[Enhance] support all image super resolution models inference

configs/dic/dic_x8c48b6_4xb2-150k_celeba-hq.py

@@ -78,6 +78,30 @@
 ]
 test_pipeline = valid_pipeline
 
+inference_pipeline = [
+    dict(
+        type='LoadImageFromFile',
+        key='img',
+        color_type='color',
+        channel_order='rgb',
+        imdecode_backend='cv2'),
+    dict(
+        type='Resize',
+        scale=(128, 128),
+        keys=['img'],
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(
+        type='Resize',
+        scale=1 / 8,
+        keep_ratio=True,
+        keys=['img'],
+        output_keys=['img'],
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(type='PackEditInputs')
+]
+
 # dataset settings
 dataset_type = 'BasicImageDataset'
 data_root = 'data'

configs/glean/glean_x8_2xb8_cat.py

@@ -85,6 +85,21 @@
     dict(type='PackEditInputs')
 ]
 
+inference_pipeline = [
+    dict(
+        type='LoadImageFromFile',
+        key='img',
+        color_type='color',
+        channel_order='rgb'),
+    dict(
+        type='Resize',
+        scale=(32, 32),
+        keys=['img'],
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(type='PackEditInputs')
+]
+
 # dataset settings
 dataset_type = 'BasicImageDataset'
 

mmedit/apis/inferencers/restoration_inferencer.py → mmedit/apis/inferencers/image_super_resolution_inferencer.py

@@ -8,17 +8,16 @@
 from mmengine import mkdir_or_exist
 from mmengine.dataset import Compose
 from mmengine.dataset.utils import default_collate as collate
-from torch.nn.parallel import scatter
 
 from mmedit.utils import tensor2img
 from .base_mmedit_inferencer import BaseMMEditInferencer, InputsType, PredType
 
 
-class RestorationInferencer(BaseMMEditInferencer):
+class ImageSuperResolutionInferencer(BaseMMEditInferencer):
     """inferencer that predicts with restoration models."""
 
     func_kwargs = dict(
-        preprocess=['img'],
+        preprocess=['img', 'ref'],
         forward=[],
         visualize=['result_out_dir'],
         postprocess=[])
@@ -38,14 +37,15 @@ def preprocess(self, img: InputsType, ref: InputsType = None) -> Dict:
         device = next(self.model.parameters()).device  # model device
 
         # select the data pipeline
-        if cfg.get('demo_pipeline', None):
+        if cfg.get('inference_pipeline', None):
+            test_pipeline = cfg.inference_pipeline
+        elif cfg.get('demo_pipeline', None):
             test_pipeline = cfg.demo_pipeline
         elif cfg.get('test_pipeline', None):
             test_pipeline = cfg.test_pipeline
         else:
             test_pipeline = cfg.val_pipeline
 
-        # remove gt from test_pipeline
         keys_to_remove = ['gt', 'gt_path']
         for key in keys_to_remove:
             for pipeline in list(test_pipeline):
@@ -57,31 +57,31 @@ def preprocess(self, img: InputsType, ref: InputsType = None) -> Dict:
                         test_pipeline.remove(pipeline)
                 if 'meta_keys' in pipeline and key in pipeline['meta_keys']:
                     pipeline['meta_keys'].remove(key)
+
         # build the data pipeline
         test_pipeline = Compose(test_pipeline)
+
         # prepare data
         if ref:  # Ref-SR
-            data = dict(img_path=img, ref_path=ref)
+            data = dict(img_path=img, gt_path=ref)
         else:  # SISR
             data = dict(img_path=img)
         _data = test_pipeline(data)
+
         data = dict()
         data_preprocessor = cfg['model']['data_preprocessor']
         mean = torch.Tensor(data_preprocessor['mean']).view([3, 1, 1])
         std = torch.Tensor(data_preprocessor['std']).view([3, 1, 1])
         data['inputs'] = (_data['inputs'] - mean) / std
         data = collate([data])
+
         if ref:
             data['data_samples'] = [_data['data_samples']]
         if 'cuda' in str(device):
-            data = scatter(data, [device])[0]
+            data['inputs'] = data['inputs'].cuda()
             if ref:
-                data['data_samples'][0].img_lq.data = data['data_samples'][
-                    0].img_lq.data.to(device)
-                data['data_samples'][0].ref_lq.data = data['data_samples'][
-                    0].ref_lq.data.to(device)
-                data['data_samples'][0].ref_img.data = data['data_samples'][
-                    0].ref_img.data.to(device)
+                data['data_samples'][0] = data['data_samples'][0].cuda()
+
         return data
 
