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()) if args['debug']: from configs.iccvw.abl_all_5 import config else: config_module = importlib.import_module(args['config']) config = config_module.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 tqdm import random import numpy as np import plotly.graph_objects as go import plotly.express as ex from PIL import Image from pathlib import Path if config['is_skirt']: from losses.losses import edge_loss_skirt as edge_loss else: from losses.losses import edge_loss import torch import torch.nn as nn import torch.optim as optim from torch.profiler import profile, record_function, ProfilerActivity from data.dataset import Dataset from models.cond_encoder import AutoEncoder from utils.checkpoints import CheckpointSaver, load_model from utils.utils import InfiniteDataLoader as DataLoader from utils.utils import text_to_html, save_stl, create_temp_frames from utils.postprocess import fix_collision from losses.losses import collision_loss, normal_loss from pytorch_msssim import MS_SSIM from models.cvae_model import CVAE from smpl.smpl_torch import SMPL from custom_utils.custom_utils import UV2Mesh, Accumulator, plot_3d, get_summary_html, maskFacesBatch, get_summary_str, plot_3d_collision def init_wandb(): wandb.login(host='https://wandb2.lnrweb.com', key='local-2a78b434e9bc51e9f12625f8321a9e3ee34af16d', relogin=True, force=True) run = wandb.init(project='iccvw', name=config['name'], notes=config['notes'], dir='/data-local/127/temp', tags=config['tags']) artifact = wandb.Artifact('code', type='code') artifact.add_file('scripts/cvae.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('models/cvae_model.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 kl_loss(mean: torch.Tensor, logvar: torch.Tensor) -> torch.Tensor: kl_loss = 1 + logvar - torch.square(mean) - torch.exp(logvar) kl_loss = torch.mean(kl_loss) kl_loss *= -0.5 return kl_loss def train_step(batch): # with profiler: if config['use_skinning']: mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed'] else: mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed'] # with record_function("forward"): model.train() pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_normals'], batch['uv_mask']]) mesh_pred = uv2mesh(pred) if config['use_skinning']: 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])]) # shape_offsets = torch.stack([torch.matmul(shape[i, :10], smpl.shapedirs[gender[i]][:, 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] else: mesh_pred_posed = mesh_pred 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(((uv_orig - 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']) kl = kl_loss(mean, logvar) reconstr_loss = l2_loss if config['use_extra_losses']: reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss + c_loss + n_loss) * config['reg_loss_coeff'] loss = g_balancing_coeff * kl + reconstr_loss # with record_function("backward"): optimizer.zero_grad() loss.backward() optimizer.step() # with record_function("eval"): model.eval() with torch.inference_mode(): mean = torch.mean(mean) logvar = torch.mean(logvar) ms_ssim = torch.mean(_MSSSIM(pred + 1, batch['uv_static'] + 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']) # profiler.step() return { 'train/loss': loss, 'train/edge_loss': e_loss, 'train/collision_loss': c_loss, 'train/euclid_error': metric, 'train/reconstr_loss': reconstr_loss, 'train/collision_ratio': c_ratio, 'train/mean': mean, 'train/logvar': logvar, 'train/kl_loss': kl, 'train/var': np.e ** logvar, 'train/l2_loss': l2_loss, 'train/l2_mesh_loss': l2_mesh_loss, 'train/normal_loss': n_loss, 