def __init__(self): """Init global variables for contextual circuit bp.""" self.name = 'contextual_model_stimuli' self.figure_name = 'f3a' self.config = Config() self.output_size = [10, 1] self.im_size = (51, 51, 10) self.model_input_image_size = [51, 51, 10] self.default_loss_function = 'pearson' self.score_metric = 'pearson' self.preprocess = [None] self.folds = { 'train': 'train', 'test': 'test'} self.targets = { 'image': tf_fun.bytes_feature, 'label': tf_fun.float_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'label': tf_fun.fixed_len_feature( dtype='float', length=self.output_size[0]) } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': self.im_size }, 'label': { 'dtype': tf.float32, 'reshape': None } }
def __init__(self): self.name = 'cifar_10' self.extension = '.png' self.config = Config() self.output_size = [10, 1] self.im_size = [32, 32, 3] self.model_input_image_size = [32, 32, 3] self.default_loss_function = 'cce' self.score_metric = 'accuracy' self.preprocess = [None] self.shuffle = True # Preshuffle data? self.folds = {'train': 'train', 'test': 'test'} self.targets = { 'image': tf_fun.bytes_feature, 'label': tf_fun.int64_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'label': tf_fun.fixed_len_feature(dtype='int64') } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': self.im_size }, 'label': { 'dtype': tf.int64, 'reshape': None } }
def __init__(self): self.name = 'coco_2014' self.aux_dir = 'coco_images' self.extension = '.jpg' self.config = Config() self.output_size = [89, 1] self.im_size = [256, 256, 3] self.model_input_image_size = [224, 224, 3] self.default_loss_function = 'sigmoid' self.image_meta_file = '_annotations.npy' self.score_metric = 'f1' self.preprocess = ['pad_resize'] self.shuffle = False # Preshuffle data? self.folds = {'train': 'train2014', 'val': 'val2014'} self.targets = { 'image': tf_fun.bytes_feature, 'label': tf_fun.int64_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'label': tf_fun.fixed_len_feature(length=self.output_size[0], dtype='int64') } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': self.im_size }, 'label': { 'dtype': tf.int64, 'reshape': None } }
def __init__(self, cell_id_list, aux_targets, aux_tf_dict, output_size=[1], im_size=None): """Allen cell tfrecord global variable init.""" self.name = 'allen_cell_%s' % cell_id_list self.config = Config() self.folds = {'train': 'training', 'test': 'testing'} self.targets = { 'image': tf_fun.bytes_feature, 'f': tf_fun.float_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'f': tf_fun.fixed_len_feature(dtype='float') } # Add aux data for ((tk, tv), (dk, dv)) in zip(aux_targets.iteritems(), aux_tf_dict.iteritems()): self.targets[tk] = tv self.tf_dict[dk] = dv self.output_size = output_size self.im_size = im_size self.preprocess = [None] self.shuffle = False # Preshuffle data?
def __init__(self): self.name = 'spikefinder' self.config = Config() self.file_extension = '.csv' self.timepoints = 5 # Data is 100hz self.trim_nans = True self.output_size = [1, self.timepoints] self.im_size = [1, self.timepoints] self.model_input_image_size = [1, self.timepoints] self.default_loss_function = 'l2' self.score_metric = 'l2' self.preprocess = [None] self.folds = {'train': 'train', 'test': 'test'} self.targets = { 'image': tf_fun.float_feature, 'label': tf_fun.float_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='float'), 'label': tf_fun.fixed_len_feature(dtype='float') } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': None }, 'label': { 'dtype': tf.float32, 'reshape': None } }
def __init__(self): self.name = 'crcns_1d_2nd_dff' self.config = Config() self.file_extension = '.csv' self.timepoints = 10 # Data is 100hz self.output_size = [2] self.im_size = [self.timepoints, 1] self.model_input_image_size = [self.timepoints, 1] self.default_loss_function = 'sigmoid_logits' self.score_metric = 'argmax_softmax_accuracy' self.fix_imbalance_train = True self.fix_imbalance_test = False self.preprocess = [None] self.train_prop = 0.80 self.binarize_spikes = True self.df_f_window = 10 self.use_df_f = True self.save_pickle = True self.shuffle_train = True self.shuffle_test = False self.pickle_name = 'cell_3_dff.p' self.overwrite_numpy = False self.validation_slice = 2 # CRCNS data pointers self.crcns_dataset = os.path.join( self.config.data_root, 'crcns', 'cai-1') self.exp_name = 'GCaMP6s_9cells_Chen2013' self.numpy = os.path.join( self.crcns_dataset, '%s_1d.npz' % self.exp_name) # CC-BP dataset vars self.folds = { 'train': 'train', 'test': 'test'} self.targets = { 'image': tf_fun.float_feature, 'label': tf_fun.int64_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature( length=self.im_size, dtype='float'), 'label': tf_fun.fixed_len_feature( length=self.output_size[-1], dtype='int64') } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': self.im_size }, 'label': { 'dtype': tf.int64, 'reshape': self.output_size } }
