Training SegLink model on my data
Opened this issue · 3 comments
Deleted user commented
I was trying to train the DSODSL512 model using my own data, which is in ICDAR-FST2015 data format.
So, when I tried to train the other models (TB, DSOD) using the same GTUtility and InputGenerators, it worked perfectly, but when I tried doing the same on SegLink models, this error was raised:
ValueError: cannot reshape array of size 5 into shape (2)
The full trace of the error is given below. Please look into it.
P.S. I had changed the GTUtility for the ICDAR2015 to reflect my folder structure.
ValueError Traceback (most recent call last)
<ipython-input-6-a02fda50a65c> in <module>
38 workers=1,
39 #use_multiprocessing=False,
---> 40 initial_epoch=initial_epoch,
41 #pickle_safe=False, # will use threading instead of multiprocessing, which is lighter on memory use but slower
42 )
e:\installed_programs\anaconda3\envs\keras\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
89 warnings.warn('Update your `' + object_name + '` call to the ' +
90 'Keras 2 API: ' + signature, stacklevel=2)
---> 91 return func(*args, **kwargs)
92 wrapper._original_function = func
93 return wrapper
e:\installed_programs\anaconda3\envs\keras\lib\site-packages\keras\engine\training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
1416 use_multiprocessing=use_multiprocessing,
1417 shuffle=shuffle,
-> 1418 initial_epoch=initial_epoch)
1419
1420 @interfaces.legacy_generator_methods_support
e:\installed_programs\anaconda3\envs\keras\lib\site-packages\keras\engine\training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
179 batch_index = 0
180 while steps_done < steps_per_epoch:
--> 181 generator_output = next(output_generator)
182
183 if not hasattr(generator_output, '__len__'):
e:\installed_programs\anaconda3\envs\keras\lib\site-packages\keras\utils\data_utils.py in get(self)
707 "`use_multiprocessing=False, workers > 1`."
708 "For more information see issue #1638.")
--> 709 six.reraise(*sys.exc_info())
e:\installed_programs\anaconda3\envs\keras\lib\site-packages\six.py in reraise(tp, value, tb)
691 if value.__traceback__ is not tb:
692 raise value.with_traceback(tb)
--> 693 raise value
694 finally:
695 value = None
e:\installed_programs\anaconda3\envs\keras\lib\site-packages\keras\utils\data_utils.py in get(self)
683 try:
684 while self.is_running():
--> 685 inputs = self.queue.get(block=True).get()
686 self.queue.task_done()
687 if inputs is not None:
e:\installed_programs\anaconda3\envs\keras\lib\multiprocessing\pool.py in get(self, timeout)
642 return self._value
643 else:
--> 644 raise self._value
645
646 def _set(self, i, obj):
e:\installed_programs\anaconda3\envs\keras\lib\multiprocessing\pool.py in worker(inqueue, outqueue, initializer, initargs, maxtasks, wrap_exception)
117 job, i, func, args, kwds = task
118 try:
--> 119 result = (True, func(*args, **kwds))
120 except Exception as e:
121 if wrap_exception and func is not _helper_reraises_exception:
e:\installed_programs\anaconda3\envs\keras\lib\site-packages\keras\utils\data_utils.py in next_sample(uid)
624 The next value of generator `uid`.
625 """
--> 626 return six.next(_SHARED_SEQUENCES[uid])
627
628
F:\SSD Detectors\ssd_detectors\ssd_data.py in generate(self, debug, encode, seed)
563 if len(targets) == batch_size:
564 if encode:
--> 565 targets = [self.prior_util.encode(y) for y in targets]
566 targets = np.array(targets, dtype=np.float32)
567 tmp_inputs = np.array(inputs, dtype=np.float32)
F:\SSD Detectors\ssd_detectors\ssd_data.py in <listcomp>(.0)
563 if len(targets) == batch_size:
564 if encode:
--> 565 targets = [self.prior_util.encode(y) for y in targets]
566 targets = np.array(targets, dtype=np.float32)
567 tmp_inputs = np.array(inputs, dtype=np.float32)
F:\SSD Detectors\ssd_detectors\sl_utils.py in encode(self, gt_data, debug)
139 polygons = []
140 for word in gt_data:
--> 141 xy = np.reshape(word[:8], (-1, 2))
142 xy = np.copy(xy) * (self.image_w, self.image_h)
143 polygons.append(xy)
