add test_layer for v2

python/paddle/v2/layer.py

@@ -74,9 +74,6 @@ from paddle.trainer_config_helpers.config_parser_utils import \
 from paddle.trainer_config_helpers.default_decorators import wrap_name_default
 
 import data_type
-import activation
-import attr
-import pooling
 
 __all__ = [
     'parse_network', 'data', 'fc', 'conv_shift', 'img_conv', 'img_pool', 'spp',
@@ -277,126 +274,3 @@ layer_list = [
 ]
 for l in layer_list:
     globals()[l[0]] = __convert_to_v2__(l[1], l[2])
-
-if __name__ == '__main__':
-    pixel = data(name='pixel', type=data_type.dense_vector(128))
-    label = data(name='label', type=data_type.integer_value(10))
-    weight = data(name='weight', type=data_type.dense_vector(10))
-    word = data(name='word', type=data_type.integer_value(12))
-    score = data(name='score', type=data_type.dense_vector(1))
-
-    hidden = fc(input=pixel,
-                size=100,
-                act=activation.Sigmoid(),
-                param_attr=attr.Param(name='hidden'))
-    inference = fc(input=hidden, size=10, act=activation.Softmax())
-    print parse_network(inference)
-
-    # test conv layers
-    conv1 = conv_shift(a=pixel, b=score)
-    conv2 = img_conv(
-        input=pixel,
-        filter_size=1,
-        filter_size_y=1,
-        num_channels=8,
-        num_filters=16,
-        act=activation.Linear())
-    print parse_network(conv1, conv2)
-
-    # test image pooling layers
-    maxpool = img_pool(
-        input=conv2,
-        pool_size=2,
-        num_channels=16,
-        padding=1,
-        pool_type=pooling.Max())
-    spp = spp(input=conv2,
-              pyramid_height=2,
-              num_channels=16,
-              pool_type=pooling.Max())
-    maxout = maxout(input=conv2, num_channels=16, groups=4)
-    print parse_network(maxpool, spp, maxout)
-
-    # test norm layers
-    norm1 = img_cmrnorm(input=maxpool, size=5)
-    norm2 = batch_norm(input=maxpool)
-    norm3 = sum_to_one_norm(input=maxpool)
-    print parse_network(norm1, norm2, norm3)
-
-    # test recurrent layers
-    recurrent = recurrent(input=word)
-    lstm = lstmemory(input=word)
-    gru = grumemory(input=word)
-    print parse_network(recurrent, lstm, gru)
-
-    # test aggregate layers
-    pool = pool(
-        input=pixel,
-        pooling_type=pooling.Avg(),
-        agg_level=AggregateLevel.EACH_SEQUENCE)
-    last_seq = last_seq(input=pixel)
-    first_seq = first_seq(input=pixel)
-    concat = concat(input=[last_seq, first_seq])
-    seq_concat = seq_concat(a=last_seq, b=first_seq)
-    print parse_network(pool, last_seq, first_seq, concat, seq_concat)
-
-    # test reshaping layers
-    block_expand = block_expand(
-        input=maxout, num_channels=4, stride_x=1, block_x=1)
-    expand = expand(
-        input=last_seq, expand_as=pixel, expand_level=ExpandLevel.FROM_TIMESTEP)
-    repeat = repeat(input=last_seq, num_repeats=4)
-    reshape = seq_reshape(input=last_seq, reshape_size=4)
-    rotate = rotate(input=pixel, height=16, width=49)
-    print parse_network(block_expand, expand, repeat, reshape, rotate)
-
-    # test math layers
-    addto = addto(input=[last_seq, first_seq])
-    linear_comb = linear_comb(weights=weight, vectors=hidden, size=10)
-    interpolation = interpolation(input=[hidden, hidden], weight=score)
-    bilinear = bilinear_interp(input=conv2, out_size_x=4, out_size_y=4)
-    power = power(input=conv1, weight=score)
-    scaling = scaling(input=conv1, weight=score)
-    slope = slope_intercept(input=conv1)
-    tensor = tensor(a=last_seq, b=first_seq, size=1000)
-    cos_sim = cos_sim(a=last_seq, b=first_seq)
-    trans = trans(input=tensor)
-    print parse_network(addto, linear_comb, interpolation, bilinear, power,
-                        scaling, slope, tensor, cos_sim, trans)
-
-    # test sampling layers
-    maxid = max_id(input=inference)
-    sampling_id = sampling_id(input=inference)
-    print parse_network(maxid, sampling_id)
-
-    # test slicing and joining layers
-    pad = pad(input=maxpool, pad_c=[2, 3], pad_h=[1, 2], pad_w=[3, 1])
-    print parse_network(pad)
-
-    # test cost layers
-    cost1 = classification_cost(input=inference, label=label)
-    cost2 = classification_cost(input=inference, label=label, weight=weight)
-    cost3 = cross_entropy_cost(input=inference, label=label)
-    cost4 = cross_entropy_with_selfnorm_cost(input=inference, label=label)
-    cost5 = regression_cost(input=inference, label=label)
-    cost6 = regression_cost(input=inference, label=label, weight=weight)
-    cost7 = multi_binary_label_cross_entropy_cost(input=inference, label=label)
-    cost8 = rank_cost(left=score, right=score, label=score)
-    cost9 = lambda_cost(input=inference, score=score)
-    cost10 = sum_cost(input=inference)
-    cost11 = huber_cost(input=score, label=label)
-    print parse_network(cost3, cost4)
-    print parse_network(cost5, cost6)
-    print parse_network(cost7, cost8, cost9, cost10, cost11)
-
-    crf = crf(input=inference, label=label)
-    crf_decoding = crf_decoding(input=inference, size=3)
-    ctc = ctc(input=inference, label=label)
-    warp_ctc = warp_ctc(input=pixel, label=label)
-    nce = nce(input=inference, label=label, num_classes=3)
-    hsigmoid = hsigmoid(input=inference, label=label, num_classes=3)
-    print parse_network(crf, crf_decoding, ctc, warp_ctc, nce, hsigmoid)
-
-    # test check layers
-    eos = eos(input=maxid, eos_id=5)
-    print parse_network(eos)

