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
* tensorflow 'SAME' padding support added to GenMobileNet models for tflite pretrained weights
* folded batch norm support (made batch norm optional and enable conv bias) for tflite pretrained weights
* add url for spnasnet1_00 weights that I recently trained
* fix SE reduction size for semnasnet models
* 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
Ross Wightman
Ross Wightman
cclauss