diff --git a/auto_encoder_body.py b/auto_encoder_body.py index 4a197e5..83eb491 100644 --- a/auto_encoder_body.py +++ b/auto_encoder_body.py @@ -1,6 +1,6 @@ import os import sys -from configs.config_ae_body_post import config +from configs.review.config_ae_body import config os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu'] import tensorflow as tf @@ -34,7 +34,7 @@ from models.cond_encoder import AutoEncoderNew as AutoEncoder def init_wandb(): - run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp') + run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp', tags=config['tags']) artifact = wandb.Artifact('code', type='code') artifact.add_file('auto_encoder_body.py') artifact.add_file('losses.py') @@ -42,7 +42,7 @@ 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.')] + config_file_names = [key for key in sys.modules.keys() if key.startswith('configs.review.')] for config_file_name in config_file_names: artifact.add_file('/'.join(config_file_name.split('.')) + '.py') run.log_artifact(artifact) @@ -137,7 +137,10 @@ def train(): if step == 0: to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } } - wandb.log(to_log, step=step) + if step % 1000 == 0: + wandb.log(to_log, step=step, commit=True) + else: + wandb.log(to_log, step=step, commit=False) if step % 10000 == 0: manager_enc.save(checkpoint_number=step) diff --git a/auto_encoder_curv.py b/auto_encoder_curv.py index 2756698..7e75b4f 100644 --- a/auto_encoder_curv.py +++ b/auto_encoder_curv.py @@ -1,6 +1,6 @@ import os import sys -from configs.config_ae_curv_post import config +from configs.review.config_ae_curv import config os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu'] import tensorflow as tf @@ -37,7 +37,7 @@ from models.cond_encoder import AutoEncoderNew as AutoEncoder 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/') + run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp', tags=config['tags']) artifact = wandb.Artifact('code', type='code') artifact.add_file('auto_encoder_curv.py') artifact.add_file('losses.py') @@ -45,7 +45,7 @@ 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.')] + config_file_names = [key for key in sys.modules.keys() if key.startswith('configs.review.')] for config_file_name in config_file_names: artifact.add_file('/'.join(config_file_name.split('.')) + '.py') run.log_artifact(artifact) @@ -177,8 +177,11 @@ def train(): if step == 0: to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } } - wandb.log(to_log, step=step) - print(step) + if step % 1000 == 0: + wandb.log(to_log, step=step, commit=True) + else: + wandb.log(to_log, step=step, commit=False) + # print(step) if step % 10000 == 0: manager_enc.save(checkpoint_number=step) @@ -206,7 +209,7 @@ def test(): if __name__ == '__main__': init_wandb() - allow_memory_growth() + # allow_memory_growth() parser = argparse.ArgumentParser(description='') parser.add_argument('--name', type=str) args = vars(parser.parse_args()) diff --git a/auto_encoder_static.py b/auto_encoder_static.py index 9de1574..87325c3 100644 --- a/auto_encoder_static.py +++ b/auto_encoder_static.py @@ -1,6 +1,6 @@ import os import sys -from configs.config_ae_static import config +from configs.review.config_ae_static import config os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu'] import tensorflow as tf @@ -37,15 +37,16 @@ from models.cond_encoder import AutoEncoderNew as AutoEncoder def init_wandb(): - run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp') + run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp', tags=config['tags']) artifact = wandb.Artifact('code', type='code') artifact.add_file('auto_encoder_static.py') artifact.add_file('losses.py') artifact.add_file('models/base.py') + artifact.add_file('models/resnet.py') 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.')] + config_file_names = [key for key in sys.modules.keys() if key.startswith('configs.review.')] for config_file_name in config_file_names: artifact.add_file('/'.join(config_file_name.split('.')) + '.py') run.log_artifact(artifact) @@ -138,7 +139,10 @@ def train(): 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) if step % 10000 == 0: diff --git a/calculate_error.py b/calculate_error.py index 61871d3..5fbe5b7 100644 --- a/calculate_error.py +++ b/calculate_error.py @@ -1,6 +1,6 @@ import os import sys -from configs.config_abl_all_post import config +from configs.review.config_abl_all import config os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu'] # import tensorflow as tf diff --git a/configs/config_ae_static.py b/configs/config_ae_static.py index c027578..82e8f73 100644 --- a/configs/config_ae_static.py +++ b/configs/config_ae_static.py @@ -31,7 +31,7 @@ config['cond_ae']['decoder_dims'] = config['cond_ae']['encoder_dims'] config['cond_ae']['activation'] = 'linear' -config['gpu'] = '5' +config['gpu'] = '0' config['lr'] = 0.0005 config['name'] = 'static_post_4' config['notes'] = 'convnext + convnext, | \ diff --git a/configs/review/config_abl_all.py b/configs/review/config_abl_all.py new file mode 100644 index 0000000..68985b5 --- /dev/null +++ b/configs/review/config_abl_all.py @@ -0,0 +1,126 @@ +config = dict() + +# DON'T FORGET TO RESET SSIM LOSS USAGE +# DON'T FORGET TO CHECK MODEL LOADING +# DON'T FORGET TO CHECK IF STORE IN MEMORY +# DON'T FORGET TO CHECK REG LOSS COEFF +# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512 +# DON'T FORGET TO CHECK AVG GARMENT USED + +config['use_skinning'] = True # and deshaping +config['remove_global_orientation'] = True +config['use_curvature'] = True +config['use_extra_losses'] = True # edge, collision, 3d mesh +config['use_offsets'] = True + +config['use_norm_loss'] = True +config['only_curvatures'] = False + +ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False) +assert ablation_count == 1 or ablation_count == 0 + +config['gpu'] = '4' +config['name'] = 'abl_all_resnet_r7' +config['notes'] = '' + +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']: + assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '') + +config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision + +config['batch_size'] = 4 +config['resolution'] = 512 +config['is_skirt'] = False +config['dataset_in_memory'] = False +config['is_cape'] = False + +config['train_dir'] = '/home/workspace/local-data/train' +config['valid_dir'] = '/home/workspace/local-data/valid' +config['test_dir'] = '/home/workspace/data/dataset/test_ready' + +# config['frames_file'] = 'frames_dress_30k.txt' +# config['frames_file_valid'] = 'frames_dress_30k.txt' +# config['frames_file_test'] = 'frames_dress_30k.txt' + +config['static_model'] = 'checkpoints/post/static_r7_3' +config['body_model'] = 'checkpoints/post/body_r7_3' +config['curvature_model'] = 'checkpoints/post/curv_r7_3' + + +config['smpl_res'] = 14475 +if config['is_cape']: + config['smpl_res'] = 3638 + + +config['pretrained_model'] = '' + +############### +config['lr'] = 0.0001# 0.0001 +############### +config['starting_balancing_coeff'] = 0.001 + +config['signature_dynamic'] = { 'id': (), 'frame_nr': (), + 'pose_shape': (72 + 11,), + 'uv_body_posed': (512, 256, 3), + 'mesh_body_posed': (config['smpl_res'], 3,), + 'mesh_posed': (config['smpl_res'], 3,), + 'uv_curvature': (512, 256, 7), + } +config['signature_static'] = { 'uv_static': (512, 256, 3), + 'mesh_mask': (config['smpl_res'],), + 'uv_mask': (512, 256,), + } + +if config['use_skinning']: + config['signature_dynamic']['uv_unposed'] = (512, 256, 3) + config['signature_dynamic']['mesh_unposed'] = (config['smpl_res'], 3,) +else: + config['signature_dynamic']['uv_posed'] = (512, 256, 