def makeProto(self, GLOBAL):
return Props.proto + (
PD('i', '', integer(), equalto('i', GLOBAL)),
PD('m3', '', integer(), equalto('m2', GLOBAL)),
PD('D3', '', integer(), equalto('D2', GLOBAL)),
PD('j', '', integerOrNone(), 2),
PD('k', '', integerOrNone(), 2),
PD('l', '', integerOrNone(), 2),
)
def makeProto(self, GLOBAL):
return Props.proto + (
PD('i', '', integer(),
LambdaVal(lambda _, __: GLOBAL.m + GLOBAL.D)),
PD('m2', '', integer(), equalto('m', GLOBAL)),
PD('D2', '', integer(), equalto('D', GLOBAL)),
PD('j', '', integerOrNone(), None),
PD('k', '', integerOrNone(), 1),
)
class GlobalParams(dlc.HyperParams):
""" Common Properties to trickle down. """
proto = (
## Data-set Properties ##
PD(
'raw_data_dir',
'Filesystem path of raw_data_folder from where the pre-processed data is stored.',
dlc.instanceof(str)),
PD(
'image_shape_unframed',
'Shape of input images. Should be a python sequence.'
'This is superseded by image_shape which optionally includes an extra padding frame around the input image'
'Value is loaded from the dataset and is not configurable.',
issequenceof(int),
# Set dynamically based on dataset.
),
PD(
'MaxSeqLen',
"Max sequence length including the end-of-sequence marker token. Is used to "
"limit the number of decoding steps. Value is loaded from the dataset and is not configurable.",
integer(151),
# Set dynamically based on dataset.
),
PD(
'K',
'Vocabulary size including zero. Value is loaded from the dataset and is not configurable.',
(358, 557, 339),
# Set dynamically based on dataset.
# LambdaVal(lambda _, d: 557+1 if d.use_ctc_loss else 557) #get_vocab_size(data_folder) + 1 for Blank-Token
),
PD(
'CTCBlankTokenID',
'ID of the space/blank token. By tf.nn.ctc requirement, blank token should be == K-1,'
'Value is loaded from the dataset and is not configurable.',
integerOrNone(),
# Set dynamically based on dataset.
),
PD(
'SpaceTokenID',
'Space Token ID if present in the dataset.',
integerOrNone(),
# Set dynamically based on dataset.
),
PD(
'NullTokenID',
'ID of the EOS token == Null Token. Must be zero. Its value is loaded from the dataset and is not configurable.',
(0, ),
# Set dynamically based on dataset.
),
PD(
'StartTokenID',
'ID of the begin-sequence token. The value is loaded from the dataset and is not configurable.',
(1, ),
# Set dynamically based on dataset.
),
###############################
PD(
'build_image_context', """
(enum): Type of decoder conv-net model to use:
0 => Do not build decoder conv-net. Use pre-generated image features instead.
1 => Use VGG16 Conv-Net model (imported from Keras).
2 => Use a custom conv-net (defined in make_hyper)
""", (0, 1, 2)),
PD(
'build_scanning_RNN',
'(boolean): Whether to build a regular RNN or a scanning RNN',
boolean,
),
PD(
'B',
'(integer): Size of mini-batch for training, validation and testing graphs/towers. '
'NOTE: Batch-size for the data-reader is different and set under property "data_reader_B"',
integer(1),
),
PD(
'n',
"The variable n in the paper. The number of units in the decoder_lstm cell(s). "
"The paper uses a value of 1000.", (1000, 1500), 1500),
PD(
'm', '(integer): dimensionality of the embedded input vector (Ex).'
"Note: For a stacked CALSTM, the upper layers will be fed output of the previous CALSTM, "
"therefore their input dimensionality will not be equal to the embedding dimensionality, rather "
" it will be equal to output_size of the previous CALSTM. That's why this value needs to be "
"appropriately adjusted for upper CALSTM layers.", (64, 3),
LambdaVal(lambda _, p: 3 if p.build_scanning_RNN else 64)),
PD(
'REGROUP_IMAGE',
"""
Specifies how the image feature vectors should be grouped together
along Height and Width axes. For e.g. if the original dimension of the context feature map was (3,33,512)
- i.e. original H=3, original W=33 and D=512- and if REGROUP_IMAGE was (3,3) then the new
context-map would have shape (1, 11, 512*3*3) resulting in H=1, W=33, D=4608 and L=33.
A None value implies no regrouping.
""",
issequenceofOrNone(int),
),
PD('image_size', 'Older image-size was "small". Newer one is "big"',
('small', 'big'), 'big'),
PD('H0',
'Height of feature-map produced by conv-net. Specific to the dataset image size.',
integer(1),
LambdaVal(lambda _, p: 4 if (p.image_size == 'big') else 3)
# LambdaVal(lambda _, p: 8 if (p.build_image_context == 2) else (4 if p.dataset == 3 else 3))
),
PD('W0',
'Width of feature-map produced by conv-net. Specific to the dataset image size.',
integer(1),
LambdaVal(lambda _, p: 34 if (p.image_size == 'big') else 33)
# LambdaVal(lambda _, p: 68 if (p.build_image_context == 2) else (34 if p.dataset == 3 else 33))
),
PD(
'L0',
'(integer): number of pixels in an image feature-map coming out of conv-net = H0xW0 (see paper or model description)',
integer(1), LambdaVal(lambda _, p: p.H0 * p.W0)),
PD(
'D0',
'(integer): number of features coming out of the conv-net. Depth/channels of the last conv-net layer.'