'train/ms_ssim': ms_ssim, } def valid_step(batch): if config['use_skinning']: mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed'] else: mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed'] model.eval() with torch.inference_mode(): pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_normals'], batch['uv_mask']]) mesh_pred = uv2mesh(pred) if config['use_skinning']: 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])]) # shape_offsets = torch.stack([torch.matmul(shape[i, :10], smpl.shapedirs[gender[i]][:, 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] else: mesh_pred_posed = mesh_pred 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(((uv_orig - 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']) kl = kl_loss(mean, logvar) reconstr_loss = l2_loss if config['use_extra_losses']: reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss + c_loss + n_loss) * config['reg_loss_coeff'] loss = reconstr_loss + g_balancing_coeff * kl mean = torch.mean(mean) logvar = torch.mean(logvar) ms_ssim = torch.mean(_MSSSIM(pred + 1, batch['uv_static'] + 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 { 'valid/loss': loss, 'valid/edge_loss': e_loss, 'valid/collision_loss': c_loss, 'valid/euclid_error': metric, 'valid/reconstr_loss': reconstr_loss, 'valid/l2_mesh_loss': l2_mesh_loss, 'valid/collision_ratio': c_ratio, 'valid/mean': mean, 'valid/logvar': logvar, 'valid/kl_loss': kl, 'valid/var': np.e ** logvar, 'valid/normal_loss': n_loss, 'valid/ms_ssim': ms_ssim, } def plot_everything(batch: dict, type: str): if config['use_skinning']: mesh_orig, uv_orig = batch['mesh_unposed'].cpu().numpy(), batch['uv_unposed'] else: mesh_orig, uv_orig = batch['mesh_posed'].cpu().numpy(), batch['uv_posed'] model.eval() with torch.inference_mode(): pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_normals'], batch['uv_mask']]) mesh_pred = uv2mesh(pred).detach().cpu().numpy() orig_img = torch.permute(uv_orig, [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]) uv_normals_img = (torch.permute(batch['uv_normals'], [0,2,3,1]) + 1.1) / 2.2 pred_img = torch.permute(pred, [0,2,3,1]) img = torch.concat((uv_static_img, uv_body_posed_img, uv_normals_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) if config['use_skinning']: 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() else: mesh_posed = mesh_pred.cpu().numpy() gt = [mesh_orig[i][mask[i], :] for i in range(pred.shape[0])] garment_unposed, faces = maskFacesBatch(mesh_pred.cpu().numpy(), F, mask) gt_posed = [batch['mesh_posed'].cpu().numpy()[i, mask[i], :] for i in range(mask.shape[0])] # # plot garment_unposed, gt and mesh_posed next to eachother in plotly subplots # from plotly.subplots import make_subplots # fig = make_subplots(rows=1, cols=4, specs=[[{'type': 'surface'}, {'type': 'surface'}, {'type': 'surface'}, {'type': 'surface'}]]) # fig.add_trace(go.Mesh3d(x=gt[0][:,0], y=gt[0][:,2], z=gt[0][:,1], i=faces[0][:,0], j=faces[0][:,1], k=faces[0][:,2], color='red', opacity=0.5), row=1, col=1) # fig.add_trace(go.Mesh3d(x=garment_unposed[0][:,0], y=garment_unposed[0][:,2], z=garment_unposed[0][:,1], i=faces[0][:,0], j=faces[0][:,1], k=faces[0][:,2], color='blue', opacity=0.5), row=1, col=2) # # add new subplot for gt_posed # fig.add_trace(go.Mesh3d(x=gt_posed[0][:,0], y=gt_posed[0][:,2], z=gt_posed[0][:,1], i=faces[0][:,0], j=faces[0][:,1], k=faces[0][:,2], color='red', opacity=0.5), row=1, col=3) # fig.add_trace(go.Mesh3d(x=mesh_posed[0][mask[0],0], y=mesh_posed[0][mask[0],2], z=mesh_posed[0][mask[0],1], i=faces[0][:,0], j=faces[0][:,1], k=faces[0][:,2], color='green', opacity=0.5), row=1, col=4) # # make