custom upscaling
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import os
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os.environ["CUDA_VISIBLE_DEVICES"] = "2"
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import time
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import argparse
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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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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 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 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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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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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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')
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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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artifact.add_file('losses.py')
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artifact.add_file('dataset.py')
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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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def plot_3d(mesh_gt, mesh_pred_unposed, faces):
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mesh_pred_unposed[:,2] += 1
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fig = go.Figure(data=[
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go.Mesh3d(
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# 8 vertices of a cube
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x=mesh_gt[:,0],
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y=mesh_gt[:,2],
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z=mesh_gt[:,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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go.Mesh3d(
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# 8 vertices of a cube
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x=mesh_pred_unposed[:,0],
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y=mesh_pred_unposed[:,2],
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z=mesh_pred_unposed[:,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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])
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fig.update_layout(scene_aspectmode='manual',
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scene_aspectratio=dict(x=1, y=2, 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=[-1,3],),
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zaxis = dict(nticks=4, range=[-1,1],),)
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)
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return fig
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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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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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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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gradients = tape.gradient(loss, model.trainable_variables)
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optimizer.apply_gradients(zip(gradients, model.trainable_variables))
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return { 'train/loss': loss, 'train/l1_loss': l1_loss, 'train/euclid_error': metric, 'train/ms_ssim': ms_ssim }
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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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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) )), axis=1))
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ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(tf.transpose(pred, [0,2,3,1]) + 1, tf.transpose(batch['uv_unposed'], [0,2,3,1]) + 1, max_val=2))
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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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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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return { 'valid/loss': loss, 'valid/l1_loss': l1_loss, 'valid/euclid_error': metric, 'valid/ms_ssim': ms_ssim }
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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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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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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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gt = [mesh_orig[i].numpy()[mask[i], :] for i in range(pred.shape[0])]
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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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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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return { **figs, **{ type + '/images': imgs} }
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def train():
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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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batch = static_dataset_train.expand_batch(batch_to_dict(batch, signature_dynamic))
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train_loss = train_step(batch)
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# data_valid = valid_iterator.get_next()
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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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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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manager.save(checkpoint_number=step)
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print(step)
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step += 1
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if __name__ == '__main__':
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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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args = vars(parser.parse_args())
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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 = 128
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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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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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'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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}
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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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model = Upscaler(bottleneck_size=512, size=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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ckpt.restore(manager.latest_checkpoint)
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train()
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