diff --git a/hpvm/projects/onnx/frontend/approx_codegen.py b/hpvm/projects/onnx/frontend/approx_codegen.py
index 2a301273f19ddb02c657c4bb5342e22c49729fd7..cafee7a2536b1a4ff1ba2d98b02c930a9189eb22 100644
--- a/hpvm/projects/onnx/frontend/approx_codegen.py
+++ b/hpvm/projects/onnx/frontend/approx_codegen.py
@@ -5,7 +5,7 @@ class GraphCodeGen:
self._root = ""
self._root_struct = ""
self._main_func = ""
-
+
def emitHeaders(self):
headers = "\n#include <stdio.h> \n"
headers += "#include <stdlib.h> \n"
@@ -17,7 +17,7 @@ class GraphCodeGen:
headers += "#include <tensorTypes.h> \n"
headers += "#include <tensorUtils.h> \n\n"
self._headers = headers
-
+
def emitRoot(self):
def emitRootNodeHeader():
root_signature = "void root("
@@ -27,29 +27,31 @@ class GraphCodeGen:
root_signature += "\t "
self.filter_names[f_name] = index
root_signature += "void* " + f_name + ", "
- root_signature += "size_t " + f_name + "_bytes"
+ root_signature += "size_t " + f_name + "_bytes"
if index < len(self.filter_names) - 1:
root_signature += ", \n"
index += 1
root_signature += "){ \n\n"
root_signature += "\n __visc__hint(visc::CPU_TARGET); \n"
- root_signature += " __visc__attributes(" + str(len(self.filter_names)) + ", "
+ root_signature += " __visc__attributes(" + \
+ str(len(self.filter_names)) + ", "
index = 0
for f_name in self.filter_names:
- root_signature += f_name
+ root_signature += f_name
if index < len(self.filter_names) - 1:
root_signature += ", "
index += 1
root_signature += ", 0); \n\n"
return root_signature
-
- def emitRootNodeFooter(self):
+ def emitRootNodeFooter(self):
last_node = self.dfg.last_node
output_var = self.output_map[last_node.layer_name]
# Binding output of last DFG node to the Root Node output
- root_footer_str = "\n __visc__bindOut(" + output_var + ", 0, 0, 0); \n"
- root_footer_str += " __visc__bindOut(" + output_var + ", 1, 1, 0); \n"
+ root_footer_str = "\n __visc__bindOut(" + \
+ output_var + ", 0, 0, 0); \n"
+ root_footer_str += " __visc__bindOut(" + \
+ output_var + ", 1, 1, 0); \n"
root_footer_str += "\n}\n\n"
return root_footer_str
@@ -71,14 +73,14 @@ class GraphCodeGen:
self._root_struct += emitRootStructure()
self.codegen(self.dfg)
self._root += emitRootNodeFooter()
-
+
def emitMainFunc(self, test_data):
main_func_str = "int main(){ \n\n"
main_func_str += self.weight_str
main_func_str += self.input_str
main_func_str += "\n__visc__init(); \n"
main_func_str += "RootIn* args = static_cast<RootIn*>(malloc(sizeof(RootIn))); \n\n"
- for f_name in self.filter_names:
+ for f_name in self.filter_names:
main_func_str += "args->" + f_name + " = " + f_name + "; \n"
main_func_str += "args->" + f_name + "_bytes = 0; \n"
main_func_str += "\nvoid* dfg = __visc__launch(0, root, (void*) args); \n\n"
@@ -86,23 +88,22 @@ class GraphCodeGen:
main_func_str += "void *result = static_cast<RootIn*>(args)->input; \n"
main_func_str += "hpvm_request_tensor(result, 0); \n\n"
main_func_str += "__visc__cleanup(); \n "
- main_func_str += "computeAccuracy3(labels, result); \n"
+ main_func_str += "computeAccuracy3(labels, result); \n"
main_func_str += "return 0; \n\n"
- main_func_str += "} \n"
+ main_func_str += "} \n"
self._main_func += main_func_str
-
def emitSource(self, dir_prefix):
source = self._headers + self._nodes + self._root
source += self._root_struct + self._main_func
- print (source)
+ print(source)
f = open(dir_prefix + "/approxhpvm_src.cc", "w+")
f.write(source)
f.close()
-
+
def codegen(self, model, weights_dir, test_data):
self.emitHeaders()
self.emitRoot()
self.emitMainFunc(test_data)
# dump generated program string to source file
- self.emitSource(weights_dir)
\ No newline at end of file
+ self.emitSource(weights_dir)
diff --git a/hpvm/projects/onnx/frontend/common.py b/hpvm/projects/onnx/frontend/common.py
index fb4f90465da4c5ce2a60044e8e33388e6f673f51..56965d04ceb1aab6357d42fa8dabc6efb79351bf 100644
--- a/hpvm/projects/onnx/frontend/common.py
+++ b/hpvm/projects/onnx/frontend/common.py
@@ -1,4 +1,24 @@
import sys
import os
+from onnx import numpy_helper
+class Tensor(object):
+ def __init__(self, proto):
+ if not proto.name.strip():
+ raise ValueError("Tensor's name is required.")
+ self.name = proto.name
+
+ def __str__(self):
+ return "Tensor: {}\n".format(self.name)
+ __repr__ = __str__
+
+# Can be either input or weight tensor
+class WeightTensor(Tensor):
+ def __init__(self, input_proto):
+ Tensor.__init__(self, input_proto)
+ self.input_data = numpy_helper.to_array(input_proto)#.reshape(tuple(input_proto.dims))
+
+class InputTensor(Tensor):
+ def __init__(self, inter_name):
+ self.name = inter_name
diff --git a/hpvm/projects/onnx/frontend/graph_builder.py b/hpvm/projects/onnx/frontend/graph_builder.py
index b9be0dcf3c02241a3eca3014b51f2fa8183e624f..e0f1b27303c8c715b84aae27977b543dcc15e063 100644
--- a/hpvm/projects/onnx/frontend/graph_builder.py
+++ b/hpvm/projects/onnx/frontend/graph_builder.py
@@ -1,6 +1,24 @@
+import sys
+from onnx import numpy_helper
+from graph_ir import Node
+from common import InputTensor, WeightTensor
+
+support_onnx_ops = {"DepthwiseConv2D" : None,
+ "Conv" : [2], # only 2d supported here
+ "MatMul" : None,
+ "MaxPool": [2], # only 2d supported here
+ "Activation" : None,
+ "BatchNormalization" : None,
+ "Flatten" : None,
+ "Add" : None,
+ "Relu" : None,
+ "Softmax" : None,
+ "Identity": None}
+
+
class DFG(object):
-
- root_set = False;
+
+ root_set = False
def __init__(self):
self.node_map = {}
@@ -15,12 +33,10 @@ class DFG(object):
"Flatten"}
self.mutliInputLayers = {"Add"}
-
def hasSingleInput(self, layer):
layer_name = layer.__class__.__name__
return layer_name in self.singleInputLayers
-
def hasMultipleInputs(self, layer):
layer_name = layer.__class__.__name__
return layer_name in self.multiInputLayers
@@ -30,36 +46,37 @@ class DFG(object):
inbound_node_name = inbound_node_name.split("/")[0]
if inbound_node_name in self.node_map:
inbound_node = self.node_map[inbound_node_name]
- print (inbound_node_name, " found!")
+ print(inbound_node_name, " found!")
inbound_node.add_output(dfg_node)
dfg_node.add_input(inbound_node)
else:
- print ("--inbound node NOT FOUND!")
+ print("--inbound node NOT FOUND!")
def add_to_graph(self, layer):
dfg_node = DFGNode(layer)
if not self.root_set:
self.root_node = dfg_node
- self.root_set = True # DFG root node is now set
+ self.root_set = True # DFG root node is now set
- if self.hasMultipleInputs(layer):
+ if self.hasMultipleInputs(layer):
for j in range(len(layer.input)):
print(type(layer.input[j]))
- print(layer.input[j].op.name)
+ print(layer.input[j].op.name)
self.add_dfg_edge(layer.input[j].op.name, dfg_node)
else:
- print (layer.input.name)
+ print(layer.input.name)
self.add_dfg_edge(layer.input.name, dfg_node)
# Adding DFG node to name mapping
self.node_map[layer.name] = dfg_node
+ # Check if all predecessor nodes have been visited thus far - reverse
+ # postorder traversal
- # Check if all predecessor nodes have been visited thus far - reverse postorder traversal
def predVisited(self, cur_node, visited_nodes):
for input_node in cur_node.inputs:
if input_node.layer_name not in visited_nodes:
- return False;
- # All predecessors are visited
+ return False
+ # All predecessors are visited
return True
def traverseNode(self, cur_node, visited_nodes):
@@ -76,19 +93,20 @@ class DFG(object):
for output_node in cur_node.outputs:
self.traverseNode(output_node, visited_nodes)
- # NOTE: Assuming that no outbound edges implies the last node in the graph
+ # NOTE: Assuming that no outbound edges implies the last node in
+ # the graph
if len(cur_node.outputs) == 0:
self.last_node = cur_node
+ # Build and Print the DFG in reverse postorder
- #Build and Print the DFG in reverse postorder
def buildDFG(self):
- print ("\n\n ****** Traversing and Printing DFG ******* \n\n")
+ print("\n\n ****** Traversing and Printing DFG ******* \n\n")
visited_nodes = {}
# Starting traversal at the DFG root node
self.traverseNode(self.root_node, visited_nodes)
-
- ## This should be the place where partial evaluation happens
+
+ # This should be the place where partial evaluation happens
def emitNode(self, layer):
if layer.op_type == "Conv":
return Conv2DNode()
@@ -108,22 +126,21 @@ class DFG(object):
pass
else:
raise ValueError("Unsupported operator type!")
+ sys.exit("Unsupported operator type!")
