487 lines
21 KiB
Python
487 lines
21 KiB
Python
import os
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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 numpy as np
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import tensorflow as tf
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import scipy.interpolate as spi
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from custom_utils.custom_utils import UV2Mesh, maskFacesBatch
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from tensorboardX import SummaryWriter
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import plotly.graph_objects as go
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import plotly.express as ex
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from plotly.subplots import make_subplots
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from utils import str_to_bool
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from tf_utils import check_tf_version, allow_memory_growth, split_gpu_for_testing
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from load_models import load_generator, load_discriminator
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from losses import d_logistic, d_logistic_r1_reg, g_logistic_non_saturating, g_logistic_ns_pathreg, edge_loss, uv_mask_loss
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# from losses import collision_penalty as collision_penalty
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from losses import collision_loss as collision_penalty
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from convnext.models.convnext_tf import ConvNeXt, ConvNeXtReverse, Block, ConvNeXtReverseWithSkip, ConvNeXtWithSkip
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from stylegan2_ae.generator import Generator
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from custom_utils.graph_utils import Reconstructor_V4_Decoder as GNNDecoder
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from custom_utils.graph_utils import Reconstructor_v2 as SimpleReconstructor
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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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name='convunext_7'
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os.environ["CUDA_VISIBLE_DEVICES"] = "6"
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run = wandb.init(config=tf.compat.v1.flags.FLAGS, sync_tensorboard=True, project='stylegan', name=name,
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notes='basic pyramid. more balanced levels. take ssim (not ms-ssim) loss at each level. higher lr. on on jumpsuits. \
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uv maps filled with nearest enighbour, up/downscaling with lanczos. only take loss at final level')
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artifact = wandb.Artifact('code', type='code')
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artifact.add_file('auto_encoder.py')
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artifact.add_file('stylegan2_ae/layers/mapping_block.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, faces):
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# mesh_pred[:,1] += 2
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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[:,1],
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# z=mesh_gt[:,2],
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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[:,0],
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# y=mesh_pred[:,1],
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# z=mesh_pred[:,2],
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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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def plot_3d(mesh_gt, mesh_pred_unposed, faces, collision, collision_faces, collision_color):
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mesh_pred_unposed[:,1] += 1
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collision[:,1] += 2
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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[:,1],
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z=mesh_gt[:,2],
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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[:,1],
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z=mesh_pred_unposed[:,2],
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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=collision[:,0],
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y=collision[:,1],
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z=collision[:,2],
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# i, j and k give the vertices of triangles
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i = collision_faces[:,0],
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j = collision_faces[:,1],
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k = collision_faces[:,2],
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vertexcolor=collision_color
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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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def plot_3d_levels(mesh_levels, faces):
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levels = len(mesh_levels)
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plot_levels = mesh_levels.copy() #[mesh_levels[i][0] for i in range(levels)]
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for i in range(levels):
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plot_levels[i][:,1] = plot_levels[i][:,1] + i * 2
