# What is this?
Functions and decorators I found myself rewriting for every pytorch project
# How do I use this?
`pip install trivial-torch-tools`
```python
from trivial_torch_tools import Sequential, init
import torch.nn as nn
class Model(nn.Module):
@init.to_device()
# ^ does self.to() and defaults to GPU if available (uses default_device variable)
@init.save_and_load_methods(model_attributes=["layers"], basic_attributes=["input_shape"])
# ^ creates self.save(path=self.path) and self.load(path=self.path)
def __init__(self):
self.input_shape = (81,81,3)
layers = Sequential(input_shape=self.input_shape)
# ^ dynamically compute the output shape/size of layers (the nn.Linear below)
layers.add_module('conv1' , nn.Conv2d(input_shape[0], 32, kernel_size=8, stride=4, padding=0))
layers.add_module('relu1' , nn.ReLU())
layers.add_module('flatten' , nn.Flatten(start_dim=1, end_dim=-1))
layers.add_module('linear1' , nn.Linear(in_features=layers.output_size, out_features=10))
layers.add_module('sigmoid1', nn.Sigmoid())
self.layers = layers
# layers.output_size
# layers.output_shape
# layers.layer_shapes
# available tools
from trivial_torch_tools import *
core.default_device # defaults to cuda if available
core.to_tensor(nested_lists_of_arrays_tuples_and_more) # aggresively converts objects to tensors
# decorators for def __init__()
@model.init.to_device(device=default_device)
@model.init.save_and_load_methods(basic_attributes=[], model_attributes=[], path_attribute="path")
@model.init.forward_sequential_method
# decorators for def forward(): # or whatever
@model.convert_each_arg.to_tensor() # Use to_tensor(which_args=[0]) to only convert first arg
@model.convert_each_arg.to_device() # Use to_device(which_args=[0]) to only convert first arg
@model.convert_each_arg.to_batched_tensor(number_of_dimensions=4) # 4 works for color images
@model.convert_each_arg.torch_tensor_from_opencv_format()
image.tensor_from_path(path)
image.pil_image_from_tensor(tensor)
image.torch_tensor_from_opencv_format(tensor_or_array)
image.opencv_tensor_from_torch_format(tensor)
image.opencv_array_from_pil_image(image_obj)
OneHotifier.tensor_from_argmax(tensor) # [0.1,99,0,0,] => [0,1,0,0,]
OneHotifier.index_from_one_hot(tensor) # [0,1,0,0,] => 2
OneHotifier.index_tensor_from_onehot_batch(tensor) # [[0,1,0,0,]] => [2]
import torch
converter = OneHotifier(possible_values=[ "thing0", ('thing', 1), {"thing":2} ])
converter.to_one_hot({"thing":2}) # >>> tensor([0,0,1])
converter.from_one_hot(torch.tensor([0,0,1])) # >>> {"thing":2}
```
Raw data
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"description": "# What is this?\n\nFunctions and decorators I found myself rewriting for every pytorch project\n\n# How do I use this?\n\n`pip install trivial-torch-tools`\n\n```python\nfrom trivial_torch_tools import Sequential, init\nimport torch.nn as nn\n\nclass Model(nn.Module):\n @init.to_device()\n # ^ does self.to() and defaults to GPU if available (uses default_device variable)\n @init.save_and_load_methods(model_attributes=[\"layers\"], basic_attributes=[\"input_shape\"])\n # ^ creates self.save(path=self.path) and self.load(path=self.path)\n def __init__(self):\n self.input_shape = (81,81,3)\n layers = Sequential(input_shape=self.input_shape)\n # ^ dynamically compute the output shape/size of layers (the nn.Linear below)\n layers.add_module('conv1' , nn.Conv2d(input_shape[0], 32, kernel_size=8, stride=4, padding=0))\n layers.add_module('relu1' , nn.ReLU())\n layers.add_module('flatten' , nn.Flatten(start_dim=1, end_dim=-1))\n layers.add_module('linear1' , nn.Linear(in_features=layers.output_size, out_features=10)) \n layers.add_module('sigmoid1', nn.Sigmoid())\n self.layers = layers\n \n # layers.output_size\n # layers.output_shape\n # layers.layer_shapes\n \n# available tools\nfrom trivial_torch_tools import *\n\ncore.default_device # defaults to cuda if available\ncore.to_tensor(nested_lists_of_arrays_tuples_and_more) # aggresively converts objects to tensors\n\n# decorators for def __init__()\n@model.init.to_device(device=default_device)\n@model.init.save_and_load_methods(basic_attributes=[], model_attributes=[], path_attribute=\"path\")\n@model.init.forward_sequential_method\n# decorators for def forward(): # or whatever \n@model.convert_each_arg.to_tensor() # Use to_tensor(which_args=[0]) to only convert first arg\n@model.convert_each_arg.to_device() # Use to_device(which_args=[0]) to only convert first arg\n@model.convert_each_arg.to_batched_tensor(number_of_dimensions=4) # 4 works for color images\n@model.convert_each_arg.torch_tensor_from_opencv_format()\n\nimage.tensor_from_path(path)\nimage.pil_image_from_tensor(tensor)\nimage.torch_tensor_from_opencv_format(tensor_or_array)\nimage.opencv_tensor_from_torch_format(tensor)\nimage.opencv_array_from_pil_image(image_obj)\n\nOneHotifier.tensor_from_argmax(tensor) # [0.1,99,0,0,] => [0,1,0,0,]\nOneHotifier.index_from_one_hot(tensor) # [0,1,0,0,] => 2\nOneHotifier.index_tensor_from_onehot_batch(tensor) # [[0,1,0,0,]] => [2]\n\nimport torch\nconverter = OneHotifier(possible_values=[ \"thing0\", ('thing', 1), {\"thing\":2} ])\nconverter.to_one_hot({\"thing\":2}) # >>> tensor([0,0,1])\nconverter.from_one_hot(torch.tensor([0,0,1])) # >>> {\"thing\":2}\n```\n\n\n",
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