remove name functions & update overview

Author: junyanz

data/__init__.py

@@ -36,7 +36,7 @@ def create_dataset(opt):
     """Create dataset given the option."""
     dataset = find_dataset_using_name(opt.dataset_mode)
     instance = dataset(opt)
-    print("dataset [%s] was created" % (instance.name()))
+    print("dataset [%s] was created" % type(instance).__name__)
     return instance
 
 

data/aligned_dataset.py

@@ -43,6 +43,3 @@ def __getitem__(self, index):
 
     def __len__(self):
         return len(self.AB_paths)
-
-    def name(self):
-        return 'AlignedDataset'

data/base_dataset.py

@@ -13,10 +13,6 @@ def __init__(self, opt):
     def modify_commandline_options(parser, is_train):
         return parser
 
-    @abstractmethod
-    def name(self):
-        return 'BaseDataset'
-
     @abstractmethod
     def __len__(self):
         return 0

data/colorization_dataset.py

@@ -33,6 +33,3 @@ def __getitem__(self, index):
 
     def __len__(self):
         return len(self.A_paths)
-
-    def name(self):
-        return 'ColorizationDataset'

data/single_dataset.py

@@ -22,6 +22,3 @@ def __getitem__(self, index):
 
     def __len__(self):
         return len(self.A_paths)
-
-    def name(self):
-        return 'SingleImageDataset'

data/template_dataset.py

@@ -10,7 +10,6 @@
     <__init__>: Initialize this dataset class.
     <__getitem__>: Return a data point and its metadata information.
     <__len__>: Return the number of images.
-    <name>: Return the name of this dataset.
 """
 from data.base_dataset import BaseDataset, get_transform
 # from data.image_folder import make_dataset
@@ -73,7 +72,3 @@ def __getitem__(self, index):
     def __len__(self):
         """Return the total number of images."""
         return len(self.image_paths)
-
-    def name(self):
-        """Return the name of this dataset."""
-        return 'TemplateDataset'

data/unaligned_dataset.py

@@ -44,6 +44,3 @@ def __getitem__(self, index):
 
     def __len__(self):
         return max(self.A_size, self.B_size)
-
-    def name(self):
-        return 'UnalignedDataset'