def __init__(self): self.name = 'cluttered_nist_2_ix1_full_semantic' self.output_name = 'cluttered_nist_2_ix1_full_semantic' self.data_name = 'ix1' self.img_dir = 'imgs' self.contour_dir = '/media/data_cifs/cluttered_nist2_plus/' self.im_extension = '.png' self.label_regex = r'(?<=length)\d+' self.config = Config() self.im_size = [350, 350] # 600, 600 self.model_input_image_size = [160, 160, 1] # [107, 160, 3] self.nhot_size = [26] self.max_ims = 20000000 self.output_size = {'output': 1, 'aux': self.nhot_size[0]} self.label_size = [1] self.default_loss_function = 'cce' self.aux_loss = {'nhot': ['bce', 1.]} # Loss type and scale self.score_metric = 'accuracy' self.store_z = False self.normalize_im = False self.all_flips = True self.shuffle = True self.input_normalization = 'none' # 'zscore' self.preprocess = ['resize'] # ['resize_nn'] self.meta = os.path.join('metadata', 'combined.npy') self.folds = { 'train': 'train', 'val': 'val' } self.cv_split = 0.9 self.cv_balance = True self.targets = { 'image': tf_fun.bytes_feature, 'nhot': tf_fun.float_feature, 'label': tf_fun.int64_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'nhot': tf_fun.fixed_len_feature( dtype='float', length=self.nhot_size), 'label': tf_fun.fixed_len_feature(dtype='int64') } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': self.im_size }, 'nhot': { 'dtype': tf.float32, 'reshape': self.nhot_size }, 'label': { 'dtype': tf.int64, 'reshape': self.label_size } }
def __init__(self): self.name = 'orientation_probe' # self.name = 'plaid_surround' self.output_name = 'orientation_probe_151_viz' self.img_dir = 'imgs' # self.contour_dir = '/media/data_cifs/cluster_projects/refactor_gammanet/plaid_surround' self.contour_dir = '/media/data_cifs_lrs/projects/prj_neural_circuits/refactor_gammanet/{}'.format( self.name) # self.perturb_a = "perturb_viz/gammanet_full_plaid_surround_outputs_data.npy" self.perturb_a = "perturb_viz/bsds_tcs.npy" self.perturb_b = "perturb_viz/0.npz" # bsds_tcs.npy" self.im_extension = '.png' self.label_regex = r'(?<=length)\d+' self.config = Config() self.im_size = [320, 480, 3] # [500, 500] # 600, 600 self.model_input_image_size = [320, 480, 3] # [107, 160, 3] # self.output_size = [112, 112, 128] # [320, 480, 1] # [321, 481, 1] self.output_size = [1, 2048 + 6] # [320, 480, 1] # [321, 481, 1] self.max_ims = 100000 self.label_size = self.output_size self.default_loss_function = 'l2' self.store_z = False self.normalize_im = False self.all_flips = True self.shuffle = True self.input_normalization = 'none' # 'zscore' self.preprocess = [] # ['resize'] # ['resize_nn'] self.meta = os.path.join('metadata', '1.npy') self.folds = { 'train': 'train', 'val': 'val', 'test': 'test', } self.cv_split = 0.1 self.targets = { 'image': tf_fun.bytes_feature, 'label': tf_fun.float_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'label': tf_fun.fixed_len_feature(dtype='float32', length=self.output_size) } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': self.im_size }, 'label': { 'dtype': tf.float32, 'reshape': self.output_size } }
def __init__(self): self.name = 'BSDS500' self.output_name = 'BSDS500_100' self.im_extension = '.jpg' self.lab_extension = '.mat' self.images_dir = 'images' self.labels_dir = 'groundTruth' self.processed_labels = 'processed_labels' self.processed_images = 'processed_images' self.config = Config() self.train_size = int(200 * 1) self.im_size = [321, 481, 3] # [321, 481, 3] # self.model_input_image_size = [196, 196, 3] # self.model_input_image_size = [320, 480, 3] # [224, 224, 3] self.model_input_image_size = [320, 320, 3] # [224, 224, 3] self.val_model_input_image_size = [320, 480, 3] # self.model_input_image_size = [160, 240, 3] # [224, 224, 