def model_network(param_dict):
"""
This model network consists of a spike source and a neuron (IF_curr_alpha).
The spike rate of the source and the weight can be specified in the
param_dict. Returns the number of spikes fired during 1000 ms of simulation.
Parameters:
param_dict - dictionary with keys
rate - the rate of the spike source (spikes/second)
weight - weight of the connection source -> neuron
Returns:
dictionary with keys:
source_rate - the rate of the spike source
weight - weight of the connection source -> neuron
neuron_rate - spike rate of the neuron
"""
#set up the network
from retina import Retina
retina = Retina(param_dict['N'])
params = retina.params
params.update(param_dict) # updates what changed in the dictionary
# simulate the experiment and get its data
data = retina.run(params) #,verbose=False)
neuron_rate = data['out_ON_DATA'].mean_rate()
print neuron_rate
# return everything, including the input parameters
return {
'snr': param_dict['snr'],
'kernelseed': param_dict['kernelseed'],
'neuron_rate': neuron_rate
}
def model_network(param_dict):
"""
This model network consists of a spike source and a neuron (IF_curr_alpha).
The spike rate of the source and the weight can be specified in the
param_dict. Returns the number of spikes fired during 1000 ms of simulation.
Parameters:
param_dict - dictionary with keys
rate - the rate of the spike source (spikes/second)
weight - weight of the connection source -> neuron
Returns:
dictionary with keys:
source_rate - the rate of the spike source
weight - weight of the connection source -> neuron
neuron_rate - spike rate of the neuron
"""
#set up the network
from retina import Retina
retina = Retina(param_dict['N'])
params = retina.params
params.update(param_dict) # updates what changed in the dictionary
# simulate the experiment and get its data
data = retina.run(params)#,verbose=False)
neuron_rate = data['out_ON_DATA'].mean_rate()
print neuron_rate
# return everything, including the input parameters
return {'snr':param_dict['snr'],
'kernelseed':param_dict['kernelseed'],
'neuron_rate': neuron_rate}
def test_view_buffer():
r = Retina()
code = """for i in [1, 2, 3, 4]:
print "The count is", i
print "Done counting"""
buf = file_to_text_buffer(StringIO(code))
s = r.view_buffer(buf, x=7, y=1)
assert_equal(s, "** ^The count **^")
def test_view_buffer():
r = Retina()
code = """for i in [1, 2, 3, 4]:
print "The count is", i
print "Done counting"""
buf = file_to_text_buffer(StringIO(code))
s = r.view_buffer(buf, x=7, y=1)
assert_equal(s, "** ^The count **^")
def __init__(self, weights, classes=['building']):
self.net = Retina(classes).eval().cuda()
chkpnt = torch.load(weights)
self.net.load_state_dict(chkpnt['state_dict'])
self.transform = transforms.Compose([
transforms.Resize((300, 300)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
def __init__(self,
text_buffer,
retina=None,
pos=(0, 0),
char_vis=(0.226, 0.404)):
self.text_buffer = text_buffer
self.retina = retina
self.pos = pos
self.char_vis = char_vis
if self.retina is None:
self.retina = Retina()
def test_view_string():
r = Retina()
