Delete dnnlib/tflib/autosummary.py

dnnlib/tflib/autosummary.py

@@ -1,193 +0,0 @@
-# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Helper for adding automatically tracked values to Tensorboard.
-
-Autosummary creates an identity op that internally keeps track of the input
-values and automatically shows up in TensorBoard. The reported value
-represents an average over input components. The average is accumulated
-constantly over time and flushed when save_summaries() is called.
-
-Notes:
-- The output tensor must be used as an input for something else in the
-  graph. Otherwise, the autosummary op will not get executed, and the average
-  value will not get accumulated.
-- It is perfectly fine to include autosummaries with the same name in
-  several places throughout the graph, even if they are executed concurrently.
-- It is ok to also pass in a python scalar or numpy array. In this case, it
-  is added to the average immediately.
-"""
-
-from collections import OrderedDict
-import numpy as np
-import tensorflow as tf
-from tensorboard import summary as summary_lib
-from tensorboard.plugins.custom_scalar import layout_pb2
-
-from . import tfutil
-from .tfutil import TfExpression
-from .tfutil import TfExpressionEx
-
-# Enable "Custom scalars" tab in TensorBoard for advanced formatting.
-# Disabled by default to reduce tfevents file size.
-enable_custom_scalars = False
-
-_dtype = tf.float64
-_vars = OrderedDict()  # name => [var, ...]
-_immediate = OrderedDict()  # name => update_op, update_value
-_finalized = False
-_merge_op = None
-
-
-def _create_var(name: str, value_expr: TfExpression) -> TfExpression:
-    """Internal helper for creating autosummary accumulators."""
-    assert not _finalized
-    name_id = name.replace("/", "_")
-    v = tf.cast(value_expr, _dtype)
-
-    if v.shape.is_fully_defined():
-        size = np.prod(v.shape.as_list())
-        size_expr = tf.constant(size, dtype=_dtype)
-    else:
-        size = None
-        size_expr = tf.reduce_prod(tf.cast(tf.shape(v), _dtype))
-
-    if size == 1:
-        if v.shape.ndims != 0:
-            v = tf.reshape(v, [])
-        v = [size_expr, v, tf.square(v)]
-    else:
-        v = [size_expr, tf.reduce_sum(v), tf.reduce_sum(tf.square(v))]
-    v = tf.cond(tf.is_finite(v[1]), lambda: tf.stack(v), lambda: tf.zeros(3, dtype=_dtype))
-
-    with tfutil.absolute_name_scope("Autosummary/" + name_id), tf.control_dependencies(None):
-        var = tf.Variable(tf.zeros(3, dtype=_dtype), trainable=False)  # [sum(1), sum(x), sum(x**2)]
-    update_op = tf.cond(tf.is_variable_initialized(var), lambda: tf.assign_add(var, v), lambda: tf.assign(var, v))
-
-    if name in _vars:
-        _vars[name].append(var)
-    else:
-        _vars[name] = [var]
-    return update_op
-
-
-def autosummary(name: str, value: TfExpressionEx, passthru: TfExpressionEx = None, condition: TfExpressionEx = True) -> TfExpressionEx:
-    """Create a new autosummary.
-
-    Args:
-        name:     Name to use in TensorBoard
-        value:    TensorFlow expression or python value to track
-        passthru: Optionally return this TF node without modifications but tack an autosummary update side-effect to this node.
