import os import sys from configs.iccv.config_abl_off import config os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu'] import tensorflow as tf gpus = tf.config.experimental.list_physical_devices('GPU') if gpus: try: # Currently, memory growth needs to be the same across GPUs for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True) logical_gpus = tf.config.experimental.list_logical_devices('GPU') print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs") except RuntimeError as e: # Memory growth must be set before GPUs have been initialized print(e) 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 postprocessing import fix_collision if config['is_skirt']: from losses import edge_loss_skirt as edge_loss else: from losses import edge_loss from losses import collision_ratio, kl_loss, normal_loss #, concept_loss # from losses import collision_penalty as collision_penalty from losses import collision_loss as collision_penalty from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision from dataset import DatasetFactory, StaticDataset from custom_utils.smpl import SMPL from models.cvae_model import CVAENew as CVAE from custom_utils.custom_utils import Accumulator def init_wandb(): run = wandb.init(project='iccv', name=config['name'], notes=config['notes'], dir='/tmp', tags=config['tags']) artifact = wandb.Artifact('code', type='code') artifact.add_file('cvae.py') artifact.add_file('models/cvae_model.py') artifact.add_file('losses.py') # artifact.add_file('configs/config_base.py') # artifact.add_file('configs/config_cvae.py') artifact.add_file('models/base.py') artifact.add_file('models/cond_encoder.py') artifact.add_file('dataset.py') artifact.add_file('convnext/models/convnext_tf.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_adaptive=False, is_smpl_orig=config['is_cape']) def kl_loss(mean, logvar): kl_loss = 1 + logvar - tf.square(mean) - tf.exp(logvar) kl_loss = tf.reduce_mean(kl_loss) kl_loss *= -0.5 return kl_loss # 3d loss on posed !!!!!!!!!!!! @tf.function def train_step(batch): with tf.GradientTape() as tape: 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'] pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=True) mesh_pred = uv2mesh(pred) if config['use_skinning']: shape = batch['pose_shape'][:, 72:] gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32) shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])]) mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis] mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped) else: mesh_pred_posed = mesh_pred # is the output already masked? e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask'])) c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']) c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio) l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2])) l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (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']) # conc_loss = concept_loss(pred, batch['uv_unposed']) kl = kl_loss(mean, logvar) reconstr_loss = l2_loss if config['use_extra_losses']: if 'block_collision_loss' in config.keys() and config['block_collision_loss'] == True: reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss) * config['reg_loss_coeff'] else: reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss + c_loss) * config['reg_loss_coeff'] if config['use_norm_loss'] and step > 2000: reconstr_loss = reconstr_loss + n_loss * config['reg_loss_coeff'] loss = reconstr_loss + g_balancing_coeff * kl mean = tf.reduce_mean(mean) logvar = tf.reduce_mean(logvar) # ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1))) metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask']) gradients = tape.gradient(loss, model.trainable_variables) optimizer.apply_gradients(zip(gradients, model.trainable_variables)) 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, } @tf.function 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'] pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False) mesh_pred = uv2mesh(pred) if config['use_skinning']: shape = batch['pose_shape'][:, 72:] gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32) shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])]) mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis] mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped) else: mesh_pred_posed = mesh_pred # is the output already masked? e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask'])) c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']) c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio) l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2])) l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (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']) # conc_loss = concept_loss(pred, batch['uv_unposed']) 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) * config['reg_loss_coeff'] if config['use_norm_loss']: reconstr_loss = reconstr_loss + n_loss * config['reg_loss_coeff'] loss = reconstr_loss + g_balancing_coeff * kl mean = tf.reduce_mean(mean) logvar = tf.reduce_mean(logvar) # ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1))) metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask']) # c_ratio = tf.reduce_mean(collision_ratio(mesh_pred_posed, batch['mesh_body_posed'], 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, } @tf.function def test_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'] pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False) mesh_pred = uv2mesh(pred) if config['use_skinning']: shape = batch['pose_shape'][:, 72:] gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32) shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])]) mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis] mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped) mesh_orig_shaped = mesh_orig + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis] mesh_orig_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_orig_shaped) else: mesh_pred_posed = mesh_pred mesh_orig_posed = mesh_orig mesh_pred_posed_fixed = fix_collision(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']) # is the output already masked? e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask'])) c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']) c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio) l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2])) l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1), axis=-1)) # conc_loss = concept_loss(pred, batch['uv_unposed']) 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) * config['reg_loss_coeff'] loss = reconstr_loss + g_balancing_coeff * kl mean = tf.reduce_mean(mean) logvar = tf.reduce_mean(logvar) ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, uv_orig + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1))) metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask']) euclid_posed = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig_posed - mesh_pred_posed) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask']) euclid_posed_fixed = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig_posed - mesh_pred_posed_fixed) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask']) return { 'test/loss': loss, 'test/edge_loss': e_loss, 'test/collision_loss': c_loss, 'test/euclid_error': metric, 'test/ms_ssim': ms_ssim, 'test/reconstr_loss': reconstr_loss, 'test/collision_ratio': c_ratio, 'test/l2_mesh_loss': l2_mesh_loss, 'test/mean': mean, 'test/logvar': logvar, 'test/kl_loss': kl, 'test/var': np.e ** logvar, 'test/euclid_posed': euclid_posed, 'test/euclid_posed_fixed': euclid_posed_fixed } def plot_everything(batch: dict, type: str): 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'] pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False) mesh_pred = uv2mesh(pred).numpy() orig_img = uv_orig uv_static_img = batch['uv_static'] uv_body_posed_img = batch['uv_body_posed'] pred_img = pred img = tf.concat((uv_static_img, uv_body_posed_img, orig_img, pred_img), axis=2) imgs = [wandb.Image((img[i] + 1) / 2.0) for i in range(4)] F = np.load("custom_utils/smpl_faces" + skirt_modifier + ("_orig" if config['is_cape'] else "") + ".npy") mask = batch['mesh_mask'].numpy().astype(bool) if config['use_skinning']: shape = batch['pose_shape'][:, 72:] gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32) shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])]) garment_unposed, faces = maskFacesBatch(mesh_pred, F, mask) garment_unposed = [garment_unposed[i] + shape_offsets.numpy()[i, mask[i], :] for i in range(mask.shape[0])] gt = [mesh_orig[i].numpy()[mask[i], :] + shape_offsets.numpy()[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.numpy()[i, mask[i], :] mesh_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred).numpy() else: mesh_posed = mesh_pred gt = [mesh_orig[i].numpy()[mask[i], :] for i in range(pred.shape[0])] garment_unposed, faces = maskFacesBatch(mesh_pred, F, mask) gt_posed = [batch['mesh_posed'].numpy()[i, mask[i], :] for i in range(mask.shape[0])] body = batch['mesh_body_posed'].numpy() mesh_posed_fixed = fix_collision(mesh_posed, body, mask).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'] and config['remove_global_orientation'])) 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'] and config['remove_global_orientation'])) for i in range(4) } mean_hist = wandb.Histogram(sequence=mean.numpy().mean(axis=0)) logvar_hist = wandb.Histogram(sequence=logvar.numpy().mean(axis=0)) return { **figs, **figs_coll, **{ 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_curvature = tf.one_hot(batch['uv_curvature'], 7) nr_of_samples=2 pred = model.sample_single(batch['uv_static'][0], batch['uv_body_posed'][0], uv_curvature[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_curvature[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 if __name__ == '__main__': init_wandb() parser = argparse.ArgumentParser(description='') parser.add_argument('--name', type=str) args = vars(parser.parse_args()) smpl = SMPL(orig_smpl=config['is_cape']) skirt_modifier = '_skirt' if config['is_skirt'] else '' factory = DatasetFactory() dataset_train = factory.get_dataset(folder=config['train_dir'], batch_size=config['batch_size'], signature=config['signature_dynamic'], resolutions=[config['resolution']], in_memory=config['dataset_in_memory'], use_one_hot=config['use_one_hot'], use_no_orient=config['remove_global_orientation'], frames_file=config['frames_file'] if 'frames_file' in config.keys() else None, is_cape=config['is_cape']) static_dataset_train = StaticDataset(folder=config['train_dir'], signature=config['signature_static'], resolutions=[config['resolution']], in_memory=True, is_cape=config['is_cape']) dataset_valid = factory.get_dataset(folder=config['valid_dir'], batch_size=config['batch_size'], signature=config['signature_dynamic'], resolutions=[config['resolution']], in_memory=config['dataset_in_memory'], use_one_hot=config['use_one_hot'], use_no_orient=config['remove_global_orientation'], frames_file=config['frames_file_valid'] if 'frames_file_valid' in config.keys() else None, is_cape=config['is_cape']) static_dataset_valid = StaticDataset(folder=config['valid_dir'], signature=config['signature_static'], resolutions=[config['resolution']], in_memory=True, is_cape=config['is_cape']) model = CVAE(config) optimizer = tf.optimizers.Adam(learning_rate=config['lr']) balancing_activated_step = 0 g_balancing_coeff = config['starting_balancing_coeff'] # sample('checkpoints/cvae_13') # test() ckpt = tf.train.Checkpoint(model=model) manager = tf.train.CheckpointManager(ckpt, 'checkpoints/iccv/' + config['name'], max_to_keep=20) train_accumulator = Accumulator() valid_accumulator = Accumulator() if config['pretrained_model'] != '': load_model(model, config['pretrained_model']) # load_model(model, 'checkpoints/' + config['name']) valid_iterator = iter(dataset_valid) step = 0 for batch in dataset_train: batch = static_dataset_train.expand_batch(batch_to_dict(batch, config['signature_dynamic'])) train_loss = train_step(batch) train_accumulator.update(train_loss) if step % 50 == 0: data_valid = valid_iterator.get_next() data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic'])) valid_loss = valid_step(data_valid) valid_accumulator.update(valid_loss) # to_log = { **train_loss, **valid_loss, **{ 'current_step': step} } # if step % 5000 == 0 or (step < 5000 and step % 500 == 0): # train_figs = plot_everything(batch, 'train') # valid_figs = plot_everything(data_valid, 'valid') # to_log = { **to_log, **train_figs, **valid_figs } # if step % 5000 == 0: # manager.save(checkpoint_number=step) # if step == 0: # to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } } if train_loss['train/euclid_error'] < (config['balancing_activation_limit'] if 'balancing_activation_limit' in config.keys() else 0.017): if balancing_activated_step == 0: balancing_activated_step = step g_balancing_coeff = g_balancing_coeff + 1 / 10000 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)) } } to_log = { **to_log, **{ 'train/balancing_coeff': g_balancing_coeff } } wandb.log(to_log, step=step) train_accumulator.reset() valid_accumulator.reset() print(step) step += 1