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+46
-34
@@ -1,5 +1,5 @@
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import os
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os.environ["CUDA_VISIBLE_DEVICES"] = "4"
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os.environ["CUDA_VISIBLE_DEVICES"] = "1"
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import tensorflow as tf
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gpus = tf.config.experimental.list_physical_devices('GPU')
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@@ -25,24 +25,26 @@ import plotly.graph_objects as go
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import plotly.express as ex
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from tf_utils import check_tf_version, allow_memory_growth
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from losses import edge_loss, uv_mask_loss
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# from losses import edge_loss, uv_mask_loss
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from dataset import DatasetFactory, StaticDataset
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from custom_utils.custom_utils import batch_to_dict
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from custom_utils.smpl import SMPL
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# from custom_utils.smpl import SMPL
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# from models.upscaler import Upscaler
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from models.upscaler import UpscalerBasic as Upscaler
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from models.upscaler import UpscalerBasicNew as Upscaler
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name='up_2_3_skirt_d32'
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name='up_3_d256_512_2'
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def init_wandb():
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run = wandb.init(project='vae', name=name,
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notes='\
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changed ms-ssim scale weights\
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inpainting style\
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MLP based\
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smaller batch\
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use mask for loss\
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started from up_2_3\
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')
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changed ms-ssim scale weights | \
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inpainting style | \
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MLP based | \
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smaller batch | \
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use mask for loss | \
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using ms ssim for higher res | \
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training from low res to low res | \
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named the layers, added tanh to final layer | \
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')
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artifact = wandb.Artifact('code', type='code')
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artifact.add_file('upscale_test.py')
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artifact.add_file('models/upscaler.py')
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@@ -51,7 +53,8 @@ started from up_2_3\
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artifact.add_file('convnext/models/convnext_tf.py')
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run.log_artifact(artifact)
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uv2mesh = UV2Mesh(res=256, is_skirt=True)
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# !!!!!!!!!! CHANGE SKIRT OR NOT
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uv2mesh = UV2Mesh(res=256, is_skirt=False)
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@@ -95,14 +98,17 @@ def plot_3d(mesh_gt, mesh_pred_unposed, faces):
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@tf.function
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def train_step(batch):
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with tf.GradientTape() as tape:
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pred = model([batch['uv_unposed' + resolution_modifier], batch['uv_mask' + resolution_modifier]])
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pred = model([batch['uv_unposed' + low_resolution_modifier], batch['uv_mask' + low_resolution_modifier]])
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pred = tf.image.resize(pred, size=(high_res, high_res//2), method=tf.image.ResizeMethod.LANCZOS5)
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mesh_pred = uv2mesh(pred)
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mesh_orig = batch['mesh_unposed']
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l1_loss = tf.reduce_mean(tf.reduce_sum(((tf.math.abs(batch['uv_unposed'] - pred * batch['uv_mask'][:,:,:,tf.newaxis]) )), axis=1))
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# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred * batch['uv_mask'][:,:,:,tf.newaxis] + 1, tf.transpose(batch['uv_unposed'], [0,2,3,1]) + 1, max_val=2, 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 * batch['uv_mask'][:,:,:,tf.newaxis] + 1, batch['uv_unposed'] * batch['uv_mask'][:,:,:,tf.newaxis] + 1, max_val=2))
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l1_loss = tf.reduce_mean(tf.reduce_sum(((tf.math.abs(batch['uv_unposed' + high_resolution_modifier] - pred * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis]) )), axis=1))
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# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred * batch['uv_mask'][:,:,:,tf.newaxis] + 1, tf.transpose(batch['uv_unposed'], [0,2,3,1]) + 1, max_val=2, power_factors=(0.5, 0.2, 0.2, 0.1)))
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# ms_ssim = tf.reduce_mean(tf.image.ssim(pred * batch['uv_mask'][:,:,:,tf.newaxis] + 1, batch['uv_unposed'] * batch['uv_mask'][:,:,:,tf.newaxis] + 1, max_val=2))
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ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis] + 1, batch['uv_unposed' + high_resolution_modifier] * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis] + 1, max_val=2, power_factors=(0.5, 0.2, 0.2, 0.1)))
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# ms_ssim = tf.reduce_mean(tf.image.ssim(pred * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis] + 1, batch['uv_unposed' + high_resolution_modifier] * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis] + 1, max_val=2))
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loss = 1e3 * (1 - ms_ssim)
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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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@@ -115,7 +121,7 @@ def train_step(batch):
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# UPDATE!
