def test_minibatch_defined_by_labels(tmpdir):
input_dim = 1000
num_output_classes = 5
def assert_data(mb_source):
features_si = mb_source.stream_info('features')
labels_si = mb_source.stream_info('labels')
mb = mb_source.next_minibatch(2)
features = mb[features_si]
# 2 samples, max seq len 4, 1000 dim
assert features.shape == (2, 4, input_dim)
assert features.end_of_sweep
assert features.num_sequences == 2
assert features.num_samples == 7
assert features.is_sparse
labels = mb[labels_si]
# 2 samples, max seq len 1, 5 dim
assert labels.shape == (2, 1, num_output_classes)
assert labels.end_of_sweep
assert labels.num_sequences == 2
assert labels.num_samples == 2
assert not labels.is_sparse
label_data = labels.asarray()
assert np.allclose(label_data,
np.asarray([
[[1., 0., 0., 0., 0.]],
[[0., 1., 0., 0., 0.]]
]))
mb = mb_source.next_minibatch(3)
features = mb[features_si]
labels = mb[labels_si]
assert features.num_samples == 10
assert labels.num_samples == 3
tmpfile = _write_data(tmpdir, MBDATA_SPARSE)
mb_source = MinibatchSource(CTFDeserializer(tmpfile, StreamDefs(
features=StreamDef(field='x', shape=input_dim, is_sparse=True),
labels=StreamDef(field='y', shape=num_output_classes, is_sparse=False, defines_mb_size=True)
)), randomize=False)
assert_data(mb_source)
tmpfile1 = _write_data(tmpdir, MBDATA_SPARSE1, '1')
tmpfile2 = _write_data(tmpdir, MBDATA_SPARSE2, '2')
combined_mb_source = MinibatchSource([ CTFDeserializer(tmpfile1, StreamDefs(
features=StreamDef(field='x', shape=input_dim, is_sparse=True))),
CTFDeserializer(tmpfile2, StreamDefs(
labels=StreamDef(field='y', shape=num_output_classes, is_sparse=False, defines_mb_size=True)
))], randomize=False)
assert_data(combined_mb_source)
def test_eval_sparse_dense(tmpdir, device_id):
from cntk import Axis
from cntk.io import MinibatchSource, CTFDeserializer, StreamDef, StreamDefs
from cntk.ops import input, times
input_vocab_dim = label_vocab_dim = 69
ctf_data = '''\
0 |S0 3:1 |# <s> |S1 3:1 |# <s>
0 |S0 4:1 |# A |S1 32:1 |# ~AH
0 |S0 5:1 |# B |S1 36:1 |# ~B
0 |S0 4:1 |# A |S1 31:1 |# ~AE
0 |S0 7:1 |# D |S1 38:1 |# ~D
0 |S0 12:1 |# I |S1 47:1 |# ~IY
0 |S0 1:1 |# </s> |S1 1:1 |# </s>
2 |S0 60:1 |# <s> |S1 3:1 |# <s>
2 |S0 61:1 |# A |S1 32:1 |# ~AH
'''
ctf_file = str(tmpdir / '2seqtest.txt')
with open(ctf_file, 'w') as f:
f.write(ctf_data)
mbs = MinibatchSource(CTFDeserializer(
ctf_file,
StreamDefs(features=StreamDef(field='S0',
shape=input_vocab_dim,
is_sparse=True),
labels=StreamDef(field='S1',
shape=label_vocab_dim,
is_sparse=True))),
randomize=False,
max_samples=2)
raw_input = sequence.input(shape=input_vocab_dim,
sequence_axis=Axis('inputAxis'),
name='raw_input',
is_sparse=True)
mb_valid = mbs.next_minibatch(minibatch_size_in_samples=100,
input_map={raw_input: mbs.streams.features},
device=cntk_device(device_id))
z = times(raw_input, np.eye(input_vocab_dim))
e_reader = z.eval(mb_valid, device=cntk_device(device_id))
# CSR with the raw_input encoding in ctf_data
one_hot_data = [[3, 4, 5, 4, 7, 12, 1], [60, 61]]
data = [
csr(np.eye(input_vocab_dim, dtype=np.float32)[d]) for d in one_hot_data
]
e_csr = z.eval({raw_input: data}, device=cntk_device(device_id))
assert np.all([np.allclose(a, b) for a, b in zip(e_reader, e_csr)])
# One-hot with the raw_input encoding in ctf_data
data = Value.one_hot(one_hot_data,
num_classes=input_vocab_dim,
device=cntk_device(device_id))
e_hot = z.eval({raw_input: data}, device=cntk_device(device_id))
assert np.all([np.allclose(a, b) for a, b in zip(e_reader, e_hot)])
def test_base64_image_deserializer(tmpdir):
