update
This commit is contained in:
+51
-122
@@ -12,7 +12,7 @@ args = vars(parser.parse_args())
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if args['debug']:
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from configs.iccvw.cvae_norm_1 import config
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from configs.iccvw.cvae_norm_5 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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@@ -28,6 +28,7 @@ import wandb
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import tqdm
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import random
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import numpy as np
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import kaleido
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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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@@ -93,7 +94,7 @@ def train_step(batch):
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kl = kl_loss(mean, logvar)
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reconstr_loss = l2_loss + l2_mesh_loss + (1 - ms_ssim)
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loss = reconstr_loss + g_balancing_coeff * kl
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loss: torch.Tensor = reconstr_loss + g_balancing_coeff * kl
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optimizer.zero_grad()
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loss.backward()
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@@ -164,125 +165,6 @@ def plot_everything(batch: dict, type: str):
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return { **{ 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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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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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 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 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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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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break
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print(count)
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count += 1
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print()
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def train():
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global g_balancing_coeff
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@@ -314,7 +196,7 @@ def train():
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print(step)
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if train_loss['train/euclid_error'] < config['balancing_activation_limit']:
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g_balancing_coeff += 1 / 10000
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g_balancing_coeff += 1 / 2000
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if step % 20000 == 0:
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ckpt_saver(model, step, optimizer, None)
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@@ -322,6 +204,49 @@ def train():
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step += 1
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def sample():
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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 train_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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batch_size = uv_static.size(0)
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for i in range(batch_size):
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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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with torch.inference_mode():
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pred = model.sample(uv_static_tiled, uv_body_posed_tiled, uv_mask_tiled)
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pred = pred * 1.4
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pred = pred.cpu().numpy().transpose(0, 2, 3, 1)
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pred = (pred + 1.4) / 2.8
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pred = pred * uv_mask_tiled[:,:,:,None].cpu().numpy()
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gt = (batch['uv_normals'][i].cpu().numpy().transpose(1, 2, 0) + 1.4) / 2.8 * uv_mask_tiled[0,:,:,None].cpu().numpy()
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img = np.concatenate(pred, axis=1)
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img = np.concatenate([gt, img], axis=1)
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# flip image along its shorter edge
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img = img[::-1,:,:]
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fig = ex.imshow(img)
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fig.update_layout(margin=dict(l=0, r=0, b=0, t=0, pad=0), width=img.shape[1], height=img.shape[0])
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fig.update_xaxes(visible=False)
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fig.update_yaxes(visible=False)
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fig.write_image(f'temp/normals/{batch["id"][i]}_{batch["frame_nr"][i].cpu().numpy()}_{batch["garment"][i]}.png')
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count += 1
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if count > 100:
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break
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if __name__ == '__main__':
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if not args['debug']:
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init_wandb()
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@@ -338,6 +263,10 @@ if __name__ == '__main__':
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model = CVAENorm(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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g_balancing_coeff = config['starting_balancing_coeff']
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