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sys_profiling.py
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from dvsgesture_i import DatasetGesture_i
from expert import SGF_expert
import numpy as np
from STLayer import Spatiotemporal_Core
from logger import Logger
from agent import SGF_agent
import cfg
class SGF_profiling(object):
def __init__(self):
super().__init__()
def dataset_profiling(self):
name = "dataset_profiling"
logger_object = Logger(name)
logger_object_0 = Logger('event_0')
logger_object_1 = Logger('event_1')
logger_object_2 = Logger('event_2')
logger_object_3 = Logger('event_3')
logger_object_4 = Logger('event_4')
logger_object_5 = Logger('event_5')
logger_object_6 = Logger('event_6')
logger_object_7 = Logger('event_7')
logger_object_8 = Logger('event_8')
logger_object_9 = Logger('event_9')
sample_code_0 = np.full((1,46),0)
sample_code_1 = np.full((1,46),0)
sample_code_2 = np.full((1,46),0)
sample_code_3 = np.full((1,46),0)
sample_code_4 = np.full((1,46),0)
sample_code_5 = np.full((1,46),0)
sample_code_6 = np.full((1,46),0)
sample_code_7 = np.full((1,46),0)
sample_code_8 = np.full((1,46),0)
sample_code_9 = np.full((1,46),0)
pattern0 = [0 for i in range (500)]
pattern1 = [0 for i in range (500)] ## 30 is the user defined type for a single event
pattern2 = [0 for i in range (500)]
pattern3 = [0 for i in range (500)]
pattern4 = [0 for i in range (500)]
pattern5 = [0 for i in range (500)] ## 30 is the user defined type for a single event
pattern6 = [0 for i in range (500)]
pattern7 = [0 for i in range (500)]
pattern8 = [0 for i in range (500)]
pattern9 = [0 for i in range (500)]
num_his_9 = 0
num_his_8 = 0
num_his_7 = 0
num_his_6 = 0
num_his_5 = 0
num_his_4 = 0
num_his_3 = 0
num_his_2 = 0
num_his_1 = 0
num_his_0 = 0
dataset = DatasetGesture_i(cfg.data_path)
batchsize = 10
cnt = 0
testdata = np.full((80,128,128,batchsize),0) ## data initializations
test_label = [0 for i in range (batchsize)]
for i in range(0,dataset.test_len(),batchsize): ## read dataset by a block 10
for j in range(0, batchsize):
index = j+ i
if index >= dataset.test_len():
index = dataset.test_len()-1
else:
index = index
video, label = dataset.get_test_sample(index)
if np.shape(video)[0] < 80:
testdata[0:np.shape(video)[0],:,:,j] = video[:,:,:]
else:
testdata[:,:,:,j] = video[0:80,:,:]
test_label[j] = label
stlayer = Spatiotemporal_Core(testdata, 2, 1, 2, 1)
stlayer.Spaceprocessing()
stlayer.Temporalprocessing()
stlayer.Stprocessing()
stlayer.stspike()
e1 = SGF_expert()
space_neuron = e1.expert_space( 2, stlayer.ST_spike, [20,10], [0,0])
space_neuron1 = e1.expert_space( 2, stlayer.ST_spike, [5,200], [0,0])
space_all = np.concatenate((space_neuron,space_neuron1), axis = 0) ## jow results along rows, generate unique code
data_ds = testdata[0:80:5,:,:,:]
#data_ds[data_ds < 0] = 0
stlayer = Spatiotemporal_Core(data_ds, 2, 1, 2, 1) ##: user defined (this should done by agent)
stlayer.Spaceprocessing() ## space integration
stlayer.Temporalprocessing() ## temporal integration
stlayer.Stprocessing() ## space-temporal integration
stlayer.stspike()
if np.any(space_neuron1[2,:] == 1): ## simple attention mechanisms
start = [0,0]
end = [64,40]
elif np.any(space_neuron1[3,:] == 1):
start = [0,20]
end = [64,64]
else:
start = [0,0]
end = [64,64]
