def test_FV_AV(path='data/pm1957090100_00589248p'):
r = RPN(path)
av = r.get_first_record_for_name('AV')
fv = r.get_first_record_for_name_and_level(varname='FV', level=5)
nx, ny = av[:, :, 0].shape
ratio = np.zeros((nx, ny))
for i in range(nx):
for j in range(ny):
if fv[i, j] != 0:
ratio[i, j] = av[i, j] / fv[i, j]
print(np.max(av))
print(np.max(fv))
print(np.max(av) / np.max(fv))
plt.figure()
plt.imshow(av[:, :, 0])
plt.colorbar()
plt.figure()
plt.imshow(fv[:, :, 0])
plt.colorbar()
plt.show()
def calculate_monthly_sum(
data_folder="/home/huziy/skynet3_rech1/gemclim/quebec/Samples",
month=6,
year=1999,
prefix="quebec_220x220_",
var_name="GWDI",
level=-1,
):
suffix = name_format % (year, month)
folder_for_month = os.path.join(data_folder, prefix + suffix)
count = 0.0
result_data = None
if not os.path.isdir(folder_for_month):
return 0, 0
for file in os.listdir(folder_for_month):
if not file.startswith("pm"):
continue
the_path = os.path.join(folder_for_month, file)
print(the_path)
r = RPN(the_path)
the_data = r.get_first_record_for_name_and_level(var_name, level=level)
if result_data == None:
result_data = the_data
else:
result_data += the_data
count += 1
r.close()
return result_data, count
pass
def test():
path = 'data/pm1957090100_00589248p'
#path = 'data/crcm_sim_with_lakes/data_selected/Coupled11_36cpu_Test_C_198505/pm1957090100_00727920p'
rpn = RPN(path)
data = rpn.get_first_record_for_name_and_level(varname = 'FV', level = 7 )
lons, lats = rpn.get_longitudes_and_latitudes()
print(lons.shape, np.min(lons), np.max(lons))
print(lats.shape, np.min(lats), np.max(lats))
# print data.shape
# plot_field_2d(lons, lats, data[:,:,0])
print(data.shape)
plot_field_2d(lons, lats, data)
plt.savefig('plot.png')
#plt.figure()
#plt.imshow(np.transpose(lons[:,:]), origin = 'lower')
#plt.imshow(np.transpose(data[:,:]), origin = 'lower') #for plotting in order to see i,j we supply j,i
plt.show()
def get_lon_lat(
path='/home/huziy/skynet3_rech1/gemclim/quebec/Samples/quebec_220x220_199912/pm1999050100_00068760p'
):
r = RPN(path)
lons, lats = r.get_longitudes_and_latitudes()
r.close()
return lons, lats
def calculate_monthly_sum(
data_folder='/home/huziy/skynet3_rech1/gemclim/quebec/Samples',
month=6,
year=1999,
prefix='quebec_220x220_',
var_name='GWDI',
level=-1):
suffix = name_format % (year, month)
folder_for_month = os.path.join(data_folder, prefix + suffix)
count = 0.0
result_data = None
if not os.path.isdir(folder_for_month):
return 0, 0
for file in os.listdir(folder_for_month):
if not file.startswith('pm'):
continue
the_path = os.path.join(folder_for_month, file)
print(the_path)
r = RPN(the_path)
the_data = r.get_first_record_for_name_and_level(var_name, level=level)
if result_data == None:
result_data = the_data
else:
result_data += the_data
count += 1
r.close()
return result_data, count
pass
def test_basic_vars(self):
r = RPN()
assert r.calc('foo bar +', {
'foo': 2,
'bar': 3
}) == 5
def test_FV_AV(path = 'data/pm1957090100_00589248p'):
r = RPN(path)
av = r.get_first_record_for_name('AV')
fv = r.get_first_record_for_name_and_level(varname = 'FV', level = 5)
nx, ny = av[:,:,0].shape
ratio = np.zeros((nx, ny))
for i in range(nx):
for j in range(ny):
if fv[i, j] != 0:
ratio[i, j] = av[i, j] / fv[i, j]
print(np.max(av))
print(np.max(fv))
print(np.max(av) / np.max(fv))
plt.figure()
plt.imshow(av[:,:,0])
plt.colorbar()
plt.figure()
plt.imshow(fv[:,:,0])
plt.colorbar()
plt.show()
def get_lakefraction(path='data/geophys_lam_na_0.5deg_170x158_class',
margin=20):
r = RPN(path)
data = r.get_first_record_for_name_and_level('VF', level=3)
r.close()
return data[margin:-margin, margin:-margin, 0]
pass
def test():
rObj = RPN('write.rpn', mode = 'w')
nx = 20
ny = 40
data = np.zeros((nx, ny))
for i in range(nx):
for j in range(ny):
data[i, j] = i ** 2 + j ** 2
print('before ', data.shape, data.min(), data.max(), data.mean())
plt.figure()
plt.title('before')
plt.pcolormesh(data)
plt.colorbar()
rObj.write_2D_field('test', level = 1, data = data, grid_type = '')
rObj.close()
rObj = RPN('write.rpn')
x = rObj.get_first_record_for_name('test')
rObj.close()
print('after ', x.shape, x.min(), x.max(), x.mean())
plt.figure()
plt.title('after')
plt.pcolormesh(x)
plt.colorbar()
plt.show()
def from_file(path):
"""Parse file
Args:
path(string): file format is:
number_of_variables(n)
0 <= x0 <= 3.3
10 <= x1 <= 323.3
11.3 <= x2 <= 323
...
0 <= xn <= 2
number_of_equations(r)
x1 + x2 <= 3
sin(tan(x3)) + (x0^4) >= 4
...
x0 + x1 + x2 <= 5.5
goal_function
goal_type
"""
with open(path) as f:
data = f.readlines()
data = [x.strip() for x in data]
data = [x for x in data if x and not x.startswith('#')]
number_of_variables = int(data[0])
# --------- BOUNDARIES
boundaries, equations = [], []
i = 1
while i < number_of_variables + 1:
boundary_equation = data[i]
bound = preparse_bound(boundary_equation)
boundaries.append(bound)
i += 1
# ------- EQUATIONS
number_of_equations = int(data[i])
i += 1
while i < number_of_equations + number_of_variables + 2:
function, equality, bound = preparse_equation(data[i])
equations.append((RPN(function), equality, bound))
i += 1
# -------- GOAL
goal = RPN(data[i])
goal_type = data[i + 1]
if goal_type not in ('min', 'max'):
raise EquationError(
"Incorrect goal type, must be one of (min, max): {}".format(
goal_type))
return goal, goal_type, equations, boundaries
def test():
path = 'data/pm1998010100-00-00_00000000p'
rObj = RPN(path)
data = rObj.get_first_record_for_name('STBM')
print(data[data < 0])
print(data.min(), data.max(), data.mean())
data = np.ma.masked_where(data < 0, data)
print(np.ma.min(data))
plt.pcolormesh(data.transpose())
plt.colorbar()
plt.show()
def main():
# Display Help in the beginning of the program.
print RPN.rpn_help()