e:\installed_programs\anaconda3\envs\keras\lib\site-packages\numpy\core\fromnumeric.py in reshape(a, newshape, order)
290 [5, 6]])
291 """
--> 292 return _wrapfunc(a, 'reshape', newshape, order=order)
293
294
e:\installed_programs\anaconda3\envs\keras\lib\site-packages\numpy\core\fromnumeric.py in _wrapfunc(obj, method, *args, **kwds)
54 def _wrapfunc(obj, method, *args, **kwds):
55 try:
---> 56 return getattr(obj, method)(*args, **kwds)
57
58 # An AttributeError occurs if the object does not have
ValueError: cannot reshape array of size 5 into shape (2)
The script used is:
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import numpy as np
import matplotlib.pyplot as plt
import keras
import os
import time
import pickle
from sl_model import SL512, DSODSL512
from ssd_data import InputGenerator
from sl_utils import PriorUtil
from sl_training import SegLinkLoss, SegLinkFocalLoss
from utils.training import Logger, LearningRateDecay
from utils.model import load_weights, calc_memory_usage
# ### Data
# In[2]:
from data_icdar2015fst import GTUtility
gt_util_train = GTUtility('data/ICDAR_Camera_photos')
gt_util_val = GTUtility('data/ICDAR_Camera_photos', test=True)
# ### Model
# In[3]:
# SegLink + DenseNet
model = DSODSL512()
#model = DSODSL512(activation='leaky_relu')
weights_path = None
batch_size = 6
experiment = 'dsodsl512_synthtext'
# In[4]:
if weights_path is not None:
if weights_path.find('ssd512') > -1:
layer_list = [
'conv1_1', 'conv1_2',
'conv2_1', 'conv2_2',
'conv3_1', 'conv3_2', 'conv3_3',
'conv4_1', 'conv4_2', 'conv4_3',
'conv5_1', 'conv5_2', 'conv5_3',
'fc6', 'fc7',
'conv6_1', 'conv6_2',
'conv7_1', 'conv7_2',
'conv8_1', 'conv8_2',
'conv9_1', 'conv9_2',
]
freeze = [
'conv1_1', 'conv1_2',
'conv2_1', 'conv2_2',
'conv3_1', 'conv3_2', 'conv3_3',
#'conv4_1', 'conv4_2', 'conv4_3',
#'conv5_1', 'conv5_2', 'conv5_3',
]
load_weights(model, weights_path, layer_list)
for layer in model.layers:
layer.trainable = not layer.name in freeze
else:
load_weights(model, weights_path)
prior_util = PriorUtil(model)
# ### Training
# In[5]:
epochs = 10
initial_epoch = 0
gen_train = InputGenerator(gt_util_train, prior_util, batch_size, model.image_size, augmentation=True)
gen_val = InputGenerator(gt_util_val, prior_util, batch_size, model.image_size, augmentation=True)
# In[6]:
checkdir = './checkpoints/' + time.strftime('%Y%m%d%H%M') + '_' + experiment
if not os.path.exists(checkdir):
os.makedirs(checkdir)
with open(checkdir+'/source.py','wb') as f:
source = ''.join(['# In[%i]\n%s\n\n' % (i, In[i]) for i in range(len(In))])
f.write(source.encode())
#optim = keras.optimizers.SGD(lr=1e-3, momentum=0.9, decay=0, nesterov=True)
optim = keras.optimizers.Adam(lr=1e-3, beta_1=0.9, beta_2=0.999, epsilon=0.001, decay=0.0)
# weight decay
regularizer = keras.regularizers.l2(5e-4) # None if disabled
#regularizer = None
for l in model.layers:
if l.__class__.__name__.startswith('Conv'):
l.kernel_regularizer = regularizer
#loss = SegLinkLoss(lambda_offsets=1.0, lambda_links=1.0, neg_pos_ratio=3.0)
loss = SegLinkFocalLoss(lambda_segments=100.0, lambda_offsets=1.0, lambda_links=100.0, gamma_segments=2, gamma_links=2)
model.compile(optimizer=optim, loss=loss.compute, metrics=loss.metrics)
history = model.fit_generator(
gen_train.generate(),
steps_per_epoch=gen_train.num_batches,
epochs=epochs,
verbose=1,
callbacks=[
keras.callbacks.ModelCheckpoint(checkdir+'/weights.{epoch:03d}.h5', verbose=1, save_weights_only=True),
Logger(checkdir),
#LearningRateDecay()
],
validation_data=gen_val.generate(),
validation_steps=gen_val.num_batches,
class_weight=None,
max_queue_size=1,
workers=1,
#use_multiprocessing=False,
initial_epoch=initial_epoch,
#pickle_safe=False, # will use threading instead of multiprocessing, which is lighter on memory use but slower
)
Deleted user commented
@mvoelk So, implementing the polygon case should solve the problem? I will get back to you in case of further assistance. Thank you.
Deleted user commented
It worked like magic. Thank you so much.