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@tf.function
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def valid_step(batch):
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pred = model(batch['uv_unposed' + resolution_modifier], batch['uv_mask'])
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pred = model(batch['uv_unposed' + low_resolution_modifier], batch['uv_mask'])
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mesh_pred = uv2mesh(pred)
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mesh_orig = batch['mesh_unposed']
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@@ -132,12 +138,13 @@ def valid_step(batch):
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def plot_everything(batch: dict, type: str):
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pred = model([batch['uv_unposed' + resolution_modifier], batch['uv_mask' + resolution_modifier]])
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pred = model([batch['uv_unposed' + low_resolution_modifier], batch['uv_mask' + low_resolution_modifier]])
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pred = tf.image.resize(pred, size=(high_res, high_res//2), method=tf.image.ResizeMethod.LANCZOS5)
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mesh_pred = uv2mesh(pred)
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mesh_orig = batch['mesh_unposed']
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F = np.load("custom_utils/smpl_faces_skirt.npy")
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F = np.load("custom_utils/smpl_faces.npy")
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mask = batch['mesh_mask'].numpy().astype(bool)
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garment_unposed, faces = maskFacesBatch(mesh_pred.numpy(), F, mask)
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# gt = [mesh_orig[i].numpy()[mask[i], :] for i in range(pred.shape[0])]
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@@ -146,7 +153,7 @@ def plot_everything(batch: dict, type: str):
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figs = { type + "/garment_" + str(i): plot_3d(gt[i], garment_unposed[i], faces[i]) for i in range(4) }
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# figs = { type + "/garment_" + str(i): plot_3d(gt[i], garment_unposed[i], faces[i], collision[i], collision_faces[i], collision_color[i]) for i in range(4) }
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orig_img = batch['uv_unposed']
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orig_img = batch['uv_unposed' + high_resolution_modifier]
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img = tf.concat((orig_img, pred), axis=2)
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imgs = [wandb.Image((img[i] + 1) / 2.0) for i in range(4)]
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@@ -166,20 +173,23 @@ def train():
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# data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, signature_dynamic))
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valid_loss = {}#valid_step(data_valid)
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if step % 2500 == 0:
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if step % 10000 == 0:
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train_figs = plot_everything(batch, 'train')
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valid_figs = {}#plot_everything(data_valid, 'valid')
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wandb.log({ **train_loss, **valid_loss, **train_figs, **valid_figs }, step=step)
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else:
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wandb.log({ **train_loss, **valid_loss }, step=step)
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if step % 10000 == 0:
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if step % 25000 == 0:
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manager.save(checkpoint_number=step)
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print(step)
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if step > 300000:
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break
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step += 1
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if __name__ == '__main__':
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@@ -189,39 +199,41 @@ if __name__ == '__main__':
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parser.add_argument('--name', type=str)
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args = vars(parser.parse_args())
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smpl = SMPL()
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# smpl = SMPL()
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# uv_mask_body = np.load('custom_utils/mask_256x512.npy')
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# uv_mask_body = cv2.dilate(uv_mask_body.astype(np.float32), cv2.getStructuringElement(cv2.MORPH_RECT, (2 * 2 + 1, 2 * 2 + 1), (2, 2)), iterations=2)
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low_res = 32
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low_res = 256
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high_res = 512
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uv_mask_body = tf.ones((low_res, low_res//2))
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resolution_modifier = '_' + str(low_res)
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low_resolution_modifier = '_' + str(low_res)
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high_resolution_modifier = ('_' + str(high_res)) if high_res != 512 else ''
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factory = DatasetFactory()
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signature_dynamic = { 'id': (),
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'pose_shape': (72 + 11,),
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'uv_unposed' + resolution_modifier: (3, low_res, low_res//2,),
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'uv_unposed': (3, 512, 256,),
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'uv_unposed' + low_resolution_modifier: (low_res, low_res//2, 3),
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'uv_unposed' + high_resolution_modifier: (high_res, high_res//2, 3),
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'mesh_unposed': (14475, 3,),
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}
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signature_static = {
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'mesh_mask': (14475,),
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'uv_mask': (512, 256,),
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'uv_mask' + resolution_modifier: (low_res, low_res//2,),
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'uv_mask' + high_resolution_modifier: (high_res, high_res//2,),
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'uv_mask' + low_resolution_modifier: (low_res, low_res//2,),
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}
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dataset_train = factory.get_dataset(folder='/home/workspace/local-data/train_skirt_25_2', batch_size=8, signature=signature_dynamic, use_filled=False, resolutions=[512, low_res])
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static_dataset_train = StaticDataset(folder='/home/workspace/local-data/train_skirt_25_2', signature=signature_static, use_filled=False, resolutions=[512, low_res])
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dataset_train = factory.get_dataset(folder='/home/workspace/local-data/train_100', batch_size=8, signature=signature_dynamic, use_filled=False, resolutions=[high_res, low_res])
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static_dataset_train = StaticDataset(folder='/home/workspace/local-data/train_100', signature=signature_static, use_filled=False, resolutions=[high_res, low_res])
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dataset_valid = None #factory.get_dataset(folder='/home/workspace/local-data/jumpsuit_20/valid', batch_size=32, signature=signature_dynamic)
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static_dataset_valid = None #StaticDataset(folder='/home/workspace/local-data/jumpsuit_20/valid', signature=signature_static)
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model = Upscaler(bottleneck_size=512, size=low_res)
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model = Upscaler(low_res=low_res)
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optimizer = tf.optimizers.Adam(learning_rate=0.0001)
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folder = 'models/ae_logs/'
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ckpt = tf.train.Checkpoint(model=model)
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manager = tf.train.CheckpointManager(ckpt, 'checkpoints/' + name, max_to_keep=3)
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load_model(model, 'checkpoints/up_2_3')
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# load_model(model, 'checkpoints/up_2_3')
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train()
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