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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2019/07/29 08:07:01 UTC
[GitHub] [incubator-mxnet] hdmjdp opened a new issue #15685: about
hybrid_forward
hdmjdp opened a new issue #15685: about hybrid_forward
URL: https://github.com/apache/incubator-mxnet/issues/15685
class ...(nn.HybridBlock):
with self.name_scope(): # 3*3 5*5 7*7
self.weight_dw3 = self.params.get('weight_dw3',
shape=(self.num_input // 3, 1, 3),
init=mx.init.Xavier(),
dtype=self.dtype,
allow_deferred_init=True)
self.weight_dw5 = self.params.get('weight_dw5',
shape=(self.num_input // 3, 1, 5),
init=mx.init.Xavier(),
dtype=self.dtype,
allow_deferred_init=True),
self.weight_dw7 = self.params.get('weight_dw7',
shape=(self.num_input // 3, 1, 7),
init=mx.init.Xavier(),
dtype=self.dtype,
allow_deferred_init=True)
self.bias_dw3 = self.params.get('bias_dw3',
shape=(self.num_input // 3,),
init=mx.init.Zero(),
dtype=self.dtype,
allow_deferred_init=True),
self.bias_dw5 = self.params.get('bias_dw5',
shape=(self.num_input // 3,),
init=mx.init.Zero(),
dtype=self.dtype,
allow_deferred_init=True),
self.bias_dw7 = self.params.get('bias_dw7',
shape=(self.num_input // 3,),
init=mx.init.Zero(),
dtype=self.dtype,
allow_deferred_init=True)
self.g_dw3 = self.params.get('gain_dw3',
shape=(self.num_input // 3, 1, 1),
init=mx.init.Constant(
mx.init.random.uniform(1, np.sqrt(5), shape=(self.num_input // 3, 1, 1))),
dtype=self.dtype,
allow_deferred_init=True),
self.g_dw5 = self.params.get('gain_dw5',
shape=(self.num_input // 3, 1, 1),
init=mx.init.Constant(
mx.init.random.uniform(1, np.sqrt(5), shape=(self.num_input // 3, 1, 1))),
dtype=self.dtype,
allow_deferred_init=True),
self.g_dw7 = self.params.get('gain_dw7',
shape=(self.num_input // 3, 1, 1),
init=mx.init.Constant(
mx.init.random.uniform(1, np.sqrt(5), shape=(self.num_input // 3, 1, 1))),
dtype=self.dtype,
allow_deferred_init=True)
self.weight_pw = self.params.get('weight_pw',
shape=(self.num_hidden, self.num_input, 1),
init=mx.init.Xavier(),
dtype=self.dtype,
allow_deferred_init=True)
self.bias_pw = self.params.get('bias_pw',
shape=(self.num_hidden,),
init=mx.init.Zero(),
dtype=self.dtype,
allow_deferred_init=True)
self.g_pw = self.params.get('gain_pw',
shape=(self.num_hidden, 1, 1),
init=mx.init.Constant(
mx.init.random.uniform(1, np.sqrt(5), shape=(self.num_hidden, 1, 1))),
dtype=self.dtype,
allow_deferred_init=True)
self.dropout = nn.Dropout(rate=dropout_rate)
def hybrid_forward(self, F, inputs, weight_dw3, weight_dw5, weight_dw7, bias_dw3, bias_dw5, bias_dw7, g_dw3, g_dw5, g_dw7, weight_pw, bias_pw, g_pw, *args, **kwargs):
'''
Args:
inputs: A 3-D tensor with shape of [batch, depth, time].
Returns:
A tensor of the same shape and dtypes as `inputs`.
'''
when I call it : self.conv_blocks_textenc[j](tensor), it will give this error:
wld, vuv_logits = Vec2Cmp(vec)
File "/home/hdm/.local/lib/python3.5/site-packages/mxnet/gluon/block.py", line 541, in __call__
out = self.forward(*args)
File "/home/hdm/.local/lib/python3.5/site-packages/mxnet/gluon/block.py", line 918, in forward
return self.hybrid_forward(ndarray, x, *args, **params)
File "/home/hdm/Documents/dctts/vec2cmp-mx-float/networks_3.py", line 94, in hybrid_forward
tensor = self.conv_blocks_textenc[j](tensor)
File "/home/hdm/.local/lib/python3.5/site-packages/mxnet/gluon/block.py", line 541, in __call__
out = self.forward(*args)
File "/home/hdm/.local/lib/python3.5/site-packages/mxnet/gluon/block.py", line 918, in forward
return self.hybrid_forward(ndarray, x, *args, **params)
TypeError: hybrid_forward() missing 5 required positional arguments: 'weight_dw5', 'bias_dw3', 'bias_dw5', 'g_dw3', and 'g_dw5'
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