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resnet condition encoders

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luni
2023-02-27 17:41:47 +00:00
parent bb7128f589
commit aa3defbf07
10 changed files with 151 additions and 111 deletions
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aaa_helpers.txt
+1 -1
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@@ -1,7 +1,7 @@
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 &
nohup python auto_encoder_static.py --name="lhunor_ae_static_iccv_resnet_1" > nohup/iccv/ae_static_iccv_resnet_1.out 2>&1 &
nohup python auto_encoder_static.py --name="lhunor_ae_static_iccv_resnet_1" > nohup/iccv/encoders/ae_static_iccv_resnet_1.out 2>&1 &
one final convolutional layer for smoothing, on the final coordinates
auto_encoder_body.py
+22 -12
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@@ -1,6 +1,6 @@
import os
import sys
from configs.review.config_ae_body import config
from configs.iccv.config_ae_body import config
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
import tensorflow as tf
@@ -31,10 +31,11 @@ from losses import edge_loss, uv_mask_loss
from dataset import DatasetFactory, StaticDataset
from custom_utils.custom_utils import batch_to_dict, plot_3d
from models.cond_encoder import AutoEncoderNew as AutoEncoder
from custom_utils.custom_utils import Accumulator
def init_wandb():
run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp', tags=config['tags'])
run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/tmp', tags=config['tags'])
artifact = wandb.Artifact('code', type='code')
artifact.add_file('auto_encoder_body.py')
artifact.add_file('losses.py')
@@ -42,9 +43,11 @@ def init_wandb():
artifact.add_file('models/cond_encoder.py')
artifact.add_file('dataset.py')
artifact.add_file('convnext/models/convnext_tf.py')
config_file_names = [key for key in sys.modules.keys() if key.startswith('configs.review.')]
config_file_names = [key for key in sys.modules.keys() if key.startswith('configs.')]
for config_file_name in config_file_names:
artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
file_name = '/'.join(config_file_name.split('.')) + '.py'
if os.path.exists(file_name):
artifact.add_file(file_name)
run.log_artifact(artifact)
uv2mesh = UV2Mesh(res=256, is_smpl_orig=config['is_cape'], is_skirt=config['is_skirt'])
@@ -60,7 +63,8 @@ def train_step(batch):
e_loss = 1e-2 * tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, tf.ones((pred.shape[0], 3638 if config['is_cape'] else 14475))))
l1_loss = tf.reduce_mean(tf.reduce_sum(((tf.math.abs(batch['uv_body_posed' + resolution_modifier] - pred))), axis=1))
ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 1, batch['uv_body_posed' + resolution_modifier] + 1, max_val=2, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 1, batch['uv_body_posed' + resolution_modifier] + 1, max_val=3, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_body_posed' + resolution_modifier] + 1, max_val=3))
loss = 1e3 * (1 - ms_ssim)
@@ -80,7 +84,8 @@ def valid_step(batch):
e_loss = 1e-2 * tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, tf.ones((pred.shape[0], 3638 if config['is_cape'] else 14475))))
l1_loss = tf.reduce_mean(tf.reduce_sum(((tf.math.abs(batch['uv_body_posed' + resolution_modifier] - pred))), axis=1))
ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 1, batch['uv_body_posed' + resolution_modifier] + 1, max_val=2, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 1, batch['uv_body_posed' + resolution_modifier] + 1, max_val=3, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_body_posed' + resolution_modifier] + 1, max_val=3))
loss = 1e3 * (1 - ms_ssim)
@@ -124,23 +129,26 @@ def train():
