From 495ffc4777522e40941753e3b1b79c02f84b25b4 Mon Sep 17 00:00:00 2001
From: Grafting Rayman <156515434+GraftingRayman@users.noreply.github.com>
Date: Fri, 17 Jan 2025 11:00:30 +0000
Subject: Add files via upload

---
 r_basicsr/models/edvr_model.py | 62 ++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 62 insertions(+)
 create mode 100644 r_basicsr/models/edvr_model.py

(limited to 'r_basicsr/models/edvr_model.py')

diff --git a/r_basicsr/models/edvr_model.py b/r_basicsr/models/edvr_model.py
new file mode 100644
index 0000000..1475033
--- /dev/null
+++ b/r_basicsr/models/edvr_model.py
@@ -0,0 +1,62 @@
+from r_basicsr.utils import get_root_logger
+from r_basicsr.utils.registry import MODEL_REGISTRY
+from .video_base_model import VideoBaseModel
+
+
+@MODEL_REGISTRY.register()
+class EDVRModel(VideoBaseModel):
+    """EDVR Model.
+
+    Paper: EDVR: Video Restoration with Enhanced Deformable Convolutional Networks.  # noqa: E501
+    """
+
+    def __init__(self, opt):
+        super(EDVRModel, self).__init__(opt)
+        if self.is_train:
+            self.train_tsa_iter = opt['train'].get('tsa_iter')
+
+    def setup_optimizers(self):
+        train_opt = self.opt['train']
+        dcn_lr_mul = train_opt.get('dcn_lr_mul', 1)
+        logger = get_root_logger()
+        logger.info(f'Multiple the learning rate for dcn with {dcn_lr_mul}.')
+        if dcn_lr_mul == 1:
+            optim_params = self.net_g.parameters()
+        else:  # separate dcn params and normal params for different lr
+            normal_params = []
+            dcn_params = []
+            for name, param in self.net_g.named_parameters():
+                if 'dcn' in name:
+                    dcn_params.append(param)
+                else:
+                    normal_params.append(param)
+            optim_params = [
+                {  # add normal params first
+                    'params': normal_params,
+                    'lr': train_opt['optim_g']['lr']
+                },
+                {
+                    'params': dcn_params,
+                    'lr': train_opt['optim_g']['lr'] * dcn_lr_mul
+                },
+            ]
+
+        optim_type = train_opt['optim_g'].pop('type')
+        self.optimizer_g = self.get_optimizer(optim_type, optim_params, **train_opt['optim_g'])
+        self.optimizers.append(self.optimizer_g)
+
+    def optimize_parameters(self, current_iter):
+        if self.train_tsa_iter:
+            if current_iter == 1:
+                logger = get_root_logger()
+                logger.info(f'Only train TSA module for {self.train_tsa_iter} iters.')
+                for name, param in self.net_g.named_parameters():
+                    if 'fusion' not in name:
+                        param.requires_grad = False
+            elif current_iter == self.train_tsa_iter:
+                logger = get_root_logger()
+                logger.warning('Train all the parameters.')
+                for param in self.net_g.parameters():
+                    param.requires_grad = True
+
+        super(EDVRModel, self).optimize_parameters(current_iter)
-- 
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