Complete unittest for trainer_config_helpers. (#108)

* Fix lots of trainer_config_helpers bug, and complete unittest for `layers.py`

python/paddle/trainer/config_parser.py

@@ -1279,7 +1279,7 @@ class LayerBase(object):
             size,
             dims=None,
             sparse = None,
-            format = "csr"):
+            format = None):
         if dims is None:
             # TODO(yuyang18): print warning and callstack here!
             dims = list()
@@ -2074,7 +2074,7 @@ class MaxLayer(LayerBase):
             active_type='linear',
             device=None,
             bias=False,
-            output_max_index=False):
+            output_max_index=None):
         super(MaxLayer, self).__init__(name, 'max', 0, inputs=inputs, device=device)
         config_assert(len(self.inputs) == 1, 'MaxLayer must have 1 input')
         self.config.trans_type =  trans_type
@@ -2083,6 +2083,7 @@ class MaxLayer(LayerBase):
             input_layer = self.get_input_layer(input_index)
             self.set_layer_size(input_layer.size)
         self.create_bias_parameter(bias, self.config.size)
+        if output_max_index is not None:
             self.config.output_max_index = output_max_index
 
 
@@ -2440,7 +2441,7 @@ class MixedLayer(LayerBase):
             inputs,
             size=0,
             bias=True,
-            error_clipping_threshold=0.0,
+            error_clipping_threshold=None,
             **xargs):
         config_assert(inputs, 'inputs cannot be empty')
         super(MixedLayer, self).__init__(
@@ -2510,6 +2511,7 @@ class MixedLayer(LayerBase):
 
         self.create_bias_parameter(bias, self.config.size)
 
+        if error_clipping_threshold is not None:
             self.config.error_clipping_threshold = error_clipping_threshold
 
 # like MixedLayer, but no bias parameter

python/paddle/trainer_config_helpers/activations.py

@@ -15,8 +15,10 @@
 __all__ = ["TanhActivation", "SigmoidActivation",
            "SoftmaxActivation", "IdentityActivation", "LinearActivation",
            'SequenceSoftmaxActivation', 'ExpActivation',
-           "ReluActivation", "BReluActivation", "SoftReluActivation", "STanhActivation",
+           "ReluActivation", "BReluActivation", "SoftReluActivation",
-           "AbsActivation", "SquareActivation", "BaseActivation"]
+           "STanhActivation",
+           "AbsActivation", "SquareActivation",
+           "BaseActivation"]
 
 
 class BaseActivation(object):
@@ -36,6 +38,9 @@ class BaseActivation(object):
         self.name = name
         self.support_hppl = support_hppl
 
+    def __repr__(self):
+        return self.name
+
 
 class TanhActivation(BaseActivation):
     """

python/paddle/trainer_config_helpers/networks.py

@@ -616,7 +616,7 @@ def lstmemory_group(input, size=None, name=None,
     cell states, or hidden states in every time step are accessible to for the
     user. This is especially useful in attention model. If you do not need to
     access to the internal states of the lstm, but merely use its outputs,
-    it is recommanded to use the lstmemory, which is relatively faster than
+    it is recommended to use the lstmemory, which is relatively faster than
     lstmemory_group.
 
     NOTE: In PaddlePaddle's implementation, the following input-to-hidden
@@ -1052,7 +1052,7 @@ def dropout_layer(input, dropout_rate, name=None):
                        layer_attr=ExtraAttr(drop_rate=dropout_rate))
 
 
-def outputs(layers):
+def outputs(layers, *args):
     """
     Declare the end of network. Currently it will only calculate the
     input/output order of network. It will calculate the predict network or
@@ -1089,9 +1089,12 @@ def outputs(layers):
     if isinstance(layers, LayerOutput):
         layers = [layers]
 
