[scripts] Add example of accessing nnet3 model from python

egs/mini_librispeech/s5/local/chain/diagnostic/report_example.py

@@ -0,0 +1,80 @@
+#!/usr/bin/env python3
+
+# I ran from the shell:
+# . ./path.sh
+# steps/nnet3/report/convert_model.py exp/chain/tdnn1g_sp/24.mdl{,.pkl}
+# steps/nnet3/report/convert_model.py exp/chain/tdnn1g_sp/25.mdl{,.pkl}
+# .. and then this script:
+# local/chain/diagnostic/report_example.py
+
+# Note: I make no claim that the information in the generated report is
+# understandable in general; it's just something I was plotting for
+# my own information.  The point of this script is to demonstrate
+# how to use steps/nnet3/report/convert_model.py.
+
+import sys
+sys.path.append("steps/nnet3/report")
+import convert_model
+import matplotlib as mpl
+mpl.use('Agg')
+import matplotlib.pyplot as plt
+import pickle
+import numpy as np
+
+# instead of the pickle.load commands, you could do in python, as follows:
+# (but dumping them to disk first is faster in case you'll be running this
+# script more than once).
+# model1 = convert_model.read_model("exp/chain/tdnn1g_sp/24.mdl")
+# model2 = convert_model.read_model("exp/chain/tdnn1g_sp/25.mdl")
+model1 = pickle.load(open("exp/chain/tdnn1g_sp/24.mdl.pkl", "rb"))
+model2 = pickle.load(open("exp/chain/tdnn1g_sp/25.mdl.pkl", "rb"))
+
+convert_model.compute_derived_quantities(model1)
+convert_model.compute_derived_quantities(model2)
+convert_model.compute_progress(model1, model2)
+
+
+f, ((ax1, ax2, ax3), (ax4, ax5, ax6)) = plt.subplots(nrows=2, ncols=3)
+plt.tight_layout()
+fs=5
+ss=4
+ax1.scatter(model1['tdnn4.affine']['col-norms-3'],
+            model1['tdnn3.affine']['row-change'], s=ss)
+ax1.set_title('row-change3 versus  column-norms4',
+              fontsize=fs)
+
+ax2.scatter(model1['tdnn4.affine']['col-norms-3'],
+            model1['tdnn3.affine']['rel-row-change'], s=ss)
+ax2.set_title('rel-row-change3 versus  column-norms4',
+              fontsize=fs)
+
+ax3.scatter(model1['tdnn4.affine']['col-norms-3'],
+              model1['tdnn3.affine']['row-norms'], s=ss)
+ax3.set_title('row-norms3 versus  column-norms4',
+              fontsize=fs)
+
+ax4.scatter(model1['tdnn4.affine']['col-norms'],
+            model1['tdnn4.affine']['rel-col-change'], s=ss)
+ax4.set_title('rel-col-change4 versus col-norms4',
+              fontsize=fs)
+
+ax5.scatter(model1['tdnn3.batchnorm']['stats-stddev'],
+            model1['tdnn4.affine']['col-norms-3'], s=ss)
+ax5.set_title('col-norms4 versus batch-norm-stddev3',
+              fontsize=fs)
+
+
+#ax6.scatter(np.reciprocal(model1['tdnn3.relu']['deriv-avg']) * model1['tdnn4.affine']['col-norms-3'],
+#            model1['tdnn3.affine']['row-norms'], s=ss)
+#ax6.set_title('row-norms3 vs predicted-row-norms3',
+#              fontsize=fs)
+
+ax6.scatter(model2['tdnn3.relu']['deriv-avg'] * model2['tdnn3.relu']['oderiv-rms'],
+            # model1['tdnn3.relu']['oderiv-rms'],
+            model2['tdnn3.affine']['row-norms'], s=ss)
+ax6.set_xlim(left=0.00, right=0.009)
+ax6.set_title('row-norms3 vs ideriv-rms3',
+              fontsize=fs)
+
+
+plt.savefig('progress.pdf')