[Fix] fix isr, vsr, vfi inferencer (open-mmlab#1845)

* [Fix] fix video inferencer

* [Fix] fix video inferencer

demo/README.md

@@ -139,6 +139,14 @@ python mmagic_inference_demo.py \
         --result-out-dir ../resources/output/restoration/demo_restoration_esrgan_res.png
 ```
 
+```shell
+python mmagic_inference_demo.py \
+        --model-name ttsr \
+        --img ../resources/input/restoration/0901x2.png \
+        --ref ../resources/input/restoration/0901x2.png \
+        --result-out-dir ../resources/output/restoration/demo_restoration_ttsr_res.png
+```
+
 #### 2.2.5 Image translation
 
 ```shell
@@ -167,6 +175,17 @@ python mmagic_inference_demo.py \
 
 #### 2.2.8 Video Super-Resolution
 
+BasicVSR / BasicVSR++ / IconVSR / RealBasicVSR
+
+```shell
+python mmagic_inference_demo.py \
+        --model-name basicvsr \
+        --video ../resources/input/video_restoration/QUuC4vJs_000084_000094_400x320.mp4 \
+        --result-out-dir ../resources/output/video_restoration/demo_video_restoration_basicvsr_res.mp4
+```
+
+EDVR
+
 ```shell
 python mmagic_inference_demo.py \
         --model-name edvr \
@@ -175,6 +194,17 @@ python mmagic_inference_demo.py \
         --result-out-dir ../resources/output/video_restoration/demo_video_restoration_edvr_res.mp4
 ```
 
+TDAN
+
+```shell
+python mmagic_inference_demo.py \
+        --model-name tdan \
+        --model-setting 2
+        --extra-parameters window_size=5 \
+        --video ../resources/input/video_restoration/QUuC4vJs_000084_000094_400x320.mp4 \
+        --result-out-dir ../resources/output/video_restoration/demo_video_restoration_edvr_res.mp4
+```
+
 #### 2.2.9 Text-to-Image
 
 ```shell

mmagic/apis/inferencers/image_super_resolution_inferencer.py

@@ -7,7 +7,6 @@
 import torch
 from mmengine import mkdir_or_exist
 from mmengine.dataset import Compose
-from mmengine.dataset.utils import default_collate as collate
 
 from mmagic.utils import tensor2img
 from .base_mmagic_inferencer import BaseMMagicInferencer, InputsType, PredType
@@ -34,7 +33,6 @@ def preprocess(self, img: InputsType, ref: InputsType = None) -> Dict:
             data(Dict): Results of preprocess.
         """
         cfg = self.model.cfg
-        device = next(self.model.parameters()).device  # model device
 
         # select the data pipeline
         if cfg.get('inference_pipeline', None):
@@ -63,31 +61,22 @@ def preprocess(self, img: InputsType, ref: InputsType = None) -> Dict:
 
         # prepare data
         if ref:  # Ref-SR
-            data = dict(img_path=img, gt_path=ref)
+            data = dict(img_path=img, ref_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['inputs'] = data['inputs'].cuda()
-            if ref:
-                data['data_samples'][0] = data['data_samples'][0].cuda()
+        data['inputs'] = [_data['inputs']]
+        data['data_samples'] = [_data['data_samples']]
 
         return data
 
     def forward(self, inputs: InputsType) -> PredType:
         """Forward the inputs to the model."""
+        inputs = self.model.data_preprocessor(inputs)
         with torch.no_grad():
-            result = self.model(mode='tensor', **inputs)
+            result = self.model(mode='predict', **inputs)
         return result
 
     def visualize(self,
@@ -105,7 +94,8 @@ def visualize(self,
         Returns:
             List[np.ndarray]: Result of visualize
         """
-        results = tensor2img(preds[0])
+        result = preds[0].output.pred_img / 255.
+        results = tensor2img(result)[..., ::-1]
         if result_out_dir:
             mkdir_or_exist(os.path.dirname(result_out_dir))
             mmcv.imwrite(results, result_out_dir)

mmagic/apis/inferencers/video_interpolation_inferencer.py

@@ -6,6 +6,7 @@
 
 import cv2
 import mmcv
+import mmengine
 import numpy as np
 import torch
 from mmengine.dataset import Compose
@@ -109,6 +110,7 @@ def forward(self,
 
         # check if the output is a video
         output_file_extension = os.path.splitext(result_out_dir)[1]
+        mmengine.utils.mkdir_or_exist(osp.dirname(result_out_dir))
         if output_file_extension in VIDEO_EXTENSIONS:
             fourcc = cv2.VideoWriter_fourcc(*'mp4v')
             target = cv2.VideoWriter(result_out_dir, fourcc, output_fps,
@@ -179,11 +181,12 @@ def forward(self,
                self.extra_parameters['end_idx']:
                 break
 
-        logger: MMLogger = MMLogger.get_current_instance()
-        logger.info(f'Output video is save at {result_out_dir}.')
         if to_video:
             target.release()
 
+        logger: MMLogger = MMLogger.get_current_instance()
+        logger.info(f'Output video is save at {result_out_dir}.')
+
         return {}
 
     def visualize(self,

mmagic/apis/inferencers/video_restoration_inferencer.py

@@ -6,10 +6,12 @@
 
 import cv2
 import mmcv
+import mmengine
 import numpy as np
 import torch
 from mmengine.dataset import Compose
 from mmengine.logging import MMLogger
+from mmengine.utils import ProgressBar
 
 from mmagic.utils import tensor2img
 from .base_mmagic_inferencer import (BaseMMagicInferencer, InputsType,
@@ -153,13 +155,16 @@ def visualize(self,
             List[np.ndarray]: Result of visualize
         """
         file_extension = os.path.splitext(result_out_dir)[1]
+        mmengine.utils.mkdir_or_exist(osp.dirname(result_out_dir))
+        prog_bar = ProgressBar(preds.size(1))
         if file_extension in VIDEO_EXTENSIONS:  # save as video
             h, w = preds.shape[-2:]
             fourcc = cv2.VideoWriter_fourcc(*'mp4v')
             video_writer = cv2.VideoWriter(result_out_dir, fourcc, 25, (w, h))
             for i in range(0, preds.size(1)):
                 img = tensor2img(preds[:, i, :, :, :])
                 video_writer.write(img.astype(np.uint8))
+                prog_bar.update()
             cv2.destroyAllWindows()
             video_writer.release()
         else:
@@ -170,8 +175,8 @@ def visualize(self,
                 output_i = tensor2img(output_i)
                 filename_tmpl = self.extra_parameters['filename_tmpl']
                 save_path_i = f'{result_out_dir}/{filename_tmpl.format(i)}'
-
                 mmcv.imwrite(output_i, save_path_i)
+                prog_bar.update()
 
         logger: MMLogger = MMLogger.get_current_instance()
         logger.info(f'Output video is save at {result_out_dir}.')