+
class GraphBuilder(object):
- def __init__(self, model, shape, dtype, opset):
+ def __init__(self, model, shape, dtype, opset, weight_dir):
self._check_model(model)
- self._nodes = {}
- self._params = {}
- self._renames = {}
- self._num_input = 0
- self._num_param = 0
- self._dtype = dtype
- self._graph = model.graph
- self._opset = opset
- self._shape = shape if shape else self._build_shape()
- self.program_str = ""
+ self.model = model
+ self.dtype = dtype
+ self.graph = model.graph
+ self.opset = opset
+ self.weight_dir = weight_dir
+ self.shape = shape if shape else self._build_shape()
self.dfg = DFG()
-
+ self.tensors = dict()
+
################################################
# Aux functions for graph building
################################################
@@ -140,18 +157,19 @@ class GraphBuilder(object):
import warnings
warnings.warn(str(e))
except ImportError as e:
- raise ImportError("Unable to import onnx.checker which is required {}".format(e))
-
+ raise ImportError(
+ "Unable to import onnx.checker which is required {}".format(e))
+
def _build_shape(self):
shape = {}
- for input in self._graph.input:
+ for input in self.graph.input:
# get type of input tensor
tensor_type = input.type.tensor_type
# check if it has a shape:
if (tensor_type.HasField("shape")):
shape[input.name] = tensor_type.shape
return shape
-
+
def _parse_array(self, tensor_proto):
try:
from onnx.numpy_helper import to_array
@@ -160,15 +178,15 @@ class GraphBuilder(object):
"Unable to import onnx which is required {}".format(e))
np_array = to_array(tensor_proto).reshape(tuple(tensor_proto.dims))
return np_array
-
+
def _parse_value_proto(self, value_proto):
"""Parse ValueProto or raw str."""
try:
name = value_proto.name
except AttributeError:
name = value_proto
- return name
-
+ return name
+
def _parse_dtype(self, value_proto, dtype):
"""Parse dtype."""
try:
@@ -177,55 +195,54 @@ class GraphBuilder(object):
except AttributeError:
return dtype
+ def _support_check(self, node):
+ op_name = node.op_type
+ if op_name not in support_onnx_ops:
+ return False
+ else:
+ if support_onnx_ops[op_name] == None:
+ return True
+ else:
+ #print(type(node.attribute))
+ for attr in node.attribute:
+ # partially evaluate the kernel shape
+ if attr.name == 'kernel_shape':
+ return len(attr.ints) in support_onnx_ops[op_name]
+ return False
+
+ def _dump_weight(self, weight_tensor):
+ print("weights dump here")
+
+
################################################
# Top level Graph Building functions
- # return the compile-ready graph
+ # return the compilation-ready graph
################################################
-
+
def build_graph(self):
- # parse init tensors
- for init_tensor in self._graph.initializer:
- if not init_tensor.name.strip():
- raise ValueError("Tensor's name is required.")
- self._params[init_tensor.name] = self._parse_array(init_tensor)
- self._nodes[init_tensor.name] = Node(init_tensor.name,
- self._params[init_tensor.name].shape,
- self._params[init_tensor.name].dtype)
-
- # parse inputs
- # from onnx v0.2, GraphProto.input has type ValueInfoProto,
- # and the name is 'i.name'
- for i in self._graph.input:
- i_name = self._parse_value_proto(i)
- d_type = self._parse_dtype(i, 'float32')
- if i_name in self._params:
- # i is a param instead of input
- self._num_param += 1
- self._params[i_name] = self._params.pop(i_name)
- self._nodes[i_name] = Node(i_name,
- self._params[i_name].shape,
- self._params[i_name].dtype)
- else:
- self._num_input += 1
- if i_name in self._shape:
- tshape = self._shape[i_name]
- else:
- raise ValueError("Must provide an input shape for `{0}`.".format(i_name))
- if isinstance(self._dtype, dict):
- dtype = self._dtype[i_name] if i_name in self._dtype else d_type
- else:
- dtype = d_type
- self._nodes[i_name] = Node(i_name,
- tshape,
- dtype)
- for node in self._graph.node:
+ for weight_tensor in self.graph.initializer:
+ self.tensors[weight_tensor.name] = WeightTensor(weight_tensor)
+ for i in self.graph.input:
+ if i.name not in self.tensors:
+ self.tensors[i.name] = InputTensor(i.name)
+ for node in self.graph.node:
op_name = node.op_type
- print("###############################")
- print(op_name)
+ #print("###############################")
+ if not self._support_check(node):
+ raise ValueError(
+ "Operator not currently supported: `{0}`!".format(op_name))
#print("attribute: " + str(node.attribute))
#print("input: " + str(node.input))
#print("output: " + str(node.output))
- #print(self._nodes)
+ #print("###############################")
+ for i in node.input:
+ if i not in self.tensors:
+ raise ValueError(
+ "Compilation Interrupted for missing input!`{0}`.".format(i))
+ for i in node.output:
+ if i not in self.tensors:
+ self.tensors[i] = InputTensor(i)
+ for tensor in self.tensors.values():
+ if isinstance(tensor, WeightTensor):
+ print(tensor.name)
return self.dfg
-
-
\ No newline at end of file
diff --git a/hpvm/projects/onnx/frontend/graph_codegen.py b/hpvm/projects/onnx/frontend/graph_codegen.py
index 2f63b24e9e175c5447939d52a0e8084c462f2b4d..25c6b2688c4b370e18ce757edbf94293f2cd5b8c 100644
--- a/hpvm/projects/onnx/frontend/graph_codegen.py
+++ b/hpvm/projects/onnx/frontend/graph_codegen.py
@@ -4,7 +4,8 @@ import os
from graph_builder import *
from graph_ir import *
-
+
+
class GraphCodeGen(object):
def __init__(self, DFG):
self.program_str = ""
@@ -16,7 +17,7 @@ class GraphCodeGen(object):
def dump_weights(self):
for init_tensor in self._graph.initializer:
- #print(init_tensor)
+ # print(init_tensor)
pass
def traverse_graph(self, cur_node, visited):
@@ -33,7 +34,7 @@ class GraphCodeGen(object):
self.build_graph()
visited_nodes = {}
self.traverse_graph(self.dfg.root, visited)
-
+
def emit_header(self):
headers = "\n#include <stdio.h> \n"
headers += "#include <stdlib.h> \n"
@@ -53,9 +54,9 @@ class GraphCodeGen(object):
def emit_footer(self, test_data):
if test_data is not None and self.dfg.last_node is not None:
- last_node = self.dfg.last_node
- output_var = self.output_map[last_node.layer_name]
-
+ last_node = self.dfg.last_node
+ output_var = self.output_map[last_node.layer_name]
+
destructors = "\nllvm_hpvm_cleanupTensorRt(); \n"
end_main = "\nreturn 0; \n\n}\n"
self.program_str += destructors
@@ -66,10 +67,10 @@ class GraphCodeGen(object):
C = x_test.shape[1]
H = x_test.shape[2]
W = x_test.shape[3]
-
+
loop_str = ""
loop_str += "\nstartMemTracking(); \n\n"
-
+
loop_str += "int test_input_size = " + str(N) + "; \n"
loop_str += "int batch_size = " + str(N) + "; \n"
loop_str += "int batch_count = test_input_size / batch_size; \n"
@@ -79,7 +80,7 @@ class GraphCodeGen(object):
loop_str += "int start = i * batch_size; \n"
loop_str += "int end = (i + 1) * batch_size; \n"
- loop_str += "\nvoid* input = readInputBatch(input_path.c_str(),0,start,end,"
+ loop_str += "\nvoid* input = readInputBatch(input_path.c_str(),0,start,end,"
loop_str += str(C) + "," + str(H) + "," + str(W) + "); \n\n"
self.program_str += loop_str
@@ -89,7 +90,8 @@ class GraphCodeGen(object):
end_loop_str += "\nuint32_t* labels = readLabelsBatch3(labels_path.c_str(),start,end); \n"
last_node = self.dfg.last_node
output_var = self.output_map[last_node.layer_name]
- accuracy_call = "\nfloat accuracy = computeAccuracy3(labels, " + output_var + "); \n"
+ accuracy_call = "\nfloat accuracy = computeAccuracy3(labels, " + \
+ output_var + "); \n"
end_loop_str += accuracy_call
#end_loop_str += "float accuracy = computeAccuracy2(labels, batch_size, var_60); "
end_loop_str += "final_accuracy += accuracy; \n"
@@ -114,8 +116,8 @@ class GraphCodeGen(object):
def codegen(self, weights_dir, test_data, test_labels):
if os.path.exists(weights_dir):
raise ValueError("Weight dir existed. Compilation interrupted!")
- os.mkdir(weights_dir)
- self.emit_header()
+ os.mkdir(weights_dir)
+ self.emit_header()
self.emit_weights(weights_dir)
self.emit_batch_loop(test_data)
self.emit_graph()
diff --git a/hpvm/projects/onnx/frontend/graph_ir.py b/hpvm/projects/onnx/frontend/graph_ir.py
index c9da1e9ef759627b56ccf98b4300628dccd75265..50f79c741fd3485d823c2cf35b0f178bcb2e96dd 100644
--- a/hpvm/projects/onnx/frontend/graph_ir.py
+++ b/hpvm/projects/onnx/frontend/graph_ir.py
@@ -1,68 +1,90 @@
################################################
# Top Level DFGNode interface
################################################
+
+
class DFGNode(object):
def add_output(self, output_node):
self.outputs.append(output_node)
+
def add_input(self, input_node):
self.inputs.append(input_node)
+
def __init__(self, layer):
self.inputs = []
self.outputs = []
self.name = layer.name
self.op_type = layer.op_type
+
'''
Element wise operatos that is for activation function
e.g. HardSigmoid, LeakyRelu, PRelu, Pow, Reciprocal,
Relu, Selu, Sigmoid, Softplus, Sqrt, ThresholdedRelu,
Abs, Ceil, Elu, Floor, Neg
'''
+
+
class ActivationNode(DFGNode):
pass
+
+
'''
ELement wise operators that is not for activation function.
