cleanup deprecations

docs/conf.py

@@ -377,8 +377,6 @@ _DEPRECATED_NAMES = set([
     'dump_dataflow_to_process_queue',
     'DistributedTrainerReplicated',
     'DistributedTrainerParameterServer',
-    'InputDesc',
-    'inputs_desc',
     'Augmentor',
     "get_model_loader",
 

tensorpack/dataflow/common.py

@@ -13,7 +13,6 @@ from termcolor import colored
 
 from ..utils import logger
 from ..utils.utils import get_rng, get_tqdm, get_tqdm_kwargs
-from ..utils.develop import log_deprecated
 from .base import DataFlow, DataFlowReentrantGuard, ProxyDataFlow, RNGDataFlow
 
 try:
@@ -622,7 +621,7 @@ class LocallyShuffleData(ProxyDataFlow, RNGDataFlow):
         because it does not make sense to stop the iteration anywhere.
     """
 
-    def __init__(self, ds, buffer_size, num_reuse=1, shuffle_interval=None, nr_reuse=None):
+    def __init__(self, ds, buffer_size, num_reuse=1, shuffle_interval=None):
         """
         Args:
             ds (DataFlow): input DataFlow.
@@ -633,11 +632,7 @@ class LocallyShuffleData(ProxyDataFlow, RNGDataFlow):
                 datapoints were produced from the given dataflow. Frequent shuffle on large buffer
                 may affect speed, but infrequent shuffle may not provide enough randomness.
                 Defaults to buffer_size / 3
-            nr_reuse: deprecated name for num_reuse
         """
-        if nr_reuse is not None:
-            log_deprecated("LocallyShuffleData(nr_reuse=...)", "Renamed to 'num_reuse'.", "2020-01-01")
-            num_reuse = nr_reuse
         ProxyDataFlow.__init__(self, ds)
         self.q = deque(maxlen=buffer_size)
         if shuffle_interval is None:

tensorpack/dataflow/parallel.py

@@ -16,7 +16,6 @@ import zmq
 from six.moves import queue, range
 
 from ..utils import logger
-from ..utils.develop import log_deprecated
 from ..utils.concurrency import (
     StoppableThread, enable_death_signal, ensure_proc_terminate, start_proc_mask_signal)
 from ..utils.serialize import dumps_once as dumps, loads_once as loads
@@ -193,24 +192,14 @@ class MultiProcessRunner(ProxyDataFlow):
                 for dp in self.ds:
                     self.queue.put(dp)
 
-    def __init__(self, ds, num_prefetch=None, num_proc=None, nr_prefetch=None, nr_proc=None):
+    def __init__(self, ds, num_prefetch, num_proc):
         """
         Args:
             ds (DataFlow): input DataFlow.
             num_prefetch (int): size of the queue to hold prefetched datapoints.
                 Required.
             num_proc (int): number of processes to use. Required.
-            nr_prefetch, nr_proc: deprecated argument names
         """
-        if nr_prefetch is not None:
-            log_deprecated("MultiProcessRunner(nr_prefetch)", "Renamed to 'num_prefetch'", "2020-01-01")
-            num_prefetch = nr_prefetch
-        if nr_proc is not None:
-            log_deprecated("MultiProcessRunner(nr_proc)", "Renamed to 'num_proc'", "2020-01-01")
-            num_proc = nr_proc
-        if num_prefetch is None or num_proc is None:
-            raise TypeError("Missing argument num_prefetch or num_proc in MultiProcessRunner!")
-
         # https://docs.python.org/3.6/library/multiprocessing.html?highlight=process#the-spawn-and-forkserver-start-methods
         if os.name == 'nt':
             logger.warn("MultiProcessRunner does support Windows. \
@@ -333,17 +322,13 @@ class MultiProcessRunnerZMQ(_MultiProcessZMQDataFlow):
                 socket.close(0)
                 context.destroy(0)
 
-    def __init__(self, ds, num_proc=1, hwm=50, nr_proc=None):
+    def __init__(self, ds, num_proc=1, hwm=50):
         """
         Args:
             ds (DataFlow): input DataFlow.
             num_proc (int): number of processes to use.
             hwm (int): the zmq "high-water mark" (queue size) for both sender and receiver.
-            nr_proc: deprecated
         """
-        if nr_proc is not None:
-            log_deprecated("MultiProcessRunnerZMQ(nr_proc)", "Renamed to 'num_proc'", "2020-01-01")
-            num_proc = nr_proc
         super(MultiProcessRunnerZMQ, self).__init__()
 
         self.ds = ds
@@ -443,7 +428,7 @@ class MultiThreadRunner(DataFlow):
             finally:
                 self.stop()
 
