Baidu-warpctc is a CTC implement by Baidu which support GPU. CTC can be used with LSTM to solve lable alignment problems in many areas such as OCR, speech recognition.
cd ~/ git clone https://github.com/baidu-research/warp-ctc cd warp-ctc mkdir build cd build cmake .. make sudo make install
comment out following lines in make/config.mk WARPCTC_PATH = $(HOME)/warp-ctc MXNET_PLUGINS += plugin/warpctc/warpctc.mk rebuild mxnet by make clean && make -j4
I implement two examples, one is just a toy example which can be used to prove ctc integration is right. The second is a OCR example with LSTM+CTC. You can run it by:
cd examples/warpctc python lstm_ocr.py
contexts = [mx.context.gpu(0)]in this file according to your hardware.
'./font/Ubuntu-M.ttf'. Copy your font to here font/yourfont.ttf. To get a free font from here.
The OCR example is constructed as follows:
Following code show detail construction of the net:
def lstm_unroll(num_lstm_layer, seq_len, num_hidden, num_label): param_cells =  last_states =  for i in range(num_lstm_layer): param_cells.append(LSTMParam(i2h_weight=mx.sym.Variable("l%d_i2h_weight" % i), i2h_bias=mx.sym.Variable("l%d_i2h_bias" % i), h2h_weight=mx.sym.Variable("l%d_h2h_weight" % i), h2h_bias=mx.sym.Variable("l%d_h2h_bias" % i))) state = LSTMState(c=mx.sym.Variable("l%d_init_c" % i), h=mx.sym.Variable("l%d_init_h" % i)) last_states.append(state) assert(len(last_states) == num_lstm_layer) data = mx.sym.Variable('data') label = mx.sym.Variable('label') #every column of image is an input, there are seq_len inputs wordvec = mx.sym.SliceChannel(data=data, num_outputs=seq_len, squeeze_axis=1) hidden_all =  for seqidx in range(seq_len): hidden = wordvec[seqidx] for i in range(num_lstm_layer): next_state = lstm(num_hidden, indata=hidden, prev_state=last_states[i], param=param_cells[i], seqidx=seqidx, layeridx=i) hidden = next_state.h last_states[i] = next_state hidden_all.append(hidden) hidden_concat = mx.sym.Concat(*hidden_all, dim=0) pred = mx.sym.FullyConnected(data=hidden_concat, num_hidden=11) # here we do NOT need to transpose label as other lstm examples do label = mx.sym.Reshape(data=label, target_shape=(0,)) #label should be int type, so use cast label = mx.sym.Cast(data = label, dtype = 'int32') sm = mx.sym.WarpCTC(data=pred, label=label, label_length = num_label, input_length = seq_len) return sm
If you label length is smaller than or equal to b. You should provide labels with length b, and for those samples which label length is smaller than b, you should append 0 to label data to make it have length b.
Here, 0 is reserved for blank label.
makein amalgamation folder.(a libmxnet_predict.so will be created in lib folder.)