385 lines
14 KiB
Python
385 lines
14 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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os.environ["CUDA_VISIBLE_DEVICES"] = "2"
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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, ConvNeXtReverseLabels
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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_curvature_7'
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testing = False
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def init_wandb():
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if not testing:
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run = wandb.init(project='vae', name=name, notes='convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments')
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artifact = wandb.Artifact('code', type='code')
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artifact.add_file('auto_encoder_curv.py')
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artifact.add_file('dataset.py')
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artifact.add_file('losses.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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class Bottleneck(tf.keras.Model):
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def __init__(self, dim, latent_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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self.flatten = tf.keras.layers.Flatten()
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self.dense = tf.keras.layers.Dense(latent_dim)
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def call(self, inputs, training=None):
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x = inputs
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x = self.bottleneck(x)
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x = self.flatten(x)
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x = self.dense(x)
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return x
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class Decoder(tf.keras.Model):
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def __init__(self, bottleneck_size, latent_dim):
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super(Decoder, self).__init__()
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self.dense = tf.keras.layers.Dense(16 * 8 * bottleneck_size)
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self.reshape = tf.keras.layers.Reshape((16, 8, bottleneck_size))
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self.decoder = ConvNeXtReverseLabels(depths=list(reversed([3, 3, 9, 3])), dims=list(reversed([bottleneck_size, 128, 64, 16])), nr_of_classes=7)
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def call(self, inputs, training=None):
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x = inputs
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x = self.dense(x)
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x = self.reshape(x)
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x = self.decoder(x)
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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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latent_dim = 128
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self.decoder = Decoder(bottleneck_size, latent_dim)
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self.bottleneck = Bottleneck(bottleneck_size, latent_dim)
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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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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.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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x = x * uv_mask[:,:,:,tf.newaxis]
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return x
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g_mean_iou = tf.keras.metrics.MeanIoU(num_classes=7)
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def get_mean_iou(y_true, y_pred):
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g_mean_iou.reset_state()
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g_mean_iou.update_state(y_true, y_pred)
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return g_mean_iou.result()
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# https://github.com/NifTK/NiftyNet/blob/dev/niftynet/layer/loss_segmentation.py
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def dice_dense(prediction, ground_truth, weight_map=None):
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"""
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Computing mean-class Dice similarity.
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:param prediction: last dimension should have ``num_classes``
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:param ground_truth: segmentation ground truth (encoded as a binary matrix)
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last dimension should be ``num_classes``
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:param weight_map:
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:return: ``1.0 - mean(Dice similarity per class)``
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"""
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if weight_map is not None:
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raise NotImplementedError
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prediction = tf.cast(prediction, dtype=tf.float32)
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ground_truth = tf.cast(ground_truth, dtype=tf.float32)
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ground_truth = tf.reshape(ground_truth, prediction.shape)
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# computing Dice over the spatial dimensions
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reduce_axes = list(range(len(prediction.shape) - 1))
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dice_numerator = 2.0 * tf.reduce_sum(
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prediction * ground_truth, axis=reduce_axes)
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dice_denominator = \
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tf.reduce_sum(tf.square(prediction), axis=reduce_axes) + \
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tf.reduce_sum(tf.square(ground_truth), axis=reduce_axes)
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epsilon = 0.00001
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dice_score = (dice_numerator + epsilon) / (dice_denominator + epsilon)
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return tf.reduce_mean(dice_score)
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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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uv_curvature = tf.one_hot(batch['uv_curvature'], 7)
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pred = model([uv_curvature, batch['uv_mask']])
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ce_uv_loss = scce(batch['uv_curvature'], pred)
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ms_ssim = tf.reduce_mean(tf.image.ssim(pred, uv_curvature, max_val=1))
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# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(tf.math.argmax(pred, axis=-1, output_type=tf.dtypes.int32)[:,:,:,tf.newaxis], batch['uv_curvature'][:,:,:,tf.newaxis], max_val=7*2))
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# loss = 1e3 * (1 - ms_ssim)
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loss = ce_uv_loss
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# mesh_pred = uv2mesh(tf.tile(tf.math.argmax(pred, axis=-1, output_type=tf.dtypes.int32)[:,:,:,tf.newaxis], [1,1,1,3]))[:,:,0]
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# mesh_orig = tf.one_hot(batch['mesh_curvature'] + tf.cast(batch['mesh_mask'], tf.int32), 7)
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# ce_mesh_loss = scce(mesh_orig, mesh_pred)
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mean_iou = get_mean_iou(batch['uv_curvature'], tf.math.argmax(pred, axis=-1))
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dice = dice_dense(pred, uv_curvature)
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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/ce_uv_loss': ce_uv_loss, 'train/ssim': ms_ssim, 'train/mean_iou': mean_iou, 'train/dice': dice #'ce_mesh_loss': ce_mesh_loss
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}
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@tf.function
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def valid_step(batch):
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uv_curvature = tf.one_hot(batch['uv_curvature'], 7)
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pred = model([uv_curvature, batch['uv_mask']])
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ce_uv_loss = scce(batch['uv_curvature'], pred)
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ms_ssim = tf.reduce_mean(tf.image.ssim(pred, uv_curvature, max_val=1))
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loss = ce_uv_loss
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mean_iou = get_mean_iou(batch['uv_curvature'], tf.math.argmax(pred, axis=-1))
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dice = dice_dense(pred, uv_curvature)
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return { 'valid/loss': loss, 'valid/ce_uv_loss': ce_uv_loss, 'valid/ssim': ms_ssim, 'valid/mean_iou': mean_iou, 'valid/dice': dice #'ce_mesh_loss': ce_mesh_loss
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}
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def train():
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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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data_valid = batch
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to_log = { **train_loss, **valid_loss}
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if step % 2500 == 0:
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uv_curvature = tf.one_hot(data_valid['uv_curvature'], 7)
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pred = model([uv_curvature, data_valid['uv_mask']])
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orig_img = data_valid['uv_curvature']
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pred_img = tf.math.argmax(pred, axis=-1, output_type=tf.dtypes.int32)
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img = tf.concat((orig_img, pred_img), axis=2).numpy()
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# imgs = { 'valid/images': [wandb.Image(img[i,:,:,np.newaxis] / 6.0) for i in range(4)] }
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imgs = { 'train/images': [wandb.Image(img[i,:,:,np.newaxis] / 6.0) for i in range(4)] }
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to_log = { **to_log, **imgs}
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wandb.log(to_log, step=step)
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print(step)
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if step % 2500 == 0:
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manager.save(checkpoint_number=step)
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step += 1
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def test():
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ckpt.restore(manager.latest_checkpoint)
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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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uv_curvature = tf.one_hot(batch['uv_curvature'], 7)
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pred = model([uv_curvature, batch['uv_mask']])
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orig_img = batch['uv_curvature']
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pred_img = tf.math.argmax(pred, axis=-1, output_type=tf.dtypes.int32)
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img = tf.concat((orig_img, pred_img), axis=2).numpy()
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ex.imshow(img[0] / 6.0).show()
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print()
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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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scce = tf.keras.losses.SparseCategoricalCrossentropy()
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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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'uv_curvature': (512, 256),
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'mesh_curvature': (14475)
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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/train_100',
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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/train_100',
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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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valid_iterator = iter(dataset_valid)
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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(0.001, decay_steps=10000, 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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ckpt = tf.train.Checkpoint(model=model)
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manager = tf.train.CheckpointManager(ckpt, 'checkpoints/' + name, max_to_keep=5)
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if testing:
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test()
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else:
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train() |