434 lines
19 KiB
Python
434 lines
19 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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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 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
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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='ae_body_1'
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# os.environ["CUDA_VISIBLE_DEVICES"] = "6"
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# run = wandb.init(project='stylegan', name=name,
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# notes='convnext + convnext, add dense with tanh at the end of reverse convnext, larger features. add lr scheduler. fft3d instead of rfft3d. \
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# fft2d per channel and separate for top and bottom. fft_loss, all data. added convnext block as bottleneck, same as before, added extra metric. \
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# . lowered learning rate because it goes to nan. shallow third stage. slower lr decay. with validation. higher lr rate.')
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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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class DatasetSimple(tf.data.Dataset):
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def _generator(folder):
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# Opening the file
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folder = folder.decode("utf-8")
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uv_folder = os.path.join(folder, 'uv_posed')
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frames = os.listdir(uv_folder)
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random.shuffle(frames)
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for frame in frames:
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id, frame_nr = frame.split('.')[0].split('_')
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mesh_mask = np.load(os.path.join(folder, 'mesh_mask', id + '.npy')); mesh_mask = mesh_mask.astype(np.float32)
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uv_mask = np.load(os.path.join(folder, 'uv_mask', id + '.npz')); uv_mask=uv_mask['uv_mask']; uv_mask = uv_mask.astype(np.float32)
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mesh_unposed = np.load(os.path.join(folder, 'mesh_unposed', id + '_' + frame_nr + '.npz')); mesh_unposed = mesh_unposed['mesh_unposed']
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uv_unposed = np.load(os.path.join(folder, 'uv_unposed', id + '_' + frame_nr + '.npz')); uv_unposed = uv_unposed['uv_unposed']; uv_unposed = np.transpose(uv_unposed, [2, 0, 1])
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yield uv_unposed, mesh_unposed, uv_mask, mesh_mask
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def __new__(cls, folder, res=256):
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return tf.data.Dataset.from_generator(
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cls._generator,
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output_signature = (tf.TensorSpec(shape = (3, res * 2, res,), dtype = tf.float32),
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tf.TensorSpec(shape = (14475, 3,), dtype = tf.float32),
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tf.TensorSpec(shape = (res * 2, res,), dtype = tf.float32),
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tf.TensorSpec(shape = (14475,), dtype = tf.float32), ),
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args=(folder, )
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)
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def filter_resolutions_featuremaps(resolutions, featuremaps, res):
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index = resolutions.index(res)
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filtered_resolutions = resolutions[:index + 1]
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filtered_featuremaps = featuremaps[:index + 1]
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return filtered_resolutions, filtered_featuremaps
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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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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 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 = 32
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self.encoder = ConvNeXt(depths=[3, 3, 9, 3], dims=[32, 64, 128, bottleneck_size], include_top=False)
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self.bottleneck = Bottleneck(bottleneck_size)
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self.decoder = ConvNeXtReverse(depths=list(reversed([3, 3, 9, 3])), dims=list(reversed([bottleneck_size, 128, 64, 16])))
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def call(self, inputs, training=None):
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x, uv_mask = inputs
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x = tf.transpose(x, [0,2,3,1])
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x = self.encoder(x, training=training)
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# x = self.pooling(x)
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# x = self.reshape(x)
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# x = self.flatten(x)
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x = self.bottleneck(x, training=training)
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x = self.decoder(x, training=training)
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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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def draw_cicle(shape,diamiter):
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'''
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Input:
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shape : tuple (height, width)
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diameter : scalar
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Output:
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np.array of shape that says True within a circle with diamiter = around center
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'''
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assert len(shape) == 2
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TF = np.zeros(shape,dtype=bool)
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center = np.array(TF.shape)/2.0
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for iy in range(shape[0]):
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for ix in range(shape[1]):
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TF[iy,ix] = (iy- center[0])**2 + (ix - center[1])**2 < diamiter **2
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return (TF)
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TFcircleIN = draw_cicle(shape=(256,256), diamiter=15)
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TFcircleIN = np.ones((512,256,3), dtype=bool) * np.concatenate((TFcircleIN, TFcircleIN), axis=0)[:,:,np.newaxis]
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TFcircleOUT = ~TFcircleIN
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def fft_loss_per_channel(pred, gt):
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pred = tf.complex(pred, tf.zeros_like(pred))
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gt = tf.complex(gt, tf.zeros_like(gt))
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fft_pred_top = tf.stack([tf.signal.fftshift(tf.signal.fft2d(pred[:,i,:256,:])) for i in range(3)], axis=-1)
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fft_gt_top = tf.stack([tf.signal.fftshift(tf.signal.fft2d(gt[:,i,:256,:])) for i in range(3)], axis=-1)
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fft_pred_bottom = tf.stack([tf.signal.fftshift(tf.signal.fft2d(pred[:,i,256:,:])) for i in range(3)], axis=-1)
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fft_gt_bottom = tf.stack([tf.signal.fftshift(tf.signal.fft2d(gt[:,i,256:,:])) for i in range(3)], axis=-1)
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fft_pred = tf.concat((fft_pred_top, fft_pred_bottom), axis=1)
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fft_gt = tf.concat((fft_gt_top, fft_gt_bottom), axis=1)
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fft_pred_low = tf.where(TFcircleIN, fft_pred, tf.zeros_like(fft_pred))
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fft_gt_low = tf.where(TFcircleIN, fft_gt, tf.zeros_like(fft_gt))
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fft_pred_high = tf.where(TFcircleOUT, fft_pred, tf.zeros_like(fft_pred))
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fft_gt_high = tf.where(TFcircleOUT, fft_gt, tf.zeros_like(fft_gt))
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low_loss = tf.reduce_mean(tf.reduce_sum(tf.math.abs(fft_pred_low - fft_gt_low), axis=[1,2,3]))
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high_loss = tf.reduce_mean(tf.reduce_sum(tf.math.abs(fft_pred_high - fft_gt_high), axis=[1,2,3]))
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total = tf.reduce_mean(tf.reduce_sum(tf.math.abs(fft_pred - fft_gt), axis=[1,2,3]))
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return total, low_loss, high_loss
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# ex.imshow(tf.math.real(gt[0,0,:256,:])).show()
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# img = tf.signal.fftshift(fft_gt[0][0])
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# ex.imshow(tf.math.abs(tf.math.log(img))).show()
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# low = tf.where(TFcircleIN, img, tf.zeros_like(img))
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# ex.imshow(tf.math.real(tf.signal.ifft2d(tf.signal.ifftshift(low)))).show()
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# high = tf.where(TFcircleOUT, img, tf.zeros_like(img))
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# # ex.imshow(tf.math.abs(tf.math.log(high))).show()
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# ex.imshow(tf.math.real(tf.signal.ifft2d(tf.signal.ifftshift(high)))).show()
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print()
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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'], batch['uv_mask']])
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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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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['mesh_unposed'] - pred)), axis=-1) * 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, tf.transpose(batch['uv_unposed'], [0,2,3,1]) + 1, max_val=2))
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rfft_loss = 0 #tf.reduce_mean(tf.reduce_sum(tf.math.abs(tf.signal.rfft3d(pred) - tf.signal.rfft3d(batch['uv_unposed'])), axis=[1,2,3]))
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fft_loss = tf.reduce_mean(tf.reduce_sum(tf.math.abs(tf.signal.fft3d(tf.complex(pred, tf.zeros_like(pred))) - tf.signal.fft3d(tf.complex(batch['uv_unposed'], tf.zeros_like(batch['uv_unposed'])))), axis=[1,2,3]))
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fft_loss_pc, fft_loss_pc_low, fft_loss_pc_high = 0,0,0 #fft_loss_per_channel(pred, batch['uv_unposed'])
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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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# gradients = [tf.clip_by_norm(g, 1.0) for g in gradients]
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# gradients = [tf.clip_by_value(g, -1.0, 1.0) for g in gradients]
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optimizer.apply_gradients(zip(gradients, model.trainable_variables))
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return { 'train/loss': loss, 'train/edge_loss': e_loss, 'train/collision_loss': c_loss, 'train/l1_loss': l1_loss, 'train/euclid_error': metric, 'train/ms_ssim': ms_ssim,
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'train/fft_loss': fft_loss, 'train/euclid_error_reconst': metric_2, 'train/rfft_loss': rfft_loss, 'train/fft_oc_low': fft_loss_pc_low, 'train/fft_pc_high': fft_loss_pc_high,
