wehub-resource-sync
162 lines
18 KiB
Plaintext
162 lines
18 KiB
Plaintext
---
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title: fastai Abbreviation Guide
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---
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As mentioned in the [fastai style](style.html),
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we name symbols following the *Huffman Coding* principle, which basically means
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> Commonly used and generic concepts should be named shorter. You shouldn't waste short sequences on less common concepts.
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fastai also follows the life-cycle naming principle:
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> The shorter life a symbol, the shorter name it should have.
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which means:
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- **Aggressive Abbreviations** are used in *list comprehensions*, *lambda* functions, *local helper* functions.
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- **Aggressive Abbreviations** are sometimes used for *local temporary variables* inside a function.
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- **Common Abbreviations** are used most elsewhere, especially for *function arguments*, *function names*, and *variables*
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- **Light or No Abbreviations** are used for *module names*, *class names* or *constructor methods*, since they basically live forever.
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However, when a class or module is very popular, we could consider using abbreviations to shorten its name.
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This document lists abbreviations of common concepts that are consistently used across the whole fastai project. For naming of
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domain-specific concepts, you should check their corresponding module documentations. Concepts are grouped and listed by semantic order.
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Note that there are always exceptions, especially when we try to comply with the naming convention in a library.
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| | **Concept** | **Abbr.** | **Combination Examples** |
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|------------------|-------------------------------------|----------------|--------------------------------------------------|
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| **Suffix** | | | |
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| | multiple of something (plural) | s | xs, ys, tfms, args, ss |
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| | internal property or method | _ | data_, V_() |
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| **Prefix** | | | |
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| | check if satisfied | is_ | is_reg, is_multi, is_single, is_test, is_correct |
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| | On/off a feature | use_ | use_bn |
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| | Number of something (plural) | n_ | n_embs, n_factors, n_users, n_items |
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| | count something | num_ | num_features(), num_gpus() |
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| | convert to something | to_ | to_gpu(), to_cpu(), to_np() |
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| **Infix** | | | |
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| | Convert between concepts | 2 | name2idx(), label2idx(), seq2seq |
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| **Aggressive** | | | |
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| | function | f | |
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| | torch input | x | |
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| | key, value | k,v | for k,v in d.items() |
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| | other pairs of short scope | p,q | listify(p,q) (same as python's stdlib) |
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| | index | i | |
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| | generic object argument | o | [o for o in list], lambda o: o |
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| | variable | v | V(), VV() |
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| | tensor | t | T() |
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| | array | a | A() |
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| | use first letter | | *w*eight -> w, *m*odel -> m |
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| **Generic** | | | |
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| | function | fn | opt_fn, init_fn, reg_fn |
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| | process | proc | proc_col |
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| | transform | tfm | tfm_y, TfmType |
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| | evaluate | eval | eval() |
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| | | | |
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| | argument | arg | |
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| | input | x | |
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| | input / output | io | |
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| | object | obj | |
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| | string | s | |
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| | class | cl | cl, classes |
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| | source | src | |
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| | destination | dst | |
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| | directory | dir | |
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| | percentage | p | |
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| | ratio, proportion of something | r | |
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| | count | cnt | |
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| | | | |
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| | configuration | cfg | |
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| | random | rand | |
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| | utility | util | |
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| | filename | fname | |
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| | | | |
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| | threshold | thresh | |
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| **Data** | | | |
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| | number of elements | n | |
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| | length | len | |
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| | size | sz | |
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| | array | arr | label_arr |
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| | dictionary | dict | |
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| | sequence | seq | |
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| | dataset | ds | train_ds |
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| | dataloader | dl | train_dl |
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| | dataframe | df | train_df |
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| | train | train | train_ds, train_dl, train_x, train_y |
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| | validation | valid | valid_ds, valid_dl, valid_x, valid_y |
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| | test | test | test_ds, test_dl |
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| | number of classes | c | |
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| | batch | b | |
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| | batch's x parts | xb | |
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| | batch's y parts | yb | |
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| | batch size | bs | |
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| | multiple targets | multi | is_multi |
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| | regression | reg | is_reg |
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| | iterate, iterator | iter | train_iter, valid_iter |
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| | | | |
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| | torch input | x | |
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| | target | y | |
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| | dependent var tensor | dep | |
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| | independent var tensor | indep | |
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| | prediction | pred | |
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| | output | out | |
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| | column | col | dep_col |
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| | continuous var | cont | conts |
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| | category var | cat | cat, cats |
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| | continuous columns | cont_cols | |
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| | category columns | cat_cols | |
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| | dependent column | dep_col | |
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| | | | |
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| | index | idx | |
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| | identity | id | |
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| | first element | head | |
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| | last element | tail | |
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| | unique | uniq | |
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| | residual | res | |
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| | label | lbl | (not common) |
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| | augment | aug | |
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| | padding | pad | |
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| | probability | pr | |
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| | image | img | |
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| | rectangle | rect | |
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| | color | colr | |
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| | anchor box | anc | |
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| | bounding box | bb | |
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| **Modeling** | | | |
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| | initialize | init | |
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| | language model | lm | |
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| | recurrent neural network | rnn | |
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| | convolutional neural network | convnet | |
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| | model data | md | |
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| | linear | lin | |
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| | embedding | emb | |
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| | batch norm | bn | |
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| | dropout | drop | |
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| | fully connected | fc | |
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| | convolution | conv | |
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| | hidden | hid | |
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| | optimizer (e.g. Adam) | opt | |
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| | layer group learning rate optimizer | layer_opt | |
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| | criteria | crit | |
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| | weight decay | wd | |
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| | momentum | mom | |
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| | cross validation | cv | |
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| | learning rate | lr | |
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| | schedule | sched | |
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| | cycle length | cl | |
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| | multiplier | mult | |
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| | activation | actn | |
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| **CV** | computer vision | | |
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| | figure | fig | |
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| | image | im | |
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| | transform image using opencv | _cv | zoom_cv(), rotate_cv(), stretch_cv() |
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| **NLP** | natural language processing (nlp) | | |
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| | token | tok | |
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| | sequence length | sl | |
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| | back propagation through time | bptt | |
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