-In other words, they are logical comparison operators
-e.g. And, Equal, Greater, GreaterOrEqual, Less, LessOrEqual,
+In other words, they are logical comparison operators
+e.g. And, Equal, Greater, GreaterOrEqual, Less, LessOrEqual,
Or, Xor
'''
+
+
class LogicalOpNode(DFGNode):
pass
+
class Node(object):
def __init__(self, name, shape, dtype):
self._name = name
self._shape = shape if shape else {}
self._dtype = dtype
+
def __str__(self):
- return "Node: " + self._name + " with shape: " + str(self._shape) + " and data type " + str(self._dtype)
+ return "Node: " + self._name + " with shape: " + \
+ str(self._shape) + " and data type " + str(self._dtype)
__repr__ = __str__
################################################
# Actually Implementation of Operators
################################################
+
class AddNode(DFGNode):
def __init__(self, layer):
DFGNode.__init__(self, layer)
+
def codegen(self):
input_vars = self.getMultipleInputNames(cur_node)
inst_str = "void* " + out_var_name1 + " = "
- inst_str += "tensorAdd(" + input_vars[0] + ", " + input_vars[1] + "); \n"
+ inst_str += "tensorAdd(" + \
+ input_vars[0] + ", " + input_vars[1] + "); \n"
return inst_str
+
class MatMulNode(DFGNode):
def __init__(self, layer):
DFGNode.__init__(self, layer)
+
def codegen(self):
pass
+
class SoftMaxNode(DFGNode):
def __init__(self, layer):
DFGNode.__init__(self, layer)
+
def codegen(self):
pass
+
class Conv2DNode(DFGNode):
def __init__(self, layer):
DFGNode.__init__(self, layer)
@@ -73,6 +95,7 @@ class Conv2DNode(DFGNode):
self.strides = layer.strides
print("\t", self.strides)
print("\tPadding = ", self.padding)
+
def codegen(self):
input_var_name = self.getSingleInputName(cur_node)
weights = cur_node.weights
@@ -81,12 +104,12 @@ class Conv2DNode(DFGNode):
if cur_node.padding.strip() == "valid":
padding = 0
else:
- padding = cur_node.padding
+ padding = cur_node.padding
padding = int((weights.shape[0] - 1) / 2)
prev_padding = self.getPrevLayerPadding(cur_node)
- if prev_padding != None:
+ if prev_padding is not None:
# FIXME: currently only supporting symmetric padding
- padding = prev_padding[0][0]
+ padding = prev_padding[0][0]
inst_str = "void* " + out_var_name1 + " = "
inst_str += "tensorConvolution(" + input_var_name + ", "
@@ -102,11 +125,13 @@ class Conv2DNode(DFGNode):
else:
inst_str += "1); \n"
if strides[0] > 1 and cur_node.padding.strip() == "same":
- print ("!ERROR: Same Padding not supported for Conv with Stride > 1")
- print ("Use: ZeroPadding2D(padding=(" + str(padding) + "," + str(padding) + "));\n");
+ print("!ERROR: Same Padding not supported for Conv with Stride > 1")
+ print("Use: ZeroPadding2D(padding=(" +
+ str(padding) + "," + str(padding) + "));\n")
sys.exit(0)
return inst_str
+
class DepthwiseConv2DNode(DFGNode):
def __init__(self, layer):
DFGNode.__init__(self, layer)
@@ -117,6 +142,7 @@ class DepthwiseConv2DNode(DFGNode):
self.strides = layer.strides
print("\t", self.strides)
print("\tPadding = ", self.padding)
+
def codegen(self):
input_var_name = self.getSingleInputName(cur_node)
weights = cur_node.weights
@@ -125,12 +151,12 @@ class DepthwiseConv2DNode(DFGNode):
if cur_node.padding.strip() == "valid":
padding = 0
else:
- padding = cur_node.padding
+ padding = cur_node.padding
padding = int((weights.shape[0] - 1) / 2)
prev_padding = self.getPrevLayerPadding(cur_node)
- if prev_padding != None:
+ if prev_padding is not None:
# FIXME: currently only supporting symmetric padding
- padding = prev_padding[0][0]
+ padding = prev_padding[0][0]
inst_str = "void* " + out_var_name1 + " = "
inst_str += "tensorConvolution(" + input_var_name + ", "
@@ -146,17 +172,20 @@ class DepthwiseConv2DNode(DFGNode):
else:
inst_str += "1); \n"
if strides[0] > 1 and cur_node.padding.strip() == "same":
- print ("!ERROR: Same Padding not supported for Conv with Stride > 1")
- print ("Use: ZeroPadding2D(padding=(" + str(padding) + "," + str(padding) + "));\n");
+ print("!ERROR: Same Padding not supported for Conv with Stride > 1")
+ print("Use: ZeroPadding2D(padding=(" +
+ str(padding) + "," + str(padding) + "));\n")
sys.exit(0)
return inst_str
+
class DenseNode(DFGNode):
def __init__(self, layer):
DFGNode.__init__(self, layer)
self.weights = layer.get_weights()[0]
print("\t", self.weights.shape)
self.use_bias = layer.use_bias
+
def codegen(self):
input_var_name = self.getSingleInputName(cur_node)
weights = cur_node.weights
@@ -166,6 +195,7 @@ class DenseNode(DFGNode):
inst_str += "); \n"
return inst_str
+
class MaxPool2DNode(DFGNode):
def __init__(self, layer):
DFGNode.__init__(self, layer)
@@ -173,12 +203,16 @@ class MaxPool2DNode(DFGNode):
self.strides = layer.strides
print("\t pool_size = ", self.pool_size)
print("\t strides = ", self.strides)
+
def codegen(self):
inst_str = "void* " + out_var_name1 + " = "
- inst_str += "tensorPooling(" + input_var_name + "," + pool_type + "," + str(pool_size[0]) + "," + str(pool_size[1])
- inst_str += "," + str(padding) + "," + str(padding) + "," + str(strides[0]) + "," + str(strides[1])
+ inst_str += "tensorPooling(" + input_var_name + "," + \
+ pool_type + "," + str(pool_size[0]) + "," + str(pool_size[1])
+ inst_str += "," + str(padding) + "," + str(padding) + \
+ "," + str(strides[0]) + "," + str(strides[1])
inst_str += "); \n"
+
class AveragePooling2DNode(DFGNode):
def __init__(self, layer):
DFGNode.__init__(self, layer)
@@ -186,22 +220,28 @@ class AveragePooling2DNode(DFGNode):
self.strides = layer.strides
print("\t pool_size = ", self.pool_size)
print("\t strides = ", self.strides)
+
def codegen(self):
inst_str = "void* " + out_var_name1 + " = "
- inst_str += "tensorPooling(" + input_var_name + "," + pool_type + "," + str(pool_size[0]) + "," + str(pool_size[1])
- inst_str += "," + str(padding) + "," + str(padding) + "," + str(strides[0]) + "," + str(strides[1])
+ inst_str += "tensorPooling(" + input_var_name + "," + \
+ pool_type + "," + str(pool_size[0]) + "," + str(pool_size[1])
+ inst_str += "," + str(padding) + "," + str(padding) + \
+ "," + str(strides[0]) + "," + str(strides[1])
inst_str += "); \n"
return insr_str
+
class ZeroPadding2DNode(DFGNode):
def __init__(self, layer):
DFGNode.__init__(self, layer)
- print ("***ZeroPaddding \n");
+ print("***ZeroPaddding \n")
self.padding = layer.padding
- print ("padding = ", self.padding)
+ print("padding = ", self.padding)
+
def codegen(self):
pass
+
class BatchNormalizationNode(DFGNode):
def __init__(self, layer):
DFGNode.__init__(self, layer)
@@ -210,6 +250,7 @@ class BatchNormalizationNode(DFGNode):
self.gamma = layer.gamma
self.moving_mean = layer.moving_mean
self.moving_variance = layer.moving_variance
+
def codegen(self):
input_var_name = self.getSingleInputName(cur_node)
inst_str = "void* " + out_var_name1 + " = "
diff --git a/hpvm/projects/onnx/frontend/main.py b/hpvm/projects/onnx/frontend/main.py
index fdde98fe835073b56be6d78e493ae8c90e5d43e2..89da216b65eece7b554ed787382597574d567178 100644
--- a/hpvm/projects/onnx/frontend/main.py
+++ b/hpvm/projects/onnx/frontend/main.py
@@ -4,42 +4,42 @@ import numpy as np
import onnx
import glob
-from onnx import numpy_helper, version_converter
-from onnxruntime.backend.backend import OnnxRuntimeBackend as backend
+from onnx import version_converter
+#from onnxruntime.backend.backend import OnnxRuntimeBackend as backend
# onnx2hpvm modules
from graph_builder import GraphBuilder
from graph_codegen import GraphCodeGen
# from approx_codegen import GraphCodeGen
-def convert_version(model):
+
+def convert_version(model, new_version):
print('The model before conversion:\n{}'.format(model))
# A full list of supported adapters can be found here:
# https://github.com/onnx/onnx/blob/master/onnx/version_converter.py#L21
# Apply the version conversion on the original model
- converted_model = version_converter.convert_version(model, 12)
