We incorporate modular and inheritance design into our config system, which is convenient to conduct various experiments.
If you wish to inspect the config file, you may run python tools/misc/print_config.py /PATH/TO/CONFIG to see the complete config.
You can learn about the usage of our config system according to the following tutorials.
- Tutorial 1: Learn about Configs in MMagic
When submitting jobs using tools/train.py or tools/test.py, you may specify --cfg-options to in-place modify the config.
-
Update config keys of dict chains.
The config options can be specified following the order of the dict keys in the original config. For example,
--cfg-options test_cfg.use_ema=Falsechanges the default sampling model to the original generator, and--cfg-options train_dataloader.batch_size=8changes the batch size of train dataloader. -
Update keys inside a list of configs.
Some config dicts are composed as a list in your config. For example, the training pipeline
train_dataloader.dataset.pipelineis normally a list e.g.[dict(type='LoadImageFromFile'), ...]. If you want to change'LoadImageFromFile'to'LoadImageFromWebcam'in the pipeline, you may specify--cfg-options train_dataloader.dataset.pipeline.0.type=LoadImageFromWebcam. The training pipelinetrain_pipelineis normally a list e.g.[dict(type='LoadImageFromFile'), ...]. If you want to change'LoadImageFromFile'to'LoadMask'in the pipeline, you may specify--cfg-options train_pipeline.0.type=LoadMask. -
Update values of list/tuples.
If the value to be updated is a list or a tuple. You can set
--cfg-options key="[a,b]"or--cfg-options key=a,b. It also allows nested list/tuple values, e.g.,--cfg-options key="[(a,b),(c,d)]". Note that the quotation mark " is necessary to support list/tuple data types, and that NO white space is allowed inside the quotation marks in the specified value.
There are 3 basic component types under config/_base_: datasets, models and default_runtime.
Many methods could be easily constructed with one of each like AOT-GAN, EDVR, GLEAN, StyleGAN2, CycleGAN, SinGAN, etc.
Configs consisting of components from _base_ are called primitive.
For all configs under the same folder, it is recommended to have only one primitive config. All other configs should inherit from the primitive config. In this way, the maximum of inheritance level is 3.
For easy understanding, we recommend contributors to inherit from existing methods.
For example, if some modification is made base on BasicVSR,
user may first inherit the basic BasicVSR structure by specifying _base_ = ../basicvsr/basicvsr_reds4.py,
then modify the necessary fields in the config files.
If some modification is made base on StyleGAN2,
user may first inherit the basic StyleGAN2 structure by specifying _base_ = ../styleganv2/stylegan2_c2_ffhq_256_b4x8_800k.py,
then modify the necessary fields in the config files.
If you are building an entirely new method that does not share the structure with any of the existing methods,
you may create a folder xxx under configs,
Please refer to MMEngine for detailed documentation.
{model}_[module setting]_{training schedule}_{dataset}
{xxx} is required field and [yyy] is optional.
{model}: model type likestylegan,dcgan,basicvsr,dim, etc. Settings referred in the original paper are included in this field as well (e.g.,Stylegan2-config-f,edvrmofedvrm_8xb4-600k_reds.)[module setting]: specific setting for some modules, including Encoder, Decoder, Generator, Discriminator, Normalization, loss, Activation, etc. E.g.c64n7ofbasicvsr-pp_c64n7_8xb1-600k_reds4, learning rateGlr4e-4_Dlr1e-4for dcgan,gamma32.8for stylegan3,woReLUInplacein sagan. In this section, information from different submodules (e.g., generator and discriminator) are connected with_.{training_scheduler}: specific setting for training, including batch_size, schedule, etc. For example, learning rate (e.g.,lr1e-3), number of gpu and batch size is used (e.g.,8xb32), and total iterations (e.g.,160kiter) or number of images shown in the discriminator (e.g.,12Mimgs).{dataset}: dataset name and data size info likeceleba-256x256ofdeepfillv1_4xb4_celeba-256x256,reds4ofbasicvsr_2xb4_reds4,ffhq,lsun-car,celeba-hq.
To help the users have a basic idea of a complete config, we make a brief comments on the config of the EDSR model we implemented as the following. For more detailed usage and the corresponding alternative for each modules, please refer to the API documentation and the tutorial in MMEngine.
In MMagic's config, we use model fields to set up a model.
model = dict(
type='BaseEditModel', # Name of the model
generator=dict( # Config of the generator
type='EDSRNet', # Type of the generator
in_channels=3, # Channel number of inputs
out_channels=3, # Channel number of outputs
mid_channels=64, # Channel number of intermediate features
num_blocks=16, # Block number in the trunk network
upscale_factor=scale, # Upsampling factor
res_scale=1, # Used to scale the residual in residual block
rgb_mean=(0.4488, 0.4371, 0.4040), # Image mean in RGB orders
rgb_std=(1.0, 1.0, 1.0)), # Image std in RGB orders
pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean') # Config for pixel loss
train_cfg=dict(), # Config of training model.
test_cfg=dict(), # Config of testing model.
