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+65
-153
@@ -11,7 +11,7 @@ parser.add_argument('--debug', action='store_true')
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args = vars(parser.parse_args())
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if args['debug']:
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from configs.iccvw.abl_all_4 import config
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from configs.iccvw.abl_all_5_var import config
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else:
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config_module = importlib.import_module(args['config'])
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config = config_module.config
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@@ -30,6 +30,7 @@ import numpy as np
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import plotly.graph_objects as go
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import plotly.express as ex
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from PIL import Image
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from pathlib import Path
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if config['is_skirt']:
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from losses.losses import edge_loss_skirt as edge_loss
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@@ -277,171 +278,80 @@ def plot_everything(batch: dict, type: str):
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return { **figs, **figs_coll, **{ f'{type}/batch_info': batch_info }, **{ type + '/images': imgs, type + '/mean_hist': mean_hist, type + '/logvar_hist': logvar_hist } }
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def sample(folder):
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# run = wandb.init(project='vae_plots', name=folder.split('/')[-1])
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load_model(model,folder)
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def sample():
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from data.utils import Frame
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from utils.utils import save_stl
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def create_temp_frames(folder: str, frame: Frame):
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with open(Path(folder) / 'frames_temp.txt', 'w') as f:
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f.writelines([f'{frame}\n' for _ in range(4)])
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model.eval()
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load_model(model, f'checkpoints/iccvw/{config["name"]}')
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count = 0
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for batch in dataset_train:
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batch = static_dataset_train.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
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uv_normals = tf.one_hot(batch['uv_normals'], 7)
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nr_of_samples=2
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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)
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pose, shape = batch['pose_shape'][0, :72], batch['pose_shape'][0, 72:]
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pose, shape = tf.tile(pose[tf.newaxis,:], [nr_of_samples,1]), tf.tile(shape[tf.newaxis,:], [nr_of_samples,1])
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mesh_pred = uv2mesh(pred)
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mesh_posed = smpl.run_garment(pose, shape, mesh_pred).numpy()
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mesh_orig = batch['mesh_posed'][0].numpy()
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mesh_orig_reposed = smpl.run_garment(pose[:1], shape[:1], batch['mesh_unposed'][:1]).numpy()[0]
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body = batch['mesh_body_posed'][0].numpy()
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body_color = np.ones_like(body, dtype=np.int32)*100
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F = np.load("custom_utils/smpl_faces.npy")
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mask = batch['mesh_mask'].numpy().astype(bool)[0]
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_, faces = _maskFaces(np.ones((config['smpl_res'], 3)), F, mask)
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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)
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mesh_pred_zero = uv2mesh(pred_zero)
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mesh_posed_zero = smpl.run_garment(pose, shape, mesh_pred_zero).numpy()
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frames = ['01852_70_Tshirt']
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frames = [Frame(frame_str=frame) for frame in frames]
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meshes = []
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meshes.append(go.Mesh3d(
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# 8 vertices of a cube
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x=mesh_orig_reposed[mask,0],
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y=mesh_orig_reposed[mask,2],
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z=mesh_orig_reposed[mask,1],
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# i, j and k give the vertices of triangles
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i = faces[:,0],
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j = faces[:,1],
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k = faces[:,2]
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))
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for i in range(nr_of_samples):
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x = mesh_posed[i][mask, :].copy()
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# x[:, 2] += 1 * (i + 1)
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x[:, 2] += 1
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meshes.append(go.Mesh3d(
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# 8 vertices of a cube
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x=x[:,0],
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y=x[:,2],
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z=x[:,1],
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# i, j and k give the vertices of triangles
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i = faces[:,0],
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j = faces[:,1],
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k = faces[:,2]
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))
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for frame in frames:
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create_temp_frames(config['train_dir'], frame)
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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'])
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dataloader = DataLoader(dataset, batch_size=4, drop_last=True, shuffle=True, )
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batch = next(iter(dataloader))
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uv_static = batch['uv_static']
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uv_body_posed = batch['uv_body_posed']
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uv_mask = batch['uv_mask']
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uv_normals = batch['uv_normals']
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batch_size = uv_static.size(0)
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for i in range(batch_size):
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counter = 0
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frame = Frame(batch['id'][i], batch['frame_nr'][i], batch['garment'][i])
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fix_data = np.load(Path(dataset_train.folder) / 'mesh_fixed' / f'{frame.id}_{frame.garment}.npz')
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mask = fix_data['mask']
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faces = fix_data['faces']
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gt = batch['mesh_posed'][i].cpu().numpy()[mask]
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save_stl(Path('temp') / 'sampling' / f'{frame.id}_{frame.frame_nr}_{frame.garment}_gt.stl', gt, faces)
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uv_static_tiled = uv_static[i][None,:,:,:].repeat(4, 1, 1, 1)
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uv_body_posed_tiled = uv_body_posed[i][None,:,:,:].repeat(4, 1, 1, 1)
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uv_mask_tiled = uv_mask[i][None,:,:].repeat(4, 1, 1)
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uv_normals_tiled = uv_normals[i][None,:,:,:].repeat(4, 1, 1, 1)
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pose_shape = batch['pose_shape'][i][None,:].repeat(4, 1)
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for i in range(nr_of_samples):
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x = mesh_posed_zero[i][mask, :].copy()
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# x[:, 2] += 1 * (i + 1)
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x[:, 2] += 2
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meshes.append(go.Mesh3d(
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# 8 vertices of a cube
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x=x[:,0],
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y=x[:,2],
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z=x[:,1],
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# i, j and k give the vertices of triangles
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i = faces[:,0],
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j = faces[:,1],
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k = faces[:,2]
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))
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for _ in range(4):
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with torch.inference_mode():
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pred = model.sample(uv_static_tiled, uv_body_posed_tiled, uv_normals_tiled, uv_mask_tiled)
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mesh_pred = uv2mesh(pred)
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# for i in range(5):
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# x = body.copy()
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# x[:, 2] += 1 * i
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# meshes.append(go.Mesh3d(
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# # 8 vertices of a cube
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# x=x[:,0],
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# y=x[:,2],
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# z=x[:,1],
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# # i, j and k give the vertices of triangles
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# i = F[:,0],
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# j = F[:,1],
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# k = F[:,2],
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# vertexcolor=body_color
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# ))
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shape = pose_shape[:, 72:]
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gender = torch.greater_equal(shape[:, 10] , 0.5).to(torch.long)
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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])])
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mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,None]
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mesh_pred_posed = smpl(pose_shape[:, :72], pose_shape[:, 72:], mesh_pred_shaped)[0]
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for k in range(4):
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posed = mesh_pred_posed.cpu().numpy()[k, mask, :]
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save_stl(Path('temp') / 'sampling' / f'{frame.id}_{frame.frame_nr}_{frame.garment}_{counter}.stl', posed, faces)
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counter += 1
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pass
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fig = go.Figure(data=meshes)
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fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=2, z=1))
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# fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=nr_of_samples-1, z=1))
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fig.update_layout(
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scene = dict(
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xaxis = dict(nticks=4, range=[-1,1],),
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# yaxis = dict(nticks=4, range=[-1,nr_of_samples+1],),
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yaxis = dict(nticks=4, range=[-1,3],),
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zaxis = dict(nticks=4, range=[-1,1],),)
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)
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# wandb.log({'plot_' + str(count): fig})
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fig.show()
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pred = pred.numpy()
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frames = [Image.fromarray(((pred[i] + 1) * 128).astype(np.int8), mode='RGB') for i in range(pred.shape[0])]
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frames[0].save("temp/garment_1.gif", format="GIF", append_images=frames[1:],
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save_all=True, duration=500, loop=0)
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if count > 10:
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if count > 100:
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break
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print(count)
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count += 1
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print()
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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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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)
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static_dataset_test = StaticDataset(folder=config['test_dir'], signature=config['signature_static'], use_filled=config['use_filled'], resolutions=[config['resolution']], in_memory=False)
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step = 0
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losses = []
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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 = test_step(batch)
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losses.append(test_loss)
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to_log = { **{ 'current_step': step }, **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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# print(step)
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step += 1
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# averages = dict()
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path_to_plotly_html = "temp/plotly_figure.html"
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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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wandb.log({ **histograms }, step = step)
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wandb.finish()
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pass
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@@ -500,6 +410,8 @@ if __name__ == '__main__':
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model = CVAE(config)
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sample()
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exit()
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optimizer = optim.Adam(model.parameters(), lr=config['lr'])
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_MSSSIM = MS_SSIM(data_range=2, size_average=True, channel=3)
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