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import os
import torch
from argparse import ArgumentParser
from torch import nn
from torch.utils.data import ConcatDataset
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
import json
import wandb
from tqdm import tqdm
from romatch.benchmarks import MegadepthDenseBenchmark
from romatch.datasets.megadepth import MegadepthBuilder
from romatch.datasets.scannet import ScanNetBuilder
from romatch.losses.robust_loss import RobustLosses
from romatch.benchmarks import MegadepthDenseBenchmark, ScanNetBenchmark
from romatch.train.train import train_k_steps
from romatch.models.matcher import *
from romatch.models.transformer import Block, TransformerDecoder, MemEffAttention
from romatch.models.encoders import *
from romatch.checkpointing import CheckPoint
resolutions = {"low":(448, 448), "medium":(14*8*5, 14*8*5), "high":(14*8*6, 14*8*6)}
def get_model(pretrained_backbone=True, resolution = "medium", **kwargs):
gp_dim = 512
feat_dim = 512
decoder_dim = gp_dim + feat_dim
cls_to_coord_res = 64
coordinate_decoder = TransformerDecoder(
nn.Sequential(*[Block(decoder_dim, 8, attn_class=MemEffAttention) for _ in range(5)]),
decoder_dim,
cls_to_coord_res**2 + 1,
is_classifier=True,
amp = True,
pos_enc = False,)
dw = True
hidden_blocks = 8
kernel_size = 5
displacement_emb = "linear"
disable_local_corr_grad = True
conv_refiner = nn.ModuleDict(
{
"16": ConvRefiner(
2 * 512+128+(2*7+1)**2,
2 * 512+128+(2*7+1)**2,
2 + 1,
kernel_size=kernel_size,
dw=dw,
hidden_blocks=hidden_blocks,
displacement_emb=displacement_emb,
displacement_emb_dim=128,
local_corr_radius = 7,
corr_in_other = True,
amp = True,
disable_local_corr_grad = disable_local_corr_grad,
bn_momentum = 0.01,
),
"8": ConvRefiner(
2 * 512+64+(2*3+1)**2,
2 * 512+64+(2*3+1)**2,
2 + 1,
kernel_size=kernel_size,
dw=dw,
hidden_blocks=hidden_blocks,
displacement_emb=displacement_emb,
displacement_emb_dim=64,
local_corr_radius = 3,
corr_in_other = True,
amp = True,
disable_local_corr_grad = disable_local_corr_grad,
bn_momentum = 0.01,
),
"4": ConvRefiner(
2 * 256+32+(2*2+1)**2,
2 * 256+32+(2*2+1)**2,
2 + 1,
kernel_size=kernel_size,
dw=dw,
hidden_blocks=hidden_blocks,
displacement_emb=displacement_emb,
displacement_emb_dim=32,
local_corr_radius = 2,
corr_in_other = True,
amp = True,
disable_local_corr_grad = disable_local_corr_grad,
bn_momentum = 0.01,
),
"2": ConvRefiner(
2 * 64+16,
128+16,
2 + 1,
kernel_size=kernel_size,
dw=dw,
hidden_blocks=hidden_blocks,
displacement_emb=displacement_emb,
displacement_emb_dim=16,
amp = True,
disable_local_corr_grad = disable_local_corr_grad,
bn_momentum = 0.01,
),
"1": ConvRefiner(
2 * 9 + 6,
24,
2 + 1,
kernel_size=kernel_size,
dw=dw,
hidden_blocks = hidden_blocks,
displacement_emb = displacement_emb,
displacement_emb_dim = 6,
amp = True,
disable_local_corr_grad = disable_local_corr_grad,
bn_momentum = 0.01,
),
}
)
kernel_temperature = 0.2
learn_temperature = False
no_cov = True
kernel = CosKernel
only_attention = False
basis = "fourier"
gp16 = GP(
kernel,
T=kernel_temperature,
learn_temperature=learn_temperature,
only_attention=only_attention,
gp_dim=gp_dim,
basis=basis,
no_cov=no_cov,
)
gps = nn.ModuleDict({"16": gp16})
proj16 = nn.Sequential(nn.Conv2d(1024, 512, 1, 1), nn.BatchNorm2d(512))
proj8 = nn.Sequential(nn.Conv2d(512, 512, 1, 1), nn.BatchNorm2d(512))
proj4 = nn.Sequential(nn.Conv2d(256, 256, 1, 1), nn.BatchNorm2d(256))