3) + + +config['upscale_enhancer'] = '' +config['freeze_encoders'] = True +config['use_one_hot'] = False + +config['cvae'] = dict() +config['cvae']['is_label'] = False +config['cvae']['add_noise'] = True +config['cvae']['noise_amount'] = 0.001 + +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['cond_ae_static'] = dict() +config['cond_ae_static']['is_label'] = False +config['cond_ae_static']['kernel_size'] = 7 +config['cond_ae_static']['bottleneck_size'] = 8 +config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 9, 3] +config['cond_ae_static']['encoder_dims'] = [16, 32, 64, 64, config['cond_ae_static']['bottleneck_size']] +config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths'] +config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims'] +config['cond_ae_static']['activation'] = 'tanh' + +config['cond_ae_curv'] = dict() +config['cond_ae_curv']['is_label'] = True +config['cond_ae_curv']['kernel_size'] = 7 +config['cond_ae_curv']['bottleneck_size'] = 40 +config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3] +config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']] +config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths'] +config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims'] +config['cond_ae_curv']['activation'] = 'tanh' + +config['cond_ae_body'] = dict() +config['cond_ae_body']['is_label'] = False +config['cond_ae_body']['kernel_size'] = 7 +config['cond_ae_body']['bottleneck_size'] = 16 +config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3] +config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']] +config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths'] +config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims'] +config['cond_ae_body']['activation'] = 'tanh' \ No newline at end of file diff --git a/configs/review/config_ae_body.py b/configs/review/config_ae_body.py new file mode 100644 index 0000000..5018050 --- /dev/null +++ b/configs/review/config_ae_body.py @@ -0,0 +1,51 @@ +config = dict() + + +config['batch_size'] = 4 +config['train_dir'] = '/home/workspace/local-data/train' +config['valid_dir'] = '/home/workspace/local-data/valid' +config['test_dir'] = '/home/workspace/data/dataset/test_ready' +config['use_filled'] = False +config['resolution'] = 512 +config['is_skirt'] = False +config['dataset_in_memory'] = False +config['is_cape'] = False +config['remove_global_orientation'] = True + +config['smpl_res'] = 14475 +if config['is_cape']: + config['smpl_res'] = 3638 + +config['latent_dim'] = 128 + +config['cond_ae'] = dict() +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']['activation'] = 'tanh' + + +config['gpu'] = '4' +config['lr'] = 0.0001 +config['name'] = 'body_r7_3' +config['notes'] = 'resnet18' +config['tags'] = ['review_7'] + +config['cond_ae']['upscale_output'] = False +config['pretrained_model'] = '' + + +config['signature_dynamic'] = { 'id': (), 'frame_nr': (), + 'pose_shape': (72 + 11,), + 'uv_body_posed': (3, 512, 256,), + 'mesh_body_posed': (config['smpl_res'], 3,), + } +config['signature_static'] = { + 'mesh_mask': (config['smpl_res'],), + } \ No newline at end of file diff --git a/configs/review/config_ae_curv.py b/configs/review/config_ae_curv.py new file mode 100644 index 0000000..3a54856 --- /dev/null +++ b/configs/review/config_ae_curv.py @@ -0,0 +1,50 @@ +config = dict() + + +config['batch_size'] = 4 +config['train_dir'] = '/home/workspace/local-data/train' +config['valid_dir'] = '/home/workspace/local-data/valid' +config['test_dir'] = '/home/workspace/data/dataset/test_ready' +config['use_filled'] = False +config['resolution'] = 512 +config['is_skirt'] = False +config['dataset_in_memory'] = False +config['is_cape'] = False +config['remove_global_orientation'] = True + +config['smpl_res'] = 14475 +if config['is_cape']: + config['smpl_res'] = 