added ga and hyper-parameter

README.md

@@ -35,16 +35,16 @@ The instruction below will do a sweep run on each of the following networks:
 To obtain individual optimization result for a specific network and a specfic number of partition, please refer to the section below.
 
 To get optimization result with 
-1. CMA-es
+1. Covariance Matrix Adaptation Evolution Strategy (CMA-es)
     ```bash
     # cd optimization_algo/scripts
     # ./sweep_nets_cma.sh
     ```
 
-1. GA
+1. Genetic Algorithm (GA)
     ```bash
     # cd optimization_algo/scripts
-    # ./sweep_nets_cma.sh
+    # ./sweep_nets_ga.sh
     ```
 
 1. CMA

optimization_algo/approaches/ga_approach.py

@@ -3,6 +3,7 @@ import csv
 import sys
 import random
 import operator
+import path_constant as pc
 
 from multiprocessing import Pool
 from os import cpu_count
@@ -69,7 +70,7 @@ class ga_approach(object):
 
     def parse_topology_file(self):
         layers = []
-        with open(self.topology_file, 'r') as f:
+        with open(pc.TOPOLOGIES_PATH+self.topology_file, 'r') as f:
             next(f)
             for line in f:
                 elems = line.strip().split(',')
@@ -82,7 +83,7 @@ class ga_approach(object):
     def parse_data_set_file(self, path_to_data_csv):
         first = True
         target_idx = 2
-        with open(path_to_data_csv, 'r') as f:
+        with open(pc.DATA_SOURCE_PATH+path_to_data_csv, 'r') as f:
             for line in f:
                 elems = line.strip().split(',')
                 # #print(elems)
@@ -296,7 +297,7 @@ class ga_approach(object):
         # print("Throughtput Ratio:", (1/max_latency)/(1/full_latency[full_max_idx]))
         # print("Latency increase:", (max_latency*self.k)/full_latency[full_max_idx])
 
-        with open('ga.csv', 'a') as csvFile:
+        with open(pc.RESULT_CSV_PATH+'ga.csv', 'a') as csvFile:
             writer = csv.writer(csvFile, delimiter=',', lineterminator="\n")
             writer.writerow([self.target_col, self.gen,self.k,self.topology_file, 1,
                 (1/max_latency), max_latency*self.k,

optimization_algo/approaches/hyper_parameter_ga.py

 import sys
 import time
 import csv
+import path_constant as pc
+
 from hyperopt import fmin, tpe, hp, STATUS_OK, Trials, pyll
 
 class hyper_parameter(object):
@@ -42,7 +44,7 @@ class hyper_parameter(object):
 
     def parse_topology_file(self):
         layers = []
-        with open(self.topology_file, 'r') as f:
+        with open(pc.TOPOLOGIES_PATH+self.topology_file, 'r') as f:
             next(f)
             for line in f:
                 elems = line.strip().split(',')
@@ -63,7 +65,7 @@ class hyper_parameter(object):
     def parse_data_set_file(self, path_to_data_csv):
         first = True
         target_idx = 2
-        with open(path_to_data_csv, 'r') as f:
+        with open(pc.DATA_SOURCE_PATH+path_to_data_csv, 'r') as f:
             for line in f:
                 elems = line.strip().split(',')
                 # #print(elems)
@@ -164,7 +166,7 @@ class hyper_parameter(object):
         full_latency, full_max_idx = self.find_max_latency([self.layers], [self.max_res_unit]*len(self.layers))
         feasable = 0
         max_latency = 100000000
-        with open('hyperopt.csv', 'a') as csvFile:
+        with open(pc.RESULT_CSV_PATH+'hyperopt.csv', 'a') as csvFile:
             for stuff in self.trials.trials:
                 self.i = stuff.get("tid")
                 self.end = stuff.get("result").get("eval_time")

optimization_algo/scripts/sweep_nets_brute.sh

@@ -12,14 +12,14 @@ do
         for res_unit in 960;
         do
             for target in DRAM_cycle;
-	    do
+	        do
                 echo $net $partitions $target
                 python3 ../approaches/brute_force_approach.py \
                     ${net} \
                     ${partitions} \
                     ${res_unit} \
 		            ${target}
-	    done
+	        done
         done
     done
 done