+    if len(args) != 0:
+        layers.extend(args)
+
     assert len(layers) > 0
     if len(layers) != 1:
-        logger.warning("EndOfNetwork routine try to calculate network's"
+        logger.warning("`outputs` routine try to calculate network's"
                        " inputs and outputs order. It might not work well."
                        "Please see follow log carefully.")
     inputs = []

python/paddle/trainer_config_helpers/poolings.py

@@ -47,9 +47,14 @@ class MaxPooling(BasePoolingType):
     ..  math::
 
         max(samples\\_of\\_a\\_sequence)
+
+    :param output_max_index: True if output sequence max index instead of max
+                             value. None means use default value in proto.
+    :type output_max_index: bool|None
     """
-    def __init__(self):
+    def __init__(self, output_max_index=None):
         BasePoolingType.__init__(self, "max")
+        self.output_max_index = output_max_index
         
 
 class AvgPooling(BasePoolingType):

python/paddle/trainer_config_helpers/tests/CMakeLists.txt

@@ -3,3 +3,8 @@ add_test(NAME layers_test
   COMMAND ${PROJ_ROOT}/paddle/.set_python_path.sh -d ${PROJ_ROOT}/python/
         python ${PROJ_ROOT}/python/paddle/trainer_config_helpers/tests/layers_test.py
     WORKING_DIRECTORY ${PROJ_ROOT}/python/paddle)
+
+add_test(NAME test_layerHelpers
+  COMMAND
+  ${PROJ_ROOT}/python/paddle/trainer_config_helpers/tests/configs/run_tests.sh
+)

python/paddle/trainer_config_helpers/tests/configs/.gitignore

@@ -0,0 +1 @@
+*protostr

python/paddle/trainer_config_helpers/tests/configs/check.md5

@@ -0,0 +1,17 @@
+7e6919d17562516e9a1d9a88de1fb3b9  img_layers.protostr
+a5d9259ff1fd7ca23d0ef090052cb1f2  last_first_seq.protostr
+9c038249ec8ff719753a746cdb04c026  layer_activations.protostr
+5913f87b39cee3b2701fa158270aca26  projections.protostr
+6b39e34beea8dfb782bee9bd3dea9eb5  simple_rnn_layers.protostr
+0fc1409600f1a3301da994ab9d28b0bf  test_cost_layers.protostr
+144bc6d3a509de74115fa623741797ed  test_expand_layer.protostr
+2378518bdb71e8c6e888b1842923df58  test_fc.protostr
+8bb44e1e5072d0c261572307e7672bda  test_grumemory_layer.protostr
+1f3510672dce7a9ed25317fc58579ac7  test_hsigmoid.protostr
+d350bd91a0dc13e854b1364c3d9339c6  test_lstmemory_layer.protostr
+251a948ba41c1071afcd3d9cf9c233f7  test_ntm_layers.protostr
+e6ff04e70aea27c7b06d808cc49c9497  test_print_layer.protostr
+2a75dd33b640c49a8821c2da6e574577  test_rnn_group.protostr
+67d6fde3afb54f389d0ce4ff14726fe1  test_sequence_pooling.protostr
+f586a548ef4350ba1ed47a81859a64cb  unused_layers.protostr
+8122477f4f65244580cec09edc590041  util_layers.protostr

python/paddle/trainer_config_helpers/tests/configs/generate_protostr.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+set -e
+cd `dirname $0`
+export PYTHONPATH=$PWD/../../../../
+
+configs=(test_fc layer_activations projections test_print_layer
+test_sequence_pooling test_lstmemory_layer test_grumemory_layer
+last_first_seq test_expand_layer test_ntm_layers test_hsigmoid
+img_layers util_layers simple_rnn_layers unused_layers test_cost_layers
+test_rnn_group)
+
+
+for conf in ${configs[*]}
+do
+    echo "Generating " $conf
+    python -m paddle.utils.dump_config $conf.py > $conf.protostr
+done

python/paddle/trainer_config_helpers/tests/configs/img_layers.py

@@ -0,0 +1,20 @@
+from paddle.trainer_config_helpers import *
+
+settings(
+    learning_rate=1e-3,
+    batch_size=1000
+)
+
+img = data_layer(name='image', size=256*256)
+
+img_conv = img_conv_layer(input=img, num_channels=1, num_filters=64,
+                          filter_size=(32, 64), padding=(1, 0), stride=(1, 1),
+                          act=LinearActivation())
+img_bn = batch_norm_layer(input=img_conv, act=ReluActivation())
+
+img_norm = img_cmrnorm_layer(input=img_bn, size=32)
+
+img_pool = img_pool_layer(input=img_conv, pool_size=32, pool_type=MaxPooling())
+
+
+outputs(img_pool, img_norm)

