Official ARM version: v5.4.0
Add CMSIS V5.4.0, please refer to index.html available under \docs folder.
Note: content of \CMSIS\Core\Include has been copied under \Include to keep the same structure
used in existing projects, and thus avoid projects mass update
Note: the following components have been removed from ARM original delivery (as not used in ST packages)
- CMSIS_EW2018.pdf
- .gitattributes
- .gitignore
- \Device
- \CMSIS
- \CoreValidation
- \DAP
- \Documentation
- \DoxyGen
- \Driver
- \Pack
- \RTOS\CMSIS_RTOS_Tutorial.pdf
- \RTOS\RTX
- \RTOS\Template
- \RTOS2\RTX
- \Utilities
- All ARM/GCC projects files are deleted from \DSP, \RTOS and \RTOS2
Change-Id: Ia026c3f0f0d016627a4fb5a9032852c33d24b4d3
This commit is contained in:
Ali Labbene
parent
76177aa280
commit
9f95ff5b6b
6402 changed files with 944683 additions and 314204 deletions
146
NN/Scripts/NNFunctions/fully_connected_opt_weight_generation.py
Normal file
146
NN/Scripts/NNFunctions/fully_connected_opt_weight_generation.py
Normal file
|
|
@ -0,0 +1,146 @@
|
|||
#!/usr/bin/env python
|
||||
|
||||
import numpy as np
|
||||
|
||||
def convert_to_x4_q7_weights(weights):
|
||||
[r, h, w, c] = weights.shape
|
||||
weights = np.reshape(weights, (r, h*w*c))
|
||||
num_of_rows = r
|
||||
num_of_cols = h*w*c
|
||||
new_weights = np.copy(weights)
|
||||
new_weights = np.reshape(new_weights, (r*h*w*c))
|
||||
counter = 0
|
||||
for i in range(int(num_of_rows)/4):
|
||||
# we only need to do the re-ordering for every 4 rows
|
||||
row_base = 4*i
|
||||
for j in range (int(num_of_cols)/4):
|
||||
# for each 4 entries
|
||||
column_base = 4*j
|
||||
new_weights[counter] = weights[row_base ][column_base ]
|
||||
new_weights[counter+1] = weights[row_base+1][column_base ]
|
||||
new_weights[counter+2] = weights[row_base ][column_base+2]
|
||||
new_weights[counter+3] = weights[row_base+1][column_base+2]
|
||||
new_weights[counter+4] = weights[row_base+2][column_base ]
|
||||
new_weights[counter+5] = weights[row_base+3][column_base ]
|
||||
new_weights[counter+6] = weights[row_base+2][column_base+2]
|
||||
new_weights[counter+7] = weights[row_base+3][column_base+2]
|
||||
|
||||
new_weights[counter+8] = weights[row_base ][column_base+1]
|
||||
new_weights[counter+9] = weights[row_base+1][column_base+1]
|
||||
new_weights[counter+10] = weights[row_base ][column_base+3]
|
||||
new_weights[counter+11] = weights[row_base+1][column_base+3]
|
||||
new_weights[counter+12] = weights[row_base+2][column_base+1]
|
||||
new_weights[counter+13] = weights[row_base+3][column_base+1]
|
||||
new_weights[counter+14] = weights[row_base+2][column_base+3]
|
||||
new_weights[counter+15] = weights[row_base+3][column_base+3]
|
||||
counter = counter + 16
|
||||
# the remaining ones are in order
|
||||
for j in range((int)(num_of_cols-num_of_cols%4), int(num_of_cols)):
|
||||
new_weights[counter] = weights[row_base][j]
|
||||
new_weights[counter+1] = weights[row_base+1][j]
|
||||
new_weights[counter+2] = weights[row_base+2][j]
|
||||
new_weights[counter+3] = weights[row_base+3][j]
|
||||
counter = counter + 4
|
||||
return new_weights
|
||||
|
||||
def convert_to_x4_q15_weights(weights):
|
||||
[r, h, w, c] = weights.shape
|
||||
weights = np.reshape(weights, (r, h*w*c))
|
||||
num_of_rows = r
|
||||
num_of_cols = h*w*c
|
||||
new_weights = np.copy(weights)
|
||||
new_weights = np.reshape(new_weights, (r*h*w*c))
|
||||
counter = 0
|
||||
for i in range(int(num_of_rows)/4):
|
||||
# we only need to do the re-ordering for every 4 rows
|
||||
row_base = 4*i
|
||||
for j in range (int(num_of_cols)/2):
|
||||
# for each 2 entries
|
||||
column_base = 2*j
|
||||
new_weights[counter] = weights[row_base ][column_base ]
|
||||
new_weights[counter+1] = weights[row_base ][column_base+1]
|
||||
new_weights[counter+2] = weights[row_base+1][column_base ]
|
||||
new_weights[counter+3] = weights[row_base+1][column_base+1]
|
||||