docs/overview.md

@@ -1,42 +1,43 @@
 ## Overview of Code Structure
-[train.py](./train.py) is a general-purpose training script. It works for various models (with model option `--model`: e.g., `pix2pix`, `cyclegan`, `colorization`) and different datasets (with dataset option `--dataset_mode`: e.g., `aligned`, `unaligned`, `single`, `colorization`). See the main [README](../README.md) and Training/test [tips](tips.md) for more details.
-
-[test.py](./test.py) is a general-purpose test script. Once you have trained your models with `train.py`, you can use this script to test the model. It will load a saved model from `--checkpoints_dir` and save the results to `--results_dir`. See the main [README](../README.md) and Training/test [tips](tips.md) for more details.
-
-
-[data](./data) directory contains all the modules related to datasets.
-* [\_\_init\_\_.py](./data/__init__.py) implements the interface between this package and training/test script. You should call `from data import CreateDataLoader` and `data_loader = CreateDataLoader(opt)` to create a dataloader given an option `opt`.
-* [base_dataset.py](./data/base_dataset.py) implements an abstract base dataset class. It also includes common transformation functions `get_transform` and `get_simple_transform` which can be used in dataset classes. To add a custom dataset class called `dummy`, you need to add a file called `dummy_dataset.py` and define a subclass `DummyDataset` inherited from `BaseDataset`. You need to implement functions: `name`, `__len__`, `__getitem__`, and `modify_commandline_options`. You can use this dataset using `--dataset_mode dummy`.
-* [image_folder.py](./data/image_folder.py) implements a modified Image folder class. We
-modify the original PyTorch code so that it also loads images from the current directory as well as the subdirectories.
-* [template_dataset.py](./data/template_dataset.py) provides a class template with detailed documentation. Check out this file if you plan to implement your own dataset class.
-* [aligned_dataset.py](./data/aligned_dataset.py) includes a dataset class that can load aligned image pairs.
-* [unaligned_dataset.py](./data/unaligned_dataset.py) includes a dataset class that can load unaligned/unpaired datasets.
-* [single_dataset.py](./data/single_dataset.py) includes a dataset class that can load a collection of single images. It is used in `test.py` when only model in one direction is being tested.
-* [colorization_dataset.py](./data/colorization_dataset.py) implements a dataset class that can load a collection of RGB images and convert it into (L, ab) pairs. It is used with pix2pix-based colorization model.
-
-
-[models](./models) directory contains all the modules related to core core formulations and network.
-* [\_\_init\_\_.py](./models/__init__.py)
-* [base_model.py](./models/base_model.py)
-* [template_model.py](./models/template_model.py)
-* [pix2pix_model.py](./models/pix2pix_model.py)
-* [colorization_model.py](./models/colorization_model.py)
-* [cycle_gan_model.py](./models/cycle_gan_model.py)
-* [networks.py](./models/networks.py) module implements network architectures (both generators and discriminators), as well as normalization layers, initialization, optimization scheduler (learning rate policy), and GAN loss function.
-* [test_model.py](./models/test_model.py)
-
-[options](./options) directory includes option modules: training options, test options and basic options (used in both training and test).
-* [\_\_init\_\_.py](./options/__init__.py) an empty file to make the `options` directory a package.
-* [base_options.py](./options/base_options.py) includes options that are used in both training and test. It also implements a few helper functions such as parsing, printing, and saving the options. It also gathers additional options defined in `modify_commandline_options` functions in both dataset class and model class.
-* [train_options.py](./options/train_options.py) includes options that are only used in training time.
-* [test_options.py](./options/test_options.py) includes options that are only used in test time.
-
-
-[util](./util) directory includes a misc collection of useful utility functions.
-  * [\_\_init\_\_.py](./util/__init__.py): an empty file to make the `util` directory a package.
-  * [get_data.py](./util/get_data.py)
-  * [html.py](./util/html.py)
-  * [image_pool.py](./util/image_pool.py)
-  * [util.py](./util/util.py)
-  * [visualizer.py](./util/visualizer.py)
+We give a brief overview of each directory and each file. Please see the documentation in each file for more details. If you have questions, you may find useful information in [training/test tips](tips.md) and [frequently asked questions](qa.md).
+
+[train.py](../train.py) is a general-purpose training script. It works for various models (with option `--model`: e.g., `pix2pix`, `cyclegan`, `colorization`) and different datasets (with option `--dataset_mode`: e.g., `aligned`, `unaligned`, `single`, `colorization`). See the main [README](.../README.md) and Training/test [tips](tips.md) for more details.
+
+[test.py](../test.py) is a general-purpose test script. Once you have trained your model with `train.py`, you can use this script to test the model. It will load a saved model from `--checkpoints_dir` and save the results to `--results_dir`. See the main [README](.../README.md) and Training/test [tips](tips.md) for more details.
+
+
+[data](../data) directory contains all the modules related to data loading and data preprocessing.
+* [\_\_init\_\_.py](../data/__init__.py) implements the interface between this package and training/test script. In the `train.py` and `test.py`, we call `from data import CreateDataLoader` and `data_loader = CreateDataLoader(opt)` to create a dataloader given the option `opt`.
+* [base_dataset.py](../data/base_dataset.py) implements an abstract base class for datasets. It also includes common transformation functions `get_transform` and `get_simple_transform` which can be used in subclasses. To add a custom dataset class called `dummy`, you need to add a file called `dummy_dataset.py` and define a subclass `DummyDataset` inherited from `BaseDataset`. You need to implement four functions: `name`, `__len__`, `__getitem__`, and optionally `modify_commandline_options`. You can then use this dataset class by specifying flag `--dataset_mode dummy`.
+* [image_folder.py](../data/image_folder.py) implements an image folder class. We modify the official PyTorch image folder [code](https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py) so that this class can load images from both the current directory and its subdirectories.
+* [template_dataset.py](../data/template_dataset.py) provides a dataset class template with detailed documentation. Check out this file if you plan to implement your own dataset class.
+* [aligned_dataset.py](../data/aligned_dataset.py) includes a dataset class that can load aligned image pairs. It assumes a single image directory `/path/to/data/train`, which contains image pairs in the form of {A,B}. See [here](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/tips.md#prepare-your-own-datasets-for-pix2pix) on how to prepare aligned datasets. During test time, you need to prepare a directory `/path/to/data/test` for test data.
+* [unaligned_dataset.py](../data/unaligned_dataset.py) includes a dataset class that can load unaligned/unpaired datasets. It assumes that two directories to host training images from domain A `/path/to/data/trainA` and from domain B `/path/to/data/trainB` separately. Then you can train the model with the dataset flag `--dataroot /path/to/data`. Similarly, you need to prepare two directories `/path/to/data/testA` and `/path/to/data/testB` during test time.
+* [single_dataset.py](../data/single_dataset.py) includes a dataset class that can load a set of single images. It is used in `test.py` when only model in one direction is being tested. The option `--model test` is used for generating CycleGAN results only for one side. This option will automatically set `--dataset_mode single`.
+* [colorization_dataset.py](../data/colorization_dataset.py) implements a dataset class that can load a set of nature images in RGB, and convert RGB format into (L, ab) pairs. It is required by pix2pix-based colorization model (`--model colorization`).
+
+
+[models](../models) directory contains core modules related to objective functions, optimizations, and network architectures.
+* [\_\_init\_\_.py](../models/__init__.py)
+* [base_model.py](../models/base_model.py)
+* [template_model.py](../models/template_model.py)
+* [pix2pix_model.py](../models/pix2pix_model.py)
+* [colorization_model.py](../models/colorization_model.py)
+* [cycle_gan_model.py](../models/cycle_gan_model.py)
+* [networks.py](../models/networks.py) module implements network architectures (both generators and discriminators), as well as normalization layers, initialization, optimization scheduler (learning rate policy), and GAN loss function.
+* [test_model.py](../models/test_model.py)
+
+[options](../options) directory includes our option modules: training options, test options and basic options (used in both training and test).
+* [\_\_init\_\_.py](../options/__init__.py) an empty file to make the `options` directory a package.
+* [base_options.py](../options/base_options.py) includes options that are used in both training and test. It also implements a few helper functions such as parsing, printing, and saving the options. It also gathers additional options defined in `modify_commandline_options` functions in both dataset class and model class.
+* [train_options.py](../options/train_options.py) includes options that are only used in training time.
+* [test_options.py](../options/test_options.py) includes options that are only used in test time.
+
+
+[util](../util) directory includes a misc collection of useful utility functions.
+  * [\_\_init\_\_.py](../util/__init__.py): an empty file to make the `util` directory a package.
+  * [get_data.py](../util/get_data.py)
+  * [html.py](../util/html.py)
+  * [image_pool.py](../util/image_pool.py)
+  * [util.py](../util/util.py)
+  * [visualizer.py](../util/visualizer.py)