3] # self.val_model_input_image_size = [160, 240, 3] # self.model_input_image_size = [80, 160, 3] # [224, 224, 3] # self.val_model_input_image_size = [80, 160, 3] self.output_size = [321, 481, 1] # [321, 481, 1] self.label_size = self.output_size self.default_loss_function = 'pearson' self.score_metric = 'sigmoid_accuracy' self.aux_scores = ['f1'] self.store_z = True self.input_normalization = 'none' # 'zscore' self.preprocess = [None] # Preprocessing before tfrecords self.folds = { 'train': 'train', 'val': 'val' } self.fold_options = { 'train': 'mean', 'val': 'mean' } self.targets = { 'image': tf_fun.bytes_feature, 'label': tf_fun.bytes_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'label': tf_fun.fixed_len_feature(dtype='string') } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': self.im_size }, 'label': { 'dtype': tf.float32, 'reshape': self.output_size } }
def __init__(self): self.output_name = 'BSDS500_100_hed' self.im_extension = '.jpg' self.lab_extension = '.mat' self.images_dir = '/media/data_cifs/pytorch_projects/datasets/BSDS500_crops/data/images/train' self.val_images_dir = '/media/data_cifs/pytorch_projects/datasets/BSDS500_crops/data/images/val' self.processed_labels = 'processed_labels' self.processed_images = 'processed_images' self.config = Config() self.train_size = int(1000 * 1) self.im_size = [320, 320, 3] # [321, 481, 3] self.model_input_image_size = [320, 320, 3] # [224, 224, 3] self.val_model_input_image_size = [320, 320, 3] self.output_size = [320, 320, 1] # [321, 481, 1] self.label_size = self.output_size self.default_loss_function = 'pearson' self.score_metric = 'sigmoid_accuracy' self.aux_scores = ['f1'] self.store_z = False self.input_normalization = 'none' # 'zscore' self.preprocess = ['hed_pad'] # Preprocessing before tfrecords self.folds = {'train': 'train', 'val': 'val'} self.fold_options = {'train': 'mean', 'val': 'mean'} self.targets = { 'image': tf_fun.bytes_feature, 'label': tf_fun.bytes_feature, 'height': tf_fun.int64_feature, 'width': tf_fun.int64_feature, } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'label': tf_fun.fixed_len_feature(dtype='string'), 'height': tf_fun.fixed_len_feature(dtype='int64'), 'width': tf_fun.fixed_len_feature(dtype='int64'), } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': self.im_size }, 'label': { 'dtype': tf.float32, 'reshape': self.output_size }, 'height': { 'dtype': tf.int64, 'reshape': [] }, 'width': { 'dtype': tf.int64, 'reshape': [] }, }
def __init__(self): self.name = 'new_LMD_512_egfr' self.output_name = 'new_LMD_512_egfr' self.kras_dir = '/media/data_cifs/andreas/pathology/2018-04-26/mar2019/LMD/4-03-2019/512_npys' self.im_extension = '.npy' self.label_regex = r'(?<=length)\d+' self.config = Config() self.im_size = [512, 512, 3] # 600, 600 self.model_input_image_size = [224, 224, 3] # [480, 480, 3] # [107, 160, 3] self.max_ims = 125000 self.output_size = [4] self.label_size = self.output_size self.default_loss_function = 'cce' self.score_metric = 'accuracy' self.store_z = False self.normalize_im = False self.all_flips = True self.balance = True self.shuffle = True self.calculate_moments = False self.input_normalization = 'none' # 'zscore' self.preprocess = [ 'rgba2rgb' ] # ['to_float32', 'crop_center'] # , 'exclude_white'] # 'rgba2rgb', self.LMD = ['3361805'] self.val_set = self.LMD # self.non_lung_cases_new # self.non_lung_kras_cases_2017 + self.non_lung_non_kras_cases_2017 # + self.non_lung_cases_new self.folds = {'train': 'train', 'val': 'val', 'test': 'test'} self.cv_split = 0.9 self.cv_balance = True self.targets = { 'image': tf_fun.bytes_feature, 'label': tf_fun.float_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'label': tf_fun.fixed_len_feature(dtype='float32', length=self.output_size[0]) } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': self.im_size }, 'label': { 'dtype': tf.float32, 'reshape': self.output_size } }
def __init__(self): self.name = 'cube_plus' self.output_name = 'cube_plus' self.main_dir = '/media/data_cifs/image_datasets/cube_plus' self.image_dir = os.path.join(self.main_dir, 'images') self.label_file = os.path.join(self.main_dir, 'cube+_gt.txt') self.im_extension = '.PNG' self.label_regex = r'(?<=length)\d+' self.config = Config() self.im_size = [1732, 2601, 3] # 600, 600 self.model_input_image_size = [288, 433, 3] # [107, 160, 3] self.max_ims = 125000 self.output_size = [3] self.label_size = self.output_size self.default_loss_function = 'bce' self.score_metric = 'accuracy' self.store_z = False self.normalize_im = False self.all_flips = True self.balance = True self.shuffle = True self.calculate_moments = True self.input_normalization = 'none' # 'zscore' self.preprocess = [] self.folds = { 'train': 'train', 'val': 'val' } self.cv_split = 0.1 self.cv_balance = True self.targets = { 'image': tf_fun.bytes_feature, 'label': tf_fun.float_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'label': tf_fun.fixed_len_feature( dtype='float32', length=self.output_size[0]) } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': self.im_size }, 'label': { 'dtype': tf.float32, 'reshape': self.output_size } }
def __init__(self): self.name = 'shape_connectome_lumcontrast' self.output_name = 'shape_connectome_lumcontrast' self.data_name = 'lumcontrast' self.img_dir = 'imgs' self.contour_dir = '/media/data_cifs/synth/synth_connectomics/' self.im_extension = '.png' self.label_regex = r'(?<=length)\d+' self.config = Config() self.im_size = [350, 350] # 600, 600 self.model_input_image_size = [320, 320, 1] # [107, 160, 3] self.nhot_size = [26] self.max_ims = 200 self.output_size = {'output': 2, 'aux': self.nhot_size[0]} self.label_size = self.im_size self.default_loss_function = 'cce' # self.aux_loss = {'nhot': ['bce', 1.]} # Loss type and scale self.score_metric = 'prop_positives' self.store_z = False self.normalize_im = False self.all_flips = True self.shuffle = True self.input_normalization = 'none' # 'zscore' self.preprocess = ['resize'] # ['resize_nn'] self.meta = os.path.join('metadata', 'combined.npy') self.folds = { 'train': 'train', 'val': 'val' } self.cv_split = 0.05 self.cv_balance = True self.targets = { 'image': tf_fun.bytes_feature, 'label': tf_fun.bytes_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'label': tf_fun.fixed_len_feature(dtype='string') } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': self.im_size }, 'label': { 'dtype': tf.float32, 'reshape': self.label_size } }
def __init__(self): self.name = 'curv_contour_length_9_full' self.output_name = 'curv_contour_length_9_full' self.data_name = 'curv_contour_length_9' self.contour_dir = '/media/data_cifs/curvy_2snakes_300/' self.im_extension = '.png' self.label_regex = r'(?<=length)\d+' self.config = Config() self.im_size = [300, 300] # 600, 600 self.model_input_image_size = [160, 160, 1] # [107, 160, 3] self.max_ims = 125000 self.output_size = [1] self.label_size = self.output_size self.default_loss_function = 'cce' self.score_metric = 'accuracy' self.store_z = False self.normalize_im = False self.all_flips = True self.balance = True self.shuffle = True self.input_normalization = 'none' # 'zscore' self.preprocess = [''] # ['resize_nn'] self.meta = os.path.join('metadata', 'combined.npy') self.negative = 'curv_contour_length_9_neg' self.folds = { 'train': 'train', 'val': 'val' } self.cv_split = 0.9 self.cv_balance = True self.targets = { 'image': tf_fun.bytes_feature, 'label': tf_fun.int64_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'label': tf_fun.fixed_len_feature(dtype='int64') } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': self.im_size }, 'label': { 'dtype': tf.int64, 'reshape': self.output_size } }
def __init__(self): self.name = 'crcns_2d' self.config = Config() self.file_extension = '.csv' self.timepoints = 10 # Data is 100hz self.output_size = [1, 1] self.im_size = [self.timepoints, 256, 256, 1] self.model_input_image_size = [128, 128, 1] self.default_loss_function = 'sigmoid_logits' self.score_metric = 'argmax_softmax_accuracy' self.fix_imbalance = True self.preprocess = ['resize'] self.train_prop = 0.80 self.binarize_spikes = True self.df_f_window = 10 self.use_df_f = False self.shuffle = True # CRCNS data pointers self.crcns_dataset = os.path.join( self.config.data_root, 'crcns', 'cai-1') self.exp_name = 'GCaMP6s_9cells_Chen2013' # CC-BP dataset vars self.folds = { 'train': 'train', 'test': 'test' } self.targets = { 'image': tf_fun.bytes_feature, 'label': tf_fun.float_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'label': tf_fun.fixed_len_feature(dtype='float') } self.tf_reader = { 'image': { 'dtype': tf.float16, 'reshape': self.im_size }, 'label': { 'dtype': tf.float32, 'reshape': None } }
def __init__(self): self.name = 'berson_005' self.output_name = 'berson_005' self.contour_dir = '/media/data_cifs/connectomics/datasets/berson_0.npz' self.config = Config() self.affinity = False self.im_size = [384, 384] # 600, 600 self.model_input_image_size = [384, 384, 1] # [107, 160, 3] self.nhot_size = [26] self.max_ims = 222 self.output_size = {'output': 1, 'aux': self.nhot_size[0]} self.label_size = self.im_size + [1] self.default_loss_function = 'cce' # self.aux_loss = {'nhot': ['bce', 1.]} # Loss type and scale self.score_metric = 'prop_positives' self.store_z = False self.normalize_im = False self.all_flips = True self.shuffle = True self.input_normalization = 'none' # 'zscore' self.meta = os.path.join('metadata', 'combined.npy') self.folds = { 'train': 'train', 'val': 'val', 'test': 'test', } self.train_split = int(307 * .05) self.val_split = 307 self.train_size = 1280 self.cv_balance = True self.targets = { 'image': tf_fun.bytes_feature, 'label': tf_fun.bytes_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'label': tf_fun.fixed_len_feature(dtype='string') } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': self.im_size }, 'label': { 'dtype': tf.float32, 'reshape': self.label_size } }
def __init__(self): self.name = 'gratings_undo_bias_main' self.output_name = 'gratings_undo_bias_main' self.img_dir = 'imgs' self.contour_dir = '/media/data_cifs/tilt_illusion' self.im_extension = '.png' self.label_regex = r'(?<=length)\d+' self.config = Config() self.im_size = [500, 500] # 600, 600 self.model_input_image_size = [224, 224, 1] # [107, 160, 3] self.output_size = [2] self.max_ims = 100000 self.label_size = self.output_size self.default_loss_function = 'l2' self.store_z = False self.normalize_im = False self.all_flips = True self.shuffle = True self.input_normalization = 'none' # 'zscore' self.preprocess = [] # ['resize'] # ['resize_nn'] self.meta = os.path.join('test', 'metadata', 'filtered_te.npy') self.folds = { 'train': 'train', 'val': 'val', 'test': 'test', } self.cv_split = 0.1 self.targets = { 'image': tf_fun.bytes_feature, 'label': tf_fun.float_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'label': tf_fun.fixed_len_feature(dtype='float32', length=self.output_size) } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': self.im_size }, 'label': { 'dtype': tf.float32, 'reshape': self.output_size } }
def __init__(self): self.name = 'seg_cluttered_nist_3_baseline_50k' self.output_name = 'seg_cluttered_nist_3_baseline_50k' self.data_name = 'baseline' self.img_dir = 'imgs' self.contour_dir = '/media/data_cifs/cluttered_nist3_seg/' self.im_extension = '.png' self.label_regex = r'(?<=length)\d+' self.config = Config() self.im_size = [350, 350] # 600, 600 self.model_input_image_size = [160, 160, 1] # [107, 160, 3] self.max_ims = 50000 self.output_size = self.im_size + [2] self.label_size = self.output_size self.default_loss_function = 'cce' self.score_metric = 'accuracy' self.store_z = False self.normalize_im = False self.all_flips = True self.shuffle = True self.input_normalization = 'none' # 'zscore' self.preprocess = ['resize', 'trim_extra_dims'] # ['resize_nn'] self.meta = os.path.join('metadata', 'combined.npy') self.folds = { 'train': 'train', 'val': 'val' } self.cv_split = 0.9 self.cv_balance = True self.targets = { 'image': tf_fun.bytes_feature, 'label': tf_fun.bytes_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'label': tf_fun.fixed_len_feature(dtype='string') } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': self.im_size }, 'label': { 'dtype': tf.float32, 'reshape': self.im_size } }
def __init__(self): self.name = 'new_LMD_whole' self.output_name = 'new_LMD_whole' self.kras_dir = '/media/data_cifs/pathology/molecular_sahar/LMD/imgs' self.im_extension = '.jpg' self.label_regex = r'(?<=length)\d+' self.config = Config() self.im_size = [2028, 2028, 3] # 600, 600 self.model_input_image_size = [2028, 2028, 3] self.max_ims = 125000 self.output_size = [1] self.label_size = self.output_size self.default_loss_function = 'cce' self.score_metric = 'accuracy' self.store_z = False self.normalize_im = False self.all_flips = True self.balance = True self.shuffle = True self.calculate_moments = False self.input_normalization = 'none' # 'zscore' self.preprocess = ['rgba2rgb', 'macenko'] self.LMD = ['3361805'] self.val_set = self.LMD self.folds = {'train': 'train', 'val': 'val', 'test': 'test'} self.cv_split = 0.9 self.cv_balance = True self.targets = { 'image': tf_fun.bytes_feature, 'label': tf_fun.int64_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'label': tf_fun.fixed_len_feature(dtype='int64') } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': self.im_size }, 'label': { 'dtype': tf.int64, 'reshape': self.output_size } }
def __init__(self): self.name = 'ilsvrc12' self.output_name = 'ilsvrc12' self.img_dir = 'imgs' self.contour_dir = '/media/data_cifs/clicktionary/webapp_data' self.train_dir = 'lmdb_trains' self.val_dir = 'lmdb_validations' self.im_extension = '.JPEG' self.label_regex = r'(?<=length)\d+' self.config = Config() self.im_size = [256, 256, 3] # 600, 600 self.model_input_image_size = [224, 224, 3] # [107, 160, 3] self.max_ims = np.inf self.output_size = [] self.force_output_size = [1000] self.label_size = self.output_size self.default_loss_function = 'cce' self.score_metric = 'accuracy' self.store_z = False self.normalize_im = False self.all_flips = True self.shuffle = True self.input_normalization = 'none' # 'zscore' self.preprocess = ['resize'] # ['resize_nn'] self.meta = os.path.join('metadata', 'combined.npy') self.folds = {'train': 'train', 'val': 'val'} self.targets = { 'image': tf_fun.bytes_feature, 'label': tf_fun.int64_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'label': tf_fun.fixed_len_feature(dtype='int64') } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': self.im_size }, 'label': { 'dtype': tf.int64, 'reshape': self.output_size } }
def __init__(self): self.name = 'BSDS500_2' self.orig_name = 'BSDS500' self.im_extension = '.jpg' self.lab_extension = '.mat' self.images_dir = 'images' self.labels_dir = 'groundTruth' self.processed_labels = 'processed_labels' self.processed_images = 'processed_images' self.config = Config() self.im_size = [321, 481, 3] self.model_input_image_size = [107, 160, 3] # [150, 240, 3] self.output_size = [321, 481, 1] # 256 x 256 pixels, 7 x 7 angle self.label_size = self.output_size self.default_loss_function = 'pearson' self.score_metric = 'pearson' self.aux_scores = ['f1'] self.preprocess = [None] # ['resize_nn'] self.folds = { 'train': 'train', 'val': 'val' } self.fold_options = { 'train': 'mean', 'val': 'duplicate' } self.targets = { 'image': tf_fun.bytes_feature, 'label': tf_fun.bytes_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'label': tf_fun.fixed_len_feature(dtype='string') } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': self.im_size }, 'label': { 'dtype': tf.float32, 'reshape': self.output_size } }
def __init__(self): self.name = 'baseline' self.data_name = 'baseline' self.output_name = 'cluttered_nist_caps_baseline' self.dataset_dir = '/media/data_cifs/cluttered_nist_caps' self.im_extension = '.png' self.label_regex = r'(?<=length)\d+' self.config = Config() self.im_size = [300, 300] self.model_input_image_size = [256, 256, 1] self.output_size =[1] self.default_loss_function = 'cce' self.score_metric = 'accuracy' self.store_z = False self.normalize_im = False self.all_flips = True self.shuffle = True self.input_normalization = 'none' # 'zscore' self.preprocess = [''] # ['resize_nn'] self.meta = os.path.join('metadata', 'combined.npy') self.folds = { 'train': 'train', 'val': 'val' } self.cv_split = 0.9 self.cv_balance = True self.targets = { 'image': tf_fun.bytes_feature, 'label': tf_fun.int64_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'label': tf_fun.fixed_len_feature(dtype='int64') } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': self.im_size }, 'label': { 'dtype': tf.int64, 'reshape': self.output_size } }
def __init__(self): self.name = 'shapes' self.output_name = 'shapes_held_out_light' self.image_dir = '/media/data_cifs/image_datasets/' self.im_extension = '.png' self.label_regex = r'(?<=length)\d' self.config = Config() self.im_size = [500, 500] self.model_input_image_size = [256, 256, 1] # [107, 160, 3] self.max_ims = None self.output_size = self.model_input_image_size self.label_size = self.output_size self.default_loss_function = 'l2' self.score_metric = 'pearson' self.store_z = False self.normalize_im = False self.shuffle = True self.img_file_ids = ['img1', 'img2', 'img3'] self.test_im = 'img1' # Which label are we using for the test set self.input_normalization = 'none' # 'zscore' self.label_string = 'slant_ms_200_1' self.preprocess = [''] # ['resize_nn'] self.folds = {'train': 'train', 'val': 'val'} self.cv_split = 0.9 self.cv_balance = False self.targets = { 'image': tf_fun.bytes_feature, 'label': tf_fun.bytes_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'label': tf_fun.fixed_len_feature(dtype='string') } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': self.im_size }, 'label': { 'dtype': tf.float32, 'reshape': self.output_size } }
def __init__(self): self.name = 'BSDS_SBD' self.dataset_roots = ['BSDS500', 'SBD'] self.orig_name = 'BSDS_SBD' self.im_extension = '.jpg' self.lab_extension = '.mat' self.images_dir = 'images' self.labels_dir = 'groundTruth' self.processed_labels = 'processed_labels' self.processed_images = 'processed_images' self.config = Config() self.im_size = [321, 481, 3] self.sum_imgs = np.zeros(self.im_size) self.lab_size = (321, 481) #Opposite to convention, opencv standards self.model_input_image_size = [ 150, 240, 3 ] #[321, 481, 3] #[150, 240, 3] # [107, 160, 3] self.output_size = [321, 481, 1] self.label_size = self.output_size self.default_loss_function = 'pearson' self.score_metric = 'pearson' self.aux_scores = ['f1'] self.preprocess = [None] # ['resize_nn'] self.folds = {'train': 'train', 'val': 'val'} self.fold_options = {'train': 'duplicate', 'val': 'mean'} self.targets = { 'image': tf_fun.bytes_feature, 'label': tf_fun.bytes_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'label': tf_fun.fixed_len_feature(dtype='string') } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': self.im_size }, 'label': { 'dtype': tf.float32, 'reshape': self.output_size } }
def __init__(self): self.name = 'multicue_boundaries' self.output_name = 'multicue_boundaries' self.image_dir = '/media/data_cifs/pytorch_projects/datasets/Multicue_crops/data/images' self.im_extension = '.jpg' self.label_regex = r'(?<=length)\d+' self.config = Config() self.im_size = [500, 500, 3] # 600, 600 self.model_input_image_size = [320, 400, 3] # [224, 224, 3] self.val_model_input_image_size = [320, 400, 3] self.output_size = [320, 400, 1] # [321, 481, 1] self.label_size = self.output_size self.default_loss_function = 'cce' self.score_metric = 'accuracy' self.store_z = False self.normalize_im = False self.all_flips = True self.balance = True self.shuffle = True self.calculate_moments = False self.input_normalization = 'none' # 'zscore' self.preprocess = [] self.folds = {'train': 'train', 'val': 'val', 'test': 'test'} self.cv_split = 0.1 self.cv_balance = True self.targets = { 'image': tf_fun.bytes_feature, 'label': tf_fun.bytes_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'label': tf_fun.fixed_len_feature(dtype='string') } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': self.im_size }, 'label': { 'dtype': tf.float32, 'reshape': self.output_size } }
def __init__(self): self.name = 'new_LMD' self.output_name = 'new_LMD' self.kras_dir = '/media/data_cifs/andreas/pathology/2018-04-26/mar2019/LMD/patch_npys' self.im_extension = '.npy' self.label_regex = r'(?<=length)\d+' self.config = Config() self.im_size = [230, 230, 3] # 600, 600 self.model_input_image_size = [200, 200, 3] # [107, 160, 3] self.output_size = [1] self.label_size = self.output_size self.default_loss_function = 'bce' self.score_metric = 'accuracy' self.store_z = False self.normalize_im = False self.all_flips = True self.balance = True self.shuffle = True self.input_normalization = 'none' # 'zscore' self.preprocess = ['rgba2rgb', 'crop_center'] # ['resize_nn'] self.LMD = ['3361805'] self.val_set = self.LMD # self.non_lung_cases_new # self.non_lung_kras_cases_2017 + self.non_lung_non_kras_cases_2017 # + self.non_lung_cases_new self.folds = {'train': 'train', 'val': 'val'} self.cv_split = 0.9 self.cv_balance = True self.targets = { 'image': tf_fun.bytes_feature, 'label': tf_fun.int64_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'label': tf_fun.fixed_len_feature(dtype='int64') } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': self.im_size }, 'label': { 'dtype': tf.int64, 'reshape': self.output_size } }
def __init__(self): self.name = 'contours_gilbert_256_sparse_contrast' self.im_extension = '.png' self.images_dir = 'images' self.label_regex = r'(?<=length)\d+' self.config = Config() self.im_size = [256, 256, 3] # 600, 600 self.model_input_image_size = [256, 256, 3] # [107, 160, 3] self.max_ims = 0 self.output_size = [1] self.label_size = self.output_size self.default_loss_function = 'cce' self.score_metric = 'accuracy' self.store_z = False self.normalize_im = True self.shuffle = True self.input_normalization = 'none' self.preprocess = ['resize'] # ['resize_nn'] self.folds = { 'train': 'train', 'val': 'val' } self.cv_split = 0.9 self.cv_balance = True self.targets = { 'image': tf_fun.bytes_feature, 'label': tf_fun.int64_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'label': tf_fun.fixed_len_feature(dtype='int64') } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': self.im_size }, 'label': { 'dtype': tf.int64, 'reshape': self.output_size } }
def __init__(self): self.name = 'hed_BSDS500' self.output_name = 'hed_BSDS500' self.images_dir = '/media/data_cifs/image_datasets/hed_bsds/HED-BSDS' self.processed_labels = 'processed_labels' self.processed_images = 'processed_images' self.config = Config() self.im_size = [321, 481, 3] # self.model_input_image_size = [196, 196, 3] self.model_input_image_size = [320, 320, 3] # [224, 224, 3] self.val_model_input_image_size = [320, 320, 3] self.output_size = [321, 481, 1] self.label_size = self.output_size self.default_loss_function = 'pearson' self.score_metric = 'sigmoid_accuracy' self.aux_scores = ['f1'] self.store_z = True self.input_normalization = 'none' # 'zscore' self.preprocess = [None] # Preprocessing before tfrecords self.folds = { 'train': 'train', 'val': 'val', 'test': 'test', } self.targets = { 'image': tf_fun.bytes_feature, 'label': tf_fun.bytes_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'label': tf_fun.fixed_len_feature(dtype='string') } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': self.im_size }, 'label': { 'dtype': tf.float32, 'reshape': self.output_size } }
def __init__(self): self.output_name = 'multicue_001_boundaries_jk' self.im_extension = '.jpg' self.lab_extension = '.mat' self.images_dir = '/media/data_cifs/pytorch_projects/datasets/Multicue_crops/data/images/train' self.val_images_dir = '/media/data_cifs/pytorch_projects/datasets/Multicue_crops/data/images/test' self.processed_labels = 'processed_labels' self.processed_images = 'processed_images' self.config = Config() self.train_size = int(760 * 0.01) self.im_size = [500, 500, 3] # [321, 481, 3] self.model_input_image_size = [320, 320, 3] # [224, 224, 3] self.val_model_input_image_size = [320, 320, 3] self.output_size = [500, 500, 1] # [321, 481, 1] self.label_size = self.output_size self.default_loss_function = 'pearson' self.score_metric = 'sigmoid_accuracy' self.aux_scores = ['f1'] self.store_z = False self.input_normalization = 'none' # 'zscore' self.preprocess = [None] # Preprocessing before tfrecords self.folds = {'train': 'train', 'val': 'val'} self.fold_options = {'train': 'mean', 'val': 'mean'} self.targets = { 'image': tf_fun.bytes_feature, 'label': tf_fun.bytes_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'label': tf_fun.fixed_len_feature(dtype='string') } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': self.im_size }, 'label': { 'dtype': tf.float32, 'reshape': self.output_size } }
def __init__(self): self.name = 'sheinberg_data_noise_subtracted' self.data_name = 'sheinberg_data' self.config = Config() self.output_size = [1, 1] self.im_size = [192, 256, 3] self.model_input_image_size = [192, 256, 3] self.num_rf_images = 2000 self.default_loss_function = 'l2' self.score_metric = 'l2' self.preprocess = [None] self.im_ext = '.jpg' self.im_folder = 'scene_images' self.neural_data = 'spike' # 'spike' self.val_set = -76 self.save_npys = True self.num_channels = 33 # 32 with indexing from 1 self.dates = ['100614', '100714', '100814', '100914'] # Recording starts 200msec before onset. # Target is 50 - 150ms. = 270 - 370. self.spike_range = [250, 350] self.resize = [192, 256] self.folds = {'train': 'train', 'test': 'test'} self.targets = { 'image': tf_fun.bytes_feature, 'label': tf_fun.float_feature } self.tf_dict = { 'image': tf_fun.fixed_len_feature(dtype='string'), 'label': tf_fun.fixed_len_feature(dtype='float') } self.tf_reader = { 'image': { 'dtype': tf.float32, 'reshape': None }, 'label': { 'dtype': tf.float32, 'reshape': None } }