import os from configs.config_abl_2_pyr_64_128_256_512_cape_m 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 tqdm import argparse import wandb import random import numpy as np import scipy as sp from custom_utils.custom_utils import UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, get_summary_str import plotly.graph_objects as go import plotly.express as ex from plotly.subplots import make_subplots from PIL import Image if config['is_skirt']: from losses import edge_loss_skirt as edge_loss else: from losses import edge_loss from tf_utils import check_tf_version, allow_memory_growth, split_gpu_for_testing from losses import uv_mask_loss, bend_loss, kl_loss, collision_ratio # 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 from dataset import DatasetFactory, StaticDataset from custom_utils.smpl import SMPL from models.pyramid_model_optimized import Pyramid def init_wandb(): run = wandb.init(project='results', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp') artifact = wandb.Artifact('code', type='code') artifact.add_file('cvae_pyramid.py') artifact.add_file('models/cvae_model.py') artifact.add_file('models/pyramid_model.py') artifact.add_file('models/pyramid_level.py') artifact.add_file('models/upscaler.py') artifact.add_file('losses.py') artifact.add_file('configs/config_base.py') artifact.add_file('configs/config_pyramid.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') run.log_artifact(artifact) uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape']) def plot_3d(mesh_gt, mesh_pred_unposed_levels, faces, mesh_posed, collision, collision_faces, collision_color): fig = make_subplots(rows=1, cols=1+len(mesh_pred_unposed_levels)+1+1, start_cell="top-left", specs = [ [{ 'type': 'scene' } for _ in range(1+len(mesh_pred_unposed_levels)+1+1)] ]) fig.add_trace(go.Mesh3d( x=mesh_gt[:,0], y=mesh_gt[:,2], z=mesh_gt[:,1], i = faces[:,0], j = faces[:,1], k = faces[:,2], text="Ground Truth" ), row=1, col=1) for i, mesh in enumerate(mesh_pred_unposed_levels): fig.add_trace(go.Mesh3d( x=mesh[:,0], y=mesh[:,2], z=mesh[:,1], i = faces[:,0], j = faces[:,1], k = faces[:,2], text='Unposed level_' + str(i) ), row=1, col=i+2) fig.add_trace(go.Mesh3d( x=mesh_posed[:,0], y=mesh_posed[:,1], z=mesh_posed[:,2], i = faces[:,0], j = faces[:,1], k = faces[:,2], text='Posed GT' ), row=1, col=1+len(mesh_pred_unposed_levels)+1) fig.add_trace(go.Mesh3d( x=collision[:,0], y=collision[:,2], z=collision[:,1], i = collision_faces[:,0], j = collision_faces[:,1], k = collision_faces[:,2], vertexcolor=collision_color, text='Posed output on body' ), row=1, col=1+len(mesh_pred_unposed_levels)+1+1) if len(mesh_pred_unposed_levels) == 1: fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1), scene2_aspectmode='manual', scene2_aspectratio=dict(x=1, y=1, z=1), scene3_aspectmode='manual', scene3_aspectratio=dict(x=1, y=1, z=1), scene4_aspectmode='manual', scene4_aspectratio=dict(x=1, y=1, z=1), ) helper = dict(xaxis = dict(nticks=4, range=[-1,1],), yaxis = dict(nticks=4, range=[-1,1],), zaxis = dict(nticks=4, range=[-1,1],),) fig.update_layout(scene = helper, scene2 = helper, scene3 = helper, scene4 = helper, ) if len(mesh_pred_unposed_levels) == 2: fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1), scene2_aspectmode='manual', scene2_aspectratio=dict(x=1, y=1, z=1), scene3_aspectmode='manual', scene3_aspectratio=dict(x=1, y=1, z=1), scene4_aspectmode='manual', scene4_aspectratio=dict(x=1, y=1, z=1), scene5_aspectmode='manual', scene5_aspectratio=dict(x=1, y=1, z=1), ) helper = dict(xaxis = dict(nticks=4, range=[-1,1],), yaxis = dict(nticks=4, range=[-1,1],), zaxis = dict(nticks=4, range=[-1,1],),) fig.update_layout(scene = helper, scene2 = helper, scene3 = helper, scene4 = helper, scene5 = helper, ) if len(mesh_pred_unposed_levels) == 3: fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1), scene2_aspectmode='manual', scene2_aspectratio=dict(x=1, y=1, z=1), scene3_aspectmode='manual', scene3_aspectratio=dict(x=1, y=1, z=1), scene4_aspectmode='manual', scene4_aspectratio=dict(x=1, y=1, z=1), scene5_aspectmode='manual', scene5_aspectratio=dict(x=1, y=1, z=1), scene6_aspectmode='manual', scene6_aspectratio=dict(x=1, y=1, z=1,) ) helper = dict(xaxis = dict(nticks=4, range=[-1,1],), yaxis = dict(nticks=4, range=[-1,1],), zaxis = dict(nticks=4, range=[-1,1],),) fig.update_layout(scene = helper, scene2 = helper, scene3 = helper, scene4 = helper, scene5 = helper, scene6 = helper, ) if len(mesh_pred_unposed_levels) == 4: fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1), scene2_aspectmode='manual', scene2_aspectratio=dict(x=1, y=1, z=1), scene3_aspectmode='manual', scene3_aspectratio=dict(x=1, y=1, z=1), scene4_aspectmode='manual', scene4_aspectratio=dict(x=1, y=1, z=1), scene5_aspectmode='manual', scene5_aspectratio=dict(x=1, y=1, z=1), scene6_aspectmode='manual', scene6_aspectratio=dict(x=1, y=1, z=1,), scene7_aspectmode='manual', scene7_aspectratio=dict(x=1, y=1, z=1,), ) helper = dict(xaxis = dict(nticks=4, range=[-1,1],), yaxis = dict(nticks=4, range=[-1,1],), zaxis = dict(nticks=4, range=[-1,1],),) fig.update_layout(scene = helper, scene2 = helper, scene3 = helper, scene4 = helper, scene5 = helper, scene6 = helper, scene7 = helper, ) if len(mesh_pred_unposed_levels) == 5: fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1), scene2_aspectmode='manual', scene2_aspectratio=dict(x=1, y=1, z=1), scene3_aspectmode='manual', scene3_aspectratio=dict(x=1, y=1, z=1), scene4_aspectmode='manual', scene4_aspectratio=dict(x=1, y=1, z=1), scene5_aspectmode='manual', scene5_aspectratio=dict(x=1, y=1, z=1), scene6_aspectmode='manual', scene6_aspectratio=dict(x=1, y=1, z=1,), scene7_aspectmode='manual', scene7_aspectratio=dict(x=1, y=1, z=1,), scene8_aspectmode='manual', scene8_aspectratio=dict(x=1, y=1, z=1,), ) helper = dict(xaxis = dict(nticks=4, range=[-1,1],), yaxis = dict(nticks=4, range=[-1,1],), zaxis = dict(nticks=4, range=[-1,1],),) fig.update_layout(scene = helper, scene2 = helper, scene3 = helper, scene4 = helper, scene5 = helper, scene6 = helper, scene7 = helper, scene8 = helper, ) return fig def group_inputs_for_levels(batch): levels_input = [] for res in config['pyramid']['resolutions']: res_modifier = '_' + str(res) if res != 512 else '' levels_input.append([batch['uv_unposed' + res_modifier], batch['uv_static' + res_modifier], batch['uv_body_posed' + res_modifier], batch['uv_curvature' + res_modifier], batch['uv_mask' + res_modifier], batch['uv_mask']]) conds = [batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature']] return levels_input, conds @tf.function def test_step(batch): levels_input, conds = group_inputs_for_levels(batch) out_levels = model([levels_input, conds], training=False) # 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_orig = batch['mesh_unposed'] # output = dict() # reconstr_loss_total = 0 # kl_total = 0 # #for i, res in enumerate(config['pyramid']['resolutions']): # i = -1 # res = config['pyramid']['resolutions'][-1] # res_modifier = '_' + str(res) if res != 512 else '' # pred, mean, logvar = out_levels[i] # mesh_pred = uv2mesh(pred) # 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) # # is