data_preprocessor=dict( # The Config to build data preprocessor
type='DataPreprocessor', mean=[0., 0., 0.], std=[255., 255.,
255.]))Dataloaders are required for the training, validation, and testing of the runner. Dataset and data pipeline need to be set to build the dataloader. Due to the complexity of this part, we use intermediate variables to simplify the writing of dataloader configs.
train_pipeline = [ # Training data processing pipeline
dict(type='LoadImageFromFile', # Load images from files
key='img', # Keys in results to find the corresponding path
color_type='color', # Color type of image
channel_order='rgb', # Channel order of image
imdecode_backend='cv2'), # decode backend
dict(type='LoadImageFromFile', # Load images from files
key='gt', # Keys in results to find the corresponding path
color_type='color', # Color type of image
channel_order='rgb', # Channel order of image
imdecode_backend='cv2'), # decode backend
dict(type='SetValues', dictionary=dict(scale=scale)), # Set value to destination keys
dict(type='PairedRandomCrop', gt_patch_size=96), # Paired random crop
dict(type='Flip', # Flip images
keys=['lq', 'gt'], # Images to be flipped
flip_ratio=0.5, # Flip ratio
direction='horizontal'), # Flip direction
dict(type='Flip', # Flip images
keys=['lq', 'gt'], # Images to be flipped
flip_ratio=0.5, # Flip ratio
direction='vertical'), # Flip direction
dict(type='RandomTransposeHW', # Random transpose h and w for images
keys=['lq', 'gt'], # Images to be transposed
transpose_ratio=0.5 # Transpose ratio
),
dict(type='PackInputs') # The config of collecting data from the current pipeline
]
test_pipeline = [ # Test pipeline
dict(type='LoadImageFromFile', # Load images from files
key='img', # Keys in results to find corresponding path
color_type='color', # Color type of image
channel_order='rgb', # Channel order of image
imdecode_backend='cv2'), # decode backend
dict(type='LoadImageFromFile', # Load images from files
key='gt', # Keys in results to find corresponding path
color_type='color', # Color type of image
channel_order='rgb', # Channel order of image
imdecode_backend='cv2'), # decode backend
dict(type='PackInputs') # The config of collecting data from the current pipeline
]dataset_type = 'BasicImageDataset' # The type of dataset
data_root = 'data' # Root path of data
train_dataloader = dict(
num_workers=4, # The number of workers to pre-fetch data for each single GPU
persistent_workers=False, # Whether maintain the workers Dataset instances alive
sampler=dict(type='InfiniteSampler', shuffle=True), # The type of data sampler
dataset=dict( # Train dataset config
type=dataset_type, # Type of dataset
ann_file='meta_info_DIV2K800sub_GT.txt', # Path of annotation file
metainfo=dict(dataset_type='div2k', task_name='sisr'),
data_root=data_root + '/DIV2K', # Root path of data
data_prefix=dict( # Prefix of image path
img='DIV2K_train_LR_bicubic/X2_sub', gt='DIV2K_train_HR_sub'),
filename_tmpl=dict(img='{}', gt='{}'), # Filename template
pipeline=train_pipeline))
val_dataloader = dict(
num_workers=4, # The number of workers to pre-fetch data for each single GPU
persistent_workers=False, # Whether maintain the workers Dataset instances alive
drop_last=False, # Whether drop the last incomplete batch
sampler=dict(type='DefaultSampler', shuffle=False), # The type of data sampler
dataset=dict( # Validation dataset config
type=dataset_type, # Type of dataset
metainfo=dict(dataset_type='set5', task_name='sisr'),
data_root=data_root + '/Set5', # Root path of data
data_prefix=dict(img='LRbicx2', gt='GTmod12'), # Prefix of image path
pipeline=test_pipeline))
test_dataloader = val_dataloaderEvaluators are used to compute the metrics of the trained model on the validation and testing datasets. The config of evaluators consists of one or a list of metric configs:
val_evaluator = [
dict(type='MAE'), # The name of metrics to evaluate
dict(type='PSNR', crop_border=scale), # The name of metrics to evaluate
dict(type='SSIM', crop_border=scale), # The name of metrics to evaluate
]
test_evaluator = val_evaluator # The config for testing evaluatorMMEngine's runner uses Loop to control the training, validation, and testing processes. Users can set the maximum training iteration and validation intervals with these fields.
train_cfg = dict(
type='IterBasedTrainLoop', # The name of train loop type
max_iters=300000, # The number of total iterations
val_interval=5000, # The number of validation interval iterations
)
val_cfg = dict(type='ValLoop') # The name of validation loop type
test_cfg = dict(type='TestLoop') # The name of test loop typeoptim_wrapper is the field to configure optimization related settings.
The optimizer wrapper not only provides the functions of the optimizer, but also supports functions such as gradient clipping, mixed precision training, etc. Find more in optimizer wrapper tutorial.
optim_wrapper = dict(
dict(
type='OptimWrapper',
optimizer=dict(type='Adam', lr=0.00001),
)
) # Config used to build optimizer, support all the optimizers in PyTorch whose arguments are also the same as those in PyTorch.param_scheduler is a field that configures methods of adjusting optimization hyper-parameters such as learning rate and momentum.
Users can combine multiple schedulers to create a desired parameter adjustment strategy.
