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CTCModel
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TextRecognition
CTCModel
Commits
86ec9bfe
Commit
86ec9bfe
authored
Dec 17, 2018
by
Yann SOULLARD
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update for last keras version
parent
095b18f3
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1
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1 changed file
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85 additions
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12 deletions
+85
-12
CTCModel.py
CTCModel.py
+85
-12
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CTCModel.py
View file @
86ec9bfe
...
...
@@ -3,21 +3,25 @@ import tensorflow as tf
import
numpy
as
np
import
os
#import warpctc_tensorflow
from
keras
import
Input
from
keras.engine
import
Model
from
keras.layers
import
Lambda
from
keras.models
import
model_from_json
from
keras.models
import
model_from_json
,
Sequential
import
pickle
from
tensorflow.python.ops
import
ctc_ops
as
ctc
from
keras.utils
import
Sequence
,
GeneratorEnqueuer
,
OrderedEnqueuer
import
warnings
from
keras.utils.generic_utils
import
Progbar
from
keras.layers
import
TimeDistributed
,
Activation
,
Dense
#from ocr_ctc.utils.utils_analysis import tf_edit_distance
#from ocr_ctc.utils.utils_keras import Kreshape_To1D
from
keras.preprocessing
import
sequence
"""
authors: Yann Soullard, Cyprien Ruffino (2017)
LITIS lab, university of Rouen (France)
...
...
@@ -50,8 +54,14 @@ class CTCModel:
self
.
model_train
=
None
self
.
model_pred
=
None
self
.
model_eval
=
None
self
.
inputs
=
inputs
self
.
outputs
=
outputs
if
not
isinstance
(
inputs
,
list
):
self
.
inputs
=
[
inputs
]
else
:
self
.
inputs
=
inputs
if
not
isinstance
(
outputs
,
list
):
self
.
outputs
=
[
outputs
]
else
:
self
.
outputs
=
outputs
self
.
greedy
=
greedy
self
.
beam_width
=
beam_width
...
...
@@ -59,6 +69,7 @@ class CTCModel:
self
.
charset
=
charset
def
compile
(
self
,
optimizer
):
"""
Configures the CTC Model for training.
...
...
@@ -760,12 +771,14 @@ class CTCModel:
or list of arrays of predictions
(if the model has multiple outputs).
"""
# num_samples = self.model_pred._check_num_samples(ins, batch_size,
# steps,
# 'steps')
num_samples
=
check_num_samples
(
ins
,
batch_size
=
batch_size
,
steps
=
steps
,
steps_name
=
'steps'
)
if
steps
is
not
None
:
# Step-based predictions.
# Since we do not know how many samples
...
...
@@ -828,7 +841,7 @@ class CTCModel:
y_pred
,
labels
,
input_length
,
label_length
=
args
return
K
.
ctc_batch_cost
(
labels
,
y_pred
,
input_length
,
label_length
)
#, ignore_longer_outputs_than_inputs=True)
# return warpctc_tensorflow.ctc(y_pred, labels, label_length, input_length)
@
staticmethod
def
ctc_complete_decoding_lambda_func
(
args
,
**
arguments
):
...
...
@@ -931,7 +944,7 @@ class CTCModel:
output
.
close
()
def
load_model
(
self
,
path_dir
,
optimizer
,
file_weights
=
None
):
def
load_model
(
self
,
path_dir
,
optimizer
,
file_weights
=
None
,
change_parameters
=
False
,
init_last_layer
=
False
):
""" Load a model in path_dir
load model_train, model_pred and model_eval from json
load inputs and outputs from json
...
...
@@ -972,10 +985,11 @@ class CTCModel:
param
=
p
.
load
()
input
.
close
()
self
.
greedy
=
param
[
'greedy'
]
if
'greedy'
in
param
.
keys
()
else
self
.
greedy
self
.
beam_width
=
param
[
'beam_width'
]
if
'beam_width'
in
param
.
keys
()
else
self
.
beam_width
self
.
top_paths
=
param
[
'top_paths'
]
if
'top_paths'
in
param
.
keys
()
else
self
.
top_paths
self
.
charset
=
param
[
'charset'
]
if
'charset'
in
param
.
keys
()
else
self
.
charset
if
not
change_parameters
:
self
.
greedy
=
param
[
'greedy'
]
if
'greedy'
in
param
.
keys
()
else
self
.
greedy
self
.
beam_width
=
param
[
'beam_width'
]
if
'beam_width'
in
param
.
keys
()
else
self
.
beam_width
self
.
top_paths
=
param
[
'top_paths'
]
if
'top_paths'
in
param
.
keys
()
else
self
.
top_paths
self
.
charset
=
param
[
'charset'
]
if
'charset'
in
param
.
keys
()
and
self
.
charset
is
None
else
self
.
charset
self
.
compile
(
optimizer
)
...
