cape evaluation script
This commit is contained in:
@@ -0,0 +1,413 @@
|
|||||||
|
import os
|
||||||
|
from configs.config_pyramid import config
|
||||||
|
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
|
||||||
|
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 time
|
||||||
|
import tqdm
|
||||||
|
import argparse
|
||||||
|
import wandb
|
||||||
|
import random
|
||||||
|
import numpy as np
|
||||||
|
import scipy as sp
|
||||||
|
from custom_utils.custom_utils import UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, get_summary_str
|
||||||
|
import plotly.graph_objects as go
|
||||||
|
import plotly.express as ex
|
||||||
|
from plotly.subplots import make_subplots
|
||||||
|
from PIL import Image
|
||||||
|
|
||||||
|
if config['is_skirt']:
|
||||||
|
from losses import edge_loss_skirt as edge_loss
|
||||||
|
else:
|
||||||
|
from losses import edge_loss
|
||||||
|
|
||||||
|
from tf_utils import check_tf_version, allow_memory_growth, split_gpu_for_testing
|
||||||
|
from losses import uv_mask_loss, bend_loss, kl_loss, collision_ratio
|
||||||
|
# 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
|
||||||
|
from dataset import DatasetFactory, StaticDataset
|
||||||
|
from custom_utils.smpl import SMPL
|
||||||
|
from models.pyramid_model_optimized import Pyramid
|
||||||
|
|
||||||
|
|
||||||
|
def init_wandb():
|
||||||
|
run = wandb.init(project='cvae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp')
|
||||||
|
|
||||||
|
artifact = wandb.Artifact('code', type='code')
|
||||||
|
artifact.add_file('cvae_pyramid.py')
|
||||||
|
artifact.add_file('models/cvae_model.py')
|
||||||
|
artifact.add_file('models/pyramid_model.py')
|
||||||
|
artifact.add_file('models/pyramid_level.py')
|
||||||
|
artifact.add_file('models/upscaler.py')
|
||||||
|
artifact.add_file('losses.py')
|
||||||
|
artifact.add_file('configs/config_base.py')
|
||||||
|
artifact.add_file('configs/config_pyramid.py')
|
||||||
|
artifact.add_file('models/base.py')
|
||||||
|
artifact.add_file('models/cond_encoder.py')
|
||||||
|
artifact.add_file('dataset.py')
|
||||||
|
artifact.add_file('convnext/models/convnext_tf.py')
|
||||||
|
run.log_artifact(artifact)
|
||||||
|
|
||||||
|
uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
|
||||||
|
|
||||||
|
def plot_3d(mesh_gt, mesh_pred_unposed_levels, faces, mesh_posed, collision, collision_faces, collision_color):
|
||||||
|
fig = make_subplots(rows=1, cols=1+len(mesh_pred_unposed_levels)+1+1, start_cell="top-left", specs = [ [{ 'type': 'scene' } for _ in range(1+len(mesh_pred_unposed_levels)+1+1)] ])
|
||||||
|
|
||||||
|
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"
|
||||||
|
),
|
||||||
|
row=1, col=1)
|
||||||
|
|
||||||
|
for i, mesh in enumerate(mesh_pred_unposed_levels):
|
||||||
|
fig.add_trace(go.Mesh3d(
|
||||||
|
x=mesh[:,0],
|
||||||
|
y=mesh[:,2],
|
||||||
|
z=mesh[:,1],
|
||||||
|
i = faces[:,0],
|
||||||
|
j = faces[:,1],
|
||||||
|
k = faces[:,2],
|
||||||
|
text='Unposed level_' + str(i)
|
||||||
|
),
|
||||||
|
row=1, col=i+2)
|
||||||
|
|
||||||
|
fig.add_trace(go.Mesh3d(
|
||||||
|
x=mesh_posed[:,0],
|
||||||
|
y=mesh_posed[:,1],
|
||||||
|
z=mesh_posed[:,2],
|
||||||
|
i = faces[:,0],
|
||||||
|
j = faces[:,1],
|
||||||
|
k = faces[:,2],
|
||||||
|
text='Posed GT'
|
||||||
|
),
|
||||||
|
row=1, col=1+len(mesh_pred_unposed_levels)+1)
|
||||||
|
|
||||||
|
fig.add_trace(go.Mesh3d(
|
||||||
|
x=collision[:,0],
|
||||||
|
y=collision[:,2],
|
||||||
|
z=collision[:,1],
|
||||||
|
i = collision_faces[:,0],
|
||||||
|
j = collision_faces[:,1],
|
||||||
|
k = collision_faces[:,2],
|
||||||
|
vertexcolor=collision_color,
|
||||||
|
text='Posed output on body'
|
||||||
|
),
|
||||||
|
row=1, col=1+len(mesh_pred_unposed_levels)+1+1)
