def __init__(self, feat_extract_name , n_processes, low_pass, high_pass, gauss_noise, roi, size_percentage,\
feature_extractor__shape_norm, feature_extractor__shape_conv, \
feature_extractor__shape_pool, feature_extractor__n_filters, \
feature_extractor__stride_pool, feature_extractor__stoc_pool, \
feature_extractor__div_norm, feature_extractor__region_shape, \
feature_extractor__region_stride, feature_extractor__top_regions, \
feature_extractor__stride_pool_recurrent, feature_extractor__analysis_shape, \
feature_extractor__method, \
feature_extractor__n_tiles, augmentation, multi_column, aug_rotate \
):
self.low_pass = low_pass
self.high_pass = high_pass
self.gauss_noise = gauss_noise
self.roi = roi
self.size_percentage = size_percentage
self.augmentation = augmentation
self.aug_rotate = aug_rotate
self.multi_column = multi_column
self.feat_extract_name = feat_extract_name
self.n_processes = n_processes
if feat_extract_name.lower() == 'convnet':
self.feature_extractor = eval(feat_extract_name + '()')
self.feature_extractor.n_filters = feature_extractor__n_filters
self.feature_extractor.shape_norm = feature_extractor__shape_norm
self.feature_extractor.shape_conv = feature_extractor__shape_conv
self.feature_extractor.shape_pool = feature_extractor__shape_pool
self.feature_extractor.stride_pool = feature_extractor__stride_pool
self.feature_extractor.div_norm = feature_extractor__div_norm
self.feature_extractor.stoc_pool = feature_extractor__stoc_pool
elif feat_extract_name.lower() == 'mrrconvnet':
self.feature_extractor = eval(feat_extract_name + '()')
convnet = ConvNet()
convnet.n_filters = feature_extractor__n_filters
convnet.shape_norm = feature_extractor__shape_norm
convnet.shape_conv = feature_extractor__shape_conv
convnet.shape_pool = feature_extractor__shape_pool
convnet.stride_pool = feature_extractor__stride_pool
convnet.div_norm = feature_extractor__div_norm
convnet.stoc_pool = feature_extractor__stoc_pool
self.feature_extractor.convnet = convnet
self.feature_extractor.region_shape = feature_extractor__region_shape
self.feature_extractor.region_stride = feature_extractor__region_stride
self.feature_extractor.top_regions = feature_extractor__top_regions
self.feature_extractor.stride_pool_recurrent = feature_extractor__stride_pool_recurrent
self.feature_extractor.analysis_shape = feature_extractor__analysis_shape
elif feat_extract_name.lower() == 'lbp':
self.feature_extractor = eval(feat_extract_name + '()')
self.feature_extractor.method = feature_extractor__method
self.feature_extractor.n_tiles = feature_extractor__n_tiles
def __init__(self, feat_extract_name , n_processes, low_pass, high_pass, gauss_noise, roi, size_percentage,\
feature_extractor__shape_norm, feature_extractor__shape_conv, \
feature_extractor__shape_pool, feature_extractor__n_filters, \
feature_extractor__stride_pool, feature_extractor__stoc_pool, \
feature_extractor__div_norm, feature_extractor__region_shape, \
feature_extractor__region_stride, feature_extractor__top_regions, \
feature_extractor__stride_pool_recurrent, feature_extractor__analysis_shape, \
feature_extractor__method, \
feature_extractor__n_tiles, augmentation, multi_column, aug_rotate \
):
self.low_pass = low_pass
self.high_pass = high_pass
self.gauss_noise = gauss_noise
self.roi = roi
self.size_percentage = size_percentage
self.augmentation = augmentation
self.aug_rotate = aug_rotate
self.multi_column = multi_column
self.feat_extract_name = feat_extract_name
self.n_processes = n_processes
if feat_extract_name.lower() == 'convnet':
self.feature_extractor = eval(feat_extract_name+'()')
self.feature_extractor.n_filters = feature_extractor__n_filters
self.feature_extractor.shape_norm = feature_extractor__shape_norm
self.feature_extractor.shape_conv = feature_extractor__shape_conv
self.feature_extractor.shape_pool = feature_extractor__shape_pool
self.feature_extractor.stride_pool = feature_extractor__stride_pool
self.feature_extractor.div_norm = feature_extractor__div_norm
self.feature_extractor.stoc_pool = feature_extractor__stoc_pool
elif feat_extract_name.lower() == 'mrrconvnet':
self.feature_extractor = eval(feat_extract_name+'()')
convnet = ConvNet()
convnet.n_filters = feature_extractor__n_filters
convnet.shape_norm = feature_extractor__shape_norm
convnet.shape_conv = feature_extractor__shape_conv
convnet.shape_pool = feature_extractor__shape_pool
convnet.stride_pool = feature_extractor__stride_pool
convnet.div_norm = feature_extractor__div_norm
convnet.stoc_pool = feature_extractor__stoc_pool
self.feature_extractor.convnet = convnet
self.feature_extractor.region_shape = feature_extractor__region_shape
self.feature_extractor.region_stride = feature_extractor__region_stride
self.feature_extractor.top_regions = feature_extractor__top_regions
self.feature_extractor.stride_pool_recurrent = feature_extractor__stride_pool_recurrent
self.feature_extractor.analysis_shape =feature_extractor__analysis_shape
elif feat_extract_name.lower() == 'lbp':
self.feature_extractor = eval(feat_extract_name+'()')
self.feature_extractor.method = feature_extractor__method
self.feature_extractor.n_tiles = feature_extractor__n_tiles