new_weights[counter+4] = weights[row_base+2][column_base ]
|
||||
new_weights[counter+5] = weights[row_base+2][column_base+1]
|
||||
new_weights[counter+6] = weights[row_base+3][column_base ]
|
||||
new_weights[counter+7] = weights[row_base+3][column_base+1]
|
||||
|
||||
counter = counter + 8
|
||||
# the remaining ones are in order
|
||||
for j in range((int)(num_of_cols-num_of_cols%2), int(num_of_cols)):
|
||||
new_weights[counter] = weights[row_base][j]
|
||||
new_weights[counter+1] = weights[row_base+1][j]
|
||||
new_weights[counter+2] = weights[row_base+2][j]
|
||||
new_weights[counter+3] = weights[row_base+3][j]
|
||||
counter = counter + 4
|
||||
return new_weights
|
||||
|
||||
def convert_q7_q15_weights(weights):
|
||||
[r, h, w, c] = weights.shape
|
||||
weights = np.reshape(weights, (r, h*w*c))
|
||||
num_of_rows = r
|
||||
num_of_cols = h*w*c
|
||||
new_weights = np.copy(weights)
|
||||
new_weights = np.reshape(new_weights, (r*h*w*c))
|
||||
counter = 0
|
||||
for i in range(int(num_of_rows)/4):
|
||||
# we only need to do the re-ordering for every 4 rows
|
||||
row_base = 4*i
|
||||
for j in range (int(num_of_cols)/2):
|
||||
# for each 2 entries
|
||||
column_base = 2*j
|
||||
new_weights[counter] = weights[row_base ][column_base ]
|
||||
new_weights[counter+1] = weights[row_base+1][column_base ]
|
||||
new_weights[counter+2] = weights[row_base ][column_base+1]
|
||||
new_weights[counter+3] = weights[row_base+1][column_base+1]
|
||||
new_weights[counter+4] = weights[row_base+2][column_base ]
|
||||
new_weights[counter+5] = weights[row_base+3][column_base ]
|
||||
new_weights[counter+6] = weights[row_base+2][column_base+1]
|
||||
new_weights[counter+7] = weights[row_base+3][column_base+1]
|
||||
|
||||
counter = counter + 8
|
||||
# the remaining ones are in order
|
||||
for j in range((int)(num_of_cols-num_of_cols%2), int(num_of_cols)):
|
||||
new_weights[counter] = weights[row_base][j]
|
||||
new_weights[counter+1] = weights[row_base+1][j]
|
||||
new_weights[counter+2] = weights[row_base+2][j]
|
||||
new_weights[counter+3] = weights[row_base+3][j]
|
||||
counter = counter + 4
|
||||
return new_weights
|
||||
|
||||
# input dimensions
|
||||
vec_dim = 127
|
||||
row_dim = 127
|
||||
|
||||
weight = np.zeros((row_dim,vec_dim), dtype=int)
|
||||
|
||||
# generate random inputs
|
||||
for i in range(row_dim):
|
||||
for j in range(vec_dim):
|
||||
weight[i][j] = np.random.randint(256)-128
|
||||
|
||||
weight = np.reshape(weight, (row_dim, vec_dim, 1, 1))
|
||||
|
||||
outfile = open("../Ref_Implementations/fully_connected_testing_weights.h", "w")
|
||||
outfile.write("#define IP2_WEIGHT {")
|
||||
weight.tofile(outfile,sep=",",format="%d")
|
||||
outfile.write("}\n\n")
|
||||
|
||||
new_weight = convert_to_x4_q7_weights(weight)
|
||||
outfile.write("#define IP4_WEIGHT {")
|
||||
new_weight.tofile(outfile,sep=",",format="%d")
|
||||
outfile.write("}\n\n")
|
||||
|
||||
new_weight = convert_q7_q15_weights(weight)
|
||||
outfile.write("#define IP4_q7_q15_WEIGHT {")
|
||||
new_weight.tofile(outfile,sep=",",format="%d")
|
||||
outfile.write("}\n\n")
|
||||
|
||||
new_weight = convert_to_x4_q15_weights(weight)
|
||||
outfile.write("#define IP4_WEIGHT_Q15 {")
|
||||
new_weight.tofile(outfile,sep=",",format="%d")
|
||||
outfile.write("}\n\n")
|
||||
|
||||
|
||||
outfile.close()
|
||||
116
NN/Scripts/NNFunctions/table_gen.py
Normal file
116
NN/Scripts/NNFunctions/table_gen.py
Normal file
|
|
@ -0,0 +1,116 @@
|
|||
#!/usr/bin/python
|
||||
|
||||
import math
|
||||
|
||||
class Table(object):
|
||||
|
||||
def __init__(self, table_entry=256, table_range=8):
|
||||
self.table_entry = table_entry
|
||||
self.table_range = table_range
|
||||
pass
|
||||
|
||||
def sigmoid(self, x):
|
||||
return 1 / (1 + math.exp(-1*x))
|
||||
|
||||
def tanh(self, x):
|
||||
return (math.exp(2*x)-1) / (math.exp(2*x)+1)
|
||||
|
||||
def fp2q7(self, x):
|
||||
x_int = math.floor(x*(2**7)+0.5)