the output already masked? # e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask'])) # c_loss = tf.reduce_mean(collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])) # c_ratio = tf.reduce_mean(collision_ratio(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])) # l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((batch['uv_unposed'] - pred) ** 2), axis=-1), axis=[1, 2])) # # 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))) # ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3)) # reconstr_loss_total += config['pyramid']['loss_weights'][i] * (l2_loss + (e_loss + c_loss) * config['reg_loss_coeff']) # # 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']) # output = { **output, **{ 'test/edge_loss' + res_modifier: e_loss, 'test/collision_loss' + res_modifier: c_loss, # 'test/ms_ssim' + res_modifier: ms_ssim, 'test/l2_loss' + res_modifier: l2_loss, # 'test/collision_ratio' + res_modifier: c_ratio } } # loss = reconstr_loss_total + balancing_coeff * kl_total # return { **{ 'test/loss': loss, 'test/reconstr_loss_total': reconstr_loss_total }, **output }, out_levels return dict(), out_levels def plot_everything(batch: dict, type: str): levels_input = group_inputs_for_levels(batch) output_levels = model(levels_input, training=False) mesh_pred_levels = [uv2mesh(output_levels[i][0]).numpy() for i in range(len(output_levels))] mesh_orig = batch['mesh_unposed'].numpy() mesh_orig_posed = batch['mesh_posed'].numpy() body = batch['mesh_body_posed'].numpy() orig_img = (batch['uv_unposed'].numpy() + 1) / 2 uv_static_img = (batch['uv_static'].numpy() + 1) / 2 uv_body_posed_img = (batch['uv_body_posed'].numpy() + 1) / 2 uv_curvature = (np.tile(batch['uv_curvature'].numpy().argmax(-1)[:,:,:,np.newaxis], [1,1,1,3]) / 6) pred_img = np.concatenate([tf.image.resize(output_levels[i][0], (512, 256), tf.image.ResizeMethod.LANCZOS5) for i in range(len(output_levels))], axis=2) pred_img = (pred_img - pred_img.min(axis=(1,2,3))[:,np.newaxis,np.newaxis,np.newaxis]) / (pred_img.max(axis=(1,2,3))[:,np.newaxis,np.newaxis,np.newaxis] - pred_img.min(axis=(1,2,3))[:,np.newaxis,np.newaxis,np.newaxis]) img = tf.concat((uv_static_img, uv_body_posed_img, uv_curvature, orig_img, pred_img), axis=2) imgs = [wandb.Image(img[i]) for i in range(4)] 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])]).numpy() F = np.load("custom_utils/smpl_faces" + skirt_modifier + ("_orig" if config['is_cape'] else "") + ".npy") mask = batch['mesh_mask'].numpy().astype(bool) _, faces = maskFacesBatch(np.ones_like(mesh_orig), F, mask) gt = [mesh_orig[i][mask[i], :] + shape_offsets[i, mask[i], :] for i in range(mask.shape[0])] gt_posed = [mesh_orig_posed[i, mask[i], :] for i in range(mask.shape[0])] mesh_unposed_shaped_levels = [[] for _ in range(mask.shape[0])] for i in range(len(output_levels)): mesh_unposed_shaped = [mesh_pred_levels[i][j, mask[j], :] + shape_offsets[j, mask[j]] for j in range(mask.shape[0])] for k in range(mask.shape[0]): mesh_unposed_shaped_levels[k].append(mesh_unposed_shaped[k]) # full res version mesh_unposed_shaped_full = np.zeros((mask.shape[0], config['smpl_res'], 3), dtype=np.float32) for i in range(mask.shape[0]): mesh_unposed_shaped_full[i, mask[i], :] = mesh_unposed_shaped[i] mesh_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_unposed_shaped_full).numpy() mesh_posed = [mesh_posed[j, mask[j], :] for j in range(mask.shape[0])] collision = [np.concatenate((body[i], mesh_posed[i]), axis=0) for i in range(len(mesh_posed))] collision_faces = [np.concatenate((F, faces[i] + config['smpl_res']), axis=0) for