+ converted_model = version_converter.convert_version(model, new_version)
print('The model after conversion:\n{}'.format(converted_model))
return converted_model
def main():
- model = onnx.load('../models/keras/alexnet.onnx')
+ model = onnx.load('../models/keras/lenet.onnx')
weights_dir = './test_src'
- test_data_dir = '../models/mnist/test_data_set_0'
+ # test_data_dir = '../models/mnist/test_data_set_0'
# converted_model = convert_version(model)
-
- graph = model.graph
try:
opset = model.opset_import[0].version if model.opset_import else 1
except AttributeError:
opset = 1 # default opset version set to 1 if not specified
print("opset version: ", opset)
- gBuilder = GraphBuilder(model, None, "float32", opset)
- gCodegen = GraphCodeGen(gBuilder.build_graph())
- gCodegen.codegen(weights_dir, test_data)#, test_labels)
+ gBuilder = GraphBuilder(model, None, "float32", opset, weights_dir)
+ gBuilder.build_graph()
+ #gCodegen = GraphCodeGen(gBuilder.build_graph())
+ # gCodegen.codegen(weights_dir, test_data)#, test_labels)
if __name__ == "__main__":
diff --git a/hpvm/projects/onnx/frontend/weight_utils.py b/hpvm/projects/onnx/frontend/weight_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..db81fa2d70664daef92e98e7230d842359912723
--- /dev/null
+++ b/hpvm/projects/onnx/frontend/weight_utils.py
@@ -0,0 +1,147 @@
+
+import numpy as np
+import struct
+import random
+
+
+def dumpLabels(file_name, Y_test):
+
+ f = open(file_name, "wb")
+
+ labels_map = {}
+ for label in Y_test:
+ label_val = 0
+ if len(Y_test.shape) > 1:
+ #label_val = np.int8(label[0])
+ label_val = np.int32(label[0])
+ else:
+ #label_val = np.int8(label)
+ label_val = np.int32(label)
+
+ if label_val not in labels_map:
+ labels_map[label_val] = 0
+ labels_map[label_val] += 1
+
+ f.write(label_val)
+
+ f.close()
+
+
+
+"""def dumpData(file_name, X_test):
+
+ N = X_test.shape[0]
+ C = X_test.shape[1]
+ H = X_test.shape[2]
+ W = X_test.shape[3]
+
+ print ("*DumpData")
+ print("-min_val = ", np.amin(X_test))
+ print("-max_val = ", np.amax(X_test))
+
+ f = open(file_name, "wb")
+ for i in range(N):
+ for j in range(C):
+ for k in range(H):
+ for l in range(W):
+ val = struct.unpack("f", struct.pack("f", X_test[i][j][k][l]))
+ f.write(np.float32(val[0]))
+
+ f.close()
+"""
+
+
+def dumpData(file_name, X_test):
+
+ N = X_test.shape[0]
+ C = X_test.shape[1]
+ H = X_test.shape[2]
+ W = X_test.shape[3]
+
+ print ("*DumpData")
+ print("-min_val = ", np.amin(X_test))
+ print("-max_val = ", np.amax(X_test))
+
+ f = open(file_name, "wb")
+
+ X_test.tofile(f)
+
+ f.close()
+
+
+
+def dumpConvWeights(file_name, weights, N, C, H, W):
+
+ print (weights.shape)
+ print ("*DumpConvWeights")
+ print("-min_val = ", np.amin(weights))
+ print("-max_val = ", np.amax(weights))
+
+
+ f = open(file_name, "wb")
+ for i in range(N):
+ for j in range(C):
+ for k in range(H):
+ for l in range(W):
+ f.write(weights[k][l][j][i])
+
+ f.close()
+
+
+
+def dumpFcWeights(file_name, weights, H, W):
+
+ print (weights.shape)
+ print ("*DumpFcWeights")
+ print("-min_val = ", np.amin(weights))
+ print("-max_val = ", np.amax(weights))
+
+
+ f = open(file_name, "wb")
+ for i in range(H):
+ for j in range(W):
+ f.write(weights[i][j])
+
+ f.close()
+
+
+
+def dumpFcBias(file_name, bias, W):
+
+ print (bias.shape)
+ print ("*DumpFcBias")
+ print("-min_val = ", np.amin(bias))
+ print("-max_val = ", np.amax(bias))
+
+
+ f = open(file_name, "wb")
+ for i in range(W):
+ f.write(bias[i])
+
+ f.close()
+
+
+
+def dumpCalibrationData(file_name, X_train, labels_fname, train_labels):
+
+ combined_list = []
+ for i in range(len(X_train)):
+ tup = (X_train[i], train_labels[i])
+ combined_list.append(tup)
+
+ np.random.shuffle(combined_list)
+ #X_calibration = X_train[0:5000]
+
+ data_list = []
+ labels_list = []
+ for i in range(5000):
+ tup = combined_list[i]
+ data_list.append(tup[0])
+ labels_list.append(tup[1])
+
+ data_list = np.array(data_list)
+ labels_list = np.array(labels_list)
+
+ dumpData(file_name, data_list)
+ dumpLabels(labels_fname, labels_list)
+
diff --git a/hpvm/projects/onnx/keras_ref/generated/vgg16/approxhpvm_src.cc b/hpvm/projects/onnx/keras_ref/generated/vgg16/approxhpvm_src.cc
deleted file mode 100644
index 2bf9bad6b967698e34af99cd128fee8fa206bd5e..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/keras_ref/generated/vgg16/approxhpvm_src.cc
+++ /dev/null
@@ -1,982 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/stat.h>
-#include <cstring>
-#include <visc.h>
-#include <tensorTypes.h>
-#include <tensorUtils.h>
-
-void var_0_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_1_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_2_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_3_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_4_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_5_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_6_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_7_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_8_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_9_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_10_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_11_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_12_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_13_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_14_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_15_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_16_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_17_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_18_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_19_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_20_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_21_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_22_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_23_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_24_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_25_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_26_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_27_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_28_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_29_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_30_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_31_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_32_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_33_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_34_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_35_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_36_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_37_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_38_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_39_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_40_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_41_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_42_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_43_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_44_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_mul(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_45_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_46_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_47_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_mul(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_48_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_49_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_softmax(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void root(void* input, size_t input_bytes,
- void* conv2d_1_w, size_t conv2d_1_w_bytes,
- void* conv2d_1_b, size_t conv2d_1_b_bytes,
- void* conv2d_2_w, size_t conv2d_2_w_bytes,
- void* conv2d_2_b, size_t conv2d_2_b_bytes,
- void* conv2d_3_w, size_t conv2d_3_w_bytes,