|
||||
if x_int >= 128 :
|
||||
x_int = 127
|
||||
if x_int < -128 :
|
||||
x_int = -128
|
||||
if x_int >= 0 :
|
||||
return x_int
|
||||
else :
|
||||
return 0x100 + x_int
|
||||
|
||||
def fp2q15(self, x):
|
||||
x_int = math.floor(x*(2**15)+0.5)
|
||||
if x_int >= 2**15 :
|
||||
x_int = 2**15-1
|
||||
if x_int < -1*2**15 :
|
||||
x_int = -1*2**15
|
||||
if x_int >= 0 :
|
||||
return x_int
|
||||
else :
|
||||
return 0x10000 + x_int
|
||||
|
||||
def table_gen(self):
|
||||
outfile = open("NNCommonTable.c", "wb")
|
||||
|
||||
outfile.write("/*\n * Common tables for NN\n *\n *\n *\n *\n */\n\n#include \"arm_math.h\"\n#include \"NNCommonTable.h\"\n\n/*\n * Table for sigmoid\n */\n")
|
||||
|
||||
for function_type in ["sigmoid", "tanh"]:
|
||||
for data_type in [7, 15]:
|
||||
out_type = "q"+str(data_type)+"_t"
|
||||
act_func = getattr(self, function_type)
|
||||
quan_func = getattr(self, 'fp2q'+str(data_type))
|
||||
|
||||
# unified table
|
||||
outfile.write('const %s %sTable_q%d[%d] = {\n' % (out_type, function_type, data_type, self.table_entry) )
|
||||
for i in range(self.table_entry):
|
||||
# convert into actual value
|
||||
if i < self.table_entry/2:
|
||||
value_q7 = self.table_range * (i)
|
||||
else:
|
||||
value_q7 = self.table_range * (i - self.table_entry)
|
||||
|
||||
if data_type == 7:
|
||||
#outfile.write('%f, ' % (act_func(float(value_q7)/256)))
|
||||
outfile.write('0x%02x, ' % (quan_func(act_func(float(value_q7)/self.table_entry))))
|
||||
else:
|
||||
#outfile.write('%f, ' % (act_func(float(value_q7)/256)))
|
||||
outfile.write('0x%04x, ' % (quan_func(act_func(float(value_q7)/self.table_entry))))
|
||||
if i % 8 == 7:
|
||||
outfile.write("\n")
|
||||
outfile.write("};\n\n")
|
||||
|
||||
for data_type in [15]:
|
||||
out_type = "q"+str(data_type)+"_t"
|
||||
act_func = getattr(self, function_type)
|
||||
quan_func = getattr(self, 'fp2q'+str(data_type))
|
||||
|
||||
# H-L tables
|
||||
outfile.write('const %s %sLTable_q%d[%d] = {\n' % (out_type, function_type, data_type, self.table_entry/2))
|
||||
for i in range(self.table_entry/2):
|
||||
# convert into actual value, max value is 16*self.table_entry/4 / 4
|
||||
# which is equivalent to self.table_entry / self.table_entry/2 = 2, i.e., 1/4 of 8
|
||||
if i < self.table_entry/4:
|
||||
value_q7 = self.table_range * i / 4
|
||||
else:
|
||||
value_q7 = self.table_range * (i - self.table_entry/2) / 4
|
||||
if data_type == 7:
|
||||
#outfile.write('%f, ' % (act_func(float(value_q7)/256)))
|
||||
outfile.write('0x%02x, ' % (quan_func(act_func(float(value_q7)/(self.table_entry/2)))))
|
||||
else:
|
||||
#outfile.write('%f, ' % (act_func(float(value_q7)/256)))
|
||||
outfile.write('0x%04x, ' % (quan_func(act_func(float(value_q7)/(self.table_entry/2)))))
|
||||
if i % 8 == 7:
|
||||
outfile.write("\n")
|
||||
outfile.write("};\n\n")
|
||||
|
||||
outfile.write('const %s %sHTable_q%d[%d] = {\n' % (out_type, function_type, data_type, 3*self.table_entry/4))
|
||||
for i in range(3 * self.table_entry/4):
|
||||
# convert into actual value, tageting range (2, 8)
|
||||
if i < 3*self.table_entry/8 :
|
||||
value_q7 = self.table_range * ( i + self.table_entry/8 )
|
||||
else:
|
||||
value_q7 = self.table_range * ( i + self.table_entry/8 - self.table_entry)
|
||||
if data_type == 7:
|
||||
#outfile.write('%f, ' % (act_func(float(value_q7)/256)))
|
||||
outfile.write('0x%02x, ' % (quan_func(act_func(float(value_q7)/self.table_entry))))
|
||||
else:
|
||||
#outfile.write('%f, ' % (act_func(float(value_q7)/256)))
|
||||
outfile.write('0x%04x, ' % (quan_func(act_func(float(value_q7)/self.table_entry))))
|
||||
if i % 8 == 7:
|
||||
outfile.write("\n")
|
||||
outfile.write("};\n\n")
|
||||
|
||||
outfile.close()
|
||||
|
||||
|
||||
mytable = Table(table_entry=256, table_range=16)
|
||||
|
||||
mytable.table_gen()
|
||||
Loading…
Add table
Add a link
Reference in a new issue