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'train/fft_loss_per_channel': fft_loss_pc, 'train/euclid_error_posed': euclid_posed }
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@tf.function
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def valid_step(batch):
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pred = model([batch['uv_unposed'], batch['uv_mask']])
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mesh_pred = uv2mesh(pred)
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mesh_orig = batch['mesh_unposed']
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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, tf.transpose(batch['uv_unposed'], [0,2,3,1]) + 1, max_val=2))
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fft_loss = tf.reduce_mean(tf.reduce_sum(tf.math.abs(tf.signal.fft3d(tf.complex(pred, tf.zeros_like(pred))) - tf.signal.fft3d(tf.complex(batch['uv_unposed'], 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 { 'valid/loss': loss, 'valid/edge_loss': e_loss, 'valid/collision_loss': c_loss, 'valid/l1_loss': l1_loss, 'valid/euclid_error': metric, 'valid/ms_ssim': ms_ssim,
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'valid/fft_loss': fft_loss, 'valid/euclid_error_reconst': metric_2
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}
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def plot_everything(batch: dict, type: str):
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pred = model([batch['uv_unposed'], batch['uv_mask']])
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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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imgs = [wandb.Image((img[i] + 1) / 2.0) for i in range(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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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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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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return { **figs, **{ type + '/images': imgs } }
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if __name__ == '__main__':
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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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# R = smpl.run_rodrigues((np.zeros((8,72), dtype=np.float32)))
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# zero_body = smpl.run(R, np.zeros((8, 11), dtype=np.float32))[0]
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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_posed': (3, 512, 256,),
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'uv_body_posed': (3, 512, 256,),
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'mesh_body_posed': (14475, 3,),
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'mesh_posed': (14475, 3,),
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'mesh_unposed': (14475, 3,),
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# 'uv_unposed': (3, 512, 256,)
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}
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signature_static = { #'uv_static': (3, 512, 256,),
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# 'mesh_static': (14475, 3,),
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'mesh_mask': (14475,),
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'uv_mask': (512, 256,),
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# 'uv_shaped_body': (3, 512, 256,),
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}
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dataset_train = factory.get_dataset(folder='/home/workspace/local-data/jumpsuit_20/train',
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batch_size=8,
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signature=signature_dynamic)
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static_dataset_train = StaticDataset(folder='/home/workspace/local-data/jumpsuit_20/train',
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signature=signature_static)
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dataset_valid = factory.get_dataset(folder='/home/workspace/local-data/jumpsuit_20/valid',
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batch_size=8,
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signature=signature_dynamic)
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static_dataset_valid = StaticDataset(folder='/home/workspace/local-data/jumpsuit_20/valid',
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signature=signature_static)
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|
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model = AutoEncoder()
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# optimizer = tf.optimizers.Adam(learning_rate=0.002, beta_1=0.0, beta_2=0.99, epsilon=1e-8)
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lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(0.0005, decay_steps=30000, decay_rate=0.1, staircase=False)
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optimizer = tf.optimizers.Adam(learning_rate=lr_schedule)
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folder = 'models/ae_logs/'
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# train_summary_writer = tf.summary.create_file_writer(folder + name + '/train')
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# valid_summary_writer = tf.summary.create_file_writer(folder + name + '/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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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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|
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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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|
|
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if step % 1000 == 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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|
|
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print(step)
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|
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step += 1 |