# Get into a controlled Daemon/infinite loop.
while True:
# Get the RPN expression
input_text = RPN.get_input()
# Check what have you got, if its an expression, call for evaluation
if RPN.is_expression(input_text):
output = RPN.evaluate_expression(input_text)
print output
def test():
path = 'data/pm1998010100-00-00_00000000p'
rObj = RPN(path)
data = rObj.get_first_record_for_name('STBM')
print(data[data < 0])
print(data.min(), data.max(), data.mean())
data = np.ma.masked_where(data < 0, data)
print(np.ma.min(data))
plt.pcolormesh(data.transpose())
plt.colorbar()
plt.show()
def _inner():
for t, datarows in self.raw_data.items():
total = None
for k, v in datarows.items():
value = v
if value is not None:
if k in self.cdefs:
cdef = self.cdefs[k]
value = RPN().calc(cdef.split(','), datarows)
value = round(value, 5)
value = value * -1 if k in self.invert_datarow_names else value
if total is None:
total = value
else:
total += value
datarows[k] = value
if self.graph.graph_total and total is not None:
datarows[self.graph.graph_total] = total
yield t, datarows
def calculate_time_integral(
data_folder='data/crcm_sim_with_lakes/data_selected',
start_date=None,
end_date=None,
var_name='FV',
level=7,
level_kind=level_kinds.ARBITRARY,
name_pattern='Coupled11_36cpu_Test_C_%Y%m',
dt=3 * 60 * 60,
file_size_limit_bytes=None):
date = start_date
time_integral = None
while date <= end_date:
current_folder = os.path.join(data_folder, date.strftime(name_pattern))
for file in os.listdir(current_folder):
if not file.startswith('pm'): #skip non physics files
continue
file_path = os.path.join(current_folder, file)
if file_size_limit_bytes != None:
if os.path.getsize(file_path) < file_size_limit_bytes:
continue
r = RPN(file_path)
print('current folder ', current_folder)
print('reading file {0}'.format(file))
data = r.get_first_record_for_name_and_level(varname=var_name,
level=level,
level_kind=level_kind)
data = data[:, :, 0]
if date == start_date:
time_integral = data
else:
time_integral += data
r.close()
#add month
if date.month == 12:
date = datetime(date.year + 1, 1, date.day, date.hour, date.minute)
else:
date = datetime(date.year, date.month + 1, date.day, date.hour,
date.minute)
return time_integral * dt
pass
def __init__(self, in_size, num_classes, visualize=False):
super(FasterRCNN, self).__init__()
self.in_size = in_size
self.num_classes = num_classes
self.visualize = visualize
self.device = torch.device(
'cuda:0' if torch.cuda.is_available() else 'cpu')
# self.backbone = self.build_backbone()
self.backbone = VGG('A', self.num_classes).to(self.device)
self.rpn = RPN(512, 512, self.in_size, 9).to(self.device)
def manually():
number_of_variables = None
while number_of_variables is None:
number_of_variables = int(input("Number of variables: "))
if number_of_variables <= 0:
number_of_variables = None
print("There must be more than 0 variables")
# --------- BOUNDARIES
boundaries = []
print("Boundaries (in the form: 10 <= x0 <= 50):")
for i in range(number_of_variables):
boundary_equation = input("for x{}: ".format(i))
bound = preparse_bound(boundary_equation)
boundaries.append(bound)
# ------- EQUATIONS
number_of_equations = int(input("Number of equations: "))
equations = []
i = 0
while i < number_of_equations:
equation = input("Gimme equation {}: ".format(i))
function, equality, bound = preparse_equation(equation)
rpn = RPN(function)
try:
rpn.infix()
equations.append((rpn, equality, bound))
except RPNError as e:
print("Wrong equation: {}".format(e))
i -= 1
i += 1
# -------- GOAL
goal = input("Gimme goal function: ")
goal = RPN(goal)
goal_type = input(
"Maximize (max) or minimize (min) goal function: ").lower()
if goal_type not in ('min', 'max'):
raise EquationError("Incorrect goal type, must be one of (min, max)")
return goal, goal_type, equations, boundaries
def get_session_and_models():
''' Define model graph, load model parameters,
create session and return session handle and tensors
'''
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
config.log_device_placement = False
# image segmentaion
with tf.Graph().as_default():
with tf.device('/gpu:' + str(GPU_INDEX)):
img_seg_net = ImgSegNet(BATCH_SIZE, NUM_POINT)
img_seg_net.load_graph(FLAGS.img_seg_model)
sess1 = tf.Session(config=config)
# point RPN
with tf.Graph().as_default():
with tf.device('/gpu:' + str(GPU_INDEX)):
rpn_model = RPN(BATCH_SIZE,
NUM_POINT,
num_channel=4,
is_training=False)
pls = rpn_model.placeholders
box_center, box_angle, box_size = rpn_model.box_encoder.tf_decode(
rpn_model.end_points)
box_center = box_center + rpn_model.end_points['fg_points_xyz']
rpn_model.end_points['box_center'] = box_center
rpn_model.end_points['box_angle'] = box_angle
rpn_model.end_points['box_size'] = box_size
sess2 = tf.Session(config=config)
saver = tf.train.Saver()
saver.restore(sess2, FLAGS.rpn_model)
with tf.Graph().as_default():
with tf.device('/gpu:' + str(GPU_INDEX)):
rcnn_model = FrustumPointNet(BATCH_SIZE_RCNN, NUM_POINT_RCNN)
sess3 = tf.Session(config=config)
saver = tf.train.Saver()
saver.restore(sess3, FLAGS.rcnn_model)
return sess1, img_seg_net, sess2, rpn_model, sess3, rcnn_model
def calculate_time_integral(data_folder = 'data/crcm_sim_with_lakes/data_selected',
start_date = None, end_date = None, var_name = 'FV', level = 7,
level_kind = level_kinds.ARBITRARY,
name_pattern = 'Coupled11_36cpu_Test_C_%Y%m',
dt = 3 * 60 * 60,
file_size_limit_bytes = None
):
date = start_date
time_integral = None
while date <= end_date:
current_folder = os.path.join(data_folder, date.strftime(name_pattern))
for file in os.listdir(current_folder):
if not file.startswith('pm'): #skip non physics files
continue
file_path = os.path.join(current_folder, file)
if file_size_limit_bytes != None:
if os.path.getsize(file_path) < file_size_limit_bytes:
continue
r = RPN(file_path)
print('current folder ', current_folder)
print('reading file {0}'.format(file))
data = r.get_first_record_for_name_and_level(varname = var_name, level = level, level_kind = level_kind)
data = data[:,:,0]
if date == start_date:
time_integral = data
else:
time_integral += data
r.close()
#add month
if date.month == 12:
date = datetime(date.year + 1, 1, date.day, date.hour, date.minute)
else:
date = datetime(date.year, date.month + 1, date.day, date.hour, date.minute)
return time_integral * dt
pass
def testMatch(self):
for pattern, test_case in self._getCases().items():
tree = SyntaxTree(RPN(pattern))
root_tree = tree.buildTree()
followpos = root_tree.getFollowpos()
drtan, nodes, start, term = root_tree.build(
followpos, tree.dies_positions)
dfa = DFA(drtan, nodes, start, term, root_tree.alphabet)
for t, accepts in test_case.items():
self.assertEqual(dfa.match(start, term, drtan, t), accepts)
def read_data(folder="data/1950-1960-ECHO-G-profiles"):
ni, nj, id_to_hor_indices, land_sea_mask = read_coordinates()
nz = -1
all_data = []
for year_folder in os.listdir(folder):
year_folder_path = os.path.join(folder, year_folder)
if not os.path.isdir(year_folder_path):
continue
data = None
for file in os.listdir(year_folder_path):
file_path = os.path.join(year_folder_path, file)
f = open(file_path)
lines = [x.strip() for x in f.readlines()]
lines = list(filter(lambda x: len(x) > 0, lines))
if data is None:
nz = len(lines)
data = np.zeros((ni, nj, nz))
the_id = int(re.findall("\d+", file)[0])
i, j = id_to_hor_indices[the_id]
profile = [float(x.split()[-1]) for x in lines]
data[i, j, :] = np.array(profile)[:]
f.close()
all_data.append(np.fliplr(data))
mean_data = np.mean(all_data, axis=0)
rpn_obj = RPN("data/soil_profiles/{0}.rpn".format("profile_200"), mode="w")
rpn_obj.write_2D_field(grid_type="A",
ig=[0, 0, 0, 0],
data=np.fliplr(land_sea_mask),
name="MASK")
for level in range(nz):
#longitudinal grid length is 360/NI. For such a grid,
#IG1 contains the domain of the grid: 0: Global
#1: Northern Hemisphere
#2: Southern Hemisphere IG2 contains the orientation of the grid:
#0: South -> North (pt (1,1) is at the bottom of the grid)
#1: North -> South (pt (1,1) is at the top of the grid) IG3 should be 0.