     def forward(self, inputs: InputsType) -> PredType:

mmedit/apis/inferencers/mmedit_inferencer.py

@@ -7,9 +7,9 @@
 from .colorization_inferencer import ColorizationInferencer
 from .conditional_inferencer import ConditionalInferencer
 from .eg3d_inferencer import EG3DInferencer
+from .image_super_resolution_inferencer import ImageSuperResolutionInferencer
 from .inpainting_inferencer import InpaintingInferencer
 from .matting_inferencer import MattingInferencer
-from .restoration_inferencer import RestorationInferencer
 from .text2image_inferencer import Text2ImageInferencer
 from .translation_inferencer import TranslationInferencer
 from .unconditional_inferencer import UnconditionalInferencer
@@ -55,8 +55,8 @@ def __init__(self,
         elif self.task in ['translation', 'Image2Image']:
             self.inferencer = TranslationInferencer(
                 config, ckpt, device, extra_parameters, seed=seed)
-        elif self.task in ['restoration', 'Image Super-Resolution']:
-            self.inferencer = RestorationInferencer(
+        elif self.task in ['Image super-resolution', 'Image Super-Resolution']:
+            self.inferencer = ImageSuperResolutionInferencer(
                 config, ckpt, device, extra_parameters, seed=seed)
         elif self.task in ['video_restoration', 'Video Super-Resolution']:
             self.inferencer = VideoRestorationInferencer(

mmedit/edit.py

@@ -39,7 +39,9 @@ class MMEdit:
         >>> # see demo/mmediting_inference_tutorial.ipynb for more examples
     """
     # unsupported now
-    # singan
+    # singan, liif
+    # output should be checked
+    # dic, glean
 
     inference_supported_models = [
         # colorization models
@@ -71,8 +73,16 @@ class MMEdit:
         'pix2pix',
         'cyclegan',
 
-        # restoration models
+        # image super-resolution models
+        'srcnn',
+        'srgan_resnet',
+        'edsr',
         'esrgan',
+        'rdn',
+        'dic',
+        'ttsr',
+        'glean',
+        'real_esrgan',
 
         # video_interpolation models
         'flavr',

tests/test_apis/test_inferencers/test_restoration_inferencer.py → tests/test_apis/test_inferencers/test_image_super_resolution_inferencer.py

@@ -5,8 +5,8 @@
 import pytest
 import torch
 
-from mmedit.apis.inferencers.restoration_inferencer import \
-    RestorationInferencer
+from mmedit.apis.inferencers.image_super_resolution_inferencer import \
+    ImageSuperResolutionInferencer
 from mmedit.utils import register_all_modules
 
 register_all_modules()
@@ -15,16 +15,16 @@
 @pytest.mark.skipif(
     'win' in platform.system().lower() and 'cu' in torch.__version__,
     reason='skip on windows-cuda due to limited RAM.')
-def test_restoration_inferencer():
+def test_image_super_resolution_inferencer():
     data_root = osp.join(osp.dirname(__file__), '../../../')
     config = data_root + 'configs/esrgan/esrgan_x4c64b23g32_1xb16-400k_div2k.py'  # noqa
     img_path = data_root + 'tests/data/image/lq/baboon_x4.png'
     result_out_dir = osp.join(
         osp.dirname(__file__), '..', '..', 'data/out',
-        'restoration_result.png')
+        'image_super_resolution_result.png')
 
     inferencer_instance = \
-        RestorationInferencer(config, None)
+        ImageSuperResolutionInferencer(config, None)
     inferencer_instance(img=img_path)
     inference_result = inferencer_instance(
         img=img_path, result_out_dir=result_out_dir)