Find more in parameter scheduler tutorial.
param_scheduler = dict( # Config of learning policy
type='MultiStepLR', by_epoch=False, milestones=[200000], gamma=0.5)Users can attach hooks to training, validation, and testing loops to insert some operations during running. There are two different hook fields, one is default_hooks and the other is custom_hooks.
default_hooks is a dict of hook configs. default_hooks are the hooks must required at runtime. They have default priority which should not be modified. If not set, runner will use the default values. To disable a default hook, users can set its config to None.
default_hooks = dict( # Used to build default hooks
checkpoint=dict( # Config to set the checkpoint hook
type='CheckpointHook',
interval=5000, # The save interval is 5000 iterations
save_optimizer=True,
by_epoch=False, # Count by iterations
out_dir=save_dir,
),
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=100), # Config to register logger hook
param_scheduler=dict(type='ParamSchedulerHook'),
sampler_seed=dict(type='DistSamplerSeedHook'),
)custom_hooks is a list of hook configs. Users can develop there own hooks and insert them in this field.
custom_hooks = [dict(type='BasicVisualizationHook', interval=1)] # Config of visualization hookdefault_scope = 'mmagic' # Used to set registries location
env_cfg = dict( # Parameters to setup distributed training, the port can also be set
cudnn_benchmark=False,
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=4),
dist_cfg=dict(backend='nccl'),
)
log_level = 'INFO' # The level of logging
log_processor = dict(type='LogProcessor', window_size=100, by_epoch=False) # Used to build log processor
load_from = None # load models as a pre-trained model from a given path. This will not resume training.
resume = False # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved.Taking Stylegan2 at 1024x1024 scale as an example, we introduce each field in the config according to different function modules.
In addition to neural network components such as generator, discriminator etc, it also requires data_preprocessor, loss_config, and some of them contains ema_config.
data_preprocessor is responsible for processing a batch of data output by dataloader.
loss_config is responsible for weight of loss terms.
ema_config is responsible for exponential moving average (EMA) operation for generator.
model = dict(
type='StyleGAN2', # The name of the model
data_preprocessor=dict(type='DataPreprocessor'), # The config of data preprocessor, usually includs image normalization and padding
generator=dict( # The config for generator
type='StyleGANv2Generator', # The name of the generator
out_size=1024, # The output resolution of the generator
style_channels=512), # The number of style channels of the generator
discriminator=dict( # The config for discriminator
type='StyleGAN2Discriminator', # The name of the discriminator
in_size=1024), # The input resolution of the discriminator
ema_config=dict( # The config for EMA
type='ExponentialMovingAverage', # Specific the type of Average model
interval=1, # The interval of EMA operation
momentum=0.9977843871238888), # The momentum of EMA operation
loss_config=dict( # The config for loss terms
r1_loss_weight=80.0, # The weight for r1 gradient penalty
r1_interval=16, # The interval of r1 gradient penalty
norm_mode='HWC', # The normalization mode for r1 gradient penalty
g_reg_interval=4, # The interval for generator's regularization
g_reg_weight=8.0, # The weight for generator's regularization
pl_batch_shrink=2)) # The factor of shrinking the batch size in path length regularizationDataloaders are required for the training, validation, and testing of the runner. Dataset and data pipeline need to be set to build the dataloader. Due to the complexity of this part, we use intermediate variables to simplify the writing of dataloader configs.
dataset_type = 'BasicImageDataset' # Dataset type, this will be used to define the dataset
data_root = './data/ffhq/' # Root path of data
train_pipeline = [ # Training data process pipeline
dict(type='LoadImageFromFile', key='img'), # First pipeline to load images from file path
dict(type='Flip', keys=['img'], direction='horizontal'), # Argumentation pipeline that flip the images
dict(type='PackInputs', keys=['img']) # The last pipeline that formats the annotation data (if have) and decides which keys in the data should be packed into data_samples
]
val_pipeline = [
dict(type='LoadImageFromFile', key='img'), # First pipeline to load images from file path
dict(type='PackInputs', keys=['img']) # The last pipeline that formats the annotation data (if have) and decides which keys in the data should be packed into data_samples
]
train_dataloader = dict( # The config of train dataloader
batch_size=4, # Batch size of a single GPU
num_workers=8, # Worker to pre-fetch data for each single GPU
persistent_workers=True, # If ``True``, the dataloader will not shutdown the worker processes after an epoch end, which can accelerate training speed.