- void* conv2d_3_b, size_t conv2d_3_b_bytes,
- void* conv2d_4_w, size_t conv2d_4_w_bytes,
- void* conv2d_4_b, size_t conv2d_4_b_bytes,
- void* conv2d_5_w, size_t conv2d_5_w_bytes,
- void* conv2d_5_b, size_t conv2d_5_b_bytes,
- void* conv2d_6_w, size_t conv2d_6_w_bytes,
- void* conv2d_6_b, size_t conv2d_6_b_bytes,
- void* conv2d_7_w, size_t conv2d_7_w_bytes,
- void* conv2d_7_b, size_t conv2d_7_b_bytes,
- void* conv2d_8_w, size_t conv2d_8_w_bytes,
- void* conv2d_8_b, size_t conv2d_8_b_bytes,
- void* conv2d_9_w, size_t conv2d_9_w_bytes,
- void* conv2d_9_b, size_t conv2d_9_b_bytes,
- void* conv2d_10_w, size_t conv2d_10_w_bytes,
- void* conv2d_10_b, size_t conv2d_10_b_bytes,
- void* conv2d_11_w, size_t conv2d_11_w_bytes,
- void* conv2d_11_b, size_t conv2d_11_b_bytes,
- void* conv2d_12_w, size_t conv2d_12_w_bytes,
- void* conv2d_12_b, size_t conv2d_12_b_bytes,
- void* conv2d_13_w, size_t conv2d_13_w_bytes,
- void* conv2d_13_b, size_t conv2d_13_b_bytes,
- void* dense_1_w, size_t dense_1_w_bytes,
- void* dense_1_b, size_t dense_1_b_bytes,
- void* dense_2_w, size_t dense_2_w_bytes,
- void* dense_2_b, size_t dense_2_b_bytes){
-
-
- __visc__hint(visc::CPU_TARGET);
- __visc__attributes(31, input, conv2d_1_w, conv2d_1_b, conv2d_2_w, conv2d_2_b, conv2d_3_w, conv2d_3_b, conv2d_4_w, conv2d_4_b, conv2d_5_w, conv2d_5_b, conv2d_6_w, conv2d_6_b, conv2d_7_w, conv2d_7_b, conv2d_8_w, conv2d_8_b, conv2d_9_w, conv2d_9_b, conv2d_10_w, conv2d_10_b, conv2d_11_w, conv2d_11_b, conv2d_12_w, conv2d_12_b, conv2d_13_w, conv2d_13_b, dense_1_w, dense_1_b, dense_2_w, dense_2_b, 0);
-
-
- void* var_0 = __visc__createNodeND(0, var_0_node);
-
- __visc__bindIn(var_0, 0, 0, 0);
- __visc__bindIn(var_0, 1, 1, 0);
- __visc__bindIn(var_0, 2, 2, 0);
- __visc__bindIn(var_0, 3, 3, 0);
-
- void* var_1 = __visc__createNodeND(0, var_1_node);
-
- __visc__edge(var_0, var_1, 1, 0, 0, 0);
- __visc__edge(var_0, var_1, 1, 1, 1, 0);
- __visc__bindIn(var_1, 4, 2, 0);
- __visc__bindIn(var_1, 5, 3, 0);
-
- void* var_2 = __visc__createNodeND(0, var_2_node);
-
- __visc__edge(var_1, var_2, 1, 0, 0, 0);
- __visc__edge(var_1, var_2, 1, 1, 1, 0);
-
- void* var_3 = __visc__createNodeND(0, var_3_node);
-
- __visc__edge(var_2, var_3, 1, 0, 0, 0);
- __visc__edge(var_2, var_3, 1, 1, 1, 0);
- __visc__bindIn(var_3, 6, 2, 0);
- __visc__bindIn(var_3, 7, 3, 0);
-
- void* var_4 = __visc__createNodeND(0, var_4_node);
-
- __visc__edge(var_3, var_4, 1, 0, 0, 0);
- __visc__edge(var_3, var_4, 1, 1, 1, 0);
- __visc__bindIn(var_4, 8, 2, 0);
- __visc__bindIn(var_4, 9, 3, 0);
-
- void* var_5 = __visc__createNodeND(0, var_5_node);
-
- __visc__edge(var_4, var_5, 1, 0, 0, 0);
- __visc__edge(var_4, var_5, 1, 1, 1, 0);
-
- void* var_6 = __visc__createNodeND(0, var_6_node);
-
- __visc__edge(var_5, var_6, 1, 0, 0, 0);
- __visc__edge(var_5, var_6, 1, 1, 1, 0);
-
- void* var_7 = __visc__createNodeND(0, var_7_node);
-
- __visc__edge(var_6, var_7, 1, 0, 0, 0);
- __visc__edge(var_6, var_7, 1, 1, 1, 0);
- __visc__bindIn(var_7, 10, 2, 0);
- __visc__bindIn(var_7, 11, 3, 0);
-
- void* var_8 = __visc__createNodeND(0, var_8_node);
-
- __visc__edge(var_7, var_8, 1, 0, 0, 0);
- __visc__edge(var_7, var_8, 1, 1, 1, 0);
- __visc__bindIn(var_8, 12, 2, 0);
- __visc__bindIn(var_8, 13, 3, 0);
-
- void* var_9 = __visc__createNodeND(0, var_9_node);
-
- __visc__edge(var_8, var_9, 1, 0, 0, 0);
- __visc__edge(var_8, var_9, 1, 1, 1, 0);
-
- void* var_10 = __visc__createNodeND(0, var_10_node);
-
- __visc__edge(var_9, var_10, 1, 0, 0, 0);
- __visc__edge(var_9, var_10, 1, 1, 1, 0);
- __visc__bindIn(var_10, 14, 2, 0);
- __visc__bindIn(var_10, 15, 3, 0);
-
- void* var_11 = __visc__createNodeND(0, var_11_node);
-
- __visc__edge(var_10, var_11, 1, 0, 0, 0);
- __visc__edge(var_10, var_11, 1, 1, 1, 0);
- __visc__bindIn(var_11, 16, 2, 0);
- __visc__bindIn(var_11, 17, 3, 0);
-
- void* var_12 = __visc__createNodeND(0, var_12_node);
-
- __visc__edge(var_11, var_12, 1, 0, 0, 0);
- __visc__edge(var_11, var_12, 1, 1, 1, 0);
-
- void* var_13 = __visc__createNodeND(0, var_13_node);
-
- __visc__edge(var_12, var_13, 1, 0, 0, 0);
- __visc__edge(var_12, var_13, 1, 1, 1, 0);
-
- void* var_14 = __visc__createNodeND(0, var_14_node);
-
- __visc__edge(var_13, var_14, 1, 0, 0, 0);
- __visc__edge(var_13, var_14, 1, 1, 1, 0);
- __visc__bindIn(var_14, 18, 2, 0);
- __visc__bindIn(var_14, 19, 3, 0);
-
- void* var_15 = __visc__createNodeND(0, var_15_node);
-
- __visc__edge(var_14, var_15, 1, 0, 0, 0);
- __visc__edge(var_14, var_15, 1, 1, 1, 0);
- __visc__bindIn(var_15, 20, 2, 0);
- __visc__bindIn(var_15, 21, 3, 0);
-
- void* var_16 = __visc__createNodeND(0, var_16_node);
-
- __visc__edge(var_15, var_16, 1, 0, 0, 0);
- __visc__edge(var_15, var_16, 1, 1, 1, 0);
-
- void* var_17 = __visc__createNodeND(0, var_17_node);
-
- __visc__edge(var_16, var_17, 1, 0, 0, 0);
- __visc__edge(var_16, var_17, 1, 1, 1, 0);
- __visc__bindIn(var_17, 22, 2, 0);
- __visc__bindIn(var_17, 23, 3, 0);
-
- void* var_18 = __visc__createNodeND(0, var_18_node);
-
- __visc__edge(var_17, var_18, 1, 0, 0, 0);
- __visc__edge(var_17, var_18, 1, 1, 1, 0);
- __visc__bindIn(var_18, 24, 2, 0);
- __visc__bindIn(var_18, 25, 3, 0);
-
- void* var_19 = __visc__createNodeND(0, var_19_node);
-
- __visc__edge(var_18, var_19, 1, 0, 0, 0);
- __visc__edge(var_18, var_19, 1, 1, 1, 0);
-
- void* var_20 = __visc__createNodeND(0, var_20_node);
-
- __visc__edge(var_19, var_20, 1, 0, 0, 0);
- __visc__edge(var_19, var_20, 1, 1, 1, 0);
- __visc__bindIn(var_20, 26, 2, 0);
- __visc__bindIn(var_20, 27, 3, 0);
-
- void* var_21 = __visc__createNodeND(0, var_21_node);
-
- __visc__edge(var_20, var_21, 1, 0, 0, 0);
- __visc__edge(var_20, var_21, 1, 1, 1, 0);
- __visc__bindIn(var_21, 28, 2, 0);
- __visc__bindIn(var_21, 29, 3, 0);
-
- void* var_22 = __visc__createNodeND(0, var_22_node);
-
- __visc__edge(var_21, var_22, 1, 0, 0, 0);
- __visc__edge(var_21, var_22, 1, 1, 1, 0);
-
- void* var_23 = __visc__createNodeND(0, var_23_node);
-
- __visc__edge(var_22, var_23, 1, 0, 0, 0);
- __visc__edge(var_22, var_23, 1, 1, 1, 0);
-
- void* var_24 = __visc__createNodeND(0, var_24_node);
-
- __visc__edge(var_23, var_24, 1, 0, 0, 0);
- __visc__edge(var_23, var_24, 1, 1, 1, 0);
- __visc__bindIn(var_24, 30, 2, 0);
- __visc__bindIn(var_24, 31, 3, 0);
-
- void* var_25 = __visc__createNodeND(0, var_25_node);
-
- __visc__edge(var_24, var_25, 1, 0, 0, 0);
- __visc__edge(var_24, var_25, 1, 1, 1, 0);
- __visc__bindIn(var_25, 32, 2, 0);
- __visc__bindIn(var_25, 33, 3, 0);
-
- void* var_26 = __visc__createNodeND(0, var_26_node);
-
- __visc__edge(var_25, var_26, 1, 0, 0, 0);
- __visc__edge(var_25, var_26, 1, 1, 1, 0);
-
- void* var_27 = __visc__createNodeND(0, var_27_node);
-
- __visc__edge(var_26, var_27, 1, 0, 0, 0);
- __visc__edge(var_26, var_27, 1, 1, 1, 0);
- __visc__bindIn(var_27, 34, 2, 0);
- __visc__bindIn(var_27, 35, 3, 0);
-
- void* var_28 = __visc__createNodeND(0, var_28_node);
-
- __visc__edge(var_27, var_28, 1, 0, 0, 0);
- __visc__edge(var_27, var_28, 1, 1, 1, 0);
- __visc__bindIn(var_28, 36, 2, 0);
- __visc__bindIn(var_28, 37, 3, 0);
-
- void* var_29 = __visc__createNodeND(0, var_29_node);
-
- __visc__edge(var_28, var_29, 1, 0, 0, 0);
- __visc__edge(var_28, var_29, 1, 1, 1, 0);
-
- void* var_30 = __visc__createNodeND(0, var_30_node);
-
- __visc__edge(var_29, var_30, 1, 0, 0, 0);
- __visc__edge(var_29, var_30, 1, 1, 1, 0);
- __visc__bindIn(var_30, 38, 2, 0);
- __visc__bindIn(var_30, 39, 3, 0);
-
- void* var_31 = __visc__createNodeND(0, var_31_node);
-
- __visc__edge(var_30, var_31, 1, 0, 0, 0);
- __visc__edge(var_30, var_31, 1, 1, 1, 0);
- __visc__bindIn(var_31, 40, 2, 0);
- __visc__bindIn(var_31, 41, 3, 0);
-
- void* var_32 = __visc__createNodeND(0, var_32_node);
-
- __visc__edge(var_31, var_32, 1, 0, 0, 0);
- __visc__edge(var_31, var_32, 1, 1, 1, 0);
-
