From 53217c6e5d7d16aff89d0ae93f3a2200a48ce087 Mon Sep 17 00:00:00 2001 From: Hunor Laczko Date: Tue, 23 May 2023 22:57:08 +0200 Subject: [PATCH] direct training --- configs/direct_1.py | 77 ++++++++++ configs/direct_2.py | 78 ++++++++++ custom_utils/custom_utils.py | 1 + models/base.py | 4 +- models/cond_encoder.py | 2 +- models/direct.py | 29 ++++ run.py | 2 +- scripts/direct_train.py | 281 +++++++++++++++++++++++++++++++++++ 8 files changed, 470 insertions(+), 4 deletions(-) create mode 100644 configs/direct_1.py create mode 100644 configs/direct_2.py create mode 100644 models/direct.py create mode 100644 scripts/direct_train.py diff --git a/configs/direct_1.py b/configs/direct_1.py new file mode 100644 index 0000000..fe2ece5 --- /dev/null +++ b/configs/direct_1.py @@ -0,0 +1,77 @@ +from utils.convnext_utils import CNBlockConfig + +config = dict() + +config['gpu'] = '5' +config['name'] = 'direct_1' +config['notes'] = ' ' +config['tags'] = ['torch', 'direct'] +config['script'] = 'scripts/direct_train.py' + +config['pretrained_model'] = '' +config['use_curvature'] = False + +config['batch_size'] = 4 +config['train_dir'] = '/data-local/127/train' +config['valid_dir'] = '/data-local/127/valid' +config['test_dir'] = '/data-net/dataset/test_ready' +config['train_frames'] = None +config['valid_frames'] = None + +config['resolution'] = 512 +config['is_skirt'] = False +config['dataset_in_memory'] = False +config['is_cape'] = False +config['remove_global_orientation'] = True +config['device'] = 'cuda' +config['input_features'] = 6 + +config['smpl_res'] = 14475 +if config['is_cape']: + config['smpl_res'] = 3638 + +config['latent_dim'] = 128 + +config['bottleneck_scale'] = 2 + +config['is_label'] = False +config['kernel_size'] = 7 +config['bottleneck_size'] = 64 * config['bottleneck_scale'] + +block_setting = [ + CNBlockConfig(16, 32, 3), + CNBlockConfig(32, 128, 3), + CNBlockConfig(128, 256, 9), + CNBlockConfig(256, config['bottleneck_size'], 9), + CNBlockConfig(config['bottleneck_size'], None, 3), +] + +block_setting_reverse = [ + CNBlockConfig(config['bottleneck_size'], 256, 3), + CNBlockConfig(256, 128, 9), + CNBlockConfig(128, 32, 9), + CNBlockConfig(32, 16, 3), + CNBlockConfig(16, 3, 3), + # CNBlockConfig(3, 3, 3), +] + +config['encoder_block_settings'] = block_setting +config['decoder_block_settings'] = block_setting_reverse +config['activation'] = 'tanh' + + +config['lr'] = 0.0001 +config['reg_loss_coeff'] = 0.1 + +config['signature'] = { 'id', + 'frame_nr', + 'pose_shape', + 'uv_body_posed', + 'mesh_mask', + 'mesh_body_posed', + 'mesh_posed', + 'uv_unposed', + 'mesh_unposed', + 'uv_static', + 'uv_mask', + } \ No newline at end of file diff --git a/configs/direct_2.py b/configs/direct_2.py new file mode 100644 index 0000000..daeae20 --- /dev/null +++ b/configs/direct_2.py @@ -0,0 +1,78 @@ +from utils.convnext_utils import CNBlockConfig + +config = dict() + +config['gpu'] = '0' +config['name'] = 'direct_2' +config['notes'] = 'with curvature input' +config['tags'] = ['torch', 'direct'] +config['script'] = 'scripts/direct_train.py' + +config['pretrained_model'] = '' +config['use_curvature'] = True + +config['batch_size'] = 4 +config['train_dir'] = '/data-local/127/train' +config['valid_dir'] = '/data-local/127/valid' +config['test_dir'] = '/data-net/dataset/test_ready' +config['train_frames'] = None +config['valid_frames'] = None + +config['resolution'] = 512 +config['is_skirt'] = False +config['dataset_in_memory'] = False +config['is_cape'] = False +config['remove_global_orientation'] = True +config['device'] = 