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sym.py
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import os,sys
curr_path = os.path.abspath(os.path.dirname(__file__))
sys.path.append(os.path.join(curr_path, "../mxnet/python"))
import mxnet as mx
import numpy as np
class Debug(mx.operator.CustomOp):
def forward(self, is_train, req, in_data, out_data, aux):
f = open('s.txt','w')
value = in_data[0].asnumpy()
for i in range(value.size):
f.write('%1.6f\n'%(value.flat[i]))
f.close()
#x = in_data[0].asnumpy()
#for i in range(144):
# print(x[i])
self.assign(out_data[0],req[0], in_data[0])
def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
self.assign(in_grad[0],req[0], out_grad[0])
@mx.operator.register('debug')
class DebugProp(mx.operator.CustomOpProp):
def __init__(self):
super(DebugProp, self).__init__(need_top_grad=True)
def list_arguments(self):
return ['data']
def list_outputs(self):
return ['output']
def infer_shape(self, in_shape):
print in_shape[0]
return [in_shape[0]],[in_shape[0]],[]
def create_operator(self,ctx, shapes, dtypes):
return Debug()
def fc_module(data, prefix, num_hidden=256):
with mx.name.Prefix(prefix):
fc1 = mx.sym.FullyConnected(data=data, num_hidden=num_hidden, name='fc1')
relu_fc1 = mx.sym.Activation(data=fc1, act_type='relu', name='relu_fc1')
return relu_fc1
def sym_gen_char(bucket_key):
num_layers = 1
num_class = 2000
num_hidden = 512
key = bucket_key.split(',')
tc_length = int(key[0])
cc_length = int(key[1])
tc_data = mx.sym.Variable('tc_array')
cc_data = mx.sym.Variable('cc_array')
label = mx.sym.Variable('label')
tc_cell = mx.rnn.FusedRNNCell(num_hidden, num_layers=num_layers, bidirectional=True, mode='lstm', prefix ='tc_')
cc_cell = mx.rnn.FusedRNNCell(num_hidden, num_layers=num_layers, bidirectional=True, mode='lstm', prefix ='cc_')
tc_slices = list(mx.symbol.SliceChannel(data=tc_data, axis=1, num_outputs=tc_length, squeeze_axis=True, name='tc_slice'))
cc_slices = list(mx.symbol.SliceChannel(data=cc_data, axis=1, num_outputs=cc_length, squeeze_axis=True, name='cc_slice'))
tc_concat, _ = tc_cell.unroll(tc_length, inputs = tc_slices, merge_outputs=True, layout='TNC')
cc_concat, _ = cc_cell.unroll(cc_length, inputs = cc_slices, merge_outputs=True, layout='TNC')
tc_concat = mx.sym.transpose(tc_concat, (1, 2, 0))
cc_concat = mx.sym.transpose(cc_concat, (1, 2, 0))
tc_concat = mx.sym.Pooling(tc_concat, kernel=(1,), global_pool = True, pool_type='max')
cc_concat = mx.sym.Pooling(cc_concat, kernel=(1,), global_pool = True, pool_type='max')
feature = mx.sym.Concat(*[tc_concat, cc_concat], name= 'concat')
feature = mx.sym.Dropout(feature, p=0.5)
feature = fc_module(feature, 'feature', num_hidden=2000)
loss = mx.sym.LogisticRegressionOutput(feature, label=label, name='regression')
return loss
def sym_gen_word(bucket_key):
num_layers = 1
num_class = 2000
num_hidden = 512
key = bucket_key.split(',')
tw_length = int(key[0])
cw_length = int(key[1])
tw_data = mx.sym.Variable('tw_array')
cw_data = mx.sym.Variable('cw_array')
label = mx.sym.Variable('label')
tw_cell = mx.rnn.FusedRNNCell(num_hidden, num_layers=num_layers, bidirectional=True, mode='lstm', prefix ='tw_')
cw_cell = mx.rnn.FusedRNNCell(num_hidden, num_layers=num_layers, bidirectional=True, mode='lstm', prefix ='cw_')
tw_slices = list(mx.symbol.SliceChannel(data=tw_data, axis=1, num_outputs=tw_length, squeeze_axis=True, name='tw_slice'))
cw_slices = list(mx.symbol.SliceChannel(data=cw_data, axis=1, num_outputs=cw_length, squeeze_axis=True, name='cw_slice'))
tw_concat, _ = tw_cell.unroll(tw_length, inputs = tw_slices, merge_outputs=True, layout='TNC')