- void* var_33 = __visc__createNodeND(0, var_33_node);
-
- __visc__edge(var_32, var_33, 1, 0, 0, 0);
- __visc__edge(var_32, var_33, 1, 1, 1, 0);
-
- void* var_34 = __visc__createNodeND(0, var_34_node);
-
- __visc__edge(var_33, var_34, 1, 0, 0, 0);
- __visc__edge(var_33, var_34, 1, 1, 1, 0);
- __visc__bindIn(var_34, 42, 2, 0);
- __visc__bindIn(var_34, 43, 3, 0);
-
- void* var_35 = __visc__createNodeND(0, var_35_node);
-
- __visc__edge(var_34, var_35, 1, 0, 0, 0);
- __visc__edge(var_34, var_35, 1, 1, 1, 0);
- __visc__bindIn(var_35, 44, 2, 0);
- __visc__bindIn(var_35, 45, 3, 0);
-
- void* var_36 = __visc__createNodeND(0, var_36_node);
-
- __visc__edge(var_35, var_36, 1, 0, 0, 0);
- __visc__edge(var_35, var_36, 1, 1, 1, 0);
-
- void* var_37 = __visc__createNodeND(0, var_37_node);
-
- __visc__edge(var_36, var_37, 1, 0, 0, 0);
- __visc__edge(var_36, var_37, 1, 1, 1, 0);
- __visc__bindIn(var_37, 46, 2, 0);
- __visc__bindIn(var_37, 47, 3, 0);
-
- void* var_38 = __visc__createNodeND(0, var_38_node);
-
- __visc__edge(var_37, var_38, 1, 0, 0, 0);
- __visc__edge(var_37, var_38, 1, 1, 1, 0);
- __visc__bindIn(var_38, 48, 2, 0);
- __visc__bindIn(var_38, 49, 3, 0);
-
- void* var_39 = __visc__createNodeND(0, var_39_node);
-
- __visc__edge(var_38, var_39, 1, 0, 0, 0);
- __visc__edge(var_38, var_39, 1, 1, 1, 0);
-
- void* var_40 = __visc__createNodeND(0, var_40_node);
-
- __visc__edge(var_39, var_40, 1, 0, 0, 0);
- __visc__edge(var_39, var_40, 1, 1, 1, 0);
- __visc__bindIn(var_40, 50, 2, 0);
- __visc__bindIn(var_40, 51, 3, 0);
-
- void* var_41 = __visc__createNodeND(0, var_41_node);
-
- __visc__edge(var_40, var_41, 1, 0, 0, 0);
- __visc__edge(var_40, var_41, 1, 1, 1, 0);
- __visc__bindIn(var_41, 52, 2, 0);
- __visc__bindIn(var_41, 53, 3, 0);
-
- void* var_42 = __visc__createNodeND(0, var_42_node);
-
- __visc__edge(var_41, var_42, 1, 0, 0, 0);
- __visc__edge(var_41, var_42, 1, 1, 1, 0);
-
- void* var_43 = __visc__createNodeND(0, var_43_node);
-
- __visc__edge(var_42, var_43, 1, 0, 0, 0);
- __visc__edge(var_42, var_43, 1, 1, 1, 0);
-
- void* var_44 = __visc__createNodeND(0, var_44_node);
-
- __visc__edge(var_43, var_44, 1, 0, 0, 0);
- __visc__edge(var_43, var_44, 1, 1, 1, 0);
- __visc__bindIn(var_44, 54, 2, 0);
- __visc__bindIn(var_44, 55, 3, 0);
-
- void* var_45 = __visc__createNodeND(0, var_45_node);
-
- __visc__edge(var_44, var_45, 1, 0, 0, 0);
- __visc__edge(var_44, var_45, 1, 1, 1, 0);
- __visc__bindIn(var_45, 56, 2, 0);
- __visc__bindIn(var_45, 57, 3, 0);
-
- void* var_46 = __visc__createNodeND(0, var_46_node);
-
- __visc__edge(var_45, var_46, 1, 0, 0, 0);
- __visc__edge(var_45, var_46, 1, 1, 1, 0);
-
- void* var_47 = __visc__createNodeND(0, var_47_node);
-
- __visc__edge(var_46, var_47, 1, 0, 0, 0);
- __visc__edge(var_46, var_47, 1, 1, 1, 0);
- __visc__bindIn(var_47, 58, 2, 0);
- __visc__bindIn(var_47, 59, 3, 0);
-
- void* var_48 = __visc__createNodeND(0, var_48_node);
-
- __visc__edge(var_47, var_48, 1, 0, 0, 0);
- __visc__edge(var_47, var_48, 1, 1, 1, 0);
- __visc__bindIn(var_48, 60, 2, 0);
- __visc__bindIn(var_48, 61, 3, 0);
-
- void* var_49 = __visc__createNodeND(0, var_49_node);
-
- __visc__edge(var_48, var_49, 1, 0, 0, 0);
- __visc__edge(var_48, var_49, 1, 1, 1, 0);
-
- __visc__bindOut(var_49, 0, 0, 0);
- __visc__bindOut(var_49, 1, 1, 0);
-
-}
-
-struct ret_t {
- void* tensor;
- size_t bytes;
-};
-
-typedef struct __attribute__((__packed__)) {
- void* input;
- size_t input_bytes;
- void* conv2d_1_w;
- size_t conv2d_1_w_bytes;
- void* conv2d_1_b;
- size_t conv2d_1_b_bytes;
- void* conv2d_2_w;
- size_t conv2d_2_w_bytes;
- void* conv2d_2_b;
- size_t conv2d_2_b_bytes;
- void* conv2d_3_w;
- size_t conv2d_3_w_bytes;
- void* conv2d_3_b;
- size_t conv2d_3_b_bytes;
- void* conv2d_4_w;
- size_t conv2d_4_w_bytes;
- void* conv2d_4_b;
- size_t conv2d_4_b_bytes;
- void* conv2d_5_w;
- size_t conv2d_5_w_bytes;
- void* conv2d_5_b;
- size_t conv2d_5_b_bytes;
- void* conv2d_6_w;
- size_t conv2d_6_w_bytes;
- void* conv2d_6_b;
- size_t conv2d_6_b_bytes;
- void* conv2d_7_w;
- size_t conv2d_7_w_bytes;
- void* conv2d_7_b;
- size_t conv2d_7_b_bytes;
- void* conv2d_8_w;
- size_t conv2d_8_w_bytes;
- void* conv2d_8_b;
- size_t conv2d_8_b_bytes;
- void* conv2d_9_w;
- size_t conv2d_9_w_bytes;
- void* conv2d_9_b;
- size_t conv2d_9_b_bytes;
- void* conv2d_10_w;
- size_t conv2d_10_w_bytes;
- void* conv2d_10_b;
- size_t conv2d_10_b_bytes;
- void* conv2d_11_w;
- size_t conv2d_11_w_bytes;
- void* conv2d_11_b;
- size_t conv2d_11_b_bytes;
- void* conv2d_12_w;
- size_t conv2d_12_w_bytes;
- void* conv2d_12_b;
- size_t conv2d_12_b_bytes;
- void* conv2d_13_w;
- size_t conv2d_13_w_bytes;
- void* conv2d_13_b;
- size_t conv2d_13_b_bytes;
- void* dense_1_w;
- size_t dense_1_w_bytes;
- void* dense_1_b;
- size_t dense_1_b_bytes;
- void* dense_2_w;
- size_t dense_2_w_bytes;
- void* dense_2_b;
- size_t dense_2_b_bytes;
-
- struct ret_t r;
-}
-RootIn;
-
-int main(){
-
-std::string dir_prefix = std::string("data/vgg16_cifar10/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
-std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
-void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
-void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
-void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
-std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
-void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
-void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
-void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
-void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
-void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
-void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
-std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
-void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,10);
-std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
-void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,10,1,1);
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
-uint8_t* labels = readLabels(labels_path.c_str(),10000);
-
-__visc__init();
-RootIn* args = static_cast<RootIn*>(malloc(sizeof(RootIn)));
-
-args->input = input;
-args->input_bytes = 0;
-args->conv2d_1_w = conv2d_1_w;
-args->conv2d_1_w_bytes = 0;
-args->conv2d_1_b = conv2d_1_b;
-args->conv2d_1_b_bytes = 0;
-args->conv2d_2_w = conv2d_2_w;
-args->conv2d_2_w_bytes = 0;
-args->conv2d_2_b = conv2d_2_b;
-args->conv2d_2_b_bytes = 0;
-args->conv2d_3_w = conv2d_3_w;
-args->conv2d_3_w_bytes = 0;
-args->conv2d_3_b = conv2d_3_b;
-args->conv2d_3_b_bytes = 0;
-args->conv2d_4_w = conv2d_4_w;
-args->conv2d_4_w_bytes = 0;
-args->conv2d_4_b = conv2d_4_b;
-args->conv2d_4_b_bytes = 0;
-args->conv2d_5_w = conv2d_5_w;
-args->conv2d_5_w_bytes = 0;
-args->conv2d_5_b = conv2d_5_b;
-args->conv2d_5_b_bytes = 0;
-args->conv2d_6_w = conv2d_6_w;
-args->conv2d_6_w_bytes = 0;
-args->conv2d_6_b = conv2d_6_b;
-args->conv2d_6_b_bytes = 0;
-args->conv2d_7_w = conv2d_7_w;
-args->conv2d_7_w_bytes = 0;
-args->conv2d_7_b = conv2d_7_b;
-args->conv2d_7_b_bytes = 0;
-args->conv2d_8_w = conv2d_8_w;
-args->conv2d_8_w_bytes = 0;
-args->conv2d_8_b = conv2d_8_b;
-args->conv2d_8_b_bytes = 0;
-args->conv2d_9_w = conv2d_9_w;
-args->conv2d_9_w_bytes = 0;
-args->conv2d_9_b = conv2d_9_b;
-args->conv2d_9_b_bytes = 0;
-args->conv2d_10_w = conv2d_10_w;
-args->conv2d_10_w_bytes = 0;
-args->conv2d_10_b = conv2d_10_b;
-args->conv2d_10_b_bytes = 0;
-args->conv2d_11_w = conv2d_11_w;
-args->conv2d_11_w_bytes = 0;
-args->conv2d_11_b = conv2d_11_b;
-args->conv2d_11_b_bytes = 0;
-args->conv2d_12_w = conv2d_12_w;
-args->conv2d_12_w_bytes = 0;
-args->conv2d_12_b = conv2d_12_b;
-args->conv2d_12_b_bytes = 0;
-args->conv2d_13_w = conv2d_13_w;
-args->conv2d_13_w_bytes = 0;
-args->conv2d_13_b = conv2d_13_b;
-args->conv2d_13_b_bytes = 0;
-args->dense_1_w = dense_1_w;
-args->dense_1_w_bytes = 0;