each subplot between -1 and 1 # fig.update_layout(scene_aspectmode='cube', scene2_aspectmode='cube', scene3_aspectmode='cube', scene4_aspectmode='cube') # fig.update_layout(scene=dict(xaxis=dict(range=[-1,1]), yaxis=dict(range=[-1,1]), zaxis=dict(range=[-1,1])), scene2=dict(xaxis=dict(range=[-1,1]), yaxis=dict(range=[-1,1]), zaxis=dict(range=[-1,1])), scene3=dict(xaxis=dict(range=[-1,1]), yaxis=dict(range=[-1,1]), zaxis=dict(range=[-1,1])), scene4=dict(xaxis=dict(range=[-1,1]), yaxis=dict(range=[-1,1]), zaxis=dict(range=[-1,1]))) # fig.show() 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], flip_xy=(not config['use_skinning'])) 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], flip_xy=(not config['use_skinning'])) for i in range(4) } mean_hist = wandb.Histogram(sequence=mean.cpu().numpy().mean(axis=0)) logvar_hist = wandb.Histogram(sequence=logvar.cpu().numpy().mean(axis=0)) batch_id = batch['id'] batch_frame_nr = batch['frame_nr'].cpu().numpy() batch_garment = batch['garment'] batch_info = [f'batch_info: id: {batch_id[i]}, frame_nr: {batch_frame_nr[i]}, garment: {batch_garment[i]}' for i in range(len(batch_id))] batch_info = text_to_html(batch_info) batch_info = wandb.Html(batch_info) return { **figs, **figs_coll, **{ f'{type}/batch_info': batch_info }, **{ type + '/images': imgs, type + '/mean_hist': mean_hist, type + '/logvar_hist': logvar_hist } } def sample(): from data.utils import Frame from utils.utils import save_stl def create_temp_frames(folder: str, frame: Frame): with open(Path(folder) / 'frames_temp.txt', 'w') as f: f.writelines([f'{frame}\n' for _ in range(4)]) model.eval() load_model(model, f'checkpoints/iccvw/{config["name"]}') count = 0 frames = ['01852_70_Tshirt'] frames = [Frame(frame_str=frame) for frame in frames] for frame in frames: create_temp_frames(config['train_dir'], frame) dataset = Dataset(config['train_dir'], config['signature'], device=config['device'], frames_file='frames_temp.txt', use_cache=True, resolutions=[512], is_cape=config['is_cape']) dataloader = DataLoader(dataset, batch_size=4, drop_last=True, shuffle=True, ) batch = next(iter(dataloader)) uv_static = batch['uv_static'] uv_body_posed = batch['uv_body_posed'] uv_mask = batch['uv_mask'] uv_normals = batch['uv_normals'] batch_size = uv_static.size(0) for i in range(batch_size): counter = 0 frame = Frame(batch['id'][i], batch['frame_nr'][i], batch['garment'][i]) fix_data = np.load(Path(dataset_train.folder) / 'mesh_fixed' / f'{frame.id}_{frame.garment}.npz') mask = fix_data['mask'] faces = fix_data['faces'] gt = batch['mesh_posed'][i].cpu().numpy()[mask] save_stl(Path('temp') / 'sampling' / f'{frame.id}_{frame.frame_nr}_{frame.garment}_gt.stl', gt, faces) uv_static_tiled = uv_static[i][None,:,:,:].repeat(4, 1, 1, 1) uv_body_posed_tiled = uv_body_posed[i][None,:,:,:].repeat(4, 1, 1, 1) uv_mask_tiled = uv_mask[i][None,:,:].repeat(4, 1, 1) uv_normals_tiled = uv_normals[i][None,:,:,:].repeat(4, 1, 1, 1) pose_shape = batch['pose_shape'][i][None,:].repeat(4, 1) for _ in range(4): with torch.inference_mode(): pred = model.sample(uv_static_tiled, uv_body_posed_tiled, uv_normals_tiled, uv_mask_tiled) mesh_pred = uv2mesh(pred) shape = 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(pose_shape[:, :72], pose_shape[:, 72:], mesh_pred_shaped)[0] for k in range(4): posed = mesh_pred_posed.cpu().numpy()[k, mask, :] save_stl(Path('temp') / 'sampling' / f'{frame.id}_{frame.frame_nr}_{frame.garment}_{counter}.stl', posed, faces) counter += 1 pass if count > 100: break def generate(): from data.utils import Frame