mmr/tkDNN
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works but it need cleaning

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This commit is contained in:
Francesco Gatti
2020-02-16 16:28:39 +01:00
parent 8736a8c3da
commit 0ac292ea48
9 changed files with 219 additions and 71 deletions
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tests/weights_exporter.py
+37 -7
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@@ -42,17 +42,47 @@ def export_layer(name, weights, bias):
bin_write(f, bias)
print ("WEIGHTS saved\n")
def export_bidir(name, params):
def export_bidir(name, params, paramsb):
print ("######## EXPORT", name, "LAYER ########")
f = open(name + ".bin", mode='wb')
print("FORWARD")
ker = params[0]
rec_ker = params[1]
bias = params[2]
print ("export kernels: ", np.shape(ker))
units = np.shape(ker)[1] // 4
bin_write(f, ker[:,:units])
bin_write(f, ker[:,units:units*2])
bin_write(f, ker[:,units*2:units*3])
bin_write(f, ker[:,units*3:])
print ("export recurrent kernels: ", np.shape(rec_ker))
bin_write(f, rec_ker[:,:units])
bin_write(f, rec_ker[:,units:units*2])
bin_write(f, rec_ker[:,units*2:units*3])
bin_write(f, rec_ker[:,units*3:])
print ("export kernels: ", np.shape(ker))
bin_write(f, bias)
print("WEIGHTS saved\n")
for w in params:
#w = w.transpose()
print(np.shape(w))
bin_write(f, w)
print("BACKWARD")
ker = paramsb[0]
rec_ker = paramsb[1]
bias = paramsb[2]
print ("export kernels: ", np.shape(ker))
units = np.shape(ker)[1] // 4
bin_write(f, ker[:,:units])
bin_write(f, ker[:,units:units*2])
bin_write(f, ker[:,units*2:units*3])
bin_write(f, ker[:,units*3:])
print ("export recurrent kernels: ", np.shape(rec_ker))
bin_write(f, rec_ker[:,:units])
bin_write(f, rec_ker[:,units:units*2])
bin_write(f, rec_ker[:,units*2:units*3])
bin_write(f, rec_ker[:,units*3:])
print ("export kernels: ", np.shape(ker))
bin_write(f, bias)
print("WEIGHTS saved\n")
#https://github.com/fchollet/keras/wiki/Converting-convolution-kernels-from-Theano-to-TensorFlow-and-vice-versa
@@ -100,7 +130,7 @@ if __name__ == '__main__':
export_layer(args.output + "/" + name, wgs[0], wgs[1])
elif name.startswith("bidirectional"):
wgs = l.forward_layer.get_weights()
export_bidir(args.output + "/" + name, wgs)
export_bidir(args.output + "/" + name, l.forward_layer.get_weights(), l.backward_layer.get_weights())
else:
print ("skip:", name, "has no weights")
continue