proj2 = nn.Sequential(nn.Conv2d(128, 64, 1, 1), nn.BatchNorm2d(64))
proj1 = nn.Sequential(nn.Conv2d(64, 9, 1, 1), nn.BatchNorm2d(9))
proj = nn.ModuleDict({
"16": proj16,
"8": proj8,
"4": proj4,
"2": proj2,
"1": proj1,
})
displacement_dropout_p = 0.0
gm_warp_dropout_p = 0.0
decoder = Decoder(coordinate_decoder,
gps,
proj,
conv_refiner,
detach=True,
scales=["16", "8", "4", "2", "1"],
displacement_dropout_p = displacement_dropout_p,
gm_warp_dropout_p = gm_warp_dropout_p)
h,w = resolutions[resolution]
encoder = CNNandDinov2(
cnn_kwargs = dict(
pretrained=pretrained_backbone,
amp = True),
amp = True,
use_vgg = True,
)
matcher = RegressionMatcher(encoder, decoder, h=h, w=w, alpha=1, beta=0,**kwargs)
return matcher
def train(args):
dist.init_process_group('nccl')
#torch._dynamo.config.verbose=True
gpus = int(os.environ['WORLD_SIZE'])
# create model and move it to GPU with id rank
rank = dist.get_rank()
print(f"Start running DDP on rank {rank}")
device_id = rank % torch.cuda.device_count()
romatch.LOCAL_RANK = device_id
torch.cuda.set_device(device_id)
resolution = args.train_resolution
wandb_log = not args.dont_log_wandb
experiment_name = os.path.splitext(os.path.basename(__file__))[0]
wandb_mode = "online" if wandb_log and rank == 0 and False else "disabled"
wandb.init(project="romatch", entity=args.wandb_entity, name=experiment_name, reinit=False, mode = wandb_mode)
checkpoint_dir = "workspace/checkpoints/"
h,w = resolutions[resolution]
model = get_model(pretrained_backbone=True, resolution=resolution, attenuate_cert = False).to(device_id)
# Num steps
global_step = 0
batch_size = args.gpu_batch_size
step_size = gpus*batch_size
romatch.STEP_SIZE = step_size
N = (32 * 250000) # 250k steps of batch size 32
# checkpoint every
k = 25000 // romatch.STEP_SIZE
# Data
mega = MegadepthBuilder(data_root="data/megadepth", loftr_ignore=True, imc21_ignore = True)
use_horizontal_flip_aug = True
rot_prob = 0
depth_interpolation_mode = "bilinear"
megadepth_train1 = mega.build_scenes(
split="train_loftr", min_overlap=0.01, shake_t=32, use_horizontal_flip_aug = use_horizontal_flip_aug, rot_prob = rot_prob,
ht=h,wt=w,
)
megadepth_train2 = mega.build_scenes(
split="train_loftr", min_overlap=0.35, shake_t=32, use_horizontal_flip_aug = use_horizontal_flip_aug, rot_prob = rot_prob,
ht=h,wt=w,
)
megadepth_train = ConcatDataset(megadepth_train1 + megadepth_train2)
mega_ws = mega.weight_scenes(megadepth_train, alpha=0.75)
scannet = ScanNetBuilder(data_root="data/scannet")
scannet_train = scannet.build_scenes(split="train", ht=h, wt=w, use_horizontal_flip_aug = use_horizontal_flip_aug)
scannet_train = ConcatDataset(scannet_train)
scannet_ws = scannet.weight_scenes(scannet_train, alpha=0.75)
# Loss and optimizer
depth_loss_scannet = RobustLosses(
ce_weight=0.0,
local_dist={1:4, 2:4, 4:8, 8:8},
local_largest_scale=8,
depth_interpolation_mode=depth_interpolation_mode,
alpha = 0.5,
c = 1e-4,)
# Loss and optimizer
depth_loss_mega = RobustLosses(
ce_weight=0.01,
local_dist={1:4, 2:4, 4:8, 8:8},
local_largest_scale=8,
depth_interpolation_mode=depth_interpolation_mode,
alpha = 0.5,
c = 1e-4,)
parameters = [
{"params": model.encoder.parameters(), "lr": romatch.STEP_SIZE * 5e-6 / 8},
{"params": model.decoder.parameters(), "lr": romatch.STEP_SIZE * 1e-4 / 8},
]
optimizer = torch.optim.AdamW(parameters, weight_decay=0.01)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, milestones=[(9*N/romatch.STEP_SIZE)//10])
