def path_to_datum(self, path, label,
image_sum = None):
"""
Creates a Datum from a path and a label
May also update image_sum, if computing mean
Arguments:
path -- path to the image (filesystem path or URL)
label -- numeric label for this image's category
Keyword arguments:
image_sum -- numpy array that stores a running sum of added images
"""
# prepend path with image_folder, if appropriate
if not utils.is_url(path) and self.image_folder and not os.path.isabs(path):
path = os.path.join(self.image_folder, path)
image = utils.image.load_image(path)
image = utils.image.resize_image(image,
self.height, self.width,
channels = self.channels,
resize_mode = self.resize_mode,
)
if self.compute_mean and image_sum is not None:
image_sum += image
if not self.encoding or self.encoding == 'none':
# Transform to caffe's format requirements
if image.ndim == 3:
# Transpose to (channels, height, width)
image = image.transpose((2,0,1))
if image.shape[0] == 3:
# channel swap
# XXX see issue #59
image = image[[2,1,0],...]
elif image.ndim == 2:
# Add a channels axis
image = image[np.newaxis,:,:]
else:
raise Exception('Image has unrecognized shape: "%s"' % image.shape)
datum = caffe.io.array_to_datum(image, label)
else:
datum = caffe_pb2.Datum()
if image.ndim == 3:
datum.channels = image.shape[2]
else:
datum.channels = 1
datum.height = image.shape[0]
datum.width = image.shape[1]
datum.label = label
s = StringIO()
if self.encoding == 'png':
PIL.Image.fromarray(image).save(s, format='PNG')
elif self.encoding == 'jpg':
PIL.Image.fromarray(image).save(s, format='JPEG', quality=90)
datum.data = s.getvalue()
datum.encoded = True
return datum
def key_value_example(use_caffe_datum=False):
lmdb_dir_path = './mylmdb'
with lmdb.open(lmdb_dir_path, readonly=True) as env:
with env.begin() as txn:
cursor = txn.cursor()
if use_caffe_datum:
#from caffe.proto import caffe_pb2
import caffe_pb2
for k, v in cursor:
# REF [site] >> https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto
datum = caffe_pb2.Datum()
datum.ParseFromString(v)
x = np.fromstring(datum.data, dtype=np.uint8)
x = x.reshape(datum.channels, datum.height, datum.width)
y = datum.label
print(k.decode(), x.shape, y)
else:
for k, v in cursor:
datum = json.loads(v.decode('ascii'))
x = np.array(datum['data'], dtype=np.uint8)
x = x.reshape(datum['channels'], datum['height'],
datum['width'])
y = datum['label']
print(k.decode(), x.shape, y)
def read_from_db_example(use_caffe_datum=False):
leveldb_dir_path = './myleveldb'
db = leveldb.LevelDB(leveldb_dir_path, create_if_missing=True)
key = b'00000000'
try:
raw_datum = db.Get(key)
except KeyError as ex:
print('Invalid key, {}: {}.'.format(key, ex))
return
if use_caffe_datum:
#from caffe.proto import caffe_pb2
import caffe_pb2
# REF [site] >> https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto
datum = caffe_pb2.Datum()
datum.ParseFromString(raw_datum)
x = np.fromstring(datum.data, dtype=np.uint8)
x = x.reshape(datum.channels, datum.height, datum.width)
y = datum.label
else:
datum = json.loads(raw_datum.decode('ascii'))
x = np.array(datum['data'], dtype=np.uint8)
x = x.reshape(datum['channels'], datum['height'], datum['width'])
y = datum['label']
print(x.shape, y)
def parse_datum(value):
"""
Parse a Caffe datum
"""
datum = caffe_pb2.Datum()
datum.ParseFromString(value)
if datum.encoded:
s = StringIO()
s.write(datum.data)
s.seek(0)
img = PIL.Image.open(s)
img = np.array(img)
else:
import caffe.io
arr = caffe.io.datum_to_array(datum)
# CHW -> HWC
arr = arr.transpose((1, 2, 0))
if arr.shape[2] == 1:
# HWC -> HW
arr = arr[:, :, 0]
elif arr.shape[2] == 3:
# BGR -> RGB
# XXX see issue #59
arr = arr[:, :, [2, 1, 0]]
img = arr
return img
def array_to_datum(self, data, scalar_label, encoding):
if data.ndim != 3:
raise ValueError('Invalid number of dimensions: %d' % data.ndim)
if encoding == 'none':
if data.shape[0] == 3:
# RGB to BGR
# XXX see issue #59
data = data[[2, 1, 0], ...]
datum = caffe.io.array_to_datum(data, scalar_label)
else:
# Transpose to (height, width, channel)
data = data.transpose((1, 2, 0))
datum = caffe_pb2.Datum()
datum.height = data.shape[0]
datum.width = data.shape[1]
datum.channels = data.shape[2]
datum.label = scalar_label
if data.shape[2] == 1:
# grayscale
data = data[:, :, 0]
s = BytesIO()
if encoding == 'png':
PIL.Image.fromarray(data).save(s, format='PNG')
elif encoding == 'jpg':
PIL.Image.fromarray(data).save(s, format='JPEG', quality=90)
else:
raise ValueError('Invalid encoding type')
datum.data = s.getvalue()
datum.encoded = True
return datum
def __getitem__(self, index):
"""Returns the i-th example."""
key = self.lmdb_cursor.key()
if len(key) == 0:
self.lmdb_cursor.first()
datum = caffe_pb2.Datum()
datum.ParseFromString(self.lmdb_cursor.value())
self.lmdb_cursor.next()
w = datum.width
h = datum.height
c = datum.channels
y = datum.label
xint8 = np.fromstring(datum.data, dtype=np.uint8).reshape(c, h, w)
if self.random:
top = random.randint(0, h - self.crop_size - 1)
left = random.randint(0, w - self.crop_size - 1)
else:
top = (h - self.crop_size) // 2
left = (w - self.crop_size) // 2
bottom = top + self.crop_size
right = left + self.crop_size
xint8 = xint8[:, top:bottom, left:right]
if self.flip:
if random.randint(0, 1):
xint8 = xint8[:, :, ::-1]
xf32 = xint8 * np.float32(self.scale)
return xf32, y
def read_from_db_example(use_caffe_datum=False):
lmdb_dir_path = './mylmdb'
with lmdb.open(lmdb_dir_path, readonly=True) as env:
with env.begin() as txn:
raw_datum = txn.get(b'00000000')
if use_caffe_datum:
#from caffe.proto import caffe_pb2
import caffe_pb2
# REF [site] >> https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto
datum = caffe_pb2.Datum()
datum.ParseFromString(raw_datum)
x = np.fromstring(datum.data, dtype=np.uint8)
x = x.reshape(datum.channels, datum.height, datum.width)
y = datum.label
else:
datum = json.loads(raw_datum.decode('ascii'))
x = np.array(datum['data'], dtype=np.uint8)
x = x.reshape(datum['channels'], datum['height'], datum['width'])
y = datum['label']
print(x.shape, y)
def explore():
"""
Returns a gallery consisting of the images of one of the dbs
"""
job = job_from_request()
# Get LMDB
db = job.path(flask.request.args.get('db'))
db_path = job.path(db)
labels = []
if COLOR_PALETTE_ATTRIBUTE in job.extension_userdata:
# assume single-channel 8-bit palette
palette = job.extension_userdata[COLOR_PALETTE_ATTRIBUTE]
palette = np.array(palette).reshape((len(palette)/3,3)) / 255.