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meshes = [go.Mesh3d(
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# 8 vertices of a cube
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x=mesh[:,0],
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y=mesh[:,1],
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z=mesh[:,2],
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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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) for mesh in mesh_levels]
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fig = go.Figure(data= meshes)
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fig.update_layout(scene_aspectmode='manual',
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scene_aspectratio=dict(x=1, y=levels, 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,levels*2],),
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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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class Bottleneck(tf.keras.Model):
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def __init__(self, dim):
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super(Bottleneck, self).__init__()
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self.bottleneck = tf.keras.Sequential(
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[Block(dim=dim, drop_path=0,
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layer_scale_init_value=1e-6, prefix=f'bottleneck{j}') for j in range(5)]
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)
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def call(self, inputs, training=None):
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x = inputs
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x = self.bottleneck(x, training=training)
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return x
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class PyramidLevel(tf.keras.Model):
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def __init__(self, depths=[3, 3, 9, 3], dims=[64, 128, 256, 256]):
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super(PyramidLevel, self).__init__()
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# reverse_dims = list(reversed(dims[:-1] + [16]))
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# reverse_dims = dims[:-1] + [16]
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reverse_dims = [16] + dims[:-1]
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self.encoder = ConvNeXtWithSkip(depths=depths, dims=dims)
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self.bottleneck = Bottleneck(dims[-1])
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self.decoder = ConvNeXtReverseWithSkip(depths=depths, dims=reverse_dims)
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def call(self, inputs, training=None):
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x = inputs
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x, skip_features = self.encoder(x, training=training)
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x = self.bottleneck(x, training=training)
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x = self.decoder([x, skip_features], training=training)
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return x
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def downscale(img, uv_mask=None):
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# img = tf.transpose(tf.squeeze(batch['uv_posed']), [1,2,0]).numpy()
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# coords = np.transpose(np.stack(np.nonzero((1 - batch['uv_mask'][0]).numpy().astype(bool))), (1,0))
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# img_idx = np.transpose(np.stack(np.nonzero((batch['uv_mask'][0]).numpy().astype(bool))), (1,0))
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# img_data = img[img_idx[:,0], img_idx[:,1], :]
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# interpolator = spi.NearestNDInterpolator(x=img_idx, y=img_data)
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# img_2 = interpolator(coords)
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# res = np.zeros((512,256,3))
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# res[coords[:,0], coords[:,1], :] = img_2
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# res[img_idx[:,0], img_idx[:,1], :] = img_data
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results = [img]
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if uv_mask is not None:
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masks = [uv_mask]
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for i in range(4):
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# print(np.array(img.shape[:2]) / (2 ** (i + 1)))
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results.append(tf.image.resize(img, size=np.array(img.shape[1:3]) / (2 ** (i + 1)), method=tf.image.ResizeMethod.LANCZOS5, antialias=True, preserve_aspect_ratio=True))
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if uv_mask is not None:
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masks.append(tf.image.resize(uv_mask[:,:,:,tf.newaxis], size=np.array(uv_mask.shape[1:]) / (2 ** (i + 1)), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR, antialias=False)[:,:,:,0])
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if uv_mask is not None:
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return results, masks
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return results
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def upscale(imgs):