temporal_neuron_final1 = e1.expert_temporal(2, stlayer.stcore, start, end, 0, int(50/64*stlayer.stcore.shape[1])) #--: expert_temporal(self, resolution, data, start, end, type, scale):
temporal_neuron_final2 = e1.expert_temporal(2, stlayer.stcore, start, end ,1, int(50/64*stlayer.stcore.shape[1])) #--: expert_temporal(self, resolution, data, start, end, type, scale):
temporal_neuron1 = [0 for i in range (len(temporal_neuron_final1))]
temporal_neuron2 = [0 for i in range (len(temporal_neuron_final2))]
for i in range(0, len(temporal_neuron_final1)):
if temporal_neuron_final1[i] > temporal_neuron_final2[i]:
temporal_neuron1[i] = 1
else:
temporal_neuron2[i] = 1
temporal_td = np.stack((temporal_neuron1,temporal_neuron2),axis = 0)
temporal_all = np.concatenate((space_all,temporal_td),axis = 0)
space_neuron3 = e1.expert_space(6, stlayer.ST_spike, [8,5], [0,0])
sp_all= np.concatenate((temporal_all,space_neuron3), axis = 0) ## combine two experts knowledge
for i in range(0, batchsize):
sample = sp_all[:,i]
file_name_id = dataset.get_test_data_file_name(cnt)
print(cnt,file_name_id)
if test_label[i] == 9: ## event is 9
pattern_num = np.shape(sample_code_9)[0] ## calculate the current pattern num
pattern_index = [0 for i in range (pattern_num)]
for j in range(0, pattern_num):
if np.all(sample == sample_code_9[j]): ## if it is the same pattern, record the number
pattern9[j] = pattern9[j] +1
pattern_index[j] = 1
logger_object_9.debug(pattern9)
if np.all(np.asarray(pattern_index) == 0):
sample_code_9 = np.vstack([sample_code_9,sp_all[:,i]])
logger_object_9.critial(sample_code_9)
logger_object_9.warn(file_name_id)
logger_object_9.debug(cnt)
num_his_9 = self.expert_profiling(sample_code_9, num_his_9)
if test_label[i] == 0: ## event is 0
pattern_num = np.shape(sample_code_0)[0] ## calculate the current pattern num
pattern_index = [0 for i in range (pattern_num)]
for j in range(0, pattern_num):
if np.all(sample == sample_code_0[j]): ## if it is the same pattern, record the number
pattern0[j] = pattern0[j] +1
pattern_index[j] = 1
logger_object_0.debug(pattern0)
if np.all(np.asarray(pattern_index) == 0):
sample_code_0 = np.vstack([sample_code_0,sp_all[:,i]])
logger_object_0.critial(sample_code_0)
logger_object_0.warn(file_name_id)
logger_object_0.debug(cnt)
num_his_0 = self.expert_profiling(sample_code_0, num_his_0)
if test_label[i] == 1: ## event is 1
pattern_num = np.shape(sample_code_1)[0] ## calculate the current pattern num
pattern_index = [0 for i in range (pattern_num)]
for j in range(0, pattern_num):
if np.all(sample == sample_code_1[j]): ## if it is the same pattern, record the number
pattern1[j] = pattern1[j] +1
pattern_index[j] = 1
logger_object_1.debug(pattern1)
if np.all(np.asarray(pattern_index) == 0):
sample_code_1 = np.vstack([sample_code_1,sp_all[:,i]])
logger_object_1.critial(sample_code_1)
logger_object_1.warn(file_name_id)
logger_object_1.debug(cnt)
num_his_1 = self.expert_profiling(sample_code_1, num_his_1)
if test_label[i] == 2: ## event is 2
pattern_num = np.shape(sample_code_2)[0] ## calculate the current pattern num
pattern_index = [0 for i in range (pattern_num)]
for j in range(0, pattern_num):
if np.all(sample == sample_code_2[j]): ## if it is the same pattern, record the number
pattern2[j] = pattern2[j] +1
pattern_index[j] = 1
logger_object_2.debug(pattern2)
if np.all(np.asarray(pattern_index) == 0):
sample_code_2 = np.vstack([sample_code_2,sp_all[:,i]])
logger_object_2.critial(sample_code_2)
logger_object_2.warn(file_name_id)
logger_object_2.debug(cnt)
num_his_2 = self.expert_profiling(sample_code_2, num_his_2)
if test_label[i] == 3: ## event is 3
pattern_num = np.shape(sample_code_3)[0] ## calculate the current pattern num
pattern_index = [0 for i in range (pattern_num)]
for j in range(0, pattern_num):
if np.all(sample == sample_code_3[j]): ## if it is the same pattern, record the number
pattern3[j] = pattern3[j] +1
pattern_index[j] = 1
logger_object_3.debug(pattern3)
if np.all(np.asarray(pattern_index) == 0):
sample_code_3 = np.vstack([sample_code_3,sp_all[:,i]])
logger_object_3.critial(sample_code_3)
logger_object_3.warn(file_name_id)
logger_object_3.debug(cnt)
num_his_3 = self.expert_profiling(sample_code_3, num_his_3)
if test_label[i] == 4: ## event is 4
pattern_num = np.shape(sample_code_4)[0] ## calculate the current pattern num
pattern_index = [0 for i in range (pattern_num)]
for j in range(0, pattern_num):
if np.all(sample == sample_code_4[j]): ## if it is the same pattern, record the number
pattern4[j] = pattern4[j] +1
pattern_index[j] = 1
logger_object_4.debug(pattern4)
if np.all(np.asarray(pattern_index) == 0):
sample_code_4 = np.vstack([sample_code_4,sp_all[:,i]])
logger_object_4.critial(sample_code_4)
logger_object_4.warn(file_name_id)
logger_object_4.debug(cnt)
num_his_4 = self.expert_profiling(sample_code_4, num_his_4)
if test_label[i] == 5: ## event is 5
pattern_num = np.shape(sample_code_5)[0] ## calculate the current pattern num
pattern_index = [0 for i in range (pattern_num)]
for j in range(0, pattern_num):
if np.all(sample == sample_code_5[j]): ## if it is the same pattern, record the number
pattern5[j] = pattern5[j] +1
pattern_index[j] = 1
logger_object_5.debug(pattern5)
if np.all(np.asarray(pattern_index) == 0):
sample_code_5 = np.vstack([sample_code_5,sp_all[:,i]])
logger_object_5.critial(sample_code_5)
logger_object_5.warn(file_name_id)
logger_object_5.debug(cnt)
num_his_5 = self.expert_profiling(sample_code_5, num_his_5)
if test_label[i] == 6: ## event is 6
pattern_num = np.shape(sample_code_6)[0] ## calculate the current pattern num
pattern_index = [0 for i in range (pattern_num)]
for j in range(0, pattern_num):
if np.all(sample == sample_code_6[j]): ## if it is the same pattern, record the number
pattern6[j] = pattern6[j] +1
pattern_index[j] = 1
logger_object_6.debug(pattern6)
if np.all(np.asarray(pattern_index) == 0):
sample_code_6 = np.vstack([sample_code_6,sp_all[:,i]])
logger_object_6.critial(sample_code_6)
logger_object_6.warn(file_name_id)
logger_object_6.debug(cnt)
if test_label[i] == 7: ## event is 7
pattern_num = np.shape(sample_code_7)[0] ## calculate the current pattern num
pattern_index = [0 for i in range (pattern_num)]
for j in range(0, pattern_num):
if np.all(sample == sample_code_7[j]): ## if it is the same pattern, record the number
pattern7[j] = pattern7[j] +1
pattern_index[j] = 1
logger_object_7.debug(pattern7)
if np.all(np.asarray(pattern_index) == 0):
sample_code_7 = np.vstack([sample_code_7,sp_all[:,i]])
logger_object_7.critial(sample_code_7)
logger_object_7.warn(file_name_id)
logger_object_7.debug(cnt)
if test_label[i] == 8: ## event is 8
pattern_num = np.shape(sample_code_8)[0] ## calculate the current pattern num