load_model(model, f'checkpoints/iccvw/{config["name"]}') frame = '01852_70_Tshirt' # frame = '03849_27_Dress' # 03849_27_Dress 00054_1_Dress frame = Frame(frame_str=frame) create_temp_frames(config['train_dir'], frame) dataset_train = Dataset(config['train_dir'], config['signature'], device=config['device'], frames_file='frames_temp.txt', use_cache=config['dataset_in_memory']) train_dataloader = DataLoader(dataset_train, batch_size=4, drop_last=True, shuffle=True, ) batch = next(iter(train_dataloader)) if config['use_skinning']: mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed'] else: mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed'] # with record_function("forward"): model.eval() with torch.inference_mode(): pred, _, _ = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_normals'], batch['uv_mask']]) mesh_pred = uv2mesh(pred) if config['use_skinning']: 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])]) # shape_offsets = torch.stack([torch.matmul(shape[i, :10], smpl.shapedirs[gender[i]][:, 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] else: mesh_pred_posed = mesh_pred fix_data = np.load(Path(dataset_train.folder) / 'mesh_fixed' / f'{frame.id}_{frame.garment}.npz') mask = fix_data['mask'] faces = fix_data['faces'] pred = mesh_pred_posed.cpu().numpy()[0, mask, :] body = batch['mesh_body_posed'].cpu().numpy()[0] body_faces = smpl.f save_stl(Path('temp') / 'baseline' / f'{frame.id}_{frame.frame_nr}_{frame.garment}_baseline_mesh.stl', pred, faces) save_stl(Path('temp') / 'baseline' / f'{frame.id}_{frame.frame_nr}_{frame.garment}_baseline_body.stl', body, body_faces) pass def train(): global g_balancing_coeff global step valid_iterator = iter(valid_dataloader) 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 train_loss['train/euclid_error'] < config['balancing_activation_limit']: # g_balancing_coeff += 1 / 10000 if step % 25000 == 0 and not args['debug']: ckpt_saver(model, step, optimizer, None) step += 1 if __name__ == '__main__': if not args['debug']: init_wandb() smpl = SMPL(orig_smpl=config['is_cape'], path='smpl', device=config['device']) skirt_modifier = '_skirt' if config['is_skirt'] else '' dataset_train = Dataset(config['train_dir'], config['signature'], device=config['device'], frames_file=config['train_frames'], use_cache=config['dataset_in_memory']) dataset_valid = Dataset(config['valid_dir'], config['signature'], device=config['device'], frames_file=config['valid_frames'], use_cache=config['dataset_in_memory']) 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 = CVAE(config) generate() exit() sample() exit() optimizer = optim.Adam(model.parameters(), lr=config['lr']) _MSSSIM = MS_SSIM(data_range=2, size_average=True, channel=3) g_balancing_coeff = config['starting_balancing_coeff'] ckpt_saver = CheckpointSaver('checkpoints/iccvw/' + config['name'], top_n=10) step = 0 if config['pretrained_model'] != '': if 'ignore_saved_optimizer' in config.keys() and config['ignore_saved_optimizer']: step = load_model(model, config['pretrained_model']) else: step = load_model(model, config['pretrained_model'], optimizer=optimizer) if not args['debug']: # model = torch.compile(model) wandb.watch(model, log='all', log_freq=10000, log_graph=True) # create a profiler object # profiler = torch.profiler.profile( # activities=[ProfilerActivity.CPU], # schedule=torch.profiler.schedule(wait=2, warmup=1, active=3, repeat=2), # on_trace_ready=torch.profiler.tensorboard_trace_handler("log_dir"), # ) # with profiler: # with record_function("model_inference"): train() # profiler.stop() # print(profiler.key_averages().table(sort_by="cpu_time_total", row_limit=25))