|
||||||
|
|
||||||
|
if len(mesh_pred_unposed_levels) == 1:
|
||||||
|
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),
|
||||||
|
scene4_aspectmode='manual', scene4_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,
|
||||||
|
scene4 = helper,
|
||||||
|
)
|
||||||
|
if len(mesh_pred_unposed_levels) == 2:
|
||||||
|
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),
|
||||||
|
scene4_aspectmode='manual', scene4_aspectratio=dict(x=1, y=1, z=1),
|
||||||
|
scene5_aspectmode='manual', scene5_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,
|
||||||
|
scene4 = helper,
|
||||||
|
scene5 = helper,
|
||||||
|
)
|
||||||
|
if len(mesh_pred_unposed_levels) == 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),
|
||||||
|
scene4_aspectmode='manual', scene4_aspectratio=dict(x=1, y=1, z=1),
|
||||||
|
scene5_aspectmode='manual', scene5_aspectratio=dict(x=1, y=1, z=1),
|
||||||
|
scene6_aspectmode='manual', scene6_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,
|
||||||
|
scene4 = helper,
|
||||||
|
scene5 = helper,
|
||||||
|
scene6 = helper,
|
||||||
|
)
|
||||||
|
if len(mesh_pred_unposed_levels) == 4:
|
||||||
|
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),
|
||||||
|
scene4_aspectmode='manual', scene4_aspectratio=dict(x=1, y=1, z=1),
|
||||||
|
scene5_aspectmode='manual', scene5_aspectratio=dict(x=1, y=1, z=1),
|
||||||
|
scene6_aspectmode='manual', scene6_aspectratio=dict(x=1, y=1, z=1,),
|
||||||
|
scene7_aspectmode='manual', scene7_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,
|
||||||
|
scene4 = helper,
|
||||||
|
scene5 = helper,
|
||||||
|
scene6 = helper,
|
||||||
|
scene7 = helper,
|
||||||
|
)
|
||||||
|
if len(mesh_pred_unposed_levels) == 5:
|
||||||
|
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),
|
||||||
|
scene4_aspectmode='manual', scene4_aspectratio=dict(x=1, y=1, z=1),
|
||||||
|
scene5_aspectmode='manual', scene5_aspectratio=dict(x=1, y=1, z=1),
|
||||||
|
scene6_aspectmode='manual', scene6_aspectratio=dict(x=1, y=1, z=1,),
|
||||||
|
scene7_aspectmode='manual', scene7_aspectratio=dict(x=1, y=1, z=1,),
|
||||||
|
scene8_aspectmode='manual', scene8_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,
|
||||||
|
scene4 = helper,
|
||||||
|
scene5 = helper,
|
||||||
|
scene6 = helper,
|
||||||
|
scene7 = helper,
|
||||||
|
scene8 = helper,
|
||||||
|
)
|
||||||
|
return fig
|
||||||
|
|
||||||
|
|
||||||
|
def group_inputs_for_levels(batch):
|
||||||
|
levels_input = []
|
||||||
|
for res in config['pyramid']['resolutions']:
|
||||||
|
res_modifier = '_' + str(res) if res != 512 else ''
|
||||||
|
levels_input.append([batch['uv_unposed' + res_modifier], batch['uv_static' + res_modifier], batch['uv_body_posed' + res_modifier], batch['uv_curvature' + res_modifier], batch['uv_mask' + res_modifier], batch['uv_mask']])
|
||||||
|
conds = [batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature']]
|
||||||
|
return levels_input, conds
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
@tf.function
|
||||||
|
def test_step(batch):
|
||||||
|
levels_input, conds = group_inputs_for_levels(batch)
|
||||||
|
out_levels = model([levels_input, conds], training=False)
|
||||||
|
|
||||||
|
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_orig = batch['mesh_unposed']
|
||||||
|
|
||||||
|
output = dict()
|
||||||
|
reconstr_loss_total = 0
|
||||||
|
kl_total = 0
|
||||||
|
|
||||||
|
#for i, res in enumerate(config['pyramid']['resolutions']):
|
||||||
|
i = -1
|
||||||
|
res = config['pyramid']['resolutions'][-1]
|
||||||
|
res_modifier = '_' + str(res) if res != 512 else ''
|
||||||
|
pred, mean, logvar = out_levels[i]
|
||||||
|
|
||||||
|
mesh_pred = uv2mesh(pred)
|
||||||
|
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)