-
-    Example use of the passthru mechanism:
-
-    n = autosummary('l2loss', loss, passthru=n)
-
-    This is a shorthand for the following code:
-
-    with tf.control_dependencies([autosummary('l2loss', loss)]):
-        n = tf.identity(n)
-    """
-    tfutil.assert_tf_initialized()
-    name_id = name.replace("/", "_")
-
-    if tfutil.is_tf_expression(value):
-        with tf.name_scope("summary_" + name_id), tf.device(value.device):
-            condition = tf.convert_to_tensor(condition, name='condition')
-            update_op = tf.cond(condition, lambda: tf.group(_create_var(name, value)), tf.no_op)
-            with tf.control_dependencies([update_op]):
-                return tf.identity(value if passthru is None else passthru)
-
-    else:  # python scalar or numpy array
-        assert not tfutil.is_tf_expression(passthru)
-        assert not tfutil.is_tf_expression(condition)
-        if condition:
-            if name not in _immediate:
-                with tfutil.absolute_name_scope("Autosummary/" + name_id), tf.device(None), tf.control_dependencies(None):
-                    update_value = tf.placeholder(_dtype)
-                    update_op = _create_var(name, update_value)
-                    _immediate[name] = update_op, update_value
-            update_op, update_value = _immediate[name]
-            tfutil.run(update_op, {update_value: value})
-        return value if passthru is None else passthru
-
-
-def finalize_autosummaries() -> None:
-    """Create the necessary ops to include autosummaries in TensorBoard report.
-    Note: This should be done only once per graph.
-    """
-    global _finalized
-    tfutil.assert_tf_initialized()
-
-    if _finalized:
-        return None
-
-    _finalized = True
-    tfutil.init_uninitialized_vars([var for vars_list in _vars.values() for var in vars_list])
-
-    # Create summary ops.
-    with tf.device(None), tf.control_dependencies(None):
-        for name, vars_list in _vars.items():
-            name_id = name.replace("/", "_")
-            with tfutil.absolute_name_scope("Autosummary/" + name_id):
-                moments = tf.add_n(vars_list)
-                moments /= moments[0]
-                with tf.control_dependencies([moments]):  # read before resetting
-                    reset_ops = [tf.assign(var, tf.zeros(3, dtype=_dtype)) for var in vars_list]
-                    with tf.name_scope(None), tf.control_dependencies(reset_ops):  # reset before reporting
-                        mean = moments[1]
-                        std = tf.sqrt(moments[2] - tf.square(moments[1]))
-                        tf.summary.scalar(name, mean)
-                        if enable_custom_scalars:
-                            tf.summary.scalar("xCustomScalars/" + name + "/margin_lo", mean - std)
-                            tf.summary.scalar("xCustomScalars/" + name + "/margin_hi", mean + std)
-
-    # Setup layout for custom scalars.
-    layout = None
-    if enable_custom_scalars:
-        cat_dict = OrderedDict()
-        for series_name in sorted(_vars.keys()):
-            p = series_name.split("/")
-            cat = p[0] if len(p) >= 2 else ""
-            chart = "/".join(p[1:-1]) if len(p) >= 3 else p[-1]
-            if cat not in cat_dict:
-                cat_dict[cat] = OrderedDict()
-            if chart not in cat_dict[cat]:
-                cat_dict[cat][chart] = []
-            cat_dict[cat][chart].append(series_name)
-        categories = []
-        for cat_name, chart_dict in cat_dict.items():
-            charts = []
-            for chart_name, series_names in chart_dict.items():
-                series = []
-                for series_name in series_names:
-                    series.append(layout_pb2.MarginChartContent.Series(
-                        value=series_name,
-                        lower="xCustomScalars/" + series_name + "/margin_lo",
-                        upper="xCustomScalars/" + series_name + "/margin_hi"))
-                margin = layout_pb2.MarginChartContent(series=series)
-                charts.append(layout_pb2.Chart(title=chart_name, margin=margin))
-            categories.append(layout_pb2.Category(title=cat_name, chart=charts))
-        layout = summary_lib.custom_scalar_pb(layout_pb2.Layout(category=categories))
-    return layout
-
-def save_summaries(file_writer, global_step=None):
-    """Call FileWriter.add_summary() with all summaries in the default graph,
-    automatically finalizing and merging them on the first call.
-    """
-    global _merge_op
-    tfutil.assert_tf_initialized()
-
-    if _merge_op is None:
-        layout = finalize_autosummaries()
-        if layout is not None:
-            file_writer.add_summary(layout)
-        with tf.device(None), tf.control_dependencies(None):
-            _merge_op = tf.summary.merge_all()
-
-    file_writer.add_summary(_merge_op.eval(), global_step)