* factor out data related constants to own file
* move data related config helpers to own file
* add a variant of RandomResizeCrop that randomizes interpolation method
* remove old Numpy version of RandomErasing
* cleanup torch version of RandomErasing and use it in either GPU loader batch mode or single image cpu Transform
Identity is not the same thing as equality in Python. In these instances, we want the latter.
Use ==/!= to compare str, bytes, and int literals.
$ __python__
```python
>>> proj = "pro"
>>> proj += 'j'
>>> proj
'proj'
>>> proj == 'proj'
True
>>> proj is 'proj'
False
>>> 0 == 0.0
True
>>> 0 is 0.0
False
```
[flake8](http://flake8.pycqa.org) testing of https://github.com/rwightman/pytorch-image-models on Python 3.7.1
$ __flake8 . --count --select=E9,F63,F72,F82 --show-source --statistics__
```
./data/loader.py:48:23: F823 local variable 'input' defined as a builtin referenced before assignment
yield input, target
^
./models/dpn.py:170:12: F632 use ==/!= to compare str, bytes, and int literals
if block_type is 'proj':
^
./models/dpn.py:173:14: F632 use ==/!= to compare str, bytes, and int literals
elif block_type is 'down':
^
./models/dpn.py:177:20: F632 use ==/!= to compare str, bytes, and int literals
assert block_type is 'normal'
^
3 F632 use ==/!= to compare str, bytes, and int literals
1 F823 local variable 'input' defined as a builtin referenced before assignment
4
```
__E901,E999,F821,F822,F823__ are the "_showstopper_" [flake8](http://flake8.pycqa.org) issues that can halt the runtime with a SyntaxError, NameError, etc. These 5 are different from most other flake8 issues which are merely "style violations" -- useful for readability but they do not effect runtime safety.
* F821: undefined name `name`
* F822: undefined name `name` in `__all__`
* F823: local variable name referenced before assignment
* E901: SyntaxError or IndentationError
* E999: SyntaxError -- failed to compile a file into an Abstract Syntax Tree
* create one resolve fn to pull together model defaults + cmd line args
* update attribution comments in some models
* test update train/validation/inference scripts
* All models have 'default_cfgs' dict
* load/resume/pretrained helpers factored out
* pretrained load operates on state_dict based on default_cfg
* test all models in validate
* schedule, optim factor factored out
* test time pool wrapper applied based on default_cfg
* Move 'test time pool' to Module that can be used by any model, remove from DPN
* Remove ResNext model file and combine with ResNet
* Remove fbresnet200 as it was an old conversion and pretrained performance not worth param count
* Cleanup adaptive avgmax pooling and add back conctat variant
* Factor out checkpoint load fn