|
||||||
|
|
||||||
|
# is the output already masked?
|
||||||
|
|
||||||
|
e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
|
||||||
|
c_loss = tf.reduce_mean(collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']))
|
||||||
|
c_ratio = tf.reduce_mean(collision_ratio(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']))
|
||||||
|
l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((batch['uv_unposed'] - pred) ** 2), axis=-1), axis=[1, 2]))
|
||||||
|
# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
|
||||||
|
ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3))
|
||||||
|
|
||||||
|
reconstr_loss_total += config['pyramid']['loss_weights'][i] * (l2_loss + (e_loss + c_loss) * config['reg_loss_coeff'])
|
||||||
|
|
||||||
|
# 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'])
|
||||||
|
|
||||||
|
output = { **output, **{ 'test/edge_loss' + res_modifier: e_loss, 'test/collision_loss' + res_modifier: c_loss,
|
||||||
|
'test/ms_ssim' + res_modifier: ms_ssim, 'test/l2_loss' + res_modifier: l2_loss,
|
||||||
|
'test/collision_ratio' + res_modifier: c_ratio } }
|
||||||
|
|
||||||
|
loss = reconstr_loss_total + balancing_coeff * kl_total
|
||||||
|
|
||||||
|
return { **{ 'test/loss': loss, 'test/reconstr_loss_total': reconstr_loss_total }, **output }, out_levels
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
def plot_everything(batch: dict, type: str):
|
||||||
|
levels_input = group_inputs_for_levels(batch)
|
||||||
|
output_levels = model(levels_input, training=False)
|
||||||
|
|
||||||
|
mesh_pred_levels = [uv2mesh(output_levels[i][0]).numpy() for i in range(len(output_levels))]
|
||||||
|
mesh_orig = batch['mesh_unposed'].numpy()
|
||||||
|
mesh_orig_posed = batch['mesh_posed'].numpy()
|
||||||
|
body = batch['mesh_body_posed'].numpy()
|
||||||
|
|
||||||
|
orig_img = (batch['uv_unposed'].numpy() + 1) / 2
|
||||||
|
uv_static_img = (batch['uv_static'].numpy() + 1) / 2
|
||||||
|
uv_body_posed_img = (batch['uv_body_posed'].numpy() + 1) / 2
|
||||||
|
uv_curvature = (np.tile(batch['uv_curvature'].numpy().argmax(-1)[:,:,:,np.newaxis], [1,1,1,3]) / 6)
|
||||||
|
pred_img = np.concatenate([tf.image.resize(output_levels[i][0], (512, 256), tf.image.ResizeMethod.LANCZOS5) for i in range(len(output_levels))], axis=2)
|
||||||
|
pred_img = (pred_img - pred_img.min(axis=(1,2,3))[:,np.newaxis,np.newaxis,np.newaxis]) / (pred_img.max(axis=(1,2,3))[:,np.newaxis,np.newaxis,np.newaxis] - pred_img.min(axis=(1,2,3))[:,np.newaxis,np.newaxis,np.newaxis])
|
||||||
|
img = tf.concat((uv_static_img, uv_body_posed_img, uv_curvature, orig_img, pred_img), axis=2)
|
||||||
|
imgs = [wandb.Image(img[i]) for i in range(4)]
|
||||||
|
|
||||||
|
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])]).numpy()
|
||||||
|
|
||||||
|
F = np.load("custom_utils/smpl_faces" + skirt_modifier + ("_orig" if config['is_cape'] else "") + ".npy")
|
||||||
|
mask = batch['mesh_mask'].numpy().astype(bool)
|
||||||
|
|
||||||
|
_, faces = maskFacesBatch(np.ones_like(mesh_orig), F, mask)
|
||||||
|
gt = [mesh_orig[i][mask[i], :] + shape_offsets[i, mask[i], :] for i in range(mask.shape[0])]
