From 20a8cfd6cb2251b4d6c9f46f7128869704a4a5ba Mon Sep 17 00:00:00 2001
From: Hashim Sharif <hsharif3@miranda.cs.illinois.edu>
Date: Tue, 16 Mar 2021 17:35:01 -0500
Subject: [PATCH] Fixed recursive directory creation
---
.../keras/frontend/approxhpvm_translator.py | 26 ++++++++++++-------
1 file changed, 17 insertions(+), 9 deletions(-)
diff --git a/hpvm/projects/keras/frontend/approxhpvm_translator.py b/hpvm/projects/keras/frontend/approxhpvm_translator.py
index ff75415a0f..7515bf67da 100644
--- a/hpvm/projects/keras/frontend/approxhpvm_translator.py
+++ b/hpvm/projects/keras/frontend/approxhpvm_translator.py
@@ -521,17 +521,16 @@ class TensorRtTranslator:
activation_type = cur_node.activation_type
out_var_name3 = self.getVariableName(cur_node)
- inst_str = genActivationCallStr(out_var_name1, out_var_name3, activation_type)
- self.program_str += inst_str
-
if activation_type == "softmax":
print ("Softmax canNOT be part of Dense/Conv Op. Insert: Activation('softmax');")
sys.exit(0)
+
+ inst_str = genActivationCallStr(out_var_name1, out_var_name3, activation_type)
+ self.program_str += inst_str
- #self.json_str += activation_type + "_" + str(self.op_count) + " : 0, \n"
- #self.op_count += 1
self.addBaselineKnob(activation_type)
-
+
+
if layer_type == "BatchNormalization":
input_var_name = self.getSingleInputName(cur_node)
@@ -1074,6 +1073,15 @@ def getUniquePath(weights_dir):
+def createRecursiveDir(target_dir):
+
+ toks = target_dir.split("/")
+ for i in range(len(toks)):
+ path_str = "/".join(toks[0:i+1])
+ if not os.path.exists(path_str):
+ os.mkdir(path_str)
+
+
#***** Top level External Function *******
def translate_to_approxhpvm(model,
@@ -1088,10 +1096,11 @@ def translate_to_approxhpvm(model,
if not reload_weights:
weights_dir = getUniquePath(weights_dir)
- os.mkdir(weights_dir)
+ createRecursiveDir(weights_dir)
+
src_dir = getUniquePath(src_dir)
- os.mkdir(src_dir)
+ createRecursiveDir(src_dir)
dfg = DFG()
for i in range(len(model.layers)):
@@ -1122,7 +1131,6 @@ def translate_to_approxhpvm(model,
-
if reload_weights:
print ("NOTE: Using existing pretrained weights \n")
else:
--
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