import io, base64, uuid; from PIL import Image
images, b64_images = [], []
np.random.seed(1)
for i in range(10):
data = np.random.randint(0, 2**8, (5,7,3))
image = Image.fromarray(data.astype('uint8'), "RGB")
buf = io.BytesIO()
image.save(buf, format='PNG')
assert image.width == 7 and image.height == 5
b64_images.append(base64.b64encode(buf.getvalue()))
images.append(np.array(image))
image_data = str(tmpdir / 'mbdata1.txt')
seq_ids = []
uid = uuid.uuid1().int >> 64
with open(image_data, 'wb') as f:
for i,data in enumerate(b64_images):
seq_id = uid ^ i
seq_id = str(seq_id).encode('ascii')
seq_ids.append(seq_id)
line = seq_id + b'\t'
label = str(i).encode('ascii')
line += label + b'\t' + data + b'\n'
f.write(line)
ctf_data = str(tmpdir / 'mbdata2.txt')
with open(ctf_data, 'wb') as f:
for i, sid in enumerate(seq_ids):
line = sid + b'\t' + b'|index '+str(i).encode('ascii') + b'\n'
f.write(line)
transforms = [xforms.scale(width=7, height=5, channels=3)]
b64_deserializer = Base64ImageDeserializer(image_data,
StreamDefs(
images=StreamDef(field='image', transforms=transforms),
labels=StreamDef(field='label', shape=10)))
ctf_deserializer = CTFDeserializer(ctf_data,
StreamDefs(index=StreamDef(field='index', shape=1)))
mb_source = MinibatchSource([ctf_deserializer, b64_deserializer])
assert isinstance(mb_source, MinibatchSource)
for j in range(100):
mb = mb_source.next_minibatch(10)
index_stream = mb_source.streams['index']
index = mb[index_stream].asarray().flatten()
image_stream = mb_source.streams['images']
results = mb[image_stream].asarray()
for i in range(10):
# original images are RBG, openCV produces BGR images,
# reverse the last dimension of the original images
bgrImage = images[int(index[i])][:,:,::-1]
assert (bgrImage == results[i][0]).all()
def create_mb_source(map_file, image_width, image_height, num_channels, num_classes, randomize=True):
transforms = []
transforms += [xforms.crop(crop_type='randomside', side_ratio=0.8)]
transforms += [xforms.scale(width=image_width, height=image_height, channels=num_channels, interpolations='linear')]
return MinibatchSource(ImageDeserializer(map_file, StreamDefs(
features =StreamDef(field='image', transforms=transforms),
labels =StreamDef(field='label', shape=num_classes))),
randomize=randomize)
def create_reader(path, is_training, input_dim, label_dim):
return MinibatchSource(
CTFDeserializer(
path,
StreamDefs(features=StreamDef(field='features', shape=input_dim),
labels=StreamDef(field='labels', shape=label_dim))),
randomize=is_training,
epoch_size=INFINITELY_REPEAT if is_training else FULL_DATA_SWEEP)
def create_reader(path, is_training, input_dim, num_label_classes):
"""
reads CNTK formatted file with 'labels' and 'features'
"""
return MinibatchSource(CTFDeserializer(path, StreamDefs(
labels = StreamDef(field='labels', shape=num_label_classes),
features = StreamDef(field='features', shape=input_dim)
)), randomize = is_training, max_sweeps = INFINITELY_REPEAT if is_training else 1)
def create_reader(path, randomize, input_vocab_dim, label_vocab_dim, size=INFINITELY_REPEAT):
if not os.path.exists(path):
raise RuntimeError("File '%s' does not exist." % (path))
return MinibatchSource(CTFDeserializer(path, StreamDefs(
features = StreamDef(field='S0', shape=input_vocab_dim, is_sparse=True),
labels = StreamDef(field='S1', shape=label_vocab_dim, is_sparse=True)
)), randomize=randomize, max_samples = size)
def create_reader_raw(path, is_training, input_dim, num_label_classes):
"""
Reads in the unstardized values.