python/paddle/trainer_config_helpers/tests/configs/last_first_seq.py

@@ -0,0 +1,26 @@
+from paddle.trainer_config_helpers import *
+
+settings(
+    batch_size=1000,
+    learning_rate=1e-5
+)
+
+din = data_layer(name='data', size=30)
+
+seq_op = [
+    first_seq,
+    last_seq
+]
+
+agg_level = [
+    AggregateLevel.EACH_SEQUENCE,
+    AggregateLevel.EACH_TIMESTEP
+]
+
+opts = []
+
+for op in seq_op:
+    for al in agg_level:
+        opts.append(op(input=din, agg_level=al))
+
+outputs(opts)

python/paddle/trainer_config_helpers/tests/configs/layer_activations.py

@@ -0,0 +1,21 @@
+'''
+Test all activations.
+'''
+
+from paddle.trainer_config_helpers import *
+
+settings(
+    learning_rate=1e-4,
+    batch_size=1000
+)
+
+din = data_layer(name='input', size=100)
+
+acts = [
+    TanhActivation, SigmoidActivation, SoftmaxActivation, IdentityActivation,
+    LinearActivation, ExpActivation, ReluActivation, BReluActivation,
+    SoftReluActivation, STanhActivation, AbsActivation, SquareActivation]
+
+outputs(
+    [fc_layer(input=din, size=100, act=act(), name="layer_%d" % i) for i, act in
+     enumerate(acts)])

python/paddle/trainer_config_helpers/tests/configs/projections.py

@@ -0,0 +1,47 @@
+'''
+Test mixed layer, projections and operators.
+'''
+from paddle.trainer_config_helpers import *
+
+settings(
+    batch_size=1000,
+    learning_rate=1e-4
+)
+
+din = data_layer(name='test', size=100)
+
+din = embedding_layer(input=din, size=256)
+
+with mixed_layer(size=100) as m1:
+    m1 += full_matrix_projection(input=din)
+
+with mixed_layer(size=100) as m2:
+    m2 += table_projection(input=m1)
+
+with mixed_layer(size=100) as m3:
+    m3 += identity_projection(input=m2)
+
+with mixed_layer(size=100) as m4:
+    m4 += dotmul_projection(input=m3)
+
+with mixed_layer() as m5:
+    m5 += context_projection(input=m4, context_len=3)
+
+with mixed_layer() as m6:
+    m6 += dotmul_operator(a=m3, b=m4)
+
+img = data_layer(name='img', size=32*32)
+flt = data_layer(name='filter', size=3*3*1*64)
+
+with mixed_layer() as m7:
+    m7 += conv_operator(img=img, filter=flt, num_filters=64,
+                        num_channel=1, filter_size=3)
+
+end = mixed_layer(input=[full_matrix_projection(input=m5),
+                         trans_full_matrix_projection(input=m6),
+                         full_matrix_projection(input=m7)],
+                  size=100,
+                  layer_attr=ExtraAttr(drop_rate=0.5,
+                                       error_clipping_threshold=40))
+
+outputs(end)

python/paddle/trainer_config_helpers/tests/configs/run_tests.sh

@@ -0,0 +1,5 @@
+#!/bin/bash
+cd `dirname $0`
+set -e
+./generate_protostr.sh
+md5sum -c check.md5

python/paddle/trainer_config_helpers/tests/configs/simple_rnn_layers.py

@@ -0,0 +1,36 @@
+from paddle.trainer_config_helpers import *
+
+settings(
+    batch_size=1000,
+    learning_rate=1e-4
+)
+
+din = data_layer(name='data', size=200)
+
+hidden = fc_layer(input=din, size=200, act=SigmoidActivation())
+
+rnn = recurrent_layer(input=hidden, act=SigmoidActivation())
+
+rnn2 = recurrent_layer(input=hidden, act=SigmoidActivation(), reverse=True)
+
+lstm1_param = fc_layer(input=hidden, size=200*4, act=LinearActivation(),
+                       bias_attr=False)
+
+lstm1 = lstmemory(input=lstm1_param, act=SigmoidActivation())
+
+lstm2_param = fc_layer(input=hidden, size=200*4, act=LinearActivation(),
+                       bias_attr=False)
+
+lstm2 = lstmemory(input=lstm2_param, act=SigmoidActivation(), reverse=True)
+
+gru1_param = fc_layer(input=hidden, size=200*3, act=LinearActivation(),
+                      bias_attr=False)
+gru1 = grumemory(input=gru1_param, act=SigmoidActivation())
+
+gru2_param = fc_layer(input=hidden, size=200*3, act=LinearActivation(),
+                      bias_attr=False)
+gru2 = grumemory(input=gru2_param, act=SigmoidActivation(), reverse=True)
+
+outputs(last_seq(input=rnn), first_seq(input=rnn2),
+        last_seq(input=lstm1), first_seq(input=lstm2),
+        last_seq(input=gru1), first_seq(gru2))