megadense_benchmark = MegadepthDenseBenchmark("data/megadepth", num_samples = 1000, h=h,w=w)
checkpointer = CheckPoint(checkpoint_dir, experiment_name)
model, optimizer, lr_scheduler, global_step = checkpointer.load(model, optimizer, lr_scheduler, global_step)
romatch.GLOBAL_STEP = global_step
ddp_model = DDP(model, device_ids=[device_id], find_unused_parameters = False, gradient_as_bucket_view=True)
grad_scaler = torch.cuda.amp.GradScaler(growth_interval=1_000_000)
grad_clip_norm = 0.01
for n in range(romatch.GLOBAL_STEP, N, k * romatch.STEP_SIZE):
mega_sampler = torch.utils.data.WeightedRandomSampler(
mega_ws, num_samples = batch_size * k, replacement=False
)
mega_dataloader = iter(
torch.utils.data.DataLoader(
megadepth_train,
batch_size = batch_size,
sampler = mega_sampler,
num_workers = 8,
)
)
scannet_ws_sampler = torch.utils.data.WeightedRandomSampler(
scannet_ws, num_samples=batch_size * k, replacement=False
)
scannet_dataloader = iter(
torch.utils.data.DataLoader(
scannet_train,
batch_size=batch_size,
sampler=scannet_ws_sampler,
num_workers=gpus * 8,
)
)
for n_k in tqdm(range(n, n + 2 * k, 2),disable = romatch.RANK > 0):
train_k_steps(
n_k, 1, mega_dataloader, ddp_model, depth_loss_mega, optimizer, lr_scheduler, grad_scaler, grad_clip_norm = grad_clip_norm, progress_bar=False
)
train_k_steps(
n_k + 1, 1, scannet_dataloader, ddp_model, depth_loss_scannet, optimizer, lr_scheduler, grad_scaler, grad_clip_norm = grad_clip_norm, progress_bar=False
)
checkpointer.save(model, optimizer, lr_scheduler, romatch.GLOBAL_STEP)
wandb.log(megadense_benchmark.benchmark(model), step = romatch.GLOBAL_STEP)
def test_scannet(model, name, resolution, sample_mode):
scannet_benchmark = ScanNetBenchmark("data/scannet")
scannet_results = scannet_benchmark.benchmark(model)
json.dump(scannet_results, open(f"results/scannet_{name}.json", "w"))
if __name__ == "__main__":
import warnings
warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated')
warnings.filterwarnings('ignore')#, category=UserWarning)#, message='WARNING batched routines are designed for small sizes.')
os.environ["TORCH_CUDNN_V8_API_ENABLED"] = "1" # For BF16 computations
os.environ["OMP_NUM_THREADS"] = "16"
import romatch
parser = ArgumentParser()
parser.add_argument("--test", action='store_true')
parser.add_argument("--debug_mode", action='store_true')
parser.add_argument("--dont_log_wandb", action='store_true')
parser.add_argument("--train_resolution", default='medium')
parser.add_argument("--gpu_batch_size", default=4, type=int)
parser.add_argument("--wandb_entity", required = False)
args, _ = parser.parse_known_args()
romatch.DEBUG_MODE = args.debug_mode
if not args.test:
train(args)
experiment_name = os.path.splitext(os.path.basename(__file__))[0]
checkpoint_dir = "workspace/"
checkpoint_name = checkpoint_dir + experiment_name + ".pth"
test_resolution = "medium"
sample_mode = "threshold_balanced"
symmetric = True
upsample_preds = False
attenuate_cert = True
model = get_model(pretrained_backbone=False, resolution = test_resolution, sample_mode = sample_mode, upsample_preds = upsample_preds, symmetric=symmetric, name=experiment_name, attenuate_cert = attenuate_cert)
model = model.cuda()
states = torch.load(checkpoint_name)
model.load_state_dict(states["model"])
test_scannet(model, experiment_name, resolution = test_resolution, sample_mode = sample_mode)
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