# normalize input pixels to [0,1]
norm = mpl.colors.Normalize(vmin=0,vmax=255)
# create map
cmap = mpl.pyplot.cm.ScalarMappable(norm=norm,
cmap=mpl.colors.ListedColormap(palette))
else:
cmap = None
page = int(flask.request.args.get('page', 0))
size = int(flask.request.args.get('size', 25))
reader = DbReader(db_path)
count = 0
imgs = []
min_page = max(0, page - 5)
total_entries = reader.total_entries
max_page = min((total_entries-1) / size, page + 5)
pages = range(min_page, max_page + 1)
for key, value in reader.entries():
if count >= page*size:
datum = caffe_pb2.Datum()
datum.ParseFromString(value)
if not datum.encoded:
raise RuntimeError("Expected encoded database")
s = StringIO()
s.write(datum.data)
s.seek(0)
img = PIL.Image.open(s)
if cmap and img.mode in ['L', '1']:
data = np.array(img)
data = cmap.to_rgba(data)*255
data = data.astype('uint8')
# keep RGB values only, remove alpha channel
data = data[:, :, 0:3]
img = PIL.Image.fromarray(data)
imgs.append({"label": None, "b64": utils.image.embed_image_html(img)})
count += 1
if len(imgs) >= size:
break
return flask.render_template('datasets/images/explore.html', page=page, size=size, job=job, imgs=imgs, labels=None, pages=pages, label=None, total_entries=total_entries, db=db)
def _array_to_label_datum(labels, single_label):
if not single_label:
label1, label2 = labels
target = np.zeros((2, 1, 1))
target[0, 0, 0] = label1
target[1, 0, 0] = label2
datum = caffe_pb2.Datum()
datum.channels, datum.height, datum.width = 2, 1, 1
datum.float_data.extend(target.flat)
else:
target = np.zeros((1, 1, 1))
target[0, 0, 0] = labels
datum = caffe_pb2.Datum()
datum.channels, datum.height, datum.width = 1, 1, 1
datum.float_data.extend(target.flat)
return datum
def explore():
"""
Returns a gallery consisting of the images of one of the dbs
"""
job = job_from_request()
# Get LMDB
db = job.path(flask.request.args.get('db'))
db_path = job.path(db)
labels = []
page = int(flask.request.args.get('page', 0))
size = int(flask.request.args.get('size', 25))
reader = DbReader(db_path)
count = 0
imgs = []
min_page = max(0, page - 5)
total_entries = reader.total_entries
max_page = min((total_entries - 1) / size, page + 5)
pages = range(min_page, max_page + 1)
for key, value in reader.entries():
if count >= page * size:
datum = caffe_pb2.Datum()
datum.ParseFromString(value)
if not datum.encoded:
raise RuntimeError("Expected encoded database")
s = StringIO()
s.write(datum.data)
s.seek(0)
img = PIL.Image.open(s)
imgs.append({
"label": None,
"b64": utils.image.embed_image_html(img)
})
count += 1
if len(imgs) >= size:
break
return flask.render_template('datasets/images/explore.html',
page=page,
size=size,
job=job,
imgs=imgs,
labels=None,
pages=pages,
label=None,
total_entries=total_entries,
db=db)
def transform(prev):
d = caffe_pb2.Datum()
d.channels = 1
d.height = 1
d.width = 2064
totals = [0] * d.width
for entry, label in prev:
totals = [t + e for t, e in zip(totals, entry)]
d.data = struct.pack("2064d", *entry)
d.label = label
yield d.SerializeToString()
d.data = struct.pack("2064d", *totals)
open("watson_mean.binaryproto", "w").write(d.SerializeToString())
def readFromDB(index, size, database):
env = lmdb.open(database, readonly=True)
#with env.begin() as txn:
# cursor = txn.cursor()
# for i in range (index , size):
# str_id = '{0:08}'.format(i)
# value = cursor.get(str_id.encode('ascii'))
# output = '{0:08}'.format(value)
# print(i, value)
datum = caffe_pb2.Datum()
with env.begin() as txn:
cursor = txn.cursor()
for key, value in cursor:
datum.proto.caffe_pb2.ParseFromString(value)
label = datum.label
channels = datum.channels
height = datum.height
width = datum.width
short = 0
#first = True
readArray = np.zeros((channels, height, width),
dtype=np.uint16)
print(channels)
print(height)
print(width)
count = 0
for c in datum.data:
readArray[(count / (height * height) % channels),
(count / height) % height,
count % height] = ord(struct.unpack('c', c)[0])
#print(struct.unpack('H',short)[0])
first = True
count = count + 1
print(label,
"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
#lmdbReadWrite2.printArray(readArray[1,:,:], readArray[0,:,:])
#print readArray[2,:,:]
print readArray[0, :, :]
def array_to_datum(arr, label=0):
"""Converts a 3-dimensional array to datum. If the array has dtype uint8,
the output data will be encoded as a string. Otherwise, the output data
will be stored in float format.