3638 + +config['latent_dim'] = 128 + +config['cond_ae'] = dict() +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']['activation'] = 'tanh' + + +config['gpu'] = '3' +config['lr'] = 0.001 +config['name'] = 'curv_r7_3' +config['notes'] = 'resnet18' +config['tags'] = ['review_7'] + +config['cond_ae']['upscale_output'] = False +config['pretrained_model'] = '' + + +config['signature_dynamic'] = { 'id': (), 'frame_nr': (), + 'uv_curvature': (512, 256, 7), + 'mesh_curvature': (config['smpl_res']) + } +config['signature_static'] = { 'mesh_mask': (config['smpl_res'],), + 'uv_mask': (512, 256,), + } \ No newline at end of file diff --git a/configs/review/config_ae_static.py b/configs/review/config_ae_static.py new file mode 100644 index 0000000..bcda780 --- /dev/null +++ b/configs/review/config_ae_static.py @@ -0,0 +1,51 @@ +config = dict() + + +config['batch_size'] = 4 +config['train_dir'] = '/home/workspace/local-data/train' +config['valid_dir'] = '/home/workspace/local-data/valid' +config['test_dir'] = '/home/workspace/data/dataset/test_ready' +config['use_filled'] = False +config['resolution'] = 512 +config['is_skirt'] = False +config['dataset_in_memory'] = True +config['is_cape'] = False +config['remove_global_orientation'] = True + +config['smpl_res'] = 14475 +if config['is_cape']: + config['smpl_res'] = 3638 + +config['latent_dim'] = 128 + +config['cond_ae'] = dict() +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 +config['cond_ae']['encoder_depths'] = [3, 3, 3, 27, 3] +config['cond_ae']['encoder_dims'] = [8, 16, 32, 32, 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']['activation'] = 'linear' + + +config['gpu'] = '6' +config['lr'] = 0.0005 +config['name'] = 'static_r7_3' +config['notes'] = 'resnet18 based, one more block' +config['tags'] = ['review_7'] + + + +config['cond_ae']['upscale_output'] = False +config['pretrained_model'] = '' + + +config['signature_dynamic'] = { 'id': (), 'frame_nr': (), } +config['signature_static'] = { 'uv_static': (512, 256, 3), + 'mesh_static': (config['smpl_res'], 3,), + 'mesh_mask': (config['smpl_res'],), + 'uv_mask': (512, 256,), + } \ No newline at end of file diff --git a/cvae.py b/cvae.py index 8bf7754..1defa5f 100644 --- a/cvae.py +++ b/cvae.py @@ -1,6 +1,6 @@ import os import sys -from configs.config_abl_all_post import config +from configs.review.config_abl_all import config os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu'] import tensorflow as tf @@ -58,7 +58,9 @@ def init_wandb(): artifact.add_file('convnext/models/convnext_tf.py') 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_adaptive=False, is_smpl_orig=config['is_cape']) @@ -455,8 +457,6 @@ def sample(folder): - - def test(): str_split = config['name'].split('_') test_idx = str_split.index('test') diff --git a/evaluate_ae.py b/evaluate_ae.py index c160969..ce23332 100644 --- a/evaluate_ae.py +++ b/evaluate_ae.py @@ -1,6 +1,6 @@ import os import sys -from configs.config_abl_all_post import config +from configs.review.config_abl_all import config os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu'] import tensorflow as tf diff --git a/models/base.py b/models/base.py index 122c18f..6f0f39b 100644 --- a/models/base.py +++ b/models/base.py @@ -1,5 +1,7 @@ import tensorflow as tf from convnext.models.convnext_tf import ConvNeXt, ConvNeXtReverse, Block, ConvNeXtReverseLabels +from models.resnet import resnet_18, make_basic_block_layer + class Bottleneck(tf.keras.Model): @@ -111,20 +113,34 @@ class Decoder(tf.keras.Model): class VariationalEncoderNew(tf.keras.Model): - def __init__(self, depths, dims, kernel_size=7): + def __init__(self, features): super(VariationalEncoderNew, self).