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results = []
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for img in imgs:
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results.append(tf.image.resize(img, size=(512, 256), method=tf.image.ResizeMethod.LANCZOS5))
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return results
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class Pyramid(tf.keras.Model):
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def __init__(self):
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super(Pyramid, self).__init__()
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self.level_1 = PyramidLevel(depths=[27], dims=[512])
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self.level_2 = PyramidLevel(depths=[9, 27], dims=[256, 256])
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self.level_3 = PyramidLevel(depths=[9, 9, 27], dims=[64, 128, 256])
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self.level_4 = PyramidLevel(depths=[3, 9, 27, 3], dims=[32, 64, 128, 256])
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self.level_5 = PyramidLevel(depths=[3, 3, 9, 27, 9], dims=[32, 64, 128, 256, 256])
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self.levels = [self.level_5, self.level_4, self.level_3, self.level_2, self.level_1]
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def call(self, inputs, training=None):
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x, uv_mask, uv_shaped_body = inputs
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x = tf.transpose(x, [0,2,3,1])
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x_levels = downscale(x)
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output = self.levels[-1](x_levels[-1])
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output_levels = [output]
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for i in reversed(range(len(x_levels)-1)):
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level_out = self.levels[i](x_levels[i])
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output = tf.image.resize(output, size=(output.shape[1] * 2, output.shape[2] * 2), method=tf.image.ResizeMethod.LANCZOS5) + level_out
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output_levels.append(output)
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x = output
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x = tf.transpose(x, [0,3,1,2])
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# x = x * uv_mask[:,tf.newaxis,:,:]
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return x, output_levels
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class AutoEncoder(tf.keras.Model):
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def __init__(self):
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super(AutoEncoder, self).__init__()
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# bottleneck_size = 256
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# self.decoder = ConvNeXtReverseWithSkip(depths=list(reversed([3, 3, 9, 3])), dims=list(reversed([bottleneck_size, 128, 64, 16])))
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# self.bottleneck = Bottleneck(bottleneck_size)
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# self.encoder = ConvNeXtWithSkip(depths=[3, 3, 9, 3], dims=[64, 128, 256, bottleneck_size])
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self.level = PyramidLevel(depths=[3, 3, 3, 3], dims=[64, 128])
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def call(self, inputs, training=None):
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x, uv_mask, uv_shaped_body = inputs
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x = tf.transpose(x, [0,2,3,1])
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x = tf.image.resize(x, size=(256, 128))
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# x, skip_features = self.encoder(x, training=training)
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# x = self.bottleneck(x, training=training)
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# x = self.decoder([x, skip_features], training=training)
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x = self.level(x)
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x = x + tf.transpose(uv_shaped_body, [0,2,3,1])
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x = tf.transpose(x, [0,3,1,2])
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# x = x * uv_mask[:,:, tf.newaxis]
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# x = x * uv_mask[:,tf.newaxis,:,:]
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return x
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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, levels_pred = model([tf.concat((batch['uv_static'], batch['uv_body_posed']), axis=1), batch['uv_mask'], batch['uv_shaped_body']])
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# pred = (pred + zero_body) * batch['mesh_mask'][:,:,tf.newaxis]
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mesh_pred = uv2mesh(pred)
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# mesh_pred = pred
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mesh_orig = batch['mesh_unposed']
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# uv_unposed_levels, uv_mask_levels = downscale(tf.transpose(batch['uv_unposed'], [0,2,3,1]), batch['uv_mask'])
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ms_ssim_levels = 0 #tf.reduce_mean([tf.reduce_mean(tf.image.ssim((levels_pred[i] + 1) * uv_mask_levels[len(levels_pred) - i - 1][:,:,:,tf.newaxis], (uv_unposed_levels[len(levels_pred) - i - 1] + 1) * uv_mask_levels[len(levels_pred) - i - 1][:,:,:,tf.newaxis], max_val=10)) for i in range(len(levels_pred))])