"""
if arr.ndim != 3:
raise ValueError('Incorrect array shape.')
datum = caffe_pb2.Datum()
datum.channels, datum.height, datum.width = arr.shape
if arr.dtype == np.uint8:
datum.data = arr.tostring()
else:
datum.float_data.extend(arr.flat)
datum.label = label
return datum
def write_to_db_example(use_caffe_datum=False):
N = 1000
X = np.zeros((N, 3, 32, 32), dtype=np.uint8)
y = np.zeros(N, dtype=np.int64)
try:
lmdb_dir_path = './mylmdb'
map_size = X.nbytes * 10
with lmdb.open(lmdb_dir_path, map_size=map_size) as env:
with env.begin(write=True) as txn: # A transaction object.
if use_caffe_datum:
#from caffe.proto import caffe_pb2
import caffe_pb2
for i in range(N):
# REF [site] >> https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto
datum = caffe_pb2.Datum()
datum.channels = X.shape[1]
datum.height = X.shape[2]
datum.width = X.shape[3]
datum.data = X[i].tobytes(
) # or .tostring() if numpy < 1.9.
datum.label = int(y[i])
str_id = '{:08}'.format(i)
# The encode is only essential in Python 3.
txn.put(str_id.encode('ascii'),
datum.SerializeToString())
else:
for i in range(N):
datum = {
'channels': X.shape[1],
'height': X.shape[2],
'width': X.shape[3],
'data': X[i].tolist(),
'label': int(y[i]),
}
str_id = '{:08}'.format(i)
# The encode is only essential in Python 3.
txn.put(str_id.encode('ascii'),
json.dumps(datum).encode('ascii'))
#--------------------
print(env.stat())
except lmdb.MapFullError as ex:
print('lmdb.MapFullError raised: {}.'.format(ex))
def _array_to_datum(image, label, encoding):
"""
Create a caffe Datum from a numpy.ndarray
"""
if not encoding:
# Transform to caffe's format requirements
if image.ndim == 3:
# Transpose to (channels, height, width)
image = image.transpose((2, 0, 1))
if image.shape[0] == 3:
# channel swap
# XXX see issue #59
image = image[[2, 1, 0], ...]
elif image.ndim == 2:
# Add a channels axis
image = image[np.newaxis, :, :]
else:
raise Exception('Image has unrecognized shape: "%s"' % image.shape)
datum = caffe.io.array_to_datum(image, label)
else:
datum = caffe_pb2.Datum()
if image.ndim == 3:
datum.channels = image.shape[2]
else:
datum.channels = 1
datum.height = image.shape[0]
datum.width = image.shape[1]
datum.label = label
s = StringIO()
if encoding == 'png':
PIL.Image.fromarray(image).save(s.getvalue(), format='PNG')
elif encoding == 'jpg':
PIL.Image.fromarray(image).save(s.getvalue(),
format='JPEG',
quality=90)
else:
raise ValueError('Invalid encoding type')
datum.data = s.getvalue()
datum.encoded = True
return datum
def write_to_db_example(use_caffe_datum=False):
N = 1000
X = np.zeros((N, 3, 32, 32), dtype=np.uint8)
y = np.zeros(N, dtype=np.int64)
leveldb_dir_path = './myleveldb'
db = leveldb.LevelDB(leveldb_dir_path, create_if_missing=True)
if use_caffe_datum:
#from caffe.proto import caffe_pb2
import caffe_pb2
for i in range(N):
# REF [site] >> https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto
datum = caffe_pb2.Datum()
datum.channels = X.shape[1]
datum.height = X.shape[2]
datum.width = X.shape[3]
datum.data = X[i].tobytes() # or .tostring() if numpy < 1.9.
datum.label = int(y[i])
str_id = '{:08}'.format(i)
# The encode is only essential in Python 3.
db.Put(str_id.encode('ascii'), datum.SerializeToString())
else:
for i in range(N):
datum = {
'channels': X.shape[1],
'height': X.shape[2],
'width': X.shape[3],
'data': X[i].tolist(),
'label': int(y[i]),
}
str_id = '{:08}'.format(i)
# The encode is only essential in Python 3.
db.Put(str_id.encode('ascii'), json.dumps(datum).encode('ascii'))
#db.Delete(b'00000000')
#--------------------
print(db.GetStats())
def key_value_example(use_caffe_datum=False):
leveldb_dir_path = './myleveldb'
db = leveldb.LevelDB(leveldb_dir_path, create_if_missing=True)
if use_caffe_datum:
#from caffe.proto import caffe_pb2
import caffe_pb2
for k, v in db.RangeIter():
# REF [site] >> https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto
datum = caffe_pb2.Datum()
datum.ParseFromString(v)
x = np.fromstring(datum.data, dtype=np.uint8)
x = x.reshape(datum.channels, datum.height, datum.width)
y = datum.label
print(k.decode(), x.shape, y)
else:
for k, v in db.RangeIter():
datum = json.loads(v.decode('ascii'))
x = np.array(datum['data'], dtype=np.uint8)
x = x.reshape(datum['channels'], datum['height'], datum['width'])
y = datum['label']
print(k.decode(), x.shape, y)
def analyze_db(database,
only_count=False,
force_same_shape=False,
print_data=False,
):
"""
Looks at the data in a prebuilt database and verifies it
Also prints out some information about it
Returns True if all entries are valid
Arguments:
database -- path to the database
Keyword arguments:
only_count -- only count the entries, don't inspect them
force_same_shape -- throw an error if not all images have the same shape
print_data -- print the array for each datum
"""
start_time = time.time()
# Open database
try:
database = validate_database_path(database)
except ValueError as e:
logger.error(e.message)
return False
reader = DbReader(database)
logger.info('Total entries: %s' % reader.total_entries)
unique_shapes = Counter()
count = 0
update_time = None
for key, value in reader.entries():
datum = caffe_pb2.Datum()
datum.ParseFromString(value)
if print_data:
array = caffe.io.datum_to_array(datum)
print(('>>> Datum #%d (shape=%s)' % (count, array.shape)))
print (array)
if (not datum.HasField('height') or datum.height == 0 or
not datum.HasField('width') or datum.width == 0):
if datum.encoded:
if force_same_shape or not len(list(unique_shapes.keys())):
# Decode datum to learn the shape
s = StringIO()
s.write(datum.data)
s.seek(0)
img = PIL.Image.open(s)
width, height = img.size
channels = len(img.split())
else:
# We've already decoded one image, don't bother reading the rest
width = '?'
height = '?'
channels = '?'
else:
errstr = 'Shape is not set and datum is not encoded'
logger.error(errstr)
raise ValueError(errstr)
else:
width, height, channels = datum.width, datum.height, datum.channels
shape = '%sx%sx%s' % (width, height, channels)
unique_shapes[shape] += 1
if force_same_shape and len(list(unique_shapes.keys())) > 1:
logger.error("Images with different shapes found: %s and %s" % tuple(unique_shapes.keys()))
return False
count += 1
# Send update every 2 seconds
if update_time is None or (time.time() - update_time) > 2:
logger.debug('>>> Key %s' % key)
print_datum(datum)
logger.debug('Progress: %s/%s' % (count, reader.total_entries))
update_time = time.time()
if only_count:
# quit after reading one
count = reader.total_entries
logger.info('Assuming all entries have same shape ...')