-args->dense_1_b = dense_1_b;
-args->dense_1_b_bytes = 0;
-args->dense_2_w = dense_2_w;
-args->dense_2_w_bytes = 0;
-args->dense_2_b = dense_2_b;
-args->dense_2_b_bytes = 0;
-
-void* dfg = __visc__launch(0, root, (void*) args);
-
-__visc__wait(dfg);
-
-void *result = static_cast<RootIn*>(args)->input;
-hpvm_request_tensor(result, 0);
-
-__visc__cleanup();
- computeAccuracy2(labels, 10000, result);
-return 0;
-
-}
diff --git a/hpvm/projects/onnx/keras_ref/generated/vgg16/src.cc b/hpvm/projects/onnx/keras_ref/generated/vgg16/src.cc
deleted file mode 100644
index 9303866d0d29d1990c858f84ccaced7f0fc0dcc7..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/keras_ref/generated/vgg16/src.cc
+++ /dev/null
@@ -1,164 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-
-std::string dir_prefix = std::string("data/vgg16_cifar10/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
-std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
-void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
-void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
-void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
-std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
-void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
-void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
-void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
-void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
-void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
-void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
-std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
-void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,10);
-std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
-void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,10,1,1);
-
-
-
-startMemTracking();
-
-int test_input_size = 10000;
-int batch_size = 10000;
-int batch_count = test_input_size / batch_size;
-float final_accuracy = 0.0;
-
-for(int i = 0; i < batch_count; i++){
-
-int start = i * batch_size;
-int end = (i + 1) * batch_size;
-
-void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
-void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
-void* var_1 = tensorAdd(var_0, conv2d_1_b);
-void* var_2 = tensorRelu(var_1);
-void* var_4 = tensorConvolution(var_2, conv2d_2_w, 1, 1, 1, 1, 1, 0);
-void* var_5 = tensorAdd(var_4, conv2d_2_b);
-void* var_6 = tensorRelu(var_5);
-void* var_7 = tensorPooling(var_6,0,2,2,0,0,2,2);
-void* var_8 = tensorConvolution(var_7, conv2d_3_w, 1, 1, 1, 1, 1, 0);
-void* var_9 = tensorAdd(var_8, conv2d_3_b);
-void* var_10 = tensorRelu(var_9);
-void* var_12 = tensorConvolution(var_10, conv2d_4_w, 1, 1, 1, 1, 1, 0);
-void* var_13 = tensorAdd(var_12, conv2d_4_b);
-void* var_14 = tensorRelu(var_13);
-void* var_15 = tensorPooling(var_14,0,2,2,0,0,2,2);
-void* var_16 = tensorConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0);
-void* var_17 = tensorAdd(var_16, conv2d_5_b);
-void* var_18 = tensorRelu(var_17);
-void* var_20 = tensorConvolution(var_18, conv2d_6_w, 1, 1, 1, 1, 1, 0);
-void* var_21 = tensorAdd(var_20, conv2d_6_b);
-void* var_22 = tensorRelu(var_21);
-void* var_24 = tensorConvolution(var_22, conv2d_7_w, 1, 1, 1, 1, 1, 0);
-void* var_25 = tensorAdd(var_24, conv2d_7_b);
-void* var_26 = tensorRelu(var_25);
-void* var_27 = tensorPooling(var_26,0,2,2,0,0,2,2);
-void* var_28 = tensorConvolution(var_27, conv2d_8_w, 1, 1, 1, 1, 1, 0);
-void* var_29 = tensorAdd(var_28, conv2d_8_b);
-void* var_30 = tensorRelu(var_29);
-void* var_32 = tensorConvolution(var_30, conv2d_9_w, 1, 1, 1, 1, 1, 0);
-void* var_33 = tensorAdd(var_32, conv2d_9_b);
-void* var_34 = tensorRelu(var_33);
-void* var_36 = tensorConvolution(var_34, conv2d_10_w, 1, 1, 1, 1, 1, 0);
-void* var_37 = tensorAdd(var_36, conv2d_10_b);
-void* var_38 = tensorRelu(var_37);
-void* var_39 = tensorPooling(var_38,0,2,2,0,0,2,2);
-void* var_40 = tensorConvolution(var_39, conv2d_11_w, 1, 1, 1, 1, 1, 0);
-void* var_41 = tensorAdd(var_40, conv2d_11_b);
-void* var_42 = tensorRelu(var_41);
-void* var_44 = tensorConvolution(var_42, conv2d_12_w, 1, 1, 1, 1, 1, 0);
-void* var_45 = tensorAdd(var_44, conv2d_12_b);
-void* var_46 = tensorRelu(var_45);
-void* var_48 = tensorConvolution(var_46, conv2d_13_w, 1, 1, 1, 1, 1, 0);
-void* var_49 = tensorAdd(var_48, conv2d_13_b);
-void* var_50 = tensorRelu(var_49);
-void* var_51 = tensorPooling(var_50,0,2,2,0,0,2,2);
-void* var_54 = tensorGemmGPU(var_51, dense_1_w);
-void* var_55 = tensorAdd(var_54, dense_1_b);
-void* var_56 = tensorRelu(var_55);
-void* var_58 = tensorGemmGPU(var_56, dense_2_w);
-void* var_59 = tensorAdd(var_58, dense_2_b);
-void* var_60 = tensorSoftmax(var_59);
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
-float accuracy = computeAccuracy2(labels, batch_size, var_60);
-final_accuracy += accuracy;
-freeBatchMemory();
-
-}
-
-final_accuracy = final_accuracy / batch_count;
-dumpFinalAccuracy(final_accuracy);
-
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/hpvm/projects/onnx/keras_ref/keras_environment.yml b/hpvm/projects/onnx/keras_ref/keras_environment.yml
deleted file mode 100644
index b0aa451b76f83618f69e8eb95ff86163b658a33a..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/keras_ref/keras_environment.yml
+++ /dev/null
@@ -1,322 +0,0 @@
-name: approxhpvm_keras
-channels:
- - pytorch
- - conda-forge
- - defaults
-dependencies:
- - absl-py=0.6.1=py36_0
- - anaconda-project=0.8.2=py36_0
- - asn1crypto=0.24.0=py36_0
- - automat=0.7.0=py36_0
- - babel=2.6.0=py36_0
- - backports=1.0=py36_1
- - backports.os=0.1.1=py36_0
- - beautifulsoup4=4.6.3=py36_0
- - bkcharts=0.2=py36_0
- - blaze=0.11.3=py36_0
- - conda=4.5.11=py36_0
- - conda-env=2.6.0=1
- - contextlib2=0.5.5=py36_0
- - cycler=0.10.0=py36_0
- - dill=0.2.8.2=py36_0
- - docutils=0.14=py36_0
- - entrypoints=0.2.3=py36_2
- - et_xmlfile=1.0.1=py36_0
- - idna=2.7=py36_0
- - imageio=2.4.1=py36_0
- - importlib_metadata=0.6=py36_0
- - ipython_genutils=0.2.0=py36_0
- - isort=4.3.4=py36_0
- - jdcal=1.4=py36_0
- - jedi=0.13.1=py36_0
- - jinja2=2.10=py36_0
- - jmespath=0.9.3=py36_0
- - jsonschema=2.6.0=py36_0
- - keyring=16.0.0=py36_0
- - libgcc=7.2.0=h69d50b8_2
- - libgfortran=3.0.0=1
- - locket=0.2.0=py36_1
- - more-itertools=4.3.0=py36_0
- - nbconvert=5.3.1=py36_0
- - nbformat=4.4.0=py36_0
- - nose=1.3.7=py36_2
- - notebook=5.7.0=py36_0
- - numpydoc=0.8.0=py36_0
- - odo=0.5.1=py36_0
- - pathlib2=2.3.2=py36_0
- - pexpect=4.6.0=py36_0
- - pickleshare=0.7.5=py36_0
- - ply=3.11=py36_0
- - ptyprocess=0.6.0=py36_0
- - pycodestyle=2.4.0=py36_0
- - pygments=2.2.0=py36_0
- - pylint=2.1.1=py36_0
- - pyopenssl=18.0.0=py36_0
- - qtconsole=4.4.2=py36_0
- - requests=2.19.1=py36_0
- - s3transfer=0.1.13=py36_0
- - secretstorage=3.1.0=py36_0
- - setuptools=40.5.0=py36_0
- - singledispatch=3.4.0.3=py36_0
- - six=1.11.0=py36_1
- - snowballstemmer=1.2.1=py36_0
- - sortedcollections=1.0.1=py36_0
- - sphinx=1.8.1=py36_0
- - spyder=3.3.1=py36_1
- - sympy=1.3=py36_0
- - tblib=1.3.2=py36_0
- - termcolor=1.1.0=py36_1
- - terminado=0.8.1=py36_1
- - testpath=0.4.2=py36_0
- - torchvision=0.2.1=py36_0
- - traitlets=4.3.2=py36_0
- - typing=3.6.4=py36_0
- - unicodecsv=0.14.1=py36_0
- - urllib3=1.23=py36_0
- - wcwidth=0.1.7=py36_0
- - wheel=0.32.2=py36_0
- - widgetsnbextension=3.4.2=py36_0
- - xlwt=1.3.0=py36_0
- - _license=1.1=py36_1
- - _tflow_select=2.1.0=gpu
- - alabaster=0.7.12=py36_0
- - anaconda-client=1.7.2=py36_0
- - anaconda=custom=py36hbbc8b67_0
- - anaconda-navigator=1.9.2=py36_0
- - appdirs=1.4.3=py36h28b3542_0
- - astor=0.7.1=py36_0
- - astroid=2.0.4=py36_0
- - astropy=3.0.5=py36h7b6447c_0
- - atomicwrites=1.2.1=py36_0
- - attrs=18.2.0=py36h28b3542_0
- - backcall=0.1.0=py36_0
- - backports.shutil_get_terminal_size=1.0.0=py36_2
- - bitarray=0.8.3=py36h14c3975_0
- - blas=1.0=mkl
- - bleach=3.0.2=py36_0
- - blosc=1.14.4=hdbcaa40_0
