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fix plotting and error calc

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luni
2023-02-17 15:31:19 +00:00
parent 33eff16c8c
commit ebbc52d0fd
3 changed files with 8 additions and 8 deletions
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aaa_helpers.txt
+1 -1
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@@ -1,4 +1,4 @@
nohup python cvae.py --name="lhunor_weights_test_2" > nohup/iccv/weights_test.out 2>&1 &
nohup python cvae.py --name="lhunor_weights_test_6" > nohup/iccv/weights_test_6.out 2>&1 &
one final convolutional layer for smoothing, on the final coordinates
configs/config_abl_all_post.py
+3 -3
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@@ -20,8 +20,8 @@ ablation_count = [config['use_skinning'], config['remove_global_orientation'], c
assert ablation_count == 1 or ablation_count == 0
config['gpu'] = '5'
config['name'] = 'iccv_weights_test_5'
config['notes'] = 'smaller weight loss coeff, 1e-6, convolutional weight learning'
config['name'] = 'iccv_weights_test_6'
config['notes'] = 'smaller weight loss coeff, 1e-6, convolutional weight learning, continued from _5'
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_2'
config['pretrained_model'] = 'checkpoints/post/iccv_weights_test_5'
###############
config['lr'] = 0.0001# 0.0001
cvae.py
+4 -4
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@@ -136,7 +136,7 @@ def train_step(batch):
logvar = tf.reduce_mean(logvar)
# 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)))
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'])
metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred_posed) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
return { 'train/loss': loss, 'train/edge_loss': e_loss, 'train/collision_loss': c_loss, 'train/euclid_error': metric,
@@ -198,7 +198,7 @@ def valid_step(batch):
logvar = tf.reduce_mean(logvar)
# 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)))
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'])
metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred_posed) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
# c_ratio = tf.reduce_mean(collision_ratio(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']))
return { 'valid/loss': loss, 'valid/edge_loss': e_loss, 'valid/collision_loss': c_loss, 'valid/euclid_error': metric,
@@ -324,8 +324,8 @@ def plot_everything(batch: dict, type: str):
collision_gt = [np.concatenate((body[i], gt_posed[i]), axis=0) for i in range(len(garment_posed))]
# figs = { type + "/garment_" + str(i): plot_3d(gt[i], garment_unposed[i], faces[i], garment_posed[i], faces[i], collision_color[i], flip_xy=(not config['use_skinning'] and config['remove_global_orientation'])) for i in range(4) }
figs = { type + "/garment_" + str(i): plot_3d(gt[i], garment_unposed[i], faces[i], collision[i], collision_faces[i], collision_color[i], flip_xy=(config['remove_global_orientation'])) for i in range(4) }
figs_coll = { type + "/garment_" + str(i) + '_collision': plot_3d_collision(collision_gt[i], collision[i], collision_fixed[i], collision_faces[i], collision_color[i], flip_xy=(config['remove_global_orientation'])) for i in range(4) }
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) }
figs_coll = { type + "/garment_" + str(i) + '_collision': plot_3d_collision(collision_gt[i], collision[i], collision_fixed[i], collision_faces[i], collision_color[i],) for i in range(4) }
figs_weights = { type + '/weights_' + str(i): plot_weights(mesh_weigths[0,:,i], body[0], F) for i in range(24) }
# figs_weights = { type + '/weights_' + str(i): plot_weights(smpl.weights[0,:,i], body[0], F) for i in range(24) }