for batch in dataset_train:
batch = static_dataset_train.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
train_loss = train_step(batch)
train_accumulator.update(train_loss)
if step % 50 == 0:
data_valid = valid_iterator.get_next()
data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
valid_loss = valid_step(data_valid)
valid_accumulator.update(valid_loss)
to_log = { **train_loss, **valid_loss, **{ 'current_step': step} }
if step % 5000 == 0 or (step < 5000 and step % 500 == 0):
if step % 1000 == 0 or (step < 1000 and step % 100 == 0):
to_log = dict({ **train_loss, **valid_loss, **{ 'current_step': step } })
if step % 5000 == 0 or (step < 5000 and step % 1000 == 0):
train_figs = plot_everything(batch, 'train')
valid_figs = plot_everything(data_valid, 'valid')
to_log = { **to_log, **train_figs, **valid_figs }
if step == 0:
to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
if step % 1000 == 0:
wandb.log(to_log, step=step, commit=True)
else:
wandb.log(to_log, step=step, commit=False)
wandb.log(to_log, step=step)
train_accumulator.reset()
valid_accumulator.reset()
if step % 10000 == 0:
manager_enc.save(checkpoint_number=step)
@@ -207,5 +215,7 @@ if __name__ == '__main__':
manager_enc = tf.train.CheckpointManager(ckpt_enc, 'checkpoints/post/' + config['name'] + '_enc', max_to_keep=20)
ckpt_dec = tf.train.Checkpoint(model=model.decoder)
manager_dec = tf.train.CheckpointManager(ckpt_dec, 'checkpoints/post/' + config['name'] + '_dec', max_to_keep=20)
train_accumulator = Accumulator()
valid_accumulator = Accumulator()
train()
auto_encoder_curv.py
+18 -11
View File
@@ -1,6 +1,6 @@
import os
import sys
from configs.review.config_ae_curv import config
from configs.iccv.config_ae_curv import config
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
import tensorflow as tf
@@ -32,12 +32,13 @@ from tf_utils import check_tf_version, allow_memory_growth
from dataset import DatasetFactory, StaticDataset
from custom_utils.custom_utils import batch_to_dict
from models.cond_encoder import AutoEncoderNew as AutoEncoder
from custom_utils.custom_utils import Accumulator
testing = False
def init_wandb():
if not testing:
run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp', tags=config['tags'])
run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/tmp', tags=config['tags'])
artifact = wandb.Artifact('code', type='code')
artifact.add_file('auto_encoder_curv.py')
artifact.add_file('losses.py')
@@ -45,9 +46,11 @@ def init_wandb():
artifact.add_file('models/cond_encoder.py')
artifact.add_file('dataset.py')
artifact.add_file('convnext/models/convnext_tf.py')
config_file_names = [key for key in sys.modules.keys() if key.startswith('configs.review.')]
config_file_names = [key for key in sys.modules.keys() if key.startswith('configs.')]
for config_file_name in config_file_names:
artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
file_name = '/'.join(config_file_name.split('.')) + '.py'
if os.path.exists(file_name):
artifact.add_file(file_name)
run.log_artifact(artifact)
uv2mesh = UV2MeshLabel(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
@@ -164,24 +167,26 @@ def train():
batch = static_dataset_train.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
train_loss = train_step(batch)
train_accumulator.update(train_loss)
if step % 50 == 0:
data_valid = valid_iterator.get_next()
data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
valid_loss = valid_step(data_valid)
valid_accumulator.update(valid_loss)
to_log = { **train_loss, **valid_loss }
if step % 5000 == 0 or (step < 5000 and step % 500 == 0):
if step % 1000 == 0 or (step < 1000 and step % 100 == 0):
to_log = dict({ **train_loss, **valid_loss, **{ 'current_step': step } })