'cuda' +config['input_features'] = 10 + +config['smpl_res'] = 14475 +if config['is_cape']: + config['smpl_res'] = 3638 + +config['latent_dim'] = 128 + +config['bottleneck_scale'] = 2 + +config['is_label'] = False +config['kernel_size'] = 7 +config['bottleneck_size'] = 64 * config['bottleneck_scale'] + +block_setting = [ + CNBlockConfig(16, 32, 3), + CNBlockConfig(32, 128, 3), + CNBlockConfig(128, 256, 9), + CNBlockConfig(256, config['bottleneck_size'], 9), + CNBlockConfig(config['bottleneck_size'], None, 3), +] + +block_setting_reverse = [ + CNBlockConfig(config['bottleneck_size'], 256, 3), + CNBlockConfig(256, 128, 9), + CNBlockConfig(128, 32, 9), + CNBlockConfig(32, 16, 3), + CNBlockConfig(16, 3, 3), + # CNBlockConfig(3, 3, 3), +] + +config['encoder_block_settings'] = block_setting +config['decoder_block_settings'] = block_setting_reverse +config['activation'] = 'tanh' + + +config['lr'] = 0.0001 +config['reg_loss_coeff'] = 0.1 + +config['signature'] = { 'id', + 'frame_nr', + 'pose_shape', + 'uv_curvature', + 'mesh_mask', + 'mesh_body_posed', + 'mesh_posed', + 'uv_unposed', + 'mesh_unposed', + 'uv_static', + 'uv_mask', + 'uv_body_posed', + } \ No newline at end of file diff --git a/custom_utils/custom_utils.py b/custom_utils/custom_utils.py index d9b256d..349ccf8 100644 --- a/custom_utils/custom_utils.py +++ b/custom_utils/custom_utils.py @@ -43,6 +43,7 @@ class UV2Mesh(nn.Module): if uv.shape[1] == 3 or uv.shape[1] == 7: uv = torch.permute(uv, [0,2,3,1]) if uv.shape[1] != 512: + raise NotImplementedError uv = Resize(uv[:,:,None], size=(512, 256), interpolation=InterpolationMode.BILINEAR) # batch_size = uv.shape[0] diff --git a/models/base.py b/models/base.py index 406d3d7..d058f0d 100644 --- a/models/base.py +++ b/models/base.py @@ -35,10 +35,10 @@ class VariationalEncoder(nn.Module): class AEEncoder(nn.Module): - def __init__(self, block_setting: CNBlockConfig, kernel_size: int = 7, activation='tanh', is_label=False, nr_of_labels=None, device='cuda'): + def __init__(self, block_setting: CNBlockConfig, kernel_size: int = 7, activation='tanh', is_label=False, nr_of_labels=None, device='cuda', input_features=3): super(AEEncoder, self).__init__() - self.encoder = ConvNeXt(block_setting=block_setting, kernel_size=kernel_size, is_label=is_label, nr_of_labels=nr_of_labels, device=device) + self.encoder = ConvNeXt(block_setting=block_setting, kernel_size=kernel_size, is_label=is_label, nr_of_labels=nr_of_labels, device=device, input_features=input_features) if activation == 'tanh': self.act = nn.Tanh() elif activation == 'sigmoid': diff --git a/models/cond_encoder.py b/models/cond_encoder.py index 66e16d3..447aa58 100644 --- a/models/cond_encoder.py +++ b/models/cond_encoder.py @@ -8,7 +8,7 @@ class AutoEncoder(nn.Module): def __init__(self, config): super(AutoEncoder, self).__init__() - self.encoder : AEEncoder = AEEncoder(config['encoder_block_settings'], config['kernel_size'], config['activation'], is_label=config['is_label'], device=config['device'], nr_of_labels=7) + self.encoder : AEEncoder = AEEncoder(config['encoder_block_settings'], config['kernel_size'], config['activation'], is_label=config['is_label'], device=config['device'], nr_of_labels=7, input_features=config['input_features']) self.decoder : Decoder = Decoder(config['decoder_block_settings'], kernel_size=config['kernel_size'], is_label=config['is_label'], device=config['device'], nr_of_classes=6) diff --git a/models/direct.py b/models/direct.py new file mode 100644 index 0000000..18ee869 --- /dev/null +++ b/models/direct.py @@ -0,0 +1,29 @@ +import torch +from torch import nn +from models.convnext_orig import ConvNeXt, CNBlock, CNBlockConfig +from typing import Any, Callable, List, Optional, Sequence + + +class DirectModel(nn.Module): + + def __init__(self, block_setting: List[CNBlockConfig], kernel_size: int = 7, input_features: int = 16, device: str = 'cuda'): + super(DirectModel, self).