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