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Merge remote-tracking branch 'origin/iccv' into new_model

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luni
2023-02-21 22:12:49 +01:00
parent be78b02bad ac27319860
commit 0912b9f390
5 changed files with 222 additions and 21 deletions
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aaa_helpers.txt
+2
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@@ -1,4 +1,6 @@
nohup python cvae.py --name="lhunor_weights_test_6" > nohup/iccv/weights_test_6.out 2>&1 &
nohup python evaluate_ae.py --name iccv_weights_test_6 > nohup/iccv/evaluate_iccv_weights_test_6.out 2>&1 &
nohup python calculate_error.py --name iccv_weights_test_6 > nohup/iccv/error_iccv_weights_test_6.out 2>&1 &
one final convolutional layer for smoothing, on the final coordinates
compare_models_saved.py
+188
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@@ -0,0 +1,188 @@
import os
import sys
from configs.config_abl_all_post import config as config_a
os.environ["CUDA_VISIBLE_DEVICES"] = '5'
import tensorflow as tf
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
# Currently, memory growth needs to be the same across GPUs
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
except RuntimeError as e:
# Memory growth must be set before GPUs have been initialized
print(e)
import glob
import time
import argparse
import wandb
import tqdm
import random
import numpy as np
import plotly.graph_objects as go
import plotly.express as ex
from PIL import Image
from postprocessing import fix_collision
if config_a['is_skirt']:
from losses import edge_loss_skirt as edge_loss
else:
from losses import edge_loss
from losses import collision_ratio, kl_loss #, concept_loss
# from losses import collision_penalty as collision_penalty
from losses import collision_loss as collision_penalty
from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
from dataset import DatasetFactory, StaticDataset
from custom_utils.smpl import SMPL
from models.cvae_model import CVAE
from custom_utils.supersmpl_io import writeOBJ
from plotly.subplots import make_subplots
# uv2mesh = UV2Mesh(res=256, is_skirt=config_a['is_skirt'], is_adaptive=False)
def plot(mesh_gt, pred_a, pred_b, body, mask):
fig = make_subplots(rows=1, cols=3, start_cell="top-left", specs = [ [{ 'type': 'scene' } for _ in range(3)] ])
_, faces_garment = _maskFaces(np.zeros((14475, 3), np.float32), F, mask)
faces = np.concatenate((F, faces_garment + 14475), axis=0)
mesh_gt = np.concatenate((body, mesh_gt[mask, :]), axis=0)
pred_a = np.concatenate((body, pred_a), axis=0)
pred_b = np.concatenate((body, pred_b), axis=0)
colors = np.concatenate((np.ones_like(body, dtype=np.int32)*100, np.ones_like(mesh_gt, dtype=np.int32)*230), axis=0)
# colors = np.ones_like(mesh_gt, dtype=np.int32)*230
fig.add_trace(go.Mesh3d(
x=mesh_gt[:,0],
y=mesh_gt[:,2],
z=mesh_gt[:,1],
i = faces[:,0],
j = faces[:,1],
k = faces[:,2],
text="Ground Truth",
vertexcolor=colors,
),
row=1, col=1)
fig.add_trace(go.Mesh3d(
x=pred_a[:,0],
y=pred_a[:,2],
z=pred_a[:,1],
i = faces[:,0],
j = faces[:,1],
k = faces[:,2],
text="Pred A",
vertexcolor=colors,
),
row=1, col=2)
fig.add_trace(go.Mesh3d(
x=pred_b[:,0],
y=pred_b[:,2],
z=pred_b[:,1],
i = faces[:,0],
j = faces[:,1],
k = faces[:,2],
text="Pred B",
vertexcolor=colors,
),
row=1, col=3)
fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1),
scene2_aspectmode='manual', scene2_aspectratio=dict(x=1, y=1, z=1),
scene3_aspectmode='manual', scene3_aspectratio=dict(x=1, y=1, z=1),
)
helper = dict(xaxis = dict(nticks=4, range=[-1,1],),
yaxis = dict(nticks=4, range=[-1,1],),