if step % 5000 == 0 or (step < 5000 and step % 1000 == 0):
train_figs = plot_everything(batch, 'train')
valid_figs = plot_everything(data_valid, 'valid')
to_log = { **to_log, **train_figs, **valid_figs }
if step == 0:
to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
if step % 1000 == 0:
wandb.log(to_log, step=step, commit=True)
else:
wandb.log(to_log, step=step, commit=False)
# print(step)
wandb.log(to_log, step=step)
train_accumulator.reset()
valid_accumulator.reset()
if step % 10000 == 0:
manager_enc.save(checkpoint_number=step)
@@ -257,6 +262,8 @@ if __name__ == '__main__':
manager_enc = tf.train.CheckpointManager(ckpt_enc, 'checkpoints/post/' + config['name'] + '_enc', max_to_keep=20)
ckpt_dec = tf.train.Checkpoint(model=model.decoder)
manager_dec = tf.train.CheckpointManager(ckpt_dec, 'checkpoints/post/' + config['name'] + '_dec', max_to_keep=20)
train_accumulator = Accumulator()
valid_accumulator = Accumulator()
if testing:
test()
auto_encoder_static.py
+21 -17
View File
@@ -34,6 +34,7 @@ from tf_utils import check_tf_version, allow_memory_growth
from dataset import DatasetFactory, StaticDataset
from custom_utils.custom_utils import batch_to_dict, plot_3d
from models.cond_encoder import AutoEncoderNew as AutoEncoder
from custom_utils.custom_utils import Accumulator
def init_wandb():
@@ -46,9 +47,11 @@ def init_wandb():
artifact.add_file('models/cond_encoder.py')
artifact.add_file('dataset.py')
artifact.add_file('convnext/models/convnext_tf.py')
config_file_names = [key for key in sys.modules.keys() if key.startswith('configs.review.')]
config_file_names = [key for key in sys.modules.keys() if key.startswith('configs.')]
for config_file_name in config_file_names:
artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
file_name = '/'.join(config_file_name.split('.')) + '.py'
if os.path.exists(file_name):
artifact.add_file(file_name)
run.log_artifact(artifact)
uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
@@ -65,11 +68,14 @@ def train_step(batch):
e_loss = 1e-2 * tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
l1_loss = tf.reduce_mean(tf.reduce_sum((tf.math.abs(batch['uv_static'] - pred)), axis=1))
ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 1, batch['uv_static'] + 1, max_val=2, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 1, batch['uv_static'] + 1, max_val=3, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_static'] + 1, max_val=3))
loss = 1e3 * (1 - ms_ssim)
# loss = l1_loss
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'])
gradients = tape.gradient(loss, model.trainable_variables)
gradients = [(tf.clip_by_norm(grad, clip_norm=2.0)) for grad in gradients]
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
return { 'train/loss': loss, 'train/edge_loss': e_loss, 'train/l1_loss': l1_loss, 'train/euclid_error': metric, 'train/ms_ssim': ms_ssim, }
@@ -84,8 +90,10 @@ def valid_step(batch):
e_loss = 1e-2 * tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
l1_loss = tf.reduce_mean(tf.reduce_sum((tf.math.abs(batch['uv_static'] - pred)), axis=1))
ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 1, batch['uv_static'] + 1, max_val=2, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 1, batch['uv_static'] + 1, max_val=3, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_static'] + 1, max_val=3))
loss = 1e3 * (1 - ms_ssim)
# loss = l1_loss
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'])
return { 'valid/loss': loss, 'valid/edge_loss': e_loss, 'valid/l1_loss': l1_loss, 'valid/euclid_error': metric, 'valid/ms_ssim': ms_ssim, }
@@ -126,21 +134,26 @@ def train():
for batch in dataset_train:
batch = static_dataset_train.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
train_loss = train_step(batch)
train_accumulator.update(train_loss)
if step % 50 == 0:
data_valid = valid_iterator.get_next()
data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
valid_loss = valid_step(data_valid)
valid_accumulator.update(valid_loss)
if step % 1000 == 0 or (step < 1000 and step % 100 == 0):
to_log = dict({ **train_loss, **valid_loss, **{ 'current_step': step } })
if step % 5000 == 0 or (step < 5000 and step % 500 == 0):
if step % 5000 == 0 or (step < 5000 and step % 1000 == 0):
train_figs = plot_everything(batch, 'train')
valid_figs = plot_everything(data_valid, 'valid')
to_log = { **to_log, **train_figs, **valid_figs }
if step == 0:
to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
wandb.log(to_log, step=step)
train_accumulator.reset()
valid_accumulator.reset()
if step % 10000 == 0:
manager_enc.save(checkpoint_number=step)
@@ -185,17 +198,6 @@ def test():
print()
class MyLRSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):
def __init__(self, initial_learning_rate):
self.initial_learning_rate = initial_learning_rate
def __call__(self, step):
if step < 1000:
return self.initial_learning_rate / 10.0
else:
return self.initial_learning_rate
if __name__ == '__main__':
init_wandb()
@@ -242,6 +244,8 @@ if __name__ == '__main__':
model : AutoEncoder = AutoEncoder(config['cond_ae'])
optimizer = tf.optimizers.Adam(learning_rate=config['lr'])
train_accumulator = Accumulator()
valid_accumulator = Accumulator()
if config['pretrained_model'] != '':
load_model(model.encoder, config['pretrained_model'] + '_enc')
configs/iccv/config_ae_body.py
+6 -9
View File
@@ -23,19 +23,16 @@ config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 16
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_depths'] = config['cond_ae']['encoder_depths']
config['cond_ae']['decoder_dims'] = config['cond_ae']['encoder_dims']
config['cond_ae']['bottleneck_size'] = 16 * 2
config['cond_ae']['layer_params'] = [3, 7, 15, 7, 3]
config['cond_ae']['activation'] = 'tanh'
config['gpu'] = '4'
config['gpu'] = '3'
config['lr'] = 0.0001
config['name'] = 'body_r7_3'
config['notes'] = 'resnet18'
config['tags'] = ['review_7']
config['name'] = 'body_resnet_1'
config['notes'] = ''
config['tags'] = ['iccv', 'resnet']
config['cond_ae']['upscale_output'] = False
config['pretrained_model'] = ''
configs/iccv/config_ae_curv.py
+7 -9
View File
@@ -23,19 +23,17 @@ config['cond_ae']['is_label'] = True
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 40
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_depths'] = config['cond_ae']['encoder_depths']
config['cond_ae']['decoder_dims'] = config['cond_ae']['encoder_dims']
config['cond_ae']['bottleneck_size'] = 40 * 2
config['cond_ae']['layer_params'] = [3, 7, 15, 7, 3]
config['cond_ae']['activation'] = 'tanh'
config['gpu'] = '3'
config['gpu'] = '2'
config['lr'] = 0.001
config['name'] = 'curv_r7_3'
config['notes'] = 'resnet18'
config['tags'] = ['review_7']
config['name'] = 'curv_resnet_1'
config['notes'] = ''
config['tags'] = ['iccv', 'resnet']
config['cond_ae']['upscale_output'] = False
config['pretrained_model'] = ''
configs/iccv/config_ae_static.py
+8 -8
View File
@@ -23,17 +23,17 @@ config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 8*4 # 8*4
config['cond_ae']['layer_params'] = [3, 4, 23, 4, 3]
# config['cond_ae']['layer_params'] = [3, 4, 23, 3, 3]
config['cond_ae']['activation'] = 'linear'
config['cond_ae']['bottleneck_size'] = 8*2 # 8*4
# config['cond_ae']['layer_params'] = [3, 4, 23, 4, 3]
config['cond_ae']['layer_params'] = [3, 7, 15, 7, 3]
config['cond_ae']['activation'] = 'tanh'
config['gpu'] = '5'
config['lr'] = 0.0001
