So i am trying to train a model in a specific dataset, Camus Dataset, but i am new to pytorch so i am a little it confused. I used the Camus Dataset code of https://www.kaggle.com/code/sontungtran/camus-eda . But when i run a part of code for display image i see that some images have different sizes of others, so when i go down the step for train the model and with batch size, i always have a problem.
So i used this code
class CamusIterator(Dataset):
def __init__(
self,
data_type,
global_transforms = [],
augment_transforms =[],
data_file = []
):
super(CamusIterator, self).__init__()
train_file='/content/drive/MyDrive/Echo_Segmentation/tries/training_set'
test_file='/content/drive/MyDrive/Echo_Segmentation/tries/testing_set'
if data_type == 'train':
data_file = train_file
self.images = os.listdir(train_file)
elif data_type == 'test':
data_file = test_file
self.images = os.listdir(test_file)
else:
raise Exception('Wrong data_type for CamusIterator')
self.data_type = data_type
self.data_file = data_file
self.global_transforms = global_transforms
self.augment_transforms = augment_transforms
def __read_image( self, patient_file, suffix ):
image_file = '{}/{}/{}'.format( self.data_file, patient_file, patient_file+suffix )
# # Stolen from a StackOverflow answer
# # https://stackoverflow.com/questions/37290631/reading-png-raw-format-in-python
image = sitk.GetArrayFromImage( sitk.ReadImage(image_file, sitk.sitkFloat32) )
#size = [1232,748]
# plt.figure(figsize=(20,16))
# plt.gray()
# plt.subplots_adjust(0,0,1,1,0.01,0.01)
# for i in range(image.shape[0]):
# plt.subplot(5,6,i+1), plt.imshow(image[i]), plt.axis('off')
# # use plt.savefig(...) here if you want to save the images as .jpg, e.g.
# plt.savefig('image.jpg')
# #plt.show()
print(image.shape)
return image
def __read_info( self, data_file ):
info = {}
with open( data_file, 'r' ) as f:
for line in f.readlines():
info_type, info_details = line.strip( '\n' ).split( ': ' )
info[ info_type ] = info_details
return info
def __len__( self ):
return len( os.listdir(self.data_file) )
def __getitem__( self, index ):
#patient_file = 'patient{}'.format( f'{index+1:04}' ) # patient{0001}, patient{0002}, etc
#patient_file= os.listdir(self.data_file)[index]
patient_file= os.listdir(self.data_file)[index]
image_2CH_ED = self.__read_image( patient_file, '_2CH_ED.mhd' )
image_2CH_ES = self.__read_image( patient_file, '_2CH_ES.mhd' )
image_4CH_ED = self.__read_image( patient_file, '_4CH_ED.mhd' )
image_4CH_ES = self.__read_image( patient_file, '_4CH_ES.mhd' )
image_2CH_sequence = self.__read_image( patient_file, '_2CH_sequence.mhd' )
image_4CH_sequence = self.__read_image( patient_file, '_4CH_sequence.mhd' )
if self.data_type == 'train':
#for train in self.data_type:
image_2CH_ED_gt = self.__read_image( patient_file, '_2CH_ED_gt.mhd' )
image_2CH_ES_gt = self.__read_image( patient_file, '_2CH_ES_gt.mhd' )
image_4CH_ED_gt = self.__read_image( patient_file, '_4CH_ED_gt.mhd' )
image_4CH_ES_gt = self.__read_image( patient_file, '_4CH_ES_gt.mhd' )
info_2CH = self.__read_info( '{}/{}/{}'.format(self.data_file, patient_file, 'Info_2CH.cfg') )
info_4CH = self.__read_info( '{}/{}/{}'.format(self.data_file, patient_file, 'Info_4CH.cfg') )
if self.data_type == 'train':
data = {
'patient': patient_file,
'2CH_ED': image_2CH_ED,
'2CH_ES': image_2CH_ES,
'4CH_ED': image_4CH_ED,
'4CH_ES': image_4CH_ES,
'2CH_sequence': image_2CH_sequence,
'4CH_sequence': image_4CH_sequence,
'2CH_ED_gt': image_2CH_ED_gt,
'2CH_ES_gt': image_2CH_ES_gt,
'4CH_ED_gt': image_4CH_ED_gt,
'4CH_ES_gt': image_4CH_ES_gt,
'info_2CH': info_2CH, # Dictionary of infos
'info_4CH': info_4CH # Dictionary of infos
}
elif self.data_type == 'test':
data = {
'patient': patient_file,
'2CH_ED': image_2CH_ED,
'2CH_ES': image_2CH_ES,
'4CH_ED': image_4CH_ED,
'4CH_ES': image_4CH_ES,
'2CH_sequence': image_2CH_sequence,
'4CH_sequence': image_4CH_sequence,
'info_2CH': info_2CH, # Dictionary of infos
'info_4CH': info_4CH # Dictionary of infos
}
# Transforms
for transform in self.global_transforms:
data = transform(data,self.data_type)
for transform in self.augment_transforms:
data = transform(data)
return data
def __iter__( self ):
for i in range( len(self) ):
yield self[ i ]
and then after this code i used:
def display_image(patient,fields):
#fields=['2CH_ED', '2CH_ES', '4CH_ED', '4CH_ES','2CH_ED_gt', '2CH_ES_gt', '4CH_ED_gt', '4CH_ES_gt']
fig = plt.figure(figsize=(40,60))
for i,fname in enumerate(fields):
test = fig.add_subplot(1, len(fields), i + 1)
image = patient[fname]
# print(len(np.unique(image)))
plt.axis('off')
# print(image.shape)
plt.imshow(image.squeeze(),cmap = 'gray')
test.set_title(fname)
fig.tight_layout()
plt.show()
for i in train_file:
fields=[#'2CH_ED',
'2CH_ED_gt',# '2CH_ES',
'2CH_ES_gt',# '4CH_ED',
'4CH_ED_gt',# '4CH_ES',
'4CH_ES_gt']
display_image(i,fields)
break
and i take as a result something like that (i will not put all the values i get cause a lot of them are the same):
(1, 779, 417)
(1, 908, 551)
(1, 779, 417)
(1, 908, 582)
(1, 843, 512)
(1, 908, 582)
(1, 1168, 708)
so the images of my dataset have different (H,W). How can i change that? i gave try some things as you can see from the comment lines(this is just an example) but i can't figure out the right solution. Should i put the images and the masks in seperate ways somehow, and if yes how do i do that?
