Corrected identation

example/kaggle-ndsb1/gen_img_list.py

@@ -6,7 +6,6 @@
 import argparse
 
 parser = argparse.ArgumentParser(description='generate train/test image list files form input directory. If training it will also split into tr and va sets.')
-
 parser.add_argument('--image-folder', type=str, default="data/train/",
                     help='the input data directory')
 parser.add_argument('--out-folder', type=str, default="data/",
@@ -22,18 +21,16 @@
                     help='if True it will split train lst into tr and va sets using stratified sampling')
 args = parser.parse_args()
 
-
 random.seed(888)
 
-
 fo_name=os.path.join(args.out_folder+args.out_file)
 fo = csv.writer(open(fo_name, "w"), delimiter='\t', lineterminator='\n')
-
+    
 if args.train:
- tr_fo_name=os.path.join(args.out_folder+"tr.lst")
- va_fo_name=os.path.join(args.out_folder+"va.lst")
- tr_fo = csv.writer(open(tr_fo_name, "w"), delimiter='\t', lineterminator='\n')
- va_fo = csv.writer(open(va_fo_name, "w"), delimiter='\t', lineterminator='\n')
+    tr_fo_name=os.path.join(args.out_folder+"tr.lst")
+    va_fo_name=os.path.join(args.out_folder+"va.lst")
+    tr_fo = csv.writer(open(tr_fo_name, "w"), delimiter='\t', lineterminator='\n')
+    va_fo = csv.writer(open(va_fo_name, "w"), delimiter='\t', lineterminator='\n')
 
 #check sampleSubmission.csv from kaggle website to view submission format
 head = "acantharia_protist_big_center,acantharia_protist_halo,acantharia_protist,amphipods,appendicularian_fritillaridae,appendicularian_s_shape,appendicularian_slight_curve,appendicularian_straight,artifacts_edge,artifacts,chaetognath_non_sagitta,chaetognath_other,chaetognath_sagitta,chordate_type1,copepod_calanoid_eggs,copepod_calanoid_eucalanus,copepod_calanoid_flatheads,copepod_calanoid_frillyAntennae,copepod_calanoid_large_side_antennatucked,copepod_calanoid_large,copepod_calanoid_octomoms,copepod_calanoid_small_longantennae,copepod_calanoid,copepod_cyclopoid_copilia,copepod_cyclopoid_oithona_eggs,copepod_cyclopoid_oithona,copepod_other,crustacean_other,ctenophore_cestid,ctenophore_cydippid_no_tentacles,ctenophore_cydippid_tentacles,ctenophore_lobate,decapods,detritus_blob,detritus_filamentous,detritus_other,diatom_chain_string,diatom_chain_tube,echinoderm_larva_pluteus_brittlestar,echinoderm_larva_pluteus_early,echinoderm_larva_pluteus_typeC,echinoderm_larva_pluteus_urchin,echinoderm_larva_seastar_bipinnaria,echinoderm_larva_seastar_brachiolaria,echinoderm_seacucumber_auricularia_larva,echinopluteus,ephyra,euphausiids_young,euphausiids,fecal_pellet,fish_larvae_deep_body,fish_larvae_leptocephali,fish_larvae_medium_body,fish_larvae_myctophids,fish_larvae_thin_body,fish_larvae_very_thin_body,heteropod,hydromedusae_aglaura,hydromedusae_bell_and_tentacles,hydromedusae_h15,hydromedusae_haliscera_small_sideview,hydromedusae_haliscera,hydromedusae_liriope,hydromedusae_narco_dark,hydromedusae_narco_young,hydromedusae_narcomedusae,hydromedusae_other,hydromedusae_partial_dark,hydromedusae_shapeA_sideview_small,hydromedusae_shapeA,hydromedusae_shapeB,hydromedusae_sideview_big,hydromedusae_solmaris,hydromedusae_solmundella,hydromedusae_typeD_bell_and_tentacles,hydromedusae_typeD,hydromedusae_typeE,hydromedusae_typeF,invertebrate_larvae_other_A,invertebrate_larvae_other_B,jellies_tentacles,polychaete,protist_dark_center,protist_fuzzy_olive,protist_noctiluca,protist_other,protist_star,pteropod_butterfly,pteropod_theco_dev_seq,pteropod_triangle,radiolarian_chain,radiolarian_colony,shrimp_caridean,shrimp_sergestidae,shrimp_zoea,shrimp-like_other,siphonophore_calycophoran_abylidae,siphonophore_calycophoran_rocketship_adult,siphonophore_calycophoran_rocketship_young,siphonophore_calycophoran_sphaeronectes_stem,siphonophore_calycophoran_sphaeronectes_young,siphonophore_calycophoran_sphaeronectes,siphonophore_other_parts,siphonophore_partial,siphonophore_physonect_young,siphonophore_physonect,stomatopod,tornaria_acorn_worm_larvae,trichodesmium_bowtie,trichodesmium_multiple,trichodesmium_puff,trichodesmium_tuft,trochophore_larvae,tunicate_doliolid_nurse,tunicate_doliolid,tunicate_partial,tunicate_salp_chains,tunicate_salp,unknown_blobs_and_smudges,unknown_sticks,unknown_unclassified".split(',')
@@ -59,7 +56,7 @@
 
 #write
 for item in img_lst:
-        fo.writerow(item)
+    fo.writerow(item)
 
 
 

example/kaggle-ndsb1/training_curves.py

@@ -1,15 +1,13 @@
 ## based on https://github.com/dmlc/mxnet/issues/1302
 ## Parses the model fit log file and generates a train/val vs epoch plot
-
-
 import matplotlib.pyplot as plt
 import numpy as np
 import re
 import argparse
 
 parser = argparse.ArgumentParser(description='Parses log file and generates train/val curves')
-parser.add_argument('--log-file', type=str,default="log_tr_va", 
-		    help='the path of log file')
+parser.add_argument('--log-file', type=str,default="log_tr_va",
+                    help='the path of log file')
 args = parser.parse_args()
 
 
@@ -26,10 +24,10 @@
 plt.xlabel("Epoch")
 plt.ylabel("Accuracy")
 plt.plot(idx, log_tr, 'o', linestyle='-', color="r",
-                      label="Train accuracy")
+         label="Train accuracy")
 
 plt.plot(idx, log_va, 'o', linestyle='-', color="b",
-                      label="Validation accuracy")
+         label="Validation accuracy")
 
 plt.legend(loc="best")
 plt.xticks(np.arange(min(idx), max(idx)+1, 5))