unique_shapes[list(unique_shapes.keys())[0]] = count
break
if count != reader.total_entries:
logger.warning('LMDB reported %s total entries, but only read %s' % (reader.total_entries, count))
for key, val in sorted(list(unique_shapes.items()), key=operator.itemgetter(1), reverse=True):
logger.info('%s entries found with shape %s (WxHxC)' % (val, key))
logger.info('Completed in %s seconds.' % (time.time() - start_time,))
return True
def image_classification_dataset_explore():
"""
Returns a gallery consisting of the images of one of the dbs
"""
job = job_from_request()
# Get LMDB
db = flask.request.args.get('db', 'train')
if 'train' in db.lower():
task = job.train_db_task()
elif 'val' in db.lower():
task = job.val_db_task()
elif 'test' in db.lower():
task = job.test_db_task()
if task == None:
raise ValueError('No create_db task for {0}'.format(db))
if task.status != 'D':
raise ValueError(
"This create_db task's status should be 'D' but is '{0}'".format(
task.status))
if task.backend != 'lmdb':
raise ValueError(
"Backend is {0} while expected backend is lmdb".format(
task.backend))
db_path = job.path(task.db_name)
category_labels = job.get_labels()
labels = {}
for k, gt_category_type in enumerate(category_labels):
fname = db_path[:-3] + '_' + str(k) + '.h5'
labels[gt_category_type] = h5py.File(fname, 'r')
page = int(flask.request.args.get('page', 0))
size = int(flask.request.args.get('size', 25))
label = flask.request.args.get('label', None)
category_type = flask.request.args.get('type', None)
if label == 'None' and category_type == 'None':
label = None
category_type = None
if label is not None:
label = int(label)
#try:
# label = int(label)
#except ValueError:
# label = None
reader = DbReader(db_path)
count = 0
imgs = []
min_page = max(0, page - 5)
if label is None:
total_entries = reader.total_entries
else:
index = category_labels.keys().index(category_type)
total_entries = task.distribution[index][label]
max_page = min((total_entries - 1) / size, page + 5)
pages = range(min_page, max_page + 1)
for key, value in reader.entries():
if count >= page * size:
datum = caffe_pb2.Datum()
datum.ParseFromString(value)
if category_type is not None:
gt_label = int(labels[category_type]['label'][int(key)])
else:
gt_label = [
int(labels[c]['label'][int(key)]) for c in category_labels
]
if label is None or gt_label == label:
if datum.encoded:
s = StringIO()
s.write(datum.data)
s.seek(0)
img = PIL.Image.open(s)
else:
import caffe.io
arr = caffe.io.datum_to_array(datum)
# CHW -> HWC
arr = arr.transpose((1, 2, 0))
if arr.shape[2] == 1:
# HWC -> HW
arr = arr[:, :, 0]
elif arr.shape[2] == 3:
# BGR -> RGB
# XXX see issue #59
arr = arr[:, :, [2, 1, 0]]
img = PIL.Image.fromarray(arr)
if category_type is not None:
gt_class = category_labels[category_type][gt_label]
else:
gt_class = [
category_labels[c][gt_label[k]]
for k, c in enumerate(category_labels)
]
gt_class = ', '.join(gt_class)
#gt_class = category_labels[category_labels.keys()[0]][gt_label[0]]
imgs.append({
"label": gt_class,
"b64": utils.image.embed_image_html(img)
})
if label is None:
count += 1
else:
gt_label = int(labels[category_type]['label'][int(key)])
if gt_label == int(label):
count += 1
if len(imgs) >= size:
break
return flask.render_template('datasets/images/classification/explore.html',
page=page,
size=size,
job=job,
imgs=imgs,
category_labels=category_labels,
pages=pages,
label=label,
type=category_type,
total_entries=total_entries,
db=db)
image[t,:] = datum.float_data[ datum.width*(t): datum.width*(t+1)]
return image
if len(sys.argv)!=3:
print "usage: python make_pngs.py [lmdb folder/db] [output dir]"
print "Will dump out a bunch of RGB pngs whose name will be the key."
sys.exit(-1)
lmdb_dir = sys.argv[1]
outdir = sys.argv[2]
lmdb_env = lmdb.open( lmdb_dir )
lmdb_txn = lmdb_env.begin()
lmdb_cursor = lmdb_txn.cursor()
datum = caffe.Datum()
for key, raw_datum in lmdb_cursor:
datum.ParseFromString(raw_datum)
label = datum.label
data = datum_to_array( datum )
rescaled = (255.0 / data.max() * (data - data.min())).astype(np.uint8)
name = '%s/%s.png'%(outdir,key)
print "Make ",name
# RGB using unscaled ADC values
matplotlib.image.imsave(name, data)
# greyscale using scaled ADC values
#im = Image.fromarray(rescaled)
#im.save(name)
def image_classification_dataset_explore():
"""
Returns a gallery consisting of the images of one of the dbs
"""
job = job_from_request()
# Get LMDB
db = flask.request.args.get('db', 'train')
if 'train' in db.lower():
task = job.train_db_task()
elif 'val' in db.lower():
task = job.val_db_task()
elif 'test' in db.lower():
task = job.test_db_task()
if task is None:
raise ValueError('No create_db task for {0}'.format(db))
if task.status != 'D':
raise ValueError("This create_db task's status should be 'D' but is '{0}'".format(task.status))
if task.backend != 'lmdb':
raise ValueError("Backend is {0} while expected backend is lmdb".format(task.backend))
db_path = job.path(task.db_name)
labels = task.get_labels()
page = int(flask.request.args.get('page', 0))
size = int(flask.request.args.get('size', 25))
label = flask.request.args.get('label', None)
if label is not None:
try:
label = int(label)
label_str = labels[label]
except ValueError:
label = None
reader = DbReader(db_path)
count = 0
imgs = []
min_page = max(0, page - 5)
if label is None:
total_entries = reader.total_entries
else:
total_entries = task.distribution[str(label)]
max_page = min((total_entries-1) / size, page + 5)
pages = range(min_page, max_page + 1)
for key, value in reader.entries():
if count >= page*size:
datum = caffe_pb2.Datum()
datum.ParseFromString(value)
if label is None or datum.label == label:
if datum.encoded:
s = StringIO()
s.write(datum.data)
s.seek(0)
img = PIL.Image.open(s)
else:
import caffe.io
arr = caffe.io.datum_to_array(datum)
# CHW -> HWC
arr = arr.transpose((1,2,0))
if arr.shape[2] == 1:
# HWC -> HW
arr = arr[:,:,0]
elif arr.shape[2] == 3:
# BGR -> RGB
# XXX see issue #59
arr = arr[:,:,[2,1,0]]
img = PIL.Image.fromarray(arr)
imgs.append({"label":labels[datum.label], "b64": utils.image.embed_image_html(img)})
if label is None:
count += 1
else:
datum = caffe_pb2.Datum()
datum.ParseFromString(value)
if datum.label == int(label):
count += 1
if len(imgs) >= size:
break
return flask.render_template('datasets/images/classification/explore.html', page=page, size=size, job=job, imgs=imgs, labels=labels, pages=pages, label=label, total_entries=total_entries, db=db)
def create_lmdbs(folder, file_list, image_count=None, db_batch_size=None):
"""
Creates LMDBs for generic inference
Returns the filename for a test image
Creates these files in "folder":
train_images/
train_labels/
val_images/
val_labels/
mean.binaryproto
test.png
"""
if image_count is None:
train_image_count = TRAIN_IMAGE_COUNT
else:
train_image_count = image_count
val_image_count = VAL_IMAGE_COUNT
if db_batch_size is None:
db_batch_size = DB_BATCH_SIZE
# read file list
images = []
f = open(file_list)
for line in f.readlines():
line = line.strip()
if not line:
continue
path = None
# might contain a numerical label at the end
match = re.match(r'(.*\S)\s+(\d+)$', line)
if match:
path = match.group(1)
ground_truth = int(match.group(2))
images.append([path, ground_truth])
print(("Found %d image paths in image list" % len(images)))
for phase, image_count in [
('train', train_image_count),
('val', val_image_count)]:
print(("Will create %d pairs of %s images" % (image_count, phase)))
# create DBs
image_db = lmdb.open(os.path.join(folder, '%s_images' % phase),
map_async=True, max_dbs=0)
label_db = lmdb.open(os.path.join(folder, '%s_labels' % phase),
map_async=True, max_dbs=0)
# add up all images to later create mean image
image_sum = None
shape = None
# save test images (one for each label)
testImagesSameClass = []
testImagesDifferentClass = []
# arrays for image and label batch writing
image_batch = []
label_batch = []
for i in range(image_count):
# pick up random indices from image list
index1 = random.randint(0, len(images) - 1)
index2 = random.randint(0, len(images) - 1)
# label=1 if images are from the same class otherwise label=0
label = 1 if int(images[index1][1]) == int(images[index2][1]) else 0
# load images from files
image1 = np.array(utils.image.load_image(images[index1][0]))
image2 = np.array(utils.image.load_image(images[index2][0]))
if not shape:
# initialize image sum for mean image
shape = image1.shape
image_sum = np.zeros((3, shape[0], shape[1]), 'float64')
assert(image1.shape == shape and image2.shape == shape)
# create BGR image: blue channel will contain first image,
# green channel will contain second image
image_pair = np.zeros(image_sum.shape)
image_pair[0] = image1
image_pair[1] = image2
image_sum += image_pair
# save test images on first pass
if label > 0 and len(testImagesSameClass) < TEST_IMAGE_COUNT:
testImagesSameClass.append(image_pair)
if label == 0 and len(testImagesDifferentClass) < TEST_IMAGE_COUNT:
testImagesDifferentClass.append(image_pair)
# encode into Datum object
image = image_pair.astype('uint8')
datum = caffe.io.array_to_datum(image, -1)
image_batch.append([str(i), datum])
# create label Datum
label_datum = caffe_pb2.Datum()
label_datum.channels, label_datum.height, label_datum.width = 1, 1, 1
label_datum.float_data.extend(np.array([label]).flat)
label_batch.append([str(i), label_datum])
if (i % db_batch_size == (db_batch_size - 1)) or (i == image_count - 1):
_write_batch_to_lmdb(image_db, image_batch)
_write_batch_to_lmdb(label_db, label_batch)
image_batch = []
label_batch = []
if i % (image_count / 20) == 0:
print("%d/%d" % (i, image_count))
# close databases
image_db.close()
label_db.close()
# save mean
mean_image = (image_sum / image_count).astype('uint8')
_save_mean(mean_image, os.path.join(folder, '%s_mean.binaryproto' % phase))
_save_mean(mean_image, os.path.join(folder, '%s_mean.png' % phase))
# create test images
for idx, image in enumerate(testImagesSameClass):
_save_image(image, os.path.join(folder, '%s_test_same_class_%d.png' % (phase, idx)))
for idx, image in enumerate(testImagesDifferentClass):
_save_image(image, os.path.join(folder, '%s_test_different_class_%d.png' % (phase, idx)))
return
def infer(input_list,
output_dir,
jobs_dir,
model_id,
epoch,
batch_size,
layers,
gpu,
input_is_db,
resize):
"""
Perform inference on a list of images using the specified model
"""
# job directory defaults to that defined in DIGITS config
if jobs_dir == 'none':
jobs_dir = digits.config.config_value('jobs_dir')
# load model job
model_dir = os.path.join(jobs_dir, model_id)
assert os.path.isdir(model_dir), "Model dir %s does not exist" % model_dir
model = Job.load(model_dir)
# load dataset job
dataset_dir = os.path.join(jobs_dir, model.dataset_id)
assert os.path.isdir(dataset_dir), "Dataset dir %s does not exist" % dataset_dir
dataset = Job.load(dataset_dir)
for task in model.tasks:
task.dataset = dataset
# retrieve snapshot file
task = model.train_task()
snapshot_filename = None
epoch = float(epoch)
if epoch == -1 and len(task.snapshots):
# use last epoch
epoch = task.snapshots[-1][1]
snapshot_filename = task.snapshots[-1][0]
else:
for f, e in task.snapshots:
if e == epoch:
snapshot_filename = f
break
if not snapshot_filename:
raise InferenceError("Unable to find snapshot for epoch=%s" % repr(epoch))
# Set color dataset
kwargs = {'colormap': 'dataset'}
vis = Visualization(dataset, **kwargs)
# Delete existing png segmented images
for filename in glob.glob("/home/scania/Scania/Agneev/Tmp/*"):
os.remove(filename)
# retrieve image dimensions and resize mode
image_dims = dataset.get_feature_dims()
height = image_dims[0]
width = image_dims[1]
channels = image_dims[2]
resize_mode = dataset.resize_mode if hasattr(dataset, 'resize_mode') else 'squash'
n_input_samples = 0 # number of samples we were able to load
input_ids = [] # indices of samples within file list
input_data = [] # sample data
input_filename = []
if input_is_db:
# load images from database
reader = DbReader(input_list)
for key, value in reader.entries():
datum = caffe_pb2.Datum()
datum.ParseFromString(value)
if datum.encoded:
s = StringIO()
s.write(datum.data)
s.seek(0)
img = PIL.Image.open(s)
img = np.array(img)
else:
import caffe.io
arr = caffe.io.datum_to_array(datum)
# CHW -> HWC
arr = arr.transpose((1, 2, 0))
if arr.shape[2] == 1:
# HWC -> HW
arr = arr[:, :, 0]
elif arr.shape[2] == 3:
# BGR -> RGB
# XXX see issue #59
arr = arr[:, :, [2, 1, 0]]
img = arr
input_ids.append(key)
input_data.append(img)
n_input_samples = n_input_samples + 1
else:
# load paths from file
paths = None
try:
if input_list.endswith('.h264') or input_list.endswith('.raw'):
logging.info('Reading video...')
## http://stackoverflow.com/questions/33650974/opencv-python-read-specific-frame-using-videocapture
cap = cv2.VideoCapture(input_list) #'/home/scania/Scania/Glantan_Recordings/2017-03-24_DrivePX2/dw_20170324_115921_0.000000_0.000000/video_front.h264')
print cap
frame_no = 0
while frame_no < sys.maxint:
cap.set(1,frame_no);
ret, cv2_im = cap.read()
#if not ret:
# break
cv2_im = cv2.cvtColor(cv2_im,cv2.COLOR_BGR2RGB)
image = PIL.Image.fromarray(cv2_im)
# print image
if resize:
image = utils.image.resize_image(
image,
height,
width,
channels=channels,
resize_mode=resize_mode)
else:
image = utils.image.image_to_array(
image,
channels=channels)
# single image inference
outputs, visualizations = model.train_task().infer_one(
image,
snapshot_epoch=epoch,
layers=layers,
gpu=gpu,
resize=resize)
out = dict([outputs.items()][0])
out['score'] = out.items()[0][1][0]
vis.process_data(n_input_samples, image, out, 'Video_file')
n_input_samples = n_input_samples + 1
frame_no = frame_no + 30
elif input_list.endswith('.txt'):
logging.info('Reading images...')
with open(input_list) as infile:
paths = infile.readlines()
# load and resize images
for idx, path in enumerate(paths):
path = path.strip()
try:
image = utils.image.load_image(path.strip())
if resize:
image = utils.image.resize_image(
image,
height,
width,
channels=channels,
resize_mode=resize_mode)
else:
image = utils.image.image_to_array(
image,
channels=channels)
# single image inference
outputs, visualizations = model.train_task().infer_one(
image,
snapshot_epoch=epoch,
layers=layers,
gpu=gpu,
resize=resize)
# Find filename
head, tail = os.path.split(path)
filename = tail.split('.')[0]
out = dict([outputs.items()][0])
out['score'] = out.items()[0][1][0]
vis.process_data(n_input_samples, image, out, filename)
n_input_samples = n_input_samples + 1
except utils.errors.LoadImageError as e:
print e
else:
print 'Cannot read image or video file. \nPlease provide .h264, .raw or .txt file only.'
except cv2.error as e:
print e
def create_lmdbs(folder, image_width=None, image_height=None, image_count=None):
"""
Creates LMDBs for generic inference
Returns the filename for a test image
Creates these files in "folder":
train_images/
train_labels/
val_images/
val_labels/
mean.binaryproto
test.png
"""
if image_width is None:
image_width = IMAGE_SIZE
if image_height is None:
image_height = IMAGE_SIZE
if image_count is None:
train_image_count = TRAIN_IMAGE_COUNT
else:
train_image_count = image_count
val_image_count = VAL_IMAGE_COUNT
# Used to calculate the gradients later
yy, xx = np.mgrid[:image_height, :image_width].astype('float')
for phase, image_count in [
('train', train_image_count),
('val', val_image_count)]:
image_db = lmdb.open(os.path.join(folder, '%s_images' % phase),
map_async=True,
max_dbs=0)
label_db = lmdb.open(os.path.join(folder, '%s_labels' % phase),
map_async=True,
max_dbs=0)
image_sum = np.zeros((image_height, image_width), 'float64')
for i in xrange(image_count):
xslope, yslope = np.random.random_sample(2) - 0.5
a = xslope * 255 / image_width
b = yslope * 255 / image_height
image = a * (xx - image_width/2) + b * (yy - image_height/2) + 127.5
image_sum += image
image = image.astype('uint8')
pil_img = PIL.Image.fromarray(image)
#pil_img.save(os.path.join(folder, '%s_%d.png' % (phase, i)))
# create image Datum
image_datum = caffe_pb2.Datum()
image_datum.height = image.shape[0]
image_datum.width = image.shape[1]
image_datum.channels = 1
s = StringIO()
pil_img.save(s, format='PNG')
image_datum.data = s.getvalue()
image_datum.encoded = True
_write_to_lmdb(image_db, str(i), image_datum.SerializeToString())
# create label Datum
label_datum = caffe_pb2.Datum()
label_datum.channels, label_datum.height, label_datum.width = 1, 1, 2
label_datum.float_data.extend(np.array([xslope, yslope]).flat)
_write_to_lmdb(label_db, str(i), label_datum.SerializeToString())
# close databases
image_db.close()
label_db.close()
# save mean
mean_image = (image_sum / image_count).astype('uint8')
_save_mean(mean_image, os.path.join(folder, '%s_mean.png' % phase))
_save_mean(mean_image, os.path.join(folder, '%s_mean.binaryproto' % phase))
# create test image
# The network should be able to easily produce two numbers >1
xslope, yslope = 0.5, 0.5
a = xslope * 255 / image_width
b = yslope * 255 / image_height
test_image = a * (xx - image_width/2) + b * (yy - image_height/2) + 127.5
test_image = test_image.astype('uint8')
pil_img = PIL.Image.fromarray(test_image)
test_image_filename = os.path.join(folder, 'test.png')
pil_img.save(test_image_filename)
return test_image_filename
def analyze_db(
database,
only_count=False,
force_same_shape=False,
):
"""
Looks at the data in a prebuilt database and verifies it
Also prints out some information about it
Returns True if all entries are valid
Arguments:
database -- path to the database
Keyword arguments:
only_count -- only count the entries, don't inspect them
force_same_shape -- throw an error if not all images have the same shape
"""
start_time = time.time()
# Open database
try:
database = validate_database_path(database)
except ValueError as e:
logger.error(e.message)
return False
reader = DbReader(database)
logger.info('Total entries: %s' % reader.total_entries)
unique_shapes = Counter()
count = 0
update_time = None
for key, value in reader.entries():
datum = caffe_pb2.Datum()
datum.ParseFromString(value)
shape = '%sx%sx%s' % (datum.width, datum.height, datum.channels)
unique_shapes[shape] += 1
if force_same_shape and len(unique_shapes.keys()) > 1:
logger.error("Images with different shapes found: %s and %s" %
tuple(unique_shapes.keys()))
return False
count += 1
# Send update every 2 seconds
if update_time is None or (time.time() - update_time) > 2:
logger.debug('>>> Key %s' % key)
print_datum(datum)
logger.debug('Progress: %s/%s' % (count, reader.total_entries))
update_time = time.time()
if only_count:
# quit after reading one
count = reader.total_entries
logger.info('Assuming all entries have same shape ...')