__init__() - self.encoder = ConvNeXt(depths=depths, dims=dims, include_top=False, kernel_size=kernel_size) + self.encoder = resnet_18(features) - self.mean_branch = tf.keras.Sequential( - [Block(dim=64, drop_path=0, kernel_size=kernel_size, - layer_scale_init_value=1e-6, prefix=f'bottleneck{j}') for j in range(5)] - ) + self.mean_branch = tf.keras.Sequential([ + tf.keras.layers.Conv2D(filters=features, + kernel_size=(3, 3), + strides=1, + padding="same"), + tf.keras.layers.BatchNormalization(), + tf.keras.layers.Conv2D(filters=features, + kernel_size=(3, 3), + strides=1, + padding="same") + ]) - self.var_branch = tf.keras.Sequential( - [Block(dim=64, drop_path=0, kernel_size=kernel_size, - layer_scale_init_value=1e-6, prefix=f'bottleneck{j}') for j in range(5)] - ) + self.var_branch = tf.keras.Sequential([ + tf.keras.layers.Conv2D(filters=features, + kernel_size=(3, 3), + strides=1, + padding="same"), + tf.keras.layers.BatchNormalization(), + tf.keras.layers.Conv2D(filters=features, + kernel_size=(3, 3), + strides=1, + padding="same") + ]) self.flatten_mean = tf.keras.layers.Flatten() self.flatten_var = tf.keras.layers.Flatten() @@ -147,10 +163,10 @@ class VariationalEncoderNew(tf.keras.Model): class AEEncoderNew(tf.keras.Model): - def __init__(self, depths, dims, kernel_size=7, activation='tanh'): + def __init__(self, features, activation='tanh'): super(AEEncoderNew, self).__init__() - self.encoder = ConvNeXt(depths=depths, dims=dims, include_top=False, kernel_size=kernel_size) + self.encoder = resnet_18(features) self.act = tf.keras.layers.Activation(activation) @@ -171,15 +187,17 @@ class AEEncoderNew(tf.keras.Model): class DecoderNew(tf.keras.Model): + def __init__(self, features, is_label=False, is_cvae=False): def __init__(self, depths, dims, kernel_size=7, is_label=False, learn_weights=False): super(DecoderNew, self).__init__() self.is_label = is_label if not is_label: + self.decoder = resnet_18(features, reversed=True, is_cvae=is_cvae) self.decoder = ConvNeXtReverse(depths=depths, dims=dims, kernel_size=kernel_size, learn_weights=learn_weights) else: - self.decoder = ConvNeXtReverseLabels(depths=depths, dims=dims, nr_of_classes=7) + self.decoder = resnet_18(features, reversed=True, labels=6, is_cvae=is_cvae) def call(self, inputs, training): diff --git a/models/cond_encoder.py b/models/cond_encoder.py index c86f54c..076357b 100644 --- a/models/cond_encoder.py +++ b/models/cond_encoder.py @@ -25,8 +25,8 @@ class AutoEncoderNew(tf.keras.Model): def __init__(self, config): super(AutoEncoderNew, self).__init__() - self.encoder : AEEncoderNew = AEEncoderNew(config['encoder_depths'], config['encoder_dims'], config['kernel_size'], config['activation']) - self.decoder : DecoderNew = DecoderNew(config['decoder_depths'], config['decoder_dims'], kernel_size=config['kernel_size'], is_label=config['is_label']) + self.encoder : AEEncoderNew = AEEncoderNew(config['bottleneck_size'], config['activation']) + self.decoder : DecoderNew = DecoderNew(config['bottleneck_size'], is_label=config['is_label']) def call(self, inputs, training): diff --git a/models/cvae_model.py b/models/cvae_model.py index c708ef1..02bef32 100644 --- a/models/cvae_model.py +++ b/models/cvae_model.py @@ -159,16 +159,16 @@ class CVAENew(tf.keras.Model): self.size = config['resolution'] if not('only_curvatures' in config.keys() and config['only_curvatures'] == True): - self.static_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_static']['encoder_depths'], config['cond_ae_static']['encoder_dims'], config['cond_ae_static']['kernel_size'], config['cond_ae_static']['activation']) + self.static_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_static']['bottleneck_size'], config['cond_ae_static']['activation']) if config['static_model'] != '': load_model(self.static_enc, config['static_model'] + '_enc') - self.body_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_body']['encoder_depths'], config['cond_ae_body']['encoder_dims'], config['cond_ae_body']['kernel_size']) + self.body_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_body']['bottleneck_size']) if