...
@@ -989,6 +1003,65 @@ class CTCModel:
self
.
model_pred
.
set_weights
(
self
.
model_train
.
get_weights
())
self
.
model_eval
.
set_weights
(
self
.
model_train
.
get_weights
())
if
init_last_layer
:
labels
=
Input
(
name
=
'labels'
,
shape
=
[
None
])
input_length
=
Input
(
name
=
'input_length'
,
shape
=
[
1
])
label_length
=
Input
(
name
=
'label_length'
,
shape
=
[
1
])
# new_model_init = Sequential() # new model
# for layer in self.model_init.layers[:-2]:
# new_model_init.add(layer)
# new_model_init.add(TimeDistributed(Dense(len(self.charset) + 1), name="DenseSoftmax"))
# new_model_init.add(Activation('softmax', name='Softmax'))
# self.model_init = new_model_init
new_layer
=
Input
(
name
=
'input'
,
shape
=
self
.
model_init
.
layers
[
0
].
output_shape
[
1
:])
self
.
inputs
=
[
new_layer
]
for
layer
in
self
.
model_init
.
layers
[
1
:
-
2
]:
new_layer
=
layer
(
new_layer
)
new_layer
=
TimeDistributed
(
Dense
(
len
(
self
.
charset
)
+
1
),
name
=
"DenseSoftmax"
)(
new_layer
)
new_layer
=
Activation
(
'softmax'
,
name
=
'Softmax'
)(
new_layer
)
self
.
outputs
=
[
new_layer
]
# new_model_train = Sequential() # new model
# nb_layers = len(self.model_train.layers)
# new_layer = Input(name='input',
# shape=self.model_train.layers[0].output_shape[1:])
# for layer in self.model_train.layers[1:-6]:
# new_layer = layer(new_layer)
# new_layer = TimeDistributed(Dense(len(self.charset) + 1), name="DenseSoftmax")(new_layer)
# new_layer = Activation('softmax', name='Softmax')(new_layer)
# Lambda layer for computing the loss function
loss_out
=
Lambda
(
self
.
ctc_loss_lambda_func
,
output_shape
=
(
1
,),
name
=
'CTCloss'
)(
self
.
outputs
+
[
labels
,
input_length
,
label_length
])
# Lambda layer for the decoding function
out_decoded_dense
=
Lambda
(
self
.
ctc_complete_decoding_lambda_func
,
output_shape
=
(
None
,
None
),
name
=
'CTCdecode'
,
arguments
=
{
'greedy'
:
self
.
greedy
,
'beam_width'
:
self
.
beam_width
,
'top_paths'
:
self
.
top_paths
},
dtype
=
"float32"
)(
self
.
outputs
+
[
input_length
])
# Lambda layer to perform an analysis (CER and SER)
out_analysis
=
Lambda
(
self
.
ctc_complete_analysis_lambda_func
,
output_shape
=
(
None
,),
name
=
'CTCanalysis'
,
arguments
=
{
'greedy'
:
self
.
greedy
,
'beam_width'
:
self
.
beam_width
,
'top_paths'
:
self
.
top_paths
},
dtype
=
"float32"
)(
self
.
outputs
+
[
labels
,
input_length
,
label_length
])
# create Keras models
self
.
model_init
=
Model
(
inputs
=
self
.
inputs
,
outputs
=
self
.
outputs
)
self
.
model_train
=
Model
(
inputs
=
self
.
inputs
+
[
labels
,
input_length
,
label_length
],
outputs
=
loss_out
)
self
.
model_pred
=
Model
(
inputs
=
self
.
inputs
+
[
input_length
],
outputs
=
out_decoded_dense
)
self
.
model_eval
=
Model
(
inputs
=
self
.
inputs
+
[
labels
,
input_length
,
label_length
],
outputs
=
out_analysis
)
# Compile models
self
.
model_train
.
compile
(
loss
=
{
'CTCloss'
:
lambda
yt
,
yp
:
yp
},
optimizer
=
optimizer
)
self
.
model_pred
.
compile
(
loss
=
{
'CTCdecode'
:
lambda
yt
,
yp
:
yp
},
optimizer
=
optimizer
)
self
.
model_eval
.
compile
(
loss
=
{
'CTCanalysis'
:
lambda
yt
,
yp
:
yp
},
optimizer
=
optimizer
)
def
_standardize_input_data
(
data
,
names
,
shapes
=
None
,
...
...
@@ -1230,4 +1303,4 @@ def check_num_samples(ins,
if
hasattr
(
ins
[
0
],
'shape'
):
return
int
(
ins
[
0
].
shape
[
0
])
return
None
# Edge case where ins == [static_learning_phase]
return
None
# Edge case where ins == [static_learning_phase]
\ No newline at end of file
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