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-56
@@ -1,5 +1,5 @@
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import os
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from configs.config_pyramid import config
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from configs.config_abl_pyr_64_128_256_cape_f import config
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os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
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import tensorflow as tf
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@@ -44,7 +44,7 @@ from models.pyramid_model_optimized import Pyramid
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def init_wandb():
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run = wandb.init(project='cvae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp')
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run = wandb.init(project='results', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp')
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artifact = wandb.Artifact('code', type='code')
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artifact.add_file('cvae_pyramid.py')
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@@ -220,45 +220,46 @@ def test_step(batch):
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levels_input, conds = group_inputs_for_levels(batch)
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out_levels = model([levels_input, conds], training=False)
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shape = batch['pose_shape'][:, 72:]
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gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
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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])])
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mesh_orig = batch['mesh_unposed']
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# shape = batch['pose_shape'][:, 72:]
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# gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
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# 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])])
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# mesh_orig = batch['mesh_unposed']
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output = dict()
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reconstr_loss_total = 0
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kl_total = 0
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# output = dict()
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# reconstr_loss_total = 0
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# kl_total = 0
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#for i, res in enumerate(config['pyramid']['resolutions']):
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i = -1
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res = config['pyramid']['resolutions'][-1]
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res_modifier = '_' + str(res) if res != 512 else ''
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pred, mean, logvar = out_levels[i]
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# #for i, res in enumerate(config['pyramid']['resolutions']):
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# i = -1
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# res = config['pyramid']['resolutions'][-1]
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# res_modifier = '_' + str(res) if res != 512 else ''
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# pred, mean, logvar = out_levels[i]
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mesh_pred = uv2mesh(pred)
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mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
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mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
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# mesh_pred = uv2mesh(pred)
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# mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
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# mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
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# is the output already masked?
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# # is the output already masked?
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e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
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c_loss = tf.reduce_mean(collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']))
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c_ratio = tf.reduce_mean(collision_ratio(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']))
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l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((batch['uv_unposed'] - pred) ** 2), axis=-1), axis=[1, 2]))
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# 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)))
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ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3))
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# e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
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# c_loss = tf.reduce_mean(collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']))
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# c_ratio = tf.reduce_mean(collision_ratio(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']))
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# l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((batch['uv_unposed'] - pred) ** 2), axis=-1), axis=[1, 2]))
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# # 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)))
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# ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3))
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reconstr_loss_total += config['pyramid']['loss_weights'][i] * (l2_loss + (e_loss + c_loss) * config['reg_loss_coeff'])
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# reconstr_loss_total += config['pyramid']['loss_weights'][i] * (l2_loss + (e_loss + c_loss) * config['reg_loss_coeff'])
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# 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'])
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# # 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'])
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output = { **output, **{ 'test/edge_loss' + res_modifier: e_loss, 'test/collision_loss' + res_modifier: c_loss,
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'test/ms_ssim' + res_modifier: ms_ssim, 'test/l2_loss' + res_modifier: l2_loss,
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'test/collision_ratio' + res_modifier: c_ratio } }
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# output = { **output, **{ 'test/edge_loss' + res_modifier: e_loss, 'test/collision_loss' + res_modifier: c_loss,
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# 'test/ms_ssim' + res_modifier: ms_ssim, 'test/l2_loss' + res_modifier: l2_loss,
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# 'test/collision_ratio' + res_modifier: c_ratio } }
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loss = reconstr_loss_total + balancing_coeff * kl_total
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# loss = reconstr_loss_total + balancing_coeff * kl_total
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return { **{ 'test/loss': loss, 'test/reconstr_loss_total': reconstr_loss_total }, **output }, out_levels
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# return { **{ 'test/loss': loss, 'test/reconstr_loss_total': reconstr_loss_total }, **output }, out_levels
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return dict(), out_levels
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@@ -322,13 +323,10 @@ def plot_everything(batch: dict, type: str):
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def test():
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str_split = config['name'].split('_')
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test_idx = str_split.index('test')
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checkpoint_name = '_'.join(str_split[:test_idx])
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load_model(model, 'checkpoints/' + checkpoint_name)
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load_model(model, 'checkpoints/' + config['name'])
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dataset_test = factory.get_dataset(folder=config['test_dir'], batch_size=config['batch_size'], signature=config['signature_dynamic'], use_filled=config['use_filled'], resolutions=dataset_resolutions, in_memory=False, do_repeat=False, is_cape=config['is_cape'])
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static_dataset_test = StaticDataset(folder=config['test_dir'], signature=config['signature_static'], use_filled=config['use_filled'], resolutions=dataset_resolutions, in_memory=False, is_cape=config['is_cape'])
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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'])
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static_dataset_test = StaticDataset(folder=config['test_dir'], signature=config['signature_static'], resolutions=dataset_resolutions, in_memory=True, is_cape=config['is_cape'])
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step = 0
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losses = []
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@@ -337,7 +335,7 @@ def test():
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for batch in tqdm.tqdm(dataset_test):
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batch = static_dataset_test.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
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test_loss, out_levels = test_step(batch)
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_, out_levels = test_step(batch)
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all_errors = []
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errors = []
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@@ -350,15 +348,15 @@ def test():
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error = tf.reduce_mean(tf.math.sqrt(tf.reduce_sum((offsets_orig - offsets_pred) ** 2, axis=-1)))
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errors.append(error)
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test_loss = { **{ 'test/error': tf.reduce_mean(errors) }, **test_loss }
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losses.append(test_loss)
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# test_loss = { **{ 'test/error': tf.reduce_mean(errors) }, **test_loss }
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# losses.append(test_loss)
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all_errors.extend(errors)
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to_log = { **{ 'current_step': step, 'test/error': np.asarray(errors).mean() }, **test_loss }
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if step % 1000 == 0:
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figs = plot_everything(batch, 'test')
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to_log = { **to_log, **figs }
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# to_log = { **{ 'current_step': step, 'test/error': np.asarray(errors).mean() }, **test_loss }
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# if step % 1000 == 0:
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# figs = plot_everything(batch, 'test')
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# to_log = { **to_log, **figs }
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wandb.log(to_log, step = step)
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# wandb.log(to_log, step = step)
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# print(step)
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step += 1
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@@ -371,25 +369,25 @@ def test():
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print('min: ' + str(errors.min()))
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print('max: ' + str(errors.max()))
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print('median: ' + str(np.median(errors)))
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np.save('temp/cape_f_pyr_64_128_256_error.npy', errors)
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np.save('temp/error_'+ config['name'] +'.npy', errors)
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histograms = dict()
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for key in test_loss.keys():
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accumulated = [losses[i][key].numpy() for i in range(len(losses))]
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fig = ex.histogram(accumulated, title='Average: ' + str(np.asarray(accumulated).mean()))
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fig.write_html(path_to_plotly_html, auto_play = False)
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# histograms = dict()
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# for key in test_loss.keys():
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# accumulated = [losses[i][key].numpy() for i in range(len(losses))]
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# fig = ex.histogram(accumulated, title='Average: ' + str(np.asarray(accumulated).mean()))
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# fig.write_html(path_to_plotly_html, auto_play = False)
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histograms['all/' + key.split('/')[-1] + '_hist'] = wandb.Html(path_to_plotly_html)
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# averages['all/' + key.split('/')[-1] + '_avg'] = np.asarray(accumulated).mean()
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# histograms['all/' + key.split('/')[-1] + '_hist'] = wandb.Html(path_to_plotly_html)
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# # averages['all/' + key.split('/')[-1] + '_avg'] = np.asarray(accumulated).mean()
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wandb.log({ **histograms }, step = step)
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wandb.finish()
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# wandb.log({ **histograms }, step = step)
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# wandb.finish()
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pass
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if __name__ == '__main__':
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init_wandb()
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# init_wandb()
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allow_memory_growth()
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parser = argparse.ArgumentParser(description='')
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parser.add_argument('--name', type=str)
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