|
||||||
|
gt_posed = [mesh_orig_posed[i, mask[i], :] for i in range(mask.shape[0])]
|
||||||
|
|
||||||
|
mesh_unposed_shaped_levels = [[] for _ in range(mask.shape[0])]
|
||||||
|
for i in range(len(output_levels)):
|
||||||
|
mesh_unposed_shaped = [mesh_pred_levels[i][j, mask[j], :] + shape_offsets[j, mask[j]] for j in range(mask.shape[0])]
|
||||||
|
for k in range(mask.shape[0]):
|
||||||
|
mesh_unposed_shaped_levels[k].append(mesh_unposed_shaped[k])
|
||||||
|
|
||||||
|
# full res version
|
||||||
|
mesh_unposed_shaped_full = np.zeros((mask.shape[0], config['smpl_res'], 3), dtype=np.float32)
|
||||||
|
for i in range(mask.shape[0]):
|
||||||
|
mesh_unposed_shaped_full[i, mask[i], :] = mesh_unposed_shaped[i]
|
||||||
|
mesh_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_unposed_shaped_full).numpy()
|
||||||
|
mesh_posed = [mesh_posed[j, mask[j], :] for j in range(mask.shape[0])]
|
||||||
|
|
||||||
|
collision = [np.concatenate((body[i], mesh_posed[i]), axis=0) for i in range(len(mesh_posed))]
|
||||||
|
collision_faces = [np.concatenate((F, faces[i] + config['smpl_res']), axis=0) for i in range(len(mesh_posed))]
|
||||||
|
collision_color = [np.concatenate((np.ones_like(body[i], dtype=np.int32)*100, np.ones_like(mesh_posed[i], dtype=np.int32)*230), axis=0) for i in range(len(mesh_posed))]
|
||||||
|
|
||||||
|
# body_2 = [b.copy() for b in body]
|
||||||
|
# for i in range(len(gt_posed)):
|
||||||
|
# body_2[i][:, 1] = body[i][:,2].copy()
|
||||||
|
# body_2[i][:, 2] = body[i][:,1].copy()
|
||||||
|
# gt_posed_with_body = [np.concatenate((body_2[i], gt_posed[i]), axis=0) for i in range(len(gt_posed))]
|
||||||
|
|
||||||
|
figs = { type + "/garment_" + str(i): plot_3d(gt[i], mesh_unposed_shaped_levels[i], faces[i], gt_posed[i], collision[i], collision_faces[i], collision_color[i]) for i in range(4) }
|
||||||
|
|
||||||
|
return { **figs, **{ type + '/images': imgs, } }
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
def test():
|
||||||
|
str_split = config['name'].split('_')
|
||||||
|
test_idx = str_split.index('test')
|
||||||
|
checkpoint_name = '_'.join(str_split[:test_idx])
|
||||||
|
load_model(model, 'checkpoints/' + checkpoint_name)
|
||||||
|
|
||||||
|
dataset_test = factory.get_dataset(folder=config['test_dir'], batch_size=config['batch_size'], signature=config['signature_dynamic'], use_filled=config['use_filled'], resolutions=dataset_resolutions, in_memory=False, do_repeat=False, is_cape=config['is_cape'])
|
||||||
|
static_dataset_test = StaticDataset(folder=config['test_dir'], signature=config['signature_static'], use_filled=config['use_filled'], resolutions=dataset_resolutions, in_memory=False, is_cape=config['is_cape'])
|
||||||
|
|
||||||
|
step = 0
|
||||||
|
losses = []
|
||||||
|
|
||||||
|
|
||||||
|
for batch in tqdm.tqdm(dataset_test):
|
||||||
|
batch = static_dataset_test.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
|
||||||
|
|
||||||
|
test_loss, out_levels = test_step(batch)
|
||||||
|
all_errors = []
|
||||||
|
|
||||||
|
errors = []
|
||||||