pattern_index = [0 for i in range (pattern_num)]
for j in range(0, pattern_num):
if np.all(sample == sample_code_8[j]): ## if it is the same pattern, record the number
pattern8[j] = pattern8[j] +1
pattern_index[j] = 1
logger_object_8.debug(pattern8)
if np.all(np.asarray(pattern_index) == 0):
sample_code_8 = np.vstack([sample_code_8,sp_all[:,i]])
logger_object_8.critial(sample_code_8)
logger_object_8.warn(file_name_id)
logger_object_8.debug(cnt)
cnt +=1
logger_object_9.debug(np.shape(sample_code_9)[0]-1)
logger_object_9.debug(pattern9)
event9_id_p = "expert4_id_p"
np.save(event9_id_p, pattern9)
event9_pattern_p = 'event9_pattern_p'
np.save(event9_pattern_p, sample_code_9)
event9_expert_p = "event9_expert_p"
np.save(event9_expert_p, num_his_9)
logger_object_8.debug(np.shape(sample_code_8)[0]-1)
logger_object_8.debug(pattern8)
event8_id_p = "expert8_id_p"
np.save(event8_id_p, pattern8)
event8_pattern_p = 'event8_pattern_p'
np.save(event8_pattern_p, sample_code_8)
logger_object_7.debug(np.shape(sample_code_7)[0]-1)
logger_object_7.debug(pattern7)
event7_id_p = "expert7_id_p"
np.save(event7_id_p, pattern7)
event7_pattern_p = 'event7_pattern_p'
np.save(event7_pattern_p, sample_code_7)
logger_object_6.debug(np.shape(sample_code_6)[0]-1)
logger_object_6.debug(pattern6)
event6_id_p = "expert6_id_p"
np.save(event6_id_p, pattern6)
event6_pattern_p = 'event6_pattern_p'
np.save(event6_pattern_p, sample_code_6)
logger_object_5.debug(np.shape(sample_code_5)[0]-1)
logger_object_5.debug(pattern5)
event5_id_p = "expert5_id_p"
np.save(event5_id_p, pattern5)
event5_pattern_p = 'event5_pattern_p'
np.save(event5_pattern_p, sample_code_5)
event5_expert_p = "event5_expert_p"
np.save(event5_expert_p, num_his_5)
logger_object_4.debug(np.shape(sample_code_4)[0]-1)
logger_object_4.debug(pattern4)
event4_id_p = "expert4_id_p"
np.save(event4_id_p, pattern4)
event4_pattern_p = 'event4_pattern_p'
np.save(event4_pattern_p, sample_code_4)
event4_expert_p = "event4_expert_p"
np.save(event4_expert_p, num_his_4)
logger_object_3.debug(pattern3)
logger_object_3.debug(np.shape(sample_code_3)[0]-1)
event3_id_p = "expert3_id_p"
np.save(event3_id_p, pattern3)
event3_pattern_p = 'event3_pattern_p'
np.save(event3_pattern_p, sample_code_3)
event3_expert_p = "event3_expert_p"
np.save(event3_expert_p, num_his_3)
logger_object_2.debug(pattern2)
logger_object_2.debug(np.shape(sample_code_2)[0]-1)
event2_id_p = "expert2_id_p"
np.save(event2_id_p, pattern2)
event2_pattern_p = 'event2_pattern_p'
np.save(event2_pattern_p, sample_code_2)
event2_expert_p = "event2_expert_p"
np.save(event2_expert_p, num_his_2)
logger_object_1.debug(pattern1)
logger_object_1.debug(np.shape(sample_code_1)[0]-1)
event1_id_p = "expert1_id_p"
np.save(event1_id_p, pattern1)
event1_pattern_p = 'event1_pattern_p'
np.save(event1_pattern_p, sample_code_1)
event1_expert_p = "event1_expert_p"
np.save(event1_expert_p, num_his_1)
logger_object_0.debug(pattern0)
logger_object_0.debug(np.shape(sample_code_0)[0]-1)
event0_id_p = "expert0_id_p"
np.save(event0_id_p, pattern0)
event0_pattern_p = 'event0_pattern_p'
np.save(event0_pattern_p, sample_code_0)
event0_expert_p = "event0_expert_p"
np.save(event0_expert_p, num_his_0)
def expert_profiling(self,sample_code,num_his):
num = np.shape(sample_code)[0]-1
num_his = np.vstack([num_his,num])
return num_his