what the f

.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto

code/config.py

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+#%%
+'''
+Learning Attributes:
+	- Color (6)
+	- Material (3)
+	- Shape (8)
+Additional Attributes:
+	- Color (2)
+	- Material (1)
+	- Shape (3)
+Flexibility:
+	- Camera angle (6)
+	- Lighting (3)
+Variability (Only in testing):
+	- Size (2) 		[Default: large]
+	- Stretch (3) 	[Default: normal]
+	- Color shade (2) [Default: base]
+
+Naming convension:
+[color]_[material]_[shape]_shade_[]_stretch_[]_scale_[]_brightness_view_[]_[tyimg].png
+e.g.
+aqua_glass_cone_shade_base_stretch_normal_scale_large_brightness_bright_view_0_-2_3_rgba.png
+'''
+# Learning attributes:
+colors = ['brown', "green", "blue", "aqua", "purple", "red", "yellow", 'white']
+materials = ['rubber', 'metal', 'plastic', 'glass']
+shapes = ["cube", "cylinder", "sphere", "cone", "torus", "gear",
+			"torus_knot", "sponge", "spot", "teapot", "suzanne"]
+vocabs = colors+materials+shapes
+
+
+# Flexibility:
+views = ['0_3_2', '-2_-2_2', '-2_2_2',  '1.5_-1.5_3', '1.5_1.5_3', '0_-2_3']
+brightness = ['dim', 'normal', 'bright']
+
+# Variability
+scale_train = ['large']
+stretch_train = ['normal']
+shade_train = ['base']
+
+scale_test = ['small', 'medium', 'large']
+stretch_test = ['normal', 'x', 'y', 'z']
+shade_test = ['base', 'light', 'dark']
+
+others = views + brightness + scale_test + stretch_test + shade_test
+
+# Types of images
+tyimgs = ['rgba', 'depth', 'normal', 'object_coordinates', 'segmentation']
+
+
+dic_train = {"color": colors,
+			"material": materials,
+			"shape": shapes,
+			"view": views,
+			'brightness': brightness,
+			"scale": scale_train,
+			'stretch': stretch_train,
+			'shade': shade_train
+			}
+dic_test = {"color": colors,
+			"material": materials,
+			"shape": shapes,
+			"view": views,
+			'brightness': brightness,
+			"scale": scale_test,
+			'stretch': stretch_test,
+			'shade': shade_test
+			}
+
+types_learning = ['color', 'material', 'shape']
+types_flebility = ['color', 'material', 'shape', 'brightness', 'view']
+types_variability = ['scale', 'stretch', 'shade']
+types_all = ['color', 'material', 'shape', 'brightness',
+				'view', 'shade', 'stretch', 'scale']
+
+# make dicts for logical traing and testing
+relations = ['and', 'or', 'not'] # <--- new 
+types_logical = [] 
+for i in types_learning:
+	for j in relations:
+		if j == 'not':
+			types_logical.append(j+' '+i)
+		else:
+			for h in types_learning:
+				if h+' '+j+' '+i not in types_logical:
+					if j == 'and' and i == h:
+						pass
+					else:
+						types_logical.append(i+' '+j+' '+h)
+types_logical_with_learning =  types_logical + types_learning 
+
+from itertools import product
+from pprint import pprint
+
+dic_train_logical = dic_train.copy()
+for rel in types_logical:
+	if rel.split(' ')[0] == 'not':
+		attr = rel.split(' ')[1]
+		dic_train_logical[rel] = [f'not {x}' for x in dic_train[attr]]
+	else:
+		attr1 = rel.split(' ')[0]
+		r = rel.split(' ')[1]
+		attr2 = rel.split(' ')[2]
+		dic_train_logical[rel] = [f'{x} {r} {y}' for x, y in product(dic_train[attr1], dic_train[attr2]) if x != y]
+
+dic_test_logical = dic_train_logical.copy()
+dic_test_logical["scale"] = dic_test["scale"]
+dic_test_logical["stretch"] = dic_test["stretch"]
+dic_test_logical["shade"] = dic_test["shade"]
+
+
+
+all_vocabs = []
+for v in dic_train_logical.values():
+	for n in v:
+		if n not in others:
+			all_vocabs.append(n)
+
+# count n of concepts
+
+types_logical_with_learning_1 = types_logical_with_learning[0:2]
+print(types_logical_with_learning_1)
+types_logical_with_learning_2 = types_logical_with_learning[2:4]
+print(types_logical_with_learning_2)
+types_logical_with_learning_3 = types_logical_with_learning[4:6]
+print(types_logical_with_learning_3)
+types_logical_with_learning_4 = types_logical_with_learning[6:8]
+print(types_logical_with_learning_4)
+types_logical_with_learning_5 = types_logical_with_learning[8:10]
+print(types_logical_with_learning_5)
+types_logical_with_learning_6 = types_logical_with_learning[10:12] 
+print(types_logical_with_learning_6)
+types_logical_with_learning_7 = types_logical_with_learning[12:]
+print(types_logical_with_learning_7)
+
+#print(all_vocabs)
+#pprint(dic_train_logical)
+#print(types_logical_with_learning)
+
+# paths and filenames
+bn_n_train = "bn_n_train.txt"
+bsn_novel_train_1 = "bsn_novel_train_1.txt"
+bsn_novel_train_2 = "bsn_novel_train_2.txt"
+bsn_novel_train_2_nw = "bsn_novel_train_2_nw.txt"
+bsn_novel_train_2_old = "bsn_novel_train_2_old.txt"
+
+bn_n_test = "bn_n_test.txt"
+bsn_novel_test_1 = "bsn_novel_test_1.txt"
+bsn_novel_test_2_nw = "bsn_novel_test_2_nw.txt"
+bsn_novel_test_2_old = "bsn_novel_test_2_old.txt"
+
+bn_train = "bn_train.txt"
+bn_test = "bn_test.txt"
+bsn_test_1 = "bsn_test_1.txt"
+bsn_test_2_nw = "bsn_test_2_nw.txt"
+bsn_test_2_old = "bsn_test_2_old.txt"
+
+# train parameters
+resize = 224
+lr = 1e-3
+epochs = 1
+
+sim_batch = 132
+gen_batch = 132
+batch_size = 33
+
+# model architecture
+hidden_dim_clip = 128
+latent_dim = 16