zaxis = dict(nticks=4, range=[-1,1],),)
fig.update_layout(scene = helper,
scene2 = helper,
scene3 = helper,
)
return fig
if __name__ == '__main__':
run = wandb.init(project='plots', name=config_a['name'], notes=config_a['notes'], dir='/wandb')
parser = argparse.ArgumentParser(description='')
parser.add_argument('--name', type=str)
args = vars(parser.parse_args())
skirt_modifier = '_skirt' if config_a['is_skirt'] else ''
folder = config_a['test_dir']
# smpl = SMPL(use_one_hot=False)
F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy").astype(np.int32)
iteration = ''
# load_model(model, 'checkpoints/' + 'abl_all_120k')
factory = DatasetFactory()
dataset_a = factory.get_dataset(folder=config_a['test_dir'],
batch_size=config_a['batch_size'],
signature=config_a['signature_dynamic'],
resolutions=[config_a['resolution']],
in_memory=False,
do_repeat=False,
use_one_hot=config_a['use_one_hot'],
use_no_orient=config_a['remove_global_orientation'],
shuffle=False,
testing=True,
# frames_file='frames_abl.txt',
)
static_dataset_a = StaticDataset(folder=config_a['test_dir'],
signature=config_a['signature_static'],
resolutions=[config_a['resolution']],
in_memory=True)
iterator_a = iter(dataset_a)
faces_folder = '/home/workspace/data/results/support/faces' + ('_skirt' if config_a['is_skirt'] else '')
ids = os.listdir(faces_folder)
ids = [id.split('.')[0] for id in ids]
faces = {id : np.load(faces_folder + '/' + id + '.npy') for id in ids }
results_folder = '/home/workspace/data/results/post'
count = 0
while True:
batch = iterator_a.get_next()
batch = static_dataset_a.expand_batch(batch_to_dict(batch, config_a['signature_dynamic']))
mask = batch['mesh_mask'].numpy().astype(bool)
gt = batch['mesh_posed'].numpy()
body = batch['mesh_body_posed'].numpy()
for i in range(mask.shape[0]):
id, frame_nr = batch['id'][i].numpy().decode("utf-8"), batch['frame_nr'][i].numpy().decode("utf-8")
pred_a = np.load(results_folder + '/al_all_post-/' + id + '_' + frame_nr + '.npy')
pred_b = np.load(results_folder + '/iccv_weights_test_6-/' + id + '_' + frame_nr + '.npy')
# pred_c = np.load(results_folder + '/iccv_weights_test_6-/' + id + '_' + frame_nr + '.npy')
fig = plot(gt[i], pred_a, pred_b, body[i], mask[i])
wandb.log({f'{id}_{frame_nr}': fig}, step = count)
count += 1
print(count)
configs/config_abl_all_post.py
+5 -5
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@@ -21,7 +21,7 @@ assert ablation_count == 1 or ablation_count == 0
config['gpu'] = '5'
config['name'] = 'iccv_weights_test_8'
config['notes'] = 'smaller weight loss coeff, 1e-6, convolutional weight learning, continued from _5'
config['notes'] = 'smaller weight loss coeff, 1e-6, convolutional weight learning, changed convnext reverse'
config['avg_garment_path'] = 'custom_data/train_no_shape_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
if config['use_skinning']:
@@ -53,7 +53,7 @@ if config['is_cape']:
config['smpl_res'] = 3638
config['pretrained_model'] = ''#'checkpoints/post/iccv_weights_test_5'
config['pretrained_model'] = ''
###############
config['lr'] = 0.0001# 0.0001
@@ -91,9 +91,9 @@ config['cvae']['noise_amount'] = 0.00001
config['cvae']['kernel_size'] = 7
config['cvae']['encoder_depths'] = [3, 3, 3, 9, 3]
config['cvae']['encoder_dims'] = [32, 64, 128, 256, 64]
config['cvae']['decoder_depths'] = config['cvae']['encoder_depths']
config['cvae']['decoder_dims'] = config['cvae']['encoder_dims']
config['cvae']['decoder_weights_depths'] = [3, 3, 3, 5, 3]
config['cvae']['decoder_depths'] = [3, 9, 3, 3, 3, 3]
config['cvae']['decoder_dims'] = [3, 32, 128, 128, 256, 512]