config['gpu'] = '1'
config['lr'] = 3e-4
config['name'] = 'static_resnet_1'
config['notes'] = 'resnet101 based, one more block'
config['tags'] = ['iccv', 'resnet']
config['notes'] = ''
config['tags'] = ['iccv', 'resnet', 'static']
custom_utils/custom_utils.py
+24
View File
@@ -433,3 +433,27 @@ def group_inputs_for_levels(batch, config):
levels_input.append([uv_orig, 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
class Accumulator():
def __init__(self):
self.dict = dict()
def update(self, dict):
for key in dict.keys():
if key in self.dict.keys():
self.dict[key].append(dict[key])
else:
self.dict[key] = [dict[key]]
def get(self):
result = dict()
for key in self.dict.keys():
result[key] = np.mean(self.dict[key])
return result
def reset(self):
self.dict = dict()
models/base.py
+3 -3
View File
@@ -166,7 +166,7 @@ class AEEncoderNew(tf.keras.Model):
def __init__(self, layer_params, features, activation='tanh'):
super(AEEncoderNew, self).__init__()
self.encoder = resnet_18(layer_params, features)
self.encoder = resnet_101(layer_params, features)
self.act = tf.keras.layers.Activation(activation)
@@ -193,9 +193,9 @@ class DecoderNew(tf.keras.Model):
self.is_label = is_label
if not is_label:
self.decoder = resnet_18(layer_params, features, reversed=True, is_cvae=is_cvae)
self.decoder = resnet_101(layer_params, features, reversed=True, is_cvae=is_cvae)
else:
self.decoder = resnet_18(layer_params, features, reversed=True, labels=6, is_cvae=is_cvae)
self.decoder = resnet_101(layer_params, features, reversed=True, labels=6, is_cvae=is_cvae)
def call(self, inputs, training):
models/resnet.py
+10 -10
View File
@@ -133,7 +133,7 @@ class ResNetTypeI(tf.keras.Model):
self.labels = labels
if not reversed:
self.conv1 = tf.keras.layers.Conv2D(filters=64,
self.conv1 = tf.keras.layers.Conv2D(filters=32,
kernel_size=(7, 7),
strides=2,
padding="same")
@@ -149,19 +149,19 @@ class ResNetTypeI(tf.keras.Model):
self.bn1 = tf.keras.layers.BatchNormalization()
self.pool1 = tf.keras.layers.UpSampling2D()
self.layer1 = make_basic_block_layer(filter_num=64,
self.layer1 = make_basic_block_layer(filter_num=32,
blocks=layer_params[0],
stride=1 if not reversed else 2,
upsample=reversed)
self.layer2 = make_basic_block_layer(filter_num=64,
self.layer2 = make_basic_block_layer(filter_num=32,
blocks=layer_params[1],
stride=2,
upsample=reversed)
self.layer3 = make_basic_block_layer(filter_num=64,
self.layer3 = make_basic_block_layer(filter_num=32,
blocks=layer_params[2],
stride=2,
upsample=reversed)
self.layer4 = make_basic_block_layer(filter_num=128,
self.layer4 = make_basic_block_layer(filter_num=64,
blocks=layer_params[3],
stride=2,
upsample=reversed)
@@ -207,7 +207,7 @@ class ResNetTypeII(tf.keras.Model):
self.labels = labels
if not reversed:
self.conv1 = tf.keras.layers.Conv2D(filters=64,
self.conv1 = tf.keras.layers.Conv2D(filters=32,
kernel_size=(7, 7),
strides=2,
padding="same")
@@ -223,19 +223,19 @@ class ResNetTypeII(tf.keras.Model):
self.bn1 = tf.keras.layers.BatchNormalization()
self.pool1 = tf.keras.layers.UpSampling2D()
self.layer1 = make_bottleneck_layer(filter_num=64,
self.layer1 = make_bottleneck_layer(filter_num=32,
blocks=layer_params[0],
stride=1 if not reversed else 2,
upsample=reversed)
self.layer2 = make_bottleneck_layer(filter_num=128,
self.layer2 = make_bottleneck_layer(filter_num=64,
blocks=layer_params[1],
stride=2,
upsample=reversed)
self.layer3 = make_bottleneck_layer(filter_num=256,
self.layer3 = make_bottleneck_layer(filter_num=64,
blocks=layer_params[2],
stride=2,
upsample=reversed)
self.layer4 = make_bottleneck_layer(filter_num=512,
self.layer4 = make_bottleneck_layer(filter_num=128,
blocks=layer_params[3],
stride=2,
upsample=reversed)