"""
return MinibatchSource(CTFDeserializer(path, StreamDefs(
labels = StreamDef(field='rawlabels', shape=num_label_classes),
features = StreamDef(field='rawfeatures', shape=input_dim)
)), randomize = is_training, max_sweeps = INFINITELY_REPEAT if is_training else 1)
def test_htk_deserializers():
mbsize = 640
epoch_size = 1000 * mbsize
lr = [0.001]
feature_dim = 33
num_classes = 132
context = 2
os.chdir(data_path)
features_file = "glob_0000.scp"
labels_file = "glob_0000.mlf"
label_mapping_file = "state.list"
fd = HTKFeatureDeserializer(
StreamDefs(amazing_features=StreamDef(
shape=feature_dim, context=(context, context), scp=features_file)))
ld = HTKMLFDeserializer(
label_mapping_file,
StreamDefs(
awesome_labels=StreamDef(shape=num_classes, mlf=labels_file)))
reader = MinibatchSource([fd, ld])
features = C.input_variable(((2 * context + 1) * feature_dim))
labels = C.input_variable((num_classes))
model = Sequential(
[For(range(3), lambda: Recurrence(LSTM(256))),
Dense(num_classes)])
z = model(features)
ce = C.cross_entropy_with_softmax(z, labels)
errs = C.classification_error(z, labels)
learner = C.adam_sgd(z.parameters,
lr=C.learning_rate_schedule(lr, C.UnitType.sample,
epoch_size),
momentum=C.momentum_as_time_constant_schedule(1000),
low_memory=True,
gradient_clipping_threshold_per_sample=15,
gradient_clipping_with_truncation=True)
trainer = C.Trainer(z, (ce, errs), learner)
input_map = {
features: reader.streams.amazing_features,
labels: reader.streams.awesome_labels
}
pp = C.ProgressPrinter(freq=0)
# just run and verify it doesn't crash
for i in range(3):
mb_data = reader.next_minibatch(mbsize, input_map=input_map)
trainer.train_minibatch(mb_data)
pp.update_with_trainer(trainer, with_metric=True)
assert True
os.chdir(abs_path)
def test_image():
map_file = "input.txt"
mean_file = "mean.txt"
feature_name = "f"
image_width = 100
image_height = 200
num_channels = 3
label_name = "l"
num_classes = 7
transforms = [
xforms.crop(crop_type='randomside', side_ratio=0.5,
jitter_type='uniratio'),
xforms.scale(width=image_width, height=image_height,
channels=num_channels, interpolations='linear'),
xforms.mean(mean_file)]
defs = StreamDefs(f=StreamDef(field='image', transforms=transforms),
l=StreamDef(field='label', shape=num_classes))
image = ImageDeserializer(map_file, defs)
config = to_dictionary(MinibatchSourceConfig([image], randomize=False))
assert len(config['deserializers']) == 1
d = config['deserializers'][0]
assert d['type'] == 'ImageDeserializer'
assert d['file'] == map_file
assert set(d['input'].keys()) == {label_name, feature_name}
l = d['input'][label_name]
assert l['labelDim'] == num_classes
f = d['input'][feature_name]
assert set(f.keys()) == {'transforms'}
t0, t1, t2, _ = f['transforms']
assert t0['type'] == 'Crop'
assert t1['type'] == 'Scale'
assert t2['type'] == 'Mean'
assert t0['cropType'] == 'randomside'
assert t0['sideRatio'] == 0.5
assert t0['aspectRatio'] == 1.0
assert t0['jitterType'] == 'uniratio'
assert t1['width'] == image_width
assert t1['height'] == image_height
assert t1['channels'] == num_channels
assert t1['interpolations'] == 'linear'
assert t2['meanFile'] == mean_file
config = to_dictionary(MinibatchSourceConfig([image, image]))
assert len(config['deserializers']) == 2
config = to_dictionary(MinibatchSourceConfig([image, image, image]))
