From 385aea398b1ca5dba365e1ef12881a8f43c15f9d Mon Sep 17 00:00:00 2001 From: Hunor Laczko Date: Fri, 9 Jun 2023 08:55:31 +0000 Subject: [PATCH] cvae norm training --- configs/cvae_norm_1.py | 172 ++++++++++++++++ models/cvae_norm_model.py | 97 +++++++++ run.py | 2 +- scripts/cvae.py | 2 +- scripts/cvae_norm.py | 400 ++++++++++++++++++++++++++++++++++++++ 5 files changed, 671 insertions(+), 2 deletions(-) create mode 100644 configs/cvae_norm_1.py create mode 100644 models/cvae_norm_model.py create mode 100644 scripts/cvae_norm.py diff --git a/configs/cvae_norm_1.py b/configs/cvae_norm_1.py new file mode 100644 index 0000000..8adc7e5 --- /dev/null +++ b/configs/cvae_norm_1.py @@ -0,0 +1,172 @@ +from utils.convnext_utils import CNBlockConfig + +config = dict() + +config['gpu'] = '0' +config['name'] = 'cvae_norm_1' +config['notes'] = '' +config['tags'] = ['iccv', 'cvae_norm', 'torch'] +config['script'] = 'scripts/cvae_norm.py' + +config['pretrained_model'] = '' + + +config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision + +config['batch_size'] = 4 +config['resolution'] = 512 +config['is_skirt'] = False +config['dataset_in_memory'] = False +config['is_cape'] = False +config['device'] = 'cuda' + +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['static_model'] = 'checkpoints/iccv/static_torch_orig_2' +config['body_model'] = 'checkpoints/iccv/body_torch_1' + +config['smpl_res'] = 14475 +if config['is_cape']: + config['smpl_res'] = 3638 + +config['latent_dim'] = 128 + +############### +config['lr'] = 0.0001# 0.0001 +############### +config['starting_balancing_coeff'] = 0.0001 + +config['signature']: set = { 'id', + 'frame_nr', + 'pose_shape', + 'uv_body_posed', + 'uv_normals', + 'uv_static', + 'mesh_mask', + 'uv_mask', + 'mesh_normals', + } + + + +config['upscale_enhancer'] = '' +config['freeze_encoders'] = True +config['use_one_hot'] = False +config['bottleneck_scale'] = 2 + +config['cvae'] = dict() +config['cvae']['device'] = config['device'] +config['cvae']['is_label'] = False +config['cvae']['input_features'] = 3+3+3 +config['cvae']['kernel_size'] = 7 +config['cvae']['bottleneck_size'] = 64 * config['bottleneck_scale'] +config['cvae']['latent_dim'] = config['cvae']['bottleneck_size'] + +block_setting = [ + CNBlockConfig(32, 64, 3), + CNBlockConfig(64, 128, 3), + CNBlockConfig(128, 256, 3), + CNBlockConfig(256, config['cvae']['bottleneck_size'], 9), + CNBlockConfig(config['cvae']['bottleneck_size'], None, 3), +] + +block_setting_reverse = [ + CNBlockConfig(176, 256, 3), + CNBlockConfig(256, 128, 9), + CNBlockConfig(128, 64, 9), + CNBlockConfig(64, 32, 3), + CNBlockConfig(32, 3, 3), +] + +config['cvae']['encoder_block_settings'] = block_setting +config['cvae']['decoder_block_settings'] = block_setting_reverse + + + +####################### cond_ae_curv ############################ +config['cond_ae_curv'] = dict() +config['cond_ae_curv']['device'] = config['device'] +config['cond_ae_curv']['is_label'] = True +config['cond_ae_curv']['latent_dim'] = config['latent_dim'] +config['cond_ae_curv']['kernel_size'] = 7 +config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale'] + +block_setting = [ + CNBlockConfig(16, 32, 3), + CNBlockConfig(32, 64, 3), + CNBlockConfig(64, 128, 9), + CNBlockConfig(128, config['cond_ae_curv']['bottleneck_size'], 9), + CNBlockConfig(config['cond_ae_curv']['bottleneck_size'], None, 3), +] + +block_setting_reverse = [ + CNBlockConfig(config['cond_ae_curv']['bottleneck_size'], 128, 3), + CNBlockConfig(128, 64, 9), + CNBlockConfig(64, 32, 9), + CNBlockConfig(32, 16, 3), + CNBlockConfig(16, 3, 3), +] + +config['cond_ae_curv']['encoder_block_settings'] = block_setting +config['cond_ae_curv']['decoder_block_settings'] = block_setting_reverse +config['cond_ae_curv']['activation'] = 'tanh' + +####################### cond_ae_body ############################ +config['cond_ae_body'] = dict() +config['cond_ae_body']['device'] = config['device'] +config['cond_ae_body']['is_label'] = False +config['cond_ae_body']['latent_dim'] = config['latent_dim'] +config['cond_ae_body']['kernel_size'] = 7 +config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale'] + +block_setting = [ + CNBlockConfig(16, 32, 3), + CNBlockConfig(32, 64, 3), + CNBlockConfig(64, 128, 9), + CNBlockConfig(128, config['cond_ae_body']['bottleneck_size'], 9), + CNBlockConfig(config['cond_ae_body']['bottleneck_size'], None, 3), +] + +block_setting_reverse = [ + CNBlockConfig(config['cond_ae_body']['bottleneck_size'], 128, 3), + CNBlockConfig(128, 64, 9), + CNBlockConfig(64, 32, 9), + CNBlockConfig(32, 16, 3), + CNBlockConfig(16, 3, 3), +] + +config['cond_ae_body']['encoder_block_settings'] = block_setting +config['cond_ae_body']['decoder_block_settings'] = block_setting_reverse +config['cond_ae_body']['activation'] = 'tanh' + +####################### cond_ae_static ############################ +config['cond_ae_static'] = dict() +config['cond_ae_static']['device'] = config['device'] +config['cond_ae_static']['is_label'] = False +config['cond_ae_static']['latent_dim'] = config['latent_dim'] +config['cond_ae_static']['kernel_size'] = 7 +config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale'] + +block_setting = [ + CNBlockConfig(8, 16, 3), + CNBlockConfig(16, 32, 3), + CNBlockConfig(32, 64, 3), + CNBlockConfig(64, config['cond_ae_static']['bottleneck_size'], 9), + CNBlockConfig(config['cond_ae_static']['bottleneck_size'], None, 3), +] + +block_setting_reverse = [ + CNBlockConfig(config['cond_ae_static']['bottleneck_size'], 64, 3), + CNBlockConfig(64, 32, 9), + CNBlockConfig(32, 16, 3), + CNBlockConfig(16, 8, 3), + CNBlockConfig(8, 3, 3), +] + +config['cond_ae_static']['encoder_block_settings'] = block_setting +config['cond_ae_static']['decoder_block_settings'] = block_setting_reverse +config['cond_ae_static']['activation'] = 'tanh' \ No newline at end of file diff --git a/models/cvae_norm_model.py b/models/cvae_norm_model.py new file mode 100644 index 0000000..327f5fb --- /dev/null +++ b/models/cvae_norm_model.py @@ -0,0 +1,97 @@ +import os +import time +import random +import torch +from torch import nn +import numpy as np +import scipy as sp +# import plotly.graph_objects as go +# import plotly.express as ex + +from utils.checkpoints import load_model +from models.base import AEEncoder, VariationalEncoder, Decoder + + +class CVAENorm(nn.Module): + + def __init__(self, config): + super(CVAENorm, self).__init__() + self.size = config['resolution'] + + self.static_enc : AEEncoder = AEEncoder(config['cond_ae_static']['encoder_block_settings'], config['cond_ae_static']['kernel_size'], config['cond_ae_static']['activation'], device=config['device']) + if config['static_model'] != '': + load_model(self.static_enc, config['static_model'] + '_enc') + + self.body_enc : AEEncoder = AEEncoder(config['cond_ae_body']['encoder_block_settings'], config['cond_ae_body']['kernel_size'], config['cond_ae_body']['activation'], device=config['device']) + if config['body_model'] != '': + load_model(self.body_enc, config['body_model'] + '_enc') + + if config['freeze_encoders']: + for param in self.static_enc.parameters(): param.requires_grad = False + for param in self.body_enc.parameters(): param.requires_grad = False + + self.encoder = VariationalEncoder(config['cvae']['encoder_block_settings'], config['cvae']['kernel_size'], config['cvae']['input_features'], device=config['device']) + self.decoder : Decoder = Decoder(config['cvae']['decoder_block_settings'], kernel_size=config['cvae']['kernel_size'], device=config['device']) + + self.latent_shape = (config['cvae']['latent_dim'], 8, 4) + + + def sample(self, uv_template, uv_body_posed, uv_curv, uv_mask, noise=None, add_noise=False): + if noise is None: + eps = tf.random.normal(shape=(uv_template.shape[0], 8, 4, self.latent_dim)) + else: + eps = noise + + z_cond = self.encode_conds(uv_template, uv_body_posed, uv_curv, training=False) + if add_noise: + z_cond = z_cond + (tf.random.uniform(z_cond.shape, maxval=self.noise_amount * 2) - self.noise_amount) + + z = tf.concat([eps, z_cond], axis=-1) + + x = self.decoder(z, training=False) + + if self.avg_garment is not None: + x = x + self.avg_garment[tf.newaxis,:,:,:] + + if self.use_offsets_over_body: + x = x + uv_body_posed + + x = x * uv_mask[:,:,:,tf.newaxis] + + return x + + + def reparameterize(self, mean: torch.Tensor, logvar: torch.Tensor, training: bool = True, fix_noise: bool = False): + if training == True: + if not fix_noise: + eps = torch.randn(size=mean.shape).to(mean.device) + else: + eps = torch.randn(size=(1, mean.shape[1])) + eps = torch.tile(eps, [mean.shape[0],1]) + out = eps * torch.exp(logvar * 0.5) + mean + else: + out = torch.exp(logvar * 0.5) + mean + return out + + + def encode_conds(self, uv_template, uv_body_posed): + z_body = self.body_enc(uv_body_posed) + z_static = self.static_enc(uv_template) + z_cond = torch.concat([z_static, z_body], dim=1) + return z_cond + + + def forward(self, inputs): + uv_unposed, uv_template, uv_body_posed, uv_mask = inputs + x = torch.concat([uv_unposed, uv_template, uv_body_posed], dim=1) + + mean, logvar = self.encoder(x) + z: torch.Tensor = self.reparameterize(mean, logvar) + z = z.view((mean.shape[0], *self.latent_shape)) + z_cond = self.encode_conds(uv_template, uv_body_posed) + + z = torch.concat([z, z_cond], dim=1) + x = self.decoder(z) + x = x * uv_mask[:,None,:,:] + + return x, mean, logvar \ No newline at end of file diff --git a/run.py b/run.py index 88c7e24..bd248e4 100644 --- a/run.py +++ b/run.py @@ -1,6 +1,6 @@ import os import sys -from configs.abl_norm import config +from configs.cvae_norm_1 import config import argparse # parser = argparse.ArgumentParser(description='') diff --git a/scripts/cvae.py b/scripts/cvae.py index 46c7261..c485b5e 100644 --- a/scripts/cvae.py +++ b/scripts/cvae.py @@ -51,7 +51,7 @@ from custom_utils.custom_utils import UV2Mesh, Accumulator, plot_3d, get_summary def init_wandb(): - run = wandb.init(project='iccv', name=config['name'], notes=config['notes'], dir='/tmp', tags=config['tags']) + 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/cvae.py') artifact.add_file('losses/losses.py') diff --git a/scripts/cvae_norm.py b/scripts/cvae_norm.py new file mode 100644 index 0000000..c749ad3 --- /dev/null +++ b/scripts/cvae_norm.py @@ -0,0 +1,400 @@ +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.cvae_norm_1 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 tqdm +import random +import numpy as np +import plotly.graph_objects as go +import plotly.express as ex +from PIL import Image + +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 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.postprocess import fix_collision +from losses.losses import collision_loss, normal_loss +from pytorch_msssim import MS_SSIM +from models.cvae_norm_model import CVAENorm +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(): + 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/cvae_norm.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_norm_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): + model.train() + pred, mean, logvar = model([batch['uv_normals'], batch['uv_static'], batch['uv_body_posed'], batch['uv_mask']]) + pred = pred * 1.1 + mesh_pred = uv2mesh(pred) + + + l2_loss = torch.mean(1e3 * torch.mean(torch.sum(((batch['uv_normals'] - pred) ** 2), axis=-1), axis=[1, 2])) + l2_mesh_loss = torch.mean(1e3 * torch.mean(torch.sum(batch['mesh_mask'][:,:,None] * (batch['mesh_normals'] - mesh_pred) ** 2, axis=-1), axis=-1)) + ms_ssim = torch.mean(_MSSSIM(pred + 1.1, batch['uv_normals'] + 1.1)) + kl = kl_loss(mean, logvar) + + reconstr_loss = l2_loss + l2_mesh_loss + (1 - ms_ssim) + loss = reconstr_loss + g_balancing_coeff * kl + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + model.eval() + with torch.inference_mode(): + mean = torch.mean(mean) + logvar = torch.mean(logvar) + metric = torch.sum(torch.sqrt(torch.sum(batch['mesh_mask'][:,:,None] * (batch['mesh_normals'] - mesh_pred.detach()) ** 2, axis=-1))) / torch.sum(batch['mesh_mask']) + + return { 'train/loss': loss, 'train/euclid_error': metric, + 'train/reconstr_loss': reconstr_loss, + '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/ms_ssim': ms_ssim, + } + + +def valid_step(batch): + model.eval() + with torch.inference_mode(): + pred, mean, logvar = model([batch['uv_normals'], batch['uv_static'], batch['uv_body_posed'], batch['uv_mask']]) + pred = pred * 1.1 + mesh_pred = uv2mesh(pred) + + l2_loss = torch.mean(1e3 * torch.mean(torch.sum(((batch['uv_normals'] - pred) ** 2), axis=-1), axis=[1, 2])) + l2_mesh_loss = torch.mean(1e3 * torch.mean(torch.sum(batch['mesh_mask'][:,:,None] * (mesh_pred - batch['mesh_normals']) ** 2, axis=-1), axis=-1)) + ms_ssim = torch.mean(_MSSSIM(pred + 1.1, batch['uv_static'] + 1.1)) + kl = kl_loss(mean, logvar) + + reconstr_loss = l2_loss + l2_mesh_loss + (1 - ms_ssim) + loss = reconstr_loss + g_balancing_coeff * kl + + mean = torch.mean(mean) + logvar = torch.mean(logvar) + metric = torch.sum(torch.sqrt(torch.sum(batch['mesh_mask'][:,:,None] * (batch['mesh_normals'] - mesh_pred.detach()) ** 2, axis=-1))) / torch.sum(batch['mesh_mask']) + + + return { 'valid/loss': loss, 'valid/euclid_error': metric, + 'valid/reconstr_loss': reconstr_loss, 'valid/l2_mesh_loss': l2_mesh_loss, + 'valid/mean': mean, 'valid/logvar': logvar, 'valid/kl_loss': kl, 'valid/var': np.e ** logvar, + 'valid/ms_ssim': ms_ssim, + } + + +def plot_everything(batch: dict, type: str): + model.eval() + with torch.inference_mode(): + pred, mean, logvar = model([batch['uv_normals'], batch['uv_static'], batch['uv_body_posed'], batch['uv_mask']]) + + pred = pred * 1.1 + # mesh_pred = uv2mesh(pred).detach().cpu().numpy() + + 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]) + orig_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, 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) + + mean_hist = wandb.Histogram(sequence=mean.cpu().numpy().mean(axis=0)) + logvar_hist = wandb.Histogram(sequence=logvar.cpu().numpy().mean(axis=0)) + + return { **{ type + '/images': imgs, type + '/mean_hist': mean_hist, type + '/logvar_hist': logvar_hist } } + + +def sample(folder): + # run = wandb.init(project='vae_plots', name=folder.split('/')[-1]) + load_model(model,folder) + count = 0 + for batch in dataset_train: + batch = static_dataset_train.expand_batch(batch_to_dict(batch, config['signature_dynamic'])) + uv_normals = tf.one_hot(batch['uv_normals'], 7) + nr_of_samples=2 + + pred = model.sample_single(batch['uv_static'][0], batch['uv_body_posed'][0], uv_normals[0], batch['uv_mask'][0], nr_of_samples=nr_of_samples) + pose, shape = batch['pose_shape'][0, :72], batch['pose_shape'][0, 72:] + pose, shape = tf.tile(pose[tf.newaxis,:], [nr_of_samples,1]), tf.tile(shape[tf.newaxis,:], [nr_of_samples,1]) + + mesh_pred = uv2mesh(pred) + mesh_posed = smpl.run_garment(pose, shape, mesh_pred).numpy() + mesh_orig = batch['mesh_posed'][0].numpy() + mesh_orig_reposed = smpl.run_garment(pose[:1], shape[:1], batch['mesh_unposed'][:1]).numpy()[0] + + body = batch['mesh_body_posed'][0].numpy() + body_color = np.ones_like(body, dtype=np.int32)*100 + + F = np.load("custom_utils/smpl_faces.npy") + mask = batch['mesh_mask'].numpy().astype(bool)[0] + _, faces = _maskFaces(np.ones((config['smpl_res'], 3)), F, mask) + + pred_zero = model.sample_single(batch['uv_static'][0], batch['uv_body_posed'][0], uv_normals[0], batch['uv_mask'][0], nr_of_samples=nr_of_samples, zero_latent=True) + mesh_pred_zero = uv2mesh(pred_zero) + mesh_posed_zero = smpl.run_garment(pose, shape, mesh_pred_zero).numpy() + + + meshes = [] + meshes.append(go.Mesh3d( + # 8 vertices of a cube + x=mesh_orig_reposed[mask,0], + y=mesh_orig_reposed[mask,2], + z=mesh_orig_reposed[mask,1], + # i, j and k give the vertices of triangles + i = faces[:,0], + j = faces[:,1], + k = faces[:,2] + )) + + for