sampler=dict( # The config of training data sampler
type='InfiniteSampler', # InfiniteSampler for iteratiion-based training. Refers to https://github.com/open-mmlab/mmengine/blob/fe0eb0a5bbc8bf816d5649bfdd34908c258eb245/mmengine/dataset/sampler.py#L107
shuffle=True), # Whether randomly shuffle the training data
dataset=dict( # The config of the training dataset
type=dataset_type,
data_root=data_root,
pipeline=train_pipeline))
val_dataloader = dict( # The config of validation dataloader
batch_size=4, # Batch size of a single GPU
num_workers=8, # Worker to pre-fetch data for each single GPU
dataset=dict( # The config of the validation dataset
type=dataset_type,
data_root=data_root,
pipeline=val_pipeline),
sampler=dict( # The config of validatioin data sampler
type='DefaultSampler', # DefaultSampler which supports both distributed and non-distributed training. Refer to https://github.com/open-mmlab/mmengine/blob/fe0eb0a5bbc8bf816d5649bfdd34908c258eb245/mmengine/dataset/sampler.py#L14
shuffle=False), # Whether randomly shuffle the validation data
persistent_workers=True)
test_dataloader = val_dataloader # The config of the testing dataloaderEvaluators are used to compute the metrics of the trained model on the validation and testing datasets. The config of evaluators consists of one or a list of metric configs:
val_evaluator = dict( # The config for validation evaluator
type='Evaluator', # The type of evaluation
metrics=[ # The config for metrics
dict(
type='FrechetInceptionDistance',
prefix='FID-Full-50k',
fake_nums=50000,
inception_style='StyleGAN',
sample_model='ema'),
dict(type='PrecisionAndRecall', fake_nums=50000, prefix='PR-50K'),
dict(type='PerceptualPathLength', fake_nums=50000, prefix='ppl-w')
])
test_evaluator = val_evaluator # The config for testing evaluatorMMEngine's runner uses Loop to control the training, validation, and testing processes. Users can set the maximum training iteration and validation intervals with these fields.
train_cfg = dict( # The config for training
by_epoch=False, # Set `by_epoch` as False to use iteration-based training
val_begin=1, # Which iteration to start the validation
val_interval=10000, # Validation intervals
max_iters=800002) # Maximum training iterations
val_cfg = dict(type='MultiValLoop') # The validation loop type
test_cfg = dict(type='MultiTestLoop') # The testing loop typeoptim_wrapper is the field to configure optimization related settings.
The optimizer wrapper not only provides the functions of the optimizer, but also supports functions such as gradient clipping, mixed precision training, etc. Find more in optimizer wrapper tutorial.
optim_wrapper = dict(
constructor='MultiOptimWrapperConstructor',
generator=dict(
optimizer=dict(type='Adam', lr=0.0016, betas=(0, 0.9919919678228657))),
discriminator=dict(
optimizer=dict(
type='Adam',
lr=0.0018823529411764706,
betas=(0, 0.9905854573074332))))param_scheduler is a field that configures methods of adjusting optimization hyperparameters such as learning rate and momentum.
Users can combine multiple schedulers to create a desired parameter adjustment strategy.
Find more in parameter scheduler tutorial.
Since StyleGAN2 do not use parameter scheduler, we use config in CycleGAN as an example:
# parameter scheduler in CycleGAN config
param_scheduler = dict(
type='LinearLrInterval', # The type of scheduler
interval=400, # The interval to update the learning rate
by_epoch=False, # The scheduler is called by iteration
start_factor=0.0002, # The number we multiply parameter value in the first iteration
end_factor=0, # The number we multiply parameter value at the end of linear changing process.
begin=40000, # The start iteration of the scheduler
end=80000) # The end iteration of the schedulerUsers can attach hooks to training, validation, and testing loops to insert some operations during running. There are two different hook fields, one is default_hooks and the other is custom_hooks.
default_hooks is a dict of hook configs. default_hooks are the hooks must required at runtime. They have default priority which should not be modified. If not set, runner will use the default values. To disable a default hook, users can set its config to None.
default_hooks = dict(
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=100, log_metric_by_epoch=False),
checkpoint=dict(
type='CheckpointHook',
interval=10000,
by_epoch=False,
less_keys=['FID-Full-50k/fid'],
greater_keys=['IS-50k/is'],
save_optimizer=True,
save_best='FID-Full-50k/fid'))custom_hooks is a list of hook configs. Users can develop there own hooks and insert them in this field.
custom_hooks = [
dict(
type='VisualizationHook',
interval=5000,
fixed_input=True,
vis_kwargs_list=dict(type='GAN', name='fake_img'))
]default_scope = 'mmagic' # The default registry scope to find modules. Refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/registry.html
# config for environment
env_cfg = dict(
cudnn_benchmark=True, # whether to enable cudnn benchmark.
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), # set multi process parameters.
dist_cfg=dict(backend='nccl'), # set distributed parameters.
)
log_level = 'INFO' # The level of logging
log_processor = dict(
type='LogProcessor', # log processor to process runtime logs
by_epoch=False) # print log by iteration
load_from = None # load model checkpoint as a pre-trained model for a given path
resume = False # Whether to resume from the checkpoint define in `load_from`. If `load_from` is `None`, it will resume the latest checkpoint in `work_dir`To help the users have a basic idea of a complete config and the modules in a inpainting system, we make brief comments on the config of Global&Local as the following. For more detailed usage and the corresponding alternative for each modules, please refer to the API documentation.