config['cvae']['decoder_weights_depths'] = [3, 9, 3, 3, 3]
config['cvae']['decoder_weights_dims'] = [24, 32, 64, 128, 256]
config['cond_ae_static'] = dict()
convnext/models/convnext_tf.py
+16 -13
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@@ -225,26 +225,28 @@ class ConvNeXtReverse(tf.keras.Model):
drop_path_rate=0., layer_scale_init_value=1e-6, head_init_scale=1., learn_weights=False,
):
super().__init__()
self.upsample_layers = [] # stem and 3 intermediate downsampling conv layers
self.upsample_layers = []
stem = tf.keras.Sequential([
Conv2DTranspose(dims[0], kernel_size=4, strides=4, padding='same'),
# UpSampling2D((4, 4), interpolation='nearest'),
# Conv2D(dims[0], kernel_size=4, strides=1, padding='same'),
# Dense(dims[0]),
LayerNormalization(epsilon=1e-6)]
)
LayerNormalization(epsilon=1e-6),
Conv2DTranspose(dims[0], kernel_size=2, strides=2, padding='same'),
])
self.upsample_layers.append(stem)
for i in range(len(depths) - 1):
for i in range(len(depths) - 2):
downsample_layer = tf.keras.Sequential([
LayerNormalization(epsilon=1e-6),
Conv2DTranspose(dims[i+1], kernel_size=2, strides=2, padding='same')
# UpSampling2D((2,2), interpolation='nearest'),
# Conv2D(dims[i+1], kernel_size=2, strides=1, padding='same'),
# Dense(dims[i+1]),
Conv2DTranspose(dims[i+1], kernel_size=2, strides=2, padding='same'),
])
self.upsample_layers.append(downsample_layer)
self.stages = [] # 4 feature resolution stages, each consisting of multiple residual blocks
downsample_layer = tf.keras.Sequential([
Conv2DTranspose(dims[i+2], kernel_size=2, strides=2, padding='same'),
LayerNormalization(epsilon=1e-6),
])
self.upsample_layers.append(downsample_layer)
self.stages = []
dp_rates = [x for x in np.linspace(0, drop_path_rate, sum(depths))]
cur = 0
for i in range(len(depths)):
@@ -257,6 +259,7 @@ class ConvNeXtReverse(tf.keras.Model):
if not learn_weights:
self.last = Dense(3, activation='tanh')
# self.last = Activation('tanh')
else:
self.last = Activation('softmax')
# self.last = Dense(24, activation='softmax')
evaluate_ae.py
+11 -3
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@@ -39,7 +39,7 @@ else:
from losses import collision_ratio, kl_loss #, concept_loss
# from losses import collision_penalty as collision_penalty
from losses import collision_loss as collision_penalty
from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, UV2MeshLabel, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
from dataset import DatasetFactory, StaticDataset
from custom_utils.smpl import SMPL
from models.cvae_model import CVAENew as CVAE
@@ -48,6 +48,7 @@ from custom_utils.supersmpl_io import writeOBJ
uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_adaptive=False, is_smpl_orig=config['is_cape'])
uv2mesh_label = UV2MeshLabel(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
@tf.function
def step(batch):
@@ -56,9 +57,10 @@ def step(batch):
else:
uv_orig = batch['uv_posed']
pred, _, _ = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
pred, _, _, weights = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
mesh_pred = uv2mesh(pred)
mesh_weigths = uv2mesh_label(weights)
if config['use_skinning']:
shape = batch['pose_shape'][:, 72:]
@@ -68,7 +70,13 @@ def step(batch):
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
else:
mesh_pred_posed = mesh_pred
shape = batch['pose_shape'][:, 72:]
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
mesh_pred_posed = smpl.run_garment_with_weights(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped, mesh_weigths)
# mesh_pred_posed_fixed = fix_collision(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])