Adding RIFE_HDv3.py to 4.25

4.25/train_log/RIFE_HDv3.py

@@ -0,0 +1,89 @@
+import torch
+import torch.nn as nn
+import numpy as np
+from torch.optim import AdamW
+import torch.optim as optim
+import itertools
+from model.warplayer import warp
+from torch.nn.parallel import DistributedDataParallel as DDP
+from train_log.IFNet_HDv3 import *
+import torch.nn.functional as F
+from model.loss import *
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    
+class Model:
+    def __init__(self, local_rank=-1):
+        self.flownet = IFNet()
+        self.device()
+        self.optimG = AdamW(self.flownet.parameters(), lr=1e-6, weight_decay=1e-4)
+        self.epe = EPE()
+        self.version = 4.25
+        # self.vgg = VGGPerceptualLoss().to(device)
+        self.sobel = SOBEL()
+        if local_rank != -1:
+            self.flownet = DDP(self.flownet, device_ids=[local_rank], output_device=local_rank)
+
+    def train(self):
+        self.flownet.train()
+
+    def eval(self):
+        self.flownet.eval()
+
+    def device(self):
+        self.flownet.to(device)
+
+    def load_model(self, path, rank=0):
+        def convert(param):
+            if rank == -1:
+                return {
+                    k.replace("module.", ""): v
+                    for k, v in param.items()
+                    if "module." in k
+                }
+            else:
+                return param
+        if rank <= 0:
+            if torch.cuda.is_available():
+                self.flownet.load_state_dict(convert(torch.load('{}/flownet.pkl'.format(path))), False)
+            else:
+                self.flownet.load_state_dict(convert(torch.load('{}/flownet.pkl'.format(path), map_location ='cpu')), False)
+        
+    def save_model(self, path, rank=0):
+        if rank == 0:
+            torch.save(self.flownet.state_dict(),'{}/flownet.pkl'.format(path))
+
+    def inference(self, img0, img1, timestep=0.5, scale=1.0):
+        imgs = torch.cat((img0, img1), 1)
+        scale_list = [16/scale, 8/scale, 4/scale, 2/scale, 1/scale]
+        flow, mask, merged = self.flownet(imgs, timestep, scale_list)
+        return merged[-1]
+    
+    def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None):
+        for param_group in self.optimG.param_groups:
+            param_group['lr'] = learning_rate
+        img0 = imgs[:, :3]
+        img1 = imgs[:, 3:]
+        if training:
+            self.train()
+        else:
+            self.eval()
+        scale = [16, 8, 4, 2, 1]
+        flow, mask, merged = self.flownet(torch.cat((imgs, gt), 1), scale=scale, training=training)
+        loss_l1 = (merged[-1] - gt).abs().mean()
+        loss_smooth = self.sobel(flow[-1], flow[-1]*0).mean()
+        # loss_vgg = self.vgg(merged[-1], gt)
+        if training:
+            self.optimG.zero_grad()
+            loss_G = loss_l1 + loss_cons + loss_smooth * 0.1
+            loss_G.backward()
+            self.optimG.step()
+        else:
+            flow_teacher = flow[2]
+        return merged[-1], {
+            'mask': mask,
+            'flow': flow[-1][:, :2],
+            'loss_l1': loss_l1,
+            'loss_cons': loss_cons,
+            'loss_smooth': loss_smooth,
+            }