model = dict(
type='GLInpaintor', # The name of inpaintor
data_preprocessor=dict(
type='DataPreprocessor', # The name of data preprocessor
mean=[127.5], # Mean value used in data normalization
std=[127.5], # Std value used in data normalization
),
encdec=dict(
type='GLEncoderDecoder', # The name of encoder-decoder
encoder=dict(type='GLEncoder', norm_cfg=dict(type='SyncBN')), # The config of encoder
decoder=dict(type='GLDecoder', norm_cfg=dict(type='SyncBN')), # The config of decoder
dilation_neck=dict(
type='GLDilationNeck', norm_cfg=dict(type='SyncBN'))), # The config of dilation neck
disc=dict(
type='GLDiscs', # The name of discriminator
global_disc_cfg=dict(
in_channels=3, # The input channel of discriminator
max_channels=512, # The maximum middle channel in discriminator
fc_in_channels=512 * 4 * 4, # The input channel of last fc layer
fc_out_channels=1024, # The output channel of last fc channel
num_convs=6, # The number of convs used in discriminator
norm_cfg=dict(type='SyncBN') # The config of norm layer
),
local_disc_cfg=dict(
in_channels=3, # The input channel of discriminator
max_channels=512, # The maximum middle channel in discriminator
fc_in_channels=512 * 4 * 4, # The input channel of last fc layer
fc_out_channels=1024, # The output channel of last fc channel
num_convs=5, # The number of convs used in discriminator
norm_cfg=dict(type='SyncBN') # The config of norm layer
),
),
loss_gan=dict(
type='GANLoss', # The name of GAN loss
gan_type='vanilla', # The type of GAN loss
loss_weight=0.001 # The weight of GAN loss
),
loss_l1_hole=dict(
type='L1Loss', # The type of l1 loss
loss_weight=1.0 # The weight of l1 loss
))
train_cfg = dict(
type='IterBasedTrainLoop',# The name of train loop type
max_iters=500002, # The number of total iterations
val_interval=50000, # The number of validation interval iterations
)
val_cfg = dict(type='ValLoop') # The name of validation loop type
test_cfg = dict(type='TestLoop') # The name of test loop type
val_evaluator = [
dict(type='MAE', mask_key='mask', scaling=100), # The name of metrics to evaluate
dict(type='PSNR'), # The name of metrics to evaluate
dict(type='SSIM'), # The name of metrics to evaluate
]
test_evaluator = val_evaluator
input_shape = (256, 256) # The shape of input image
train_pipeline = [
dict(type='LoadImageFromFile', key='gt'), # The config of loading image
dict(
type='LoadMask', # The type of loading mask pipeline
mask_mode='bbox', # The type of mask
mask_config=dict(
max_bbox_shape=(128, 128), # The shape of bbox
max_bbox_delta=40, # The changing delta of bbox height and width
min_margin=20, # The minimum margin from bbox to the image border
img_shape=input_shape)), # The input image shape
dict(
type='Crop', # The type of crop pipeline
keys=['gt'], # The keys of images to be cropped
crop_size=(384, 384), # The size of cropped patch
random_crop=True, # Whether to use random crop
),
dict(
type='Resize', # The type of resizing pipeline
keys=['gt'], # They keys of images to be resized
scale=input_shape, # The scale of resizing function
keep_ratio=False, # Whether to keep ratio during resizing
),
dict(
type='Normalize', # The type of normalizing pipeline
keys=['gt_img'], # The keys of images to be normed
mean=[127.5] * 3, # Mean value used in normalization
std=[127.5] * 3, # Std value used in normalization
to_rgb=False), # Whether to transfer image channels to rgb
dict(type='GetMaskedImage'), # The config of getting masked image pipeline
dict(type='PackInputs'), # The config of collecting data from the current pipeline
]
test_pipeline = train_pipeline # Constructing testing/validation pipeline
dataset_type = 'BasicImageDataset' # The type of dataset
data_root = 'data/places' # Root path of data
train_dataloader = dict(
batch_size=12, # Batch size of a single GPU
num_workers=4, # The number of workers to pre-fetch data for each single GPU
persistent_workers=False, # Whether maintain the workers Dataset instances alive
sampler=dict(type='InfiniteSampler', shuffle=False), # The type of data sampler
dataset=dict( # Train dataset config
type=dataset_type, # Type of dataset
data_root=data_root, # Root path of data
data_prefix=dict(gt='data_large'), # Prefix of image path
ann_file='meta/places365_train_challenge.txt', # Path of annotation file
test_mode=False,
pipeline=train_pipeline,
))
val_dataloader = dict(
batch_size=1, # Batch size of a single GPU
num_workers=4, # The number of workers to pre-fetch data for each single GPU
persistent_workers=False, # Whether maintain the workers Dataset instances alive
drop_last=False, # Whether drop the last incomplete batch
sampler=dict(type='DefaultSampler', shuffle=False), # The type of data sampler
dataset=dict( # Validation dataset config
type=dataset_type, # Type of dataset
data_root=data_root, # Root path of data
data_prefix=dict(gt='val_large'), # Prefix of image path
ann_file='meta/places365_val.txt', # Path of annotation file
test_mode=True,
pipeline=test_pipeline,
))
test_dataloader = val_dataloader
model_wrapper_cfg = dict(type='MMSeparateDistributedDataParallel') # The name of model wrapper
optim_wrapper = dict( # Config used to build optimizer, support all the optimizers in PyTorch whose arguments are also the same as those in PyTorch
constructor='MultiOptimWrapperConstructor',
generator=dict(
type='OptimWrapper', optimizer=dict(type='Adam', lr=0.0004)),
disc=dict(type='OptimWrapper', optimizer=dict(type='Adam', lr=0.0004)))
default_scope = 'mmagic' # Used to set registries location
save_dir = './work_dirs' # Directory to save the model checkpoints and logs for the current experiments
exp_name = 'gl_places' # The experiment name
default_hooks = dict( # Used to build default hooks
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=100), # Config to register logger hook
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict( # Config to set the checkpoint hook
type='CheckpointHook',
interval=50000,
by_epoch=False,
out_dir=save_dir),
sampler_seed=dict(type='DistSamplerSeedHook'),
)
env_cfg = dict( # Parameters to setup distributed training, the port can also be set
cudnn_benchmark=False,
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
dist_cfg=dict(backend='nccl'),
)
vis_backends = [dict(type='LocalVisBackend')] # The name of visualization backend
visualizer = dict( # Config used to build visualizer
type='ConcatImageVisualizer',
vis_backends=vis_backends,
fn_key='gt_path',
img_keys=['gt_img', 'input', 'pred_img'],
bgr2rgb=True)
custom_hooks = [dict(type='BasicVisualizationHook', interval=1)] # Used to build custom hooks
log_level = 'INFO' # The level of logging
log_processor = dict(type='LogProcessor', by_epoch=False) # Used to build log processor
load_from = None # load models as a pre-trained model from a given path. This will not resume training.