assert len(config['deserializers']) == 3
# TODO depends on ImageReader.dll
'''
def test_minibatch(tmpdir):
mbdata = r'''0 |S0 0 |S1 0
0 |S0 1 |S1 1
0 |S0 2
0 |S0 3 |S1 3
1 |S0 4
1 |S0 5 |S1 1
1 |S0 6 |S1 2
'''
tmpfile = str(tmpdir/'mbtest.txt')
with open(tmpfile, 'w') as f:
f.write(mbdata)
from cntk.io import CTFDeserializer, MinibatchSource, StreamDef, StreamDefs
mb_source = MinibatchSource(CTFDeserializer(tmpfile, StreamDefs(
features = StreamDef(field='S0', shape=1),
labels = StreamDef(field='S1', shape=1))))
features_si = mb_source.stream_info('features')
labels_si = mb_source.stream_info('labels')
mb = mb_source.next_minibatch(1000)
assert mb[features_si].num_sequences == 2
assert mb[labels_si].num_sequences == 2
features = mb[features_si]
assert len(features.value) == 2
expected_features = \
[
[[0],[1],[2],[3]],
[[4],[5],[6]]
]
for res, exp in zip (features.value, expected_features):
assert np.allclose(res, exp)
assert np.allclose(features.mask,
[[2, 1, 1, 1],
[2, 1, 1, 0]])
labels = mb[labels_si]
assert len(labels.value) == 2
expected_labels = \
[
[[0],[1],[3]],
[[1],[2]]
]
for res, exp in zip (labels.value, expected_labels):
assert np.allclose(res, exp)
assert np.allclose(labels.mask,
[[2, 1, 1],
[2, 1, 0]])
def create_reader(path, randomize, size=INFINITELY_REPEAT):
return MinibatchSource(CTFDeserializer(
path,
StreamDefs(features=StreamDef(field='S0',
shape=input_vocab_size,
is_sparse=True),
labels=StreamDef(field='S1',
shape=label_vocab_size,
is_sparse=True))),
randomize=randomize,
epoch_size=size)
def mb_source(tmpdir, fileprefix, max_samples=FULL_DATA_SWEEP, ctf=ctf_data, streams = ['S0', 'S1']):
ctf_file = str(tmpdir / (fileprefix + '2seqtest.txt'))
with open(ctf_file, 'w') as f:
f.write(ctf)
mbs = MinibatchSource(CTFDeserializer(ctf_file, StreamDefs(
features=StreamDef(field=streams[0], shape=input_dim, is_sparse=True),
labels=StreamDef(field=streams[1], shape=input_dim, is_sparse=True)
)),
randomize=False, max_samples=max_samples)
return mbs
def create_reader(path, is_training):
return MinibatchSource(CTFDeserializer(
path,
StreamDefs(features=StreamDef(field='S0',
shape=input_vocab_dim,
is_sparse=True),
labels=StreamDef(field='S1',
shape=label_vocab_dim,
is_sparse=True))),
randomize=is_training,
max_sweeps=INFINITELY_REPEAT if is_training else 1)
def create_reader(path, is_training, input_dim, output_dim):
return MinibatchSource(CTFDeserializer(
path,
StreamDefs(features=StreamDef(field='attribs',
shape=input_dim,
is_sparse=False),
labels=StreamDef(field='species',
shape=output_dim,
is_sparse=False))),
randomize=is_training,
max_sweeps=INFINITELY_REPEAT if is_training else 1)
def create_reader(path):
return MinibatchSource(
CTFDeserializer(
path,
StreamDefs(
query=StreamDef(field='S0', shape=input_dim, is_sparse=True),
intent_unused=StreamDef(
field='S1', shape=num_intents,
is_sparse=True), # BUGBUG: unused, and should infer dim
slot_labels=StreamDef(field='S2',
shape=label_dim,
is_sparse=True))))
def create_reader(path, is_training, input_dim, label_dim):
"""Create MinibatchSource for reaching training data from given file"""
return MinibatchSource(CTFDeserializer(
path,
StreamDefs(features=StreamDef(field='features',
shape=input_dim,
is_sparse=False),
labels=StreamDef(field='labels',