cw_concat, _ = cw_cell.unroll(cw_length, inputs = cw_slices, merge_outputs=True, layout='TNC')
tw_concat = mx.sym.transpose(tw_concat, (1, 2, 0))
cw_concat = mx.sym.transpose(cw_concat, (1, 2, 0))
tw_concat = mx.sym.Pooling(tw_concat, kernel=(1,), global_pool = True, pool_type='max')
cw_concat = mx.sym.Pooling(cw_concat, kernel=(1,), global_pool = True, pool_type='max')
feature = mx.sym.Concat(*[tw_concat, cw_concat], name= 'concat')
feature = mx.sym.Dropout(feature, p=0.5)
feature = fc_module(feature, 'feature', num_hidden=2000)
loss = mx.sym.LogisticRegressionOutput(feature, label=label, name='regression')
data_name = ['tw_array', 'cw_array']
label_name = ['label']
return loss, data_name, label_name
def sym_gen_both(bucket_key):
num_layers = 1
num_class = 2000
num_hidden = 512
key = bucket_key.split(',')
tc_length = int(key[0])
cc_length = int(key[1])
tw_length = int(key[2])
cw_length = int(key[3])
tc_data = mx.sym.Variable('tc_array')
cc_data = mx.sym.Variable('cc_array')
tw_data = mx.sym.Variable('tw_array')
cw_data = mx.sym.Variable('cw_array')
label = mx.sym.Variable('label')
tc_cell = mx.rnn.FusedRNNCell(num_hidden, num_layers=num_layers, bidirectional=True, mode='lstm', prefix ='tc_')
cc_cell = mx.rnn.FusedRNNCell(num_hidden, num_layers=num_layers, bidirectional=True, mode='lstm', prefix ='cc_')
tw_cell = mx.rnn.FusedRNNCell(num_hidden, num_layers=num_layers, bidirectional=True, mode='lstm', prefix ='tw_')
cw_cell = mx.rnn.FusedRNNCell(num_hidden, num_layers=num_layers, bidirectional=True, mode='lstm', prefix ='cw_')
tc_slices = list(mx.symbol.SliceChannel(data=tc_data, axis=1, num_outputs=tc_length, squeeze_axis=True, name='tc_slice'))
cc_slices = list(mx.symbol.SliceChannel(data=cc_data, axis=1, num_outputs=cc_length, squeeze_axis=True, name='cc_slice'))
tw_slices = list(mx.symbol.SliceChannel(data=tw_data, axis=1, num_outputs=tw_length, squeeze_axis=True, name='tw_slice'))
cw_slices = list(mx.symbol.SliceChannel(data=cw_data, axis=1, num_outputs=cw_length, squeeze_axis=True, name='cw_slice'))
tc_concat, _ = tc_cell.unroll(tc_length, inputs = tc_slices, merge_outputs=True, layout='TNC')
cc_concat, _ = cc_cell.unroll(cc_length, inputs = cc_slices, merge_outputs=True, layout='TNC')
tw_concat, _ = tw_cell.unroll(tw_length, inputs = tw_slices, merge_outputs=True, layout='TNC')
cw_concat, _ = cw_cell.unroll(cw_length, inputs = cw_slices, merge_outputs=True, layout='TNC')
tc_concat = mx.sym.transpose(tc_concat, (1, 2, 0))
cc_concat = mx.sym.transpose(cc_concat, (1, 2, 0))
tw_concat = mx.sym.transpose(tw_concat, (1, 2, 0))
cw_concat = mx.sym.transpose(cw_concat, (1, 2, 0))
tc_concat = mx.sym.Pooling(tc_concat, kernel=(1,), global_pool = True, pool_type='max')
cc_concat = mx.sym.Pooling(cc_concat, kernel=(1,), global_pool = True, pool_type='max')
tw_concat = mx.sym.Pooling(tw_concat, kernel=(1,), global_pool = True, pool_type='max')
cw_concat = mx.sym.Pooling(cw_concat, kernel=(1,), global_pool = True, pool_type='max')
feature = mx.sym.Concat(*[tc_concat, cc_concat, tw_concat, cw_concat], name= 'concat')
feature = mx.sym.Dropout(feature, p=0.5)
feature = fc_module(feature, 'feature', num_hidden=2000)
loss = mx.sym.LogisticRegressionOutput(feature, label=label, name='regression')
return loss
if __name__ == '__main__':
sym = sym_gen_both('100,33,11,21')
batch_size = 32
dim = 256
length = 100
shapes = sym.infer_shape_partial(tc_array=(batch_size,100,dim),
cc_array=(batch_size,33,dim),
tw_array=(batch_size,11,dim),
cw_array=(batch_size,21,dim),
label=(batch_size,2000))
names = sym.list_arguments()
for name, shape in zip(names, shapes[0]):
print name, shape