import os,time import torch,wandb from tqdm import trange from einops import rearrange import vis,geometry from copy import deepcopy import numpy as np import piqa,kornia from torchvision.utils import make_grid from einops import rearrange, repeat from models import ch_sec from torch.cuda.amp import autocast, GradScaler import torch.nn.functional as F import getpass import data,models import viser,nerfview from gsplat import rasterization torch.inverse(torch.ones((0, 0), device="cuda:0")) def to_gpu(ob): return {k: to_gpu(torch.tensor(v)) for k, v in ob.items()} if isinstance(ob, dict) else ob.cuda() def train_flowmap(run,train_dataset,until_img=25,until_vid=100,until_save=500,optim=None,single_data=None): def loss_fn(model_out, gt, model_input,model,step): rays = lambda x,y: torch.stack([x[i,:,y[i].long()] for i in range(len(x))]) losses = { } #if "res_depth" in model_out:losses["metrics/res_depth_reg"] = model_out["res_depth"].square().mean()*1e-1 if "point_track_loss" in model_out: losses["metrics/tracks_err"] = model_out["point_track_loss"]*1e3 if "affinity_emb_unnorm" in model_out and 0: #rig_targ=torch.ones_like(model_out["affinity_emb_unnorm"]) losses["metrics/aff_emb_reg"] = (1-model_out["affinity_emb_unnorm"]).square().mean()*2e-1 print(losses) #if "point_track_loss" in model_out: losses["metrics/tracks_err"] = model_out["point_track_loss"]*1e3 #if "track_reprojs" in model_out: # #rig_tracks=F.grid_sample(model_input["rig_flow_masks"].flatten(0,1),model_input["pred_tracks"][:,1:].flatten(0,1).unsqueeze(-2)*2-1,padding_mode="border",) # #rig_tracks=torch.cat((torch.ones_like(rig_tracks[:1]),rig_tracks)).squeeze()[None] # #vis_mask=torch.minimum(repeat(model_input["pred_visibility"]*rig_tracks,"b t k -> b k t s 1",s=model_input["pred_visibility"].size(1)), # # repeat(model_input["pred_visibility"]*rig_tracks,"b t k -> b k s t 1",s=model_input["pred_visibility"].size(1))) # vis_mask=torch.minimum(repeat(model_input["pred_visibility"],"b t k -> b k t s 1",s=model_input["pred_visibility"].size(1)), # repeat(model_input["pred_visibility"],"b t k -> b k s t 1",s=model_input["pred_visibility"].size(1))) # #losses["metrics/tracks_err"]=( # # repeat(model_input["pred_tracks"][:,:,model_out["track_idxs"]],"b t p c -> b p s t c", s=model_input["pred_visibility"].size(1))*vis_mask[:,model_out["track_idxs"]], # # model_out["track_reprojs"]*vis_mask[:,model_out["track_idxs"]],delta=2e-4 # # )*2e6 # losses["metrics/tracks_err"]= ( repeat(model_input["pred_tracks"][:,:,model_out["track_idxs"]],"b t p c -> b p s t c", s=model_input["pred_visibility"].size(1))*vis_mask[:,model_out["track_idxs"]] - model_out["track_reprojs"]*vis_mask[:,model_out["track_idxs"]] ).square().mean()*5e3 #if "flow_from_pose" in model_out: losses["metrics/flow_from_pose"] = F.huber_loss( model_out["flow_from_pose"].clip(-.2,.2), ch_sec(model_out["flow_inp_"]).clip(-.2,.2),delta=1.5e-05 )*3e7 #if "flow_from_pose" in model_out: losses["metrics/flow_from_pose"] = ( (model_out["flow_from_pose"].clip(-.2,.2) -ch_sec(model_out["flow_inp_"])).square().mean()*1e6 )#.clip(max=10) #if "flow_from_pose" in model_out: #for i in range(1,3): losses["metrics/flow_from_pose_%d"%i] = ( ((model_out["flow_from_pose"].clip(-.2,.2) -ch_sec(model_out["flow_inp_"]))*ch_sec(model_input["rig_flow_masks"])[...,[i]]).square().sum()/ch_sec(model_input["rig_flow_masks"])[...,[i]].sum()*1e5 )#.clip(max=10) #if "flow_from_pose" in model_out: losses["metrics/flow_from_pose_sobel_grad"] = (kornia.filters.sobel(model_out["flow_from_pose"].clip(-.2,.2).flatten(0,1).permute(0,2,1).unflatten(-1,model_input["rgb"].shape[-2:])) - kornia.filters.sobel(model_out["flow_inp_"].flatten(0,1)) ).square().mean()*1e6 #if "flow_from_pose" in model_out: losses["metrics/flow_from_pose"] = (model_out["flow_from_pose"].clip(-.2,.2)*ch_sec(model_input["rig_flow_masks"]) # -ch_sec(model_out["flow_inp_"])*ch_sec(model_input["rig_flow_masks"])).square().mean()*6e3 #if "flow_from_pose" in model_out: losses["metrics/flow_from_pose"] = ( (model_out["flow_from_pose"].clip(-.2,.2)*ch_sec(model_input["rig_flow_masks"]) # -ch_sec(model_out["flow_inp_"])*ch_sec(model_input["rig_flow_masks"])).square().mean()*6e5 ).clip(max=10) wandb.log({"est/fx": model_input["intrinsics"][0,0,0,0]},step=step) wandb.log({"est/fy": model_input["intrinsics"][0,0,1,1]},step=step) return losses losses_agg=[] optim = torch.optim.Adam(lr=run.args.lr, params=run.model.parameters()) model_input = ground_truth = single_data = to_gpu(run.dataset.collate_fn([train_dataset[0][0]])) # Train loop for step in trange(run.args.n_train_steps, desc="Fitting"): # train until user interruption # Run model and calculate losses total_loss = 0. #with torch.autocast(device_type="cuda", dtype=torch.bfloat16): out=run.model(model_input) losses = loss_fn(out, ground_truth, model_input,run.model,step) for loss_name, loss in losses.items(): wandb.log({loss_name: loss.item()}, step=step) total_loss += loss wandb.log({"loss": total_loss.item()}, step=step) total_loss.backward();optim.step();optim.zero_grad(); #run.splat_vars = geometry.format_splat_vars(out) # for splat vis # Image summaries and checkpoint if step==0:run.time=time.time() losses_agg.append({k:v.detach().item() for k,v in losses.items()}|{"time elapsed (seconds)":time.time()-run.time}) with torch.no_grad(): wandb_imgs=None if step%until_img==0 and 1: out=run.model.forward_allpts(model_input) # collect predictions for all point tracks wandb_imgs=vis.wandb_summary( 0, out, model_input, ground_truth, None,step=step) if step%until_save == 0 and step: # save model print(f"Saving to {run.save_dir}"); torch.save({ 'step': step, 'model_state_dict': run.model.state_dict(), }, os.path.join(run.save_dir, f"checkpoint_{step}.pt")) #save(run.splat_vars) if run.args.export_poses and step%100==0: # export poses+geom if overfitting on single scene out["intrinsics"] = model_input["intrinsics"] if "c2w" in model_input: out["gt_poses"],out["gt_intrinsics"]=model_input["c2w"],model_input["gt_intrinsics"] torch.save({k:v.detach() for k,v in out.items() if type(v)==torch.Tensor and len(v.shape)>1} ,f"/data/cameron/pose_exps/poses_{run.args.name}.pt") print("exported poses") # Data/args setup and run import argparse parser = argparse.ArgumentParser(description='simple training job') # logging parameters parser.add_argument('-n','--name', type=str,default="",required=False,help="wandb training name") parser.add_argument('-c','--init_ckpt', type=str,default=None,required=False,help="File for checkpoint loading. If folder specific, will use latest .pt file") parser.add_argument('-o','--online', default=False, action='store_true') parser.add_argument('--viser', default=False, action='store_true') parser.add_argument('--save_opt_vis', default=False, action='store_true') # data/training parameters parser.add_argument('-d','--dataset', type=str,default="hydrant") parser.add_argument('--imgpath', type=str,default="") parser.add_argument('-b','--batch_size', type=int,default=1,help="number of videos/sequences per training step") parser.add_argument('-v','--vid_len', type=int,default=6,help="video length or number of images per batch") parser.add_argument('--n_workers',type=int,default=0,help="number of workers per dataloader") parser.add_argument('--until_save',type=int,default=500,help="number of steps until model save") parser.add_argument('--lr',type=float,default=1e-4,help="learning rate") parser.add_argument('--n_train_steps',type=int,default=int(1e8),help="learning rate") parser.add_argument('--overfit', default=True, action='store_true',help="Whether to overfit on a single scene") parser.add_argument('--until_img', type=int,default=50,help="Number of steps until image summary. ") parser.add_argument('--sf', type=float,default=1,help="Image resolution scale factor (fractional is cheaper)") parser.add_argument('--load_save', default=False, action='store_true',help="Whether to load the previously saved data if overfitting (to avoid running flow again)") parser.add_argument('--splat_src', type=str,default=None,required=False,help="splat src pt file") # model parameters parser.add_argument('--time_stride', type=int,default=1,help="Number of frames to flatten in the encoder like a temporal convolution (to save memory). ") parser.add_argument('--n_skip', type=int,default=1,help="Number of frames to skip between adjacent frames in dataloader. ") parser.add_argument('--use_gt_intrinsics', default=True, action='store_true',help="Whether to use GT intrinsics instead of predicting them. Useful for pretraining scene rep.") parser.add_argument('--point_track', default=True, action='store_true',help="Whether to use point tracking") parser.add_argument('--export_poses', default=False, action='store_true',help="Export poses when overfitting") def make_run(args=None,val=False): args = parser.parse_args(args) self = argparse.Namespace() user = getpass.getuser() print(f"user={user}") # Wandb init run = wandb.init(entity="cameronsmithbusiness",project="biasing",mode="online" if args.online else "disabled",name=args.name,dir=f"/tmp/wandb") wandb.run.log_code(".") self.save_dir = "/tmp/"+args.name#os.path.join(os.environ.get('LOGDIR', "") , run.name) os.makedirs(self.save_dir,exist_ok=True) wandb.save(os.path.join(self.save_dir, "checkpoint*")) wandb.save(os.path.join(self.save_dir, "video*")) self.args=args self.wandb=run if args.viser: self.viser_server=viser.ViserServer() # Make dataset self.dataset = data.ImageFolder(path=args.imgpath,num_trgt=args.vid_len+1,n_skip = args.n_skip,sf=args.sf) # Make model and load checkpoint self.model = models.FlowMap(args).cuda() if args.init_ckpt is not None: ckpt_file = args.init_ckpt if os.path.isfile(os.path.expanduser(args.init_ckpt)) else max(glob.glob(os.path.join(args.init_ckpt,"*.pt")), key=os.path.getctime) self.model.load_state_dict(torch.load(ckpt_file)["model_state_dict"],strict=False) return self run = make_run() torch.autograd.set_detect_anomaly(False) train_flowmap(run,run.dataset,until_save=run.args.until_save, until_vid=100 if not run.args.overfit else 300, until_img=run.args.until_img)