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crnn.py
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crnn.py
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
"""
LeCun, Yann, Leon Bottou, Yoshua Bengio, and Patrick Haffner.
Gradient-based learning applied to document recognition.
Proceedings of the IEEE (1998)
"""
import mxnet as mx
from fit.ctc_loss import add_ctc_loss
from fit.lstm import lstm
def crnn_no_lstm(hp):
# input
data = mx.sym.Variable('data')
label = mx.sym.Variable('label')
kernel_size = [(3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3)]
padding_size = [(1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1)]
layer_size = [min(32*2**(i+1), 512) for i in range(len(kernel_size))]
def convRelu(i, input_data, bn=True):
layer = mx.symbol.Convolution(name='conv-%d' % i, data=input_data, kernel=kernel_size[i], pad=padding_size[i],
num_filter=layer_size[i])
if bn:
layer = mx.sym.BatchNorm(data=layer, name='batchnorm-%d' % i)
layer = mx.sym.LeakyReLU(data=layer,name='leakyrelu-%d' % i)
return layer
net = convRelu(0, data) # bz x f x 32 x 200
max = mx.sym.Pooling(data=net, name='pool-0_m', pool_type='max', kernel=(2, 2), stride=(2, 2))
avg = mx.sym.Pooling(data=net, name='pool-0_a', pool_type='avg', kernel=(2, 2), stride=(2, 2))
net = max - avg # 16 x 100
net = convRelu(1, net)
net = mx.sym.Pooling(data=net, name='pool-1', pool_type='max', kernel=(2, 2), stride=(2, 2)) # bz x f x 8 x 50
net = convRelu(2, net, True)
net = convRelu(3, net)
net = mx.sym.Pooling(data=net, name='pool-2', pool_type='max', kernel=(2, 2), stride=(2, 2)) # bz x f x 4 x 25
net = convRelu(4, net, True)
net = convRelu(5, net)
net = mx.symbol.Pooling(data=net, kernel=(4, 1), pool_type='avg', name='pool1') # bz x f x 1 x 25
if hp.dropout > 0:
net = mx.symbol.Dropout(data=net, p=hp.dropout)
net = mx.sym.transpose(data=net, axes=[1,0,2,3]) # f x bz x 1 x 25
net = mx.sym.flatten(data=net) # f x (bz x 25)
hidden_concat = mx.sym.transpose(data=net, axes=[1,0]) # (bz x 25) x f
# mx.sym.transpose(net, [])
pred = mx.sym.FullyConnected(data=hidden_concat, num_hidden=hp.num_classes) # (bz x 25) x num_classes
if hp.loss_type:
# Training mode, add loss
return add_ctc_loss(pred, hp.seq_length, hp.num_label, hp.loss_type)
else:
# Inference mode, add softmax
return mx.sym.softmax(data=pred, name='softmax')
def crnn_lstm(hp):
# input
data = mx.sym.Variable('data')
label = mx.sym.Variable('label')
kernel_size = [(3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3)]
padding_size = [(1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1)]
layer_size = [min(32*2**(i+1), 512) for i in range(len(kernel_size))]
def convRelu(i, input_data, bn=True):
layer = mx.symbol.Convolution(name='conv-%d' % i, data=input_data, kernel=kernel_size[i], pad=padding_size[i],
num_filter=layer_size[i])
if bn:
layer = mx.sym.BatchNorm(data=layer, name='batchnorm-%d' % i)
layer = mx.sym.LeakyReLU(data=layer,name='leakyrelu-%d' % i)
layer = mx.symbol.Convolution(name='conv-%d-1x1' % i, data=layer, kernel=(1, 1), pad=(0, 0),
num_filter=layer_size[i])
if bn:
layer = mx.sym.BatchNorm(data=layer, name='batchnorm-%d-1x1' % i)
layer = mx.sym.LeakyReLU(data=layer, name='leakyrelu-%d-1x1' % i)
return layer
net = convRelu(0, data) # bz x f x 32 x 200
max = mx.sym.Pooling(data=net, name='pool-0_m', pool_type='max', kernel=(2, 2), stride=(2, 2))
avg = mx.sym.Pooling(data=net, name='pool-0_a', pool_type='avg', kernel=(2, 2), stride=(2, 2))
net = max - avg # 16 x 100
net = convRelu(1, net)
net = mx.sym.Pooling(data=net, name='pool-1', pool_type='max', kernel=(2, 2), stride=(2, 2)) # bz x f x 8 x 50
net = convRelu(2, net, True)
net = convRelu(3, net)
net = mx.sym.Pooling(data=net, name='pool-2', pool_type='max', kernel=(2, 2), stride=(2, 2)) # bz x f x 4 x 25
net = convRelu(4, net, True)
net = convRelu(5, net)
net = mx.symbol.Pooling(data=net, kernel=(4, 1), pool_type='avg', name='pool1') # bz x f x 1 x 25
if hp.dropout > 0:
net = mx.symbol.Dropout(data=net, p=hp.dropout)
hidden_concat = lstm(net,num_lstm_layer=hp.num_lstm_layer, num_hidden=hp.num_hidden, seq_length=hp.seq_length)
# mx.sym.transpose(net, [])
pred = mx.sym.FullyConnected(data=hidden_concat, num_hidden=hp.num_classes) # (bz x 25) x num_classes
if hp.loss_type:
# Training mode, add loss
return add_ctc_loss(pred, hp.seq_length, hp.num_label, hp.loss_type)
else:
# Inference mode, add softmax
return mx.sym.softmax(data=pred, name='softmax')
from hyperparams.hyperparams import Hyperparams
if __name__ == '__main__':
hp = Hyperparams()
init_states = {}
init_states['data'] = (hp.batch_size, 1, hp.img_height, hp.img_width)
init_states['label'] = (hp.batch_size, hp.num_label)
# init_c = {('l%d_init_c' % l): (hp.batch_size, hp.num_hidden) for l in range(hp.num_lstm_layer*2)}
# init_h = {('l%d_init_h' % l): (hp.batch_size, hp.num_hidden) for l in range(hp.num_lstm_layer*2)}
#
# for item in init_c:
# init_states[item] = init_c[item]
# for item in init_h:
# init_states[item] = init_h[item]
symbol = crnn_no_lstm(hp)
interals = symbol.get_internals()
_, out_shapes, _ = interals.infer_shape(**init_states)
shape_dict = dict(zip(interals.list_outputs(), out_shapes))
for item in shape_dict:
print(item,shape_dict[item])