|
for i, id in enumerate(batch['id']):
|
||||||
|
id_str = id.numpy().decode("utf-8")
|
||||||
|
pred = uv2mesh(out_levels[-1][0])
|
||||||
|
min_body = np.load(os.path.join('/home/workspace/data/dataset/cape/cape_release', 'minimal_body_shape', id_str.split('_')[0], id_str.split('_')[0] + '_minimal.npy'))[np.newaxis, :, :].astype(np.float32)
|
||||||
|
offsets_pred = pred[i] - min_body[0,smpl.to_keep,:]
|
||||||
|
offsets_orig = batch['mesh_unposed'][i] - min_body[0,smpl.to_keep,:]
|
||||||
|
error = tf.reduce_mean(tf.math.sqrt(tf.reduce_sum((offsets_orig - offsets_pred) ** 2, axis=-1)))
|
||||||
|
errors.append(error)
|
||||||
|
|
||||||
|
test_loss = { **{ 'test/error': tf.reduce_mean(errors) }, **test_loss }
|
||||||
|
losses.append(test_loss)
|
||||||
|
all_errors.extend(errors)
|
||||||
|
to_log = { **{ 'current_step': step, 'test/error': np.asarray(errors).mean() }, **test_loss }
|
||||||
|
if step % 1000 == 0:
|
||||||
|
figs = plot_everything(batch, 'test')
|
||||||
|
to_log = { **to_log, **figs }
|
||||||
|
|
||||||
|
wandb.log(to_log, step = step)
|
||||||
|
|
||||||
|
# print(step)
|
||||||
|
step += 1
|
||||||
|
|
||||||
|
# averages = dict()
|
||||||
|
path_to_plotly_html = "temp/plotly_figure.html"
|
||||||
|
|
||||||
|
errors = np.asarray(all_errors)
|
||||||
|
print('mean: ' + str(errors.mean()))
|
||||||
|
print('min: ' + str(errors.min()))
|
||||||
|
print('max: ' + str(errors.max()))
|
||||||
|
print('median: ' + str(np.median(errors)))
|
||||||
|
np.save('temp/cape_f_pyr_64_128_256_error.npy', errors)
|
||||||
|
|
||||||
|
histograms = dict()
|
||||||
|
for key in test_loss.keys():
|
||||||
|
accumulated = [losses[i][key].numpy() for i in range(len(losses))]
|
||||||
|
fig = ex.histogram(accumulated, title='Average: ' + str(np.asarray(accumulated).mean()))
|
||||||
|
fig.write_html(path_to_plotly_html, auto_play = False)
|
||||||
|
|
||||||
|
histograms['all/' + key.split('/')[-1] + '_hist'] = wandb.Html(path_to_plotly_html)
|
||||||
|
# averages['all/' + key.split('/')[-1] + '_avg'] = np.asarray(accumulated).mean()
|
||||||
|
|
||||||
|
wandb.log({ **histograms }, step = step)
|
||||||
|
wandb.finish()
|
||||||
|
pass
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
init_wandb()
|
||||||
|
allow_memory_growth()
|
||||||
|
parser = argparse.ArgumentParser(description='')
|
||||||
|
parser.add_argument('--name', type=str)
|
||||||
|
args = vars(parser.parse_args())
|
||||||
|
|
||||||
|
smpl = SMPL(orig_smpl=config['is_cape'])
|
||||||
|
skirt_modifier = '_skirt' if config['is_skirt'] else ''
|
||||||
|
dataset_resolutions = config['pyramid']['resolutions'].copy()
|
||||||
|
if 512 not in dataset_resolutions:
|
||||||
|
dataset_resolutions.append(512)
|
||||||
|
|
||||||
|
factory = DatasetFactory()
|
||||||
|
|
||||||
|
model: Pyramid = Pyramid(config)
|
||||||
|
|
||||||
|
optimizer = tf.optimizers.Adam(learning_rate=config['lr'])
|
||||||
|
balancing_coeff = config['starting_balancing_coeff']
|
||||||
|
balancing_activated_step = 0
|
||||||
|
|
||||||
|
test()
|
||||||
|
|
||||||
Reference in New Issue
Block a user