__init__() + + self.encoder = ConvNeXt(block_setting=block_setting, kernel_size=kernel_size, input_features=input_features, device=device) + self.decoder = ConvNeXt(block_setting=block_setting, kernel_size=kernel_size, device=device, reversed=True) + + layers = [CNBlock(block_setting[-1].input_channels, kernel_size=kernel_size) for _ in range(5)] + self.mean_branch = nn.Sequential(*layers).to(device) + layers = [CNBlock(block_setting[-1].input_channels, kernel_size=kernel_size) for _ in range(5)] + self.var_branch = nn.Sequential(*layers).to(device) + + self.flatten_mean = nn.Flatten() + self.flatten_var = nn.Flatten() + + + def forward(self, inputs): + body, template = inputs + + x = self.encoder(x) + + return mean \ No newline at end of file diff --git a/run.py b/run.py index e1d7d52..13272a1 100644 --- a/run.py +++ b/run.py @@ -1,6 +1,6 @@ import os import sys -from configs.pyr_64_cape_m import config +from configs.direct_1 import config import argparse # parser = argparse.ArgumentParser(description='') diff --git a/scripts/direct_train.py b/scripts/direct_train.py new file mode 100644 index 0000000..fe8c2b1 --- /dev/null +++ b/scripts/direct_train.py @@ -0,0 +1,281 @@ +import os +import sys +sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..') + +import importlib +import argparse +parser = argparse.ArgumentParser(description='') +parser.add_argument('--name', type=str) +parser.add_argument('--config', type=str) +parser.add_argument('--debug', action='store_true') +args = vars(parser.parse_args()) + +config_module = importlib.import_module(args['config']) +config = config_module.config +if args['debug']: + from configs.direct_1 import config + # from configs.ae_static_orig_2 import config +os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" +os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu'] +if args['debug']: + os.environ["CUDA_LAUNCH_BLOCKING"] = '1' + +import time +import argparse +import wandb +import random +import numpy as np +import torch +# from torchmetrics import MultiScaleStructuralSimilarityIndexMeasure as MSSSIM +from custom_utils.custom_utils import UV2Mesh, get_summary_html, maskFacesBatch, plot_3d_collision +import plotly.graph_objects as go +import plotly.express as ex +import torch.optim as optim + +if config['is_skirt']: + from losses.losses import edge_loss_skirt as edge_loss +else: + from losses.losses import edge_loss + +from data.dataset import Dataset +# from torch.utils.data import DataLoader +from losses.losses import collision_loss, normal_loss +from utils.postprocess import fix_collision +from custom_utils.custom_utils import plot_3d +from utils.checkpoints import CheckpointSaver, load_model +from custom_utils.custom_utils import Accumulator +from utils.utils import InfiniteDataLoader as DataLoader +from models.cond_encoder import AutoEncoder as Model +from smpl.smpl_torch import SMPL + +from pytorch_msssim import MS_SSIM + + +def init_wandb(): + run = wandb.init(project='iccv', name=config['name'], notes=config['notes'], dir='/data-local/127/temp', tags=config['tags']) + artifact = wandb.Artifact('code', type='code') + artifact.add_file('scripts/direct_train.py') + artifact.add_file('losses/losses.py') + artifact.add_file('models/base.py') + artifact.add_file('models/cond_encoder.py') + artifact.add_file('models/convnext.py') + artifact.add_file('data/dataset.py') + config_file_names = [key for key in sys.modules.keys() if key.startswith('configs.')] + for config_file_name in config_file_names: + file_name = '/'.join(config_file_name.split('.')) + '.py' + if os.path.exists(file_name): + artifact.add_file(file_name) + run.log_artifact(artifact) + +uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape']) + + + +def train_step(batch): + model.train() + # with torch.autocast(device_type='cuda', dtype=torch.float16, enabled=use_amp): + if config['use_curvature']: + input = torch.cat([batch['uv_curvature'], batch['uv_static']], axis=1) + else: + input = torch.cat([batch['uv_body_posed'], batch['uv_static']], axis=1) + pred = model([input, batch['uv_mask']]) + + mesh_pred = uv2mesh(pred) + mesh_orig = batch['mesh_unposed'] + shape = batch['pose_shape'][:, 72:] + gender = torch.greater_equal(shape[:, 10] , 0.5).to(torch.long) + shape_offsets = torch.stack([torch.tensordot(shape[i,:10][None, :], smpl.shapedirs[gender[i]], dims=[[1], [2]])[0,smpl.to_keep,:] for i in range(shape.shape[0])]) + mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,None] + mesh_pred_posed = smpl(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)[0] + + e_loss = torch.mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask'])) + c_loss, c_ratio = collision_loss(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']) + c_loss, c_ratio = torch.mean(c_loss), torch.mean(c_ratio) + # l2_loss = torch.mean(1e3 * torch.mean(torch.sum(((batch['uv_unposed'] - pred) ** 2), axis=-1), axis=[1, 2])) + l2_mesh_loss = torch.mean(1e3 * torch.mean(torch.sum(batch['mesh_mask'][:,:,None] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1)) + n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape']) + + loss = l2_mesh_loss + (e_loss + c_loss + n_loss) * config['reg_loss_coeff'] + + optimizer.zero_grad() + loss.backward() + optimizer.step() + # scaler.scale(loss).backward() + # scaler.step(optimizer) + # scaler.update() + # optimizer.zero_grad() + + model.eval() + with torch.inference_mode(): + ms_ssim = torch.mean(_MSSSIM(pred + 1, batch['uv_unposed'] + 1)) + metric = torch.sum(torch.sqrt(torch.sum(batch['mesh_mask'][:,:,None] * (mesh_orig - mesh_pred.detach()) ** 2, axis=-1))) / torch.sum(batch['mesh_mask']) + + return { 'train/loss': loss, 'train/edge_loss': e_loss, 'train/euclid_error': metric, 'train/collision_ratio': c_ratio, + 'train/ms_ssim': ms_ssim, 'train/collision_loss': c_loss, 'train/l2_mesh_loss': l2_mesh_loss, + 'train/normal_loss': n_loss } + + +def valid_step(batch): + model.eval() + + with torch.inference_mode(): + if config['use_curvature']: + input = torch.cat([batch['uv_curvature'], batch['uv_static']], axis=1) + else: + input = torch.cat([batch['uv_body_posed'], batch['uv_static']], axis=1) + pred = model([input, batch['uv_mask']]) + mesh_pred = uv2mesh(pred) + mesh_orig = batch['mesh_unposed'] + shape = batch['pose_shape'][:, 72:] + gender = torch.greater_equal(shape[:, 10] , 0.5).to(torch.long) + shape_offsets = torch.stack([torch.tensordot(shape[i,:10][None, :], smpl.shapedirs[gender[i]], dims=[[1], [2]])[0,smpl.to_keep,:] for i in range(shape.shape[0])]) + mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,None] + mesh_pred_posed = smpl(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)[0] + + e_loss = torch.mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask'])) + c_loss, c_ratio = collision_loss(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']) + c_loss, c_ratio = torch.mean(c_loss), torch.mean(c_ratio) + # l2_loss = torch.mean(1e3 * torch.mean(torch.sum(((batch['uv_unposed'] - pred) ** 2), axis=-1), axis=[1, 2])) + l2_mesh_loss = torch.mean(1e3 * torch.mean(torch.sum(batch['mesh_mask'][:,:,None] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1)) + n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape']) + + ms_ssim = torch.mean(_MSSSIM(pred + 1, batch['uv_unposed'] + 1)) + loss = l2_mesh_loss + (e_loss + c_loss + n_loss) * config['reg_loss_coeff'] + + metric = torch.sum(torch.sqrt(torch.sum(batch['mesh_mask'][:,:,None] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / torch.sum(batch['mesh_mask']) + + return { 'valid/loss': loss, 'valid/edge_loss': e_loss, 'valid/euclid_error': metric, 'valid/collision_ratio': c_ratio, + 'valid/ms_ssim': ms_ssim, 'valid/collision_loss': c_loss, 'valid/l2_mesh_loss': l2_mesh_loss, + 'valid/normal_loss': n_loss } + + +def plot_everything(batch: dict, type: str): + model.eval() + with torch.inference_mode(): + if config['use_curvature']: + input = torch.cat([batch['uv_curvature'], batch['uv_static']], axis=1) + else: + input = torch.cat([batch['uv_body_posed'], batch['uv_static']], axis=1) + pred = model([input, batch['uv_mask']]).detach() + + mesh_pred = uv2mesh(pred).cpu().numpy() + mesh_orig = batch['mesh_unposed'].cpu().numpy() + + orig_img = torch.permute(batch['uv_unposed'], [0,2,3,1]) + uv_static_img = torch.permute(batch['uv_static'], [0,2,3,1]) + uv_body_posed_img = torch.permute(batch['uv_body_posed'], [0,2,3,1]) + pred_img = torch.permute(pred, [0,2,3,1]) + img = torch.concat((uv_static_img, uv_body_posed_img, orig_img, pred_img), axis=2) + imgs = [wandb.Image((img[i].cpu().numpy() + 1) / 2.0) for i in range(4)] + + F = np.load("smpl/smpl_faces" + skirt_modifier + ("_orig" if config['is_cape'] else "") + ".npy") + mask = batch['mesh_mask'].cpu().numpy().astype(bool) + + shape = batch['pose_shape'][:, 72:] + gender = torch.greater_equal(shape[:, 10] , 0.5).to(torch.long) + shape_offsets = torch.stack([torch.tensordot(shape[i,:10][None, :], smpl.shapedirs[gender[i]], dims=[[1], [2]])[0,smpl.to_keep,:] for i in range(shape.shape[0])]).cpu().numpy() + + garment_unposed, faces = maskFacesBatch(mesh_pred, F, mask) + garment_unposed = [garment_unposed[i] + shape_offsets[i, mask[i], :] for i in range(mask.shape[0])] + gt = [mesh_orig[i][mask[i], :] + shape_offsets[i, mask[i], :] for i in range(pred.shape[0])] + + for i in range(mesh_orig.shape[0]): + mesh_pred[i, mask[i], :] += shape_offsets[i, mask[i], :] + mesh_posed = smpl(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], torch.tensor(mesh_pred, device=config['device']))[0].cpu().numpy() + + gt_posed = [batch['mesh_posed'].cpu().numpy()[i, mask[i], :] for i in range(mask.shape[0])] + + mesh_posed_fixed = fix_collision(torch.tensor(mesh_posed, device=config['device']), batch['mesh_body_posed'], torch.tensor(mask, device=config['device'])).cpu().numpy() + body = batch['mesh_body_posed'].cpu().numpy() + + garment_posed = [mesh_posed[i, mask[i], :] for i in range(body.shape[0])] + collision = [np.concatenate((body[i], garment_posed[i]), axis=0) for i in range(len(garment_posed))] + collision_faces = [np.concatenate((F, faces[i] + config['smpl_res']), axis=0) for i in range(len(garment_posed))] + collision_color = [np.concatenate((np.ones_like(body[i], dtype=np.int32)*100, np.ones_like(garment_posed[i], dtype=np.int32)*230), axis=0) for i in range(len(garment_posed))] + + garment_posed_fixed = [mesh_posed_fixed[i, mask[i], :] for i in range(body.shape[0])] + collision_fixed = [np.concatenate((body[i], garment_posed_fixed[i]), axis=0) for i in range(len(garment_posed))] + collision_gt = [np.concatenate((body[i], gt_posed[i]), axis=0) for i in range(len(garment_posed))] + + figs = { type + "/garment_" + str(i): plot_3d(gt[i], garment_unposed[i], faces[i], collision[i], collision_faces[i], collision_color[i]) for i in range(4) } + figs_coll = { type + "/garment_" + str(i) + '_collision': plot_3d_collision(collision_gt[i], collision[i], collision_fixed[i], collision_faces[i], collision_color[i]) for i in range(4) } + + return { **figs, **figs_coll, **{ type + '/images': imgs, } } + + +def train(): + valid_iterator = iter(valid_dataloader) + + step = 0 + for batch in train_dataloader: + train_loss = train_step(batch) + train_accumulator.update(train_loss) + + if step % 25 == 0: + data_valid = next(valid_iterator) + valid_loss = valid_step(data_valid) + valid_accumulator.update(valid_loss) + + if step % 1000 == 0 or (step < 1000 and step % 100 == 0): + to_log = dict({ **train_loss, **valid_loss, **{ 'current_step': step } }) + if step % 5000 == 0 or (step < 5000 and step % 1000 == 0): + train_figs = plot_everything(batch, 'train') + valid_figs = plot_everything(data_valid, 'valid') + to_log = { **to_log, **train_figs, **valid_figs } + if step == 0: + to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } } + + if not args['debug']: + wandb.log(to_log, step=step) + train_accumulator.reset() + valid_accumulator.reset() + print(step) + + if step % 10000 == 0: + ckpt_saver_enc(model.encoder, step, optimizer, scaler) + ckpt_saver_dec(model.decoder, step, optimizer, scaler) + + step += 1 + + +if __name__ == '__main__': + if not args['debug']: + init_wandb() + + use_amp = True + skirt_modifier = '_skirt' if config['is_skirt'] else '' + + # dataset = Dataset('/data-local/127/train', config['signature'], resolutions=[512], device=device, frames_file='frames_jumpsuit.txt') + dataset_train = Dataset(config['train_dir'], config['signature'], device=config['device'], frames_file=config['train_frames'], use_cache=config['dataset_in_memory'], is_cape=config['is_cape']) + dataset_valid = Dataset(config['valid_dir'], config['signature'], device=config['device'], frames_file=config['valid_frames'], use_cache=config['dataset_in_memory'], is_cape=config['is_cape']) + train_dataloader = DataLoader(dataset_train, batch_size=4, drop_last=True, shuffle=True, ) + valid_dataloader = DataLoader(dataset_valid, batch_size=4, drop_last=True, shuffle=True) + train_accumulator = Accumulator() + valid_accumulator = Accumulator() + + model : Model = Model(config) + optimizer = optim.Adam(model.parameters(), lr=config['lr']) + scaler = torch.cuda.amp.GradScaler(enabled=use_amp) + smpl = SMPL(orig_smpl=config['is_cape'], path='smpl', device=config['device']) + + # _MSSSIM = MSSSIM(data_range=2).to(config['device']) + _MSSSIM = MS_SSIM(data_range=2, size_average=True, channel=3) + + if config['pretrained_model'] != '': + load_model(model.encoder, config['pretrained_model'] + '_enc') + load_model(model.decoder, config['pretrained_model'] + '_dec') + + ckpt_saver_enc = CheckpointSaver('checkpoints/iccv/torch/' + config['name'] + '_enc', top_n=5) + ckpt_saver_dec = CheckpointSaver('checkpoints/iccv/torch/' + config['name'] + '_dec', top_n=5) + + run_name = config['name'] + print(f'started training: {run_name}') + + if not args['debug']: + # model = torch.compile(model) + wandb.watch(model, log='all', log_freq=1000, log_graph=True) + + train() + + +