unique_shapes[unique_shapes.keys()[0]] = count
break
if count != reader.total_entries:
logger.warning('LMDB reported %s total entries, but only read %s' %
(reader.total_entries, count))
for key, val in sorted(unique_shapes.items(),
key=operator.itemgetter(1),
reverse=True):
logger.info('%s entries found with shape %s (WxHxC)' % (val, key))
logger.info('Completed in %s seconds.' % (time.time() - start_time, ))
return True
def infer(input_list, output_dir, jobs_dir, model_id, epoch, batch_size,
layers, gpu, input_is_db, resize):
"""
Perform inference on a list of images using the specified model
"""
# job directory defaults to that defined in DIGITS config
if jobs_dir == 'none':
jobs_dir = digits.config.config_value('jobs_dir')
# load model job
model_dir = os.path.join(jobs_dir, model_id)
assert os.path.isdir(model_dir), "Model dir %s does not exist" % model_dir
model = Job.load(model_dir)
# load dataset job
dataset_dir = os.path.join(jobs_dir, model.dataset_id)
assert os.path.isdir(
dataset_dir), "Dataset dir %s does not exist" % dataset_dir
dataset = Job.load(dataset_dir)
for task in model.tasks:
task.dataset = dataset
# retrieve snapshot file
task = model.train_task()
snapshot_filename = None
epoch = float(epoch)
if epoch == -1 and len(task.snapshots):
# use last epoch
epoch = task.snapshots[-1][1]
snapshot_filename = task.snapshots[-1][0]
else:
for f, e in task.snapshots:
if e == epoch:
snapshot_filename = f
break
if not snapshot_filename:
raise InferenceError("Unable to find snapshot for epoch=%s" %
repr(epoch))
# retrieve image dimensions and resize mode
image_dims = dataset.get_feature_dims()
height = image_dims[0]
width = image_dims[1]
channels = image_dims[2]
resize_mode = dataset.resize_mode if hasattr(dataset,
'resize_mode') else 'squash'
n_input_samples = 0 # number of samples we were able to load
input_ids = [] # indices of samples within file list
input_data = [] # sample data
if input_is_db:
# load images from database
reader = DbReader(input_list)
for key, value in reader.entries():
datum = caffe_pb2.Datum()
datum.ParseFromString(value)
if datum.encoded:
s = StringIO()
s.write(datum.data)
s.seek(0)
img = PIL.Image.open(s)
img = np.array(img)
else:
import caffe.io
arr = caffe.io.datum_to_array(datum)
# CHW -> HWC
arr = arr.transpose((1, 2, 0))
if arr.shape[2] == 1:
# HWC -> HW
arr = arr[:, :, 0]
elif arr.shape[2] == 3:
# BGR -> RGB
# XXX see issue #59
arr = arr[:, :, [2, 1, 0]]
img = arr
input_ids.append(key)
input_data.append(img)
n_input_samples = n_input_samples + 1
else:
# load paths from file
paths = None
with open(input_list) as infile:
paths = infile.readlines()
# load and resize images
for idx, path in enumerate(paths):
path = path.strip()
try:
image = utils.image.load_image(path.strip())
if resize:
image = utils.image.resize_image(image,
height,
width,
channels=channels,
resize_mode=resize_mode)
else:
image = utils.image.image_to_array(image,
channels=channels)
input_ids.append(idx)
input_data.append(image)
n_input_samples = n_input_samples + 1
except utils.errors.LoadImageError as e:
print e
# perform inference
visualizations = None
predictions = []
if n_input_samples == 0:
raise InferenceError("Unable to load any image from file '%s'" %
repr(input_list))
elif n_input_samples == 1:
# single image inference
outputs, visualizations = model.train_task().infer_one(
input_data[0],
snapshot_epoch=epoch,
layers=layers,
gpu=gpu,
resize=resize)
else:
if layers != 'none':
raise InferenceError(
"Layer visualization is not supported for multiple inference")
outputs = model.train_task().infer_many(input_data,
snapshot_epoch=epoch,
gpu=gpu,
resize=resize)
# write to hdf5 file
db_path = os.path.join(output_dir, 'inference.hdf5')
db = h5py.File(db_path, 'w')
# write input paths and images to database
db.create_dataset("input_ids", data=input_ids)
db.create_dataset("input_data", data=input_data)
# write outputs to database
db_outputs = db.create_group("outputs")
for output_id, output_name in enumerate(outputs.keys()):
output_data = outputs[output_name]
output_key = base64.urlsafe_b64encode(str(output_name))
dset = db_outputs.create_dataset(output_key, data=output_data)
# add ID attribute so outputs can be sorted in
# the order they appear in here
dset.attrs['id'] = output_id
# write visualization data
if visualizations is not None and len(visualizations) > 0:
db_layers = db.create_group("layers")
for idx, layer in enumerate(visualizations):
vis = layer['vis'] if layer['vis'] is not None else np.empty(0)
dset = db_layers.create_dataset(str(idx), data=vis)
dset.attrs['name'] = layer['name']
dset.attrs['vis_type'] = layer['vis_type']
if 'param_count' in layer:
dset.attrs['param_count'] = layer['param_count']
if 'layer_type' in layer:
dset.attrs['layer_type'] = layer['layer_type']
dset.attrs['shape'] = layer['data_stats']['shape']
dset.attrs['mean'] = layer['data_stats']['mean']
dset.attrs['stddev'] = layer['data_stats']['stddev']
dset.attrs['histogram_y'] = layer['data_stats']['histogram'][0]
dset.attrs['histogram_x'] = layer['data_stats']['histogram'][1]
dset.attrs['histogram_ticks'] = layer['data_stats']['histogram'][2]
db.close()
logger.info('Saved data to %s', db_path)
def infer(input_list, output_dir, jobs_dir, model_id, epoch, batch_size,
layers, gpu, input_is_db, label_file, resize):
"""
Perform inference on a list of images using the specified model
"""
# job directory defaults to that defined in DIGITS config
if jobs_dir == 'none':
jobs_dir = digits.config.config_value('jobs_dir')
# load model job
model_dir = os.path.join(jobs_dir, model_id)
assert os.path.isdir(model_dir), "Model dir %s does not exist" % model_dir
model = Job.load(model_dir)