shape=label_dim,
is_sparse=False))),
randomize=is_training,
max_sweeps=INFINITELY_REPEAT if is_training else 1)
def create_feature_deserializer(path): transforms = [xforms.scale(width = ImageW, height = ImageH, channels = ImageC, interpolations = "linear")] deserializer = ImageDeserializer( path, StreamDefs( features = StreamDef(field = 'image', transforms = transforms), ignored = StreamDef(field = 'label', shape = 1) ) ) deserializer['grayscale'] = Grayscale return deserializer
def test_text_format(tmpdir):
tmpfile = _write_data(tmpdir, MBDATA_SPARSE)
input_dim = 1000
num_output_classes = 5
mb_source = MinibatchSource(CTFDeserializer(
tmpfile,
StreamDefs(features=StreamDef(field='x',
shape=input_dim,
is_sparse=True),
labels=StreamDef(field='y',
shape=num_output_classes,
is_sparse=False))),
randomize=False)
assert isinstance(mb_source, MinibatchSource)
features_si = mb_source.stream_info('features')
labels_si = mb_source.stream_info('labels')
mb = mb_source.next_minibatch(7)
features = mb[features_si]
# 2 samples, max seq len 4, 1000 dim
assert features.shape == (2, 4, input_dim)
assert features.end_of_sweep
assert features.num_sequences == 2
assert features.num_samples == 7
assert features.is_sparse
labels = mb[labels_si]
# 2 samples, max seq len 1, 5 dim
assert labels.shape == (2, 1, num_output_classes)
assert labels.end_of_sweep
assert labels.num_sequences == 2
assert labels.num_samples == 2
assert not labels.is_sparse
label_data = labels.asarray()
assert np.allclose(
label_data, np.asarray([[[1., 0., 0., 0., 0.]], [[0., 1., 0., 0.,
0.]]]))
mb = mb_source.next_minibatch(1)
features = mb[features_si]
labels = mb[labels_si]
assert not features.end_of_sweep
assert not labels.end_of_sweep
assert features.num_samples < 7
assert labels.num_samples == 1
def create_mb_source(features_file, labels_file, label_mapping_filem, total_number_of_samples):
for file_name in [features_file, labels_file, label_mapping_file]:
if not os.path.exists(file_name):
raise RuntimeError("File '%s' does not exist. Please check that datadir argument is set correctly." % (file_name))
fd = HTKFeatureDeserializer(StreamDefs(
amazing_features = StreamDef(shape=feature_dim, context=(context,context), scp=features_file)))
ld = HTKMLFDeserializer(label_mapping_file, StreamDefs(
awesome_labels = StreamDef(shape=num_classes, mlf=labels_file)))
# Enabling BPTT with truncated_length > 0
return MinibatchSource([fd,ld], truncation_length=250, epoch_size=total_number_of_samples)
def cbf_reader(path, is_training, max_samples):
"""
Returns a MinibatchSource for data at the given path
:param path: Path to a CBF file
:param is_training: Set to true if reader is for training set, else false
:param max_samples: Max no. of samples to read
"""
deserializer = CBFDeserializer(path, StreamDefs(
label=StreamDef(field='label', shape=num_classes, is_sparse=True),
front=StreamDef(field='pixels', shape=num_channels * frame_height * frame_width, is_sparse=False),
))
return MinibatchSource(deserializer, randomize=is_training, max_samples=max_samples)
def test_text_format(tmpdir):
from cntk.io import CTFDeserializer, MinibatchSource, StreamDef, StreamDefs
mbdata = r'''0 |x 560:1 |y 1 0 0 0 0
0 |x 0:1
0 |x 0:1
1 |x 560:1 |y 0 1 0 0 0
1 |x 0:1
1 |x 0:1
1 |x 424:1
'''
tmpfile = str(tmpdir/'mbdata.txt')