'See paper or model description.', integer(1), 512),
PD(
'H', 'Height of feature-map produced fed to the decoder.',
integer(1),
LambdaVal(lambda _, p: p.H0 if (p.REGROUP_IMAGE is None) else p.H0
// p.REGROUP_IMAGE[0])),
PD(
'W', 'Width of feature-map fed to the decoder.', integer(1),
LambdaVal(lambda _, p: p.W0 if (p.REGROUP_IMAGE is None) else p.W0
// p.REGROUP_IMAGE[1])),
PD(
'L',
'(integer): number of pixels in an image feature-map fed to the decoder = HxW (see paper or model description)',
integer(1), LambdaVal(lambda _, p: p.H * p.W)),
PD(
'D',
'(integer): number of image-features fed to the decoder. Depth/channels of the last conv-net layer.'
'See paper or model description.', integer(1),
LambdaVal(lambda _, p: p.D0 if (p.REGROUP_IMAGE is None) else p.D0
* p.REGROUP_IMAGE[0] * p.REGROUP_IMAGE[1])),
PD(
'tb',
"Tensorboard Params.",
instanceof(TensorboardParams),
),
PD(
'dropout',
'Dropout parameters if any - global. Absence of this property '
'signals no dropouts. If this is non-None, then weights regularizer should be None.',
instanceofOrNone(DropoutParams)),
PD('dtype', 'tensorflow float type for the entire model.',
(tf.float32, tf.float64), tf.float32),
PD('dtype_np', 'dtype for the entire model.', (np.float32, np.float64),
np.float32),
PD('int_type', 'tensorflow int type for the entire model.',
(tf.int32, tf.int64), tf.int32),
PD('int_type_np', 'numpy inttype for the entire model.',
(np.int32, np.int64), np.int32),
PD(
'weights_initializer',
'Tensorflow weights initializer function',
iscallable(),
tf.contrib.layers.xavier_initializer(
uniform=True, dtype=tf.float32) ## = glorot_uniform
# tf.contrib.layers.variance_scaling_initializer()
),
PD(
'biases_initializer',
'Tensorflow biases initializer function, e.g. tf.zeros_initializer(). ',
iscallable(), tf.zeros_initializer()),
PD(
'rLambda',
'Lambda value (scale) for regularizer.',
decimal(),
),
PD(
'weights_regularizer',
'L1 / L2 norm regularization. If this is non-None then dropout should be None.',
iscallableOrNone(),
# tf.contrib.layers.l2_regularizer(scale=1.0, scope='L2_Regularizer')
# tf.contrib.layers.l1_regularizer(scale=1.0, scope="L1_Regularizer")
),
PD(
'use_ctc_loss',
"Whether to train using ctc_loss or cross-entropy/log-loss/log-likelihood. In either case "
"ctc_loss will be logged. Also, use_ctc_loss must be turned on if building scanning-RNN.",
boolean, LambdaVal(lambda _, p: p.build_scanning_RNN)),
PD('biases_regularizer', 'L1 / L2 norm regularization',
iscallable(noneokay=True), None),
PD(
'use_peephole',
'(boolean): whether to employ peephole connections in the decoder LSTM',
(True, False), True),
PD('logger', 'Python logger object for logging.',
instanceof(logging.Logger)),
)
def __init__(self, initVals=None):
dlc.HyperParams.__init__(self, self.proto, initVals)
self._trickledown()
def _trickledown(self):
with open(os.path.join(self.raw_data_dir, 'data_props.pkl'),
'rb') as pickle_file:
data_props = np.load(pickle_file,
encoding="latin1",
allow_pickle=True)
num_channels = 1 if (self.build_image_context == 2) else 3
self.image_shape_unframed = (data_props['padded_image_dim']['height'],
data_props['padded_image_dim']['width'],
num_channels)
self.SpaceTokenID = data_props['SpaceTokenID']
self.NullTokenID = data_props['NullTokenID']
self.StartTokenID = data_props['StartTokenID']
self.MaxSeqLen = int(data_props['MaxSeqLen'])
if self.SpaceTokenID is not None:
if False: # self.use_ctc_loss:
self.K = int(data_props['K']) + 1
self.CTCBlankTokenID = self.K - 1
else:
self.K = int(data_props['K'])
self.CTCBlankTokenID = None
else:
self.K = int(data_props['K']) + 1
self.CTCBlankTokenID = self.K - 1
def __copy__(self):
## Shallow copy
return self.__class__(self)
def copy(self, override_vals={}):
## Shallow copy
return self.__class__(self).updated(override_vals)