python/paddle/v2/tests/test_layer.py

@@ -19,18 +19,106 @@ import paddle.v2.activation as activation
 import paddle.v2.attr as attr
 import paddle.v2.data_type as data_type
 import paddle.v2.layer as layer
+import paddle.v2.pooling as pooling
 from paddle.trainer_config_helpers.config_parser_utils import \
     parse_network_config as parse_network
 
-pixel = layer.data(name='pixel', type=data_type.dense_vector(784))
+pixel = layer.data(name='pixel', type=data_type.dense_vector(128))
 label = layer.data(name='label', type=data_type.integer_value(10))
 weight = layer.data(name='weight', type=data_type.dense_vector(10))
 score = layer.data(name='score', type=data_type.dense_vector(1))
+
 hidden = layer.fc(input=pixel,
                   size=100,
                   act=activation.Sigmoid(),
                   param_attr=attr.Param(name='hidden'))
 inference = layer.fc(input=hidden, size=10, act=activation.Softmax())
+conv = layer.img_conv(
+    input=pixel,
+    filter_size=1,
+    filter_size_y=1,
+    num_channels=8,
+    num_filters=16,
+    act=activation.Linear())
+
+
+class ImageLayerTest(unittest.TestCase):
+    def test_conv_layer(self):
+        conv_shift = layer.conv_shift(a=pixel, b=score)
+        print layer.parse_network(conv, conv_shift)
+
+    def test_pooling_layer(self):
+        maxpool = layer.img_pool(
+            input=conv,
+            pool_size=2,
+            num_channels=16,
+            padding=1,
+            pool_type=pooling.Max())
+        spp = layer.spp(input=conv,
+                        pyramid_height=2,
+                        num_channels=16,
+                        pool_type=pooling.Max())
+        maxout = layer.maxout(input=conv, num_channels=16, groups=4)
+        print layer.parse_network(maxpool, spp, maxout)
+
+    def test_norm_layer(self):
+        norm1 = layer.img_cmrnorm(input=conv, size=5)
+        norm2 = layer.batch_norm(input=conv)
+        norm3 = layer.sum_to_one_norm(input=conv)
+        print layer.parse_network(norm1, norm2, norm3)
+
+
+class AggregateLayerTest(unittest.TestCase):
+    def test_aggregate_layer(self):
+        pool = layer.pool(
+            input=pixel,
+            pooling_type=pooling.Avg(),
+            agg_level=layer.AggregateLevel.EACH_SEQUENCE)
+        last_seq = layer.last_seq(input=pixel)
+        first_seq = layer.first_seq(input=pixel)
+        concat = layer.concat(input=[last_seq, first_seq])
+        seq_concat = layer.seq_concat(a=last_seq, b=first_seq)
+        print layer.parse_network(pool, last_seq, first_seq, concat, seq_concat)
+
+
+class MathLayerTest(unittest.TestCase):
+    def test_math_layer(self):
+        addto = layer.addto(input=[pixel, pixel])
+        linear_comb = layer.linear_comb(weights=weight, vectors=hidden, size=10)
+        interpolation = layer.interpolation(
+            input=[hidden, hidden], weight=score)
+        bilinear = layer.bilinear_interp(input=conv, out_size_x=4, out_size_y=4)
+        power = layer.power(input=pixel, weight=score)
+        scaling = layer.scaling(input=pixel, weight=score)