#IG4 should be 0.
rpn_obj.write_2D_field(
grid_type="A",
ig=[0, 0, 0, 0],
data=mean_data[:, :, level],
level=level,
level_kind=level_kinds.HEIGHT_METERS_OVER_SEA_LEVEL,
name="TBAR")
rpn_obj.close()
def testConstruct(self):
test_regex = [
'(0|1(01*0)*1)*#', '(01*1)*1#', '(a|b)*abb#', '(a|b)*#',
'(a*|b*)*#', '((000)|(001)|(010)|(011)|(100)|(101)|(110)|(111))*#'
]
for test in test_regex:
tree = SyntaxTree(RPN(test))
root_tree = tree.buildTree()
followpos = root_tree.getFollowpos()
drtan, nodes, start, term = root_tree.build(
followpos, tree.dies_positions)
dfa = DFA(drtan, nodes, start, term, root_tree.alphabet)
def __init__(self):
super(fastnet, self).__init__()
# prepare
self.extractor, classifier = vgg()
self.rpn = RPN()
self.head = RoiHead(classifier)
self.anchor_target = utils.anchor_target()
self.proposal_target = utils.proposal_target()
self.rpn_sigma = 3.
self.roi_sigma = 1.
def test_limit(self):
r = RPN()
assert r.calc('1 1 10 LIMIT') == 1
assert r.calc('10 1 10 LIMIT') == 10
assert r.calc('0 1 10 LIMIT') is None
assert r.calc('-1 1 10 LIMIT') is None
assert r.calc('100 1 10 LIMIT') is None
def __init__(self, cnn_net, num_class, batch_size=1, is_training=True):
self._scope = 'vgg_16'
if not is_training:
self.reuse = tf.AUTO_REUSE
else:
self.reuse = None
with tf.variable_scope(self._scope, self._scope, reuse=self.reuse):
self.image = tf.placeholder(tf.float32, [1, None, None, 3])
self.gt_boxes = tf.placeholder(tf.float32, [None, 5])
self.im_info = tf.placeholder(tf.float32, [3])
self.cnn_net = cnn_net
self.batch_size = batch_size
self.num_class = num_class
self.is_training = is_training
self._feat_stride = 16
self.anchor_ratio = [0.5, 1, 2]
self.base_anchors = [8, 16, 32]
self.num_anchors = len(self.anchor_ratio) * len(self.base_anchors)
if is_training:
self.initializer = tf.truncated_normal_initializer(mean=0.0,
stddev=0.01)
self.initializer_bbox = tf.truncated_normal_initializer(
mean=0.0, stddev=0.001)
else:
self.initializer = tf.random_normal_initializer(mean=0.0,
stddev=0.01)
self.initializer_bbox = tf.random_normal_initializer(mean=0.0,
stddev=0.001)
self.rpn = RPN(self.num_class, is_training, self.initializer,
self.batch_size)
self.proposal = Proposal(self.num_class, is_training, self.initializer,
self.batch_size)
def testMinimizeAndMatch(self):
for pattern, test_case in self._getCases().items():
tree = SyntaxTree(RPN(pattern))
root_tree = tree.buildTree()
followpos = root_tree.getFollowpos()
drtan, nodes, start, term = root_tree.build(
followpos, tree.dies_positions)
dfa = DFA(drtan, nodes, start, term, root_tree.alphabet)
dfa.minimize()
min_dfa, min_start, min_end = dfa.build_minimized(dfa.get_equal())
for t, accepts in test_case.items():
self.assertEqual(dfa.match(min_start, min_end, min_dfa, t),
accepts)
def test_evaluate_invalid_expressions():
#Testing the Negative Test cases
test_io_cases = {
"2 +": "error : Not enough Operands to proceed with binary operation",
"6sad": "error : Not a Valid RPN, try until you succeed...e.g. 2 3 +",
"2 3 4 +":
"error : Not a Valid RPN, try until you succeed...e.g. 2 3 +",
"-6": "error : Not a Valid RPN, try until you succeed...e.g. 2 3 +",
"!": "error : Not enough Operands to proceed with unary operation",
"%": "error : Not enough Operands to proceed with unary operation"
}
for expression, expected_output in test_io_cases.iteritems():
actual_op = RPN.evaluate_expression(expression)
print "Output is %s for %s" % (expected_output, expression)
assert actual_op == expected_output
def test_evaluate_valid_expressions():
#Testing the positive Test cases
test_io_cases = {
"1 2 3 + -": -4,
"6 2 * 3 /": 4,
"2 3 ^ 4 5 + +": 17,
"50 % 2 *": 1,
"3 ! 4 5 * +": 26,
"12 3 / !": 24,
"5 1 2 + 4 * + 3 -": 14,
}
for expression, expected_output in test_io_cases.iteritems():
actual_op = RPN.evaluate_expression(expression)
print "Output is %s for %s" % (expected_output, expression)
assert actual_op == expected_output
def calculate_mean_field(data_path="", field_name="STFL", file_prefix=None):
"""
Calculates annual mean field from rpn files
data_path = path to the Samples folder
"""
result = None
field_count = 0.0
lons, lats = None, None
for monthFolder in os.listdir(data_path):
monthPath = os.path.join(data_path, monthFolder)
for fName in os.listdir(monthPath):
if file_prefix is not None:
if not fName.startswith(file_prefix):
continue
rObj = RPN(os.path.join(monthPath, fName))
field = rObj.get_first_record_for_name(field_name)
vDate = rObj.get_current_validity_date()
originDate = rObj.get_dateo_of_last_read_record()
print("-" * 10)
print("validity date, origin date", vDate, originDate)
print(rObj.get_datetime_for_the_last_read_record())
print("-" * 10)
if result is None:
result = field
lons, lats = rObj.get_longitudes_and_latitudes()
else:
result = (field + result * field_count) / (field_count + 1.0)
rObj.close()
field_count += 1.0
return lons, lats, result
def main(model_path:str, img_path:str):
test_transforms = InferenceTransforms()
img,data = load_data(img_path)
data = test_transforms(data)
st = torch.load(model_path,map_location='cpu')
backbone = models.alexnet().features[:-1]
rpn = RPN(backbone, features=256, n=3, effective_stride=16,
iou_threshold=0.7, conf_threshold=0.0, keep_n=300)
rpn.debug = False
rpn.load_state_dict(st, strict=True)
rpn.to('cuda')
rpn.eval()
with torch.no_grad():
preds,regs = rpn(data.cuda())
dets = rpn.detection_layer._inference_postprocess(preds, regs, torch.tensor([*img.shape[:2]]).cuda())
for x1,y1,x2,y2 in dets[0][:,:4].cpu().long().numpy():
img2 = cv2.rectangle(img.copy(), (x1,y1),(x2,y2), (0,0,255), 1)
cv2.imshow("",img2)
cv2.waitKey(200)
def calculate_seasonal_mean(data_path="",
field_name="STFL",
file_prefix=None,
months=None):
"""
calculates seasonal means,
months - list of months when the averaging is performed 1 = Jan, ..., 12 = Dec
TODO: implement
"""
result = None
field_count = 0.0
lons, lats = None, None
for monthFolder in os.listdir(data_path):
monthPath = os.path.join(data_path, monthFolder)
for fName in os.listdir(monthPath):
if file_prefix is not None:
if not fName.startswith(file_prefix):
continue
rObj = RPN(os.path.join(monthPath, fName))
field = rObj.get_first_record_for_name(field_name)
vDate = rObj.get_current_validity_date()
originDate = rObj.get_dateo_of_last_read_record()
print("-" * 10)
print("validity date, origin date", vDate, originDate)
print("-" * 10)
if result is None:
result = field
lons, lats = rObj.get_longitudes_and_latitudes()
else:
result = (field + result * field_count) / (field_count + 1.0)
rObj.close()
field_count += 1.0
pass
def read_data(folder = "data/1950-1960-ECHO-G-profiles"):
ni, nj, id_to_hor_indices, land_sea_mask = read_coordinates()
nz = -1
all_data = []
for year_folder in os.listdir(folder):
year_folder_path = os.path.join(folder, year_folder)
if not os.path.isdir(year_folder_path):
continue
data = None
for file in os.listdir(year_folder_path):
file_path = os.path.join(year_folder_path, file)
f = open(file_path)
lines = [x.strip() for x in f.readlines()]
lines = list(filter(lambda x: len(x) > 0, lines))
if data is None:
nz = len(lines)
data = np.zeros((ni, nj, nz))
the_id = int(re.findall("\d+", file)[0])
i, j = id_to_hor_indices[the_id]
profile = [float(x.split()[-1]) for x in lines]
data[i, j, :] = np.array(profile)[:]
f.close()
all_data.append( np.fliplr( data ))
mean_data = np.mean(all_data, axis = 0)
rpn_obj = RPN("data/soil_profiles/{0}.rpn".format("profile_200"), mode="w")
rpn_obj.write_2D_field(grid_type="A", ig = [0, 0, 0, 0],data=np.fliplr(land_sea_mask), name = "MASK")
for level in range(nz):
#longitudinal grid length is 360/NI. For such a grid,
#IG1 contains the domain of the grid: 0: Global
#1: Northern Hemisphere
#2: Southern Hemisphere IG2 contains the orientation of the grid:
#0: South -> North (pt (1,1) is at the bottom of the grid)
#1: North -> South (pt (1,1) is at the top of the grid) IG3 should be 0.
#IG4 should be 0.
rpn_obj.write_2D_field(grid_type="A", ig = [0, 0, 0, 0],
data= mean_data[:,:, level], level = level, level_kind=level_kinds.HEIGHT_METERS_OVER_SEA_LEVEL,
name="TBAR"
)
rpn_obj.close()
def calculate_mean_field(data_path = "", field_name = "STFL", file_prefix = None):
"""
Calculates annual mean field from rpn files
data_path = path to the Samples folder
"""
result = None
field_count = 0.0
lons, lats = None, None
for monthFolder in os.listdir(data_path):
monthPath = os.path.join(data_path, monthFolder)
for fName in os.listdir(monthPath):
if file_prefix is not None:
if not fName.startswith(file_prefix):
continue
rObj = RPN(os.path.join(monthPath, fName))
field = rObj.get_first_record_for_name(field_name)
vDate = rObj.get_current_validity_date()
originDate = rObj.get_dateo_of_last_read_record()
print("-" * 10)
print("validity date, origin date", vDate, originDate)
print(rObj.get_datetime_for_the_last_read_record())
print("-" * 10)
if result is None:
result = field
lons, lats = rObj.get_longitudes_and_latitudes()
else:
result = (field + result * field_count) / ( field_count + 1.0 )
rObj.close()
field_count += 1.0
return lons, lats, result
def calculate_seasonal_mean(data_path = "", field_name = "STFL", file_prefix = None, months = None):
"""
calculates seasonal means,
months - list of months when the averaging is performed 1 = Jan, ..., 12 = Dec
TODO: implement
"""
result = None
field_count = 0.0
lons, lats = None, None
for monthFolder in os.listdir(data_path):
monthPath = os.path.join(data_path, monthFolder)
for fName in os.listdir(monthPath):
if file_prefix is not None:
if not fName.startswith(file_prefix):
continue
rObj = RPN(os.path.join(monthPath, fName))
field = rObj.get_first_record_for_name(field_name)
vDate = rObj.get_current_validity_date()
originDate = rObj.get_dateo_of_last_read_record()
print("-" * 10)
print("validity date, origin date", vDate, originDate)
print("-" * 10)
if result is None:
result = field
lons, lats = rObj.get_longitudes_and_latitudes()
else:
result = (field + result * field_count) / ( field_count + 1.0 )
rObj.close()
field_count += 1.0
pass
def get_lon_lat(path="/home/huziy/skynet3_rech1/gemclim/quebec/Samples/quebec_220x220_199912/pm1999050100_00068760p"):
r = RPN(path)
lons, lats = r.get_longitudes_and_latitudes()
r.close()
return lons, lats
def test_min(self):
r = RPN()
assert r.calc('1 2 MIN') == 1
assert r.calc('3 2 MIN') == 2
class FasterRCNN:
def __init__(self, cnn_net, num_class, batch_size=1, is_training=True):
self._scope = 'vgg_16'
if not is_training:
self.reuse = tf.AUTO_REUSE
else:
self.reuse = None
with tf.variable_scope(self._scope, self._scope, reuse=self.reuse):
self.image = tf.placeholder(tf.float32, [1, None, None, 3])
self.gt_boxes = tf.placeholder(tf.float32, [None, 5])
self.im_info = tf.placeholder(tf.float32, [3])
self.cnn_net = cnn_net
self.batch_size = batch_size
self.num_class = num_class
self.is_training = is_training
self._feat_stride = 16
self.anchor_ratio = [0.5, 1, 2]
self.base_anchors = [8, 16, 32]
self.num_anchors = len(self.anchor_ratio) * len(self.base_anchors)
if is_training:
self.initializer = tf.truncated_normal_initializer(mean=0.0,
stddev=0.01)
self.initializer_bbox = tf.truncated_normal_initializer(
mean=0.0, stddev=0.001)
else:
self.initializer = tf.random_normal_initializer(mean=0.0,
stddev=0.01)
self.initializer_bbox = tf.random_normal_initializer(mean=0.0,
stddev=0.001)
self.rpn = RPN(self.num_class, is_training, self.initializer,
self.batch_size)
self.proposal = Proposal(self.num_class, is_training, self.initializer,
self.batch_size)
def build(self, mode):
weights_regularizer = tf.contrib.layers.l2_regularizer(WEIGHT_DECAY)
biases_regularizer = tf.no_regularizer
with arg_scope([slim.conv2d, slim.conv2d_in_plane, \
slim.conv2d_transpose, slim.separable_conv2d, slim.fully_connected],
weights_regularizer=weights_regularizer,
biases_regularizer=biases_regularizer,
biases_initializer=tf.constant_initializer(0.0)):
self.cnn_net.build(self.image,
is_training=self.is_training,
mode=mode)
self.feature_input = self.cnn_net.get_output()
with tf.variable_scope(self._scope, self._scope, reuse=self.reuse):
rois = self.build_proposal()
pool5 = self._crop_pool_layer(self.feature_input, rois, "crop")
self.build_tail(pool5)
if mode == 'train' or mode == 'val':
self.build_loss()
self.lr, self.train_op = self.build_train_op()
def build_proposal(self):
self.anchor_list = get_anchors(self.feature_input, self.im_info,
self.anchor_ratio, self.base_anchors)
self.rpn_layer()
return self.proposal_layer()
def rpn_layer(self):
self.rpn_cls, self.rpn_bbox = self.rpn.build(self.feature_input,
self.gt_boxes,
self.im_info,
self.num_anchors,
self.anchor_list)
def proposal_layer(self):
self.proposal.build(self.rpn_cls, self.rpn_bbox, self.gt_boxes,
self.im_info, self.num_anchors, self.anchor_list)
return self.proposal.rois
def build_tail(self, rois):
flatten_rois = slim.flatten(rois, scope='flatten')
fc5 = slim.fully_connected(flatten_rois, 4096, scope="fc6")
if self.is_training:
fc5 = slim.dropout(fc5,
keep_prob=0.5,
is_training=True,
scope='dropout6')
fc6 = slim.fully_connected(fc5, 4096, scope="fc7")
if self.is_training:
fc6 = slim.dropout(fc6,
keep_prob=0.5,
is_training=True,
scope='dropout7')
self.cls_logit = slim.fully_connected(
fc6,
self.num_class,
weights_initializer=self.initializer,
trainable=self.is_training,
activation_fn=None,
scope='cls_logit')
self.cls_prob = tf.nn.softmax(self.cls_logit)
self.cls_pred = tf.argmax(self.cls_prob, axis=1, name="cls_pred")
self.bbox_logit = slim.fully_connected(
fc6,
self.num_class * 4,
weights_initializer=self.initializer_bbox,
trainable=self.is_training,
activation_fn=None,
scope='bbox_logit')
self.bbox_delta_pred = self.bbox_logit
def _crop_pool_layer(self, bottom, rois, name):
batch_ids = tf.squeeze(
tf.slice(rois, [0, 0], [-1, 1], name="batch_id"), [1])
# Get the normalized coordinates of bounding boxes
bottom_shape = tf.shape(bottom)
height = (tf.to_float(bottom_shape[1]) - 1.) * np.float32(
self._feat_stride)
width = (tf.to_float(bottom_shape[2]) - 1.) * np.float32(
self._feat_stride)
x1 = tf.slice(rois, [0, 1], [-1, 1], name="x1") / width
y1 = tf.slice(rois, [0, 2], [-1, 1], name="y1") / height
x2 = tf.slice(rois, [0, 3], [-1, 1], name="x2") / width
y2 = tf.slice(rois, [0, 4], [-1, 1], name="y2") / height
# Won't be back-propagated to rois anyway, but to save time
bboxes = tf.stop_gradient(tf.concat([y1, x1, y2, x2], axis=1))
pre_pool_size = POOLING_SIZE * 2
crops = tf.image.crop_and_resize(bottom,
bboxes,
tf.to_int32(batch_ids),
[pre_pool_size, pre_pool_size],
name="crops")
return slim.max_pool2d(crops, [2, 2], padding='SAME')
def build_loss(self):
rpn_cls_loss = self.get_cls_loss(self.rpn.cls_logit,
self.rpn.cls_label)
self.rpn_cross_entropy = rpn_cls_loss
rpn_bbox_loss = self._smooth_l1_loss(self.rpn.bbox_logit,
self.rpn.bbox_target,
self.rpn.bbox_target_in_weight,
self.rpn.bbox_target_out_weight,
sigma=3.0,
dim=[1, 2, 3])
self.rpn_loss_box = rpn_bbox_loss
# RCNN, class loss
cls_label = tf.to_int32(self.proposal.cls_label, name="to_int32")
cls_label = tf.reshape(cls_label, [-1])
print("loss:", cls_label.shape)
cls_loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=self.cls_logit, labels=cls_label)) #[-1, 21], [-1,1]
self.cross_entropy = cls_loss
bbox_loss = self._smooth_l1_loss(self.bbox_logit,
self.proposal.bbox_target,
self.proposal.bbox_target_in_weight,
self.proposal.bbox_target_out_weight)
self.loss_box = bbox_loss
loss = rpn_cls_loss + rpn_bbox_loss + cls_loss + bbox_loss
regularization_loss = tf.add_n(tf.losses.get_regularization_losses(),
'regu')
self.loss = loss + regularization_loss
#return self.loss
def _smooth_l1_loss(self,
bbox_pred,
bbox_targets,
bbox_inside_weights,
bbox_outside_weights,
sigma=1.0,
dim=[1]):
sigma_2 = sigma**2
box_diff = bbox_pred - bbox_targets
in_box_diff = bbox_inside_weights * box_diff
abs_in_box_diff = tf.abs(in_box_diff)
smoothL1_sign = tf.stop_gradient(
tf.to_float(tf.less(abs_in_box_diff, 1. / sigma_2)))
in_loss_box = tf.pow(in_box_diff, 2) * (sigma_2 / 2.) * smoothL1_sign \
+ (abs_in_box_diff - (0.5 / sigma_2)) * (1. - smoothL1_sign)
out_loss_box = bbox_outside_weights * in_loss_box
loss_box = tf.reduce_mean(tf.reduce_sum(out_loss_box, axis=dim))
return loss_box
def build_train_op(self):
lr = tf.Variable(0.01, trainable=False)
i_global_op = tf.train.get_or_create_global_step()
self.global_op = i_global_op
self.optimizer = tf.train.MomentumOptimizer(lr, MOMENTUM)
# Compute the gradients with regard to the loss
gvs = self.optimizer.compute_gradients(self.loss)
# Double the gradient of the bias if set
if DOUBLE_BIAS:
final_gvs = []
with tf.variable_scope('Gradient_Mult') as scope:
for grad, var in gvs:
scale = 1.
if '/biases:' in var.name:
scale *= 2.
if not np.allclose(scale, 1.0):
grad = tf.multiply(grad, scale)
final_gvs.append((grad, var))
train_op = self.optimizer.apply_gradients(final_gvs,
global_step=i_global_op)
for grad, var in final_gvs:
if grad is not None:
tf.summary.histogram(var.op.name + '/gradients', grad)
else:
train_op = self.optimizer.apply_gradients(gvs,
global_step=i_global_op)
for grad, var in gvs:
if grad is not None:
tf.summary.histogram(var.op.name + '/gradients', grad)
self.summary_op = tf.summary.merge_all()
return lr, train_op
def get_cls_loss(self, predict, target):
'''
[1,wieght, height, 9*2]
'''
rpn_cls_score = tf.reshape(predict, [-1, 2])
rpn_label = tf.reshape(target, [-1])
rpn_select = tf.where(tf.not_equal(rpn_label, -1))
rpn_cls_score = tf.reshape(tf.gather(rpn_cls_score, rpn_select),
[-1, 2])
rpn_label = tf.reshape(tf.gather(rpn_label, rpn_select), [-1])
return tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=rpn_cls_score, labels=rpn_label))
def train_step(self, sess, image, gt_boxes, im_info):
loss, lr, global_step, _, summary_str = sess.run(
[
self.loss, self.lr, self.global_op, self.train_op,
self.summary_op
],
feed_dict={
self.image: image,
self.gt_boxes: gt_boxes,
self.im_info: im_info.reshape(-1)
})
import math
assert not math.isnan(loss)
return loss, lr, global_step, summary_str
def get_loss(self, sess, image, gt_boxes, im_info):
loss = sess.run(self.loss,
feed_dict={
self.image: image,
self.gt_boxes: gt_boxes,
self.im_info: im_info.reshape(-1)
})
import math
assert not math.isnan(loss)
return loss
def predict(self, sess, image, im_info):
#score and delta bbox
score, delta_bbox, rois = sess.run(
[self.cls_prob, self.bbox_delta_pred, self.proposal.rois],
feed_dict={
self.image: image,
self.im_info: im_info.reshape(-1)
})
bbox = self.proposal.bbox_target_inv(rois, delta_bbox, im_info)
assert score.shape[1] == self.num_class
image_score_list = []
image_bbox_list = []
thresh = 0
for i in range(self.num_class):
inds = np.where(score[:, i] > thresh)[0]
image_score = score[inds, i]
image_bbox = bbox[inds, i * 4:(i + 1) * 4]
image_score_list.append(image_score)
image_bbox_list.append(image_bbox)
image_scores = np.hstack(
[image_score_list[i] for i in range(1, self.num_class)])
image_thresh = np.sort(image_scores)[-3]
print("thresh:", image_thresh)
res_score = []
res_bbox = []
for i in range(1, self.num_class):
#print ("class i:",i,image_score_list[i])
keep = image_score_list[i] >= image_thresh
image_score = image_score_list[i][keep]
image_bbox = image_bbox_list[i][keep]
res_score.append(image_score)
res_bbox.append(image_bbox)
#print ("get sore:",keep, i,image_score_list[i][keep].shape)
return res_score, res_bbox
def assign_lr(self, sess, rate):
sess.run(tf.assign(self.lr, rate))
def test_less_than(self):
r = RPN()
self._assert_result(r.calc('1 2 LT'))
self._assert_result(r.calc('2 2 LT'), False)
self._assert_result(r.calc('3 2 LT'), False)
def test():
rObj = RPN('write.rpn', mode='w')
nx = 20
ny = 40
data = np.zeros((nx, ny))
for i in range(nx):
for j in range(ny):
data[i, j] = i**2 + j**2
print('before ', data.shape, data.min(), data.max(), data.mean())
plt.figure()
plt.title('before')
plt.pcolormesh(data)
plt.colorbar()
rObj.write_2D_field('test', level=1, data=data, grid_type='')
rObj.close()
rObj = RPN('write.rpn')
x = rObj.get_first_record_for_name('test')
rObj.close()
print('after ', x.shape, x.min(), x.max(), x.mean())
plt.figure()
plt.title('after')
plt.pcolormesh(x)
plt.colorbar()
plt.show()
def __init__(self, training, num_classes):
super(FasterRCNN, self).__init__()
self.training = training
self.extractor = VGGNet()
self.rpn = RPN(training=training)
self.roi_pooling = RoIPolling()
def test_greater_than(self):
r = RPN()
self._assert_result(r.calc('2 1 GT'))
self._assert_result(r.calc('2 2 GT'), False)
self._assert_result(r.calc('2 3 GT'), False)
def test_greater_than_or_equal(self):
r = RPN()
self._assert_result(r.calc('2 1 GE'))
self._assert_result(r.calc('2 2 GE'))
self._assert_result(r.calc('2 3 GE'), False)
def test_equal(self):
r = RPN()
self._assert_result(r.calc('2 2 EQ'))
self._assert_result(r.calc('2 1 EQ'), False)
def test_RPN_stack_underflow():
inst = RPN()
s = "1 +"
assert inst.evaluate(s) == "Stack underflow" and inst.stack.items == []
def test_RPN_evaluate():
inst = RPN()
s = "12 7 * 18 + 47 8 / - 3 +"
assert inst.evaluate(s) == [99.125] and inst.stack.items == []
def test_not_equal(self):
r = RPN()
self._assert_result(r.calc('2 1 NE'))
self._assert_result(r.calc('2 2 NE'), False)
def test_if(self):
r = RPN()
assert r.calc('1 2 3 IF') == 2
assert r.calc('0 2 3 IF') == 3
def test_max(self):
r = RPN()
assert r.calc('1 2 MAX') == 2
assert r.calc('3 2 MAX') == 3
def test_less_than_or_equal(self):
r = RPN()
self._assert_result(r.calc('1 2 LE'))
self._assert_result(r.calc('2 2 LE'))
self._assert_result(r.calc('3 2 LE'), False)
def test_multiple(self):
r = RPN()
assert r.calc('2 3 + 4 *') == 20
def train():
''' Main function for training and simple evaluation. '''
with tf.Graph().as_default():
with tf.device('/gpu:0'):
# is_training_pl = tf.placeholder(tf.bool, shape=())
# Note the global_step=batch parameter to minimize.
# That tells the optimizer to increment the 'batch' parameter
# for you every time it trains.
batch = tf.get_variable('batch', [],
initializer=tf.constant_initializer(0),
trainable=False)
bn_decay = get_bn_decay(batch)
tf.summary.scalar('bn_decay', bn_decay)
# Get model and losses
rpn_model = RPN(BATCH_SIZE,
NUM_POINT,
num_channel=NUM_CHANNEL,
bn_decay=bn_decay,
is_training=True)
placeholders = rpn_model.placeholders
end_points = rpn_model.end_points
loss, loss_endpoints = rpn_model.get_loss()
iou2ds, iou3ds = tf.py_func(train_util.compute_box3d_iou, [
end_points['proposal_boxes'], end_points['gt_box_of_point'],
end_points['nms_indices']
], [tf.float32, tf.float32])
end_points['iou2ds'] = iou2ds
end_points['iou3ds'] = iou3ds
# Get training operator
learning_rate = get_learning_rate(batch)
tf.summary.scalar('learning_rate', learning_rate)
if OPTIMIZER == 'momentum':
optimizer = tf.train.MomentumOptimizer(learning_rate,
momentum=MOMENTUM)
elif OPTIMIZER == 'adam':
optimizer = tf.train.AdamOptimizer(learning_rate)
# Note: when training, the moving_mean and moving_variance need to be updated.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
#train_op = optimizer.minimize(loss, global_step=batch)
train_op = slim.learning.create_train_op(
loss, optimizer, clip_gradient_norm=1.0, global_step=batch)
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
# Create a session
config = tf.ConfigProto()
config.gpu_options.allow_growth = False
config.allow_soft_placement = True
config.log_device_placement = False
sess = tf.Session(config=config)
# Add summary writers
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'train'),
sess.graph)
test_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'test'),
sess.graph)
# Init variables
if FLAGS.restore_model_path is None:
init = tf.global_variables_initializer()
sess.run(init)
else:
saver.restore(sess, FLAGS.restore_model_path)
ops = {
'loss': loss,
'train_op': train_op,
'step': batch,
'merged': merged,
'loss_endpoints': loss_endpoints,
'end_points': end_points
}
for epoch in range(MAX_EPOCH):
log_string('**** EPOCH %03d ****' % (epoch))
sys.stdout.flush()
# eval iou and recall is slow
#eval_iou_recall = epoch > 10
#eval_iou_recall = False
eval_iou_recall = epoch % 2 == 0
train_one_epoch(sess, ops, placeholders, train_writer,
eval_iou_recall)
# Save the variables to disk.
# if val_loss < best_val_loss:
# best_val_loss = val_loss
# save_path = saver.save(sess, os.path.join(LOG_DIR, "model.ckpt"))
# log_string("Model saved in file: {0}, val_loss: {1}".format(save_path, val_loss))
save_path = saver.save(
sess, os.path.join(LOG_DIR, "model.ckpt.%03d" % epoch))
log_string("Model saved in file: {0}".format(save_path))
val_loss = eval_one_epoch(sess, ops, placeholders, test_writer,
True)
TRAIN_DATASET.stop_loading()
train_produce_thread.join()
def get_lon_lat(path = 'data/pm1957090100_00589248p'):
print('reading lons and lats from the file %s' % path)
r = RPN(path)
lons, lats = r.get_longitudes_and_latitudes()
r.close()
return lons, lats
def test(split, save_result=False):
if save_result and not os.path.exists('./rcnn_data_' + split):
os.mkdir('./rcnn_data_' + split)
is_training = False
#dataset = Dataset(NUM_POINT, '/data/ssd/public/jlliu/Kitti/object', split, is_training=is_training)
dataset = Dataset(NUM_POINT, KITTI_PATH, split, is_training=is_training)
# data loading threads
produce_thread = Thread(target=dataset.load, args=(False, ))
produce_thread.start()
with tf.Graph().as_default():
with tf.device('/gpu:0'):
rpn_model = RPN(BATCH_SIZE,
NUM_POINT,
num_channel=4,
is_training=is_training)
pls = rpn_model.placeholders
end_points = rpn_model.end_points
box_center, box_angle, box_size = rpn_model.box_encoder.tf_decode(
end_points)
box_center = box_center + end_points['fg_points_xyz']
#box_center = tf.reshape(box_center, [BATCH_SIZE * NUM_FG_POINT,3])
#box_angle = tf.reshape(box_angle, [BATCH_SIZE * NUM_FG_POINT])
#box_size = tf.reshape(box_size, [BATCH_SIZE * NUM_FG_POINT,3])
saver = tf.train.Saver()
# Create a session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
config.log_device_placement = False
sess = tf.Session(config=config)
saver.restore(sess, FLAGS.model_path)
with tf.Graph().as_default():
with tf.device('/gpu:0'):
#img_seg_net = ImgSegNet(BATCH_SIZE, NUM_POINT, num_channel=4, bn_decay=None, is_training=is_training)
#seg_softmax = img_seg_net.get_seg_softmax()
#saver1 = tf.train.Saver()
img_seg_net = ImgSegNet(BATCH_SIZE, NUM_POINT)
#img_seg_net.load_graph('/data/ssd/public/jlliu/models/research/deeplab/exp/frozen_inference_graph.pb')
img_seg_net.load_graph(FLAGS.img_model_path)
seg_softmax = img_seg_net.get_seg_softmax()
full_seg = img_seg_net.get_semantic_seg()
# Create another session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
config.log_device_placement = False
sess1 = tf.Session(config=config)
#saver1.restore(sess1, FLAGS.img_model_path)
log_string(str(datetime.now()))
# To collect statistics
total_correct = 0
total_seen = 0
num_batches = 0
tp = {'Car': 0, 'Pedestrian': 0, 'Cyclist': 0}
fp = {'Car': 0, 'Pedestrian': 0, 'Cyclist': 0}
fn = {'Car': 0, 'Pedestrian': 0, 'Cyclist': 0}
proposal_boxes = []
gt_boxes = []
nms_indices = []
while (True):
batch_data, is_last_batch = dataset.get_next_batch(BATCH_SIZE,
need_id=True)
start = datetime.now()
feed_dict = {
pls['pointclouds']: batch_data['pointcloud'],
pls['img_inputs']: batch_data['images'],
pls['calib']: batch_data['calib'],
pls['seg_labels']: batch_data['seg_label'],
pls['center_bin_x_labels']: batch_data['center_x_cls'],
pls['center_bin_z_labels']: batch_data['center_z_cls'],
pls['center_x_residuals_labels']: batch_data['center_x_res'],
pls['center_y_residuals_labels']: batch_data['center_y_res'],
pls['center_z_residuals_labels']: batch_data['center_z_res'],
pls['heading_bin_labels']: batch_data['angle_cls'],
pls['heading_residuals_labels']: batch_data['angle_res'],
pls['size_class_labels']: batch_data['size_cls'],
pls['size_residuals_labels']: batch_data['size_res'],
pls['gt_box_of_point']: batch_data['gt_box_of_point'],
pls['is_training_pl']: is_training,
}
# segmentaion with image
seg_pls = img_seg_net.placeholders
img_seg_logits, full_img_seg = sess1.run(
[seg_softmax, full_seg],
feed_dict={
seg_pls['pointclouds']: batch_data['pointcloud'],
seg_pls['img_inputs']: batch_data['images'],
seg_pls['calib']: batch_data['calib'],
seg_pls['seg_labels']: batch_data['seg_label'],
#seg_pls['is_training_pl']: is_training
})
# convert to binary segmentation
img_seg_binary = np.zeros((BATCH_SIZE, NUM_POINT, 2))
img_seg_binary[..., 0] = img_seg_logits[..., 0]
img_seg_binary[..., 1] = np.sum(img_seg_logits[..., 1:], axis=-1)
img_seg_binary *= np.array([0, 1]) # weights
feed_dict[pls['img_seg_softmax']] = img_seg_binary
'''
# label to one_hot
targets = batch_data['seg_label']
img_seg_logits = np.eye(NUM_SEG_CLASSES)[targets]
#img_seg_logits *= np.array([2, 2, 2, 2]) # weights
feed_dict[pls['img_seg_softmax']] = img_seg_logits
'''
logits_val, indices_val, centers_val, angles_val, sizes_val, corners_val, ind_val, scores_val \
= sess.run([
end_points['foreground_logits'], end_points['fg_point_indices'],
box_center, box_angle, box_size, end_points['proposal_boxes'],
end_points['nms_indices'], end_points['proposal_scores']], feed_dict=feed_dict)
print('inference time: ', datetime.now() - start)
# segmentation acc
preds_val = np.argmax(logits_val, 2)
num_batches += 1
for c in ['Car', 'Pedestrian', 'Cyclist']:
one_hot_class = g_type2onehotclass[c]
tp[c] += np.sum(
np.logical_and(preds_val == batch_data['seg_label'],
batch_data['seg_label'] == one_hot_class))
fp[c] += np.sum(
np.logical_and(preds_val != batch_data['seg_label'],
batch_data['seg_label'] != one_hot_class))
fn[c] += np.sum(
np.logical_and(preds_val != batch_data['seg_label'],
batch_data['seg_label'] == one_hot_class))
# results
for i in range(BATCH_SIZE):
proposal_boxes.append(corners_val[i])
gt_boxes.append(batch_data['gt_boxes'][i])
nms_indices.append(ind_val[i])
frame_data = {
'frame_id': batch_data['ids'][i],
'segmentation': preds_val[i],
'centers': centers_val[i],
'angles': angles_val[i],
'sizes': sizes_val[i],
'proposal_boxes': corners_val[i],
'nms_indices': ind_val[i],
'scores': scores_val[i],
'pc_choices': batch_data['pc_choice'][i]
}
# save frame data
if save_result:
with open(
os.path.join('./rcnn_data_' + split,
batch_data['ids'][i] + '.pkl'),
'wb') as fout:
pickle.dump(frame_data, fout)
np.save(
os.path.join('./rcnn_data_' + split,
batch_data['ids'][i] + '_seg.npy'),
full_img_seg[i])
if is_last_batch:
break
log_string('saved prediction')
dataset.stop_loading()
produce_thread.join()
'''
all_indices = np.tile(np.arange(1024), (len(proposal_boxes),))
iou2d, iou3d = compute_box3d_iou(proposal_boxes, point_gt_boxes, all_indices)
print('IOU2d: ', np.mean(iou2d))
print('IOU3d: ', np.mean(iou3d))
'''
if split in ['train', 'val']:
recall = compute_proposal_recall(proposal_boxes, gt_boxes, nms_indices)
print('Average recall: ', recall)
print(tp, fp, fn)
for c in ['Car', 'Pedestrian', 'Cyclist']:
if (tp[c] + fn[c] == 0) or (tp[c] + fp[c]) == 0:
continue
print(c + ' segmentation recall: %f'% \
(float(tp[c])/(tp[c]+fn[c])))
print(c + ' segmentation precision: %f'% \
(float(tp[c])/(tp[c]+fp[c])))
def get_lakefraction(path = 'data/geophys_lam_na_0.5deg_170x158_class', margin = 20):
r = RPN(path)
data = r.get_first_record_for_name_and_level('VF', level = 3)
r.close()
return data[margin:-margin, margin:-margin, 0]
pass
def main_worker(index, opt):
random.seed(opt.manual_seed)
np.random.seed(opt.manual_seed)
torch.manual_seed(opt.manual_seed)
if index >= 0 and opt.device.type == 'cuda':
# opt.device = torch.device(f'cuda:{index}')
opt.device = torch.device('cuda:{}'.format(index))
if opt.distributed:
opt.dist_rank = opt.dist_rank * opt.ngpus_per_node + index
dist.init_process_group(backend='nccl',
init_method=opt.dist_url,
world_size=opt.world_size,
rank=opt.dist_rank)
opt.batch_size = int(opt.batch_size / opt.ngpus_per_node)
opt.n_threads = int(
(opt.n_threads + opt.ngpus_per_node - 1) / opt.ngpus_per_node)
opt.is_master_node = not opt.distributed or opt.dist_rank == 0
model = generate_model(opt)
if opt.batchnorm_sync:
assert opt.distributed, 'SyncBatchNorm only supports DistributedDataParallel.'
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
if opt.pretrain_path:
model = load_pretrained_model(model, opt.pretrain_path, opt.model,
opt.n_finetune_classes, opt.strg)
if opt.strg:
model = STRG(model, nclass=opt.n_classes, nrois=opt.nrois)
rpn = RPN(nrois=opt.nrois)
rpn = make_data_parallel(rpn, opt.distributed, opt.device)
else:
rpn = None
if opt.resume_path is not None:
model = resume_model(opt.resume_path, opt.arch, model)
model = make_data_parallel(model, opt.distributed, opt.device)
# if opt.pretrain_path:
# parameters = get_fine_tuning_parameters(model, opt.ft_begin_module)
# else:
parameters = model.parameters()
if opt.is_master_node:
print(model)
criterion = CrossEntropyLoss().to(opt.device)
if not opt.no_train:
(train_loader, train_sampler, train_logger, train_batch_logger,
optimizer, scheduler) = get_train_utils(opt, parameters)
if opt.resume_path is not None:
opt.begin_epoch, optimizer, scheduler = resume_train_utils(
opt.resume_path, opt.begin_epoch, optimizer, scheduler)
if opt.overwrite_milestones:
scheduler.milestones = opt.multistep_milestones
if not opt.no_val:
val_loader, val_logger = get_val_utils(opt)
if opt.tensorboard and opt.is_master_node:
#from torch.utils.tensorboard import SummaryWriter
from tensorboardX import SummaryWriter
if opt.begin_epoch == 1:
tb_writer = SummaryWriter(log_dir=opt.result_path)
else:
tb_writer = SummaryWriter(log_dir=opt.result_path,
purge_step=opt.begin_epoch)
else:
tb_writer = None
if opt.wandb:
name = str(opt.result_path)
wandb.init(
project='strg',
name=name,
config=opt,
dir=name,
# resume=str(opt.resume_path) != '',
sync_tensorboard=True)
prev_val_loss = None
for i in range(opt.begin_epoch, opt.n_epochs + 1):
if not opt.no_train:
if opt.distributed:
train_sampler.set_epoch(i)
current_lr = get_lr(optimizer)
train_epoch(i,
train_loader,
model,
criterion,
optimizer,
opt.device,
current_lr,
train_logger,
train_batch_logger,
tb_writer,
opt.distributed,
rpn=rpn,
det_interval=opt.det_interval,
nrois=opt.nrois)
if i % opt.checkpoint == 0 and opt.is_master_node:
save_file_path = opt.result_path / 'save_{}.pth'.format(i)
save_checkpoint(save_file_path, i, opt.arch, model, optimizer,
scheduler)
if not opt.no_val:
prev_val_loss = val_epoch(i,
val_loader,
model,
criterion,
opt.device,
val_logger,
tb_writer,
opt.distributed,
rpn=rpn,
det_interval=opt.det_interval,
nrois=opt.nrois)
if not opt.no_train and opt.lr_scheduler == 'multistep':
scheduler.step()
elif not opt.no_train and opt.lr_scheduler == 'plateau':
scheduler.step(prev_val_loss)
if opt.inference:
inference_loader, inference_class_names = get_inference_utils(opt)
inference_result_path = opt.result_path / '{}.json'.format(
opt.inference_subset)
inference.inference(inference_loader, model, inference_result_path,
inference_class_names, opt.inference_no_average,
opt.output_topk)
def test_complex(self):
r = RPN()
assert r.calc('3 4 2 * 1 5 - 2 3 ^ ^ / +') == 3.0001220703125
