fix: examples updated (#6)

catalyst-team · Aug 11, 2021 · cfa084c · cfa084c
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diff --git a/README.md b/README.md
@@ -30,13 +30,6 @@ dataset:
  _target_: torchvision.datasets.CIFAR100
  root: ./data
  train: false
- transform:
- _target_: torchvision.transforms.Compose
- transforms:
- - _target_: torchvision.transforms.ToTensor
- - _target_: torchvision.transforms.Normalize
- mean: [0.1307]
- std: [0.3081]
  download: true
 ```
 
@@ -55,10 +48,6 @@ config["dataset"]
 # Root location: ./data
 # Split: Test
 # StandardTransform
-# Transform: Compose(
-# ToTensor()
-# Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-# )
 ```
 
 Please check [documentation](https://catalyst-team.github.io/hydra-slayer/master/pages/examples) for more examples.

diff --git a/docs/pages/examples.rst b/docs/pages/examples.rst
@@ -11,10 +11,42 @@ Basic Level
 
  <div>
  <img src="../_static/basic_fire_magic.png" style="float: right; padding-left: 24px;" />
- <p>Create <a href="https://www.cs.toronto.edu/~kriz/cifar.html">CIFAR100</a> dataset from config file with params.</p>
+ <p>Create instance from config file with params.</p>
  <blockquote>Please note that <b>Basic Fire Magic</b> also allows your hero to cast fire spells at reduced cost.</blockquote>
  </div>
 
+.. code-block:: yaml
+
+ # transform.yaml
+ _target_: torchvision.transforms.Normalize
+ mean: [0.5, 0.5, 0.5]
+ std: [0.5, 0.5, 0.5]
+
+.. code-block:: python
+
+ import hydra_slayer
+ import yaml
+
+ registry = hydra_slayer.Registry()
+ with open("dataset.yaml") as stream:
+ raw_config = yaml.safe_load(stream)
+
+ transform = registry.get_from_params(**raw_config)
+ transform
+ # Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+
+Advanced Level
+^^^^^^^^^^^^^^
+
+.. raw:: html
+
+ <div>
+ <img src="../_static/advanced_fire_magic.png" style="float: right; padding-left: 24px;" />
+ <p>Create <a href="https://www.cs.toronto.edu/~kriz/cifar.html">CIFAR100</a> dataset from config file with params.</p>
+ <blockquote>Please note that <b>Advanced Fire Magic</b> also allows your hero to cast fire spells at reduced cost and increased effectiveness.</blockquote>
+ </div>
+
 .. code-block:: yaml
 
  # dataset.yaml
@@ -26,8 +58,8 @@ Basic Level
  transforms:
  - _target_: torchvision.transforms.ToTensor
  - _target_: torchvision.transforms.Normalize
- mean: [0.1307]
- std: [0.3081]
+ mean: [0.5, 0.5, 0.5]
+ std: [0.5, 0.5, 0.5]
  download: true
 
 .. code-block:: python
@@ -52,15 +84,15 @@ Basic Level
  # )
 
 
-Advanced Level
-^^^^^^^^^^^^^^
+Expert level
+^^^^^^^^^^^^
 
 .. raw:: html
 
  <div>
- <img src="../_static/advanced_fire_magic.png" style="float: right; padding-left: 24px;" />
+ <img src="../_static/expert_fire_magic.png" style="float: right; padding-left: 24px;" />
  <p>Read multiple CSV files as pandas dataframes and merge them.</p>
- <blockquote>Please note that <b>Advanced Fire Magic</b> also allows your hero to cast fire spells at reduced cost and increased effectiveness.</blockquote>
+ <blockquote>Please note that <b>Expert Fire Magic</b> also allows your hero to cast fire spells at reduced cost and maximum effectiveness.</blockquote>
  </div>
 
 .. code-block:: yaml
@@ -106,72 +138,73 @@ Advanced Level
  # ... ... ... ...
 