with open(tmpfile, 'w') as f:
f.write(mbdata)
input_dim = 1000
num_output_classes = 5
mb_source = MinibatchSource(CTFDeserializer(tmpfile, StreamDefs(
features = StreamDef(field='x', shape=input_dim, is_sparse=True),
labels = StreamDef(field='y', shape=num_output_classes, is_sparse=False)
)))
assert isinstance(mb_source, MinibatchSource)
features_si = mb_source.stream_info('features')
labels_si = mb_source.stream_info('labels')
mb = mb_source.next_minibatch(7)
features = mb[features_si]
# 2 samples, max seq len 4, 1000 dim
assert features.shape == (2, 4, input_dim)
assert features.is_sparse
# TODO features is sparse and cannot be accessed right now:
# *** RuntimeError: DataBuffer/WritableDataBuffer methods can only be called for NDArrayiew objects with dense storage format
# 2 samples, max seq len 4, 1000 dim
#assert features.data().shape().dimensions() == (2, 4, input_dim)
#assert features.data().is_sparse()
labels = mb[labels_si]
# 2 samples, max seq len 1, 5 dim
assert labels.shape == (2, 1, num_output_classes)
assert not labels.is_sparse
label_data = np.asarray(labels)
assert np.allclose(label_data,
np.asarray([
[[ 1., 0., 0., 0., 0.]],
[[ 0., 1., 0., 0., 0.]]
]))
def create_reader(path, is_training):
return MinibatchSource(
CTFDeserializer(
path,
StreamDefs(
query=StreamDef(field='S0', shape=vocab_size, is_sparse=True),
intent_labels=StreamDef(
field='S1', shape=num_intents, is_sparse=True
), # (used for intent classification variant)
slot_labels=StreamDef(field='S2',
shape=num_labels,
is_sparse=True))),
randomize=is_training,
max_sweeps=INFINITELY_REPEAT if is_training else 1)
def test_image_with_crop_range():
map_file = "input.txt"
feature_name = "f"
image_width = 100
image_height = 200
num_channels = 3
label_name = "l"
num_classes = 7
transforms = [
xforms.crop(crop_type='randomside',
crop_size=(512, 424),
side_ratio=(0.2, 0.5),
area_ratio=(0.1, 0.75),
aspect_ratio=(0.3, 0.8),
jitter_type='uniratio')
]
defs = StreamDefs(f=StreamDef(field='image', transforms=transforms),
l=StreamDef(field='label', shape=num_classes))
image = ImageDeserializer(map_file, defs)
config = to_dictionary(MinibatchSourceConfig([image], randomize=False))
assert len(config['deserializers']) == 1
d = config['deserializers'][0]
assert d['type'] == 'ImageDeserializer'
assert d['file'] == map_file
assert set(d['input'].keys()) == {label_name, feature_name}
l = d['input'][label_name]
assert l['labelDim'] == num_classes
f = d['input'][feature_name]
assert set(f.keys()) == {'transforms'}
t0, _ = f['transforms']
assert t0['type'] == 'Crop'
assert t0['cropType'] == 'randomside'
assert t0['cropSize'] == '512:424'
assert t0['sideRatio'] == '0.2:0.5'
assert t0['aspectRatio'] == '0.3:0.8'
assert t0['areaRatio'] == '0.1:0.75'
assert t0['jitterType'] == 'uniratio'
config = to_dictionary(MinibatchSourceConfig([image, image]))
assert len(config['deserializers']) == 2
config = to_dictionary(MinibatchSourceConfig([image, image, image]))
assert len(config['deserializers']) == 3
def test_prefetch_with_unpacking(tmpdir):
data = r'''0 |S0 1 1 1 1 |S1 1000
1 |S0 2 2 2 2 |S1 100
2 |S0 3 3 3 3 |S1 100
3 |S0 1 1 1 1 |S1 10
4 |S0 2 2 2 2 |S1 1
5 |S0 3 3 3 3 |S1 2000
6 |S0 1 1 1 1 |S1 200
7 |S0 2 2 2 2 |S1 200
8 |S0 3 3 3 3 |S1 20
9 |S0 1 1 1 1 |S1 2
'''
import time