models/__init__.py

@@ -34,5 +34,5 @@ def get_option_setter(model_name):
 def create_model(opt):
     model = find_model_using_name(opt.model)
     instance = model(opt)
-    print("model [%s] was created" % (instance.name()))
+    print("model [%s] was created" % type(instance).__name__)
     return instance

models/base_model.py

@@ -28,11 +28,6 @@ def modify_commandline_options(parser, is_train):
         """Modify parser to add command line options;  change the default values if needed"""
         return parser
 
-    @abstractmethod
-    def name(self):
-        """Return the name of this class"""
-        pass
-
     @abstractmethod
     def set_input(self, input):
         """Unpack input data from the dataloader and perform necessary pre-processing steps."""

models/colorization_model.py

@@ -5,9 +5,6 @@
 
 
 class ColorizationModel(Pix2PixModel):
-    def name(self):
-        return 'ColorizationModel'
-
     @staticmethod
     def modify_commandline_options(parser, is_train=True):
         Pix2PixModel.modify_commandline_options(parser, is_train)

models/cycle_gan_model.py

@@ -6,9 +6,6 @@
 
 
 class CycleGANModel(BaseModel):
-    def name(self):
-        return 'CycleGANModel'
-
     @staticmethod
     def modify_commandline_options(parser, is_train=True):
         parser.set_defaults(no_dropout=True)  # default CycleGAN did not use dropout

models/pix2pix_model.py

@@ -5,9 +5,6 @@
 
 
 class Pix2PixModel(BaseModel):
-    def name(self):
-        return 'Pix2PixModel'
-
     @staticmethod
     def modify_commandline_options(parser, is_train=True):
         # changing the default values to match the pix2pix paper (https://phillipi.github.io/pix2pix/)

models/template_model.py

@@ -21,10 +21,6 @@
 
 
 class TemplateModel(BaseModel):
-    def name(self):
-        """Return the name of this model"""
-        return 'TemplateModel'
-
     @staticmethod
     def modify_commandline_options(parser, is_train=True):
         """Add new dataset-specific options and rewrite default values for existing options.

models/test_model.py

@@ -3,9 +3,6 @@
 
 
 class TestModel(BaseModel):
-    def name(self):
-        return 'TestModel'
-
     @staticmethod
     def modify_commandline_options(parser, is_train=True):
         assert not is_train, 'TestModel cannot be used in train mode'

options/base_options.py

@@ -32,7 +32,7 @@ def initialize(self, parser):
         parser.add_argument('--load_iter', type=int, default='0', help='which iteration to load? if load_iter > 0, the code will load models by iter_[load_iter]; otherwise, the code will load models by [epoch]')
         parser.add_argument('--num_threads', default=4, type=int, help='# threads for loading data')
         parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
-        parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization [batch | norm | none]')
+        parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization [instance | batch | none]')
         parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')
         parser.add_argument('--no_dropout', action='store_true', help='no dropout for the generator')
         parser.add_argument('--max_dataset_size', type=int, default=float("inf"), help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.')