i in range(len(mesh_posed))] collision_color = [np.concatenate((np.ones_like(body[i], dtype=np.int32)*100, np.ones_like(mesh_posed[i], dtype=np.int32)*230), axis=0) for i in range(len(mesh_posed))] # body_2 = [b.copy() for b in body] # for i in range(len(gt_posed)): # body_2[i][:, 1] = body[i][:,2].copy() # body_2[i][:, 2] = body[i][:,1].copy() # gt_posed_with_body = [np.concatenate((body_2[i], gt_posed[i]), axis=0) for i in range(len(gt_posed))] figs = { type + "/garment_" + str(i): plot_3d(gt[i], mesh_unposed_shaped_levels[i], faces[i], gt_posed[i], collision[i], collision_faces[i], collision_color[i]) for i in range(4) } return { **figs, **{ type + '/images': imgs, } } def test(): load_model(model, 'checkpoints/' + config['name']) # , iteration='60000' clothing_verts_idx = np.load(os.path.join('CAPE', 'data', 'clothing_verts_idx.npy')) cape_mask = np.zeros((6890), bool) cape_mask[clothing_verts_idx] = True dataset_test = factory.get_dataset(folder=config['test_dir'], batch_size=config['batch_size'], signature=config['signature_dynamic'], resolutions=dataset_resolutions, in_memory=False, do_repeat=False, is_cape=config['is_cape'], shuffle=False) static_dataset_test = StaticDataset(folder=config['test_dir'], signature=config['signature_static'], resolutions=dataset_resolutions, in_memory=True, is_cape=config['is_cape']) step = 0 losses = [] lines = [] all_errors = [] for batch in tqdm.tqdm(dataset_test): batch = static_dataset_test.expand_batch(batch_to_dict(batch, config['signature_dynamic'])) if step == 0: _, out_levels = test_step(batch) # load_model(model, 'checkpoints/' + config['name']) # , iteration='60000' # _, out_levels = test_step(batch) else: _, out_levels = test_step(batch) errors = [] for i, id in enumerate(batch['id']): id_str = id.numpy().decode("utf-8") frame_nr_str = batch['frame_nr'][i].numpy().decode("utf-8") pred = uv2mesh(out_levels[-1][0]) min_body = np.load(os.path.join('/home/workspace/data/dataset/cape/cape_release', 'minimal_body_shape', id_str.split('_')[0], id_str.split('_')[0] + '_minimal.npy'))[np.newaxis, :, :].astype(np.float32) offsets_pred = pred[i].numpy()[cape_mask[smpl.to_keep],:] - min_body[0,clothing_verts_idx,:] offsets_orig = batch['mesh_unposed'][i].numpy()[cape_mask[smpl.to_keep],:] - min_body[0,clothing_verts_idx,:] error = tf.reduce_mean(tf.math.sqrt(tf.reduce_sum((offsets_orig - offsets_pred) ** 2, axis=-1))) lines.append(id_str + '_' + frame_nr_str + ' ' + str(error.numpy())) errors.append(error) # test_loss = { **{ 'test/error': tf.reduce_mean(errors) }, **test_loss } # losses.append(test_loss) all_errors.extend(errors) # to_log = { **{ 'current_step': step, 'test/error': np.asarray(errors).mean() }, **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" with open('temp/error_' + config['name'] + '.txt', 'w') as f: f.writelines('\n'.join(lines)) errors = np.asarray(all_errors) print('mean: ' + str(errors.mean())) print('min: ' + str(errors.min())) print('max: ' + str(errors.max())) print('median: ' + str(np.median(errors))) np.save('temp/error_'+ config['name'] +'.npy', errors) # 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() allow_memory_growth() 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 '' dataset_resolutions = config['pyramid']['resolutions'].copy() if 512 not in dataset_resolutions: dataset_resolutions.append(512) factory = DatasetFactory() model: Pyramid = Pyramid(config) optimizer = tf.optimizers.Adam(learning_rate=config['lr']) balancing_coeff = config['starting_balancing_coeff'] balancing_activated_step = 0 test()