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e_loss = 1e-2 * tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
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c_loss = 1e-2 * tf.reduce_mean(collision_penalty(mesh_pred, batch['mesh_body_posed'], batch['mesh_mask']))
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l1_loss = tf.reduce_mean(tf.reduce_sum((tf.math.abs(batch['uv_unposed'] - pred)), axis=1) * batch['uv_mask'])
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ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale((tf.transpose(pred, [0,2,3,1]) + 1) * batch['uv_mask'][:,:,:,tf.newaxis], (tf.transpose(batch['uv_unposed'], [0,2,3,1]) + 1) * batch['uv_mask'][:,:,:,tf.newaxis], max_val=10))
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fft_loss = tf.reduce_mean(tf.reduce_sum(tf.math.abs(tf.signal.fft3d(tf.complex(pred * batch['uv_mask'][:,tf.newaxis,:,:], tf.zeros_like(pred))) - tf.signal.fft3d(tf.complex(batch['uv_unposed'] * batch['uv_mask'][:,tf.newaxis,:,:], tf.zeros_like(batch['uv_unposed'])))), axis=[1,2,3]))
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# loss = fft_loss_pc # + e_loss + c_loss
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loss = 1e3 * (1 - ms_ssim)
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mesh_orig_2 = uv2mesh(batch['uv_unposed'])
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# mesh_skinned = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred)
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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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metric_2 = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig_2 - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
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euclid_posed = 0 # tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_skinned - batch['mesh_posed']) ** 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 { 'loss': loss, 'edge_loss': e_loss, 'collision_loss': c_loss, 'l1_loss': l1_loss, 'euclid_error': metric, 'ms_ssim': ms_ssim,
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'fft_loss': fft_loss, 'euclid_error_reconst': metric_2,
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'euclid_error_posed': euclid_posed, 'ms_ssim_levels': ms_ssim_levels }
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@tf.function
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def valid_step(batch):
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pred, levels_pred = model([tf.concat((batch['uv_static'], batch['uv_body_posed']), axis=1), batch['uv_mask'], batch['uv_shaped_body']])
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mesh_pred = uv2mesh(pred)
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mesh_orig = batch['mesh_unposed']
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# uv_unposed_levels, uv_mask_levels = downscale(tf.transpose(batch['uv_unposed'], [0,2,3,1]), batch['uv_mask'])
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ms_ssim_levels = 0#tf.reduce_mean([tf.reduce_mean(tf.image.ssim(levels_pred[i] + 1, uv_unposed_levels[len(levels_pred) - i - 1] + 1, max_val=10)) for i in range(len(levels_pred))])
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e_loss = 1e-2 * tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
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c_loss = 1e-2 * tf.reduce_mean(collision_penalty(mesh_pred, batch['mesh_body_posed'], batch['mesh_mask']))
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l1_loss = tf.reduce_mean(tf.reduce_sum((tf.math.abs(batch['uv_unposed'] - pred)), axis=1) * batch['uv_mask'])
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ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale((tf.transpose(pred, [0,2,3,1]) + 1) * batch['uv_mask'][:,:,:,tf.newaxis], (tf.transpose(batch['uv_unposed'], [0,2,3,1]) + 1) * batch['uv_mask'][:,:,:,tf.newaxis], max_val=10))
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fft_loss = tf.reduce_mean(tf.reduce_sum(tf.math.abs(tf.signal.fft3d(tf.complex(pred * batch['uv_mask'][:,tf.newaxis,:,:], tf.zeros_like(pred))) - tf.signal.fft3d(tf.complex(batch['uv_unposed'] * batch['uv_mask'][:,tf.newaxis,:,:], tf.zeros_like(batch['uv_unposed'])))), axis=[1,2,3]))
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loss = 1e3 * (1 - ms_ssim)
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mesh_orig_2 = uv2mesh(batch['uv_unposed'])
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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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metric_2 = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig_2 - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
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return { 'loss': loss, 'edge_loss': e_loss, 'collision_loss': c_loss, 'l1_loss': l1_loss, 'euclid_error': metric, 'ms_ssim': ms_ssim,
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'fft_loss': fft_loss, 'euclid_error_reconst': metric_2, 'ms_ssim_levels': ms_ssim_levels
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}
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def plot_everything(writer, batch: dict, type: str):
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pred, levels = model([tf.concat((batch['uv_static'], batch['uv_body_posed']), axis=1), batch['uv_mask'], batch['uv_shaped_body']])
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mesh_pred = uv2mesh(pred)
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mesh_orig = batch['mesh_unposed']
