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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/11/07 18:22:41 UTC
[GitHub] szha closed pull request #13158: fixed symbols naming in RNNCell,
LSTMCell, GRUCell (v1.3.x)
szha closed pull request #13158: fixed symbols naming in RNNCell, LSTMCell, GRUCell (v1.3.x)
URL: https://github.com/apache/incubator-mxnet/pull/13158
This is a PR merged from a forked repository.
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diff --git a/python/mxnet/gluon/rnn/rnn_cell.py b/python/mxnet/gluon/rnn/rnn_cell.py
index 21cc8043154..f4303eab1a2 100644
--- a/python/mxnet/gluon/rnn/rnn_cell.py
+++ b/python/mxnet/gluon/rnn/rnn_cell.py
@@ -400,7 +400,8 @@ def hybrid_forward(self, F, inputs, states, i2h_weight,
h2h = F.FullyConnected(data=states[0], weight=h2h_weight, bias=h2h_bias,
num_hidden=self._hidden_size,
name=prefix+'h2h')
- output = self._get_activation(F, i2h + h2h, self._activation,
+ i2h_plus_h2h = F.elemwise_add(i2h, h2h, name=prefix+'plus0')
+ output = self._get_activation(F, i2h_plus_h2h, self._activation,
name=prefix+'out')
return output, [output]
@@ -513,7 +514,7 @@ def hybrid_forward(self, F, inputs, states, i2h_weight,
num_hidden=self._hidden_size*4, name=prefix+'i2h')
h2h = F.FullyConnected(data=states[0], weight=h2h_weight, bias=h2h_bias,
num_hidden=self._hidden_size*4, name=prefix+'h2h')
- gates = i2h + h2h
+ gates = F.elemwise_add(i2h, h2h, name=prefix+'plus0')
slice_gates = F.SliceChannel(gates, num_outputs=4, name=prefix+'slice')
in_gate = self._get_activation(
F, slice_gates[0], self._recurrent_activation, name=prefix+'i')
@@ -523,9 +524,10 @@ def hybrid_forward(self, F, inputs, states, i2h_weight,
F, slice_gates[2], self._activation, name=prefix+'c')
out_gate = self._get_activation(
F, slice_gates[3], self._recurrent_activation, name=prefix+'o')
- next_c = F._internal._plus(forget_gate * states[1], in_gate * in_transform,
+ next_c = F._internal._plus(F.elemwise_mul(forget_gate, states[1], name=prefix+'mul0'),
+ F.elemwise_mul(in_gate, in_transform, name=prefix+'mul1'),
name=prefix+'state')
- next_h = F._internal._mul(out_gate, F.Activation(next_c, act_type=self._activation),
+ next_h = F._internal._mul(out_gate, F.Activation(next_c, act_type=self._activation, name=prefix+'activation0'),
name=prefix+'out')
return next_h, [next_h, next_c]
@@ -637,15 +639,22 @@ def hybrid_forward(self, F, inputs, states, i2h_weight,
h2h_r, h2h_z, h2h = F.SliceChannel(h2h, num_outputs=3,
name=prefix+'h2h_slice')
- reset_gate = F.Activation(i2h_r + h2h_r, act_type="sigmoid",
+ reset_gate = F.Activation(F.elemwise_add(i2h_r, h2h_r, name=prefix+'plus0'), act_type="sigmoid",
name=prefix+'r_act')
- update_gate = F.Activation(i2h_z + h2h_z, act_type="sigmoid",
+ update_gate = F.Activation(F.elemwise_add(i2h_z, h2h_z, name=prefix+'plus1'), act_type="sigmoid",
name=prefix+'z_act')
- next_h_tmp = F.Activation(i2h + reset_gate * h2h, act_type="tanh",
+ next_h_tmp = F.Activation(F.elemwise_add(i2h,
+ F.elemwise_mul(reset_gate, h2h, name=prefix+'mul0'),
+ name=prefix+'plus2'),
+ act_type="tanh",
name=prefix+'h_act')
- next_h = F._internal._plus((1. - update_gate) * next_h_tmp, update_gate * prev_state_h,
+ ones = F.ones_like(update_gate, name=prefix+"ones_like0")
+ next_h = F._internal._plus(F.elemwise_mul(F.elemwise_sub(ones, update_gate, name=prefix+'minus0'),
+ next_h_tmp,
+ name=prefix+'mul1'),
+ F.elemwise_mul(update_gate, prev_state_h, name=prefix+'mul20'),
name=prefix+'out')
return next_h, [next_h]
diff --git a/tests/python/unittest/test_gluon_rnn.py b/tests/python/unittest/test_gluon_rnn.py
index 4e8241ffc1e..c1d5f6a590f 100644
--- a/tests/python/unittest/test_gluon_rnn.py
+++ b/tests/python/unittest/test_gluon_rnn.py
@@ -379,6 +379,54 @@ def test_rnn_cells():
net.add(gluon.rnn.GRUCell(100, input_size=100))
check_rnn_forward(net, mx.nd.ones((8, 3, 200)))
+
+def test_rnn_cells_export_import():
+ class RNNLayer(gluon.HybridBlock):
+ def __init__(self):
+ super(RNNLayer, self).__init__()
+ with self.name_scope():
+ self.cell = gluon.rnn.RNNCell(hidden_size=1)
+
+ def hybrid_forward(self, F, seq):
+ outputs, state = self.cell.unroll(inputs=seq, length=2, merge_outputs=True)
+ return outputs
+
+ class LSTMLayer(gluon.HybridBlock):
+ def __init__(self):
+ super(LSTMLayer, self).__init__()
+ with self.name_scope():
+ self.cell = gluon.rnn.LSTMCell(hidden_size=1)
+
+ def hybrid_forward(self, F, seq):
+ outputs, state = self.cell.unroll(inputs=seq, length=2, merge_outputs=True)
+ return outputs
+
+ class GRULayer(gluon.HybridBlock):
+ def __init__(self):
+ super(GRULayer, self).__init__()
+ with self.name_scope():
+ self.cell = gluon.rnn.GRUCell(hidden_size=1)
+
+ def hybrid_forward(self, F, seq):
+ outputs, state = self.cell.unroll(inputs=seq, length=2, merge_outputs=True)
+ return outputs
+
+ for hybrid in [RNNLayer(), LSTMLayer(), GRULayer()]:
+ hybrid.initialize()
+ hybrid.hybridize()
+ input = mx.nd.ones(shape=(1, 2, 1))
+ output1 = hybrid(input)
+ hybrid.export(path="./model", epoch=0)
+ symbol = mx.gluon.SymbolBlock.imports(
+ symbol_file="./model-symbol.json",
+ input_names=["data"],
+ param_file="./model-0000.params",
+ ctx=mx.Context.default_ctx
+ )
+ output2 = symbol(input)
+ assert_almost_equal(output1.asnumpy(), output2.asnumpy())
+
+
def check_rnn_layer_forward(layer, inputs, states=None, run_only=False):
layer.collect_params().initialize()
inputs.attach_grad()
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