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https://github.com/qurator-spk/eynollah.git
synced 2025-10-06 14:39:55 +02:00
remove redundant parentheses
This commit is contained in:
cneud
parent
f2f93e0251
commit
91d2a74ac9
10 changed files with 29 additions and 29 deletions
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@ -4886,9 +4886,9 @@ class Eynollah:
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textline_mask_tot_ea_org[img_revised_tab==drop_label_in_full_layout] = 0
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text_only = ((img_revised_tab[:, :] == 1)) * 1
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text_only = (img_revised_tab[:, :] == 1) * 1
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if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
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text_only_d = ((text_regions_p_1_n[:, :] == 1)) * 1
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text_only_d = (text_regions_p_1_n[:, :] == 1) * 1
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#print("text region early 2 in %.1fs", time.time() - t0)
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###min_con_area = 0.000005
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@ -12,7 +12,7 @@ from .utils import crop_image_inside_box
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from .utils.rotate import rotate_image_different
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from .utils.resize import resize_image
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class EynollahPlotter():
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class EynollahPlotter:
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"""
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Class collecting all the plotting and image writing methods
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"""
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@ -1267,11 +1267,11 @@ def order_of_regions(textline_mask, contours_main, contours_header, y_ref):
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top = peaks_neg_new[i]
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down = peaks_neg_new[i + 1]
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indexes_in = matrix_of_orders[:, 0][(matrix_of_orders[:, 3] >= top) &
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((matrix_of_orders[:, 3] < down))]
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(matrix_of_orders[:, 3] < down)]
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cxs_in = matrix_of_orders[:, 2][(matrix_of_orders[:, 3] >= top) &
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((matrix_of_orders[:, 3] < down))]
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(matrix_of_orders[:, 3] < down)]
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cys_in = matrix_of_orders[:, 3][(matrix_of_orders[:, 3] >= top) &
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((matrix_of_orders[:, 3] < down))]
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(matrix_of_orders[:, 3] < down)]
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types_of_text = matrix_of_orders[:, 1][(matrix_of_orders[:, 3] >= top) &
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(matrix_of_orders[:, 3] < down)]
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index_types_of_text = matrix_of_orders[:, 4][(matrix_of_orders[:, 3] >= top) &
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@ -1408,7 +1408,7 @@ def return_points_with_boundies(peaks_neg_fin, first_point, last_point):
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def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables, pixel_lines, contours_h=None):
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t_ins_c0 = time.time()
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separators_closeup=( (region_pre_p[:,:,:]==pixel_lines))*1
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separators_closeup= (region_pre_p[:, :, :] == pixel_lines) * 1
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separators_closeup[0:110,:,:]=0
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separators_closeup[separators_closeup.shape[0]-150:,:,:]=0
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@ -3,7 +3,7 @@ from collections import Counter
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REGION_ID_TEMPLATE = 'region_%04d'
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LINE_ID_TEMPLATE = 'region_%04d_line_%04d'
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class EynollahIdCounter():
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class EynollahIdCounter:
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def __init__(self, region_idx=0, line_idx=0):
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self._counter = Counter()
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@ -76,7 +76,7 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, light_ve
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peaks, _ = find_peaks(text_with_lines_y_rev, height=0)
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peaks=np.array(peaks)
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peaks=peaks[(peaks>first_nonzero) & ((peaks<last_nonzero))]
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peaks=peaks[(peaks>first_nonzero) & (peaks < last_nonzero)]
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peaks=peaks[region_sum_0[peaks]<min_textline_thickness ]
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@ -1428,9 +1428,9 @@ def separate_lines_new2(img_path, thetha, num_col, slope_region, logger=None, pl
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img_int = np.zeros((img_xline.shape[0], img_xline.shape[1]))
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img_int[:, :] = img_xline[:, :] # img_patch_org[:,:,0]
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img_resized = np.zeros((int(img_int.shape[0] * (1.2)), int(img_int.shape[1] * (3))))
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img_resized[int(img_int.shape[0] * (0.1)) : int(img_int.shape[0] * (0.1)) + img_int.shape[0],
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int(img_int.shape[1] * (1.0)) : int(img_int.shape[1] * (1.0)) + img_int.shape[1]] = img_int[:, :]
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img_resized = np.zeros((int(img_int.shape[0] * 1.2), int(img_int.shape[1] * 3)))
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img_resized[int(img_int.shape[0] * 0.1): int(img_int.shape[0] * 0.1) + img_int.shape[0],
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int(img_int.shape[1] * 1.0): int(img_int.shape[1] * 1.0) + img_int.shape[1]] = img_int[:, :]
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# plt.imshow(img_xline)
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# plt.show()
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img_line_rotated = rotate_image(img_resized, slopes_tile_wise[i])
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@ -1442,8 +1442,8 @@ def separate_lines_new2(img_path, thetha, num_col, slope_region, logger=None, pl
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img_patch_separated_returned[:, :][img_patch_separated_returned[:, :] != 0] = 1
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img_patch_separated_returned_true_size = img_patch_separated_returned[
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int(img_int.shape[0] * (0.1)) : int(img_int.shape[0] * (0.1)) + img_int.shape[0],
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int(img_int.shape[1] * (1.0)) : int(img_int.shape[1] * (1.0)) + img_int.shape[1]]
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int(img_int.shape[0] * 0.1): int(img_int.shape[0] * 0.1) + img_int.shape[0],
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int(img_int.shape[1] * 1.0): int(img_int.shape[1] * 1.0) + img_int.shape[1]]
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img_patch_separated_returned_true_size = img_patch_separated_returned_true_size[:, margin : length_x - margin]
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img_patch_ineterst_revised[:, index_x_d + margin : index_x_u - margin] = img_patch_separated_returned_true_size
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@ -1471,7 +1471,7 @@ def return_deskew_slop(img_patch_org, sigma_des,n_tot_angles=100,
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img_int[:,:]=img_patch_org[:,:]#img_patch_org[:,:,0]
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max_shape=np.max(img_int.shape)
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img_resized=np.zeros((int( max_shape*(1.1) ) , int( max_shape*(1.1) ) ))
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img_resized=np.zeros((int(max_shape * 1.1) , int(max_shape * 1.1)))
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onset_x=int((img_resized.shape[1]-img_int.shape[1])/2.)
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onset_y=int((img_resized.shape[0]-img_int.shape[0])/2.)
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@ -1536,7 +1536,7 @@ def return_deskew_slop_old_mp(img_patch_org, sigma_des,n_tot_angles=100,
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img_int[:,:]=img_patch_org[:,:]#img_patch_org[:,:,0]
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max_shape=np.max(img_int.shape)
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img_resized=np.zeros((int( max_shape*(1.1) ) , int( max_shape*(1.1) ) ))
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img_resized=np.zeros((int(max_shape * 1.1) , int(max_shape * 1.1)))
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onset_x=int((img_resized.shape[1]-img_int.shape[1])/2.)
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onset_y=int((img_resized.shape[0]-img_int.shape[0])/2.)
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@ -21,7 +21,7 @@ from ocrd_models.ocrd_page import (
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)
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import numpy as np
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class EynollahXmlWriter():
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class EynollahXmlWriter:
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def __init__(self, *, dir_out, image_filename, curved_line,textline_light, pcgts=None):
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self.logger = getLogger('eynollah.writer')
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@ -173,7 +173,7 @@ class sbb_predict:
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##if self.weights_dir!=None:
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##self.model.load_weights(self.weights_dir)
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if (self.task != 'classification' and self.task != 'reading_order'):
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if self.task != 'classification' and self.task != 'reading_order':
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self.img_height=self.model.layers[len(self.model.layers)-1].output_shape[1]
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self.img_width=self.model.layers[len(self.model.layers)-1].output_shape[2]
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self.n_classes=self.model.layers[len(self.model.layers)-1].output_shape[3]
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@ -560,7 +560,7 @@ class sbb_predict:
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if self.image:
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res=self.predict(image_dir = self.image)
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if (self.task == 'classification' or self.task == 'reading_order'):
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if self.task == 'classification' or self.task == 'reading_order':
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pass
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elif self.task == 'enhancement':
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if self.save:
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@ -583,7 +583,7 @@ class sbb_predict:
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image_dir = os.path.join(self.dir_in, ind_image)
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res=self.predict(image_dir)
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if (self.task == 'classification' or self.task == 'reading_order'):
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if self.task == 'classification' or self.task == 'reading_order':
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pass
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elif self.task == 'enhancement':
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self.save = os.path.join(self.out, f_name+'.png')
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@ -664,7 +664,7 @@ def main(image, dir_in, model, patches, save, save_layout, ground_truth, xml_fil
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with open(os.path.join(model,'config.json')) as f:
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config_params_model = json.load(f)
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task = config_params_model['task']
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if (task != 'classification' and task != 'reading_order'):
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if task != 'classification' and task != 'reading_order':
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if image and not save:
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print("Error: You used one of segmentation or binarization task with image input but not set -s, you need a filename to save visualized output with -s")
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sys.exit(1)
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@ -269,10 +269,10 @@ def run(_config, n_classes, n_epochs, input_height,
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num_patches = num_patches_x * num_patches_y
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if transformer_cnn_first:
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if (input_height != (num_patches_y * transformer_patchsize_y * 32) ):
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if input_height != (num_patches_y * transformer_patchsize_y * 32):
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print("Error: transformer_patchsize_y or transformer_num_patches_xy height value error . input_height should be equal to ( transformer_num_patches_xy height value * transformer_patchsize_y * 32)")
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sys.exit(1)
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if (input_width != (num_patches_x * transformer_patchsize_x * 32) ):
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if input_width != (num_patches_x * transformer_patchsize_x * 32):
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print("Error: transformer_patchsize_x or transformer_num_patches_xy width value error . input_width should be equal to ( transformer_num_patches_xy width value * transformer_patchsize_x * 32)")
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sys.exit(1)
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if (transformer_projection_dim % (transformer_patchsize_y * transformer_patchsize_x)) != 0:
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@ -282,10 +282,10 @@ def run(_config, n_classes, n_epochs, input_height,
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model = vit_resnet50_unet(n_classes, transformer_patchsize_x, transformer_patchsize_y, num_patches, transformer_mlp_head_units, transformer_layers, transformer_num_heads, transformer_projection_dim, input_height, input_width, task, weight_decay, pretraining)
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else:
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if (input_height != (num_patches_y * transformer_patchsize_y) ):
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if input_height != (num_patches_y * transformer_patchsize_y):
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print("Error: transformer_patchsize_y or transformer_num_patches_xy height value error . input_height should be equal to ( transformer_num_patches_xy height value * transformer_patchsize_y)")
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sys.exit(1)
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if (input_width != (num_patches_x * transformer_patchsize_x) ):
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if input_width != (num_patches_x * transformer_patchsize_x):
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print("Error: transformer_patchsize_x or transformer_num_patches_xy width value error . input_width should be equal to ( transformer_num_patches_xy width value * transformer_patchsize_x)")
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sys.exit(1)
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if (transformer_projection_dim % (transformer_patchsize_y * transformer_patchsize_x)) != 0:
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@ -297,7 +297,7 @@ def run(_config, n_classes, n_epochs, input_height,
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model.summary()
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if (task == "segmentation" or task == "binarization"):
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if task == "segmentation" or task == "binarization":
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if not is_loss_soft_dice and not weighted_loss:
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model.compile(loss='categorical_crossentropy',
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optimizer=Adam(learning_rate=learning_rate), metrics=['accuracy'])
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@ -260,7 +260,7 @@ def generate_data_from_folder_training(path_classes, batchsize, height, width, n
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if batchcount>=batchsize:
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ret_x = ret_x/255.
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yield (ret_x, ret_y)
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yield ret_x, ret_y
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ret_x= np.zeros((batchsize, height,width, 3)).astype(np.int16)
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ret_y= np.zeros((batchsize, n_classes)).astype(np.int16)
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batchcount = 0
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@ -446,7 +446,7 @@ def generate_arrays_from_folder_reading_order(classes_file_dir, modal_dir, batch
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ret_y[batchcount, :] = label_class
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batchcount+=1
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if batchcount>=batchsize:
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yield (ret_x, ret_y)
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yield ret_x, ret_y
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ret_x= np.zeros((batchsize, height, width, 3))#.astype(np.int16)
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ret_y= np.zeros((batchsize, n_classes)).astype(np.int16)
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batchcount = 0
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@ -464,7 +464,7 @@ def generate_arrays_from_folder_reading_order(classes_file_dir, modal_dir, batch
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ret_y[batchcount, :] = label_class
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batchcount+=1
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if batchcount>=batchsize:
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yield (ret_x, ret_y)
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yield ret_x, ret_y
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ret_x= np.zeros((batchsize, height, width, 3))#.astype(np.int16)
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ret_y= np.zeros((batchsize, n_classes)).astype(np.int16)
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batchcount = 0
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