From a5f68c946e47c2e40d46d7540a88c684673eb037 Mon Sep 17 00:00:00 2001
From: Hashim Sharif <hsharif3@tyler.cs.illinois.edu>
Date: Wed, 10 Jul 2019 02:38:58 -0500
Subject: [PATCH] TensorRT frontend changes for DepthwiseConv2D
---
.../keras/frontend/approxhpvm_translator.py | 41 +++++++++++++------
llvm/projects/keras/frontend/utils.py | 19 +++++++++
2 files changed, 48 insertions(+), 12 deletions(-)
create mode 100644 llvm/projects/keras/frontend/utils.py
diff --git a/llvm/projects/keras/frontend/approxhpvm_translator.py b/llvm/projects/keras/frontend/approxhpvm_translator.py
index b70b90cfac..f6f989ef85 100644
--- a/llvm/projects/keras/frontend/approxhpvm_translator.py
+++ b/llvm/projects/keras/frontend/approxhpvm_translator.py
@@ -4,6 +4,7 @@ import numpy as np
from frontend.promise_translator import PromiseRtTranslator
from frontend.hpvm_dfg_translator import HPVMTranslator
from frontend.weight_utils import dumpLabels, dumpData, dumpConvWeights, dumpFcWeights, dumpFcBias
+from frontend.utils import *
import keras
import os
@@ -23,6 +24,7 @@ class DFG:
layer_name = layer.__class__.__name__
singleInLayers = {}
+ singleInLayers["DepthwiseConv2D"] = True
singleInLayers["Conv2D"] = True
singleInLayers["Dense"] = True
singleInLayers["MaxPooling2D"] = True
@@ -145,7 +147,7 @@ class DFGNode:
self.layer_name = layer.name # unique layer identifier
print (self.layer_name)
- if layer_type == "Conv2D" or layer_type == "Dense":
+ if layer_type == "Conv2D" or layer_type == "DepthwiseConv2D" or layer_type == "Dense":
self.weights = layer.get_weights()[0]
print("\t", self.weights.shape)
self.use_bias = layer.use_bias
@@ -154,20 +156,23 @@ class DFGNode:
self.use_bias = layer.use_bias
self.bias_weights = layer.get_weights()[1]
print("\t", self.bias_weights.shape)
-
- if layer_type == "Conv2D":
+
+
+ if layer_type == "Conv2D" or layer_type == "DepthwiseConv2D":
self.padding = layer.padding
self.strides = layer.strides
print("\t", self.strides)
print("\tPadding = ", self.padding)
-
+
+
if layer_type == "MaxPooling2D" or layer_type == "AveragePooling2D":
self.pool_size = layer.pool_size
self.strides = layer.strides
print("\t pool_size = ", self.pool_size)
print("\t strides = ", self.strides)
-
- if layer_type == "Conv2D" or layer_type == "Dense" or layer_type == "Activation":
+
+
+ if nodeHasActivation(self):
self.activation_type = layer.activation.__name__
print ("\t Activation = ", self.activation_type)
@@ -317,7 +322,7 @@ class TensorRtTranslator:
def hasBiasAdd(self, cur_node):
- if cur_node.layer_type == "Conv2D" or cur_node.layer_type == "Dense":
+ if nodeHasBias(cur_node):
return cur_node.use_bias
return False
@@ -325,8 +330,8 @@ class TensorRtTranslator:
def hasActivation(self, cur_node):
- if cur_node.layer_type == "Conv2D" or cur_node.layer_type == "Dense":
- return cur_node.activation_type != "linear"
+ if nodeHasActivation(cur_node):
+ return cur_node.activation_type != "linear"
return False
@@ -355,7 +360,7 @@ class TensorRtTranslator:
out_var_name1 = self.getVariableName(cur_node)
layer_type = cur_node.layer_type
- if layer_type == "Conv2D":
+ if layer_type == "Conv2D" or layer_type == "DepthwiseConv2D":
input_var_name = self.getSingleInputName(cur_node)
weights = cur_node.weights
strides = cur_node.strides
@@ -379,7 +384,14 @@ class TensorRtTranslator:
inst_str += str(padding) + ", "
inst_str += str(strides[0]) + ", "
inst_str += str(strides[1]) + ", "
- inst_str += "1, 0); \n"
+ inst_str += "1, "
+
+ if layer_type == "DepthwiseConv2D":
+ C = weights.shape[2]
+ inst_str += str(C) + "); \n"
+ else:
+ inst_str += "1); \n"
+
self.program_str += inst_str
@@ -501,7 +513,7 @@ class TensorRtTranslator:
layer_type = layer.__class__.__name__
layer_name = layer.name
- if layer_type == "Conv2D":
+ if layer_type == "Conv2D" or layer_type == "DepthwiseConv2D":
weights = layer.get_weights()[0]
w_name = layer_name + "_w"
@@ -522,6 +534,11 @@ class TensorRtTranslator:
file_path_str += unique_file_name + "\"); \n"
self.weight_str += file_path_str
+ # NOTE: Special handling for DepthwiseConv2D
+ if layer_type == "DepthwiseConv2D":
+ N = C
+ C = 1
+
# FIXME: Be flexible for datatypes (currently only FP32 weights)
# NOTE: '0' specified for floating point type
self.weight_str += "void* " + w_name + " = " + " readTrainedWeights("
diff --git a/llvm/projects/keras/frontend/utils.py b/llvm/projects/keras/frontend/utils.py
new file mode 100644
index 0000000000..6343dd9eec
--- /dev/null
+++ b/llvm/projects/keras/frontend/utils.py
@@ -0,0 +1,19 @@
+
+
+
+
+def nodeHasBias(cur_node):
+
+ if cur_node.layer_type == "Conv2D" or cur_node.layer_type == "DepthwiseConv2D" or cur_node.layer_type == "Dense":
+ return True
+ else:
+ return False
+
+
+def nodeHasActivation(cur_node):
+
+ if cur_node.layer_type == "Conv2D" or cur_node.layer_type == "DepthwiseConv2D" \
+ or cur_node.layer_type == "Dense" or cur_node.layer_type == "Activation":
+ return True
+ else:
+ return False
--
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