# Empty string (whitespace for all retina slots)
s = r.view_string("")
assert_equal(s, "".join([Retina.LOW_WHITESPACE] * len(r.slots)))
# Letters
s = r.view_string(" Hello World")
assert_equal(s, " ***lo World ")
# Numbers
s = r.view_string("12 This is a test")
assert_equal(s, "## *his is a ****")
def test_view_string():
r = Retina()
# Empty string (whitespace for all retina slots)
s = r.view_string("")
assert_equal(s, "".join([Retina.LOW_WHITESPACE] * len(r.slots)))
# Letters
s = r.view_string(" Hello World")
assert_equal(s, " ***lo World ")
# Numbers
s = r.view_string("12 This is a test")
assert_equal(s, "## *his is a ****")
def __init__(self, weights, classes=['building'], cuda = True):
chkpnt = torch.load(weights)
self.config = chkpnt['args']
self.net = Retina(self.config).eval()
self.net.load_state_dict(chkpnt['state_dict'])
self.transform = transforms.Compose([
transforms.Resize((self.config.model_input_size, self.config.model_input_size)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
self.net = self.net.cuda()
self.net.anchors.anchors = self.net.anchors.anchors.cuda()
torch.set_default_tensor_type('torch.cuda.FloatTensor')
self.cuda = cuda
class Eye:
TIME_PREP = 150
TIME_MOTOR = 50
TIME_SACCADE = 20
TIME_PER_DEGREE = 2
def __init__(self,
text_buffer,
retina=None,
pos=(0, 0),
char_vis=(0.226, 0.404)):
self.text_buffer = text_buffer
self.retina = retina
self.pos = pos
self.char_vis = char_vis
if self.retina is None:
self.retina = Retina()
def move_to(self, new_pos):
x, y = new_pos
time = Eye.TIME_PREP + Eye.TIME_MOTOR + Eye.TIME_SACCADE
# Distance in degrees of visual angle
dist = math.sqrt(((self.pos[0] - x) * self.char_vis[0])**2 +
((self.pos[1] - y) * self.char_vis[1])**2)
time += (dist * Eye.TIME_PER_DEGREE)
self.pos = (x, y)
return time
def view(self):
x, y = self.pos
return self.retina.view_buffer(self.text_buffer, x, y)
def __init__(self, content, retina=False):
self.CAMERA_INITIAL_ANGLE_V = deg2rad(10.0)
# TODO: 他の環境も指定できるようにする
self.content = content
self.env = Environment(self.content)
self.egocentric_images = None
self.allocentric_images = None
self.retina = Retina() if retina else None
def __init__(self, text_buffer=None, retina=None, pos=(0, 0),
char_vis=(0.226, 0.404)):
self.busy = False
self.text_buffer = text_buffer
self.retina = retina
self.pos = pos
self.char_vis = char_vis
if self.retina is None:
self.retina = Retina()
class MrChipsEye(ccm.Model):
TIME_PREP = 0.150
TIME_MOTOR = 0.050
TIME_SACCADE = 0.020
TIME_PER_DEGREE = 0.002
def __init__(self,
text_buffer=None,
retina=None,
pos=(0, 0),
char_vis=(0.226, 0.404)):
self.busy = False
self.text_buffer = text_buffer
self.retina = retina
self.pos = pos
self.char_vis = char_vis
if self.retina is None:
self.retina = Retina()
def move_to(self, new_pos):
if self.busy:
return
self.busy = True
x, y = new_pos
self.log._ = "Eye movement preparation"
yield MrChipsEye.TIME_PREP
self.log._ = "Eye movement motor preparation"
yield MrChipsEye.TIME_MOTOR
# Distance in degrees of visual angle
dist = math.sqrt(((self.pos[0] - x) * self.char_vis[0])**2 +
((self.pos[1] - y) * self.char_vis[1])**2)