- - bokeh=1.0.1=py36_0
- - boto=2.49.0=py36_0
- - boto3=1.9.35=py36_0
- - botocore=1.12.35=py36_0
- - bottleneck=1.2.1=py36h035aef0_1
- - bz2file=0.98=py36_1
- - bzip2=1.0.6=h14c3975_5
- - ca-certificates=2018.03.07=0
- - cairo=1.14.12=h8948797_3
- - certifi=2018.10.15=py36_0
- - cffi=1.11.5=py36he75722e_1
- - chardet=3.0.4=py36_1
- - chest=0.2.3=py36_1
- - click=7.0=py36_0
- - cloudpickle=0.6.1=py36_0
- - clyent=1.2.2=py36_1
- - colorama=0.4.0=py36_0
- - configobj=5.0.6=py36_1
- - constantly=15.1.0=py36h28b3542_0
- - cryptography=2.3.1=py36hc365091_0
- - cudatoolkit=9.0=h13b8566_0
- - cudnn=7.1.2=cuda9.0_0
- - cupti=9.0.176=0
- - curl=7.61.0=h84994c4_0
- - cython=0.29=py36he6710b0_0
- - cytoolz=0.9.0.1=py36h14c3975_1
- - dask=0.20.0=py36_0
- - dask-core=0.20.0=py36_0
- - datashape=0.5.4=py36_1
- - dbus=1.13.2=h714fa37_1
- - decorator=4.3.0=py36_0
- - defusedxml=0.5.0=py36_1
- - distributed=1.24.0=py36_0
- - expat=2.2.6=he6710b0_0
- - fastcache=1.0.2=py36h14c3975_2
- - filelock=3.0.10=py36_0
- - flask=1.0.2=py36_1
- - flask-cors=3.0.6=py36_0
- - fontconfig=2.13.0=h9420a91_0
- - freetype=2.9.1=h8a8886c_1
- - fribidi=1.0.5=h7b6447c_0
- - gast=0.2.0=py36_0
- - gensim=3.4.0=py36h14c3975_0
- - get_terminal_size=1.0.0=haa9412d_0
- - gevent=1.3.7=py36h7b6447c_1
- - glib=2.56.2=hd408876_0
- - glob2=0.6=py36_1
- - gmp=6.1.2=h6c8ec71_1
- - gmpy2=2.0.8=py36h10f8cd9_2
- - graphite2=1.3.12=h23475e2_2
- - greenlet=0.4.15=py36h7b6447c_0
- - grpcio=1.12.1=py36hdbcaa40_0
- - gst-plugins-base=1.14.0=hbbd80ab_1
- - gstreamer=1.14.0=hb453b48_1
- - h5py=2.8.0=py36h989c5e5_3
- - harfbuzz=1.8.8=hffaf4a1_0
- - hdf5=1.10.2=hba1933b_1
- - heapdict=1.0.0=py36_2
- - html5lib=1.0.1=py36_0
- - hyperlink=18.0.0=py36_0
- - icu=58.2=h9c2bf20_1
- - imagesize=1.1.0=py36_0
- - incremental=17.5.0=py36_0
- - ipykernel=5.1.0=py36h39e3cac_0
- - ipython=7.1.1=py36h39e3cac_0
- - ipywidgets=7.4.2=py36_0
- - itsdangerous=1.1.0=py36_0
- - jbig=2.1=hdba287a_0
- - jeepney=0.4=py36_0
- - jpeg=9b=h024ee3a_2
- - keras=2.1.6=py36_0
- - keras-applications=1.0.6=py36_0
- - keras-preprocessing=1.0.5=py36_0
- - kiwisolver=1.0.1=py36hf484d3e_0
- - lazy-object-proxy=1.3.1=py36h14c3975_2
- - libcurl=7.61.0=h1ad7b7a_0
- - libedit=3.1.20170329=h6b74fdf_2
- - libffi=3.2.1=hd88cf55_4
- - libgcc-ng=8.2.0=hdf63c60_1
- - libgfortran-ng=7.3.0=hdf63c60_0
- - libiconv=1.15=h63c8f33_5
- - libpng=1.6.35=hbc83047_0
- - libprotobuf=3.6.1=hd408876_0
- - libsodium=1.0.16=h1bed415_0
- - libssh2=1.8.0=h9cfc8f7_4
- - libstdcxx-ng=8.2.0=hdf63c60_1
- - libtiff=4.0.9=he85c1e1_2
- - libtool=2.4.6=h7b6447c_5
- - libuuid=1.0.3=h1bed415_2
- - libxcb=1.13=h1bed415_1
- - libxml2=2.9.8=h26e45fe_1
- - libxslt=1.1.32=h1312cb7_0
- - llvmlite=0.25.0=py36hd408876_0
- - lxml=4.2.5=py36hefd8a0e_0
- - lzo=2.10=h49e0be7_2
- - markdown=3.0.1=py36_0
- - markupsafe=1.0=py36h14c3975_1
- - matplotlib=3.0.1=py36h5429711_0
- - mccabe=0.6.1=py36_1
- - mistune=0.8.4=py36h7b6447c_0
- - mkl=2018.0.3=1
- - mkl-service=1.1.2=py36h90e4bf4_5
- - mkl_fft=1.0.6=py36h7dd41cf_0
- - mkl_random=1.0.1=py36h4414c95_1
- - mpc=1.1.0=h10f8cd9_1
- - mpfr=4.0.1=hdf1c602_3
- - mpmath=1.0.0=py36_2
- - msgpack-python=0.5.6=py36h6bb024c_1
- - multipledispatch=0.6.0=py36_0
- - navigator-updater=0.2.1=py36_0
- - nccl=1.3.5=cuda9.0_0
- - ncurses=6.1=hf484d3e_0
- - networkx=2.2=py36_1
- - ninja=1.8.2=py36h6bb024c_1
- - nltk=3.3.0=py36_0
- - numba=0.40.0=py36h962f231_0
- - numexpr=2.6.8=py36hd89afb7_0
- - numpy=1.15.3=py36h1d66e8a_0
- - numpy-base=1.15.3=py36h81de0dd_0
- - olefile=0.46=py36_0
- - openpyxl=2.5.9=py36_0
- - openssl=1.0.2p=h14c3975_0
- - packaging=18.0=py36_0
- - pandas=0.23.4=py36h04863e7_0
- - pandoc=2.2.3.2=0
- - pandocfilters=1.4.2=py36_1
- - pango=1.42.4=h049681c_0
- - parso=0.3.1=py36_0
- - partd=0.3.9=py36_0
- - patchelf=0.9=he6710b0_3
- - path.py=11.5.0=py36_0
- - patsy=0.5.1=py36_0
- - pcre=8.42=h439df22_0
- - pep8=1.7.1=py36_0
- - pillow=5.3.0=py36h34e0f95_0
- - pip=18.1=py36_0
- - pixman=0.34.0=hceecf20_3
- - pkginfo=1.4.2=py36_1
- - pluggy=0.8.0=py36_0
- - prometheus_client=0.4.2=py36_0
- - prompt_toolkit=2.0.7=py36_0
- - protobuf=3.6.1=py36he6710b0_0
- - psutil=5.4.8=py36h7b6447c_0
- - py=1.7.0=py36_0
- - pyasn1=0.4.4=py36h28b3542_0
- - pyasn1-modules=0.2.2=py36_0
- - pycosat=0.6.3=py36h14c3975_0
- - pycparser=2.19=py36_0
- - pycrypto=2.6.1=py36h14c3975_9
- - pycurl=7.43.0.2=py36hb7f436b_0
- - pyflakes=2.0.0=py36_0
- - pyhamcrest=1.9.0=py36_2
- - pyodbc=4.0.24=py36he6710b0_0
- - pyparsing=2.2.2=py36_0
- - pyqt=5.9.2=py36h05f1152_2
- - pysocks=1.6.8=py36_0
- - pytables=3.4.4=py36ha205bf6_0
- - pytest=3.9.3=py36_0
- - pytest-arraydiff=0.2=py36h39e3cac_0
- - pytest-astropy=0.4.0=py36_0
- - pytest-doctestplus=0.1.3=py36_0
- - pytest-openfiles=0.3.0=py36_0
- - pytest-remotedata=0.3.1=py36_0
- - python=3.6.6=h6e4f718_2
- - python-dateutil=2.7.5=py36_0
- - pytorch=0.4.1=py36ha74772b_0
- - pytz=2018.7=py36_0
- - pywavelets=1.0.1=py36hdd07704_0
- - pyyaml=3.13=py36h14c3975_0
- - pyzmq=17.1.2=py36h14c3975_0
- - qt=5.9.6=h8703b6f_2
- - qtawesome=0.5.2=py36_0
- - qtpy=1.5.2=py36_0
- - readline=7.0=h7b6447c_5
- - redis=5.0.0=h7b6447c_0
- - redis-py=2.10.6=py36_0
- - rope=0.11.0=py36_0
- - ruamel_yaml=0.15.46=py36h14c3975_0
- - scikit-image=0.14.0=py36hf484d3e_1
- - scikit-learn=0.20.0=py36h4989274_1
- - scipy=1.1.0=py36hfa4b5c9_1
- - seaborn=0.9.0=py36_0
- - send2trash=1.5.0=py36_0
- - service_identity=17.0.0=py36h28b3542_0
- - simplegeneric=0.8.1=py36_2
- - sip=4.19.8=py36hf484d3e_0
- - smart_open=1.7.1=py36_0
- - snappy=1.1.7=hbae5bb6_3
- - sockjs-tornado=1.0.6=py36_0
- - sortedcontainers=2.0.5=py36_0
- - sphinxcontrib=1.0=py36_1
- - sphinxcontrib-websupport=1.1.0=py36_1
- - spyder-kernels=0.2.6=py36_0
- - sqlalchemy=1.2.12=py36h7b6447c_0
- - sqlite=3.25.2=h7b6447c_0
- - statsmodels=0.9.0=py36h035aef0_0
- - tensorboard=1.11.0=py36hf484d3e_0
- - tensorflow=1.11.0=gpu_py36h4459f94_0
- - tensorflow-base=1.11.0=gpu_py36h8e0ae2d_0
- - tensorflow-gpu=1.11.0=h0d30ee6_0
- - tk=8.6.8=hbc83047_0
- - toolz=0.9.0=py36_0
- - tornado=5.1.1=py36h7b6447c_0
- - tqdm=4.28.1=py36h28b3542_0
- - twisted=18.9.0=py36h7b6447c_0
- - typed-ast=1.1.0=py36h14c3975_0
- - unixodbc=2.3.7=h14c3975_0
- - webencodings=0.5.1=py36_1
- - werkzeug=0.14.1=py36_0
- - wrapt=1.10.11=py36h14c3975_2
- - xlrd=1.1.0=py36_1
- - xlsxwriter=1.1.2=py36_0
- - xz=5.2.4=h14c3975_4
- - yaml=0.1.7=had09818_2
- - zeromq=4.2.5=hf484d3e_1
- - zict=0.1.3=py36_0
- - zlib=1.2.11=ha838bed_2
- - zope=1.0=py36_1
- - zope.interface=4.6.0=py36h7b6447c_0
- - cuda91=1.0=h4c16780_0
- - pip:
- - msgpack==0.5.6
- - tables==3.4.4
- - torch==0.4.1
-
diff --git a/hpvm/projects/onnx/keras_ref/keras_frontend/__init__.py b/hpvm/projects/onnx/keras_ref/keras_frontend/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/hpvm/projects/onnx/keras_ref/keras_frontend/setup.py b/hpvm/projects/onnx/keras_ref/keras_frontend/setup.py
deleted file mode 100644
index 9da7193379454d1d5cdc1e63f6b436d3771e15a5..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/keras_ref/keras_frontend/setup.py
+++ /dev/null
@@ -1,12 +0,0 @@
-
-from setuptools import setup
-
-setup(
- name='frontend',
- version='1.0',
- description='ApproxHPVM frontend modules',
- author='Hashim',
- author_email='hsharif3@illinois.edu',
- packages=['frontend'],
- install_requires=[],
-)
diff --git a/hpvm/projects/onnx/keras_ref/setup.py b/hpvm/projects/onnx/keras_ref/setup.py
deleted file mode 100644
index 9da7193379454d1d5cdc1e63f6b436d3771e15a5..0000000000000000000000000000000000000000
--- a/hpvm/projects/onnx/keras_ref/setup.py
+++ /dev/null
@@ -1,12 +0,0 @@
-
-from setuptools import setup
-
-setup(
- name='frontend',
- version='1.0',
- description='ApproxHPVM frontend modules',
- author='Hashim',
- author_email='hsharif3@illinois.edu',
- packages=['frontend'],
- install_requires=[],
-)