highdimensionals / deep-image-matching Goto Github PK

## Hyperparameters
batch_size = 32
# Triplet loss computation parameter
margin = 0.1
#minimum number of samples required in a class in the train dataset, for that class, to be considered
min_count_th = 0 
#samples set aside, per class, for validation. They are also samples set aside, per class, for test.
samples_per_class_val = 100
samples_per_class_test = 100
#Learning rate
learning_rate = 1e-5
# Total epochs for training
num_epochs = 300
# Epochs for re-sampling of triplets
epochs_per_triplets = 1
# Candidate triplet images generated per triplet of labels
triplets_fixed_class = 70
# Number of op candidate triplets selected for training
top_triplets = 1600
# Augmentation - True or False
augmentation_flag = False
# Length of feature vectors (512 for VGG, 2048 for Resnet50)
len_features = 512
######################################
## Pooling type: 'mac' or 'spoc'
pooling_type = 'spoc'
######################################
## Layers to be fine-tuned
# Fine-tune pca layer
tune_pca = True
# Fine-tune conv5 layer
tune_conv5 = True
# For naming files
tag = 'mac-keras-new-data-margin01'
trainingLayer = 'conv5_pca'

Number of classes:  5
Number of training samples:  3100
Number of validation samples:  500
Number of test samples:  500
Data split into train, validation and test dataset, based on shape, successfully...

Generating feature vectors...
Feature vectors genereated for all train vectors...
Candidate triplets generated...
Ranking the triplets...
500 triplets evaluated...
1000 triplets evaluated...
Number of candidates with non-zero loss:  1400
Total number of candidates:  1400
Candidate triplets ranked and saved, successfully...

Loading Deep Image Retrieval (pre-trained weights) model...
Done!
Model: "model_8"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_17 (InputLayer)           (None, 224, 224, 3)  0                                            
__________________________________________________________________________________________________
input_18 (InputLayer)           (None, 224, 224, 3)  0                                            
__________________________________________________________________________________________________
input_19 (InputLayer)           (None, 224, 224, 3)  0                                            
__________________________________________________________________________________________________
vgg16 (Model)                   (None, 7, 7, 512)    14714688    input_17[0][0]                   
                                                                 input_18[0][0]                   
                                                                 input_19[0][0]                   
__________________________________________________________________________________________________
query_pooling (Lambda)          (None, 512)          0           vgg16[1][0]                      
__________________________________________________________________________________________________
relevant_pooling (Lambda)       (None, 512)          0           vgg16[2][0]                      
__________________________________________________________________________________________________
irrelevant_pooling (Lambda)     (None, 512)          0           vgg16[3][0]                      
__________________________________________________________________________________________________
query_norm1 (Lambda)            (None, 512)          0           query_pooling[0][0]              
__________________________________________________________________________________________________
relevant_norm1 (Lambda)         (None, 512)          0           relevant_pooling[0][0]           
__________________________________________________________________________________________________
irrelevant_norm1 (Lambda)       (None, 512)          0           irrelevant_pooling[0][0]         
__________________________________________________________________________________________________
model_7 (Model)                 multiple             262656      query_norm1[0][0]                
                                                                 relevant_norm1[0][0]             
                                                                 irrelevant_norm1[0][0]           
__________________________________________________________________________________________________
query_norm2 (Lambda)            (None, 512)          0           model_7[1][0]                    
__________________________________________________________________________________________________
relevant_norm2 (Lambda)         (None, 512)          0           model_7[2][0]                    
__________________________________________________________________________________________________
irrelevant_norm2 (Lambda)       (None, 512)          0           model_7[3][0]                    
__________________________________________________________________________________________________
lambda_4 (Lambda)               (None, 3, 512)       0           query_norm2[0][0]                
                                                                 relevant_norm2[0][0]             
                                                                 irrelevant_norm2[0][0]           
==================================================================================================
Total params: 14,977,344
Trainable params: 7,342,080
Non-trainable params: 7,635,264
__________________________________________________________________________________________________

The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload
Epoch 1/300
43/50 [========================>.....] - ETA: 32s - loss: 0.0997
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-27-53724cbf2a60> in <module>
     41 #===========================================
     42 
---> 43 history = model.fit_generator(train_generator,steps_per_epoch = batches_per_epoch,epochs = num_epochs,callbacks=[resampleTriplets,checkpoint])#, lr_scheduler

~/env/lib/python3.5/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

~/env/lib/python3.5/site-packages/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
   1730             use_multiprocessing=use_multiprocessing,
   1731             shuffle=shuffle,
-> 1732             initial_epoch=initial_epoch)
   1733 
   1734     @interfaces.legacy_generator_methods_support

~/env/lib/python3.5/site-packages/keras/engine/training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
    183             batch_index = 0
    184             while steps_done < steps_per_epoch:
--> 185                 generator_output = next(output_generator)
    186 
    187                 if not hasattr(generator_output, '__len__'):

~/env/lib/python3.5/site-packages/keras/utils/data_utils.py in get(self)
    740                     "`use_multiprocessing=False, workers > 1`."
    741                     "For more information see issue #1638.")
--> 742             six.reraise(*sys.exc_info())

~/env/lib/python3.5/site-packages/six.py in reraise(tp, value, tb)
    694             if value.__traceback__ is not tb:
    695                 raise value.with_traceback(tb)
--> 696             raise value
    697         finally:
    698             value = None

~/env/lib/python3.5/site-packages/keras/utils/data_utils.py in get(self)
    709                 try:
    710                     future = self.queue.get(block=True)
--> 711                     inputs = future.get(timeout=30)
    712                     self.queue.task_done()
    713                 except mp.TimeoutError:

/usr/lib/python3.5/multiprocessing/pool.py in get(self, timeout)
    606             return self._value
    607         else:
--> 608             raise self._value
    609 
    610     def _set(self, i, obj):

/usr/lib/python3.5/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:

~/env/lib/python3.5/site-packages/keras/utils/data_utils.py in next_sample(uid)
    648         The next value of generator `uid`.
    649     """
--> 650     return six.next(_SHARED_SEQUENCES[uid])
    651 
    652 

~/jupyter/Deep-image-matching/modules/modules_custom_callbacks.py in generator(input_csv_path, top_triplets, batch_size, img_height, img_width, channels, imagesDir, batches_per_epoch, epochs_per_triplets, len_features, augmentation_flag, rotation_max)
    152             for i in range(batch_size):
    153                 # Extract query image
--> 154                 img = image.load_img(imagesDir+"/"+ls_query_batch[i], target_size=(img_height, img_width))
    155                 img = image.img_to_array(img)
    156                 batch_query[i] = img/255

IndexError: list index out of range