saccade_time = MrChipsEye.TIME_SACCADE + (dist *
MrChipsEye.TIME_PER_DEGREE)
self.log._ = "Eye movement saccade"
yield saccade_time
self.log._ = "Finished eye movement"
self.pos = (x, y)
self.busy = False
def view(self):
assert self.text_buffer is not None
x, y = self.pos
return self.retina.view_buffer(self.text_buffer, x, y)
class MrChipsEye(ccm.Model):
TIME_PREP = 0.150
TIME_MOTOR = 0.050
TIME_SACCADE = 0.020
TIME_PER_DEGREE = 0.002
def __init__(self, text_buffer=None, retina=None, pos=(0, 0),
char_vis=(0.226, 0.404)):
self.busy = False
self.text_buffer = text_buffer
self.retina = retina
self.pos = pos
self.char_vis = char_vis
if self.retina is None:
self.retina = Retina()
def move_to(self, new_pos):
if self.busy:
return
self.busy = True
x, y = new_pos
self.log._ = "Eye movement preparation"
yield MrChipsEye.TIME_PREP
self.log._ = "Eye movement motor preparation"
yield MrChipsEye.TIME_MOTOR
# Distance in degrees of visual angle
dist = math.sqrt(((self.pos[0] - x) * self.char_vis[0])**2 +
((self.pos[1] - y) * self.char_vis[1])**2)
saccade_time = MrChipsEye.TIME_SACCADE + (dist * MrChipsEye.TIME_PER_DEGREE)
self.log._ = "Eye movement saccade"
yield saccade_time
self.log._ = "Finished eye movement"
self.pos = (x, y)
self.busy = False
def view(self):
assert self.text_buffer is not None
x, y = self.pos
return self.retina.view_buffer(self.text_buffer, x, y)
chip = nsetup.chips['mn256r1']
nsetup.mapper._init_fpga_mapper()
p = pyNCS.Population('', '')
p.populate_all(nsetup, 'mn256r1', 'excitatory')
# we use the first 128 neurons to receive input from the retina
inputpop = pyNCS.Population('','')
inputpop.populate_by_id(nsetup,'mn256r1', 'excitatory', np.linspace(0,255,256))
#reset multiplexer
chip.configurator._set_multiplexer(0)
#init class synapses learning
#sl = SynapsesLearning(p, 'learning')
#matrix_learning = np.ones([256,256])
#init retina
ret = Retina(inputpop)
#program all excitatory synapses for the programmable syn
matrix_exc = np.ones([256,256])
nsetup.mapper._program_onchip_exc_inh(matrix_exc)
#set to zeros recurrent and learning synapses
matrix_off = np.zeros([256,256])
matrix_rec_off = np.zeros([256,512])
nsetup.mapper._program_onchip_programmable_connections(matrix_off)
nsetup.mapper._program_onchip_recurrent(matrix_rec_off)
matrix_weights = np.ones([256,256])
nsetup.mapper._program_onchip_weight_matrix_programmable(matrix_weights)
#ret.map_retina_to_mn256r1()
#ret.map_retina_to_mn256r1(inputpop) #this function map retina output pixels to mn256r1 programmable syn inputpop.synapses['programmable'].paddr[1::2]
#we first init the mappings
ret._init_fpga_mapper()
default=300,
type=int,
help='Input dimensions for SSD')
args = parser.parse_args()
if 'VOC' in args.train_data:
dataset = VOC(args.train_data, transform=Transform(args.ssd_size))
else:
dataset = SpaceNet(args.train_data, transform=Transform(args.ssd_size))
args.checkpoint_dir = os.path.join(args.save_folder,
'ssd_%s' % datetime.now().isoformat())
args.means = (104, 117, 123) # only support voc now
args.num_classes = len(dataset.classes) + 1
args.stepvalues = (20, 50, 70)
args.start_iter = 0
args.writer = SummaryWriter()
os.makedirs(args.save_folder, exist_ok=True)
default_type = 'torch.cuda.FloatTensor' if args.cuda else 'torch.FloatTensor'
torch.set_default_tensor_type(default_type)
net = Retina(dataset.classes, args.ssd_size)
if args.cuda:
net = net.cuda()
load_checkpoint(net, args)
train(net, dataset, args)
perceptron_pop.populate_by_id(nsetup, 'mn256r1', 'excitatory', perceptron_neu)
net = Perceptrons(perceptron_pop,feature_pop)
net.matrix_learning_pot[:] = 0
net.upload_config()
#test
#syn = feature_pop.synapses['programmable'][::16]
#stim = syn.spiketrains_poisson(10)
#nsetup.stimulate(stim,send_reset_event=False)
#set up filters and connect retina
inputpop = pyNCS.Population('','')
inputpop.populate_by_id(nsetup,'mn256r1', 'excitatory', np.linspace(0,255,256))
#reset multiplexer
chip.configurator._set_multiplexer(0)
ret = Retina(inputpop)
ret._init_fpga_mapper()
pre_teach, post_teach, pre_address, post_address = ret.map_retina_to_mn256r1_randomproj()
nsetup.chips['mn256r1'].load_parameters('biases/biases_wijlearning_ret_perceptrons_1.biases')
#two different biases for teacher and inputs
#matrix_w = np.zeros([256,256])
#matrix_w[:,0:128] = 2
#matrix_w[:,128:256] = 1
#nsetup.mapper._program_onchip_programmable_connections(matrix_w)
#retina, pre_address, post_address = ret.map_retina_to_mn256r1_macro_pixels(syntype='learning')
#on off retina nsetup.mapper._program_detail_mapping(2**6) on -> 7
is_configured = True
if measure_scope:
def test_view_line():
r = Retina()
s = r.view_line("x = [2, 8, 7, 9, -5, 0, 2]", 2)
assert_equal(s, "- (#, 8, 7, 9. -#")
class RetinaNet:
name = NAME
@classmethod
def mk_hash(cls, path):
'''
Create an MD5 hash from a models weight file.
Arguments:
path : str - path to RetinaNet checkpoint
'''
dirs = path.split('/')
if 'retina_net' in dirs:
dirs = dirs[dirs.index('retina_net'):]
path = '/'.join(dirs)
else:
path = os.path.join('retina_net', path)
md5 = hashlib.md5()
md5.update(path.encode('utf-8'))
return md5.hexdigest()
@classmethod
def zip_weights(cls, path, base_dir='./'):
if os.path.splitext(path)[1] != '.pth':
raise ValueError('Invalid checkpoint')
dirs = path.split('/')
res = {
'name' : 'RetinaNet',
'instance' : '_'.join(dirs[-2:]),
'id' : cls.mk_hash(path)
}
zipfile = os.path.join(base_dir, res['id'] + '.zip')
if os.path.exists(zipfile):
os.remove(zipfile)
weight_dir = os.path.dirname(path)
with ZipFile(zipfile, 'w') as z:
z.write(path, os.path.join(res['id'], os.path.basename(path)))
return zipfile
def __init__(self, weights, classes=['building'], cuda = True):
chkpnt = torch.load(weights)
self.config = chkpnt['args']
self.net = Retina(self.config).eval()
self.net.load_state_dict(chkpnt['state_dict'])
self.transform = transforms.Compose([
transforms.Resize((self.config.model_input_size, self.config.model_input_size)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
self.net = self.net.cuda()
self.net.anchors.anchors = self.net.anchors.anchors.cuda()
torch.set_default_tensor_type('torch.cuda.FloatTensor')
self.cuda = cuda
def predict_image(self, image, eval_mode = False):
"""
Infer buildings for a single image.
