mirror of https://github.com/XingangPan/DragGAN
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255 lines
9.5 KiB
Python
255 lines
9.5 KiB
Python
# Copyright (c) SenseTime Research. All rights reserved.
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# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
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#
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# This work is made available under the Nvidia Source Code License-NC.
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# To view a copy of this license, visit
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# https://nvlabs.github.io/stylegan2/license.html
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"""Miscellaneous helper utils for Tensorflow."""
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import os
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import numpy as np
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import tensorflow as tf
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# Silence deprecation warnings from TensorFlow 1.13 onwards
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import logging
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logging.getLogger('tensorflow').setLevel(logging.ERROR)
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import tensorflow.contrib # requires TensorFlow 1.x!
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tf.contrib = tensorflow.contrib
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from typing import Any, Iterable, List, Union
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TfExpression = Union[tf.Tensor, tf.Variable, tf.Operation]
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"""A type that represents a valid Tensorflow expression."""
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TfExpressionEx = Union[TfExpression, int, float, np.ndarray]
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"""A type that can be converted to a valid Tensorflow expression."""
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def run(*args, **kwargs) -> Any:
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"""Run the specified ops in the default session."""
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assert_tf_initialized()
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return tf.get_default_session().run(*args, **kwargs)
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def is_tf_expression(x: Any) -> bool:
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"""Check whether the input is a valid Tensorflow expression, i.e., Tensorflow Tensor, Variable, or Operation."""
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return isinstance(x, (tf.Tensor, tf.Variable, tf.Operation))
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def shape_to_list(shape: Iterable[tf.Dimension]) -> List[Union[int, None]]:
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"""Convert a Tensorflow shape to a list of ints. Retained for backwards compatibility -- use TensorShape.as_list() in new code."""
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return [dim.value for dim in shape]
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def flatten(x: TfExpressionEx) -> TfExpression:
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"""Shortcut function for flattening a tensor."""
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with tf.name_scope("Flatten"):
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return tf.reshape(x, [-1])
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def log2(x: TfExpressionEx) -> TfExpression:
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"""Logarithm in base 2."""
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with tf.name_scope("Log2"):
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return tf.log(x) * np.float32(1.0 / np.log(2.0))
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def exp2(x: TfExpressionEx) -> TfExpression:
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"""Exponent in base 2."""
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with tf.name_scope("Exp2"):
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return tf.exp(x * np.float32(np.log(2.0)))
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def lerp(a: TfExpressionEx, b: TfExpressionEx, t: TfExpressionEx) -> TfExpressionEx:
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"""Linear interpolation."""
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with tf.name_scope("Lerp"):
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return a + (b - a) * t
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def lerp_clip(a: TfExpressionEx, b: TfExpressionEx, t: TfExpressionEx) -> TfExpression:
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"""Linear interpolation with clip."""
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with tf.name_scope("LerpClip"):
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return a + (b - a) * tf.clip_by_value(t, 0.0, 1.0)
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def absolute_name_scope(scope: str) -> tf.name_scope:
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"""Forcefully enter the specified name scope, ignoring any surrounding scopes."""
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return tf.name_scope(scope + "/")
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def absolute_variable_scope(scope: str, **kwargs) -> tf.variable_scope:
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"""Forcefully enter the specified variable scope, ignoring any surrounding scopes."""
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return tf.variable_scope(tf.VariableScope(name=scope, **kwargs), auxiliary_name_scope=False)
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def _sanitize_tf_config(config_dict: dict = None) -> dict:
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# Defaults.
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cfg = dict()
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cfg["rnd.np_random_seed"] = None # Random seed for NumPy. None = keep as is.
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cfg["rnd.tf_random_seed"] = "auto" # Random seed for TensorFlow. 'auto' = derive from NumPy random state. None = keep as is.
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cfg["env.TF_CPP_MIN_LOG_LEVEL"] = "1" # 0 = Print all available debug info from TensorFlow. 1 = Print warnings and errors, but disable debug info.
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cfg["graph_options.place_pruned_graph"] = True # False = Check that all ops are available on the designated device. True = Skip the check for ops that are not used.
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cfg["gpu_options.allow_growth"] = True # False = Allocate all GPU memory at the beginning. True = Allocate only as much GPU memory as needed.
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# Remove defaults for environment variables that are already set.
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for key in list(cfg):
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fields = key.split(".")
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if fields[0] == "env":
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assert len(fields) == 2
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if fields[1] in os.environ:
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del cfg[key]
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# User overrides.
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if config_dict is not None:
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cfg.update(config_dict)
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return cfg
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def init_tf(config_dict: dict = None) -> None:
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"""Initialize TensorFlow session using good default settings."""
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# Skip if already initialized.
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if tf.get_default_session() is not None:
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return
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# Setup config dict and random seeds.
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cfg = _sanitize_tf_config(config_dict)
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np_random_seed = cfg["rnd.np_random_seed"]
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if np_random_seed is not None:
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np.random.seed(np_random_seed)
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tf_random_seed = cfg["rnd.tf_random_seed"]
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if tf_random_seed == "auto":
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tf_random_seed = np.random.randint(1 << 31)
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if tf_random_seed is not None:
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tf.set_random_seed(tf_random_seed)
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# Setup environment variables.