python/paddle/trainer_config_helpers/tests/configs/test_cost_layers.py

@@ -0,0 +1,26 @@
+from paddle.trainer_config_helpers import *
+
+settings(
+    learning_rate=1e-4,
+    batch_size=1000
+)
+
+seq_in = data_layer(name='input', size=200)
+labels = data_layer(name='labels', size=5000)
+
+probs = data_layer(name='probs', size=10)
+xe_label = data_layer(name='xe-label', size=10)
+
+outputs(ctc_layer(input=seq_in, label=labels),
+        crf_layer(input=fc_layer(input=seq_in, size=4),
+                  label=data_layer(name='crf_label', size=4)),
+        rank_cost(left=data_layer(name='left', size=1),
+                  right=data_layer(name='right', size=1),
+                  label=data_layer(name='label', size=1)),
+        lambda_cost(input=data_layer(name='list_feature', size=100),
+                    score=data_layer(name='list_scores', size=1)),
+        cross_entropy(input=probs, label=xe_label),
+        cross_entropy_with_selfnorm(input=probs, label=xe_label),
+        huber_cost(input=data_layer(name='huber_probs', size=1),
+                   label=data_layer(name='huber_label', size=1)),
+        multi_binary_label_cross_entropy(input=probs, label=xe_label))

python/paddle/trainer_config_helpers/tests/configs/test_expand_layer.py

@@ -0,0 +1,14 @@
+from paddle.trainer_config_helpers import *
+
+settings(
+    batch_size=1000,
+    learning_rate=1e-5
+)
+
+din = data_layer(name='data', size=30)
+data_seq = data_layer(name='data_seq', size=30)
+
+outputs(expand_layer(input=din, expand_as=data_seq,
+                     expand_level=ExpandLevel.FROM_SEQUENCE),
+        expand_layer(input=din, expand_as=data_seq,
+                     expand_level=ExpandLevel.FROM_TIMESTEP))

python/paddle/trainer_config_helpers/tests/configs/test_fc.py

@@ -0,0 +1,20 @@
+from paddle.trainer_config_helpers import *
+
+settings(
+    batch_size=1000,
+    learning_rate=1e-5
+)
+
+din = data_layer(name='data', size=100)
+
+trans = trans_layer(input=din)
+
+hidden = fc_layer(input=trans, size=100,
+                  bias_attr=False)
+
+mask = data_layer(name='mask', size=100)
+
+hidden_sel = selective_fc_layer(input=din, select=mask, size=100,
+                                act=SigmoidActivation())
+
+outputs(hidden, hidden_sel)

python/paddle/trainer_config_helpers/tests/configs/test_grumemory_layer.py

@@ -0,0 +1,11 @@
+from paddle.trainer_config_helpers import *
+
+settings(
+    batch_size=1000,
+    learning_rate=1e-4
+)
+
+din = data_layer(name='data', size=120)
+
+outputs(grumemory(input=din, size=40, reverse=True, gate_act=TanhActivation(),
+                  act=SigmoidActivation()))

python/paddle/trainer_config_helpers/tests/configs/test_hsigmoid.py

@@ -0,0 +1,11 @@
+from paddle.trainer_config_helpers import *
+
+settings(
+    learning_rate=1e-4,
+    batch_size=1000
+)
+
+din = data_layer(name='data', size=100)
+label = data_layer(name='label', size=10)
+
+outputs(hsigmoid(input=din, label=label, num_classes=10))