Inputs:
image :: n x m x 3 ndarray - Should be in RGB format
"""
t0 = time.time()
img = self.transform(image)
if self.cuda:
img = img.cuda()
out = self.net(Variable(img.unsqueeze(0), requires_grad=False)).squeeze().data.cpu().numpy()
total_time = time.time() - t0
out = out[1] # ignore background class
out[:, (1, 3)] = np.clip(out[:, (1, 3)] * image.width, a_min=0, a_max=image.width)
out[:, (2, 4)] = np.clip(out[:, (2, 4)] * image.height, a_min=0, a_max=image.height)
out = out[out[:, 0] > 0]
return pandas.DataFrame(out, columns=['score', 'x1' ,'y1', 'x2', 'y2'])
def predict_all(self, test_boxes_file, batch_size=8, data_dir = None):
if data_dir is None:
data_dir = os.path.join(os.path.dirname(test_boxes_file))
annos = json.load(open(test_boxes_file))
total_time = 0.0
for batch in range(0, len(annos), batch_size):
images, sizes = [], []
for i in range(min(batch_size, len(annos) - batch)):
img = Image.open(os.path.join(data_dir, annos[batch + i]['image_path']))
images.append(self.transform(img))
sizes.append(torch.Tensor([img.width, img.height]))
images = torch.stack(images)
sizes = torch.stack(sizes)
if self.cuda:
images = images.cuda()
sizes = sizes.cuda()
out = self.net(Variable(images, requires_grad=False)).data
hws = torch.cat([sizes, sizes], dim=1).view(-1, 1, 1, 4).expand(-1, out.shape[1], out.shape[2], -1)
out[:, :, :, 1:] *= hws
out = out[:, 1, :, :].cpu().numpy()
for i, detections in enumerate(out):
anno = annos[batch + i]
pred = cv2.imread('../data/' + anno['image_path'])
detections = detections[detections[:, 0] > 0]
df = pandas.DataFrame(detections, columns=['score', 'x1', 'y1', 'x2', 'y2'])
df['image_id'] = anno['image_path']
truth = pred.copy()
for box in df[['x1', 'y1', 'x2', 'y2']].values.round().astype(int):
cv2.rectangle(pred, tuple(box[:2]), tuple(box[2:4]), (0,0,255))
for r in anno['rects']:
box = list(map(lambda x: int(r[x]), ['x1', 'y1', 'x2', 'y2']))
cv2.rectangle(truth, tuple(box[:2]), tuple(box[2:]), (0, 0, 255))
data = np.concatenate([pred, truth], axis=1)
cv2.imwrite('samples/image_%d.jpg' % (batch + i), data)
yield df
def test_view_line():
r = Retina()
s = r.view_line("x = [2, 8, 7, 9, -5, 0, 2]", 2)
assert_equal(s, "- (#, 8, 7, 9. -#")
class SSD:
name = NAME
@classmethod
def mk_hash(cls, path):
'''
Create an MD5 hash from a models weight file.
Arguments:
path : str - path to TensorBox checkpoint
'''
dirs = path.split('/')
if 'ssd.pytorch' in dirs:
dirs = dirs[dirs.index('ssd.pytorch'):]
path = '/'.join(dirs)
else:
path = os.path.join('ssd.pytorch', path)
md5 = hashlib.md5()
md5.update(path.encode('utf-8'))
return md5.hexdigest()
@classmethod
def zip_weights(cls, path, base_dir='./'):
if os.path.splitext(path)[1] != '.pth':
raise ValueError('Invalid checkpoint')
dirs = path.split('/')
res = {
'name': 'TensorBox',
'instance': '_'.join(dirs[-2:]),
'id': cls.mk_hash(path)
}
zipfile = os.path.join(base_dir, res['id'] + '.zip')
if os.path.exists(zipfile):
os.remove(zipfile)
weight_dir = os.path.dirname(path)
with ZipFile(zipfile, 'w') as z:
z.write(path, os.path.join(res['id'], os.path.basename(file)))
return zipfile
def __init__(self, weights, classes=['building']):
self.net = Retina(classes).eval().cuda()
chkpnt = torch.load(weights)
self.net.load_state_dict(chkpnt['state_dict'])
self.transform = transforms.Compose([
transforms.Resize((300, 300)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
def predict_image(self, image, threshold, eval_mode=False):
"""
Infer buildings for a single image.
