fanwei-china commented on issue #6931:
URL: https://github.com/apache/incubator-tvm/issues/6931#issuecomment-728785484
the tensorflow frontend code is:
with tf_compat_v1.gfile.GFile(model_path, 'rb') as f:
graph_def = tf_compat_v1.GraphDef()
graph_def.ParseFromString(f.read())
graph = tf.import_graph_def(graph_def, name='')
# Call the utility to import the graph definition into default graph.
graph_def = tf_testing.ProcessGraphDefParam(graph_def)
# Add shapes to the graph.
with tf_compat_v1.Session() as sess:
graph_def = tf_testing.AddShapesToGraphDef(sess,
'bert/cls/predictions/label_score')
qt = np.full((512, 50), 1, "int32")
qt_mask = np.full((512, 50), 1.1, "float32")
site = np.full((512, 1), 1, "int32")
o_k = np.full((512, 10), 1, "int32")
o_k_m = np.full((512, 10), 1.1, "float32")
n_k = np.full((512, 10), 1, "int32")
n_k_m = np.full((512, 10), 1.1, "float32")
n_k_i = np.full((512, 10), 1, "int32")
n_k_o = np.full((512, 10), 1, "int32")
# Import the graph to Relay
# -------------------------
# Import tensorflow graph definition to relay frontend.
#
# Results:
# sym: relay expr for given tensorflow protobuf.
# params: params converted from tensorflow params (tensor protobuf).
shape_dict = {
'qt_sent': qt.shape,
'qt_mask': qt_mask.shape,
'url_sent': site.shape,
'keyword_in': o_k.shape,
'keyword_mask': o_k_m.shape,
'n_keyword_in': n_k.shape,
'n_keyword_mask':n_k_m.shape,
'n_keyword_imp': n_k_i.shape,
'n_keyword_occ': n_k_o.shape}
mod, params = relay.frontend.from_tensorflow(graph_def,
layout=layout,
shape=shape_dic)
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