python/paddle/trainer_config_helpers/tests/configs/test_lstmemory_layer.py

@@ -0,0 +1,11 @@
+from paddle.trainer_config_helpers import *
+
+settings(
+    batch_size=1000,
+    learning_rate=1e-5
+)
+
+din = data_layer(name='data', size=128)
+
+outputs(lstmemory(input=din, reverse=True, gate_act=TanhActivation(),
+                  act=TanhActivation(), size=32))

python/paddle/trainer_config_helpers/tests/configs/test_ntm_layers.py

@@ -0,0 +1,23 @@
+from paddle.trainer_config_helpers import *
+
+settings(
+    batch_size=1000,
+    learning_rate=1e-5
+)
+
+weight = data_layer(name='w', size=1)
+a = data_layer(name='a', size=100)
+b = data_layer(name='b', size=100)
+c = data_layer(name='c', size=200)
+d = data_layer(name='d', size=31)
+
+outputs(interpolation_layer(input=[a, b], weight=weight),
+        power_layer(input=a, weight=weight),
+        scaling_layer(input=a, weight=weight),
+        cos_sim(a=a, b=b),
+        cos_sim(a=a, b=c, size=2),
+        sum_to_one_norm_layer(input=a),
+        conv_shift_layer(a=a, b=d),
+        tensor_layer(a=a, b=b, size=1000),
+        slope_intercept_layer(input=a, slope=0.7, intercept=0.9),
+        linear_comb_layer(weights=b, vectors=c))

python/paddle/trainer_config_helpers/tests/configs/test_print_layer.py

@@ -0,0 +1,12 @@
+from paddle.trainer_config_helpers import *
+
+settings(
+    learning_rate=1e-4,
+    batch_size=1000
+)
+
+din = data_layer(name='input', size=100)
+
+print_layer(input=din)
+
+outputs(din)

python/paddle/trainer_config_helpers/tests/configs/test_rnn_group.py

@@ -0,0 +1,35 @@
+from paddle.trainer_config_helpers import *
+
+settings(
+    learning_rate=1e-4,
+    batch_size=1000
+)
+
+seq = data_layer(name='seq_input', size=100)
+sub_seq = data_layer(name='sub_seq_input', size=100)
+lbl = data_layer(name='label', size=1)
+
+
+def generate_rnn_simple(name):
+    def rnn_simple(s):
+        m = memory(name=name, size=200)
+        fc = fc_layer(input=[s, m], size=200, name=name)
+        return fc
+
+    return rnn_simple
+
+
+with mixed_layer() as lstm_param:  # test lstm unit, rnn group
+    lstm_param += full_matrix_projection(input=seq, size=100 * 4)
+
+with mixed_layer() as gru_param:
+    gru_param += full_matrix_projection(input=seq, size=100 * 3)
+
+outputs(last_seq(input=recurrent_group(step=generate_rnn_simple('rnn_forward'),
+                                       input=seq)),
+        first_seq(input=recurrent_group(step=generate_rnn_simple('rnn_back'),
+                                        input=seq, reverse=True)),
+        last_seq(input=recurrent_group(step=generate_rnn_simple(
+            'rnn_subseq_forward'), input=SubsequenceInput(input=sub_seq))),
+        last_seq(input=lstmemory_group(input=lstm_param, size=100)),
+        last_seq(input=gru_group(input=gru_param, size=100)))

python/paddle/trainer_config_helpers/tests/configs/test_sequence_pooling.py

@@ -0,0 +1,30 @@
+from paddle.trainer_config_helpers import *
+
+settings(
+    learning_rate=1e-4,
+    batch_size=1000
+)
+
+din = data_layer(name='dat_in', size=100)
+
+POOL_TYPE = [
+    MaxPooling,
+    AvgPooling,
+    SumPooling
+]
+
+AGG_LEVEL = [
+    AggregateLevel.EACH_SEQUENCE,
+    AggregateLevel.EACH_TIMESTEP
+]
+
+opts = []
+
+for pt in POOL_TYPE:
+    for al in AGG_LEVEL:
+        opts.append(pooling_layer(input=din, agg_level=al, pooling_type=pt()))
+
+opts.append(pooling_layer(input=din,
+                          pooling_type=MaxPooling(output_max_index=True)))
+
+outputs(opts)