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for key, value in cfg.items():
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fields = key.split(".")
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if fields[0] == "env":
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assert len(fields) == 2
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os.environ[fields[1]] = str(value)
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# Create default TensorFlow session.
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create_session(cfg, force_as_default=True)
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def assert_tf_initialized():
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"""Check that TensorFlow session has been initialized."""
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if tf.get_default_session() is None:
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raise RuntimeError("No default TensorFlow session found. Please call dnnlib.tflib.init_tf().")
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def create_session(config_dict: dict = None, force_as_default: bool = False) -> tf.Session:
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"""Create tf.Session based on config dict."""
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# Setup TensorFlow config proto.
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cfg = _sanitize_tf_config(config_dict)
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config_proto = tf.ConfigProto()
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for key, value in cfg.items():
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fields = key.split(".")
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if fields[0] not in ["rnd", "env"]:
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obj = config_proto
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for field in fields[:-1]:
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obj = getattr(obj, field)
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setattr(obj, fields[-1], value)
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# Create session.
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session = tf.Session(config=config_proto)
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if force_as_default:
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# pylint: disable=protected-access
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session._default_session = session.as_default()
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session._default_session.enforce_nesting = False
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session._default_session.__enter__()
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return session
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def init_uninitialized_vars(target_vars: List[tf.Variable] = None) -> None:
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"""Initialize all tf.Variables that have not already been initialized.
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Equivalent to the following, but more efficient and does not bloat the tf graph:
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tf.variables_initializer(tf.report_uninitialized_variables()).run()
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"""
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assert_tf_initialized()
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if target_vars is None:
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target_vars = tf.global_variables()
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test_vars = []
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test_ops = []
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with tf.control_dependencies(None): # ignore surrounding control_dependencies
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for var in target_vars:
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assert is_tf_expression(var)
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try:
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tf.get_default_graph().get_tensor_by_name(var.name.replace(":0", "/IsVariableInitialized:0"))
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except KeyError:
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# Op does not exist => variable may be uninitialized.
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test_vars.append(var)
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with absolute_name_scope(var.name.split(":")[0]):
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test_ops.append(tf.is_variable_initialized(var))
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init_vars = [var for var, inited in zip(test_vars, run(test_ops)) if not inited]
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run([var.initializer for var in init_vars])
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def set_vars(var_to_value_dict: dict) -> None:
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"""Set the values of given tf.Variables.
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Equivalent to the following, but more efficient and does not bloat the tf graph:
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tflib.run([tf.assign(var, value) for var, value in var_to_value_dict.items()]
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"""
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assert_tf_initialized()
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ops = []
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feed_dict = {}
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for var, value in var_to_value_dict.items():
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assert is_tf_expression(var)
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try:
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setter = tf.get_default_graph().get_tensor_by_name(var.name.replace(":0", "/setter:0")) # look for existing op
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except KeyError:
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with absolute_name_scope(var.name.split(":")[0]):
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with tf.control_dependencies(None): # ignore surrounding control_dependencies
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setter = tf.assign(var, tf.placeholder(var.dtype, var.shape, "new_value"), name="setter") # create new setter
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ops.append(setter)
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feed_dict[setter.op.inputs[1]] = value
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run(ops, feed_dict)
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def create_var_with_large_initial_value(initial_value: np.ndarray, *args, **kwargs):
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"""Create tf.Variable with large initial value without bloating the tf graph."""
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assert_tf_initialized()
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assert isinstance(initial_value, np.ndarray)
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zeros = tf.zeros(initial_value.shape, initial_value.dtype)
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var = tf.Variable(zeros, *args, **kwargs)
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set_vars({var: initial_value})
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return var
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def convert_images_from_uint8(images, drange=[-1,1], nhwc_to_nchw=False):
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"""Convert a minibatch of images from uint8 to float32 with configurable dynamic range.
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Can be used as an input transformation for Network.run().
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"""
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images = tf.cast(images, tf.float32)
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if nhwc_to_nchw:
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images = tf.transpose(images, [0, 3, 1, 2])
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return images * ((drange[1] - drange[0]) / 255) + drange[0]
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def convert_images_to_uint8(images, drange=[-1,1], nchw_to_nhwc=False, shrink=1):
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"""Convert a minibatch of images from float32 to uint8 with configurable dynamic range.
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Can be used as an output transformation for Network.run().
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"""
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images = tf.cast(images, tf.float32)
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if shrink > 1:
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ksize = [1, 1, shrink, shrink]
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images = tf.nn.avg_pool(images, ksize=ksize, strides=ksize, padding="VALID", data_format="NCHW")
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if nchw_to_nhwc:
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images = tf.transpose(images, [0, 2, 3, 1])
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scale = 255 / (drange[1] - drange[0])
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images = images * scale + (0.5 - drange[0] * scale)
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return tf.saturate_cast(images, tf.uint8)
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