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orig_img = tf.transpose(batch['uv_unposed'], [0,2,3,1])
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pred_img = tf.transpose(pred, [0,2,3,1])
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img = tf.concat((orig_img, pred_img), axis=2)
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levels = upscale(levels)
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mesh_pred_levels = [uv2mesh(tf.transpose(levels[i], [0,3,1,2])) for i in range(len(levels))]
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with writer.as_default():
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tf.summary.image('images', img, step=step, max_outputs=4)
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tf.summary.image('levels', tf.concat(levels, axis=2) , step=step, max_outputs=4)
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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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body = batch['mesh_body_posed'].numpy()
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mesh_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred).numpy()
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mesh_posed_levels = [smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_levels[i]).numpy() for i in range(len(levels))]
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mesh_posed_levels = [mesh_posed_levels[j][0, mask[0], :] for j in range(len(levels))]
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garment_posed = [mesh_posed[i, mask[i], :] for i in range(body.shape[0])]
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collision = [np.concatenate((body[i], garment_posed[i]), axis=0) for i in range(len(garment_posed))]
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collision_faces = [np.concatenate((F, faces[i] + 14475), axis=0) for i in range(len(garment_posed))]
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collision_color = [np.concatenate((np.ones_like(body[i], dtype=np.int32)*100, np.ones_like(garment_posed[i], dtype=np.int32)*230), axis=0) for i in range(len(garment_posed))]
|
|
|
|
|
|
|
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fig_levels = plot_3d_levels(mesh_posed_levels, faces[0])
|
|
|
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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(pred.shape[0]) }
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# fig = plot_3d(gt[i], garment_unposed[i], faces[i], collision[i], collision_faces[i], collision_color[i])
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wandb.log( { **figs, **{ type + '/levels_garments': fig_levels } } )
|
|
|
|
|
|
if __name__ == '__main__':
|
|
allow_memory_growth()
|
|
parser = argparse.ArgumentParser(description='')
|
|
parser.add_argument('--name', type=str)
|
|
args = vars(parser.parse_args())
|
|
|
|
smpl = SMPL()
|
|
# R = smpl.run_rodrigues((np.zeros((8,72), dtype=np.float32)))
|
|
# zero_body = smpl.run(R, np.zeros((8, 11), dtype=np.float32))[0]
|
|
|
|
|
|
factory = DatasetFactory()
|
|
signature_dynamic = { 'id': (),
|
|
'pose_shape': (72 + 11,),
|
|
# 'uv_posed': (3, 512, 256,),
|
|
'uv_body_posed': (3, 512, 256,),
|
|
'mesh_body_posed': (14475, 3,),
|
|
'mesh_posed': (14475, 3,),
|
|
'mesh_unposed': (14475, 3,),
|
|
'uv_unposed': (3, 512, 256,)
|
|
}
|
|
signature_static = { 'uv_static': (3, 512, 256,),
|
|
# 'mesh_static': (14475, 3,),
|
|
'mesh_mask': (14475,),
|
|
'uv_mask': (512, 256,),
|
|
'uv_shaped_body': (3, 512, 256,),
|
|
}
|
|
|
|
dataset_train = factory.get_dataset(folder='/home/workspace/local-data/jumpsuit_20/train',
|
|
batch_size=8,
|
|
signature=signature_dynamic)
|
|
static_dataset_train = StaticDataset(folder='/home/workspace/local-data/jumpsuit_20/train',
|
|
signature=signature_static)
|
|
dataset_valid = factory.get_dataset(folder='/home/workspace/local-data/jumpsuit_20/valid',
|
|
batch_size=8,
|
|
signature=signature_dynamic)
|
|
static_dataset_valid = StaticDataset(folder='/home/workspace/local-data/jumpsuit_20/valid',
|
|
signature=signature_static)
|
|
|
|
|
|
# model = AutoEncoder()
|
|
model = Pyramid()
|
|
# optimizer = tf.optimizers.Adam(learning_rate=0.002, beta_1=0.0, beta_2=0.99, epsilon=1e-8)
|
|
lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(0.001, decay_steps=30000, decay_rate=0.1, staircase=False)
|
|
optimizer = tf.optimizers.Adam(learning_rate=lr_schedule)
|
|
folder = 'models/ae_logs/'
|
|
train_summary_writer = tf.summary.create_file_writer(folder + name + '/train')
|
|
valid_summary_writer = tf.summary.create_file_writer(folder + name + '/valid')
|
|
valid_iterator = iter(dataset_valid)
|
|
|
|
step = 0
|
|
for batch in dataset_train:
|
|
# batch = batch_to_dict(batch, signature_dynamic)
|
|
batch = static_dataset_train.expand_batch(batch_to_dict(batch, signature_dynamic))
|
|
|
|
train_loss = train_step(batch)
|
|
|
|
for key in train_loss.keys():
|
|
with train_summary_writer.as_default():
|
|
tf.summary.scalar(key, train_loss[key], step=step)
|
|
|
|
# data_valid = batch
|
|
data_valid = valid_iterator.get_next()
|
|
data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, signature_dynamic))
|
|
valid_loss = valid_step(data_valid)
|
|
|
|
for key in valid_loss.keys():
|
|
with valid_summary_writer.as_default():
|
|
tf.summary.scalar(key, valid_loss[key], step=step)
|
|
|
|
if step % 500 == 0:
|
|
plot_everything(train_summary_writer, batch, 'train')
|
|
plot_everything(valid_summary_writer, data_valid, 'valid')
|
|
|
|
# fig = make_subplots(rows=1, cols=5, start_cell="top-left")
|
|
|
|
# for i, l in enumerate(levels):
|
|
# fig.add_trace(ex.imshow((l[0] + 1) * 128).data[0], row=1, col=i+1)
|
|
|
|
|
|
|
|
print(step)
|
|
|
|
step += 1 |