making ready for skirts
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+31
-18
@@ -1,5 +1,18 @@
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import os
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os.environ["CUDA_VISIBLE_DEVICES"] = "2"
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os.environ["CUDA_VISIBLE_DEVICES"] = "4"
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import tensorflow as tf
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gpus = tf.config.experimental.list_physical_devices('GPU')
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if gpus:
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try:
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# Currently, memory growth needs to be the same across GPUs
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for gpu in gpus:
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tf.config.experimental.set_memory_growth(gpu, True)
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logical_gpus = tf.config.experimental.list_logical_devices('GPU')
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print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
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except RuntimeError as e:
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# Memory growth must be set before GPUs have been initialized
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print(e)
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import time
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import argparse
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@@ -7,8 +20,7 @@ import wandb
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import random
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import cv2
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import numpy as np
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import tensorflow as tf
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from custom_utils.custom_utils import UV2Mesh, maskFacesBatch
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from custom_utils.custom_utils import UV2Mesh, load_model, maskFacesBatch
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import plotly.graph_objects as go
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import plotly.express as ex
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@@ -20,15 +32,16 @@ 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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name='up_2_3_d128'
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name='up_2_3_skirt_d32'
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def init_wandb():
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run = wandb.init(project='vae', name=name,
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notes='convnext upscaling\
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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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artifact = wandb.Artifact('code', type='code')
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artifact.add_file('upscale_test.py')
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@@ -38,7 +51,7 @@ use mask for loss\
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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)
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uv2mesh = UV2Mesh(res=256, is_skirt=True)
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@@ -87,9 +100,9 @@ def train_step(batch):
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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(tf.transpose(batch['uv_unposed'], [0,2,3,1]) - 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(tf.transpose(pred * uv_mask_body[tf.newaxis,tf.newaxis,:,:], [0,2,3,1]) + 1, tf.transpose(batch['uv_body_posed' + resolution_modifier] * uv_mask_body[tf.newaxis,tf.newaxis,:,:], [0,2,3,1]) + 1, max_val=2))
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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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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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@@ -124,7 +137,7 @@ def plot_everything(batch: dict, type: str):
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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.npy")
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F = np.load("custom_utils/smpl_faces_skirt.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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@@ -133,7 +146,7 @@ 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 = tf.transpose(batch['uv_unposed'], [0,2,3,1])
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orig_img = batch['uv_unposed']
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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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@@ -142,7 +155,7 @@ def plot_everything(batch: dict, type: str):
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def train():
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valid_iterator = iter(dataset_valid)
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# valid_iterator = iter(dataset_valid)
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step = 0
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for batch in dataset_train:
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@@ -180,7 +193,7 @@ if __name__ == '__main__':
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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 = 128
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low_res = 32
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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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@@ -197,10 +210,10 @@ if __name__ == '__main__':
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'uv_mask' + 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_100', 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_100', signature=signature_static, use_filled=False, resolutions=[512, low_res])
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dataset_valid = factory.get_dataset(folder='/home/workspace/local-data/jumpsuit_20/valid', batch_size=32, signature=signature_dynamic)
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static_dataset_valid = StaticDataset(folder='/home/workspace/local-data/jumpsuit_20/valid', signature=signature_static)
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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_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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@@ -209,6 +222,6 @@ if __name__ == '__main__':
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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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ckpt.restore(manager.latest_checkpoint)
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load_model(model, 'checkpoints/up_2_3')
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train()
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