feat: support PyTorch mps backend on macOS

.gitignore

@@ -4,11 +4,13 @@
 
 *.pkl
 output/*
-train_log/*
+# train_log/*
 *.mp4
 
 test/
 .idea/
 *.npz
 
 *.zip
+
+.DS_Store

inference_img.py

@@ -6,7 +6,7 @@
 import warnings
 warnings.filterwarnings("ignore")
 
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+device = torch.device("cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu")
 torch.set_grad_enabled(False)
 if torch.cuda.is_available():
     torch.backends.cudnn.enabled = True

inference_img_SR.py

@@ -6,7 +6,7 @@
 import warnings
 warnings.filterwarnings("ignore")
 
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+device = torch.device("cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu")
 torch.set_grad_enabled(False)
 if torch.cuda.is_available():
     torch.backends.cudnn.enabled = True

inference_video.py

@@ -81,7 +81,7 @@ def transferAudio(sourceVideo, targetVideo):
 if not args.img is None:
     args.png = True
 
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+device = torch.device("cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu")
 torch.set_grad_enabled(False)
 if torch.cuda.is_available():
     torch.backends.cudnn.enabled = True

inference_video_enhance.py

@@ -67,7 +67,7 @@ def transferAudio(sourceVideo, targetVideo):
 if not args.img is None:
     args.png = True
 
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+device = torch.device("cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu")
 torch.set_grad_enabled(False)
 if torch.cuda.is_available():
     torch.backends.cudnn.enabled = True

model/loss.py

@@ -4,7 +4,7 @@
 import torch.nn.functional as F
 import torchvision.models as models
 
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+device = torch.device("cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu")
 
 
 class EPE(nn.Module):

model/pytorch_msssim/__init__.py

@@ -3,7 +3,7 @@
 from math import exp
 import numpy as np
 
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+device = torch.device("cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu")
 
 def gaussian(window_size, sigma):
     gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])

model/warplayer.py

@@ -1,7 +1,7 @@
 import torch
 import torch.nn as nn
 
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+device = torch.device("cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu")
 backwarp_tenGrid = {}
 
 
@@ -19,4 +19,12 @@ def warp(tenInput, tenFlow):
                          tenFlow[:, 1:2, :, :] / ((tenInput.shape[2] - 1.0) / 2.0)], 1)
 
     g = (backwarp_tenGrid[k] + tenFlow).permute(0, 2, 3, 1)
-    return torch.nn.functional.grid_sample(input=tenInput, grid=g, mode='bilinear', padding_mode='border', align_corners=True)
+
+    pd = 'border'
+
+    # mps does not support 'border' padding mode, use 'zero' instead
+    if tenInput.device.type == "mps":
+        pd = 'zeros'
+        g = g.clamp(-1, 1)
+
+    return torch.nn.functional.grid_sample(input=tenInput, grid=g, mode='bilinear', padding_mode=pd, align_corners=True) 

train_log/IFNet_HDv3.py

@@ -0,0 +1,169 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from model.warplayer import warp
+# from train_log.refine import *
+
+device = torch.device("cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu")
+
+def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=True),        
+        nn.LeakyReLU(0.2, True)
+    )
+
+def conv_bn(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=False),
+        nn.BatchNorm2d(out_planes),
+        nn.LeakyReLU(0.2, True)
+    )
+
+class Head(nn.Module):
+    def __init__(self):
+        super(Head, self).__init__()
+        self.cnn0 = nn.Conv2d(3, 16, 3, 2, 1)
+        self.cnn1 = nn.Conv2d(16, 16, 3, 1, 1)
+        self.cnn2 = nn.Conv2d(16, 16, 3, 1, 1)
+        self.cnn3 = nn.ConvTranspose2d(16, 16, 4, 2, 1)
+        self.relu = nn.LeakyReLU(0.2, True)
+
+    def forward(self, x, feat=False):
+        x0 = self.cnn0(x)
+        x = self.relu(x0)
+        x1 = self.cnn1(x)
+        x = self.relu(x1)
+        x2 = self.cnn2(x)
+        x = self.relu(x2)
+        x3 = self.cnn3(x)
+        if feat:
+            return [x0, x1, x2, x3]
+        return x3
+
+class ResConv(nn.Module):
+    def __init__(self, c, dilation=1):
+        super(ResConv, self).__init__()
+        self.conv = nn.Conv2d(c, c, 3, 1, dilation, dilation=dilation, groups=1\
+)
+        self.beta = nn.Parameter(torch.ones((1, c, 1, 1)), requires_grad=True)
+        self.relu = nn.LeakyReLU(0.2, True)
+
+    def forward(self, x):
+        return self.relu(self.conv(x) * self.beta + x)
+
+class IFBlock(nn.Module):
+    def __init__(self, in_planes, c=64):
+        super(IFBlock, self).__init__()
+        self.conv0 = nn.Sequential(
+            conv(in_planes, c//2, 3, 2, 1),
+            conv(c//2, c, 3, 2, 1),
+            )
+        self.convblock = nn.Sequential(
+            ResConv(c),
+            ResConv(c),
+            ResConv(c),
+            ResConv(c),
+            ResConv(c),
+            ResConv(c),
+            ResConv(c),
+            ResConv(c),
+        )
+        self.lastconv = nn.Sequential(
+            nn.ConvTranspose2d(c, 4*13, 4, 2, 1),
+            nn.PixelShuffle(2)
+        )
+
+    def forward(self, x, flow=None, scale=1):
+        x = F.interpolate(x, scale_factor= 1. / scale, mode="bilinear", align_corners=False)
+        if flow is not None:
+            flow = F.interpolate(flow, scale_factor= 1. / scale, mode="bilinear", align_corners=False) * 1. / scale
+            x = torch.cat((x, flow), 1)
+        feat = self.conv0(x)
+        feat = self.convblock(feat)
+        tmp = self.lastconv(feat)
+        tmp = F.interpolate(tmp, scale_factor=scale, mode="bilinear", align_corners=False)
+        flow = tmp[:, :4] * scale
+        mask = tmp[:, 4:5]
+        feat = tmp[:, 5:]
+        return flow, mask, feat
+
+class IFNet(nn.Module):
+    def __init__(self):
+        super(IFNet, self).__init__()
+        self.block0 = IFBlock(7+32, c=192)
+        self.block1 = IFBlock(8+4+8+32, c=128)
+        self.block2 = IFBlock(8+4+8+32, c=96)
+        self.block3 = IFBlock(8+4+8+32, c=64)
+        self.block4 = IFBlock(8+4+8+32, c=32)
+        self.encode = Head()
+
+        # not used during inference
+        '''
+        self.teacher = IFBlock(8+4+8+3+32, c=64)
+        self.caltime = nn.Sequential(
+            nn.Conv2d(32+9, 8, 3, 2, 1),
+            nn.LeakyReLU(0.2, True),
+            nn.Conv2d(32, 64, 3, 2, 1),
+            nn.LeakyReLU(0.2, True),
+            nn.Conv2d(64, 64, 3, 1, 1),
+            nn.LeakyReLU(0.2, True),
+            nn.Conv2d(64, 64, 3, 1, 1),
+            nn.LeakyReLU(0.2, True),
+            nn.Conv2d(64, 1, 3, 1, 1),
+            nn.Sigmoid()
+        )
+        '''
+
+    def forward(self, x, timestep=0.5, scale_list=[8, 4, 2, 1], training=False, fastmode=True, ensemble=False):
+        if training == False:
+            channel = x.shape[1] // 2
+            img0 = x[:, :channel]
+            img1 = x[:, channel:]
+        if not torch.is_tensor(timestep):
+            timestep = (x[:, :1].clone() * 0 + 1) * timestep
+        else:
+            timestep = timestep.repeat(1, 1, img0.shape[2], img0.shape[3])
+        f0 = self.encode(img0[:, :3])
+        f1 = self.encode(img1[:, :3])
+        flow_list = []
+        merged = []
+        mask_list = []
+        warped_img0 = img0
+        warped_img1 = img1
+        flow = None
+        mask = None
+        loss_cons = 0
+        block = [self.block0, self.block1, self.block2, self.block3, self.block4]
+        for i in range(5):
+            if flow is None:
+                flow, mask, feat = block[i](torch.cat((img0[:, :3], img1[:, :3], f0, f1, timestep), 1), None, scale=scale_list[i])
+                if ensemble:
+                    print("warning: ensemble is not supported since RIFEv4.21")
+            else:
+                wf0 = warp(f0, flow[:, :2])
+                wf1 = warp(f1, flow[:, 2:4])
+                fd, m0, feat = block[i](torch.cat((warped_img0[:, :3], warped_img1[:, :3], wf0, wf1, timestep, mask, feat), 1), flow, scale=scale_list[i])
+                if ensemble:
+                    print("warning: ensemble is not supported since RIFEv4.21")
+                else:
+                    mask = m0
+                flow = flow + fd
+            mask_list.append(mask)
+            flow_list.append(flow)
+            warped_img0 = warp(img0, flow[:, :2])
+            warped_img1 = warp(img1, flow[:, 2:4])
+            merged.append((warped_img0, warped_img1))
+        mask = torch.sigmoid(mask)
+        merged[4] = (warped_img0 * mask + warped_img1 * (1 - mask))
+        if not fastmode:
+            print('contextnet is removed')
+            '''
+            c0 = self.contextnet(img0, flow[:, :2])
+            c1 = self.contextnet(img1, flow[:, 2:4])
+            tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1)
+            res = tmp[:, :3] * 2 - 1
+            merged[4] = torch.clamp(merged[4] + res, 0, 1)
+            '''
+        return flow_list, mask_list[4], merged

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 "mps" if torch.backends.mps.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,
+            }