 
-Expert level
-^^^^^^^^^^^^
-
-.. raw:: html
-
- <div>
- <img src="../_static/expert_fire_magic.png" style="float: right; padding-left: 24px;" />
- <p>Sorry, the person who is responsible for the expert level example was eaten by hydras last week.</p>
- <blockquote>Please note that <b>Expert Fire Magic</b> also allows your hero to cast fire spells at reduced cost and maximum effectiveness.</blockquote>
- </div>
-
 ..
- .. code-block:: yaml
-
- # dataset.yaml
- _target_: torchvision.datasets.CIFAR100
- root: ./data
- train: false
- transform:
- _target_: torchvision.transforms.Compose
- transforms:
- - _target_: torchvision.transforms.ToTensor
- - _target_: torchvision.transforms.Normalize
- mean: [0.5,0.5,0.5]
- std: [0.5,0.5,0.5]
- download: true
-
- .. code-block:: yaml
-
- # config.yaml
- dataset:
- _target_: torch.utils.data.DataLoader
- # TODO: will not work as dict will be retuned, not dataset
+ Master level
+ ^^^^^^^^^^^^
+
+ .. raw:: html
+
+ <div>
+ <img src="../_static/expert_fire_magic.png" style="float: right; padding-left: 24px;" />
+ <p>Sorry, the person who is responsible for the expert level example was eaten by hydras last week.</p>
+ <blockquote>Please note that <b>Maximum Fire Magic</b> also allows your hero to cast fire spells at reduced cost and maximum effectiveness.</blockquote>
+ </div>
+
+ ..
+ .. code-block:: yaml
+
+ # dataset.yaml
+ _target_: torchvision.datasets.CIFAR100
+ root: ./data
+ train: false
+ transform:
+ _target_: torchvision.transforms.Compose
+ transforms:
+ - _target_: torchvision.transforms.ToTensor
+ - _target_: torchvision.transforms.Normalize
+ mean: [0.5,0.5,0.5]
+ std: [0.5,0.5,0.5]
+ download: true
+
+ .. code-block:: yaml
+
+ # config.yaml
  dataset:
- # Read dataset from "dataset.yaml", roughly equivalent to
- # with open("dataset.yaml") as stream:
- # params = yaml.safe_load(stream)
- _target_: yaml.safe_load
- stream:
- _target_: open
- file: dataset_config.yaml
+ _target_: torch.utils.data.DataLoader
+ # TODO: will not work as dict will be retuned, not dataset
+ dataset:
+ # Read dataset from "dataset.yaml", roughly equivalent to
+ # with open("dataset.yaml") as stream:
+ # params = yaml.safe_load(stream)
+ _target_: yaml.safe_load
+ stream:
+ _target_: open
+ file: dataset_config.yaml
+ meta_factory:
+ _target_: hydra_slayer.call_meta_factory
+ batch_size: 32
+ shuffle: false
+
+ model:
+ _target_: torchvision.models.resnet18
+ pretrained: true
  meta_factory:
  _target_: hydra_slayer.call_meta_factory
- batch_size: 32
- shuffle: false
-
- model:
- _target_: torchvision.models.resnet18
- pretrained: true
- meta_factory:
- _target_: hydra_slayer.call_meta_factory
 
- .. code-block:: python
+  .. code-block:: python
 
- import hydra_slayer
- import torch
- import yaml
+  import hydra_slayer
+  import torch
+  import yaml
 
- registry = hydra_slayer.Registry()
- with open("config.yaml") as stream:
- raw_config = yaml.safe_load(stream)
+  registry = hydra_slayer.Registry()
+  with open("config.yaml") as stream:
+  raw_config = yaml.safe_load(stream)
 
- config = registry.get_from_params(**raw_config)
- model, dataset = config["model"], config["dataset"]
+  config = registry.get_from_params(**raw_config)
+  model, dataset = config["model"], config["dataset"]
 
- model.eval()
- with torch.no_grad():
- for batch, y_true in dataset:
- y_preds = model(batch)
+  model.eval()
+  with torch.no_grad():
+  for batch, y_true in dataset:
+  y_preds = model(batch)