Inputs:
image :: n x m x 3 ndarray - Should be in RGB format
"""
t0 = time.time()
img = self.transform(image)
out = self.net(Variable(img.unsqueeze(0).cuda(),
volatile=True)).squeeze().data.cpu()
total_time = time.time() - t0
scores = out[:, :,
0] # class X top K X (score, minx, miny, maxx, maxy)
max_scores, inds = scores.max(dim=0)
linear = torch.arange(0, out.shape[1]).long()
boxes = out[inds, linear].numpy()
boxes[:, (1, 3)] = np.clip(boxes[:, (1, 3)] * image.width,
a_min=0,
a_max=image.width)
boxes[:, (2, 4)] = np.clip(boxes[:, (2, 4)] * image.height,
a_min=0,
a_max=image.height)
df = pandas.DataFrame(boxes, columns=['score', 'x1', 'y1', 'x2', 'y2'])
if eval_mode:
return df[df['score'] > threshold], df, total_time
else:
return df[df['score'] > threshold]
pdb.set_trace()
def predict_all(self, test_boxes_file, threshold, data_dir=None):
test_boxes = json.load(open(test_boxes_file))
true_annolist = al.parse(test_boxes_file)
if data_dir is None:
data_dir = os.path.join(os.path.dirname(test_boxes_file))
total_time = 0.0
for i in range(len(true_annolist)):
true_anno = true_annolist[i]
orig_img = imread('%s/%s' %
(data_dir, true_anno.imageName))[:, :, :3]
pred, all_rects, time = self.predict_image(orig_img,
threshold,
eval_mode=True)
pred['image_id'] = i
all_rects['image_id'] = i
yield pred, all_rects, test_boxes[i]
color = label_color(labels[i])
draw_box(img, box, color=color)
caption = "{} {:.3f}".format(labels_to_names[labels[i]], scores[i])
draw_caption(img, box, caption)
return img
if __name__ == '__main__':
labels_path = './models/coco.names'
labels_to_names = open(labels_path).read().strip().split("\n")
robot = Robot()
yolo_detector = Yolo()
retina_detector = Retina()
robot.start()
while True:
frame = robot.getFrame()
frame = draw(yolo_detector, retina_detector, frame)
cv2.imshow('ai2thor', frame)
key = chr(cv2.waitKey(0))
if key == 'q':
break
robot.apply(key)
robot.stop()
cv2.destroyAllWindows()
net = Perceptrons(perceptron_pop, feature_pop)
net.matrix_learning_pot[:] = 0
net.upload_config()
#test
#syn = feature_pop.synapses['programmable'][::16]
#stim = syn.spiketrains_poisson(10)
#nsetup.stimulate(stim,send_reset_event=False)
#set up filters and connect retina
inputpop = pyNCS.Population('', '')
inputpop.populate_by_id(nsetup, 'mn256r1', 'excitatory',
np.linspace(0, 255, 256))
#reset multiplexer
chip.configurator._set_multiplexer(0)
ret = Retina(inputpop)
ret._init_fpga_mapper()
pre_teach, post_teach, pre_address, post_address = ret.map_retina_to_mn256r1_randomproj(
)
nsetup.chips['mn256r1'].load_parameters(
'biases/biases_wijlearning_ret_perceptrons_1.biases')
#two different biases for teacher and inputs
#matrix_w = np.zeros([256,256])
#matrix_w[:,0:128] = 2
#matrix_w[:,128:256] = 1
#nsetup.mapper._program_onchip_programmable_connections(matrix_w)
#retina, pre_address, post_address = ret.map_retina_to_mn256r1_macro_pixels(syntype='learning')
#on off retina nsetup.mapper._program_detail_mapping(2**6) on -> 7
is_configured = True
help='Path to training data')
parser.add_argument('--data_dir',
default=None,
help='Directory of training data')
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
args.cuda = args.gpu is not None
default_type = 'torch.cuda.FloatTensor' if args.cuda else 'torch.FloatTensor'
torch.set_default_tensor_type(default_type)
DS_Class = VOC if 'VOC' in args.train_data else SpaceNet
net = Retina(args)
dataset = DS_Class(args.train_data,
Transform(args, net.anchors),
args,
root_dir=args.data_dir)
args.checkpoint_dir = os.path.join(args.save_folder,
'ssd_%s' % datetime.now().isoformat())
args.start_iter = 0
if args.resume:
args.checkpoint_dir = os.path.dirname(args.resume)
os.makedirs(args.save_folder, exist_ok=True)
if args.cuda:
