def norm_jit2d(a):
sm = 0
n, m = a.shape
for i in xrange(n):
for j in xrange(m):
sm += a[i, j] ** 2
return np.sqrt(sm)
def sum0(arr):
m, n = arr.shape
a = np.zeros(n)
for i in xrange(m):
for j in xrange(n):
a[j] += arr[i, j]
return a
def sum0(arr):
m, n = arr.shape
a = np.zeros(n)
for i in xrange(m):
for j in xrange(n):
a[j] += arr[i, j]
return a
def metrics_to_tuples(raw_metrics):
""" Converts metric dictionary of name:values_array into list of tuples
Use case: writing out benchmark to CSV, etc
Input:
{'metric':[value1,value2...], 'metric2':[value1,value2,...]...}
Output: list, with tuple header row, then list of tuples of values
[('metric','metric',...), (metric1_value1,metric2_value1, ...) ... ]
"""
if not isinstance(raw_metrics, dict):
raise TypeError("Input must be dictionary!")
metrics = sorted(raw_metrics.keys())
arrays = [raw_metrics[metric] for metric in metrics]
num_rows = len(arrays[0]) # Assume all same size or this fails
output = list()
output.append(tuple(metrics)) # Add headers
# Create list of tuples mimicking 2D array from input
for row in xrange(0, num_rows):
new_row = tuple([arrays[col][row] for col in xrange(0, len(arrays))])
output.append(new_row)
return output
def norm_jit2d(a):
sm = 0
n, m = a.shape
for i in xrange(n):
for j in xrange(m):
sm += a[i, j]**2
return np.sqrt(sm)
def test_factory_text(self):
""" Test the basic generator """
charsets = [string.ascii_letters, string.digits,
string.ascii_uppercase, string.hexdigits]
# Test multiple charsets and string lengths
for charset in charsets:
# Test different lengths for charset
for my_length in xrange(1, 17):
gen = generators.factory_generate_text(
legal_characters=charset, min_length=my_length, max_length=my_length)()
for x in xrange(0, 10):
val = next(gen)
self.assertEqual(my_length, len(val))
def _box_neighbors(self, v):
row, col = v.split('_')
_row = int((ord(row) - 65) // 3)
_col = int(int(col) // 3)
neighbors = []
for i in xrange(3 * _row, 3 * _row + 3):
for j in xrange(3 * _col, 3 * _col + 3):
excluded_cell = '{}_{}'.format(chr(i + 65), j)
if v != excluded_cell:
neighbors.append('{}_{}'.format(chr(i + 65), j))
return neighbors
def test_factory_text(self):
""" Test the basic generator """
charsets = [string.ascii_letters, string.digits,
string.ascii_uppercase, string.hexdigits]
# Test multiple charsets and string lengths
for charset in charsets:
# Test different lengths for charset
for my_length in xrange(1, 17):
gen = generators.factory_generate_text(
legal_characters=charset, min_length=my_length, max_length=my_length)()
for x in xrange(0, 10):
val = next(gen)
self.assertEqual(my_length, len(val))
def step(self):
"""Take a step in the optimization"""
rnd_cross = _random((self.npop, self.ndim))
for i in xrange(self.npop):
t0, t1, t2 = i, i, i
while t0 == i:
t0 = _randint(self.npop)
while t1 == i or t1 == t0:
t1 = _randint(self.npop)
while t2 == i or t2 == t0 or t2 == t1:
t2 = _randint(self.npop)
v = self.population[t0, :] + self.F * (self.population[t1, :] -
self.population[t2, :])
crossover = rnd_cross[i] <= self.C
u = np.where(crossover, v, self.population[i, :])
ri = _randint(self.ndim)
u[ri] = v[ri]
ufit = self.m * self.fun(u)
if ufit < self.fitness[i]:
self.population[i, :] = u
self.fitness[i] = ufit
def __setitem__(self, n, v):
if isinstance(n, int): # d[1]
self._dir[n] = v
elif isinstance(n, slice):
# d['foo':] adds a new foo
n = n.start
if self._dNS and not islst(n):
n = (self._dNS, n)
nv = Element(n)
self._dir.append(nv)
else: # d["foo"] replaces first <foo> and dels rest
if self._dNS and not islst(n):
n = (self._dNS, n)
nv = Element(n)
nv._dir.append(v)
replaced = False
todel = []
for i in xrange(len(self)):
if self[i]._name == n:
if replaced:
todel.append(i)
else:
self[i] = nv
replaced = True
if not replaced:
self._dir.append(nv)
for i in sorted(todel, reverse=True):
del self[i]
def _col_neighbors(self, v):
row, col = v.split('_')
neighbors = []
for i in xrange(9):
if i != int(col):
neighbors.append('{}_{}'.format(row, i))
return neighbors
def _row_neighbors(self, v):
row, col = v.split('_')
neighbors = []
for i in xrange(9):
if (i + 65) != ord(row):
neighbors.append('{}_{}'.format(chr(i + 65), col))
return neighbors
def generate_text():
local_min_len = min_length
local_max_len = max_length
rand = random.Random()
while(True):
length = random.randint(local_min_len, local_max_len)
array = [random.choice(legal_characters)
for x in xrange(0, length)]
yield ''.join(array)
def generate_text():
local_min_len = min_length
local_max_len = max_length
rand = random.Random()
while(True):
length = random.randint(local_min_len, local_max_len)
array = [random.choice(legal_characters)
for x in xrange(0, length)]
yield ''.join(array)
def generator_repeat_test(self, generator_input):
""" Basic test of a configured generator """
val = next(generator_input)
# Check for not repeating easily
for x in xrange(0, 5):
val2 = next(generator_input)
self.assertTrue(val)
self.assertTrue(val != val2)
val = val2
def generator_basic_test(self, generator, value_test_function=None):
""" Basic test for a generator, checks values and applies test function """
self.assertTrue(isinstance(generator, types.GeneratorType))
for x in xrange(0, 100):
val = next(generator)
self.assertTrue(val is not None)
if value_test_function:
self.assertTrue(value_test_function(
val), 'Test failed with value {0}'.format(val))
def convert_time_couples_to_qdb_filtered_range_t_vector(time_couples):
vec = impl.FilteredRangeVec()
c = len(time_couples)
vec.resize(c)
for i in xrange(c):
vec[i] = convert_time_couple_to_qdb_filtered_range_t(time_couples[i])
return vec
def xrange(*args, **kwargs):
major_version = sys.version_info.major
if major_version == 3:
import builtins
return builtins.range(*args, **kwargs)
elif major_version == 2:
import builtins
return builtins.xrange(*args, **kwargs)
else:
raise RuntimeError("Unsupported version of Python.")
def generator_basic_test(self, generator, value_test_function=None):
""" Basic test for a generator, checks values and applies test function """
self.assertTrue(isinstance(generator, types.GeneratorType))
for x in xrange(0, 100):
val = next(generator)
self.assertTrue(val is not None)
if value_test_function:
self.assertTrue(value_test_function(
val), 'Test failed with value {0}'.format(val))
def generator_repeat_test(self, generator_input):
""" Basic test of a configured generator """
val = next(generator_input)
# Check for not repeating easily
for x in xrange(0, 5):
val2 = next(generator_input)
self.assertTrue(val)
self.assertTrue(val != val2)
val = val2
def generate_text():
""" generates the random values for string"""
local_min_len = min_length
local_max_len = max_length
rand = random.Random()
while True:
length = random.randint(local_min_len, local_max_len)
array = [
random.choice(legal_characters) for x in xrange(0, length)
]
yield ''.join(array)
def test_parse_text_generator(self):
""" Test the text generator parsing """
config = dict()
config['type'] = 'random_text'
config['character_set'] = 'reallyINVALID'
try:
gen = generators.parse_generator(config)
self.fail(
"Should never parse an invalid character_set successfully, but did!"
)
except ValueError:
pass
# Test for character set handling
for charset in generators.CHARACTER_SETS:
try:
config['character_set'] = charset
gen = generators.parse_generator(config)
myset = set(generators.CHARACTER_SETS[charset])
for x in xrange(0, 50):
val = next(gen)
self.assertTrue(set(val).issubset(set(myset)))
except Exception as e:
print('Exception occurred with charset: ' + charset)
raise e
my_min = 1
my_max = 10
# Test for explicit character setting
del config['character_set']
temp_chars = 'ay78%&'
config['characters'] = temp_chars
gen = generators.parse_generator(config)
self.generator_basic_test(
gen,
value_test_function=lambda x: set(x).issubset(set(temp_chars)))
# Test for length setting
config['length'] = '3'
gen = generators.parse_generator(config)
self.generator_basic_test(gen,
value_test_function=lambda x: len(x) == 3)
del config['length']
# Test for explicit min/max length
config['min_length'] = '9'
config['max_length'] = 12
gen = generators.parse_generator(config)
self.generator_basic_test(
gen, value_test_function=lambda x: len(x) >= 9 and len(x) <= 12)
def make_qdb_ts_int64_point_vector(time_points):
vec = Int64PointsVector()
c = len(time_points)
vec.resize(c)
for i in xrange(c):
vec[i].timestamp.tv_sec = time_to_unix_timestamp(time_points[i][0])
vec[i].timestamp.tv_nsec = time_points[i][0].microsecond * long(1000)
vec[i].value = time_points[i][1]
return vec
def convert_to_wrap_ts_blop_points_vector(tuples):
vec = BlobPointsVector()
c = len(tuples)
vec.resize(c)
for i in xrange(c):
vec[i].timestamp.tv_sec = time_to_unix_timestamp(tuples[i][0])
vec[i].timestamp.tv_nsec = tuples[i][0].microsecond * long(1000)
vec[i].data = tuples[i][1]
return vec
def write_coordinates(atoms, V, title=""):
"""
Print coordinates V
"""
N, D = V.shape
print(str(N))
print(title)
for i in xrange(N):
atom = atoms[i]
atom = atom[0].upper() + atom[1:]
print("{0:2s} {1:15.8f} {2:15.8f} {3:15.8f}".format(
atom, V[i, 0], V[i, 1], V[i, 2]))
def test_parse_text_generator(self):
""" Test the text generator parsing """
config = dict()
config['type'] = 'random_text'
config['character_set'] = 'reallyINVALID'
try:
gen = generators.parse_generator(config)
self.fail(
"Should never parse an invalid character_set successfully, but did!")
except ValueError:
pass
# Test for character set handling
for charset in generators.CHARACTER_SETS:
try:
config['character_set'] = charset
gen = generators.parse_generator(config)
myset = set(generators.CHARACTER_SETS[charset])
for x in xrange(0, 50):
val = next(gen)
self.assertTrue(set(val).issubset(set(myset)))
except Exception as e:
print('Exception occurred with charset: ' + charset)
raise e
my_min = 1
my_max = 10
# Test for explicit character setting
del config['character_set']
temp_chars = 'ay78%&'
config['characters'] = temp_chars
gen = generators.parse_generator(config)
self.generator_basic_test(
gen, value_test_function=lambda x: set(x).issubset(set(temp_chars)))
# Test for length setting
config['length'] = '3'
gen = generators.parse_generator(config)
self.generator_basic_test(
gen, value_test_function=lambda x: len(x) == 3)
del config['length']
# Test for explicit min/max length
config['min_length'] = '9'
config['max_length'] = 12
gen = generators.parse_generator(config)
self.generator_basic_test(
gen, value_test_function=lambda x: len(x) >= 9 and len(x) <= 12)
def iteroptimize(self, ngen=1):
"""Iterator to the optimizer for ``ngen`` generations
Parameters
----------
ngen: int
number of iterations
Returns
-------
population: ndarray
population locations, [Npop x Ndim]
fitness: ndarray
population values, [Npop]
"""
for i in xrange(self.npop):
self.fitness[i] = self.m * self.fun(self.population[i, :])
for j in xrange(ngen):
self.step()
self._minidx = np.argmin(self.fitness)
yield self.population[self._minidx, :], self.fitness[self._minidx]
def __init__(self, sudoku, variable_heuristic, domain_heuristic):
sudoku_domains = {}
for idx, value in enumerate(sudoku):
value = int(value)
row = idx // 9
col = idx % 9
sudoku_domains['{}_{}'.format(
chr(row + 65),
col)] = [value] if value > 0 else [x for x in xrange(1, 10)]
sudoku_variables = {key: UNASSIGNED for key in sudoku_domains}
sudoku_constraints = lambda x, y: x != y
super(Sudoku, self).__init__(sudoku_variables, sudoku_domains,
sudoku_constraints, variable_heuristic,
domain_heuristic)
self.build_neighbors()
def createDataset(outputPath, imageListFile, checkValid=True):
"""
Create LMDB dataset for CRNN training.
ARGS:
outputPath : LMDB output path
imagePathList : list of image path
checkValid : if true, check the validity of every image
"""
with open(imageListFile) as fp:
imagePathList = fp.readlines()
nSamples = len(imagePathList)
env = lmdb.open(outputPath, map_size=1099511627776)
cache = {}
cnt = 1
for i in xrange(nSamples):
imagePath = imagePathList[i].rstrip()
labelPath = imagePath.replace('images', 'labels').replace('JPEGImages', 'labels').replace('.jpg', '.txt').replace('.png','.txt')
with open(labelPath) as f:
label = f.readlines()
label = ''.join(label)
if not os.path.exists(imagePath):
print('%s does not exist' % imagePath)
continue
with open(imagePath, 'r') as f:
imageBin = f.read()
if checkValid:
if not checkImageIsValid(imageBin):
print('%s is not a valid image' % imagePath)
continue
imageKey = 'image-%09d' % cnt
labelKey = 'label-%09d' % cnt
cache[imageKey] = imageBin
cache[labelKey] = label
if cnt % 1000 == 0:
writeCache(env, cache)
cache = {}
print('Written %d / %d' % (cnt, nSamples))
cnt += 1
nSamples = cnt-1
cache['num-samples'] = str(nSamples)
writeCache(env, cache)
print('Created dataset with %d samples' % nSamples)
def rnn_backward(dh, cache):
"""
Compute the backward pass for a vanilla RNN over an entire sequence of data.
Inputs:
- dh: Upstream gradients of all hidden states, of shape (N, T, H)
Returns a tuple of:
- dx: Gradient of inputs, of shape (N, T, D)
- dh0: Gradient of initial hidden state, of shape (N, H)
- dWx: Gradient of input-to-hidden weights, of shape (D, H)
- dWh: Gradient of hidden-to-hidden weights, of shape (H, H)
- db: Gradient of biases, of shape (H,)
"""
dx, dh0, dWx, dWh, db, dprev_h = None, None, None, None, None, None
N, T, H = dh.shape
x, h0, Wx, Wh, b, h = cache
D = x.shape[2]
dx, dh0, dWx, dWh, db = np.zeros((N, T, D)), np.zeros((N, H)), np.zeros((D, H)), np.zeros((H, H)), np.zeros((H, ))
dprev_h = np.zeros((N, H))
##############################################################################
# TODO: Implement the backward pass for a vanilla RNN running an entire #
# sequence of data. You should use the rnn_step_backward function that you #
# defined above. You can use a for loop to help compute the backward pass. #
##############################################################################
for i in xrange(T - 1, -1, -1):
temp_cache = (h[:, i, :], x[:, i, :], h[:, i - 1, :] if i >= 1 else h0, Wx, Wh, b)
dx[:, i, :], dprev_h, dWx_t, dWh_t, db_t = rnn_step_backward(dh[:, i, :] + dprev_h, temp_cache)
dWx += dWx_t
dWh += dWh_t
db += db_t
dh0 = dprev_h
##############################################################################
# END OF YOUR CODE #
##############################################################################
return dx, dh0, dWx, dWh, db
def rnn_forward(x, h0, Wx, Wh, b):
"""
Run a vanilla RNN forward on an entire sequence of data. We assume an input
sequence composed of T vectors, each of dimension D. The RNN uses a hidden
size of H, and we work over a minibatch containing N sequences. After running
the RNN forward, we return the hidden states for all timesteps.
Inputs:
- x: Input data for the entire timeseries, of shape (N, T, D).
- h0: Initial hidden state, of shape (N, H)
- Wx: Weight matrix for input-to-hidden connections, of shape (D, H)
- Wh: Weight matrix for hidden-to-hidden connections, of shape (H, H)
- b: Biases of shape (H,)
Returns a tuple of:
- h: Hidden states for the entire timeseries, of shape (N, T, H).
- cache: Values needed in the backward pass
"""
h, cache = None, None
N, H = h0.shape
T = x.shape[1]
##############################################################################
# TODO: Implement forward pass for a vanilla RNN running on a sequence of #
# input data. You should use the rnn_step_forward function that you defined #
# above. You can use a for loop to help compute the forward pass. #
##############################################################################
h = np.zeros((N, T, H))
h[:, -1, :] = h0
for i in xrange(T):
h[:, i, :], temp_cache = rnn_step_forward(x[:, i, :], h[:, i - 1, :], Wx, Wh, b)
cache = (x, h0, Wx, Wh, b, h)
##############################################################################
# END OF YOUR CODE #
##############################################################################
cache = tuple(cache)
return h, cache
def run_benchmark(benchmark, test_config=TestConfig(), context=None, *args, **kwargs):
""" Perform a benchmark, (re)using a given, configured CURL call to do so
The actual analysis of metrics is performed separately, to allow for testing
"""
# Context handling
my_context = context
if my_context is None:
my_context = Context()
warmup_runs = benchmark.warmup_runs
benchmark_runs = benchmark.benchmark_runs
message = '' # Message is name of benchmark... print it?
if (benchmark_runs <= 0):
raise Exception(
"Invalid number of benchmark runs, must be > 0 :" + benchmark_runs)
result = TestResponse()
# TODO create and use a curl-returning configuration function
# TODO create and use a post-benchmark cleanup function
# They should use is_dynamic/is_context_modifier to determine if they need to
# worry about context and re-reading/retemplating and only do it if needed
# - Also, they will need to be smart enough to handle extraction functions
# For performance reasons, we don't want to re-run templating/extraction if
# we do not need to, and do not want to save request bodies.
# Initialize variables to store output
output = BenchmarkResult()
output.name = benchmark.name
output.group = benchmark.group
metricnames = list(benchmark.metrics)
# Metric variable for curl, to avoid hash lookup for every metric name
metricvalues = [METRICS[name] for name in metricnames]
# Initialize arrays to store results for each metric
results = [list() for x in xrange(0, len(metricnames))]
curl = pycurl.Curl()
# Benchmark warm-up to allow for caching, JIT compiling, on client
logger.info('Warmup: ' + message + ' started')
for x in xrange(0, warmup_runs):
benchmark.update_context_before(my_context)
templated = benchmark.realize(my_context)
curl = templated.configure_curl(
timeout=test_config.timeout, context=my_context, curl_handle=curl)
# Do not store actual response body at all.
curl.setopt(pycurl.WRITEFUNCTION, lambda x: None)
curl.perform()
logger.info('Warmup: ' + message + ' finished')
logger.info('Benchmark: ' + message + ' starting')
for x in xrange(0, benchmark_runs): # Run the actual benchmarks
# Setup benchmark
benchmark.update_context_before(my_context)
templated = benchmark.realize(my_context)
curl = templated.configure_curl(
timeout=test_config.timeout, context=my_context, curl_handle=curl)
# Do not store actual response body at all.
curl.setopt(pycurl.WRITEFUNCTION, lambda x: None)
try: # Run the curl call, if it errors, then add to failure counts for benchmark
curl.perform()
except Exception:
output.failures = output.failures + 1
curl.close()
curl = pycurl.Curl()
continue # Skip metrics collection
# Get all metrics values for this run, and store to metric lists
for i in xrange(0, len(metricnames)):
results[i].append(curl.getinfo(metricvalues[i]))
logger.info('Benchmark: ' + message + ' ending')
temp_results = dict()
for i in xrange(0, len(metricnames)):
temp_results[metricnames[i]] = results[i]
output.results = temp_results
return analyze_benchmark_results(output, benchmark)
def build_targets(pred_boxes, target, anchors, num_anchors, num_classes, nH,
nW, noobject_scale, object_scale, sil_thresh, seen):
# nH, nW here are number of grids in y and x directions (7, 7 here)
nB = target.size(0) # batch size
nA = num_anchors # 5 for our case
nC = num_classes
anchor_step = len(anchors) // num_anchors
conf_mask = torch.ones(nB, nA, nH, nW) * noobject_scale
coord_mask = torch.zeros(nB, nA, nH, nW)
cls_mask = torch.zeros(nB, nA, nH, nW)
tx = torch.zeros(nB, nA, nH, nW)
ty = torch.zeros(nB, nA, nH, nW)
tw = torch.zeros(nB, nA, nH, nW)
th = torch.zeros(nB, nA, nH, nW)
tconf = torch.zeros(nB, nA, nH, nW)
tcls = torch.zeros(nB, nA, nH, nW)
# for each grid there are nA anchors
# nAnchors is the number of anchor for one image
nAnchors = nA * nH * nW
nPixels = nH * nW
# for each image
for b in xrange(nB):
# get all anchor boxes in one image
# (4 * nAnchors)
cur_pred_boxes = pred_boxes[b * nAnchors:(b + 1) * nAnchors].t()
# initialize iou score for each anchor
cur_ious = torch.zeros(nAnchors)
for t in xrange(50):
# for each anchor 4 coordinate parameters, already in the coordinate system for the whole image
# this loop is for anchors in each image
# for each anchor 5 parameters are available (class, x, y, w, h)
if target[b][t * 5 + 1] == 0:
break
gx = target[b][t * 5 + 1] * nW
gy = target[b][t * 5 + 2] * nH
gw = target[b][t * 5 + 3] * nW
gh = target[b][t * 5 + 4] * nH
# groud truth boxes
cur_gt_boxes = torch.FloatTensor([gx, gy, gw,
gh]).repeat(nAnchors, 1).t()
# bbox_ious is the iou value between orediction and groud truth
cur_ious = torch.max(
cur_ious,
bbox_ious(cur_pred_boxes, cur_gt_boxes, x1y1x2y2=False))
# if iou > a given threshold, it is seen as it includes an object
# conf_mask[b][cur_ious>sil_thresh] = 0
conf_mask_t = conf_mask.view(nB, -1)
conf_mask_t[b][cur_ious > sil_thresh] = 0
conf_mask_tt = conf_mask_t[b].view(nA, nH, nW)
conf_mask[b] = conf_mask_tt
if seen < 12800:
if anchor_step == 4:
tx = torch.FloatTensor(anchors).view(nA, anchor_step).index_select(
1, torch.LongTensor([2])).view(1, nA, 1,
1).repeat(nB, 1, nH, nW)
ty = torch.FloatTensor(anchors).view(
num_anchors, anchor_step).index_select(1, torch.LongTensor(
[2])).view(1, nA, 1, 1).repeat(nB, 1, nH, nW)
else:
tx.fill_(0.5)
ty.fill_(0.5)
tw.zero_()
th.zero_()
coord_mask.fill_(1)
# number of ground truth
nGT = 0
nCorrect = 0
for b in xrange(nB):
# anchors for one batch (at least batch size, and for some specific classes, there might exist more than one anchor)
for t in xrange(50):
if target[b][t * 5 + 1] == 0:
break
nGT = nGT + 1
best_iou = 0.0
best_n = -1
min_dist = 10000
# the values saved in target is ratios
# times by the width and height of the output feature maps nW and nH
gx = target[b][t * 5 + 1] * nW
gy = target[b][t * 5 + 2] * nH
gi = int(gx)
gj = int(gy)
gw = target[b][t * 5 + 3] * nW
gh = target[b][t * 5 + 4] * nH
gt_box = [0, 0, gw, gh]
for n in xrange(nA):
# get anchor parameters (2 values)
aw = anchors[anchor_step * n]
ah = anchors[anchor_step * n + 1]
anchor_box = [0, 0, aw, ah]
# only consider the size (width and height) of the anchor box
iou = bbox_iou(anchor_box, gt_box, x1y1x2y2=False)
if anchor_step == 4:
ax = anchors[anchor_step * n + 2]
ay = anchors[anchor_step * n + 3]
dist = pow(((gi + ax) - gx), 2) + pow(((gj + ay) - gy), 2)
# get the best anchor form with the highest iou
if iou > best_iou:
best_iou = iou
best_n = n
elif anchor_step == 4 and iou == best_iou and dist < min_dist:
best_iou = iou
best_n = n
min_dist = dist
# then we determine the parameters for an anchor (4 values together)
gt_box = [gx, gy, gw, gh]
# find corresponding prediction box
pred_box = pred_boxes[b * nAnchors + best_n * nPixels + gj * nW +
gi]
# only consider the best anchor box, for each image
coord_mask[b][best_n][gj][gi] = 1
cls_mask[b][best_n][gj][gi] = 1
# in this cell of the output feature map, there exists an object
conf_mask[b][best_n][gj][gi] = object_scale
tx[b][best_n][gj][gi] = target[b][t * 5 + 1] * nW - gi
ty[b][best_n][gj][gi] = target[b][t * 5 + 2] * nH - gj
tw[b][best_n][gj][gi] = math.log(gw /
anchors[anchor_step * best_n])
th[b][best_n][gj][gi] = math.log(gh /
anchors[anchor_step * best_n + 1])
iou = bbox_iou(gt_box, pred_box, x1y1x2y2=False) # best_iou
# confidence equals to iou of the corresponding anchor
tconf[b][best_n][gj][gi] = iou
tcls[b][best_n][gj][gi] = target[b][t * 5]
# if ious larger than 0.5, we justify it as a correct prediction
if iou > 0.5:
nCorrect = nCorrect + 1
# true values are returned
return nGT, nCorrect, coord_mask, conf_mask, cls_mask, tx, ty, tw, th, tconf, tcls
print('----- loss --------------------')
print(loss)
save_grad = None
def extract(grad):
global saved_grad
saved_grad = convert2cpu(grad.data)
output.register_hook(extract)
loss.backward()
saved_grad = saved_grad.view(-1)
for i in xrange(saved_grad.size(0)):
if abs(saved_grad[i]) >= 0.001:
print('%d : %f' % (i, saved_grad[i]))
print(m.state_dict().keys())
#print(m.models[0][0].weight.grad.data.storage()[0:100])
#print(m.models[14][0].weight.data.storage()[0:100])
weight = m.models[13][0].weight.data
grad = m.models[13][0].weight.grad.data
mask = torch.abs(grad) >= 0.1
print(weight[mask])
print(grad[mask])
optimizer.step()
weight2 = m.models[13][0].weight.data
print(weight2[mask])
def build_targets(pred_boxes, target, anchors, num_anchors, num_classes, nH,
nW, noobject_scale, object_scale, sil_thresh, seen):
nB = target.size(0)
nA = num_anchors
nC = num_classes
anchor_step = len(anchors) // num_anchors
conf_mask = torch.ones(nB, nA, nH, nW) * noobject_scale
coord_mask = torch.zeros(nB, nA, nH, nW)
cls_mask = torch.zeros(nB, nA, nH, nW)
tx = torch.zeros(nB, nA, nH, nW)
ty = torch.zeros(nB, nA, nH, nW)
tw = torch.zeros(nB, nA, nH, nW)
th = torch.zeros(nB, nA, nH, nW)
tconf = torch.zeros(nB, nA, nH, nW)
tcls = torch.zeros(nB, nA, nH, nW)
nAnchors = nA * nH * nW
nPixels = nH * nW
for b in xrange(nB):
cur_pred_boxes = pred_boxes[b * nAnchors:(b + 1) * nAnchors].t()
cur_ious = torch.zeros(nAnchors)
for t in xrange(50):
if target[b][t * 5 + 1] == 0:
break
gx = target[b][t * 5 + 1] * nW
gy = target[b][t * 5 + 2] * nH
gw = target[b][t * 5 + 3] * nW
gh = target[b][t * 5 + 4] * nH
cur_gt_boxes = torch.FloatTensor([gx, gy, gw,
gh]).repeat(nAnchors, 1).t()
cur_ious = torch.max(
cur_ious,
bbox_ious(cur_pred_boxes, cur_gt_boxes, x1y1x2y2=False))
conf_mask[b][cur_ious > sil_thresh] = 0
if seen < 12800:
if anchor_step == 4:
tx = torch.FloatTensor(anchors).view(nA, anchor_step).index_select(
1, torch.LongTensor([2])).view(1, nA, 1,
1).repeat(nB, 1, nH, nW)
ty = torch.FloatTensor(anchors).view(
num_anchors, anchor_step).index_select(1, torch.LongTensor(
[2])).view(1, nA, 1, 1).repeat(nB, 1, nH, nW)
else:
tx.fill_(0.5)
ty.fill_(0.5)
tw.zero_()
th.zero_()
coord_mask.fill_(1)
nGT = 0
nCorrect = 0
for b in xrange(nB):
for t in xrange(50):
if target[b][t * 5 + 1] == 0:
break
nGT = nGT + 1
best_iou = 0.0
best_n = -1
min_dist = 10000
gx = target[b][t * 5 + 1] * nW
gy = target[b][t * 5 + 2] * nH
gi = int(gx)
gj = int(gy)
gw = target[b][t * 5 + 3] * nW
gh = target[b][t * 5 + 4] * nH
gt_box = [0, 0, gw, gh]
for n in xrange(nA):
aw = anchors[anchor_step * n]
ah = anchors[anchor_step * n + 1]
anchor_box = [0, 0, aw, ah]
iou = bbox_iou(anchor_box, gt_box, x1y1x2y2=False)
if anchor_step == 4:
ax = anchors[anchor_step * n + 2]
ay = anchors[anchor_step * n + 3]
dist = pow(((gi + ax) - gx), 2) + pow(((gj + ay) - gy), 2)
if iou > best_iou:
best_iou = iou
best_n = n
elif anchor_step == 4 and iou == best_iou and dist < min_dist:
best_iou = iou
best_n = n
min_dist = dist
gt_box = [gx, gy, gw, gh]
pred_box = pred_boxes[b * nAnchors + best_n * nPixels + gj * nW +
gi]
coord_mask[b][best_n][gj][gi] = 1
cls_mask[b][best_n][gj][gi] = 1
conf_mask[b][best_n][gj][gi] = object_scale
tx[b][best_n][gj][gi] = target[b][t * 5 + 1] * nW - gi
ty[b][best_n][gj][gi] = target[b][t * 5 + 2] * nH - gj
tw[b][best_n][gj][gi] = math.log(gw /
anchors[anchor_step * best_n])
th[b][best_n][gj][gi] = math.log(gh /
anchors[anchor_step * best_n + 1])
iou = bbox_iou(gt_box, pred_box, x1y1x2y2=False) # best_iou
tconf[b][best_n][gj][gi] = iou
tcls[b][best_n][gj][gi] = target[b][t * 5]
if iou > 0.5:
nCorrect = nCorrect + 1
return nGT, nCorrect, coord_mask, conf_mask, cls_mask, tx, ty, tw, th, tconf, tcls
#!/usr/bin/env python
"""
Calculate RMSD between two XYZ files
by: Jimmy Charnley Kromann <*****@*****.**> and Lars Andersen Bratholm <*****@*****.**>
project: https://github.com/charnley/rmsd
license: https://github.com/charnley/rmsd/blob/master/LICENSE
"""
import numpy as np
import re
#enable Python2/3 compatability
try:
xrange(1)
except:
from builtins import range as xrange
def kabsch_rmsd(P, Q):
"""
Rotate matrix P unto Q and calculate the RMSD
"""
P = kabsch_rotate(P, Q)
return rmsd(P, Q)
def kabsch_rotate(P, Q):
"""
Rotate matrix P unto matrix Q using Kabsch algorithm
def syslog_parse(logfile, logdata, headers, output):
x = 0
data = []
singlelines = {}
multilines = {}
for i in logdata:
x += 1
if (not isinstance(i, str)):
if re.search('^[A-Za-z]{3}'.encode('utf-8'), i):
singlelines[x] = i.rstrip()
else:
data.append(x)
data.append(x - 1)
data.append(x + 1)
multilines[x] = i.rstrip()
else:
if re.search('^[A-Za-z]{3}', i):
singlelines[x] = i.rstrip()
else:
data.append(x)
data.append(x - 1)
data.append(x + 1)
multilines[x] = i.rstrip()
data = list(OrderedDict.fromkeys(sorted(data)))
ranges = []
#for key, group in groupby(enumerate(data), lambda (index, item): index - item):
for group in enumerate(data):
#group = map(itemgetter(1), group)
if len(group) > 1:
ranges.append(xrange(group[0], group[-1]))
else:
ranges.append(group[0])
for u in ranges:
chain = []
for z in u:
if multilines.get(z) is None:
lno = z
anchor = singlelines.get(z)
else:
if (not isinstance(multilines.get(z), str)):
val = multilines.get(z).decode('utf-8').replace(
'\t', '').replace(' ', '')
else:
val = multilines.get(z).replace('\t', '').replace(' ', '')
chain.append(val)
if (isinstance(anchor, str)):
singlelines[lno] = anchor + ' ' + ''.join(chain)
else:
singlelines[lno] = anchor.decode('utf-8') + ' ' + ''.join(chain)
for k, v in singlelines.items():
line = v
if (not isinstance(line, str)):
line = line.decode('utf-8')
if not 'last message repeated' in line:
record = OrderedDict((h, '') for h in headers)
m = re.match(
r"(?P<month>\w\w\w)\s{1,2}(?P<day>\d{1,2}) (?P<time>\w\w:\w\w:\w\w) (?P<systemname>.*?) (?P<processName>.*?)\[(?P<PID>[0-9]+)\].*?:\s{0,1}(?P<message>.*)",
line)
record['src_file'] = logfile
record['timestamp'] = str(
m.group('month') + " " + m.group('day') + " " +
m.group('time'))
record['log_systemname'] = m.group('systemname')
record['processname'] = m.group('processName')
record['pid'] = m.group('PID')
record['message'] = m.group('message')
output.write_entry(record.values())
def range(start, stop):
if sys.version_info < (3, 0):
return builtins.xrange(start, stop)
else:
return builtins.range(start, stop)
