] update readme --- README.md | 2 +- ~tensorboardX/__init__.py | 5 - ~tensorboardX/crc32c.py | 314 --------- ~tensorboardX/embedding.py | 52 -- ~tensorboardX/event_file_writer.py | 192 ------ ~tensorboardX/graph.py | 73 -- ~tensorboardX/graph_onnx.py | 159 ----- ~tensorboardX/record_writer.py | 47 -- ~tensorboardX/src/__init__.py | 0 ~tensorboardX/src/attr_value_pb2.py | 600 ---------------- ~tensorboardX/src/event_pb2.py | 506 -------------- ~tensorboardX/src/graph_pb2.py | 129 ---- ~tensorboardX/src/node_def_pb2.py | 224 ------ ~tensorboardX/src/plugin_pr_curve_pb2.py | 97 --- ~tensorboardX/src/resource_handle_pb2.py | 152 ----- ~tensorboardX/src/summary_pb2.py | 834 ----------------------- ~tensorboardX/src/tensor_pb2.py | 351 ---------- ~tensorboardX/src/tensor_shape_pb2.py | 167 ----- ~tensorboardX/src/types_pb2.py | 135 ---- ~tensorboardX/src/versions_pb2.py | 118 ---- ~tensorboardX/summary.py | 269 -------- ~tensorboardX/writer.py | 482 ------------- ~tensorboardX/x2num.py | 93 --- 23 files changed, 1 insertion(+), 5000 deletions(-) delete mode 100644 ~tensorboardX/__init__.py delete mode 100644 ~tensorboardX/crc32c.py delete mode 100644 ~tensorboardX/embedding.py delete mode 100644 ~tensorboardX/event_file_writer.py delete mode 100644 ~tensorboardX/graph.py delete mode 100644 ~tensorboardX/graph_onnx.py delete mode 100644 ~tensorboardX/record_writer.py delete mode 100644 ~tensorboardX/src/__init__.py delete mode 100644 ~tensorboardX/src/attr_value_pb2.py delete mode 100644 ~tensorboardX/src/event_pb2.py delete mode 100644 ~tensorboardX/src/graph_pb2.py delete mode 100644 ~tensorboardX/src/node_def_pb2.py delete mode 100644 ~tensorboardX/src/plugin_pr_curve_pb2.py delete mode 100644 ~tensorboardX/src/resource_handle_pb2.py delete mode 100644 ~tensorboardX/src/summary_pb2.py delete mode 100644 ~tensorboardX/src/tensor_pb2.py delete mode 100644 ~tensorboardX/src/tensor_shape_pb2.py delete mode 100644 ~tensorboardX/src/types_pb2.py delete mode 100644 ~tensorboardX/src/versions_pb2.py delete mode 100644 ~tensorboardX/summary.py delete mode 100644 ~tensorboardX/writer.py delete mode 100644 ~tensorboardX/x2num.py diff --git a/README.md b/README.md index 6fc61f7..4c893ff 100644 --- a/README.md +++ b/README.md @@ -43,7 +43,7 @@ In order to colorize your own video, it requires to extract the video frames, an - Place your video frames into one folder, _e.g._, `./sample_videos/v32_180` - Place your reference images into another folder, _e.g._, `./sample_videos/v32` -If you want to _automatically_ retrieve color images, you can try the retrieval algorithm from \[[1](https://github.com/hmmlillian/Gray-Image-Retrieval)\] which will retrieve similar images from the ImageNet dataset. Or you can try [this link](https://github.com/pochih/CBIR) on your own image database. +If you want to _automatically_ retrieve color images, you can try the retrieval algorithm from [this link](https://github.com/hmmlillian/Gray-Image-Retrieval) which will retrieve similar images from the ImageNet dataset. Or you can try [this link](https://github.com/pochih/CBIR) on your own image database. ## Test diff --git a/~tensorboardX/__init__.py b/~tensorboardX/__init__.py deleted file mode 100644 index 472510a..0000000 --- a/~tensorboardX/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -"""A module for visualization with tensorboard -""" - -from .record_writer import RecordWriter -from .writer import FileWriter, SummaryWriter diff --git a/~tensorboardX/crc32c.py b/~tensorboardX/crc32c.py deleted file mode 100644 index 0870b1c..0000000 --- a/~tensorboardX/crc32c.py +++ /dev/null @@ -1,314 +0,0 @@ -import array - -CRC_TABLE = ( - 0x00000000, - 0xF26B8303, - 0xE13B70F7, - 0x1350F3F4, - 0xC79A971F, - 0x35F1141C, - 0x26A1E7E8, - 0xD4CA64EB, - 0x8AD958CF, - 0x78B2DBCC, - 0x6BE22838, - 0x9989AB3B, - 0x4D43CFD0, - 0xBF284CD3, - 0xAC78BF27, - 0x5E133C24, - 0x105EC76F, - 0xE235446C, - 0xF165B798, - 0x030E349B, - 0xD7C45070, - 0x25AFD373, - 0x36FF2087, - 0xC494A384, - 0x9A879FA0, - 0x68EC1CA3, - 0x7BBCEF57, - 0x89D76C54, - 0x5D1D08BF, - 0xAF768BBC, - 0xBC267848, - 0x4E4DFB4B, - 0x20BD8EDE, - 0xD2D60DDD, - 0xC186FE29, - 0x33ED7D2A, - 0xE72719C1, - 0x154C9AC2, - 0x061C6936, - 0xF477EA35, - 0xAA64D611, - 0x580F5512, - 0x4B5FA6E6, - 0xB93425E5, - 0x6DFE410E, - 0x9F95C20D, - 0x8CC531F9, - 0x7EAEB2FA, - 0x30E349B1, - 0xC288CAB2, - 0xD1D83946, - 0x23B3BA45, - 0xF779DEAE, - 0x05125DAD, - 0x1642AE59, - 0xE4292D5A, - 0xBA3A117E, - 0x4851927D, - 0x5B016189, - 0xA96AE28A, - 0x7DA08661, - 0x8FCB0562, - 0x9C9BF696, - 0x6EF07595, - 0x417B1DBC, - 0xB3109EBF, - 0xA0406D4B, - 0x522BEE48, - 0x86E18AA3, - 0x748A09A0, - 0x67DAFA54, - 0x95B17957, - 0xCBA24573, - 0x39C9C670, - 0x2A993584, - 0xD8F2B687, - 0x0C38D26C, - 0xFE53516F, - 0xED03A29B, - 0x1F682198, - 0x5125DAD3, - 0xA34E59D0, - 0xB01EAA24, - 0x42752927, - 0x96BF4DCC, - 0x64D4CECF, - 0x77843D3B, - 0x85EFBE38, - 0xDBFC821C, - 0x2997011F, - 0x3AC7F2EB, - 0xC8AC71E8, - 0x1C661503, - 0xEE0D9600, - 0xFD5D65F4, - 0x0F36E6F7, - 0x61C69362, - 0x93AD1061, - 0x80FDE395, - 0x72966096, - 0xA65C047D, - 0x5437877E, - 0x4767748A, - 0xB50CF789, - 0xEB1FCBAD, - 0x197448AE, - 0x0A24BB5A, - 0xF84F3859, - 0x2C855CB2, - 0xDEEEDFB1, - 0xCDBE2C45, - 0x3FD5AF46, - 0x7198540D, - 0x83F3D70E, - 0x90A324FA, - 0x62C8A7F9, - 0xB602C312, - 0x44694011, - 0x5739B3E5, - 0xA55230E6, - 0xFB410CC2, - 0x092A8FC1, - 0x1A7A7C35, - 0xE811FF36, - 0x3CDB9BDD, - 0xCEB018DE, - 0xDDE0EB2A, - 0x2F8B6829, - 0x82F63B78, - 0x709DB87B, - 0x63CD4B8F, - 0x91A6C88C, - 0x456CAC67, - 0xB7072F64, - 0xA457DC90, - 0x563C5F93, - 0x082F63B7, - 0xFA44E0B4, - 0xE9141340, - 0x1B7F9043, - 0xCFB5F4A8, - 0x3DDE77AB, - 0x2E8E845F, - 0xDCE5075C, - 0x92A8FC17, - 0x60C37F14, - 0x73938CE0, - 0x81F80FE3, - 0x55326B08, - 0xA759E80B, - 0xB4091BFF, - 0x466298FC, - 0x1871A4D8, - 0xEA1A27DB, - 0xF94AD42F, - 0x0B21572C, - 0xDFEB33C7, - 0x2D80B0C4, - 0x3ED04330, - 0xCCBBC033, - 0xA24BB5A6, - 0x502036A5, - 0x4370C551, - 0xB11B4652, - 0x65D122B9, - 0x97BAA1BA, - 0x84EA524E, - 0x7681D14D, - 0x2892ED69, - 0xDAF96E6A, - 0xC9A99D9E, - 0x3BC21E9D, - 0xEF087A76, - 0x1D63F975, - 0x0E330A81, - 0xFC588982, - 0xB21572C9, - 0x407EF1CA, - 0x532E023E, - 0xA145813D, - 0x758FE5D6, - 0x87E466D5, - 0x94B49521, - 0x66DF1622, - 0x38CC2A06, - 0xCAA7A905, - 0xD9F75AF1, - 0x2B9CD9F2, - 0xFF56BD19, - 0x0D3D3E1A, - 0x1E6DCDEE, - 0xEC064EED, - 0xC38D26C4, - 0x31E6A5C7, - 0x22B65633, - 0xD0DDD530, - 0x0417B1DB, - 0xF67C32D8, - 0xE52CC12C, - 0x1747422F, - 0x49547E0B, - 0xBB3FFD08, - 0xA86F0EFC, - 0x5A048DFF, - 0x8ECEE914, - 0x7CA56A17, - 0x6FF599E3, - 0x9D9E1AE0, - 0xD3D3E1AB, - 0x21B862A8, - 0x32E8915C, - 0xC083125F, - 0x144976B4, - 0xE622F5B7, - 0xF5720643, - 0x07198540, - 0x590AB964, - 0xAB613A67, - 0xB831C993, - 0x4A5A4A90, - 0x9E902E7B, - 0x6CFBAD78, - 0x7FAB5E8C, - 0x8DC0DD8F, - 0xE330A81A, - 0x115B2B19, - 0x020BD8ED, - 0xF0605BEE, - 0x24AA3F05, - 0xD6C1BC06, - 0xC5914FF2, - 0x37FACCF1, - 0x69E9F0D5, - 0x9B8273D6, - 0x88D28022, - 0x7AB90321, - 0xAE7367CA, - 0x5C18E4C9, - 0x4F48173D, - 0xBD23943E, - 0xF36E6F75, - 0x0105EC76, - 0x12551F82, - 0xE03E9C81, - 0x34F4F86A, - 0xC69F7B69, - 0xD5CF889D, - 0x27A40B9E, - 0x79B737BA, - 0x8BDCB4B9, - 0x988C474D, - 0x6AE7C44E, - 0xBE2DA0A5, - 0x4C4623A6, - 0x5F16D052, - 0xAD7D5351, -) - - -CRC_INIT = 0 - -_MASK = 0xFFFFFFFF - - -def crc_update(crc, data): - """Update CRC-32C checksum with data. - - Args: - crc: 32-bit checksum to update as long. - data: byte array, string or iterable over bytes. - - Returns: - 32-bit updated CRC-32C as long. - """ - - if type(data) != array.array or data.itemsize != 1: - buf = array.array("B", data) - else: - buf = data - - crc ^= _MASK - for b in buf: - table_index = (crc ^ b) & 0xFF - crc = (CRC_TABLE[table_index] ^ (crc >> 8)) & _MASK - return crc ^ _MASK - - -def crc_finalize(crc): - """Finalize CRC-32C checksum. - - This function should be called as last step of crc calculation. - - Args: - crc: 32-bit checksum as long. - - Returns: - finalized 32-bit checksum as long - """ - return crc & _MASK - - -def crc32c(data): - """Compute CRC-32C checksum of the data. - - Args: - data: byte array, string or iterable over bytes. - - Returns: - 32-bit CRC-32C checksum of data as long. - """ - return crc_finalize(crc_update(CRC_INIT, data)) diff --git a/~tensorboardX/embedding.py b/~tensorboardX/embedding.py deleted file mode 100644 index a5c8259..0000000 --- a/~tensorboardX/embedding.py +++ /dev/null @@ -1,52 +0,0 @@ -import os - - -def make_tsv(metadata, save_path): - metadata = [str(x) for x in metadata] - with open(os.path.join(save_path, "metadata.tsv"), "w") as f: - for x in metadata: - f.write(x + "\n") - - -# https://github.com/tensorflow/tensorboard/issues/44 image label will be squared -def make_sprite(label_img, save_path): - import math - - import torch - import torchvision - - from .x2num import makenp - - # this ensures the sprite image has correct dimension as described in - # https://www.tensorflow.org/get_started/embedding_viz - nrow = int(math.ceil((label_img.size(0)) ** 0.5)) - - label_img = torch.from_numpy(makenp(label_img)) # for other framework - # augment images so that #images equals nrow*nrow - label_img = torch.cat((label_img, torch.randn(nrow ** 2 - label_img.size(0), *label_img.size()[1:]) * 255), 0) - - torchvision.utils.save_image(label_img, os.path.join(save_path, "sprite.png"), nrow=nrow, padding=0) - - -def append_pbtxt(metadata, label_img, save_path, global_step, tag): - with open(os.path.join(save_path, "projector_config.pbtxt"), "a") as f: - # step = os.path.split(save_path)[-1] - f.write("embeddings {\n") - f.write('tensor_name: "{}:{}"\n'.format(tag, global_step)) - f.write('tensor_path: "{}"\n'.format(os.path.join(global_step, "tensors.tsv"))) - if metadata is not None: - f.write('metadata_path: "{}"\n'.format(os.path.join(global_step, "metadata.tsv"))) - if label_img is not None: - f.write("sprite {\n") - f.write('image_path: "{}"\n'.format(os.path.join(global_step, "sprite.png"))) - f.write("single_image_dim: {}\n".format(label_img.size(3))) - f.write("single_image_dim: {}\n".format(label_img.size(2))) - f.write("}\n") - f.write("}\n") - - -def make_mat(matlist, save_path): - with open(os.path.join(save_path, "tensors.tsv"), "w") as f: - for x in matlist: - x = [str(i) for i in x] - f.write("\t".join(x) + "\n") diff --git a/~tensorboardX/event_file_writer.py b/~tensorboardX/event_file_writer.py deleted file mode 100644 index b9d826b..0000000 --- a/~tensorboardX/event_file_writer.py +++ /dev/null @@ -1,192 +0,0 @@ -# Copyright 2015 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Writes events to disk in a logdir.""" - -from __future__ import absolute_import, division, print_function - -import logging -import os.path -import socket -import threading -import time - -import six - -from .record_writer import RecordWriter -from .src import event_pb2 - - -def directory_check(path): - """Initialize the directory for log files.""" - # If the direcotry does not exist, create it! - if not os.path.exists(path): - os.makedirs(path) - - -class EventsWriter(object): - """Writes `Event` protocol buffers to an event file.""" - - def __init__(self, file_prefix): - """ - Events files have a name of the form - '/some/file/path/events.out.tfevents.[timestamp].[hostname]' - """ - self._file_prefix = file_prefix + ".out.tfevents." + str(time.time())[:10] + "." + socket.gethostname() - - # Open(Create) the log file with the particular form of name. - logging.basicConfig(filename=self._file_prefix) - - self._num_outstanding_events = 0 - - self._py_recordio_writer = RecordWriter(self._file_prefix) - - # Initialize an event instance. - self._event = event_pb2.Event() - - self._event.wall_time = time.time() - - self.write_event(self._event) - - def write_event(self, event): - """Append "event" to the file.""" - - # Check if event is of type event_pb2.Event proto. - if not isinstance(event, event_pb2.Event): - raise TypeError("Expected an event_pb2.Event proto, " " but got %s" % type(event)) - return self._write_serialized_event(event.SerializeToString()) - - def _write_serialized_event(self, event_str): - self._num_outstanding_events += 1 - self._py_recordio_writer.write(event_str) - - def flush(self): - """Flushes the event file to disk.""" - self._num_outstanding_events = 0 - return True - - def close(self): - """Call self.flush().""" - return_value = self.flush() - return return_value - - -class EventFileWriter(object): - """Writes `Event` protocol buffers to an event file. - The `EventFileWriter` class creates an event file in the specified directory, - and asynchronously writes Event protocol buffers to the file. The Event file - is encoded using the tfrecord format, which is similar to RecordIO. - @@__init__ - @@add_event - @@flush - @@close - """ - - def __init__(self, logdir, max_queue=10, flush_secs=120): - """Creates a `EventFileWriter` and an event file to write to. - On construction the summary writer creates a new event file in `logdir`. - This event file will contain `Event` protocol buffers, which are written to - disk via the add_event method. - The other arguments to the constructor control the asynchronous writes to - the event file: - * `flush_secs`: How often, in seconds, to flush the added summaries - and events to disk. - * `max_queue`: Maximum number of summaries or events pending to be - written to disk before one of the 'add' calls block. - Args: - logdir: A string. Directory where event file will be written. - max_queue: Integer. Size of the queue for pending events and summaries. - flush_secs: Number. How often, in seconds, to flush the - pending events and summaries to disk. - """ - self._logdir = logdir - directory_check(self._logdir) - self._event_queue = six.moves.queue.Queue(max_queue) - self._ev_writer = EventsWriter(os.path.join(self._logdir, "events")) - self._closed = False - self._worker = _EventLoggerThread(self._event_queue, self._ev_writer, flush_secs) - - self._worker.start() - - def get_logdir(self): - """Returns the directory where event file will be written.""" - return self._logdir - - def reopen(self): - """Reopens the EventFileWriter. - Can be called after `close()` to add more events in the same directory. - The events will go into a new events file. - Does nothing if the EventFileWriter was not closed. - """ - if self._closed: - self._closed = False - - def add_event(self, event): - """Adds an event to the event file. - Args: - event: An `Event` protocol buffer. - """ - if not self._closed: - self._event_queue.put(event) - - def flush(self): - """Flushes the event file to disk. - Call this method to make sure that all pending events have been written to - disk. - """ - self._event_queue.join() - self._ev_writer.flush() - - def close(self): - """Flushes the event file to disk and close the file. - Call this method when you do not need the summary writer anymore. - """ - self.flush() - self._ev_writer.close() - self._closed = True - - -class _EventLoggerThread(threading.Thread): - """Thread that logs events.""" - - def __init__(self, queue, ev_writer, flush_secs): - """Creates an _EventLoggerThread. - Args: - queue: A Queue from which to dequeue events. - ev_writer: An event writer. Used to log brain events for - the visualizer. - flush_secs: How often, in seconds, to flush the - pending file to disk. - """ - threading.Thread.__init__(self) - self.daemon = True - self._queue = queue - self._ev_writer = ev_writer - self._flush_secs = flush_secs - # The first event will be flushed immediately. - self._next_event_flush_time = 0 - - def run(self): - while True: - event = self._queue.get() - try: - self._ev_writer.write_event(event) - # Flush the event writer every so often. - now = time.time() - if now > self._next_event_flush_time: - self._ev_writer.flush() - # Do it again in two minutes. - self._next_event_flush_time = now + self._flush_secs - finally: - self._queue.task_done() diff --git a/~tensorboardX/graph.py b/~tensorboardX/graph.py deleted file mode 100644 index afb5329..0000000 --- a/~tensorboardX/graph.py +++ /dev/null @@ -1,73 +0,0 @@ -from distutils.version import LooseVersion - -from .src.attr_value_pb2 import AttrValue -from .src.graph_pb2 import GraphDef -from .src.node_def_pb2 import NodeDef -from .src.tensor_shape_pb2 import TensorShapeProto -from .src.versions_pb2 import VersionDef - - -def replace(name, scope): - return "/".join([scope[name], name]) - - -def parse(graph): - import torch - - scope = {} - for n in graph.nodes(): - inputs = [i.uniqueName() for i in n.inputs()] - for i in range(1, len(inputs)): - scope[inputs[i]] = n.scopeName() - - uname = next(iter(n.outputs())).uniqueName() - assert n.scopeName() != "", "{} has empty scope name".format(n) - scope[uname] = n.scopeName() - if LooseVersion(torch.__version__) >= LooseVersion("0.4"): - scope["0"] = "input" - else: - scope["1"] = "input" - - nodes = [] - for n in graph.nodes(): - attrs = {k: n[k] for k in n.attributeNames()} - attrs = str(attrs).replace("'", " ") # singlequote will be escaped by tensorboard - if any(i.uniqueName() not in scope.keys() for i in n.inputs()): # 0.3.1 workaround - continue - inputs = [replace(i.uniqueName(), scope) for i in n.inputs()] - uname = next(iter(n.outputs())).uniqueName() # FIXME: only first output is considered - nodes.append({"name": replace(uname, scope), "op": n.kind(), "inputs": inputs, "attr": attrs}) - - for n in graph.inputs(): - uname = n.uniqueName() - if uname not in scope.keys(): - scope[uname] = "unused" - nodes.append({"name": replace(uname, scope), "op": "Parameter", "inputs": [], "attr": str(n.type())}) - - return nodes - - -def graph(model, args, verbose=False): - import torch - - with torch.onnx.set_training(model, False): - trace, _ = torch.jit.trace(model, args) - if LooseVersion(torch.__version__) >= LooseVersion("0.4"): - torch.onnx._optimize_trace(trace, False) - else: - torch.onnx._optimize_trace(trace) - graph = trace.graph() - if verbose: - print(graph) - list_of_nodes = parse(graph) - nodes = [] - for node in list_of_nodes: - nodes.append( - NodeDef( - name=node["name"], - op=node["op"], - input=node["inputs"], - attr={"lanpa": AttrValue(s=node["attr"].encode(encoding="utf_8"))}, - ) - ) - return GraphDef(node=nodes, versions=VersionDef(producer=22)) diff --git a/~tensorboardX/graph_onnx.py b/~tensorboardX/graph_onnx.py deleted file mode 100644 index ac12b16..0000000 --- a/~tensorboardX/graph_onnx.py +++ /dev/null @@ -1,159 +0,0 @@ -from .src.attr_value_pb2 import AttrValue -from .src.graph_pb2 import GraphDef -from .src.node_def_pb2 import NodeDef -from .src.tensor_shape_pb2 import TensorShapeProto -from .src.versions_pb2 import VersionDef - -# from .src.onnx_pb2 import ModelProto - - -def gg(fname): - import onnx # 0.2.1 - - m = onnx.load(fname) - nodes_proto = [] - nodes = [] - g = m.graph - import itertools - - for node in itertools.chain(g.input, g.output): - nodes_proto.append(node) - - for node in nodes_proto: - shapeproto = TensorShapeProto( - dim=[TensorShapeProto.Dim(size=d.dim_value) for d in node.type.tensor_type.shape.dim] - ) - nodes.append( - NodeDef( - name=node.name, - op="Variable", - input=[], - attr={"dtype": AttrValue(type=node.type.tensor_type.elem_type), "shape": AttrValue(shape=shapeproto),}, - ) - ) - - for node in g.node: - attr = [] - for s in node.attribute: - attr.append(" = ".join([str(f[1]) for f in s.ListFields()])) - attr = ", ".join(attr).encode(encoding="utf_8") - - nodes.append( - NodeDef(name=node.output[0], op=node.op_type, input=node.input, attr={"parameters": AttrValue(s=attr)},) - ) - # two pass token replacement, appends opname to object id - mapping = {} - for node in nodes: - mapping[node.name] = node.op + "_" + node.name - - nodes, mapping = updatenodes(nodes, mapping) - mapping = smartGrouping(nodes, mapping) - nodes, mapping = updatenodes(nodes, mapping) - - return GraphDef(node=nodes, versions=VersionDef(producer=22)) - - -def updatenodes(nodes, mapping): - for node in nodes: - newname = mapping[node.name] - node.name = newname - newinput = [] - for inputnode in list(node.input): - newinput.append(mapping[inputnode]) - node.input.remove(inputnode) - node.input.extend(newinput) - newmap = {} - for k, v in mapping.items(): - newmap[v] = v - return nodes, newmap - - -def findnode(nodes, name): - """ input: node name - returns: node object - """ - for n in nodes: - if n.name == name: - return n - - -def parser(s, nodes, node): - print(s) - if len(s) == 0: - return - if len(s) > 0: - if s[0] == node.op: - print(s[0], node.name, s[1], node.input) - for n in node.input: - print(n, s[1]) - parser(s[1], nodes, findnode(nodes, n)) - else: - return False - - -# TODO: use recursive parse - - -def smartGrouping(nodes, mapping): - # a Fully Conv is: (TODO: check var1.size(0)==var2.size(0)) - # GEMM <-- Variable (c1) - # ^-- Transpose (c2) <-- Variable (c3) - - # a Conv with bias is: (TODO: check var1.size(0)==var2.size(0)) - # Add <-- Conv (c2) <-- Variable (c3) - # ^-- Variable (c1) - # - # gemm = ('Gemm', ('Variable', ('Transpose', ('Variable')))) - - FCcounter = 1 - Convcounter = 1 - for node in nodes: - if node.op == "Gemm": - c1 = c2 = c3 = False - for name_in in node.input: - n = findnode(nodes, name_in) - if n.op == "Variable": - c1 = True - c1name = n.name - if n.op == "Transpose": - c2 = True - c2name = n.name - if len(n.input) == 1: - nn = findnode(nodes, n.input[0]) - if nn.op == "Variable": - c3 = True - c3name = nn.name - # print(n.op, n.name, c1, c2, c3) - if c1 and c2 and c3: - # print(c1name, c2name, c3name) - mapping[c1name] = "FC{}/{}".format(FCcounter, c1name) - mapping[c2name] = "FC{}/{}".format(FCcounter, c2name) - mapping[c3name] = "FC{}/{}".format(FCcounter, c3name) - mapping[node.name] = "FC{}/{}".format(FCcounter, node.name) - FCcounter += 1 - continue - if node.op == "Add": - c1 = c2 = c3 = False - for name_in in node.input: - n = findnode(nodes, name_in) - if n.op == "Variable": - c1 = True - c1name = n.name - if n.op == "Conv": - c2 = True - c2name = n.name - if len(n.input) >= 1: - for nn_name in n.input: - nn = findnode(nodes, nn_name) - if nn.op == "Variable": - c3 = True - c3name = nn.name - - if c1 and c2 and c3: - # print(c1name, c2name, c3name) - mapping[c1name] = "Conv{}/{}".format(Convcounter, c1name) - mapping[c2name] = "Conv{}/{}".format(Convcounter, c2name) - mapping[c3name] = "Conv{}/{}".format(Convcounter, c3name) - mapping[node.name] = "Conv{}/{}".format(Convcounter, node.name) - Convcounter += 1 - return mapping diff --git a/~tensorboardX/record_writer.py b/~tensorboardX/record_writer.py deleted file mode 100644 index 85da287..0000000 --- a/~tensorboardX/record_writer.py +++ /dev/null @@ -1,47 +0,0 @@ -""" -To write tf_record into file. Here we use it for tensorboard's event writting. -The code was borrow from https://github.com/TeamHG-Memex/tensorboard_logger -""" - -import re -import struct - -from .crc32c import crc32c - -_VALID_OP_NAME_START = re.compile("^[A-Za-z0-9.]") -_VALID_OP_NAME_PART = re.compile("[A-Za-z0-9_.\\-/]+") - - -class RecordWriter(object): - def __init__(self, path, flush_secs=2): - self._name_to_tf_name = {} - self._tf_names = set() - self.path = path - self.flush_secs = flush_secs # TODO. flush every flush_secs, not every time. - self._writer = None - self._writer = open(path, "wb") - - def write(self, event_str): - w = self._writer.write - header = struct.pack("Q", len(event_str)) - w(header) - w(struct.pack("I", masked_crc32c(header))) - w(event_str) - w(struct.pack("I", masked_crc32c(event_str))) - self._writer.flush() - - -def masked_crc32c(data): - x = u32(crc32c(data)) - return u32(((x >> 15) | u32(x << 17)) + 0xA282EAD8) - - -def u32(x): - return x & 0xFFFFFFFF - - -def make_valid_tf_name(name): - if not _VALID_OP_NAME_START.match(name): - # Must make it valid somehow, but don't want to remove stuff - name = "." + name - return "_".join(_VALID_OP_NAME_PART.findall(name)) diff --git a/~tensorboardX/src/__init__.py b/~tensorboardX/src/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/~tensorboardX/src/attr_value_pb2.py b/~tensorboardX/src/attr_value_pb2.py deleted file mode 100644 index a9900c0..0000000 --- a/~tensorboardX/src/attr_value_pb2.py +++ /dev/null @@ -1,600 +0,0 @@ -# Generated by the protocol buffer compiler. DO NOT EDIT! -# source: tensorboardX/src/attr_value.proto - -import sys - -_b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode("latin1")) -from google.protobuf import descriptor as _descriptor -from google.protobuf import descriptor_pb2 -from google.protobuf import message as _message -from google.protobuf import reflection as _reflection -from google.protobuf import symbol_database as _symbol_database - -# @@protoc_insertion_point(imports) - -_sym_db = _symbol_database.Default() - - -from tensorboardX.src import tensor_pb2 as tensorboardX_dot_src_dot_tensor__pb2 -from tensorboardX.src import tensor_shape_pb2 as tensorboardX_dot_src_dot_tensor__shape__pb2 -from tensorboardX.src import types_pb2 as tensorboardX_dot_src_dot_types__pb2 - -DESCRIPTOR = _descriptor.FileDescriptor( - name="tensorboardX/src/attr_value.proto", - package="tensorboard", - syntax="proto3", - serialized_pb=_b( - "\n!tensorboardX/src/attr_value.proto\x12\x0btensorboard\x1a\x1dtensorboardX/src/tensor.proto\x1a#tensorboardX/src/tensor_shape.proto\x1a\x1ctensorboardX/src/types.proto\"\xaf\x04\n\tAttrValue\x12\x0b\n\x01s\x18\x02 \x01(\x0cH\x00\x12\x0b\n\x01i\x18\x03 \x01(\x03H\x00\x12\x0b\n\x01\x66\x18\x04 \x01(\x02H\x00\x12\x0b\n\x01\x62\x18\x05 \x01(\x08H\x00\x12%\n\x04type\x18\x06 \x01(\x0e\x32\x15.tensorboard.DataTypeH\x00\x12.\n\x05shape\x18\x07 \x01(\x0b\x32\x1d.tensorboard.TensorShapeProtoH\x00\x12*\n\x06tensor\x18\x08 \x01(\x0b\x32\x18.tensorboard.TensorProtoH\x00\x12\x30\n\x04list\x18\x01 \x01(\x0b\x32 .tensorboard.AttrValue.ListValueH\x00\x12)\n\x04\x66unc\x18\n \x01(\x0b\x32\x19.tensorboard.NameAttrListH\x00\x12\x15\n\x0bplaceholder\x18\t \x01(\tH\x00\x1a\xed\x01\n\tListValue\x12\t\n\x01s\x18\x02 \x03(\x0c\x12\r\n\x01i\x18\x03 \x03(\x03\x42\x02\x10\x01\x12\r\n\x01\x66\x18\x04 \x03(\x02\x42\x02\x10\x01\x12\r\n\x01\x62\x18\x05 \x03(\x08\x42\x02\x10\x01\x12'\n\x04type\x18\x06 \x03(\x0e\x32\x15.tensorboard.DataTypeB\x02\x10\x01\x12,\n\x05shape\x18\x07 \x03(\x0b\x32\x1d.tensorboard.TensorShapeProto\x12(\n\x06tensor\x18\x08 \x03(\x0b\x32\x18.tensorboard.TensorProto\x12'\n\x04\x66unc\x18\t \x03(\x0b\x32\x19.tensorboard.NameAttrListB\x07\n\x05value\"\x94\x01\n\x0cNameAttrList\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x31\n\x04\x61ttr\x18\x02 \x03(\x0b\x32#.tensorboard.NameAttrList.AttrEntry\x1a\x43\n\tAttrEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12%\n\x05value\x18\x02 \x01(\x0b\x32\x16.tensorboard.AttrValue:\x02\x38\x01\x42\x30\n\x18org.tensorflow.frameworkB\x0f\x41ttrValueProtosP\x01\xf8\x01\x01\x62\x06proto3" - ), - dependencies=[ - tensorboardX_dot_src_dot_tensor__pb2.DESCRIPTOR, - tensorboardX_dot_src_dot_tensor__shape__pb2.DESCRIPTOR, - tensorboardX_dot_src_dot_types__pb2.DESCRIPTOR, - ], -) - - -_ATTRVALUE_LISTVALUE = _descriptor.Descriptor( - name="ListValue", - full_name="tensorboard.AttrValue.ListValue", - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name="s", - full_name="tensorboard.AttrValue.ListValue.s", - index=0, - number=2, - type=12, - cpp_type=9, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="i", - full_name="tensorboard.AttrValue.ListValue.i", - index=1, - number=3, - type=3, - cpp_type=2, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b("\020\001")), - ), - _descriptor.FieldDescriptor( - name="f", - full_name="tensorboard.AttrValue.ListValue.f", - index=2, - number=4, - type=2, - cpp_type=6, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b("\020\001")), - ), - _descriptor.FieldDescriptor( - name="b", - full_name="tensorboard.AttrValue.ListValue.b", - index=3, - number=5, - type=8, - cpp_type=7, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b("\020\001")), - ), - _descriptor.FieldDescriptor( - name="type", - full_name="tensorboard.AttrValue.ListValue.type", - index=4, - number=6, - type=14, - cpp_type=8, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b("\020\001")), - ), - _descriptor.FieldDescriptor( - name="shape", - full_name="tensorboard.AttrValue.ListValue.shape", - index=5, - number=7, - type=11, - cpp_type=10, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="tensor", - full_name="tensorboard.AttrValue.ListValue.tensor", - index=6, - number=8, - type=11, - cpp_type=10, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="func", - full_name="tensorboard.AttrValue.ListValue.func", - index=7, - number=9, - type=11, - cpp_type=10, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - ], - extensions=[], - nested_types=[], - enum_types=[], - options=None, - is_extendable=False, - syntax="proto3", - extension_ranges=[], - oneofs=[], - serialized_start=462, - serialized_end=699, -) - -_ATTRVALUE = _descriptor.Descriptor( - name="AttrValue", - full_name="tensorboard.AttrValue", - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name="s", - full_name="tensorboard.AttrValue.s", - index=0, - number=2, - type=12, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b(""), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="i", - full_name="tensorboard.AttrValue.i", - index=1, - number=3, - type=3, - cpp_type=2, - label=1, - has_default_value=False, - default_value=0, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="f", - full_name="tensorboard.AttrValue.f", - index=2, - number=4, - type=2, - cpp_type=6, - label=1, - has_default_value=False, - default_value=float(0), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="b", - full_name="tensorboard.AttrValue.b", - index=3, - number=5, - type=8, - cpp_type=7, - label=1, - has_default_value=False, - default_value=False, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="type", - full_name="tensorboard.AttrValue.type", - index=4, - number=6, - type=14, - cpp_type=8, - label=1, - has_default_value=False, - default_value=0, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="shape", - full_name="tensorboard.AttrValue.shape", - index=5, - number=7, - type=11, - cpp_type=10, - label=1, - has_default_value=False, - default_value=None, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="tensor", - full_name="tensorboard.AttrValue.tensor", - index=6, - number=8, - type=11, - cpp_type=10, - label=1, - has_default_value=False, - default_value=None, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="list", - full_name="tensorboard.AttrValue.list", - index=7, - number=1, - type=11, - cpp_type=10, - label=1, - has_default_value=False, - default_value=None, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="func", - full_name="tensorboard.AttrValue.func", - index=8, - number=10, - type=11, - cpp_type=10, - label=1, - has_default_value=False, - default_value=None, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="placeholder", - full_name="tensorboard.AttrValue.placeholder", - index=9, - number=9, - type=9, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b("").decode("utf-8"), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - ], - extensions=[], - nested_types=[_ATTRVALUE_LISTVALUE,], - enum_types=[], - options=None, - is_extendable=False, - syntax="proto3", - extension_ranges=[], - oneofs=[ - _descriptor.OneofDescriptor( - name="value", full_name="tensorboard.AttrValue.value", index=0, containing_type=None, fields=[] - ), - ], - serialized_start=149, - serialized_end=708, -) - - -_NAMEATTRLIST_ATTRENTRY = _descriptor.Descriptor( - name="AttrEntry", - full_name="tensorboard.NameAttrList.AttrEntry", - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name="key", - full_name="tensorboard.NameAttrList.AttrEntry.key", - index=0, - number=1, - type=9, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b("").decode("utf-8"), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="value", - full_name="tensorboard.NameAttrList.AttrEntry.value", - index=1, - number=2, - type=11, - cpp_type=10, - label=1, - has_default_value=False, - default_value=None, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - ], - extensions=[], - nested_types=[], - enum_types=[], - options=_descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b("8\001")), - is_extendable=False, - syntax="proto3", - extension_ranges=[], - oneofs=[], - serialized_start=792, - serialized_end=859, -) - -_NAMEATTRLIST = _descriptor.Descriptor( - name="NameAttrList", - full_name="tensorboard.NameAttrList", - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name="name", - full_name="tensorboard.NameAttrList.name", - index=0, - number=1, - type=9, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b("").decode("utf-8"), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="attr", - full_name="tensorboard.NameAttrList.attr", - index=1, - number=2, - type=11, - cpp_type=10, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - ], - extensions=[], - nested_types=[_NAMEATTRLIST_ATTRENTRY,], - enum_types=[], - options=None, - is_extendable=False, - syntax="proto3", - extension_ranges=[], - oneofs=[], - serialized_start=711, - serialized_end=859, -) - -_ATTRVALUE_LISTVALUE.fields_by_name["type"].enum_type = tensorboardX_dot_src_dot_types__pb2._DATATYPE -_ATTRVALUE_LISTVALUE.fields_by_name[ - "shape" -].message_type = tensorboardX_dot_src_dot_tensor__shape__pb2._TENSORSHAPEPROTO -_ATTRVALUE_LISTVALUE.fields_by_name["tensor"].message_type = tensorboardX_dot_src_dot_tensor__pb2._TENSORPROTO -_ATTRVALUE_LISTVALUE.fields_by_name["func"].message_type = _NAMEATTRLIST -_ATTRVALUE_LISTVALUE.containing_type = _ATTRVALUE -_ATTRVALUE.fields_by_name["type"].enum_type = tensorboardX_dot_src_dot_types__pb2._DATATYPE -_ATTRVALUE.fields_by_name["shape"].message_type = tensorboardX_dot_src_dot_tensor__shape__pb2._TENSORSHAPEPROTO -_ATTRVALUE.fields_by_name["tensor"].message_type = tensorboardX_dot_src_dot_tensor__pb2._TENSORPROTO -_ATTRVALUE.fields_by_name["list"].message_type = _ATTRVALUE_LISTVALUE -_ATTRVALUE.fields_by_name["func"].message_type = _NAMEATTRLIST -_ATTRVALUE.oneofs_by_name["value"].fields.append(_ATTRVALUE.fields_by_name["s"]) -_ATTRVALUE.fields_by_name["s"].containing_oneof = _ATTRVALUE.oneofs_by_name["value"] -_ATTRVALUE.oneofs_by_name["value"].fields.append(_ATTRVALUE.fields_by_name["i"]) -_ATTRVALUE.fields_by_name["i"].containing_oneof = _ATTRVALUE.oneofs_by_name["value"] -_ATTRVALUE.oneofs_by_name["value"].fields.append(_ATTRVALUE.fields_by_name["f"]) -_ATTRVALUE.fields_by_name["f"].containing_oneof = _ATTRVALUE.oneofs_by_name["value"] -_ATTRVALUE.oneofs_by_name["value"].fields.append(_ATTRVALUE.fields_by_name["b"]) -_ATTRVALUE.fields_by_name["b"].containing_oneof = _ATTRVALUE.oneofs_by_name["value"] -_ATTRVALUE.oneofs_by_name["value"].fields.append(_ATTRVALUE.fields_by_name["type"]) -_ATTRVALUE.fields_by_name["type"].containing_oneof = _ATTRVALUE.oneofs_by_name["value"] -_ATTRVALUE.oneofs_by_name["value"].fields.append(_ATTRVALUE.fields_by_name["shape"]) -_ATTRVALUE.fields_by_name["shape"].containing_oneof = _ATTRVALUE.oneofs_by_name["value"] -_ATTRVALUE.oneofs_by_name["value"].fields.append(_ATTRVALUE.fields_by_name["tensor"]) -_ATTRVALUE.fields_by_name["tensor"].containing_oneof = _ATTRVALUE.oneofs_by_name["value"] -_ATTRVALUE.oneofs_by_name["value"].fields.append(_ATTRVALUE.fields_by_name["list"]) -_ATTRVALUE.fields_by_name["list"].containing_oneof = _ATTRVALUE.oneofs_by_name["value"] -_ATTRVALUE.oneofs_by_name["value"].fields.append(_ATTRVALUE.fields_by_name["func"]) -_ATTRVALUE.fields_by_name["func"].containing_oneof = _ATTRVALUE.oneofs_by_name["value"] -_ATTRVALUE.oneofs_by_name["value"].fields.append(_ATTRVALUE.fields_by_name["placeholder"]) -_ATTRVALUE.fields_by_name["placeholder"].containing_oneof = _ATTRVALUE.oneofs_by_name["value"] -_NAMEATTRLIST_ATTRENTRY.fields_by_name["value"].message_type = _ATTRVALUE -_NAMEATTRLIST_ATTRENTRY.containing_type = _NAMEATTRLIST -_NAMEATTRLIST.fields_by_name["attr"].message_type = _NAMEATTRLIST_ATTRENTRY -DESCRIPTOR.message_types_by_name["AttrValue"] = _ATTRVALUE -DESCRIPTOR.message_types_by_name["NameAttrList"] = _NAMEATTRLIST -_sym_db.RegisterFileDescriptor(DESCRIPTOR) - -AttrValue = _reflection.GeneratedProtocolMessageType( - "AttrValue", - (_message.Message,), - dict( - ListValue=_reflection.GeneratedProtocolMessageType( - "ListValue", - (_message.Message,), - dict( - DESCRIPTOR=_ATTRVALUE_LISTVALUE, - __module__="tensorboardX.src.attr_value_pb2" - # @@protoc_insertion_point(class_scope:tensorboard.AttrValue.ListValue) - ), - ), - DESCRIPTOR=_ATTRVALUE, - __module__="tensorboardX.src.attr_value_pb2" - # @@protoc_insertion_point(class_scope:tensorboard.AttrValue) - ), -) -_sym_db.RegisterMessage(AttrValue) -_sym_db.RegisterMessage(AttrValue.ListValue) - -NameAttrList = _reflection.GeneratedProtocolMessageType( - "NameAttrList", - (_message.Message,), - dict( - AttrEntry=_reflection.GeneratedProtocolMessageType( - "AttrEntry", - (_message.Message,), - dict( - DESCRIPTOR=_NAMEATTRLIST_ATTRENTRY, - __module__="tensorboardX.src.attr_value_pb2" - # @@protoc_insertion_point(class_scope:tensorboard.NameAttrList.AttrEntry) - ), - ), - DESCRIPTOR=_NAMEATTRLIST, - __module__="tensorboardX.src.attr_value_pb2" - # @@protoc_insertion_point(class_scope:tensorboard.NameAttrList) - ), -) -_sym_db.RegisterMessage(NameAttrList) -_sym_db.RegisterMessage(NameAttrList.AttrEntry) - - -DESCRIPTOR.has_options = True -DESCRIPTOR._options = _descriptor._ParseOptions( - descriptor_pb2.FileOptions(), _b("\n\030org.tensorflow.frameworkB\017AttrValueProtosP\001\370\001\001") -) -_ATTRVALUE_LISTVALUE.fields_by_name["i"].has_options = True -_ATTRVALUE_LISTVALUE.fields_by_name["i"]._options = _descriptor._ParseOptions( - descriptor_pb2.FieldOptions(), _b("\020\001") -) -_ATTRVALUE_LISTVALUE.fields_by_name["f"].has_options = True -_ATTRVALUE_LISTVALUE.fields_by_name["f"]._options = _descriptor._ParseOptions( - descriptor_pb2.FieldOptions(), _b("\020\001") -) -_ATTRVALUE_LISTVALUE.fields_by_name["b"].has_options = True -_ATTRVALUE_LISTVALUE.fields_by_name["b"]._options = _descriptor._ParseOptions( - descriptor_pb2.FieldOptions(), _b("\020\001") -) -_ATTRVALUE_LISTVALUE.fields_by_name["type"].has_options = True -_ATTRVALUE_LISTVALUE.fields_by_name["type"]._options = _descriptor._ParseOptions( - descriptor_pb2.FieldOptions(), _b("\020\001") -) -_NAMEATTRLIST_ATTRENTRY.has_options = True -_NAMEATTRLIST_ATTRENTRY._options = _descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b("8\001")) -# @@protoc_insertion_point(module_scope) diff --git a/~tensorboardX/src/event_pb2.py b/~tensorboardX/src/event_pb2.py deleted file mode 100644 index 24d2c99..0000000 --- a/~tensorboardX/src/event_pb2.py +++ /dev/null @@ -1,506 +0,0 @@ -# Generated by the protocol buffer compiler. DO NOT EDIT! -# source: tensorboardX/src/event.proto - -import sys - -_b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode("latin1")) -from google.protobuf import descriptor as _descriptor -from google.protobuf import descriptor_pb2 -from google.protobuf import message as _message -from google.protobuf import reflection as _reflection -from google.protobuf import symbol_database as _symbol_database - -# @@protoc_insertion_point(imports) - -_sym_db = _symbol_database.Default() - - -from tensorboardX.src import summary_pb2 as tensorboardX_dot_src_dot_summary__pb2 - -DESCRIPTOR = _descriptor.FileDescriptor( - name="tensorboardX/src/event.proto", - package="tensorboard", - syntax="proto3", - serialized_pb=_b( - '\n\x1ctensorboardX/src/event.proto\x12\x0btensorboard\x1a\x1etensorboardX/src/summary.proto"\xbf\x02\n\x05\x45vent\x12\x11\n\twall_time\x18\x01 \x01(\x01\x12\x0c\n\x04step\x18\x02 \x01(\x03\x12\x16\n\x0c\x66ile_version\x18\x03 \x01(\tH\x00\x12\x13\n\tgraph_def\x18\x04 \x01(\x0cH\x00\x12\'\n\x07summary\x18\x05 \x01(\x0b\x32\x14.tensorboard.SummaryH\x00\x12.\n\x0blog_message\x18\x06 \x01(\x0b\x32\x17.tensorboard.LogMessageH\x00\x12.\n\x0bsession_log\x18\x07 \x01(\x0b\x32\x17.tensorboard.SessionLogH\x00\x12=\n\x13tagged_run_metadata\x18\x08 \x01(\x0b\x32\x1e.tensorboard.TaggedRunMetadataH\x00\x12\x18\n\x0emeta_graph_def\x18\t \x01(\x0cH\x00\x42\x06\n\x04what"\x96\x01\n\nLogMessage\x12,\n\x05level\x18\x01 \x01(\x0e\x32\x1d.tensorboard.LogMessage.Level\x12\x0f\n\x07message\x18\x02 \x01(\t"I\n\x05Level\x12\x0b\n\x07UNKNOWN\x10\x00\x12\t\n\x05\x44\x45\x42UG\x10\n\x12\x08\n\x04INFO\x10\x14\x12\x08\n\x04WARN\x10\x1e\x12\t\n\x05\x45RROR\x10(\x12\t\n\x05\x46\x41TAL\x10\x32"\xb7\x01\n\nSessionLog\x12\x35\n\x06status\x18\x01 \x01(\x0e\x32%.tensorboard.SessionLog.SessionStatus\x12\x17\n\x0f\x63heckpoint_path\x18\x02 \x01(\t\x12\x0b\n\x03msg\x18\x03 \x01(\t"L\n\rSessionStatus\x12\x16\n\x12STATUS_UNSPECIFIED\x10\x00\x12\t\n\x05START\x10\x01\x12\x08\n\x04STOP\x10\x02\x12\x0e\n\nCHECKPOINT\x10\x03"6\n\x11TaggedRunMetadata\x12\x0b\n\x03tag\x18\x01 \x01(\t\x12\x14\n\x0crun_metadata\x18\x02 \x01(\x0c\x42\'\n\x13org.tensorflow.utilB\x0b\x45ventProtosP\x01\xf8\x01\x01\x62\x06proto3' - ), - dependencies=[tensorboardX_dot_src_dot_summary__pb2.DESCRIPTOR,], -) - - -_LOGMESSAGE_LEVEL = _descriptor.EnumDescriptor( - name="Level", - full_name="tensorboard.LogMessage.Level", - filename=None, - file=DESCRIPTOR, - values=[ - _descriptor.EnumValueDescriptor(name="UNKNOWN", index=0, number=0, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DEBUG", index=1, number=10, options=None, type=None), - _descriptor.EnumValueDescriptor(name="INFO", index=2, number=20, options=None, type=None), - _descriptor.EnumValueDescriptor(name="WARN", index=3, number=30, options=None, type=None), - _descriptor.EnumValueDescriptor(name="ERROR", index=4, number=40, options=None, type=None), - _descriptor.EnumValueDescriptor(name="FATAL", index=5, number=50, options=None, type=None), - ], - containing_type=None, - options=None, - serialized_start=477, - serialized_end=550, -) -_sym_db.RegisterEnumDescriptor(_LOGMESSAGE_LEVEL) - -_SESSIONLOG_SESSIONSTATUS = _descriptor.EnumDescriptor( - name="SessionStatus", - full_name="tensorboard.SessionLog.SessionStatus", - filename=None, - file=DESCRIPTOR, - values=[ - _descriptor.EnumValueDescriptor(name="STATUS_UNSPECIFIED", index=0, number=0, options=None, type=None), - _descriptor.EnumValueDescriptor(name="START", index=1, number=1, options=None, type=None), - _descriptor.EnumValueDescriptor(name="STOP", index=2, number=2, options=None, type=None), - _descriptor.EnumValueDescriptor(name="CHECKPOINT", index=3, number=3, options=None, type=None), - ], - containing_type=None, - options=None, - serialized_start=660, - serialized_end=736, -) -_sym_db.RegisterEnumDescriptor(_SESSIONLOG_SESSIONSTATUS) - - -_EVENT = _descriptor.Descriptor( - name="Event", - full_name="tensorboard.Event", - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name="wall_time", - full_name="tensorboard.Event.wall_time", - index=0, - number=1, - type=1, - cpp_type=5, - label=1, - has_default_value=False, - default_value=float(0), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="step", - full_name="tensorboard.Event.step", - index=1, - number=2, - type=3, - cpp_type=2, - label=1, - has_default_value=False, - default_value=0, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="file_version", - full_name="tensorboard.Event.file_version", - index=2, - number=3, - type=9, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b("").decode("utf-8"), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="graph_def", - full_name="tensorboard.Event.graph_def", - index=3, - number=4, - type=12, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b(""), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="summary", - full_name="tensorboard.Event.summary", - index=4, - number=5, - type=11, - cpp_type=10, - label=1, - has_default_value=False, - default_value=None, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="log_message", - full_name="tensorboard.Event.log_message", - index=5, - number=6, - type=11, - cpp_type=10, - label=1, - has_default_value=False, - default_value=None, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="session_log", - full_name="tensorboard.Event.session_log", - index=6, - number=7, - type=11, - cpp_type=10, - label=1, - has_default_value=False, - default_value=None, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="tagged_run_metadata", - full_name="tensorboard.Event.tagged_run_metadata", - index=7, - number=8, - type=11, - cpp_type=10, - label=1, - has_default_value=False, - default_value=None, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="meta_graph_def", - full_name="tensorboard.Event.meta_graph_def", - index=8, - number=9, - type=12, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b(""), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - ], - extensions=[], - nested_types=[], - enum_types=[], - options=None, - is_extendable=False, - syntax="proto3", - extension_ranges=[], - oneofs=[ - _descriptor.OneofDescriptor( - name="what", full_name="tensorboard.Event.what", index=0, containing_type=None, fields=[] - ), - ], - serialized_start=78, - serialized_end=397, -) - - -_LOGMESSAGE = _descriptor.Descriptor( - name="LogMessage", - full_name="tensorboard.LogMessage", - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name="level", - full_name="tensorboard.LogMessage.level", - index=0, - number=1, - type=14, - cpp_type=8, - label=1, - has_default_value=False, - default_value=0, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="message", - full_name="tensorboard.LogMessage.message", - index=1, - number=2, - type=9, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b("").decode("utf-8"), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - ], - extensions=[], - nested_types=[], - enum_types=[_LOGMESSAGE_LEVEL,], - options=None, - is_extendable=False, - syntax="proto3", - extension_ranges=[], - oneofs=[], - serialized_start=400, - serialized_end=550, -) - - -_SESSIONLOG = _descriptor.Descriptor( - name="SessionLog", - full_name="tensorboard.SessionLog", - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name="status", - full_name="tensorboard.SessionLog.status", - index=0, - number=1, - type=14, - cpp_type=8, - label=1, - has_default_value=False, - default_value=0, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="checkpoint_path", - full_name="tensorboard.SessionLog.checkpoint_path", - index=1, - number=2, - type=9, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b("").decode("utf-8"), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="msg", - full_name="tensorboard.SessionLog.msg", - index=2, - number=3, - type=9, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b("").decode("utf-8"), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - ], - extensions=[], - nested_types=[], - enum_types=[_SESSIONLOG_SESSIONSTATUS,], - options=None, - is_extendable=False, - syntax="proto3", - extension_ranges=[], - oneofs=[], - serialized_start=553, - serialized_end=736, -) - - -_TAGGEDRUNMETADATA = _descriptor.Descriptor( - name="TaggedRunMetadata", - full_name="tensorboard.TaggedRunMetadata", - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name="tag", - full_name="tensorboard.TaggedRunMetadata.tag", - index=0, - number=1, - type=9, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b("").decode("utf-8"), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="run_metadata", - full_name="tensorboard.TaggedRunMetadata.run_metadata", - index=1, - number=2, - type=12, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b(""), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - ], - extensions=[], - nested_types=[], - enum_types=[], - options=None, - is_extendable=False, - syntax="proto3", - extension_ranges=[], - oneofs=[], - serialized_start=738, - serialized_end=792, -) - -_EVENT.fields_by_name["summary"].message_type = tensorboardX_dot_src_dot_summary__pb2._SUMMARY -_EVENT.fields_by_name["log_message"].message_type = _LOGMESSAGE -_EVENT.fields_by_name["session_log"].message_type = _SESSIONLOG -_EVENT.fields_by_name["tagged_run_metadata"].message_type = _TAGGEDRUNMETADATA -_EVENT.oneofs_by_name["what"].fields.append(_EVENT.fields_by_name["file_version"]) -_EVENT.fields_by_name["file_version"].containing_oneof = _EVENT.oneofs_by_name["what"] -_EVENT.oneofs_by_name["what"].fields.append(_EVENT.fields_by_name["graph_def"]) -_EVENT.fields_by_name["graph_def"].containing_oneof = _EVENT.oneofs_by_name["what"] -_EVENT.oneofs_by_name["what"].fields.append(_EVENT.fields_by_name["summary"]) -_EVENT.fields_by_name["summary"].containing_oneof = _EVENT.oneofs_by_name["what"] -_EVENT.oneofs_by_name["what"].fields.append(_EVENT.fields_by_name["log_message"]) -_EVENT.fields_by_name["log_message"].containing_oneof = _EVENT.oneofs_by_name["what"] -_EVENT.oneofs_by_name["what"].fields.append(_EVENT.fields_by_name["session_log"]) -_EVENT.fields_by_name["session_log"].containing_oneof = _EVENT.oneofs_by_name["what"] -_EVENT.oneofs_by_name["what"].fields.append(_EVENT.fields_by_name["tagged_run_metadata"]) -_EVENT.fields_by_name["tagged_run_metadata"].containing_oneof = _EVENT.oneofs_by_name["what"] -_EVENT.oneofs_by_name["what"].fields.append(_EVENT.fields_by_name["meta_graph_def"]) -_EVENT.fields_by_name["meta_graph_def"].containing_oneof = _EVENT.oneofs_by_name["what"] -_LOGMESSAGE.fields_by_name["level"].enum_type = _LOGMESSAGE_LEVEL -_LOGMESSAGE_LEVEL.containing_type = _LOGMESSAGE -_SESSIONLOG.fields_by_name["status"].enum_type = _SESSIONLOG_SESSIONSTATUS -_SESSIONLOG_SESSIONSTATUS.containing_type = _SESSIONLOG -DESCRIPTOR.message_types_by_name["Event"] = _EVENT -DESCRIPTOR.message_types_by_name["LogMessage"] = _LOGMESSAGE -DESCRIPTOR.message_types_by_name["SessionLog"] = _SESSIONLOG -DESCRIPTOR.message_types_by_name["TaggedRunMetadata"] = _TAGGEDRUNMETADATA -_sym_db.RegisterFileDescriptor(DESCRIPTOR) - -Event = _reflection.GeneratedProtocolMessageType( - "Event", - (_message.Message,), - dict( - DESCRIPTOR=_EVENT, - __module__="tensorboardX.src.event_pb2" - # @@protoc_insertion_point(class_scope:tensorboard.Event) - ), -) -_sym_db.RegisterMessage(Event) - -LogMessage = _reflection.GeneratedProtocolMessageType( - "LogMessage", - (_message.Message,), - dict( - DESCRIPTOR=_LOGMESSAGE, - __module__="tensorboardX.src.event_pb2" - # @@protoc_insertion_point(class_scope:tensorboard.LogMessage) - ), -) -_sym_db.RegisterMessage(LogMessage) - -SessionLog = _reflection.GeneratedProtocolMessageType( - "SessionLog", - (_message.Message,), - dict( - DESCRIPTOR=_SESSIONLOG, - __module__="tensorboardX.src.event_pb2" - # @@protoc_insertion_point(class_scope:tensorboard.SessionLog) - ), -) -_sym_db.RegisterMessage(SessionLog) - -TaggedRunMetadata = _reflection.GeneratedProtocolMessageType( - "TaggedRunMetadata", - (_message.Message,), - dict( - DESCRIPTOR=_TAGGEDRUNMETADATA, - __module__="tensorboardX.src.event_pb2" - # @@protoc_insertion_point(class_scope:tensorboard.TaggedRunMetadata) - ), -) -_sym_db.RegisterMessage(TaggedRunMetadata) - - -DESCRIPTOR.has_options = True -DESCRIPTOR._options = _descriptor._ParseOptions( - descriptor_pb2.FileOptions(), _b("\n\023org.tensorflow.utilB\013EventProtosP\001\370\001\001") -) -# @@protoc_insertion_point(module_scope) diff --git a/~tensorboardX/src/graph_pb2.py b/~tensorboardX/src/graph_pb2.py deleted file mode 100644 index ea98390..0000000 --- a/~tensorboardX/src/graph_pb2.py +++ /dev/null @@ -1,129 +0,0 @@ -# Generated by the protocol buffer compiler. DO NOT EDIT! -# source: tensorboardX/src/graph.proto - -import sys - -_b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode("latin1")) -from google.protobuf import descriptor as _descriptor -from google.protobuf import descriptor_pb2 -from google.protobuf import message as _message -from google.protobuf import reflection as _reflection -from google.protobuf import symbol_database as _symbol_database - -# @@protoc_insertion_point(imports) - -_sym_db = _symbol_database.Default() - - -from tensorboardX.src import node_def_pb2 as tensorboardX_dot_src_dot_node__def__pb2 -from tensorboardX.src import versions_pb2 as tensorboardX_dot_src_dot_versions__pb2 - -DESCRIPTOR = _descriptor.FileDescriptor( - name="tensorboardX/src/graph.proto", - package="tensorboard", - syntax="proto3", - serialized_pb=_b( - '\n\x1ctensorboardX/src/graph.proto\x12\x0btensorboard\x1a\x1ftensorboardX/src/node_def.proto\x1a\x1ftensorboardX/src/versions.proto"n\n\x08GraphDef\x12"\n\x04node\x18\x01 \x03(\x0b\x32\x14.tensorboard.NodeDef\x12)\n\x08versions\x18\x04 \x01(\x0b\x32\x17.tensorboard.VersionDef\x12\x13\n\x07version\x18\x03 \x01(\x05\x42\x02\x18\x01\x42,\n\x18org.tensorflow.frameworkB\x0bGraphProtosP\x01\xf8\x01\x01\x62\x06proto3' - ), - dependencies=[ - tensorboardX_dot_src_dot_node__def__pb2.DESCRIPTOR, - tensorboardX_dot_src_dot_versions__pb2.DESCRIPTOR, - ], -) - - -_GRAPHDEF = _descriptor.Descriptor( - name="GraphDef", - full_name="tensorboard.GraphDef", - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name="node", - full_name="tensorboard.GraphDef.node", - index=0, - number=1, - type=11, - cpp_type=10, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="versions", - full_name="tensorboard.GraphDef.versions", - index=1, - number=4, - type=11, - cpp_type=10, - label=1, - has_default_value=False, - default_value=None, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="version", - full_name="tensorboard.GraphDef.version", - index=2, - number=3, - type=5, - cpp_type=1, - label=1, - has_default_value=False, - default_value=0, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b("\030\001")), - ), - ], - extensions=[], - nested_types=[], - enum_types=[], - options=None, - is_extendable=False, - syntax="proto3", - extension_ranges=[], - oneofs=[], - serialized_start=111, - serialized_end=221, -) - -_GRAPHDEF.fields_by_name["node"].message_type = tensorboardX_dot_src_dot_node__def__pb2._NODEDEF -_GRAPHDEF.fields_by_name["versions"].message_type = tensorboardX_dot_src_dot_versions__pb2._VERSIONDEF -DESCRIPTOR.message_types_by_name["GraphDef"] = _GRAPHDEF -_sym_db.RegisterFileDescriptor(DESCRIPTOR) - -GraphDef = _reflection.GeneratedProtocolMessageType( - "GraphDef", - (_message.Message,), - dict( - DESCRIPTOR=_GRAPHDEF, - __module__="tensorboardX.src.graph_pb2" - # @@protoc_insertion_point(class_scope:tensorboard.GraphDef) - ), -) -_sym_db.RegisterMessage(GraphDef) - - -DESCRIPTOR.has_options = True -DESCRIPTOR._options = _descriptor._ParseOptions( - descriptor_pb2.FileOptions(), _b("\n\030org.tensorflow.frameworkB\013GraphProtosP\001\370\001\001") -) -_GRAPHDEF.fields_by_name["version"].has_options = True -_GRAPHDEF.fields_by_name["version"]._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b("\030\001")) -# @@protoc_insertion_point(module_scope) diff --git a/~tensorboardX/src/node_def_pb2.py b/~tensorboardX/src/node_def_pb2.py deleted file mode 100644 index e7d3a14..0000000 --- a/~tensorboardX/src/node_def_pb2.py +++ /dev/null @@ -1,224 +0,0 @@ -# Generated by the protocol buffer compiler. DO NOT EDIT! -# source: tensorboardX/src/node_def.proto - -import sys - -_b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode("latin1")) -from google.protobuf import descriptor as _descriptor -from google.protobuf import descriptor_pb2 -from google.protobuf import message as _message -from google.protobuf import reflection as _reflection -from google.protobuf import symbol_database as _symbol_database - -# @@protoc_insertion_point(imports) - -_sym_db = _symbol_database.Default() - - -from tensorboardX.src import attr_value_pb2 as tensorboardX_dot_src_dot_attr__value__pb2 - -DESCRIPTOR = _descriptor.FileDescriptor( - name="tensorboardX/src/node_def.proto", - package="tensorboard", - syntax="proto3", - serialized_pb=_b( - '\n\x1ftensorboardX/src/node_def.proto\x12\x0btensorboard\x1a!tensorboardX/src/attr_value.proto"\xb5\x01\n\x07NodeDef\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\n\n\x02op\x18\x02 \x01(\t\x12\r\n\x05input\x18\x03 \x03(\t\x12\x0e\n\x06\x64\x65vice\x18\x04 \x01(\t\x12,\n\x04\x61ttr\x18\x05 \x03(\x0b\x32\x1e.tensorboard.NodeDef.AttrEntry\x1a\x43\n\tAttrEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12%\n\x05value\x18\x02 \x01(\x0b\x32\x16.tensorboard.AttrValue:\x02\x38\x01\x42*\n\x18org.tensorflow.frameworkB\tNodeProtoP\x01\xf8\x01\x01\x62\x06proto3' - ), - dependencies=[tensorboardX_dot_src_dot_attr__value__pb2.DESCRIPTOR,], -) - - -_NODEDEF_ATTRENTRY = _descriptor.Descriptor( - name="AttrEntry", - full_name="tensorboard.NodeDef.AttrEntry", - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name="key", - full_name="tensorboard.NodeDef.AttrEntry.key", - index=0, - number=1, - type=9, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b("").decode("utf-8"), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="value", - full_name="tensorboard.NodeDef.AttrEntry.value", - index=1, - number=2, - type=11, - cpp_type=10, - label=1, - has_default_value=False, - default_value=None, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - ], - extensions=[], - nested_types=[], - enum_types=[], - options=_descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b("8\001")), - is_extendable=False, - syntax="proto3", - extension_ranges=[], - oneofs=[], - serialized_start=198, - serialized_end=265, -) - -_NODEDEF = _descriptor.Descriptor( - name="NodeDef", - full_name="tensorboard.NodeDef", - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name="name", - full_name="tensorboard.NodeDef.name", - index=0, - number=1, - type=9, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b("").decode("utf-8"), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="op", - full_name="tensorboard.NodeDef.op", - index=1, - number=2, - type=9, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b("").decode("utf-8"), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="input", - full_name="tensorboard.NodeDef.input", - index=2, - number=3, - type=9, - cpp_type=9, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="device", - full_name="tensorboard.NodeDef.device", - index=3, - number=4, - type=9, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b("").decode("utf-8"), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="attr", - full_name="tensorboard.NodeDef.attr", - index=4, - number=5, - type=11, - cpp_type=10, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - ], - extensions=[], - nested_types=[_NODEDEF_ATTRENTRY,], - enum_types=[], - options=None, - is_extendable=False, - syntax="proto3", - extension_ranges=[], - oneofs=[], - serialized_start=84, - serialized_end=265, -) - -_NODEDEF_ATTRENTRY.fields_by_name["value"].message_type = tensorboardX_dot_src_dot_attr__value__pb2._ATTRVALUE -_NODEDEF_ATTRENTRY.containing_type = _NODEDEF -_NODEDEF.fields_by_name["attr"].message_type = _NODEDEF_ATTRENTRY -DESCRIPTOR.message_types_by_name["NodeDef"] = _NODEDEF -_sym_db.RegisterFileDescriptor(DESCRIPTOR) - -NodeDef = _reflection.GeneratedProtocolMessageType( - "NodeDef", - (_message.Message,), - dict( - AttrEntry=_reflection.GeneratedProtocolMessageType( - "AttrEntry", - (_message.Message,), - dict( - DESCRIPTOR=_NODEDEF_ATTRENTRY, - __module__="tensorboardX.src.node_def_pb2" - # @@protoc_insertion_point(class_scope:tensorboard.NodeDef.AttrEntry) - ), - ), - DESCRIPTOR=_NODEDEF, - __module__="tensorboardX.src.node_def_pb2" - # @@protoc_insertion_point(class_scope:tensorboard.NodeDef) - ), -) -_sym_db.RegisterMessage(NodeDef) -_sym_db.RegisterMessage(NodeDef.AttrEntry) - - -DESCRIPTOR.has_options = True -DESCRIPTOR._options = _descriptor._ParseOptions( - descriptor_pb2.FileOptions(), _b("\n\030org.tensorflow.frameworkB\tNodeProtoP\001\370\001\001") -) -_NODEDEF_ATTRENTRY.has_options = True -_NODEDEF_ATTRENTRY._options = _descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b("8\001")) -# @@protoc_insertion_point(module_scope) diff --git a/~tensorboardX/src/plugin_pr_curve_pb2.py b/~tensorboardX/src/plugin_pr_curve_pb2.py deleted file mode 100644 index d0e0d9b..0000000 --- a/~tensorboardX/src/plugin_pr_curve_pb2.py +++ /dev/null @@ -1,97 +0,0 @@ -# Generated by the protocol buffer compiler. DO NOT EDIT! -# source: tensorboardX/src/plugin_pr_curve.proto - -import sys - -_b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode("latin1")) -from google.protobuf import descriptor as _descriptor -from google.protobuf import descriptor_pb2 -from google.protobuf import message as _message -from google.protobuf import reflection as _reflection -from google.protobuf import symbol_database as _symbol_database - -# @@protoc_insertion_point(imports) - -_sym_db = _symbol_database.Default() - - -DESCRIPTOR = _descriptor.FileDescriptor( - name="tensorboardX/src/plugin_pr_curve.proto", - package="tensorboard", - syntax="proto3", - serialized_pb=_b( - '\n&tensorboardX/src/plugin_pr_curve.proto\x12\x0btensorboard"<\n\x11PrCurvePluginData\x12\x0f\n\x07version\x18\x01 \x01(\x05\x12\x16\n\x0enum_thresholds\x18\x02 \x01(\rb\x06proto3' - ), -) - - -_PRCURVEPLUGINDATA = _descriptor.Descriptor( - name="PrCurvePluginData", - full_name="tensorboard.PrCurvePluginData", - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name="version", - full_name="tensorboard.PrCurvePluginData.version", - index=0, - number=1, - type=5, - cpp_type=1, - label=1, - has_default_value=False, - default_value=0, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="num_thresholds", - full_name="tensorboard.PrCurvePluginData.num_thresholds", - index=1, - number=2, - type=13, - cpp_type=3, - label=1, - has_default_value=False, - default_value=0, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - ], - extensions=[], - nested_types=[], - enum_types=[], - options=None, - is_extendable=False, - syntax="proto3", - extension_ranges=[], - oneofs=[], - serialized_start=55, - serialized_end=115, -) - -DESCRIPTOR.message_types_by_name["PrCurvePluginData"] = _PRCURVEPLUGINDATA -_sym_db.RegisterFileDescriptor(DESCRIPTOR) - -PrCurvePluginData = _reflection.GeneratedProtocolMessageType( - "PrCurvePluginData", - (_message.Message,), - dict( - DESCRIPTOR=_PRCURVEPLUGINDATA, - __module__="tensorboardX.src.plugin_pr_curve_pb2" - # @@protoc_insertion_point(class_scope:tensorboard.PrCurvePluginData) - ), -) -_sym_db.RegisterMessage(PrCurvePluginData) - - -# @@protoc_insertion_point(module_scope) diff --git a/~tensorboardX/src/resource_handle_pb2.py b/~tensorboardX/src/resource_handle_pb2.py deleted file mode 100644 index 54bae9a..0000000 --- a/~tensorboardX/src/resource_handle_pb2.py +++ /dev/null @@ -1,152 +0,0 @@ -# Generated by the protocol buffer compiler. DO NOT EDIT! -# source: tensorboardX/src/resource_handle.proto - -import sys - -_b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode("latin1")) -from google.protobuf import descriptor as _descriptor -from google.protobuf import descriptor_pb2 -from google.protobuf import message as _message -from google.protobuf import reflection as _reflection -from google.protobuf import symbol_database as _symbol_database - -# @@protoc_insertion_point(imports) - -_sym_db = _symbol_database.Default() - - -DESCRIPTOR = _descriptor.FileDescriptor( - name="tensorboardX/src/resource_handle.proto", - package="tensorboard", - syntax="proto3", - serialized_pb=_b( - '\n&tensorboardX/src/resource_handle.proto\x12\x0btensorboard"r\n\x13ResourceHandleProto\x12\x0e\n\x06\x64\x65vice\x18\x01 \x01(\t\x12\x11\n\tcontainer\x18\x02 \x01(\t\x12\x0c\n\x04name\x18\x03 \x01(\t\x12\x11\n\thash_code\x18\x04 \x01(\x04\x12\x17\n\x0fmaybe_type_name\x18\x05 \x01(\tB/\n\x18org.tensorflow.frameworkB\x0eResourceHandleP\x01\xf8\x01\x01\x62\x06proto3' - ), -) - - -_RESOURCEHANDLEPROTO = _descriptor.Descriptor( - name="ResourceHandleProto", - full_name="tensorboard.ResourceHandleProto", - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name="device", - full_name="tensorboard.ResourceHandleProto.device", - index=0, - number=1, - type=9, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b("").decode("utf-8"), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="container", - full_name="tensorboard.ResourceHandleProto.container", - index=1, - number=2, - type=9, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b("").decode("utf-8"), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="name", - full_name="tensorboard.ResourceHandleProto.name", - index=2, - number=3, - type=9, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b("").decode("utf-8"), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="hash_code", - full_name="tensorboard.ResourceHandleProto.hash_code", - index=3, - number=4, - type=4, - cpp_type=4, - label=1, - has_default_value=False, - default_value=0, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="maybe_type_name", - full_name="tensorboard.ResourceHandleProto.maybe_type_name", - index=4, - number=5, - type=9, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b("").decode("utf-8"), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - ], - extensions=[], - nested_types=[], - enum_types=[], - options=None, - is_extendable=False, - syntax="proto3", - extension_ranges=[], - oneofs=[], - serialized_start=55, - serialized_end=169, -) - -DESCRIPTOR.message_types_by_name["ResourceHandleProto"] = _RESOURCEHANDLEPROTO -_sym_db.RegisterFileDescriptor(DESCRIPTOR) - -ResourceHandleProto = _reflection.GeneratedProtocolMessageType( - "ResourceHandleProto", - (_message.Message,), - dict( - DESCRIPTOR=_RESOURCEHANDLEPROTO, - __module__="tensorboardX.src.resource_handle_pb2" - # @@protoc_insertion_point(class_scope:tensorboard.ResourceHandleProto) - ), -) -_sym_db.RegisterMessage(ResourceHandleProto) - - -DESCRIPTOR.has_options = True -DESCRIPTOR._options = _descriptor._ParseOptions( - descriptor_pb2.FileOptions(), _b("\n\030org.tensorflow.frameworkB\016ResourceHandleP\001\370\001\001") -) -# @@protoc_insertion_point(module_scope) diff --git a/~tensorboardX/src/summary_pb2.py b/~tensorboardX/src/summary_pb2.py deleted file mode 100644 index fe92ec1..0000000 --- a/~tensorboardX/src/summary_pb2.py +++ /dev/null @@ -1,834 +0,0 @@ -# Generated by the protocol buffer compiler. DO NOT EDIT! -# source: tensorboardX/src/summary.proto - -import sys - -_b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode("latin1")) -from google.protobuf import descriptor as _descriptor -from google.protobuf import descriptor_pb2 -from google.protobuf import message as _message -from google.protobuf import reflection as _reflection -from google.protobuf import symbol_database as _symbol_database - -# @@protoc_insertion_point(imports) - -_sym_db = _symbol_database.Default() - - -from tensorboardX.src import tensor_pb2 as tensorboardX_dot_src_dot_tensor__pb2 - -DESCRIPTOR = _descriptor.FileDescriptor( - name="tensorboardX/src/summary.proto", - package="tensorboard", - syntax="proto3", - serialized_pb=_b( - '\n\x1etensorboardX/src/summary.proto\x12\x0btensorboard\x1a\x1dtensorboardX/src/tensor.proto"\'\n\x12SummaryDescription\x12\x11\n\ttype_hint\x18\x01 \x01(\t"\x87\x01\n\x0eHistogramProto\x12\x0b\n\x03min\x18\x01 \x01(\x01\x12\x0b\n\x03max\x18\x02 \x01(\x01\x12\x0b\n\x03num\x18\x03 \x01(\x01\x12\x0b\n\x03sum\x18\x04 \x01(\x01\x12\x13\n\x0bsum_squares\x18\x05 \x01(\x01\x12\x18\n\x0c\x62ucket_limit\x18\x06 \x03(\x01\x42\x02\x10\x01\x12\x12\n\x06\x62ucket\x18\x07 \x03(\x01\x42\x02\x10\x01"\x83\x01\n\x0fSummaryMetadata\x12<\n\x0bplugin_data\x18\x01 \x03(\x0b\x32\'.tensorboard.SummaryMetadata.PluginData\x1a\x32\n\nPluginData\x12\x13\n\x0bplugin_name\x18\x01 \x01(\t\x12\x0f\n\x07\x63ontent\x18\x02 \x01(\t"\xe4\x04\n\x07Summary\x12)\n\x05value\x18\x01 \x03(\x0b\x32\x1a.tensorboard.Summary.Value\x1aX\n\x05Image\x12\x0e\n\x06height\x18\x01 \x01(\x05\x12\r\n\x05width\x18\x02 \x01(\x05\x12\x12\n\ncolorspace\x18\x03 \x01(\x05\x12\x1c\n\x14\x65ncoded_image_string\x18\x04 \x01(\x0c\x1a}\n\x05\x41udio\x12\x13\n\x0bsample_rate\x18\x01 \x01(\x02\x12\x14\n\x0cnum_channels\x18\x02 \x01(\x03\x12\x15\n\rlength_frames\x18\x03 \x01(\x03\x12\x1c\n\x14\x65ncoded_audio_string\x18\x04 \x01(\x0c\x12\x14\n\x0c\x63ontent_type\x18\x05 \x01(\t\x1a\xd4\x02\n\x05Value\x12\x11\n\tnode_name\x18\x07 \x01(\t\x12\x0b\n\x03tag\x18\x01 \x01(\t\x12.\n\x08metadata\x18\t \x01(\x0b\x32\x1c.tensorboard.SummaryMetadata\x12\x16\n\x0csimple_value\x18\x02 \x01(\x02H\x00\x12&\n\x1cobsolete_old_style_histogram\x18\x03 \x01(\x0cH\x00\x12+\n\x05image\x18\x04 \x01(\x0b\x32\x1a.tensorboard.Summary.ImageH\x00\x12,\n\x05histo\x18\x05 \x01(\x0b\x32\x1b.tensorboard.HistogramProtoH\x00\x12+\n\x05\x61udio\x18\x06 \x01(\x0b\x32\x1a.tensorboard.Summary.AudioH\x00\x12*\n\x06tensor\x18\x08 \x01(\x0b\x32\x18.tensorboard.TensorProtoH\x00\x42\x07\n\x05valueB.\n\x18org.tensorflow.frameworkB\rSummaryProtosP\x01\xf8\x01\x01\x62\x06proto3' - ), - dependencies=[tensorboardX_dot_src_dot_tensor__pb2.DESCRIPTOR,], -) - - -_SUMMARYDESCRIPTION = _descriptor.Descriptor( - name="SummaryDescription", - full_name="tensorboard.SummaryDescription", - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name="type_hint", - full_name="tensorboard.SummaryDescription.type_hint", - index=0, - number=1, - type=9, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b("").decode("utf-8"), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - ], - extensions=[], - nested_types=[], - enum_types=[], - options=None, - is_extendable=False, - syntax="proto3", - extension_ranges=[], - oneofs=[], - serialized_start=78, - serialized_end=117, -) - - -_HISTOGRAMPROTO = _descriptor.Descriptor( - name="HistogramProto", - full_name="tensorboard.HistogramProto", - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name="min", - full_name="tensorboard.HistogramProto.min", - index=0, - number=1, - type=1, - cpp_type=5, - label=1, - has_default_value=False, - default_value=float(0), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="max", - full_name="tensorboard.HistogramProto.max", - index=1, - number=2, - type=1, - cpp_type=5, - label=1, - has_default_value=False, - default_value=float(0), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="num", - full_name="tensorboard.HistogramProto.num", - index=2, - number=3, - type=1, - cpp_type=5, - label=1, - has_default_value=False, - default_value=float(0), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="sum", - full_name="tensorboard.HistogramProto.sum", - index=3, - number=4, - type=1, - cpp_type=5, - label=1, - has_default_value=False, - default_value=float(0), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="sum_squares", - full_name="tensorboard.HistogramProto.sum_squares", - index=4, - number=5, - type=1, - cpp_type=5, - label=1, - has_default_value=False, - default_value=float(0), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="bucket_limit", - full_name="tensorboard.HistogramProto.bucket_limit", - index=5, - number=6, - type=1, - cpp_type=5, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b("\020\001")), - ), - _descriptor.FieldDescriptor( - name="bucket", - full_name="tensorboard.HistogramProto.bucket", - index=6, - number=7, - type=1, - cpp_type=5, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b("\020\001")), - ), - ], - extensions=[], - nested_types=[], - enum_types=[], - options=None, - is_extendable=False, - syntax="proto3", - extension_ranges=[], - oneofs=[], - serialized_start=120, - serialized_end=255, -) - - -_SUMMARYMETADATA_PLUGINDATA = _descriptor.Descriptor( - name="PluginData", - full_name="tensorboard.SummaryMetadata.PluginData", - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name="plugin_name", - full_name="tensorboard.SummaryMetadata.PluginData.plugin_name", - index=0, - number=1, - type=9, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b("").decode("utf-8"), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="content", - full_name="tensorboard.SummaryMetadata.PluginData.content", - index=1, - number=2, - type=9, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b("").decode("utf-8"), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - ], - extensions=[], - nested_types=[], - enum_types=[], - options=None, - is_extendable=False, - syntax="proto3", - extension_ranges=[], - oneofs=[], - serialized_start=339, - serialized_end=389, -) - -_SUMMARYMETADATA = _descriptor.Descriptor( - name="SummaryMetadata", - full_name="tensorboard.SummaryMetadata", - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name="plugin_data", - full_name="tensorboard.SummaryMetadata.plugin_data", - index=0, - number=1, - type=11, - cpp_type=10, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - ], - extensions=[], - nested_types=[_SUMMARYMETADATA_PLUGINDATA,], - enum_types=[], - options=None, - is_extendable=False, - syntax="proto3", - extension_ranges=[], - oneofs=[], - serialized_start=258, - serialized_end=389, -) - - -_SUMMARY_IMAGE = _descriptor.Descriptor( - name="Image", - full_name="tensorboard.Summary.Image", - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name="height", - full_name="tensorboard.Summary.Image.height", - index=0, - number=1, - type=5, - cpp_type=1, - label=1, - has_default_value=False, - default_value=0, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="width", - full_name="tensorboard.Summary.Image.width", - index=1, - number=2, - type=5, - cpp_type=1, - label=1, - has_default_value=False, - default_value=0, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="colorspace", - full_name="tensorboard.Summary.Image.colorspace", - index=2, - number=3, - type=5, - cpp_type=1, - label=1, - has_default_value=False, - default_value=0, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="encoded_image_string", - full_name="tensorboard.Summary.Image.encoded_image_string", - index=3, - number=4, - type=12, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b(""), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - ], - extensions=[], - nested_types=[], - enum_types=[], - options=None, - is_extendable=False, - syntax="proto3", - extension_ranges=[], - oneofs=[], - serialized_start=446, - serialized_end=534, -) - -_SUMMARY_AUDIO = _descriptor.Descriptor( - name="Audio", - full_name="tensorboard.Summary.Audio", - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name="sample_rate", - full_name="tensorboard.Summary.Audio.sample_rate", - index=0, - number=1, - type=2, - cpp_type=6, - label=1, - has_default_value=False, - default_value=float(0), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="num_channels", - full_name="tensorboard.Summary.Audio.num_channels", - index=1, - number=2, - type=3, - cpp_type=2, - label=1, - has_default_value=False, - default_value=0, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="length_frames", - full_name="tensorboard.Summary.Audio.length_frames", - index=2, - number=3, - type=3, - cpp_type=2, - label=1, - has_default_value=False, - default_value=0, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="encoded_audio_string", - full_name="tensorboard.Summary.Audio.encoded_audio_string", - index=3, - number=4, - type=12, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b(""), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="content_type", - full_name="tensorboard.Summary.Audio.content_type", - index=4, - number=5, - type=9, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b("").decode("utf-8"), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - ], - extensions=[], - nested_types=[], - enum_types=[], - options=None, - is_extendable=False, - syntax="proto3", - extension_ranges=[], - oneofs=[], - serialized_start=536, - serialized_end=661, -) - -_SUMMARY_VALUE = _descriptor.Descriptor( - name="Value", - full_name="tensorboard.Summary.Value", - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name="node_name", - full_name="tensorboard.Summary.Value.node_name", - index=0, - number=7, - type=9, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b("").decode("utf-8"), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="tag", - full_name="tensorboard.Summary.Value.tag", - index=1, - number=1, - type=9, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b("").decode("utf-8"), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="metadata", - full_name="tensorboard.Summary.Value.metadata", - index=2, - number=9, - type=11, - cpp_type=10, - label=1, - has_default_value=False, - default_value=None, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="simple_value", - full_name="tensorboard.Summary.Value.simple_value", - index=3, - number=2, - type=2, - cpp_type=6, - label=1, - has_default_value=False, - default_value=float(0), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="obsolete_old_style_histogram", - full_name="tensorboard.Summary.Value.obsolete_old_style_histogram", - index=4, - number=3, - type=12, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b(""), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="image", - full_name="tensorboard.Summary.Value.image", - index=5, - number=4, - type=11, - cpp_type=10, - label=1, - has_default_value=False, - default_value=None, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="histo", - full_name="tensorboard.Summary.Value.histo", - index=6, - number=5, - type=11, - cpp_type=10, - label=1, - has_default_value=False, - default_value=None, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="audio", - full_name="tensorboard.Summary.Value.audio", - index=7, - number=6, - type=11, - cpp_type=10, - label=1, - has_default_value=False, - default_value=None, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="tensor", - full_name="tensorboard.Summary.Value.tensor", - index=8, - number=8, - type=11, - cpp_type=10, - label=1, - has_default_value=False, - default_value=None, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - ], - extensions=[], - nested_types=[], - enum_types=[], - options=None, - is_extendable=False, - syntax="proto3", - extension_ranges=[], - oneofs=[ - _descriptor.OneofDescriptor( - name="value", full_name="tensorboard.Summary.Value.value", index=0, containing_type=None, fields=[] - ), - ], - serialized_start=664, - serialized_end=1004, -) - -_SUMMARY = _descriptor.Descriptor( - name="Summary", - full_name="tensorboard.Summary", - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name="value", - full_name="tensorboard.Summary.value", - index=0, - number=1, - type=11, - cpp_type=10, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - ], - extensions=[], - nested_types=[_SUMMARY_IMAGE, _SUMMARY_AUDIO, _SUMMARY_VALUE,], - enum_types=[], - options=None, - is_extendable=False, - syntax="proto3", - extension_ranges=[], - oneofs=[], - serialized_start=392, - serialized_end=1004, -) - -_SUMMARYMETADATA_PLUGINDATA.containing_type = _SUMMARYMETADATA -_SUMMARYMETADATA.fields_by_name["plugin_data"].message_type = _SUMMARYMETADATA_PLUGINDATA -_SUMMARY_IMAGE.containing_type = _SUMMARY -_SUMMARY_AUDIO.containing_type = _SUMMARY -_SUMMARY_VALUE.fields_by_name["metadata"].message_type = _SUMMARYMETADATA -_SUMMARY_VALUE.fields_by_name["image"].message_type = _SUMMARY_IMAGE -_SUMMARY_VALUE.fields_by_name["histo"].message_type = _HISTOGRAMPROTO -_SUMMARY_VALUE.fields_by_name["audio"].message_type = _SUMMARY_AUDIO -_SUMMARY_VALUE.fields_by_name["tensor"].message_type = tensorboardX_dot_src_dot_tensor__pb2._TENSORPROTO -_SUMMARY_VALUE.containing_type = _SUMMARY -_SUMMARY_VALUE.oneofs_by_name["value"].fields.append(_SUMMARY_VALUE.fields_by_name["simple_value"]) -_SUMMARY_VALUE.fields_by_name["simple_value"].containing_oneof = _SUMMARY_VALUE.oneofs_by_name["value"] -_SUMMARY_VALUE.oneofs_by_name["value"].fields.append(_SUMMARY_VALUE.fields_by_name["obsolete_old_style_histogram"]) -_SUMMARY_VALUE.fields_by_name["obsolete_old_style_histogram"].containing_oneof = _SUMMARY_VALUE.oneofs_by_name["value"] -_SUMMARY_VALUE.oneofs_by_name["value"].fields.append(_SUMMARY_VALUE.fields_by_name["image"]) -_SUMMARY_VALUE.fields_by_name["image"].containing_oneof = _SUMMARY_VALUE.oneofs_by_name["value"] -_SUMMARY_VALUE.oneofs_by_name["value"].fields.append(_SUMMARY_VALUE.fields_by_name["histo"]) -_SUMMARY_VALUE.fields_by_name["histo"].containing_oneof = _SUMMARY_VALUE.oneofs_by_name["value"] -_SUMMARY_VALUE.oneofs_by_name["value"].fields.append(_SUMMARY_VALUE.fields_by_name["audio"]) -_SUMMARY_VALUE.fields_by_name["audio"].containing_oneof = _SUMMARY_VALUE.oneofs_by_name["value"] -_SUMMARY_VALUE.oneofs_by_name["value"].fields.append(_SUMMARY_VALUE.fields_by_name["tensor"]) -_SUMMARY_VALUE.fields_by_name["tensor"].containing_oneof = _SUMMARY_VALUE.oneofs_by_name["value"] -_SUMMARY.fields_by_name["value"].message_type = _SUMMARY_VALUE -DESCRIPTOR.message_types_by_name["SummaryDescription"] = _SUMMARYDESCRIPTION -DESCRIPTOR.message_types_by_name["HistogramProto"] = _HISTOGRAMPROTO -DESCRIPTOR.message_types_by_name["SummaryMetadata"] = _SUMMARYMETADATA -DESCRIPTOR.message_types_by_name["Summary"] = _SUMMARY -_sym_db.RegisterFileDescriptor(DESCRIPTOR) - -SummaryDescription = _reflection.GeneratedProtocolMessageType( - "SummaryDescription", - (_message.Message,), - dict( - DESCRIPTOR=_SUMMARYDESCRIPTION, - __module__="tensorboardX.src.summary_pb2" - # @@protoc_insertion_point(class_scope:tensorboard.SummaryDescription) - ), -) -_sym_db.RegisterMessage(SummaryDescription) - -HistogramProto = _reflection.GeneratedProtocolMessageType( - "HistogramProto", - (_message.Message,), - dict( - DESCRIPTOR=_HISTOGRAMPROTO, - __module__="tensorboardX.src.summary_pb2" - # @@protoc_insertion_point(class_scope:tensorboard.HistogramProto) - ), -) -_sym_db.RegisterMessage(HistogramProto) - -SummaryMetadata = _reflection.GeneratedProtocolMessageType( - "SummaryMetadata", - (_message.Message,), - dict( - PluginData=_reflection.GeneratedProtocolMessageType( - "PluginData", - (_message.Message,), - dict( - DESCRIPTOR=_SUMMARYMETADATA_PLUGINDATA, - __module__="tensorboardX.src.summary_pb2" - # @@protoc_insertion_point(class_scope:tensorboard.SummaryMetadata.PluginData) - ), - ), - DESCRIPTOR=_SUMMARYMETADATA, - __module__="tensorboardX.src.summary_pb2" - # @@protoc_insertion_point(class_scope:tensorboard.SummaryMetadata) - ), -) -_sym_db.RegisterMessage(SummaryMetadata) -_sym_db.RegisterMessage(SummaryMetadata.PluginData) - -Summary = _reflection.GeneratedProtocolMessageType( - "Summary", - (_message.Message,), - dict( - Image=_reflection.GeneratedProtocolMessageType( - "Image", - (_message.Message,), - dict( - DESCRIPTOR=_SUMMARY_IMAGE, - __module__="tensorboardX.src.summary_pb2" - # @@protoc_insertion_point(class_scope:tensorboard.Summary.Image) - ), - ), - Audio=_reflection.GeneratedProtocolMessageType( - "Audio", - (_message.Message,), - dict( - DESCRIPTOR=_SUMMARY_AUDIO, - __module__="tensorboardX.src.summary_pb2" - # @@protoc_insertion_point(class_scope:tensorboard.Summary.Audio) - ), - ), - Value=_reflection.GeneratedProtocolMessageType( - "Value", - (_message.Message,), - dict( - DESCRIPTOR=_SUMMARY_VALUE, - __module__="tensorboardX.src.summary_pb2" - # @@protoc_insertion_point(class_scope:tensorboard.Summary.Value) - ), - ), - DESCRIPTOR=_SUMMARY, - __module__="tensorboardX.src.summary_pb2" - # @@protoc_insertion_point(class_scope:tensorboard.Summary) - ), -) -_sym_db.RegisterMessage(Summary) -_sym_db.RegisterMessage(Summary.Image) -_sym_db.RegisterMessage(Summary.Audio) -_sym_db.RegisterMessage(Summary.Value) - - -DESCRIPTOR.has_options = True -DESCRIPTOR._options = _descriptor._ParseOptions( - descriptor_pb2.FileOptions(), _b("\n\030org.tensorflow.frameworkB\rSummaryProtosP\001\370\001\001") -) -_HISTOGRAMPROTO.fields_by_name["bucket_limit"].has_options = True -_HISTOGRAMPROTO.fields_by_name["bucket_limit"]._options = _descriptor._ParseOptions( - descriptor_pb2.FieldOptions(), _b("\020\001") -) -_HISTOGRAMPROTO.fields_by_name["bucket"].has_options = True -_HISTOGRAMPROTO.fields_by_name["bucket"]._options = _descriptor._ParseOptions( - descriptor_pb2.FieldOptions(), _b("\020\001") -) -# @@protoc_insertion_point(module_scope) diff --git a/~tensorboardX/src/tensor_pb2.py b/~tensorboardX/src/tensor_pb2.py deleted file mode 100644 index f572208..0000000 --- a/~tensorboardX/src/tensor_pb2.py +++ /dev/null @@ -1,351 +0,0 @@ -# Generated by the protocol buffer compiler. DO NOT EDIT! -# source: tensorboardX/src/tensor.proto - -import sys - -_b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode("latin1")) -from google.protobuf import descriptor as _descriptor -from google.protobuf import descriptor_pb2 -from google.protobuf import message as _message -from google.protobuf import reflection as _reflection -from google.protobuf import symbol_database as _symbol_database - -# @@protoc_insertion_point(imports) - -_sym_db = _symbol_database.Default() - - -from tensorboardX.src import resource_handle_pb2 as tensorboardX_dot_src_dot_resource__handle__pb2 -from tensorboardX.src import tensor_shape_pb2 as tensorboardX_dot_src_dot_tensor__shape__pb2 -from tensorboardX.src import types_pb2 as tensorboardX_dot_src_dot_types__pb2 - -DESCRIPTOR = _descriptor.FileDescriptor( - name="tensorboardX/src/tensor.proto", - package="tensorboard", - syntax="proto3", - serialized_pb=_b( - '\n\x1dtensorboardX/src/tensor.proto\x12\x0btensorboard\x1a&tensorboardX/src/resource_handle.proto\x1a#tensorboardX/src/tensor_shape.proto\x1a\x1ctensorboardX/src/types.proto"\xa6\x03\n\x0bTensorProto\x12$\n\x05\x64type\x18\x01 \x01(\x0e\x32\x15.tensorboard.DataType\x12\x33\n\x0ctensor_shape\x18\x02 \x01(\x0b\x32\x1d.tensorboard.TensorShapeProto\x12\x16\n\x0eversion_number\x18\x03 \x01(\x05\x12\x16\n\x0etensor_content\x18\x04 \x01(\x0c\x12\x14\n\x08half_val\x18\r \x03(\x05\x42\x02\x10\x01\x12\x15\n\tfloat_val\x18\x05 \x03(\x02\x42\x02\x10\x01\x12\x16\n\ndouble_val\x18\x06 \x03(\x01\x42\x02\x10\x01\x12\x13\n\x07int_val\x18\x07 \x03(\x05\x42\x02\x10\x01\x12\x12\n\nstring_val\x18\x08 \x03(\x0c\x12\x18\n\x0cscomplex_val\x18\t \x03(\x02\x42\x02\x10\x01\x12\x15\n\tint64_val\x18\n \x03(\x03\x42\x02\x10\x01\x12\x14\n\x08\x62ool_val\x18\x0b \x03(\x08\x42\x02\x10\x01\x12\x18\n\x0c\x64\x63omplex_val\x18\x0c \x03(\x01\x42\x02\x10\x01\x12=\n\x13resource_handle_val\x18\x0e \x03(\x0b\x32 .tensorboard.ResourceHandleProtoB-\n\x18org.tensorflow.frameworkB\x0cTensorProtosP\x01\xf8\x01\x01\x62\x06proto3' - ), - dependencies=[ - tensorboardX_dot_src_dot_resource__handle__pb2.DESCRIPTOR, - tensorboardX_dot_src_dot_tensor__shape__pb2.DESCRIPTOR, - tensorboardX_dot_src_dot_types__pb2.DESCRIPTOR, - ], -) - - -_TENSORPROTO = _descriptor.Descriptor( - name="TensorProto", - full_name="tensorboard.TensorProto", - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name="dtype", - full_name="tensorboard.TensorProto.dtype", - index=0, - number=1, - type=14, - cpp_type=8, - label=1, - has_default_value=False, - default_value=0, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="tensor_shape", - full_name="tensorboard.TensorProto.tensor_shape", - index=1, - number=2, - type=11, - cpp_type=10, - label=1, - has_default_value=False, - default_value=None, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="version_number", - full_name="tensorboard.TensorProto.version_number", - index=2, - number=3, - type=5, - cpp_type=1, - label=1, - has_default_value=False, - default_value=0, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="tensor_content", - full_name="tensorboard.TensorProto.tensor_content", - index=3, - number=4, - type=12, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b(""), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="half_val", - full_name="tensorboard.TensorProto.half_val", - index=4, - number=13, - type=5, - cpp_type=1, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b("\020\001")), - ), - _descriptor.FieldDescriptor( - name="float_val", - full_name="tensorboard.TensorProto.float_val", - index=5, - number=5, - type=2, - cpp_type=6, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b("\020\001")), - ), - _descriptor.FieldDescriptor( - name="double_val", - full_name="tensorboard.TensorProto.double_val", - index=6, - number=6, - type=1, - cpp_type=5, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b("\020\001")), - ), - _descriptor.FieldDescriptor( - name="int_val", - full_name="tensorboard.TensorProto.int_val", - index=7, - number=7, - type=5, - cpp_type=1, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b("\020\001")), - ), - _descriptor.FieldDescriptor( - name="string_val", - full_name="tensorboard.TensorProto.string_val", - index=8, - number=8, - type=12, - cpp_type=9, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="scomplex_val", - full_name="tensorboard.TensorProto.scomplex_val", - index=9, - number=9, - type=2, - cpp_type=6, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b("\020\001")), - ), - _descriptor.FieldDescriptor( - name="int64_val", - full_name="tensorboard.TensorProto.int64_val", - index=10, - number=10, - type=3, - cpp_type=2, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b("\020\001")), - ), - _descriptor.FieldDescriptor( - name="bool_val", - full_name="tensorboard.TensorProto.bool_val", - index=11, - number=11, - type=8, - cpp_type=7, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b("\020\001")), - ), - _descriptor.FieldDescriptor( - name="dcomplex_val", - full_name="tensorboard.TensorProto.dcomplex_val", - index=12, - number=12, - type=1, - cpp_type=5, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b("\020\001")), - ), - _descriptor.FieldDescriptor( - name="resource_handle_val", - full_name="tensorboard.TensorProto.resource_handle_val", - index=13, - number=14, - type=11, - cpp_type=10, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - ], - extensions=[], - nested_types=[], - enum_types=[], - options=None, - is_extendable=False, - syntax="proto3", - extension_ranges=[], - oneofs=[], - serialized_start=154, - serialized_end=576, -) - -_TENSORPROTO.fields_by_name["dtype"].enum_type = tensorboardX_dot_src_dot_types__pb2._DATATYPE -_TENSORPROTO.fields_by_name["tensor_shape"].message_type = tensorboardX_dot_src_dot_tensor__shape__pb2._TENSORSHAPEPROTO -_TENSORPROTO.fields_by_name[ - "resource_handle_val" -].message_type = tensorboardX_dot_src_dot_resource__handle__pb2._RESOURCEHANDLEPROTO -DESCRIPTOR.message_types_by_name["TensorProto"] = _TENSORPROTO -_sym_db.RegisterFileDescriptor(DESCRIPTOR) - -TensorProto = _reflection.GeneratedProtocolMessageType( - "TensorProto", - (_message.Message,), - dict( - DESCRIPTOR=_TENSORPROTO, - __module__="tensorboardX.src.tensor_pb2" - # @@protoc_insertion_point(class_scope:tensorboard.TensorProto) - ), -) -_sym_db.RegisterMessage(TensorProto) - - -DESCRIPTOR.has_options = True -DESCRIPTOR._options = _descriptor._ParseOptions( - descriptor_pb2.FileOptions(), _b("\n\030org.tensorflow.frameworkB\014TensorProtosP\001\370\001\001") -) -_TENSORPROTO.fields_by_name["half_val"].has_options = True -_TENSORPROTO.fields_by_name["half_val"]._options = _descriptor._ParseOptions( - descriptor_pb2.FieldOptions(), _b("\020\001") -) -_TENSORPROTO.fields_by_name["float_val"].has_options = True -_TENSORPROTO.fields_by_name["float_val"]._options = _descriptor._ParseOptions( - descriptor_pb2.FieldOptions(), _b("\020\001") -) -_TENSORPROTO.fields_by_name["double_val"].has_options = True -_TENSORPROTO.fields_by_name["double_val"]._options = _descriptor._ParseOptions( - descriptor_pb2.FieldOptions(), _b("\020\001") -) -_TENSORPROTO.fields_by_name["int_val"].has_options = True -_TENSORPROTO.fields_by_name["int_val"]._options = _descriptor._ParseOptions( - descriptor_pb2.FieldOptions(), _b("\020\001") -) -_TENSORPROTO.fields_by_name["scomplex_val"].has_options = True -_TENSORPROTO.fields_by_name["scomplex_val"]._options = _descriptor._ParseOptions( - descriptor_pb2.FieldOptions(), _b("\020\001") -) -_TENSORPROTO.fields_by_name["int64_val"].has_options = True -_TENSORPROTO.fields_by_name["int64_val"]._options = _descriptor._ParseOptions( - descriptor_pb2.FieldOptions(), _b("\020\001") -) -_TENSORPROTO.fields_by_name["bool_val"].has_options = True -_TENSORPROTO.fields_by_name["bool_val"]._options = _descriptor._ParseOptions( - descriptor_pb2.FieldOptions(), _b("\020\001") -) -_TENSORPROTO.fields_by_name["dcomplex_val"].has_options = True -_TENSORPROTO.fields_by_name["dcomplex_val"]._options = _descriptor._ParseOptions( - descriptor_pb2.FieldOptions(), _b("\020\001") -) -# @@protoc_insertion_point(module_scope) diff --git a/~tensorboardX/src/tensor_shape_pb2.py b/~tensorboardX/src/tensor_shape_pb2.py deleted file mode 100644 index 6f0b680..0000000 --- a/~tensorboardX/src/tensor_shape_pb2.py +++ /dev/null @@ -1,167 +0,0 @@ -# Generated by the protocol buffer compiler. DO NOT EDIT! -# source: tensorboardX/src/tensor_shape.proto - -import sys - -_b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode("latin1")) -from google.protobuf import descriptor as _descriptor -from google.protobuf import descriptor_pb2 -from google.protobuf import message as _message -from google.protobuf import reflection as _reflection -from google.protobuf import symbol_database as _symbol_database - -# @@protoc_insertion_point(imports) - -_sym_db = _symbol_database.Default() - - -DESCRIPTOR = _descriptor.FileDescriptor( - name="tensorboardX/src/tensor_shape.proto", - package="tensorboard", - syntax="proto3", - serialized_pb=_b( - '\n#tensorboardX/src/tensor_shape.proto\x12\x0btensorboard"{\n\x10TensorShapeProto\x12.\n\x03\x64im\x18\x02 \x03(\x0b\x32!.tensorboard.TensorShapeProto.Dim\x12\x14\n\x0cunknown_rank\x18\x03 \x01(\x08\x1a!\n\x03\x44im\x12\x0c\n\x04size\x18\x01 \x01(\x03\x12\x0c\n\x04name\x18\x02 \x01(\tB2\n\x18org.tensorflow.frameworkB\x11TensorShapeProtosP\x01\xf8\x01\x01\x62\x06proto3' - ), -) - - -_TENSORSHAPEPROTO_DIM = _descriptor.Descriptor( - name="Dim", - full_name="tensorboard.TensorShapeProto.Dim", - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name="size", - full_name="tensorboard.TensorShapeProto.Dim.size", - index=0, - number=1, - type=3, - cpp_type=2, - label=1, - has_default_value=False, - default_value=0, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="name", - full_name="tensorboard.TensorShapeProto.Dim.name", - index=1, - number=2, - type=9, - cpp_type=9, - label=1, - has_default_value=False, - default_value=_b("").decode("utf-8"), - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - ], - extensions=[], - nested_types=[], - enum_types=[], - options=None, - is_extendable=False, - syntax="proto3", - extension_ranges=[], - oneofs=[], - serialized_start=142, - serialized_end=175, -) - -_TENSORSHAPEPROTO = _descriptor.Descriptor( - name="TensorShapeProto", - full_name="tensorboard.TensorShapeProto", - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name="dim", - full_name="tensorboard.TensorShapeProto.dim", - index=0, - number=2, - type=11, - cpp_type=10, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="unknown_rank", - full_name="tensorboard.TensorShapeProto.unknown_rank", - index=1, - number=3, - type=8, - cpp_type=7, - label=1, - has_default_value=False, - default_value=False, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - ], - extensions=[], - nested_types=[_TENSORSHAPEPROTO_DIM,], - enum_types=[], - options=None, - is_extendable=False, - syntax="proto3", - extension_ranges=[], - oneofs=[], - serialized_start=52, - serialized_end=175, -) - -_TENSORSHAPEPROTO_DIM.containing_type = _TENSORSHAPEPROTO -_TENSORSHAPEPROTO.fields_by_name["dim"].message_type = _TENSORSHAPEPROTO_DIM -DESCRIPTOR.message_types_by_name["TensorShapeProto"] = _TENSORSHAPEPROTO -_sym_db.RegisterFileDescriptor(DESCRIPTOR) - -TensorShapeProto = _reflection.GeneratedProtocolMessageType( - "TensorShapeProto", - (_message.Message,), - dict( - Dim=_reflection.GeneratedProtocolMessageType( - "Dim", - (_message.Message,), - dict( - DESCRIPTOR=_TENSORSHAPEPROTO_DIM, - __module__="tensorboardX.src.tensor_shape_pb2" - # @@protoc_insertion_point(class_scope:tensorboard.TensorShapeProto.Dim) - ), - ), - DESCRIPTOR=_TENSORSHAPEPROTO, - __module__="tensorboardX.src.tensor_shape_pb2" - # @@protoc_insertion_point(class_scope:tensorboard.TensorShapeProto) - ), -) -_sym_db.RegisterMessage(TensorShapeProto) -_sym_db.RegisterMessage(TensorShapeProto.Dim) - - -DESCRIPTOR.has_options = True -DESCRIPTOR._options = _descriptor._ParseOptions( - descriptor_pb2.FileOptions(), _b("\n\030org.tensorflow.frameworkB\021TensorShapeProtosP\001\370\001\001") -) -# @@protoc_insertion_point(module_scope) diff --git a/~tensorboardX/src/types_pb2.py b/~tensorboardX/src/types_pb2.py deleted file mode 100644 index 4e3924e..0000000 --- a/~tensorboardX/src/types_pb2.py +++ /dev/null @@ -1,135 +0,0 @@ -# Generated by the protocol buffer compiler. DO NOT EDIT! -# source: tensorboardX/src/types.proto - -import sys - -_b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode("latin1")) -from google.protobuf import descriptor as _descriptor -from google.protobuf import descriptor_pb2 -from google.protobuf import message as _message -from google.protobuf import reflection as _reflection -from google.protobuf import symbol_database as _symbol_database -from google.protobuf.internal import enum_type_wrapper - -# @@protoc_insertion_point(imports) - -_sym_db = _symbol_database.Default() - - -DESCRIPTOR = _descriptor.FileDescriptor( - name="tensorboardX/src/types.proto", - package="tensorboard", - syntax="proto3", - serialized_pb=_b( - "\n\x1ctensorboardX/src/types.proto\x12\x0btensorboard*\xc2\x05\n\x08\x44\x61taType\x12\x0e\n\nDT_INVALID\x10\x00\x12\x0c\n\x08\x44T_FLOAT\x10\x01\x12\r\n\tDT_DOUBLE\x10\x02\x12\x0c\n\x08\x44T_INT32\x10\x03\x12\x0c\n\x08\x44T_UINT8\x10\x04\x12\x0c\n\x08\x44T_INT16\x10\x05\x12\x0b\n\x07\x44T_INT8\x10\x06\x12\r\n\tDT_STRING\x10\x07\x12\x10\n\x0c\x44T_COMPLEX64\x10\x08\x12\x0c\n\x08\x44T_INT64\x10\t\x12\x0b\n\x07\x44T_BOOL\x10\n\x12\x0c\n\x08\x44T_QINT8\x10\x0b\x12\r\n\tDT_QUINT8\x10\x0c\x12\r\n\tDT_QINT32\x10\r\x12\x0f\n\x0b\x44T_BFLOAT16\x10\x0e\x12\r\n\tDT_QINT16\x10\x0f\x12\x0e\n\nDT_QUINT16\x10\x10\x12\r\n\tDT_UINT16\x10\x11\x12\x11\n\rDT_COMPLEX128\x10\x12\x12\x0b\n\x07\x44T_HALF\x10\x13\x12\x0f\n\x0b\x44T_RESOURCE\x10\x14\x12\x10\n\x0c\x44T_FLOAT_REF\x10\x65\x12\x11\n\rDT_DOUBLE_REF\x10\x66\x12\x10\n\x0c\x44T_INT32_REF\x10g\x12\x10\n\x0c\x44T_UINT8_REF\x10h\x12\x10\n\x0c\x44T_INT16_REF\x10i\x12\x0f\n\x0b\x44T_INT8_REF\x10j\x12\x11\n\rDT_STRING_REF\x10k\x12\x14\n\x10\x44T_COMPLEX64_REF\x10l\x12\x10\n\x0c\x44T_INT64_REF\x10m\x12\x0f\n\x0b\x44T_BOOL_REF\x10n\x12\x10\n\x0c\x44T_QINT8_REF\x10o\x12\x11\n\rDT_QUINT8_REF\x10p\x12\x11\n\rDT_QINT32_REF\x10q\x12\x13\n\x0f\x44T_BFLOAT16_REF\x10r\x12\x11\n\rDT_QINT16_REF\x10s\x12\x12\n\x0e\x44T_QUINT16_REF\x10t\x12\x11\n\rDT_UINT16_REF\x10u\x12\x15\n\x11\x44T_COMPLEX128_REF\x10v\x12\x0f\n\x0b\x44T_HALF_REF\x10w\x12\x13\n\x0f\x44T_RESOURCE_REF\x10xB,\n\x18org.tensorflow.frameworkB\x0bTypesProtosP\x01\xf8\x01\x01\x62\x06proto3" - ), -) - -_DATATYPE = _descriptor.EnumDescriptor( - name="DataType", - full_name="tensorboard.DataType", - filename=None, - file=DESCRIPTOR, - values=[ - _descriptor.EnumValueDescriptor(name="DT_INVALID", index=0, number=0, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_FLOAT", index=1, number=1, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_DOUBLE", index=2, number=2, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_INT32", index=3, number=3, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_UINT8", index=4, number=4, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_INT16", index=5, number=5, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_INT8", index=6, number=6, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_STRING", index=7, number=7, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_COMPLEX64", index=8, number=8, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_INT64", index=9, number=9, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_BOOL", index=10, number=10, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_QINT8", index=11, number=11, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_QUINT8", index=12, number=12, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_QINT32", index=13, number=13, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_BFLOAT16", index=14, number=14, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_QINT16", index=15, number=15, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_QUINT16", index=16, number=16, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_UINT16", index=17, number=17, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_COMPLEX128", index=18, number=18, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_HALF", index=19, number=19, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_RESOURCE", index=20, number=20, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_FLOAT_REF", index=21, number=101, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_DOUBLE_REF", index=22, number=102, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_INT32_REF", index=23, number=103, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_UINT8_REF", index=24, number=104, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_INT16_REF", index=25, number=105, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_INT8_REF", index=26, number=106, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_STRING_REF", index=27, number=107, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_COMPLEX64_REF", index=28, number=108, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_INT64_REF", index=29, number=109, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_BOOL_REF", index=30, number=110, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_QINT8_REF", index=31, number=111, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_QUINT8_REF", index=32, number=112, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_QINT32_REF", index=33, number=113, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_BFLOAT16_REF", index=34, number=114, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_QINT16_REF", index=35, number=115, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_QUINT16_REF", index=36, number=116, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_UINT16_REF", index=37, number=117, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_COMPLEX128_REF", index=38, number=118, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_HALF_REF", index=39, number=119, options=None, type=None), - _descriptor.EnumValueDescriptor(name="DT_RESOURCE_REF", index=40, number=120, options=None, type=None), - ], - containing_type=None, - options=None, - serialized_start=46, - serialized_end=752, -) -_sym_db.RegisterEnumDescriptor(_DATATYPE) - -DataType = enum_type_wrapper.EnumTypeWrapper(_DATATYPE) -DT_INVALID = 0 -DT_FLOAT = 1 -DT_DOUBLE = 2 -DT_INT32 = 3 -DT_UINT8 = 4 -DT_INT16 = 5 -DT_INT8 = 6 -DT_STRING = 7 -DT_COMPLEX64 = 8 -DT_INT64 = 9 -DT_BOOL = 10 -DT_QINT8 = 11 -DT_QUINT8 = 12 -DT_QINT32 = 13 -DT_BFLOAT16 = 14 -DT_QINT16 = 15 -DT_QUINT16 = 16 -DT_UINT16 = 17 -DT_COMPLEX128 = 18 -DT_HALF = 19 -DT_RESOURCE = 20 -DT_FLOAT_REF = 101 -DT_DOUBLE_REF = 102 -DT_INT32_REF = 103 -DT_UINT8_REF = 104 -DT_INT16_REF = 105 -DT_INT8_REF = 106 -DT_STRING_REF = 107 -DT_COMPLEX64_REF = 108 -DT_INT64_REF = 109 -DT_BOOL_REF = 110 -DT_QINT8_REF = 111 -DT_QUINT8_REF = 112 -DT_QINT32_REF = 113 -DT_BFLOAT16_REF = 114 -DT_QINT16_REF = 115 -DT_QUINT16_REF = 116 -DT_UINT16_REF = 117 -DT_COMPLEX128_REF = 118 -DT_HALF_REF = 119 -DT_RESOURCE_REF = 120 - - -DESCRIPTOR.enum_types_by_name["DataType"] = _DATATYPE -_sym_db.RegisterFileDescriptor(DESCRIPTOR) - - -DESCRIPTOR.has_options = True -DESCRIPTOR._options = _descriptor._ParseOptions( - descriptor_pb2.FileOptions(), _b("\n\030org.tensorflow.frameworkB\013TypesProtosP\001\370\001\001") -) -# @@protoc_insertion_point(module_scope) diff --git a/~tensorboardX/src/versions_pb2.py b/~tensorboardX/src/versions_pb2.py deleted file mode 100644 index f26386c..0000000 --- a/~tensorboardX/src/versions_pb2.py +++ /dev/null @@ -1,118 +0,0 @@ -# Generated by the protocol buffer compiler. DO NOT EDIT! -# source: tensorboardX/src/versions.proto - -import sys - -_b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode("latin1")) -from google.protobuf import descriptor as _descriptor -from google.protobuf import descriptor_pb2 -from google.protobuf import message as _message -from google.protobuf import reflection as _reflection -from google.protobuf import symbol_database as _symbol_database - -# @@protoc_insertion_point(imports) - -_sym_db = _symbol_database.Default() - - -DESCRIPTOR = _descriptor.FileDescriptor( - name="tensorboardX/src/versions.proto", - package="tensorboard", - syntax="proto3", - serialized_pb=_b( - '\n\x1ftensorboardX/src/versions.proto\x12\x0btensorboard"K\n\nVersionDef\x12\x10\n\x08producer\x18\x01 \x01(\x05\x12\x14\n\x0cmin_consumer\x18\x02 \x01(\x05\x12\x15\n\rbad_consumers\x18\x03 \x03(\x05\x42/\n\x18org.tensorflow.frameworkB\x0eVersionsProtosP\x01\xf8\x01\x01\x62\x06proto3' - ), -) - - -_VERSIONDEF = _descriptor.Descriptor( - name="VersionDef", - full_name="tensorboard.VersionDef", - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name="producer", - full_name="tensorboard.VersionDef.producer", - index=0, - number=1, - type=5, - cpp_type=1, - label=1, - has_default_value=False, - default_value=0, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="min_consumer", - full_name="tensorboard.VersionDef.min_consumer", - index=1, - number=2, - type=5, - cpp_type=1, - label=1, - has_default_value=False, - default_value=0, - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - _descriptor.FieldDescriptor( - name="bad_consumers", - full_name="tensorboard.VersionDef.bad_consumers", - index=2, - number=3, - type=5, - cpp_type=1, - label=3, - has_default_value=False, - default_value=[], - message_type=None, - enum_type=None, - containing_type=None, - is_extension=False, - extension_scope=None, - options=None, - ), - ], - extensions=[], - nested_types=[], - enum_types=[], - options=None, - is_extendable=False, - syntax="proto3", - extension_ranges=[], - oneofs=[], - serialized_start=48, - serialized_end=123, -) - -DESCRIPTOR.message_types_by_name["VersionDef"] = _VERSIONDEF -_sym_db.RegisterFileDescriptor(DESCRIPTOR) - -VersionDef = _reflection.GeneratedProtocolMessageType( - "VersionDef", - (_message.Message,), - dict( - DESCRIPTOR=_VERSIONDEF, - __module__="tensorboardX.src.versions_pb2" - # @@protoc_insertion_point(class_scope:tensorboard.VersionDef) - ), -) -_sym_db.RegisterMessage(VersionDef) - - -DESCRIPTOR.has_options = True -DESCRIPTOR._options = _descriptor._ParseOptions( - descriptor_pb2.FileOptions(), _b("\n\030org.tensorflow.frameworkB\016VersionsProtosP\001\370\001\001") -) -# @@protoc_insertion_point(module_scope) diff --git a/~tensorboardX/summary.py b/~tensorboardX/summary.py deleted file mode 100644 index 15eb5b2..0000000 --- a/~tensorboardX/summary.py +++ /dev/null @@ -1,269 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -"""## Generation of summaries. -### Class for writing Summaries -@@FileWriter -@@FileWriterCache -### Summary Ops -@@tensor_summary -@@scalar -@@histogram -@@audio -@@image -@@merge -@@merge_all -## Utilities -@@get_summary_description -""" - -from __future__ import absolute_import, division, print_function - -import bisect -import logging -import re as _re - -import numpy as np -from six import StringIO -from six.moves import range - -from .src.plugin_pr_curve_pb2 import PrCurvePluginData - -# pylint: disable=unused-import -from .src.summary_pb2 import HistogramProto, Summary, SummaryMetadata -from .src.tensor_pb2 import TensorProto -from .src.tensor_shape_pb2 import TensorShapeProto -from .x2num import makenp - -_INVALID_TAG_CHARACTERS = _re.compile(r"[^-/\w\.]") - - -def _clean_tag(name): - # In the past, the first argument to summary ops was a tag, which allowed - # arbitrary characters. Now we are changing the first argument to be the node - # name. This has a number of advantages (users of summary ops now can - # take advantage of the tf name scope system) but risks breaking existing - # usage, because a much smaller set of characters are allowed in node names. - # This function replaces all illegal characters with _s, and logs a warning. - # It also strips leading slashes from the name. - if name is not None: - new_name = _INVALID_TAG_CHARACTERS.sub("_", name) - new_name = new_name.lstrip("/") # Remove leading slashes - if new_name != name: - logging.info("Summary name %s is illegal; using %s instead." % (name, new_name)) - name = new_name - return name - - -def scalar(name, scalar, collections=None): - """Outputs a `Summary` protocol buffer containing a single scalar value. - The generated Summary has a Tensor.proto containing the input Tensor. - Args: - name: A name for the generated node. Will also serve as the series name in - TensorBoard. - tensor: A real numeric Tensor containing a single value. - collections: Optional list of graph collections keys. The new summary op is - added to these collections. Defaults to `[GraphKeys.SUMMARIES]`. - Returns: - A scalar `Tensor` of type `string`. Which contains a `Summary` protobuf. - Raises: - ValueError: If tensor has the wrong shape or type. - """ - name = _clean_tag(name) - scalar = makenp(scalar) - assert scalar.squeeze().ndim == 0, "scalar should be 0D" - scalar = float(scalar) - return Summary(value=[Summary.Value(tag=name, simple_value=scalar)]) - - -def histogram(name, values, bins, collections=None): - # pylint: disable=line-too-long - """Outputs a `Summary` protocol buffer with a histogram. - 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: - name: A name for the generated node. Will also serve as a series name in - TensorBoard. - 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]`. - Returns: - A scalar `Tensor` of type `string`. The serialized `Summary` protocol - buffer. - """ - name = _clean_tag(name) - values = makenp(values) - hist = make_histogram(values.astype(float), bins) - return Summary(value=[Summary.Value(tag=name, histo=hist)]) - - -def make_histogram(values, bins): - """Convert values into a histogram proto using logic from histogram.cc.""" - values = values.reshape(-1) - counts, limits = np.histogram(values, bins=bins) - limits = limits[1:] - - sum_sq = values.dot(values) - return HistogramProto( - min=values.min(), - max=values.max(), - num=len(values), - sum=values.sum(), - sum_squares=sum_sq, - bucket_limit=limits, - bucket=counts, - ) - - -def image(tag, tensor): - """Outputs a `Summary` protocol buffer with images. - The summary has up to `max_images` summary values containing images. The - images are built from `tensor` which must be 3-D with shape `[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 `name` in the outputted Summary.Value protobufs is generated based on the - name, with a suffix depending on the max_outputs setting: - * If `max_outputs` is 1, the summary value tag is '*name*/image'. - * If `max_outputs` is greater than 1, the summary value tags are - generated sequentially as '*name*/image/0', '*name*/image/1', etc. - Args: - tag: A name for the generated node. Will also serve as a series name in - TensorBoard. - tensor: A 3-D `uint8` or `float32` `Tensor` of shape `[height, width, - channels]` where `channels` is 1, 3, or 4. - Returns: - A scalar `Tensor` of type `string`. The serialized `Summary` protocol - buffer. - """ - tag = _clean_tag(tag) - tensor = makenp(tensor, "IMG") - tensor = tensor.astype(np.float32) - tensor = (tensor * 255).astype(np.uint8) - image = make_image(tensor) - return Summary(value=[Summary.Value(tag=tag, image=image)]) - - -def make_image(tensor): - """Convert an numpy representation image to Image protobuf""" - from PIL import Image - - height, width, channel = tensor.shape - image = Image.fromarray(tensor) - import io - - output = io.BytesIO() - image.save(output, format="PNG") - image_string = output.getvalue() - output.close() - return Summary.Image(height=height, width=width, colorspace=channel, encoded_image_string=image_string) - - -def audio(tag, tensor, sample_rate=44100): - tensor = makenp(tensor) - tensor = tensor.squeeze() - if abs(tensor).max() > 1: - print("warning: audio amplitude out of range, auto clipped.") - tensor = tensor.clip(-1, 1) - assert tensor.ndim == 1, "input tensor should be 1 dimensional." - - tensor_list = [int(32767.0 * x) for x in tensor] - import io - import struct - import wave - - fio = io.BytesIO() - Wave_write = wave.open(fio, "wb") - Wave_write.setnchannels(1) - Wave_write.setsampwidth(2) - Wave_write.setframerate(sample_rate) - tensor_enc = b"" - for v in tensor_list: - tensor_enc += struct.pack(" 127: # wierd, value > 127 breaks protobuf - num_thresholds = 127 - data = compute_curve(labels, predictions, num_thresholds=num_thresholds, weights=weights) - pr_curve_plugin_data = PrCurvePluginData(version=0, num_thresholds=num_thresholds).SerializeToString() - PluginData = [SummaryMetadata.PluginData(plugin_name="pr_curves", content=pr_curve_plugin_data)] - smd = SummaryMetadata(plugin_data=PluginData) - tensor = TensorProto( - dtype="DT_FLOAT", - float_val=data.reshape(-1).tolist(), - tensor_shape=TensorShapeProto( - dim=[TensorShapeProto.Dim(size=data.shape[0]), TensorShapeProto.Dim(size=data.shape[1])] - ), - ) - return Summary(value=[Summary.Value(tag=tag, metadata=smd, tensor=tensor)]) - - -# https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/pr_curve/summary.py -def compute_curve(labels, predictions, num_thresholds=None, weights=None): - _MINIMUM_COUNT = 1e-7 - - if weights is None: - weights = 1.0 - - # Compute bins of true positives and false positives. - bucket_indices = np.int32(np.floor(predictions * (num_thresholds - 1))) - float_labels = labels.astype(np.float) - histogram_range = (0, num_thresholds - 1) - tp_buckets, _ = np.histogram( - bucket_indices, bins=num_thresholds, range=histogram_range, weights=float_labels * weights - ) - fp_buckets, _ = np.histogram( - bucket_indices, bins=num_thresholds, range=histogram_range, weights=(1.0 - float_labels) * weights - ) - - # Obtain the reverse cumulative sum. - tp = np.cumsum(tp_buckets[::-1])[::-1] - fp = np.cumsum(fp_buckets[::-1])[::-1] - tn = fp[0] - fp - fn = tp[0] - tp - precision = tp / np.maximum(_MINIMUM_COUNT, tp + fp) - recall = tp / np.maximum(_MINIMUM_COUNT, tp + fn) - return np.stack((tp, fp, tn, fn, precision, recall)) diff --git a/~tensorboardX/writer.py b/~tensorboardX/writer.py deleted file mode 100644 index 97ca7c9..0000000 --- a/~tensorboardX/writer.py +++ /dev/null @@ -1,482 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Provides an API for generating Event protocol buffers.""" - -from __future__ import absolute_import, division, print_function - -import json -import os -import time - -from .embedding import append_pbtxt, make_mat, make_sprite, make_tsv -from .event_file_writer import EventFileWriter -from .graph import graph -from .graph_onnx import gg -from .src import event_pb2, graph_pb2, summary_pb2 -from .summary import audio, histogram, image, pr_curve, scalar, text - - -class SummaryToEventTransformer(object): - """Abstractly implements the SummaryWriter API. - This API basically implements a number of endpoints (add_summary, - add_session_log, etc). The endpoints all generate an event protobuf, which is - passed to the contained event_writer. - @@__init__ - @@add_summary - @@add_session_log - @@add_graph - @@add_meta_graph - @@add_run_metadata - """ - - def __init__(self, event_writer, graph=None, graph_def=None): - """Creates a `SummaryWriter` and an event file. - On construction the summary writer creates a new event file in `logdir`. - This event file will contain `Event` protocol buffers constructed when you - call one of the following functions: `add_summary()`, `add_session_log()`, - `add_event()`, or `add_graph()`. - If you pass a `Graph` to the constructor it is added to - the event file. (This is equivalent to calling `add_graph()` later). - TensorBoard will pick the graph from the file and display it graphically so - you can interactively explore the graph you built. You will usually pass - the graph from the session in which you launched it: - ```python - ...create a graph... - # Launch the graph in a session. - sess = tf.Session() - # Create a summary writer, add the 'graph' to the event file. - writer = tf.summary.FileWriter(, sess.graph) - ``` - Args: - event_writer: An EventWriter. Implements add_event method. - graph: A `Graph` object, such as `sess.graph`. - graph_def: DEPRECATED: Use the `graph` argument instead. - """ - self.event_writer = event_writer - # For storing used tags for session.run() outputs. - self._session_run_tags = {} - # TODO(zihaolucky). pass this an empty graph to check whether it's necessary. - # currently we don't support graph in MXNet using tensorboard. - - def add_summary(self, summary, global_step=None): - """Adds a `Summary` protocol buffer to the event file. - This method wraps the provided summary in an `Event` protocol buffer - and adds it to the event file. - You can pass the result of evaluating any summary op, using - [`Session.run()`](client.md#Session.run) or - [`Tensor.eval()`](framework.md#Tensor.eval), to this - function. Alternatively, you can pass a `tf.Summary` protocol - buffer that you populate with your own data. The latter is - commonly done to report evaluation results in event files. - Args: - summary: A `Summary` protocol buffer, optionally serialized as a string. - global_step: Number. Optional global step value to record with the - summary. - """ - if isinstance(summary, bytes): - summ = summary_pb2.Summary() - summ.ParseFromString(summary) - summary = summ - event = event_pb2.Event(summary=summary) - self._add_event(event, global_step) - - def add_graph_onnx(self, graph): - """Adds a `Graph` protocol buffer to the event file. - """ - event = event_pb2.Event(graph_def=graph.SerializeToString()) - self._add_event(event, None) - - def add_graph(self, graph): - """Adds a `Graph` protocol buffer to the event file. - """ - event = event_pb2.Event(graph_def=graph.SerializeToString()) - self._add_event(event, None) - - def add_session_log(self, session_log, global_step=None): - """Adds a `SessionLog` protocol buffer to the event file. - This method wraps the provided session in an `Event` protocol buffer - and adds it to the event file. - Args: - session_log: A `SessionLog` protocol buffer. - global_step: Number. Optional global step value to record with the - summary. - """ - event = event_pb2.Event(session_log=session_log) - self._add_event(event, global_step) - - def _add_event(self, event, step): - event.wall_time = time.time() - if step is not None: - event.step = int(step) - self.event_writer.add_event(event) - - -class FileWriter(SummaryToEventTransformer): - """Writes `Summary` protocol buffers to event files. - The `FileWriter` class provides a mechanism to create an event file in a - given directory and add summaries and events to it. The class updates the - file contents asynchronously. This allows a training program to call methods - to add data to the file directly from the training loop, without slowing down - training. - @@__init__ - @@add_summary - @@add_session_log - @@add_event - @@add_graph - @@add_run_metadata - @@get_logdir - @@flush - @@close - """ - - def __init__(self, logdir, graph=None, max_queue=10, flush_secs=120, graph_def=None): - """Creates a `FileWriter` and an event file. - On construction the summary writer creates a new event file in `logdir`. - This event file will contain `Event` protocol buffers constructed when you - call one of the following functions: `add_summary()`, `add_session_log()`, - `add_event()`, or `add_graph()`. - If you pass a `Graph` to the constructor it is added to - the event file. (This is equivalent to calling `add_graph()` later). - TensorBoard will pick the graph from the file and display it graphically so - you can interactively explore the graph you built. You will usually pass - the graph from the session in which you launched it: - ```python - ...create a graph... - # Launch the graph in a session. - sess = tf.Session() - # Create a summary writer, add the 'graph' to the event file. - writer = tf.summary.FileWriter(, sess.graph) - ``` - The other arguments to the constructor control the asynchronous writes to - the event file: - * `flush_secs`: How often, in seconds, to flush the added summaries - and events to disk. - * `max_queue`: Maximum number of summaries or events pending to be - written to disk before one of the 'add' calls block. - Args: - logdir: A string. Directory where event file will be written. - graph: A `Graph` object, such as `sess.graph`. - max_queue: Integer. Size of the queue for pending events and summaries. - flush_secs: Number. How often, in seconds, to flush the - pending events and summaries to disk. - graph_def: DEPRECATED: Use the `graph` argument instead. - """ - event_writer = EventFileWriter(logdir, max_queue, flush_secs) - super(FileWriter, self).__init__(event_writer, graph, graph_def) - - def get_logdir(self): - """Returns the directory where event file will be written.""" - return self.event_writer.get_logdir() - - def add_event(self, event): - """Adds an event to the event file. - Args: - event: An `Event` protocol buffer. - """ - self.event_writer.add_event(event) - - def flush(self): - """Flushes the event file to disk. - Call this method to make sure that all pending events have been written to - disk. - """ - self.event_writer.flush() - - def close(self): - """Flushes the event file to disk and close the file. - Call this method when you do not need the summary writer anymore. - """ - self.event_writer.close() - - def reopen(self): - """Reopens the EventFileWriter. - Can be called after `close()` to add more events in the same directory. - The events will go into a new events file. - Does nothing if the EventFileWriter was not closed. - """ - self.event_writer.reopen() - - -class SummaryWriter(object): - """Writes `Summary` directly to event files. - The `SummaryWriter` class provides a high-level api to create an event file in a - given directory and add summaries and events to it. The class updates the - file contents asynchronously. This allows a training program to call methods - to add data to the file directly from the training loop, without slowing down - training. - """ - - def __init__(self, log_dir=None, comment=""): - """ - Args: - log_dir (string): save location, default is: runs/**CURRENT_DATETIME_HOSTNAME**, which changes after each - run. Use hierarchical folder structure to compare between runs easily. e.g. 'runs/exp1', 'runs/exp2' - comment (string): comment that appends to the default log_dir - """ - if not log_dir: - import socket - from datetime import datetime - - current_time = datetime.now().strftime("%b%d_%H-%M-%S") - log_dir = os.path.join("runs", current_time + "_" + socket.gethostname() + comment) - self.file_writer = FileWriter(logdir=log_dir) - v = 1e-12 - buckets = [] - neg_buckets = [] - while v < 1e20: - buckets.append(v) - neg_buckets.append(-v) - v *= 1.1 - self.default_bins = neg_buckets[::-1] + [0] + buckets - self.text_tags = [] - # - self.all_writers = {self.file_writer.get_logdir(): self.file_writer} - self.scalar_dict = {} # {writer_id : [[timestamp, step, value],...],...} - - def __append_to_scalar_dict(self, tag, scalar_value, global_step, timestamp): - """This adds an entry to the self.scalar_dict datastructure with format - {writer_id : [[timestamp, step, value], ...], ...}. - """ - from .x2num import makenp - - if tag not in self.scalar_dict.keys(): - self.scalar_dict[tag] = [] - self.scalar_dict[tag].append([timestamp, global_step, float(makenp(scalar_value))]) - - def add_scalar(self, tag, scalar_value, global_step=None): - """Add scalar data to summary. - - Args: - tag (string): Data identifier - scalar_value (float): Value to save - global_step (int): Global step value to record - """ - self.file_writer.add_summary(scalar(tag, scalar_value), global_step) - self.__append_to_scalar_dict(tag, scalar_value, global_step, time.time()) - - def add_scalars(self, main_tag, tag_scalar_dict, global_step=None): - """Adds many scalar data to summary. - - Args: - tag (string): Data identifier - main_tag (string): The parent name for the tags - tag_scalar_dict (dict): Key-value pair storing the tag and corresponding values - global_step (int): Global step value to record - - Examples:: - - writer.add_scalars('run_14h',{'xsinx':i*np.sin(i/r), - 'xcosx':i*np.cos(i/r), - 'arctanx': numsteps*np.arctan(i/r)}, i) - # This function adds three values to the same scalar plot with the tag - # 'run_14h' in TensorBoard's scalar section. - """ - timestamp = time.time() - fw_logdir = self.file_writer.get_logdir() - for tag, scalar_value in tag_scalar_dict.items(): - fw_tag = fw_logdir + "/" + main_tag + "/" + tag - if fw_tag in self.all_writers.keys(): - fw = self.all_writers[fw_tag] - else: - fw = FileWriter(logdir=fw_tag) - self.all_writers[fw_tag] = fw - fw.add_summary(scalar(main_tag, scalar_value), global_step) - self.__append_to_scalar_dict(fw_tag, scalar_value, global_step, timestamp) - - def export_scalars_to_json(self, path): - """Exports to the given path an ASCII file containing all the scalars written - so far by this instance, with the following format: - {writer_id : [[timestamp, step, value], ...], ...} - """ - with open(path, "w") as f: - json.dump(self.scalar_dict, f) - - def add_histogram(self, tag, values, global_step=None, bins="tensorflow"): - """Add histogram to summary. - - Args: - tag (string): Data identifier - values (numpy.array): Values to build histogram - global_step (int): Global step value to record - bins (string): one of {'tensorflow','auto', 'fd', ...}, this determines how the bins are made. You can find - other options in: https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html - """ - if bins == "tensorflow": - bins = self.default_bins - self.file_writer.add_summary(histogram(tag, values, bins), global_step) - - def add_image(self, tag, img_tensor, global_step=None): - """Add image data to summary. - - Note that this requires the ``pillow`` package. - - Args: - tag (string): Data identifier - img_tensor (torch.Tensor): Image data - global_step (int): Global step value to record - Shape: - img_tensor: :math:`(3, H, W)`. Use ``torchvision.utils.make_grid()`` to prepare it is a good idea. - """ - self.file_writer.add_summary(image(tag, img_tensor), global_step) - - def add_audio(self, tag, snd_tensor, global_step=None, sample_rate=44100): - """Add audio data to summary. - - Args: - tag (string): Data identifier - snd_tensor (torch.Tensor): Sound data - global_step (int): Global step value to record - sample_rate (int): sample rate in Hz - - Shape: - snd_tensor: :math:`(1, L)`. The values should lie between [-1, 1]. - """ - self.file_writer.add_summary(audio(tag, snd_tensor, sample_rate=sample_rate), global_step) - - def add_text(self, tag, text_string, global_step=None): - """Add text data to summary. - - Args: - tag (string): Data identifier - text_string (string): String to save - global_step (int): Global step value to record - - Examples:: - - writer.add_text('lstm', 'This is an lstm', 0) - writer.add_text('rnn', 'This is an rnn', 10) - """ - self.file_writer.add_summary(text(tag, text_string), global_step) - if tag not in self.text_tags: - self.text_tags.append(tag) - extension_dir = self.file_writer.get_logdir() + "/plugins/tensorboard_text/" - if not os.path.exists(extension_dir): - os.makedirs(extension_dir) - with open(extension_dir + "tensors.json", "w") as fp: - json.dump(self.text_tags, fp) - - def add_graph_onnx(self, prototxt): - self.file_writer.add_graph_onnx(gg(prototxt)) - - def add_graph(self, model, input_to_model, verbose=False): - # prohibit second call? - # no, let tensorboard handles it and show its warning message. - """Add graph data to summary. - - Args: - model (torch.nn.Module): model to draw. - input_to_model (torch.autograd.Variable): a variable or a tuple of variables to be fed. - - """ - from distutils.version import LooseVersion - - import torch - - if LooseVersion(torch.__version__) >= LooseVersion("0.3.1"): - pass - else: - if LooseVersion(torch.__version__) >= LooseVersion("0.3.0"): - print("You are using PyTorch==0.3.0, use add_graph_onnx()") - return - if not hasattr(torch.autograd.Variable, "grad_fn"): - print("add_graph() only supports PyTorch v0.2.") - return - self.file_writer.add_graph(graph(model, input_to_model, verbose)) - - def add_embedding(self, mat, metadata=None, label_img=None, global_step=None, tag="default"): - """Add embedding projector data to summary. - - Args: - mat (torch.Tensor): A matrix which each row is the feature vector of the data point - metadata (list): A list of labels, each element will be convert to string - label_img (torch.Tensor): Images correspond to each data point - global_step (int): Global step value to record - tag (string): Name for the embedding - Shape: - mat: :math:`(N, D)`, where N is number of data and D is feature dimension - - label_img: :math:`(N, C, H, W)` - - Examples:: - - import keyword - import torch - meta = [] - while len(meta)<100: - meta = meta+keyword.kwlist # get some strings - meta = meta[:100] - - for i, v in enumerate(meta): - meta[i] = v+str(i) - - label_img = torch.rand(100, 3, 10, 32) - for i in range(100): - label_img[i]*=i/100.0 - - writer.add_embedding(torch.randn(100, 5), metadata=meta, label_img=label_img) - writer.add_embedding(torch.randn(100, 5), label_img=label_img) - writer.add_embedding(torch.randn(100, 5), metadata=meta) - """ - if global_step is None: - global_step = 0 - # clear pbtxt? - save_path = os.path.join(self.file_writer.get_logdir(), str(global_step).zfill(5)) - try: - os.makedirs(save_path) - except OSError: - print("warning: Embedding dir exists, did you set global_step for add_embedding()?") - if metadata is not None: - assert mat.size(0) == len(metadata), "#labels should equal with #data points" - make_tsv(metadata, save_path) - if label_img is not None: - assert mat.size(0) == label_img.size(0), "#images should equal with #data points" - make_sprite(label_img, save_path) - assert mat.dim() == 2, "mat should be 2D, where mat.size(0) is the number of data points" - make_mat(mat.tolist(), save_path) - # new funcion to append to the config file a new embedding - append_pbtxt(metadata, label_img, self.file_writer.get_logdir(), str(global_step).zfill(5), tag) - - def add_pr_curve(self, tag, labels, predictions, global_step=None, num_thresholds=127, weights=None): - """Adds precision recall curve. - - Args: - tag (string): Data identifier - labels (torch.Tensor): Ground truth data. Binary label for each element. - predictions (torch.Tensor): The probability that an element be classified as true. Value should in [0, 1] - global_step (int): Global step value to record - num_thresholds (int): Number of thresholds used to draw the curve. - - """ - from .x2num import makenp - - labels = makenp(labels) - predictions = makenp(predictions) - self.file_writer.add_summary(pr_curve(tag, labels, predictions, num_thresholds, weights), global_step) - - def close(self): - if self.file_writer is None: - return # ignore double close - self.file_writer.flush() - self.file_writer.close() - for path, writer in self.all_writers.items(): - writer.flush() - writer.close() - self.file_writer = self.all_writers = None - - def __enter__(self): - return self - - def __exit__(self, exc_type, exc_val, exc_tb): - self.close() diff --git a/~tensorboardX/x2num.py b/~tensorboardX/x2num.py deleted file mode 100644 index d81c5c7..0000000 --- a/~tensorboardX/x2num.py +++ /dev/null @@ -1,93 +0,0 @@ -# DO NOT alter/distruct/free input object ! - -import numpy as np - - -def makenp(x, modality=None): - # if already numpy, return - if isinstance(x, np.ndarray): - if modality == "IMG" and x.dtype == np.uint8: - return x.astype(np.float32) / 255.0 - return x - if np.isscalar(x): - return np.array([x]) - if "torch" in str(type(x)): - return pytorch_np(x, modality) - if "chainer" in str(type(x)): - return chainer_np(x, modality) - if "mxnet" in str(type(x)): - return mxnet_np(x, modality) - - -def pytorch_np(x, modality): - import torch - - if isinstance(x, torch.autograd.Variable): - x = x.data - x = x.cpu().numpy() - if modality == "IMG": - x = _prepare_image(x) - return x - - -def theano_np(x): - import theano - - pass - - -def caffe2_np(x): - pass - - -def mxnet_np(x, modality): - x = x.asnumpy() - if modality == "IMG": - x = _prepare_image(x) - return x - - -def chainer_np(x, modality): - import chainer - - x = chainer.cuda.to_cpu(x.data) - if modality == "IMG": - x = _prepare_image(x) - return x - - -def make_grid(I, ncols=8): - assert isinstance(I, np.ndarray), "plugin error, should pass numpy array here" - assert I.ndim == 4 and I.shape[1] == 3 - nimg = I.shape[0] - H = I.shape[2] - W = I.shape[3] - ncols = min(nimg, ncols) - nrows = int(np.ceil(float(nimg) / ncols)) - canvas = np.zeros((3, H * nrows, W * ncols)) - i = 0 - for y in range(nrows): - for x in range(ncols): - if i >= nimg: - break - canvas[:, y * H : (y + 1) * H, x * W : (x + 1) * W] = I[i] - i = i + 1 - return canvas - - -def _prepare_image(I): - assert isinstance(I, np.ndarray), "plugin error, should pass numpy array here" - assert I.ndim == 2 or I.ndim == 3 or I.ndim == 4 - if I.ndim == 4: # NCHW - if I.shape[1] == 1: # N1HW - I = np.concatenate((I, I, I), 1) # N3HW - assert I.shape[1] == 3 - I = make_grid(I) # 3xHxW - if I.ndim == 3 and I.shape[0] == 1: # 1xHxW - I = np.concatenate((I, I, I), 0) # 3xHxW - if I.ndim == 2: # HxW - I = np.expand_dims(I, 0) # 1xHxW - I = np.concatenate((I, I, I), 0) # 3xHxW - I = I.transpose(1, 2, 0) - - return I