resume = False # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved.
find_unused_parameters = False # Whether to set find unused parameters in ddpTo help the users have a basic idea of a complete config, we make a brief comments on the config of the original DIM model we implemented as the following. For more detailed usage and the corresponding alternative for each modules, please refer to the API documentation.
# model settings
model = dict(
type='DIM', # The name of model (we call mattor).
data_preprocessor=dict( # The Config to build data preprocessor
type='DataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
proc_inputs='normalize',
proc_trimap='rescale_to_zero_one',
proc_gt='rescale_to_zero_one',
),
backbone=dict( # The config of the backbone.
type='SimpleEncoderDecoder', # The type of the backbone.
encoder=dict( # The config of the encoder.
type='VGG16'), # The type of the encoder.
decoder=dict( # The config of the decoder.
type='PlainDecoder')), # The type of the decoder.
pretrained='./weights/vgg_state_dict.pth', # The pretrained weight of the encoder to be loaded.
loss_alpha=dict( # The config of the alpha loss.
type='CharbonnierLoss', # The type of the loss for predicted alpha matte.
loss_weight=0.5), # The weight of the alpha loss.
loss_comp=dict( # The config of the composition loss.
type='CharbonnierCompLoss', # The type of the composition loss.
loss_weight=0.5), # The weight of the composition loss.
train_cfg=dict( # Config of training DIM model.
train_backbone=True, # In DIM stage1, backbone is trained.
train_refiner=False), # In DIM stage1, refiner is not trained.
test_cfg=dict( # Config of testing DIM model.
refine=False, # Whether use refiner output as output, in stage1, we don't use it.
resize_method='pad',
resize_mode='reflect',
size_divisor=32,
),
)
# data settings
dataset_type = 'AdobeComp1kDataset' # Dataset type, this will be used to define the dataset.
data_root = 'data/adobe_composition-1k' # Root path of data.
train_pipeline = [ # Training data processing pipeline.
dict(
type='LoadImageFromFile', # Load alpha matte from file.
key='alpha', # Key of alpha matte in annotation file. The pipeline will read alpha matte from path `alpha_path`.
color_type='grayscale'), # Load as grayscale image which has shape (height, width).
dict(
type='LoadImageFromFile', # Load image from file.
key='fg'), # Key of image to load. The pipeline will read fg from path `fg_path`.
dict(
type='LoadImageFromFile', # Load image from file.
key='bg'), # Key of image to load. The pipeline will read bg from path `bg_path`.
dict(
type='LoadImageFromFile', # Load image from file.
key='merged'), # Key of image to load. The pipeline will read merged from path `merged_path`.
dict(
type='CropAroundUnknown', # Crop images around unknown area (semi-transparent area).
keys=['alpha', 'merged', 'fg', 'bg'], # Images to crop.
crop_sizes=[320, 480, 640]), # Candidate crop size.
dict(
type='Flip', # Augmentation pipeline that flips the images.
keys=['alpha', 'merged', 'fg', 'bg']), # Images to be flipped.
dict(
type='Resize', # Augmentation pipeline that resizes the images.
keys=['alpha', 'merged', 'fg', 'bg'], # Images to be resized.
scale=(320, 320), # Target size.
keep_ratio=False), # Whether to keep the ratio between height and width.
dict(
type='GenerateTrimap', # Generate trimap from alpha matte.
kernel_size=(1, 30)), # Kernel size range of the erode/dilate kernel.
dict(type='PackInputs'), # The config of collecting data from the current pipeline
]
test_pipeline = [
dict(
type='LoadImageFromFile', # Load alpha matte.