i in range(nr_of_samples): + x = mesh_posed[i][mask, :].copy() + # x[:, 2] += 1 * (i + 1) + x[:, 2] += 1 + meshes.append(go.Mesh3d( + # 8 vertices of a cube + x=x[:,0], + y=x[:,2], + z=x[:,1], + # i, j and k give the vertices of triangles + i = faces[:,0], + j = faces[:,1], + k = faces[:,2] + )) + + + for i in range(nr_of_samples): + x = mesh_posed_zero[i][mask, :].copy() + # x[:, 2] += 1 * (i + 1) + x[:, 2] += 2 + meshes.append(go.Mesh3d( + # 8 vertices of a cube + x=x[:,0], + y=x[:,2], + z=x[:,1], + # i, j and k give the vertices of triangles + i = faces[:,0], + j = faces[:,1], + k = faces[:,2] + )) + + # for i in range(5): + # x = body.copy() + # x[:, 2] += 1 * i + # meshes.append(go.Mesh3d( + # # 8 vertices of a cube + # x=x[:,0], + # y=x[:,2], + # z=x[:,1], + # # i, j and k give the vertices of triangles + # i = F[:,0], + # j = F[:,1], + # k = F[:,2], + # vertexcolor=body_color + # )) + + + fig = go.Figure(data=meshes) + + fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=2, z=1)) + # fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=nr_of_samples-1, z=1)) + fig.update_layout( + scene = dict( + xaxis = dict(nticks=4, range=[-1,1],), + # yaxis = dict(nticks=4, range=[-1,nr_of_samples+1],), + yaxis = dict(nticks=4, range=[-1,3],), + zaxis = dict(nticks=4, range=[-1,1],),) + ) + + # wandb.log({'plot_' + str(count): fig}) + fig.show() + + pred = pred.numpy() + frames = [Image.fromarray(((pred[i] + 1) * 128).astype(np.int8), mode='RGB') for i in range(pred.shape[0])] + frames[0].save("temp/garment_1.gif", format="GIF", append_images=frames[1:], + save_all=True, duration=500, loop=0) + + + if count > 10: + break + + print(count) + count += 1 + + print() + + + +def test(): + str_split = config['name'].split('_') + test_idx = str_split.index('test') + checkpoint_name = '_'.join(str_split[:test_idx]) + load_model(model, 'checkpoints/' + checkpoint_name) + + dataset_test = factory.get_dataset(folder=config['test_dir'], batch_size=config['batch_size'], signature=config['signature_dynamic'], use_filled=config['use_filled'], resolutions=[config['resolution']], in_memory=False, do_repeat=False) + static_dataset_test = StaticDataset(folder=config['test_dir'], signature=config['signature_static'], use_filled=config['use_filled'], resolutions=[config['resolution']], in_memory=False) + + step = 0 + losses = [] + + for batch in tqdm.tqdm(dataset_test): + batch = static_dataset_test.expand_batch(batch_to_dict(batch, config['signature_dynamic'])) + + test_loss = test_step(batch) + losses.append(test_loss) + + to_log = { **{ 'current_step': step }, **test_loss } + if step % 1000 == 0: + figs = plot_everything(batch, 'test') + to_log = { **to_log, **figs } + + wandb.log(to_log, step = step) + + # print(step) + step += 1 + + # averages = dict() + path_to_plotly_html = "temp/plotly_figure.html" + + histograms = dict() + for key in test_loss.keys(): + accumulated = [losses[i][key].numpy() for i in range(len(losses))] + fig = ex.histogram(accumulated, title='Average: ' + str(np.asarray(accumulated).mean())) + fig.write_html(path_to_plotly_html, auto_play = False) + + histograms['all/' + key.split('/')[-1] + '_hist'] = wandb.Html(path_to_plotly_html) + # averages['all/' + key.split('/')[-1] + '_avg'] = np.asarray(accumulated).mean() + + + + wandb.log({ **histograms }, step = step) + wandb.finish() + 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'] if 'balancing_activation_limit' in config.keys() else 0.017): + g_balancing_coeff += 1 / 10000 + + if step % 10000 == 0: + 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 = CVAENorm(config) + + 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/iccv/' + config['name'], top_n=5) + + step = 0 + if config['pretrained_model'] != '': + step = load_model(model, config['pretrained_model']) + + if not args['debug']: + # model = torch.compile(model) + wandb.watch(model, log='all', log_freq=10000, log_graph=True) + + train() \ No newline at end of file