# load dataset job
dataset_dir = os.path.join(jobs_dir, model.dataset_id)
assert os.path.isdir(
dataset_dir), "Dataset dir %s does not exist" % dataset_dir
dataset = Job.load(dataset_dir)
for task in model.tasks:
task.dataset = dataset
# retrieve snapshot file
task = model.train_task()
snapshot_filename = None
epoch = float(epoch)
if epoch == -1 and len(task.snapshots):
# use last epoch
epoch = task.snapshots[-1][1]
snapshot_filename = task.snapshots[-1][0]
else:
for f, e in task.snapshots:
if e == epoch:
snapshot_filename = f
break
if not snapshot_filename:
raise InferenceError("Unable to find snapshot for epoch=%s" %
repr(epoch))
# retrieve image dimensions and resize mode
image_dims = dataset.get_feature_dims()
height = image_dims[0]
width = image_dims[1]
channels = image_dims[2]
resize_mode = dataset.resize_mode if hasattr(dataset,
'resize_mode') else 'squash'
n_input_samples = 0 # number of samples we were able to load
input_ids = [] # indices of samples within file list
input_data = [] # sample data
if input_is_db:
# load images from database
reader = DbReader(input_list)
for key, value in reader.entries():
datum = caffe_pb2.Datum()
datum.ParseFromString(value)
if datum.encoded:
s = StringIO()
s.write(datum.data)
s.seek(0)
img = PIL.Image.open(s)
img = np.array(img)
else:
import caffe.io
arr = caffe.io.datum_to_array(datum)
# CHW -> HWC
arr = arr.transpose((1, 2, 0))
if arr.shape[2] == 1:
# HWC -> HW
arr = arr[:, :, 0]
elif arr.shape[2] == 3:
# BGR -> RGB
# XXX see issue #59
arr = arr[:, :, [2, 1, 0]]
img = arr
input_ids.append(key)
input_data.append(img)
n_input_samples = n_input_samples + 1
else:
# load paths from file
paths = None
with open(input_list) as infile:
paths = infile.readlines()
# load and resize images
for idx, path in enumerate(paths):
path = path.strip()
try:
image = utils.image.load_image(path.strip())
if resize:
image = utils.image.resize_image(image,
height,
width,
channels=channels,
resize_mode=resize_mode)
else:
image = utils.image.image_to_array(image,
channels=channels)
input_ids.append(idx)
input_data.append(image)
n_input_samples = n_input_samples + 1
except utils.errors.LoadImageError as e:
print e
labels = np.loadtxt(label_file, dtype='object')
if fig is not None:
# Plot original images to grid
for row in range(NUM_ROWS):
for col in range(NUM_COLS):
idx = row * NUM_COLS + col
pl.subplot(NUM_ROWS * 2, NUM_COLS,
row * 2 * NUM_COLS + col + 1)
pl.xticks([])
pl.yticks([])
pl.imshow(input_data[idx], interpolation='nearest')
# perform inference
visualizations = None
logger.info('Inference')
if n_input_samples == 0:
raise InferenceError("Unable to load any image from file '%s'" %
repr(input_list))
elif n_input_samples == 1:
# single image inference
logger.info('Start')
outputs, visualizations = model.train_task().infer_one(
input_data[0],
snapshot_epoch=epoch,
layers=layers,
gpu=gpu,
resize=resize)
logger.info('Done!')
else:
if layers != 'none':
raise InferenceError(
"Layer visualization is not supported for multiple inference")
outputs = model.train_task().infer_many(input_data,
snapshot_epoch=epoch,
gpu=gpu,
resize=resize)
logger.info('Now it\'s time to pass results to write')
# write to hdf5 file
db_path = os.path.join(output_dir, 'inference.hdf5')
db = h5py.File(db_path, 'w')
# write input paths and images to database
db.create_dataset("input_ids", data=input_ids)
db.create_dataset("input_data", data=input_data)
# write outputs to database
db_outputs = db.create_group("outputs")
for output_id, output_name in enumerate(outputs.keys()):
output_data = outputs[output_name]
if fig is not None:
# Plot top-K inferences on grids
for elem_id, elem_data in enumerate(output_data):
row = elem_id // NUM_COLS
col = elem_id % NUM_COLS
img_labels = sorted(zip(elem_data, labels),
key=lambda x: x[0])[-NUM_TOPK_CLASSES:]
ax = pl.subplot(NUM_ROWS * 2,
NUM_COLS, (row * 2 + 1) * NUM_COLS + col + 1,
aspect='equal')
ax.yaxis.set_label_position("right")
ax.yaxis.set_label_coords(1.25, 0.5)
pl.ylabel('Confidence score', rotation=-90, fontsize=16)
height = 0.5
ylocs = np.array(range(NUM_TOPK_CLASSES)) * height + 0.1
width = max(ylocs)
top_class = img_labels[-1][1]
pl.barh(ylocs, [l[0]*width for l in img_labels], height=height, \
color=['r' if l[1] == top_class else 'b' for l in img_labels]) #color=['r' if l[1] == labels[true_label] else 'b' for l in img_labels])
pl.yticks(ylocs + height / 2, [l[1] for l in img_labels],
fontsize=14)
pl.xticks([0, width / 2.0, width], ['0%', '50%', '100%'])
pl.ylim(0, ylocs[-1] + height + 0.1)
pl.tight_layout()
pl.show()
fig.savefig('./test.pdf', dpi=300)
output_key = base64.urlsafe_b64encode(str(output_name))
dset = db_outputs.create_dataset(output_key, data=output_data)
# add ID attribute so outputs can be sorted in
# the order they appear in here
dset.attrs['id'] = output_id
# write visualization data
if visualizations is not None and len(visualizations) > 0:
db_layers = db.create_group("layers")
for idx, layer in enumerate(visualizations):
vis = layer['vis'] if layer['vis'] is not None else np.empty(0)
dset = db_layers.create_dataset(str(idx), data=vis)
dset.attrs['name'] = layer['name']
dset.attrs['vis_type'] = layer['vis_type']
if 'param_count' in layer:
dset.attrs['param_count'] = layer['param_count']
if 'layer_type' in layer:
dset.attrs['layer_type'] = layer['layer_type']
dset.attrs['shape'] = layer['data_stats']['shape']
dset.attrs['mean'] = layer['data_stats']['mean']
dset.attrs['stddev'] = layer['data_stats']['stddev']
dset.attrs['histogram_y'] = layer['data_stats']['histogram'][0]
dset.attrs['histogram_x'] = layer['data_stats']['histogram'][1]
dset.attrs['histogram_ticks'] = layer['data_stats']['histogram'][2]
db.close()
logger.info('Saved data to %s', db_path)