key='alpha', # Key of alpha matte in annotation file. The pipeline will read alpha matte from path `alpha_path`.
color_type='grayscale',
save_original_img=True),
dict(
type='LoadImageFromFile', # Load image from file
key='trimap', # Key of image to load. The pipeline will read trimap from path `trimap_path`.
color_type='grayscale', # Load as grayscale image which has shape (height, width).
save_original_img=True), # Save a copy of trimap for calculating metrics. It will be saved with key `ori_trimap`
dict(
type='LoadImageFromFile', # Load image from file
key='merged'), # Key of image to load. The pipeline will read merged from path `merged_path`.
dict(type='PackInputs'), # The config of collecting data from the current pipeline
]
train_dataloader = dict(
batch_size=1, # Batch size of a single GPU
num_workers=4, # The number of workers to pre-fetch data for each single GPU
persistent_workers=False, # Whether maintain the workers Dataset instances alive
sampler=dict(type='InfiniteSampler', shuffle=True), # The type of data sampler
dataset=dict( # Train dataset config
type=dataset_type, # Type of dataset
data_root=data_root, # Root path of data
ann_file='training_list.json', # Path of annotation file
test_mode=False,
pipeline=train_pipeline,
))
val_dataloader = dict(
batch_size=1, # Batch size of a single GPU
num_workers=4, # The number of workers to pre-fetch data for each single GPU
persistent_workers=False, # Whether maintain the workers Dataset instances alive
drop_last=False, # Whether drop the last incomplete batch
sampler=dict(type='DefaultSampler', shuffle=False), # The type of data sampler
dataset=dict( # Validation dataset config
type=dataset_type, # Type of dataset
data_root=data_root, # Root path of data
ann_file='test_list.json', # Path of annotation file
test_mode=True,
pipeline=test_pipeline,
))
test_dataloader = val_dataloader
val_evaluator = [
dict(type='SAD'), # The name of metrics to evaluate
dict(type='MattingMSE'), # The name of metrics to evaluate
dict(type='GradientError'), # The name of metrics to evaluate
dict(type='ConnectivityError'), # The name of metrics to evaluate
]
test_evaluator = val_evaluator
train_cfg = dict(
type='IterBasedTrainLoop', # The name of train loop type
max_iters=1_000_000, # The number of total iterations
val_interval=40000, # The number of validation interval iterations
)
val_cfg = dict(type='ValLoop') # The name of validation loop type
test_cfg = dict(type='TestLoop') # The name of test loop type
# optimizer
optim_wrapper = dict(
dict(
type='OptimWrapper',
optimizer=dict(type='Adam', lr=0.00001),
)
) # Config used to build optimizer, support all the optimizers in PyTorch whose arguments are also the same as those in PyTorch.
default_scope = 'mmagic' # Used to set registries location
save_dir = './work_dirs' # Directory to save the model checkpoints and logs for the current experiments.
default_hooks = dict( # Used to build default hooks
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=100), # Config to register logger hook
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict( # Config to set the checkpoint hook
type='CheckpointHook',
interval=40000, # The save interval is 40000 iterations.
by_epoch=False, # Count by iterations.
out_dir=save_dir),
sampler_seed=dict(type='DistSamplerSeedHook'),
)
env_cfg = dict( # Parameters to setup distributed training, the port can also be set
cudnn_benchmark=False,
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=4),
dist_cfg=dict(backend='nccl'),
)
log_level = 'INFO' # The level of logging
log_processor = dict(type='LogProcessor', by_epoch=False) # Used to build log processor
load_from = None # load models as a pre-trained model from a given path. This will not resume training.
resume = False # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved.To help the users have a basic idea of a complete config, we make a brief comments on the config of the EDSR model we implemented as the following. For more detailed usage and the corresponding alternative for each modules, please refer to the API documentation.
exp_name = 'edsr_x2c64b16_1x16_300k_div2k' # The experiment name
work_dir = f'./work_dirs/{experiment_name}'
save_dir = './work_dirs/'
load_from = None # based on pre-trained x2 model
scale = 2 # Scale factor for upsampling
# model settings
model = dict(
type='BaseEditModel', # Name of the model
generator=dict( # Config of the generator
type='EDSRNet', # Type of the generator
in_channels=3, # Channel number of inputs
out_channels=3, # Channel number of outputs
mid_channels=64, # Channel number of intermediate features
num_blocks=16, # Block number in the trunk network
upscale_factor=scale, # Upsampling factor
res_scale=1, # Used to scale the residual in residual block
rgb_mean=(0.4488, 0.4371, 0.4040), # Image mean in RGB orders
rgb_std=(1.0, 1.0, 1.0)), # Image std in RGB orders
pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean') # Config for pixel loss
train_cfg=dict(), # Config of training model.
test_cfg=dict(), # Config of testing model.
data_preprocessor=dict( # The Config to build data preprocessor
type='DataPreprocessor', mean=[0., 0., 0.], std=[255., 255.,
255.]))