-    def __init__(self, get_df, num_prefetch=None, num_thread=None, nr_prefetch=None, nr_thread=None):
+    def __init__(self, get_df, num_prefetch, num_thread):
         """
         Args:
             get_df ( -> DataFlow): a callable which returns a DataFlow.
@@ -452,17 +437,7 @@ class MultiThreadRunner(DataFlow):
                 unless your dataflow is stateless.
             num_prefetch (int): size of the queue
             num_thread (int): number of threads
-            nr_prefetch, nr_thread: deprecated names
         """
-        if nr_prefetch is not None:
-            log_deprecated("MultiThreadRunner(nr_prefetch)", "Renamed to 'num_prefetch'", "2020-01-01")
-            num_prefetch = nr_prefetch
-        if nr_thread is not None:
-            log_deprecated("MultiThreadRunner(nr_thread)", "Renamed to 'num_thread'", "2020-01-01")
-            num_thread = nr_thread
-        if num_prefetch is None or num_thread is None:
-            raise TypeError("Missing argument num_prefetch or num_thread in MultiThreadRunner!")
-
         assert num_thread > 0, num_thread
         assert num_prefetch > 0, num_prefetch
         self.num_thread = num_thread

tensorpack/dataflow/parallel_map.py

@@ -10,7 +10,6 @@ from six.moves import queue
 
 from ..utils.concurrency import StoppableThread, enable_death_signal
 from ..utils.serialize import dumps_once as dumps, loads_once as loads
-from ..utils.develop import log_deprecated
 from .base import DataFlow, DataFlowReentrantGuard, ProxyDataFlow
 from .common import RepeatedData, BatchData
 from .parallel import _bind_guard, _get_pipe_name, _MultiProcessZMQDataFlow, _repeat_iter, _zmq_catch_error
@@ -143,7 +142,7 @@ class MultiThreadMapData(_ParallelMapData):
             finally:
                 self.stop()
 
-    def __init__(self, ds, num_thread=None, map_func=None, buffer_size=200, strict=False, nr_thread=None):
+    def __init__(self, ds, num_thread=None, map_func=None, *, buffer_size=200, strict=False):
         """
         Args:
             ds (DataFlow): the dataflow to map
@@ -152,12 +151,7 @@ class MultiThreadMapData(_ParallelMapData):
                 discard/skip the datapoint.
             buffer_size (int): number of datapoints in the buffer
             strict (bool): use "strict mode", see notes above.
-            nr_thread: deprecated name
         """
-        if nr_thread is not None:
-            log_deprecated("MultiThreadMapData(nr_thread)", "Renamed to 'num_thread'", "2020-01-01")
-            num_thread = nr_thread
-
         if strict:
             # In strict mode, buffer size cannot be larger than the total number of datapoints
             try:
@@ -255,7 +249,7 @@ class MultiProcessMapDataZMQ(_ParallelMapData, _MultiProcessZMQDataFlow):
                 dp = self.map_func(dp)
                 socket.send(dumps(dp), copy=False)
 
-    def __init__(self, ds, num_proc=None, map_func=None, buffer_size=200, strict=False, nr_proc=None):
+    def __init__(self, ds, num_proc=None, map_func=None, *, buffer_size=200, strict=False):
         """
         Args:
             ds (DataFlow): the dataflow to map
@@ -264,11 +258,7 @@ class MultiProcessMapDataZMQ(_ParallelMapData, _MultiProcessZMQDataFlow):
                 discard/skip the datapoint.
             buffer_size (int): number of datapoints in the buffer
             strict (bool): use "strict mode", see notes above.
-            nr_proc: deprecated name
         """
-        if nr_proc is not None:
-            log_deprecated("MultiProcessMapDataZMQ(nr_proc)", "Renamed to 'num_proc'", "2020-01-01")
-            num_proc = nr_proc
         if strict:
             # In strict mode, buffer size cannot be larger than the total number of datapoints
             try:

tensorpack/graph_builder/model_desc.py

@@ -2,35 +2,7 @@
 # File: model_desc.py
 
 
-from collections import namedtuple
-import tensorflow as tf
-
-from ..utils.develop import log_deprecated
 from ..train.model_desc import ModelDesc, ModelDescBase  # kept for BC # noqa
 
 
-__all__ = ['InputDesc']
-
-
-class InputDesc(
-        namedtuple('InputDescTuple', ['type', 'shape', 'name'])):
-    """
-    An equivalent of `tf.TensorSpec`.
-
-    History: this concept is used to represent metadata about the inputs,
-    which can be later used to build placeholders or other types of input source.
-    It is introduced much much earlier than the equivalent concept `tf.TensorSpec`
-    was introduced in TensorFlow.
-    Therefore, we now switched to use `tf.TensorSpec`, but keep this here for compatibility reasons.
-    """
-
-    def __new__(cls, type, shape, name):
-        """
-        Args:
-            type (tf.DType):
-            shape (tuple):
-            name (str):
-        """
-        log_deprecated("InputDesc", "Use tf.TensorSpec instead!", "2020-03-01")
-        assert isinstance(type, tf.DType), type
-        return tf.TensorSpec(shape=shape, dtype=type, name=name)
+__all__ = []

tensorpack/predict/config.py

@@ -10,7 +10,6 @@ from ..tfutils.sessinit import JustCurrentSession, SessionInit
 from ..tfutils.sesscreate import NewSessionCreator
 from ..tfutils.tower import TowerFunc
 from ..utils import logger
-from ..utils.develop import log_deprecated
 
 __all__ = ['PredictConfig']
 
@@ -28,7 +27,6 @@ class PredictConfig(object):
                  session_init=None,
                  return_input=False,
                  create_graph=True,
-                 inputs_desc=None
                  ):
         """
         Users need to provide enough arguments to create a tower function,
@@ -69,18 +67,12 @@ class PredictConfig(object):
             return_input (bool): same as in :attr:`PredictorBase.return_input`.
             create_graph (bool): create a new graph, or use the default graph
                 when predictor is first initialized.
-            inputs_desc (list[tf.TensorSpec]): old (deprecated) name for `input_signature`.
         """
         def assert_type(v, tp, name):
             assert isinstance(v, tp), \
                 "Argument '{}' has to be type '{}', but an object of type '{}' found.".format(
                     name, tp.__name__, v.__class__.__name__)
 
-        if inputs_desc is not None:
-            log_deprecated("PredictConfig(inputs_desc)", "Use input_signature instead!", "2020-03-01")
-            assert input_signature is None, "Cannot set both inputs_desc and input_signature!"
-            input_signature = inputs_desc
-
         if model is not None:
             assert_type(model, ModelDescBase, 'model')
             assert input_signature is None and tower_func is None
@@ -120,8 +112,6 @@ class PredictConfig(object):
         self.return_input = bool(return_input)
         self.create_graph = bool(create_graph)
 
-        self.inputs_desc = input_signature  # TODO a little bit of compatibility
-
     def _maybe_create_graph(self):
         if self.create_graph:
             return tf.Graph()

tensorpack/tfutils/tower.py

@@ -8,7 +8,7 @@ import six
 from ..compat import tfv1 as tf
 from ..utils import logger
 from ..utils.argtools import call_only_once
-from ..utils.develop import HIDE_DOC, log_deprecated
+from ..utils.develop import HIDE_DOC
 from ..utils.naming import MOVING_SUMMARY_OPS_KEY
 from .collection import CollectionGuard
 from .common import get_op_or_tensor_by_name, get_op_tensor_name
@@ -305,11 +305,6 @@ class TowerFunc(object):
     def input_signature(self):
         return self._input_signature
 
-    @property
-    def inputs_desc(self):
-        log_deprecated("TowerFunc.inputs_desc", "Use .input_signature instead", "2020-03-01")
-        return self._input_signature
-
 
 TowerFuncWrapper = TowerFunc
 

tensorpack/train/model_desc.py

@@ -4,7 +4,6 @@
 
 import tensorflow as tf
 
-from ..utils.develop import log_deprecated, HIDE_DOC
 from ..utils.argtools import memoized_method
 from ..tfutils.common import get_op_tensor_name
 from ..tfutils.tower import get_current_tower_context
@@ -27,11 +26,6 @@ class ModelDescBase(object):
     together define a tower function.
     """
 
-    @HIDE_DOC
-    def get_inputs_desc(self):
-        log_deprecated("ModelDesc.get_inputs_desc", "Use get_input_signature instead!", "2020-03-01")
-        return self.get_input_signature()
-
     @memoized_method
     def get_input_signature(self):
         """

tensorpack/train/tower.py

@@ -12,7 +12,7 @@ from ..tfutils.gradproc import FilterNoneGrad
 from ..tfutils.tower import PredictTowerContext, TowerFunc, get_current_tower_context
 from ..utils import logger
 from ..utils.argtools import call_only_once, memoized
-from ..utils.develop import HIDE_DOC, log_deprecated
+from ..utils.develop import HIDE_DOC
 from .base import Trainer
 
 __all__ = ['SingleCostTrainer', 'TowerTrainer']
@@ -56,11 +56,6 @@ class TowerTrainer(Trainer):
     def tower_func(self, val):
         self._set_tower_func(val)
 
-    @property
-    def inputs_desc(self):
-        log_deprecated("TowerTrainer.inputs_desc", "Use .input_signature instead!", "2020-03-01")
-        return self.input_signature
-
     @property
     def input_signature(self):
         """