def createTestSet(ark_file, sDataFileTemplate, sDataDir, sDataFilePattern,
sDataProtoFile):
dbInfo = dl.DatabaseInfo()
dbInfo.name = 'dataset_test'
dbInfo.data_handler = 'deeplearn'
dbInfo.path_prefix = sDataDir
datasetInfo = dbInfo.data.add()
datasetInfo.data_format = dl.DatasetInfo.PBM
datasetInfo.size = 0
datasetInfo.sparse_label = True
datasetInfo.type = dl.DatasetInfo.TEST_SET
datasetInfo.file_pattern = sDataFilePattern
uttIDBatch = []
uttIDLength = []
featMatBatch = None
batchSz = -1
iFileIdx = 0
kaldiIn = KaldiReadIn(ark_file)
kaldiIn.open()
uttID, featMat = kaldiIn.next()
while featMat is not None:
if batchSz < 0:
batchSz = 400 * 1024 * 1024 / (4 * featMat.shape[1])
datasetInfo.dimensions = featMat.shape[1] + 1
datasetInfo.label_start_index = datasetInfo.dimensions - 1
if featMatBatch is None:
featMatBatch = featMat
else:
featMatBatch = np.vstack([featMatBatch, featMat])
uttIDBatch.append(uttID)
uttIDLength.append(featMat.shape[0])
if featMatBatch.shape[0] >= batchSz:
util.WriteProto(
sDataFileTemplate % iFileIdx,
util.npy2ProtoMat(
np.hstack(
[featMatBatch,
np.zeros((featMatBatch.shape[0], 1))])))
iFileIdx += 1
datasetInfo.size += featMatBatch.shape[0]
featMatBatch = None
uttID, featMat = kaldiIn.next()
kaldiIn.close()
# last batch
if featMatBatch is not None:
util.WriteProto(
sDataFileTemplate % iFileIdx,
util.npy2ProtoMat(
np.hstack([featMatBatch,
np.zeros((featMatBatch.shape[0], 1))])))
iFileIdx += 1
datasetInfo.size += featMatBatch.shape[0]
util.WriteProto(sDataProtoFile, dbInfo)
return (uttIDBatch, uttIDLength)
def createTestSet(ark_file, sDataFileTemplate, sDataDir, sDataFilePattern, sDataProtoFile):
dbInfo = dl.DatabaseInfo()
dbInfo.name = 'dataset_test'
dbInfo.data_handler = 'deeplearn'
dbInfo.path_prefix = sDataDir
datasetInfo = dbInfo.data.add()
datasetInfo.data_format = dl.DatasetInfo.PBM
datasetInfo.size = 0
datasetInfo.sparse_label = True
datasetInfo.type = dl.DatasetInfo.TEST_SET
datasetInfo.file_pattern = sDataFilePattern
uttIDBatch = []
uttIDLength = []
featMatBatch = None
batchSz = -1
iFileIdx = 0
kaldiIn = KaldiReadIn(ark_file)
kaldiIn.open()
uttID, featMat = kaldiIn.next()
while featMat is not None:
if batchSz < 0:
batchSz = 400*1024*1024 / (4*featMat.shape[1])
datasetInfo.dimensions = featMat.shape[1] + 1
datasetInfo.label_start_index = datasetInfo.dimensions - 1
if featMatBatch is None:
featMatBatch = featMat
else:
featMatBatch = np.vstack([featMatBatch, featMat])
uttIDBatch.append(uttID)
uttIDLength.append(featMat.shape[0])
if featMatBatch.shape[0] >= batchSz:
util.WriteProto(sDataFileTemplate % iFileIdx, util.npy2ProtoMat(np.hstack([featMatBatch, np.zeros((featMatBatch.shape[0], 1))])))
iFileIdx += 1
datasetInfo.size += featMatBatch.shape[0]
featMatBatch = None
uttID, featMat = kaldiIn.next()
kaldiIn.close()
# last batch
if featMatBatch is not None:
util.WriteProto(sDataFileTemplate % iFileIdx, util.npy2ProtoMat(np.hstack([featMatBatch, np.zeros((featMatBatch.shape[0], 1))])))
iFileIdx += 1
datasetInfo.size += featMatBatch.shape[0]
util.WriteProto(sDataProtoFile, dbInfo)
return (uttIDBatch, uttIDLength)
def pfile2Proto(pfilePath, filePrefix, pbmDir):
pfile = read_dataset(pfilePath, {'partition': 1024*1024*400})
dsInfo = dl.DatasetInfo()
dsInfo.data_format = dl.DatasetInfo.PBM
dsInfo.sparse_label = True
dsInfo.file_pattern = '%s[0-9]*.pbm' % filePrefix
dim = None
sz = 0
for i, (data, label) in enumerate(zip(pfile.feat_mats, pfile.label_vecs)):
dataset = util.npy2ProtoMat(np.hstack([data, label[:, None]]))
util.WriteProto(os.path.join(pbmDir, '%s%05d.pbm' % (filePrefix, i)), dataset)
if dim is None:
dim = data.shape[1] + 1
if dim != data.shape[1] + 1:
print dim, sz, data,shape, label.shape
assert dim == data.shape[1] + 1
sz += data.shape[0]
dsInfo.size = sz
dsInfo.dimensions = dim
dsInfo.label_start_index = dim - 1
return dsInfo
def pfile2Proto(pfilePath, filePrefix, pbmDir):
pfile = read_dataset(pfilePath, {'partition': 1024 * 1024 * 400})
dsInfo = dl.DatasetInfo()
dsInfo.data_format = dl.DatasetInfo.PBM
dsInfo.sparse_label = True
dsInfo.file_pattern = '%s[0-9]*.pbm' % filePrefix
dim = None
sz = 0
for i, (data, label) in enumerate(zip(pfile.feat_mats, pfile.label_vecs)):
dataset = util.npy2ProtoMat(np.hstack([data, label[:, None]]))
util.WriteProto(os.path.join(pbmDir, '%s%05d.pbm' % (filePrefix, i)),
dataset)
if dim is None:
dim = data.shape[1] + 1
if dim != data.shape[1] + 1:
print dim, sz, data, shape, label.shape
assert dim == data.shape[1] + 1
sz += data.shape[0]
dsInfo.size = sz
dsInfo.dimensions = dim
dsInfo.label_start_index = dim - 1
return dsInfo
def extractRepresentation(data, wdir, sDeeplearnPath, sModelFile):
# append label column
data = np.hstack([data, np.zeros((data.shape[0], 1))])
npyData = util.npy2ProtoMat(data)
sDataFile = os.path.join(wdir, 'input.pbm')
util.WriteProto(sDataFile, npyData)
sDataProtoFile = os.path.join(wdir, 'data.pbtxt')
dbInfo = dl.DatabaseInfo()
dbInfo.name = 'dataset_extract'
dbInfo.data_handler = 'deeplearn'
dbInfo.path_prefix = wdir
datasetInfo = dbInfo.data.add()
datasetInfo.data_format = dl.DatasetInfo.PBM
datasetInfo.size = data.shape[0]
datasetInfo.dimensions = data.shape[1]
datasetInfo.label_start_index = datasetInfo.dimensions - 1
datasetInfo.sparse_label = True
datasetInfo.type = dl.DatasetInfo.TEST_SET
datasetInfo.file_pattern = 'input.pbm'
util.WriteProto(sDataProtoFile, dbInfo)
sEvalOpFile = os.path.join(wdir, 'eval.pbtxt')
sExtractedActs = os.path.join(wdir, 'acts')
sLayerName = 'conv2'
if not os.path.exists(sExtractedActs):
os.mkdir(sExtractedActs)
evalOp = dl.Operation()
evalOp.name = 'extract'
evalOp.stop_condition.all_processed = True
evalOp.operation_type = dl.Operation.TEST
evalOp.data_proto = sDataProtoFile
evalOp.randomize = False
evalOp.get_last_piece = True
evalOp.verbose = False
evalOp.extracted_layers.append(sLayerName)
evalOp.extracted_output_dir = sExtractedActs
evalOp.extracted_data_format = dl.DatasetInfo.PBM
evalOp.extracted_data_sets.append(dl.DatasetInfo.TEST_SET)
util.WriteProto(sEvalOpFile, evalOp)
sOutFileTemplate = os.path.join(sExtractedActs, sLayerName, '*.pbm')
for s in sorted(glob.glob(sOutFileTemplate)):
try:
os.remove(s)
except Exception:
pass
# run the network...
args = [sDeeplearnPath, 'extract', sModelFile, '--eval-op=%s' % sEvalOpFile]
pr = subprocess.Popen(args, stderr=subprocess.STDOUT)
pr.wait()
if pr.returncode != 0:
print 'Failed to extract representations'
exit(1)
# read Dataset
mOutput = None
for s in sorted(glob.glob(sOutFileTemplate)):
m = util.proto2Npy(util.ReadProto(s, dl.Matrix()))
if mOutput is None:
mOutput = m
else:
mOutput = np.vstack([mOutput, m])
if mOutput.shape[0] != data.shape[0]:
print 'Invalid results'
exit(1)
return mOutput
def extractRepresentation(data, wdir, sDeeplearnPath, sModelFile):
# append label column
data = np.hstack([data, np.zeros((data.shape[0], 1))])
npyData = util.npy2ProtoMat(data)
sDataFile = os.path.join(wdir, 'input.pbm')
util.WriteProto(sDataFile, npyData)
sDataProtoFile = os.path.join(wdir, 'data.pbtxt')
dbInfo = dl.DatabaseInfo()
dbInfo.name = 'dataset_extract'
dbInfo.data_handler = 'deeplearn'
dbInfo.path_prefix = wdir
datasetInfo = dbInfo.data.add()
datasetInfo.data_format = dl.DatasetInfo.PBM
datasetInfo.size = data.shape[0]
datasetInfo.dimensions = data.shape[1]
datasetInfo.label_start_index = datasetInfo.dimensions - 1
datasetInfo.sparse_label = True
datasetInfo.type = dl.DatasetInfo.TEST_SET
datasetInfo.file_pattern = 'input.pbm'
util.WriteProto(sDataProtoFile, dbInfo)
sEvalOpFile = os.path.join(wdir, 'eval.pbtxt')
sExtractedActs = os.path.join(wdir, 'acts')
sLayerName = 'conv2'
if not os.path.exists(sExtractedActs):
os.mkdir(sExtractedActs)
evalOp = dl.Operation()
evalOp.name = 'extract'
evalOp.stop_condition.all_processed = True
evalOp.operation_type = dl.Operation.TEST
evalOp.data_proto = sDataProtoFile
evalOp.randomize = False
evalOp.get_last_piece = True
evalOp.verbose = False
evalOp.extracted_layers.append(sLayerName)
evalOp.extracted_output_dir = sExtractedActs
evalOp.extracted_data_format = dl.DatasetInfo.PBM
evalOp.extracted_data_sets.append(dl.DatasetInfo.TEST_SET)
util.WriteProto(sEvalOpFile, evalOp)
sOutFileTemplate = os.path.join(sExtractedActs, sLayerName, '*.pbm')
for s in sorted(glob.glob(sOutFileTemplate)):
try:
os.remove(s)
except Exception:
pass
# run the network...
args = [
sDeeplearnPath, 'extract', sModelFile,
'--eval-op=%s' % sEvalOpFile
]
pr = subprocess.Popen(args, stderr=subprocess.STDOUT)
pr.wait()
if pr.returncode != 0:
print 'Failed to extract representations'
exit(1)
# read Dataset
mOutput = None
for s in sorted(glob.glob(sOutFileTemplate)):
m = util.proto2Npy(util.ReadProto(s, dl.Matrix()))
if mOutput is None:
mOutput = m
else:
mOutput = np.vstack([mOutput, m])
if mOutput.shape[0] != data.shape[0]:
print 'Invalid results'
exit(1)
return mOutput