train_pipeline = [ # Training data processing pipeline
dict(type='LoadImageFromFile', # Load images from files
key='img', # Keys in results to find corresponding path
color_type='color', # Color type of image
channel_order='rgb', # Channel order of image
imdecode_backend='cv2'), # decode backend
dict(type='LoadImageFromFile', # Load images from files
key='gt', # Keys in results to find corresponding path
color_type='color', # Color type of image
channel_order='rgb', # Channel order of image
imdecode_backend='cv2'), # decode backend
dict(type='SetValues', dictionary=dict(scale=scale)), # Set value to destination keys
dict(type='PairedRandomCrop', gt_patch_size=96), # Paired random crop
dict(type='Flip', # Flip images
keys=['lq', 'gt'], # Images to be flipped
flip_ratio=0.5, # Flip ratio
direction='horizontal'), # Flip direction
dict(type='Flip', # Flip images
keys=['lq', 'gt'], # Images to be flipped
flip_ratio=0.5, # Flip ratio
direction='vertical'), # Flip direction
dict(type='RandomTransposeHW', # Random transpose h and w for images
keys=['lq', 'gt'], # Images to be transposed
transpose_ratio=0.5 # Transpose ratio
),
dict(type='PackInputs') # The config of collecting data from the current pipeline
]
test_pipeline = [ # Test pipeline
dict(type='LoadImageFromFile', # Load images from files
key='img', # Keys in results to find corresponding path
color_type='color', # Color type of image
channel_order='rgb', # Channel order of image
imdecode_backend='cv2'), # decode backend
dict(type='LoadImageFromFile', # Load images from files
key='gt', # Keys in results to find corresponding path
color_type='color', # Color type of image
channel_order='rgb', # Channel order of image
imdecode_backend='cv2'), # decode backend
dict(type='ToTensor', keys=['img', 'gt']), # Convert images to tensor
dict(type='PackInputs') # The config of collecting data from the current pipeline
]
# dataset settings
dataset_type = 'BasicImageDataset' # The type of dataset
data_root = 'data' # Root path of data
train_dataloader = dict(
num_workers=4, # The number of workers to pre-fetch data for each single GPU
persistent_workers=False, # Whether maintain the workers Dataset instances alive
sampler=dict(type='InfiniteSampler', shuffle=True), # The type of data sampler
dataset=dict( # Train dataset config
type=dataset_type, # Type of dataset
ann_file='meta_info_DIV2K800sub_GT.txt', # Path of annotation file
metainfo=dict(dataset_type='div2k', task_name='sisr'),
data_root=data_root + '/DIV2K', # Root path of data
data_prefix=dict( # Prefix of image path
img='DIV2K_train_LR_bicubic/X2_sub', gt='DIV2K_train_HR_sub'),
filename_tmpl=dict(img='{}', gt='{}'), # Filename template
pipeline=train_pipeline))
val_dataloader = dict(
num_workers=4, # The number of workers to pre-fetch data for each single GPU
persistent_workers=False, # Whether maintain the workers Dataset instances alive
drop_last=False, # Whether drop the last incomplete batch
sampler=dict(type='DefaultSampler', shuffle=False), # The type of data sampler
dataset=dict( # Validation dataset config
type=dataset_type, # Type of dataset
metainfo=dict(dataset_type='set5', task_name='sisr'),
data_root=data_root + '/Set5', # Root path of data
data_prefix=dict(img='LRbicx2', gt='GTmod12'), # Prefix of image path
pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = [
dict(type='MAE'), # The name of metrics to evaluate
dict(type='PSNR', crop_border=scale), # The name of metrics to evaluate
dict(type='SSIM', crop_border=scale), # The name of metrics to evaluate
]
test_evaluator = val_evaluator
train_cfg = dict(
type='IterBasedTrainLoop', max_iters=300000, val_interval=5000) # Config of train loop type
val_cfg = dict(type='ValLoop') # The name of validation loop type
test_cfg = dict(type='TestLoop') # The name of test loop type
# optimizer
optim_wrapper = dict(
dict(
type='OptimWrapper',
optimizer=dict(type='Adam', lr=0.00001),
)
) # Config used to build optimizer, support all the optimizers in PyTorch whose arguments are also the same as those in PyTorch.
param_scheduler = dict( # Config of learning policy
type='MultiStepLR', by_epoch=False, milestones=[200000], gamma=0.5)
default_hooks = dict( # Used to build default hooks
checkpoint=dict( # Config to set the checkpoint hook
type='CheckpointHook',
interval=5000, # The save interval is 5000 iterations
save_optimizer=True,
by_epoch=False, # Count by iterations
out_dir=save_dir,
),
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=100), # Config to register logger hook
param_scheduler=dict(type='ParamSchedulerHook'),
sampler_seed=dict(type='DistSamplerSeedHook'),
)
default_scope = 'mmagic' # Used to set registries location
save_dir = './work_dirs' # Directory to save the model checkpoints and logs for the current experiments.
env_cfg = dict( # Parameters to setup distributed training, the port can also be set
cudnn_benchmark=False,
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=4),
dist_cfg=dict(backend='nccl'),
)
log_level = 'INFO' # The level of logging
log_processor = dict(type='LogProcessor', window_size=100, by_epoch=False) # Used to build log processor
load_from = None # load models as a pre-trained model from a given path. This will not resume training.
resume = False # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved.