[WIP] TensorLayer 2.0

CHANGELOG.md

@@ -72,8 +72,29 @@ To release a new version, please update the changelog as followed:
 ### Added
 - Layer:
   - Release `GroupNormLayer` (PR #850)
+- Tests:
+  - `test_model_compilednetwork.py` has been introduced to test the class `tl.models.CompiledNetwork` (PR #755).
+  - `test_network_custom_input_layers.py` has been introduced to test the class `tl.networks.CustomModel` (PR #755).
+  - `test_network_custom_multiple_inputs.py` has been introduced to test the class `tl.networks.CustomModel` (PR #755).
+  - `test_network_custom_multiple_outputs.py` has been introduced to test the class `tl.networks.CustomModel` (PR #755).
+  - `test_network_custom_2d.py` has been introduced to test the class `tl.networks.CustomModel` (PR #755).
+  - `test_network_sequential_1d.py` has been introduced to test the class `tl.networks.Sequential` with 1D data Layers (PR #755).
+  - `test_network_sequential_2d.py` has been introduced to test the class `tl.networks.Sequential` with 2D data Layers (PR #755).
+  - `test_network_sequential_3d.py` has been introduced to test the class `tl.networks.Sequential` with 3D data Layers (PR #755).
+  - `test_network_sequential_rnn.py` has been introduced to test the class `tl.networks.Sequential` with 3D data Layers (PR #755).
 
 ### Changed
+- API:
+  - `tl.models` has been renamed to `tl.nets` to comply with TF Slim naming convention: https://github.com/tensorflow/models/tree/master/research/slim (PR #755)
+  - `layer.all_params` renamed `layer.all_weights` to reduce confusion (params != hyperparameters, params = weights) (PR #755)
+  - `layer.count_params()` renamed `layer.count_weights()` to reduce confusion (params != hyperparameters, params = weights) (PR #755)
+  - `layer.get_all_params()` renamed `layer.get_all_weights()` to reduce confusion (params != hyperparameters, params = weights) (PR #755)
+  - `layer.print_params()` renamed `layer.print_weights()` to reduce confusion (params != hyperparameters, params = weights) (PR #755)
+  - `tl.layers.ROIPoolingLayer` has been moved to `tl.layers.contrib.ROIPoolingLayer` (PR #755)
+  - `tl.layers.QuanDenseLayer` renamed to `tl.layers.QuantizedDense` (PR #755)
+  - `tl.layers.QuanDenseLayerWithBN` renamed to `tl.layers.QuantizedDenseWithBN` (PR #755)
+  - `tl.layers.QuanConv2d` renamed to `tl.layers.QuantizedConv2d` (PR #755)
+  - `tl.layers.QuanConv2dWithBN` renamed to `tl.layers.QuantizedConv2dWithBN` (PR #755)
 
 ### Dependencies Update
 - yapf>=0.22,<0.24 => yapf>=0.22,<0.25 (PR #829)
@@ -88,12 +109,16 @@ To release a new version, please update the changelog as followed:
 - Correct offset calculation in `tl.prepro.transform_matrix_offset_center` (PR #855)
 
 ### Removed
+- `tl.layers.EstimatorLayer` has been removed in favor of `tl.layers.LambdaLayer` (PR #755)
+- `tl.layers.ReconLayer` has been removed in favor of `tl.layers.DenseLayer` (#755)
+- `tl.layers.MultiplexerLayer` has been removed in favor of eager execution mode (#755)
 
 ### Security
 
 ### Contributors
 - @2wins: #850 #855
-- @DEKHTIARJonathan: #853
+- @DEKHTIARJonathan: #755 #853
+- @zsdonghao: #755
 
 ## [1.10.1] - 2018-09-07
 
@@ -104,7 +129,7 @@ To release a new version, please update the changelog as followed:
  - remove 'tensorboard' param, replaced by 'tensorboard_dir' in `tensorlayer/utils.py` with customizable tensorboard directory (PR #819)
 
 ### Removed
-- TL Graph API removed. Memory Leaks Issues with this API, will be fixed and integrated in TL 2.0 (PR #818)
+- TL Graph API removed. Memory Leaks Issues with Graph API, will be fixed and integrated in TL 2.0 (PR #818)
 
 ### Fixed
 - Issue #817 fixed: TL 1.10.0 - Memory Leaks and very slow network creation.
@@ -117,7 +142,7 @@ To release a new version, please update the changelog as followed:
 
 ### Contributors
 - @DEKHTIARJonathan: #815 #818 #820 #823
-- @ndiy: #819 
+- @ndiy: #819
 - @zsdonghao: #818
 
 ## [1.10.0] - 2018-09-02

docs/Makefile

@@ -5,7 +5,7 @@
 SPHINXOPTS    =
 SPHINXBUILD   = sphinx-build
 PAPER         =
-BUILDDIR      = _build
+BUILDDIR      = build
 
 # Internal variables.
 PAPEROPT_a4     = -D latex_paper_size=a4

docs/conf.py

@@ -1,6 +1,6 @@
-#!/usr/bin/env python3
+#! /usr/bin/python
 # -*- coding: utf-8 -*-
-#
+
 # TensorLayer documentation build configuration file, created by
 # sphinx-quickstart on Tue Aug  2 15:30:55 2016.
 #
@@ -16,13 +16,25 @@
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.
-#
-import os, sys, datetime
-sys.path.insert(0, os.path.abspath("../"))  # Important
-sys.path.insert(0, os.path.abspath(os.path.join("..", "tensorlayer"))) # Important
 
-from package_info import __shortversion__
-from package_info import __version__
+import os
+
+import datetime
+import importlib
+
+os.environ["SPHINXBUILD"] = "True"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+
+pkg_info_path = os.path.join("..", "tensorlayer", "package_info.py")
+
+
+def import_pyfile_as_module(absolute_path):
+    spec = importlib.util.spec_from_file_location(absolute_path, absolute_path)
+    module = spec.loader.load_module(spec.name)
+    return module
+
+
+pkg_info = import_pyfile_as_module(pkg_info_path)
 
 # -- General configuration ------------------------------------------------
 
@@ -36,14 +48,17 @@
 
 # extensions = [
 #    'sphinx.ext.coverage',
+#    'sphinx.ext.doctest',
 #    'sphinx.ext.githubpages',
 #    'numpydoc',
 # ]
 
+conf_file_path = os.path.realpath(__file__)
+tensorlayer_dir = os.path.dirname(os.path.dirname(conf_file_path))
+
 extensions = [
     'sphinx.ext.autodoc',
     'sphinx.ext.autosummary',
-    'sphinx.ext.doctest',
     'sphinx.ext.ifconfig',
     'sphinx.ext.inheritance_diagram',
     'sphinx.ext.intersphinx',
@@ -102,9 +117,9 @@
 # built documents.
 #
 # The short X.Y version.
-version = __shortversion__
+version = pkg_info.__version__
 # The full version, including alpha/beta/rc tags.
-release = __version__
+release = pkg_info.__version__
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
@@ -125,7 +140,7 @@
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This patterns also effect to html_static_path and html_extra_path
-exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+exclude_patterns = ['build', 'Thumbs.db', '.DS_Store']
 
 # The reST default role (used for this markup: `text`) to use for all
 # documents.

docs/make.bat

@@ -5,7 +5,7 @@ REM Command file for Sphinx documentation
 if "%SPHINXBUILD%" == "" (
 	set SPHINXBUILD=sphinx-build
 )
-set BUILDDIR=_build
+set BUILDDIR=build
 set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% .
 set I18NSPHINXOPTS=%SPHINXOPTS% .
 if NOT "%PAPER%" == "" (

docs/modules/layers.rst

@@ -142,7 +142,6 @@ Layer list
    AverageEmbeddingInputlayer
 
    DenseLayer
-   ReconLayer
    DropoutLayer
    GaussianNoiseLayer
    DropconnectDenseLayer
@@ -248,17 +247,15 @@ Layer list
    TernaryConv2d
    DorefaDenseLayer
    DorefaConv2d
-   QuanDenseLayer
-   QuanDenseLayerWithBN
-   QuanConv2d
-   QuanConv2dWithBN
+   QuantizedDense
+   QuantizedDenseWithBN
+   QuantizedConv2d
+   QuantizedConv2dWithBN
 
    PReluLayer
    PRelu6Layer
    PTRelu6Layer
 
-   MultiplexerLayer
-
    flatten_reshape
    clear_layers_name
    initialize_rnn_state
@@ -298,7 +295,7 @@ The following is an example implementation of a layer that multiplies its input
           self.outputs = self.inputs * 2
 
           # update layer (customized)
-          self._add_layers(self.outputs)
+
 
 Your Dense Layer
 ^^^^^^^^^^^^^^^^
@@ -337,8 +334,8 @@ At the end, for a layer with parameters, we also append the parameters into ``al
             self.outputs = self._apply_activation(tf.matmul(self.inputs, W) + b)
 
         # update layer (customized)
-        self._add_layers(self.outputs)
-        self._add_params([W, b])
+
+
 
 .. -----------------------------------------------------------
 ..                        Basic Layers
@@ -587,14 +584,6 @@ see `Slim-model <https://github.com/tensorflow/models/tree/master/research/slim>
 
 
 
-.. -----------------------------------------------------------
-..                    Flow Control Layer
-.. -----------------------------------------------------------
-
-Flow Control Layer
-----------------------
-.. autoclass:: MultiplexerLayer
-
 .. -----------------------------------------------------------
 ..                  Image Resampling Layers
 .. -----------------------------------------------------------
@@ -838,24 +827,24 @@ DorefaConv2d
 Quantization Dense Layer
 ^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-QuanDenseLayer
+QuantizedDense
 """""""""""""""""""""
-.. autoclass:: QuanDenseLayer
+.. autoclass:: QuantizedDense
 
-QuanDenseLayerWithBN
+QuantizedDenseWithBN
 """"""""""""""""""""""""""""""""""""
-.. autoclass:: QuanDenseLayerWithBN
+.. autoclass:: QuantizedDenseWithBN
 
 Quantization Convolutions
 ^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Quantization
 """""""""""""""""""""
-.. autoclass:: QuanConv2d
+.. autoclass:: QuantizedConv2d
 
-QuanConv2dWithBN
+QuantizedConv2dWithBN
 """""""""""""""""""""
-.. autoclass:: QuanConv2dWithBN
+.. autoclass:: QuantizedConv2dWithBN
 
 
 .. -----------------------------------------------------------

examples/basic_tutorials/tutorial_cifar10_datasetapi.py

@@ -1,5 +1,6 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
+
 """Using Dataset API and tf.image can obtain the best performance."""
 
 import time
@@ -26,19 +27,19 @@ def model_batch_norm(x_crop, y_, is_train, reuse):
     W_init2 = tf.truncated_normal_initializer(stddev=0.04)
     b_init2 = tf.constant_initializer(value=0.1)
     with tf.variable_scope("model", reuse=reuse):
-        net = InputLayer(x_crop, name='input')
-        net = Conv2d(net, 64, (5, 5), (1, 1), padding='SAME', W_init=W_init, b_init=None, name='cnn1')
-        net = BatchNormLayer(net, decay=0.99, is_train=is_train, act=tf.nn.relu, name='batch1')
-        net = MaxPool2d(net, (3, 3), (2, 2), padding='SAME', name='pool1')
-
-        net = Conv2d(net, 64, (5, 5), (1, 1), padding='SAME', W_init=W_init, b_init=None, name='cnn2')
-        net = BatchNormLayer(net, decay=0.99, is_train=is_train, act=tf.nn.relu, name='batch2')
-        net = MaxPool2d(net, (3, 3), (2, 2), padding='SAME', name='pool2')
-
-        net = FlattenLayer(net, name='flatten')
-        net = DenseLayer(net, 384, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d1relu')
-        net = DenseLayer(net, 192, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d2relu')
-        net = DenseLayer(net, n_units=10, act=None, W_init=W_init2, name='output')
+        net = InputLayer(name='input')(x_crop)
+        net = Conv2d(64, (5, 5), (1, 1), padding='SAME', W_init=W_init, b_init=None, name='cnn1')(net)
+        net = BatchNormLayer(decay=0.99, act=tf.nn.relu, name='batch1')(net, is_train=is_train)
+        net = MaxPool2d((3, 3), (2, 2), padding='SAME', name='pool1')(net)
+
+        net = Conv2d(64, (5, 5), (1, 1), padding='SAME', W_init=W_init, b_init=None, name='cnn2')(net)
+        net = BatchNormLayer(decay=0.99, act=tf.nn.relu, name='batch2')(net, is_train=is_train)
+        net = MaxPool2d((3, 3), (2, 2), padding='SAME', name='pool2')(net)
+
+        net = FlattenLayer(name='flatten')
+        net = DenseLayer(384, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d1relu')(net)
+        net = DenseLayer(192, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d2relu')(net)
+        net = DenseLayer(n_units=10, act=None, W_init=W_init2, name='output')(net)
         y = net.outputs
 
         ce = tl.cost.cross_entropy(y, y_, name='cost')
@@ -100,17 +101,19 @@ def _map_fn_test(img, target):
 
 
 # dataset API and augmentation
-ds = tf.data.Dataset().from_generator(generator_train, output_types=(tf.float32,
-                                                                     tf.int32))  # , output_shapes=((24, 24, 3), (1)))
+ds = tf.data.Dataset().from_generator(
+    generator_train, output_types=(tf.float32, tf.int32)
+)  # , output_shapes=((24, 24, 3), (1)))
 ds = ds.map(_map_fn_train, num_parallel_calls=multiprocessing.cpu_count())
 ds = ds.repeat(n_epoch)
 ds = ds.shuffle(shuffle_buffer_size)
 ds = ds.prefetch(buffer_size=4096)
 ds = ds.batch(batch_size)
 value = ds.make_one_shot_iterator().get_next()
 
-ds = tf.data.Dataset().from_generator(generator_test, output_types=(tf.float32,
-                                                                    tf.int32))  # , output_shapes=((24, 24, 3), (1)))
+ds = tf.data.Dataset().from_generator(
+    generator_test, output_types=(tf.float32, tf.int32)
+)  # , output_shapes=((24, 24, 3), (1)))
 ds = ds.shuffle(shuffle_buffer_size)
 ds = ds.map(_map_fn_test, num_parallel_calls=multiprocessing.cpu_count())
 ds = ds.repeat(n_epoch)