+        slope = layer.slope_intercept(input=pixel)
+        tensor = layer.tensor(a=pixel, b=pixel, size=1000)
+        cos_sim = layer.cos_sim(a=pixel, b=pixel)
+        trans = layer.trans(input=tensor)
+        print layer.parse_network(addto, linear_comb, interpolation, power,
+                                  scaling, slope, tensor, cos_sim, trans)
+
+
+class ReshapeLayerTest(unittest.TestCase):
+    def test_reshape_layer(self):
+        block_expand = layer.block_expand(
+            input=conv, num_channels=4, stride_x=1, block_x=1)
+        expand = layer.expand(
+            input=weight,
+            expand_as=pixel,
+            expand_level=layer.ExpandLevel.FROM_TIMESTEP)
+        repeat = layer.repeat(input=pixel, num_repeats=4)
+        reshape = layer.seq_reshape(input=pixel, reshape_size=4)
+        rotate = layer.rotate(input=pixel, height=16, width=49)
+        print layer.parse_network(block_expand, expand, repeat, reshape, rotate)
+
+
+class RecurrentLayerTest(unittest.TestCase):
+    def test_recurrent_layer(self):
+        word = layer.data(name='word', type=data_type.integer_value(12))
+        recurrent = layer.recurrent(input=word)
+        lstm = layer.lstmemory(input=word)
+        gru = layer.grumemory(input=word)
+        print layer.parse_network(recurrent, lstm, gru)
 
 
 class CostLayerTest(unittest.TestCase):
@@ -51,12 +139,32 @@ class CostLayerTest(unittest.TestCase):
         cost10 = layer.sum_cost(input=inference)
         cost11 = layer.huber_cost(input=score, label=label)
 
-        print dir(layer)
-        layer.parse_network(cost1, cost2)
-        print dir(layer)
-        #print layer.parse_network(cost3, cost4)
-        #print layer.parse_network(cost5, cost6)
-        #print layer.parse_network(cost7, cost8, cost9, cost10, cost11)
+        print layer.parse_network(cost1, cost2)
+        print layer.parse_network(cost3, cost4)
+        print layer.parse_network(cost5, cost6)
+        print layer.parse_network(cost7, cost8, cost9, cost10, cost11)
+
+        crf = layer.crf(input=inference, label=label)
+        crf_decoding = layer.crf_decoding(input=inference, size=3)
+        ctc = layer.ctc(input=inference, label=label)
+        warp_ctc = layer.warp_ctc(input=pixel, label=label)
+        nce = layer.nce(input=inference, label=label, num_classes=3)
+        hsigmoid = layer.hsigmoid(input=inference, label=label, num_classes=3)
+
+        print layer.parse_network(crf, crf_decoding, ctc, warp_ctc, nce,
+                                  hsigmoid)
+
+
+class OtherLayerTest(unittest.TestCase):
+    def test_sampling_layer(self):
+        maxid = layer.max_id(input=inference)
+        sampling_id = layer.sampling_id(input=inference)
+        eos = layer.eos(input=maxid, eos_id=5)
+        print layer.parse_network(maxid, sampling_id, eos)
+
+    def test_slicing_joining_layer(self):
+        pad = layer.pad(input=conv, pad_c=[2, 3], pad_h=[1, 2], pad_w=[3, 1])
+        print layer.parse_network(pad)
 
 
 if __name__ == '__main__':