tmpfile = _write_data(tmpdir, data)
input_dim = 4
num_output_classes = 1
mb_source = MinibatchSource(CTFDeserializer(
tmpfile,
StreamDefs(features=StreamDef(field='S0',
shape=input_dim,
is_sparse=False),
labels=StreamDef(field='S1',
shape=num_output_classes,
is_sparse=False))),
randomize=False,
max_samples=FULL_DATA_SWEEP)
input_map = {
'S0': mb_source.streams.features,
'S1': mb_source.streams.labels
}
empty = False
mb_size = 3
# On the last minibatch there will be resize called,
# due to 10%3 = 1 sample in the minibatch
while not empty:
mb = mb_source.next_minibatch(mb_size, input_map=input_map)
time.sleep(1) # make sure the prefetch kicks in
if mb:
# Force unpacking to check that we do
# not break prefetch
actual_size = mb['S0'].shape[0]
assert (mb['S0'].asarray() == np.array(
[[[1, 1, 1, 1]], [[2, 2, 2, 2]], [[3, 3, 3, 3]]],
dtype=np.float32)[0:actual_size]).all()
else:
empty = True
def test_crop_dimensionality(tmpdir):
import io; from PIL import Image
np.random.seed(1)
file_mapping_path = str(tmpdir / 'file_mapping.txt')
with open(file_mapping_path, 'w') as file_mapping:
for i in range(5):
data = np.random.randint(0, 2**8, (20, 40, 3))
image = Image.fromarray(data.astype('uint8'), "RGB")
buf = io.BytesIO()
image.save(buf, format='PNG')
assert image.width == 40 and image.height == 20
label = str(i)
# save to mapping + png file
file_name = label + '.png'
with open(str(tmpdir/file_name), 'wb') as f:
f.write(buf.getvalue())
file_mapping.write('.../%s\t%s\n' % (file_name, label))
transforms1 = [
xforms.scale(width=40, height=20, channels=3),
xforms.crop(crop_type='randomside',
crop_size=(20, 10), side_ratio=(0.2, 0.5),
jitter_type='uniratio')]
transforms2 = [
xforms.crop(crop_type='randomside',
crop_size=(20, 10), side_ratio=(0.2, 0.5),
jitter_type='uniratio')]
d1 = ImageDeserializer(file_mapping_path,
StreamDefs(
images1=StreamDef(field='image', transforms=transforms1),
labels1=StreamDef(field='label', shape=10)))
d2 = ImageDeserializer(file_mapping_path,
StreamDefs(
images2=StreamDef(field='image', transforms=transforms2),
labels2=StreamDef(field='label', shape=10)))
mbs = MinibatchSource([d1, d2])
for j in range(5):
mb = mbs.next_minibatch(1)
images1 = mb[mbs.streams.images1].asarray()
images2 = mb[mbs.streams.images2].asarray()
assert images1.shape == (1, 1, 3, 10, 20)
assert (images1 == images2).all()
def create_mb_source(img_height, img_width, img_channels, n_classes, n_rois,
data_path, data_set):
rois_dim = 4 * n_rois
label_dim = n_classes * n_rois
path = os.path.normpath(os.path.join(abs_path, data_path))
if data_set == 'test':
map_file = os.path.join(path, test_map_filename)
else:
map_file = os.path.join(path, train_map_filename)
roi_file = os.path.join(path, data_set + rois_filename_postfix)
label_file = os.path.join(path, data_set + roilabels_filename_postfix)
if not os.path.exists(map_file) or not os.path.exists(
roi_file) or not os.path.exists(label_file):
raise RuntimeError(
"File '%s', '%s' or '%s' does not exist. "
"Please run install_data_and_model.py from Examples/Image/Detection/FastRCNN to fetch them"
% (map_file, roi_file, label_file))
# read images
transforms = [
scale(width=img_width,
height=img_height,
channels=img_channels,
scale_mode="pad",
pad_value=114,
interpolations='linear')
]
image_source = ImageDeserializer(
map_file,
StreamDefs(features=StreamDef(field='image', transforms=transforms)))
# read rois and labels
roi_source = CTFDeserializer(
roi_file,
StreamDefs(rois=StreamDef(
field=roi_stream_name, shape=rois_dim, is_sparse=False)))
label_source = CTFDeserializer(
label_file,
StreamDefs(roiLabels=StreamDef(
field=label_stream_name, shape=label_dim, is_sparse=False)))
# define a composite reader
return MinibatchSource([image_source, roi_source, label_source],
max_samples=sys.maxsize,
randomize=data_set == "train")
def test_base64_is_equal_image(tmpdir):
import io, base64
from PIL import Image
np.random.seed(1)
file_mapping_path = str(tmpdir / 'file_mapping.txt')
base64_mapping_path = str(tmpdir / 'base64_mapping.txt')
with open(file_mapping_path, 'w') as file_mapping:
with open(base64_mapping_path, 'w') as base64_mapping:
for i in range(10):
data = np.random.randint(0, 2**8, (5, 7, 3))
image = Image.fromarray(data.astype('uint8'), "RGB")
buf = io.BytesIO()
image.save(buf, format='PNG')
assert image.width == 7 and image.height == 5
label = str(i)
# save to base 64 mapping file
encoded = base64.b64encode(buf.getvalue()).decode('ascii')
base64_mapping.write('%s\t%s\n' % (label, encoded))
# save to mapping + png file
file_name = label + '.png'
with open(str(tmpdir / file_name), 'wb') as f:
f.write(buf.getvalue())
file_mapping.write('.../%s\t%s\n' % (file_name, label))
transforms = [xforms.scale(width=7, height=5, channels=3)]
b64_deserializer = Base64ImageDeserializer(
base64_mapping_path,
StreamDefs(images1=StreamDef(field='image', transforms=transforms),
labels1=StreamDef(field='label', shape=10)))
file_image_deserializer = ImageDeserializer(
file_mapping_path,
StreamDefs(images2=StreamDef(field='image', transforms=transforms),
labels2=StreamDef(field='label', shape=10)))
mb_source = MinibatchSource([b64_deserializer, file_image_deserializer])
for j in range(20):
mb = mb_source.next_minibatch(1)
images1_stream = mb_source.streams['images1']
images1 = mb[images1_stream].asarray()
images2_stream = mb_source.streams['images2']
images2 = mb[images2_stream].asarray()
assert (images1 == images2).all()
def test_MinibatchData_and_Value_as_input(tmpdir):
mbdata = r'''0 |S0 100'''
tmpfile = str(tmpdir / 'mbtest.txt')
with open(tmpfile, 'w') as f:
f.write(mbdata)
defs = StreamDefs(f1=StreamDef(field='S0', shape=1))
mb_source = MinibatchSource(CTFDeserializer(tmpfile, defs),
randomize=False)
f1_si = mb_source.stream_info('f1')
mb = mb_source.next_minibatch(1)
f1 = input(shape=(1, ), needs_gradient=True, name='f')
res = f1 * 2
assert res.eval({f1: mb[f1_si]}) == [[200]]
# Test MinibatchData
assert res.eval(mb[f1_si]) == [[200]]
# Test Value
assert res.eval(mb[f1_si].data) == [[200]]
# Test NumPy (converted back from MinibatchData)
assert res.eval(mb[f1_si].asarray()) == [[200]]
# Test Value
assert res.eval(mb[f1_si].data) == [[200]]
def mb_source(tmpdir, fileprefix, epoch_size=FULL_DATA_SWEEP):
ctf_file = str(tmpdir / (fileprefix + '2seqtest.txt'))
with open(ctf_file, 'w') as f:
f.write(ctf_data)
mbs = MinibatchSource(CTFDeserializer(
ctf_file,
StreamDefs(features=StreamDef(field='S0',
shape=input_dim,
is_sparse=True),
labels=StreamDef(field='S1',
shape=input_dim,
is_sparse=True))),
randomize=False,
epoch_size=epoch_size)
return mbs