optimization_algo/scripts/sweep_nets_cma.sh

@@ -10,7 +10,7 @@ declare -a max_partitions=(23 14 9 29 8 8 8 27 13)
 #tp_gain, latency_penalty, \
 #best_layer_partition, best_resource_partition, \
 #time_taken, sigma, seed, valid_sampling_percentage, \
-#trial, popsize, res_unit, seeding_type" > cma.csv
+#trial, popsize, res_unit, seeding_type" > ../resulting_csv/cma.csv
 
 for i in 8;
 do
@@ -25,14 +25,14 @@ do
                 do
                     for target in DRAM_cycle Cycles;
                     do
-                    echo $net $partitions $strategy
-                    python3 ../approaches/cma_approach.py \
-                        ${net} \
-                        ${partitions} \
-                        ${popsize} \
-                        ${res_unit} \
-                        ${strategy} \
-                        ${target}
+                        echo $net $partitions $strategy
+                        python3 ../approaches/cma_approach.py \
+                            ${net} \
+                            ${partitions} \
+                            ${popsize} \
+                            ${res_unit} \
+                            ${strategy} \
+                            ${target}
                     done
                 done
             done

optimization_algo/scripts/sweep_nets_ga.sh

@@ -4,13 +4,13 @@ declare -a nets=(FasterRCNN mobilenet yolo_tiny googlenet alexnet AlphaGoZero nc
 declare -a layers=(46 27 10 58 8 8 8 53)
 declare -a max_partitions=(23 14 9 29 8 8 8 27)
 
-echo "target, evo_counter,partitions,topology, feasable, \
-tp_partition, latency_partition, \
-tp_fullmap, latency_fm, \
-tp_gain, latency_penalty, \
-best_layer_partition, best_resource_partition, \
-time_taken, sigma, seed, valid_sampling_percentage, \
-trial, popsize, res_unit, seeding_type" > ga.csv
+# echo "target, evo_counter,partitions,topology, feasable, \
+# tp_partition, latency_partition, \
+# tp_fullmap, latency_fm, \
+# tp_gain, latency_penalty, \
+# best_layer_partition, best_resource_partition, \
+# time_taken, sigma, seed, valid_sampling_percentage, \
+# trial, popsize, res_unit, seeding_type" > ../resulting_csv/ga.csv
 
 for i in 0 1 2 3 4 5 6 7;
 do
@@ -24,16 +24,16 @@ do
             for res_unit in 960;
             do
                 for target in DRAM_cycle Cycles;
-	        do
+	            do
                     echo $net $partitions $target
-                    python3 ga_approach.py \
+                    python3 ../approaches/ga_approach.py \
                         ${net} \
                         ${partitions} \
                         ${elite_popsize} \
                         ${popsize} \
                         ${res_unit} \
-	    	        ${target}
-	        done
+	    	            ${target}
+	            done
             done
         done
     done

optimization_algo/scripts/sweep_nets_ho.sh

@@ -4,13 +4,13 @@ declare -a nets=(FasterRCNN mobilenet yolo_tiny googlenet alexnet AlphaGoZero nc
 declare -a layers=(46 27 10 58 8 8 8 53)
 declare -a max_partitions=(23 14 9 29 8 8 8 27)
 
-echo "target, evo_counter,partitions,topology, feasable, \
-tp_partition, latency_partition, \
-tp_fullmap, latency_fm, \
-tp_gain, latency_penalty, \
-best_layer_partition, best_resource_partition, \
-time_taken, sigma, seed, valid_sampling_percentage, \
-trial, popsize, res_unit, seeding_type" > hyperopt.csv
+# echo "target, evo_counter,partitions,topology, feasable, \
+# tp_partition, latency_partition, \
+# tp_fullmap, latency_fm, \
+# tp_gain, latency_penalty, \
+# best_layer_partition, best_resource_partition, \
+# time_taken, sigma, seed, valid_sampling_percentage, \
+# trial, popsize, res_unit, seeding_type" > ../resulting_csv/hyperopt.csv
 
 for i in 0 1 2 3 4 5 6 7;
 do
@@ -20,15 +20,15 @@ do
         for res_unit in 960;
         do
             for target in DRAM_cycle Cycles;
-	    do
+	        do
                 echo $net $partitions $target
-                python3 hyper_parameter_ga.py \
+                python3 ../approaches/hyper_parameter_ga.py \
                     ${net} \
                     ${partitions} \
                     ${res_unit} \
 		            ${target} \
                     2500
-	    done
+	        done
         done
     done
 done