config['body_model'] != '': load_model(self.body_enc, config['body_model'] + '_enc') if config['use_curvature']: - self.curv_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_curv']['encoder_depths'], config['cond_ae_curv']['encoder_dims'], config['cond_ae_curv']['kernel_size'], config['cond_ae_curv']['activation']) + self.curv_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_curv']['bottleneck_size'], config['cond_ae_curv']['activation']) if config['curvature_model'] != '': load_model(self.curv_enc, config['curvature_model'] + '_enc') @@ -182,8 +182,10 @@ class CVAENew(tf.keras.Model): self.encoder = VariationalEncoderNew(config['cvae']['encoder_depths'], config['cvae']['encoder_dims'], kernel_size=config['cvae']['kernel_size']) self.decoder = DecoderNew(config['cvae']['decoder_depths'], config['cvae']['decoder_dims'], config['cvae']['kernel_size']) self.decoder_weights = DecoderNew(config['cvae']['decoder_weights_depths'], config['cvae']['decoder_weights_dims'], config['cvae']['kernel_size'], learn_weights=True) + self.encoder = VariationalEncoderNew(config['cvae']['encoder_dims'][-1]) + self.decoder = DecoderNew(config['cvae']['decoder_dims'][-1], is_cvae=True) - self.reshape = tf.keras.layers.Reshape((8, 4, 64)) + self.reshape = tf.keras.layers.Reshape((8, 4, config['cvae']['decoder_dims'][-1])) if config['avg_garment_path'] != '': self.avg_garment = np.load(config['avg_garment_path']) diff --git a/models/resnet.py b/models/resnet.py new file mode 100644 index 0000000..4673404 --- /dev/null +++ b/models/resnet.py @@ -0,0 +1,254 @@ +# taken from https://github.com/calmisential/TensorFlow2.0_ResNet/blob/master/models/resnet.py + +import tensorflow as tf + + +class BasicBlock(tf.keras.layers.Layer): + + def __init__(self, filter_num, stride=1, upsample=False): + super(BasicBlock, self).__init__() + if not upsample: + self.conv1 = tf.keras.layers.Conv2D(filters=filter_num, + kernel_size=(3, 3), + strides=stride, + padding="same") + else: + self.conv1 = tf.keras.layers.Conv2DTranspose(filters=filter_num, + kernel_size=(3, 3), + strides=stride, + padding="same") + self.bn1 = tf.keras.layers.BatchNormalization() + self.conv2 = tf.keras.layers.Conv2D(filters=filter_num, + kernel_size=(3, 3), + strides=1, + padding="same") + self.bn2 = tf.keras.layers.BatchNormalization() + if stride != 1: + if not upsample: + self.updownsample = tf.keras.Sequential() + self.updownsample.add(tf.keras.layers.Conv2D(filters=filter_num, + kernel_size=(1, 1), + strides=stride)) + self.updownsample.add(tf.keras.layers.BatchNormalization()) + else: + self.updownsample = tf.keras.Sequential() + self.updownsample.add(tf.keras.layers.Conv2DTranspose(filters=filter_num, + kernel_size=(1, 1), + strides=stride)) + self.updownsample.add(tf.keras.layers.BatchNormalization()) + else: + self.updownsample = lambda x: x + + def call(self, inputs, training=None, **kwargs): + residual = self.updownsample(inputs) + + x = self.conv1(inputs) + x = self.bn1(x, training=training) + x = tf.nn.relu(x) + x = self.conv2(x) + x = self.bn2(x, training=training) + + output = tf.nn.relu(tf.keras.layers.add([residual, x])) + + return output + + +class BottleNeck(tf.keras.layers.Layer): + def __init__(self, filter_num, stride=1): + super(BottleNeck, self).__init__() + self.conv1 = tf.keras.layers.Conv2D(filters=filter_num, + kernel_size=(1, 1), + strides=1, + padding='same') + self.bn1 = tf.keras.layers.BatchNormalization() + self.conv2 = tf.keras.layers.Conv2D(filters=filter_num, + kernel_size=(3, 3), + strides=stride, + padding='same') + self.bn2 = tf.keras.layers.BatchNormalization() + self.conv3 = tf.keras.layers.Conv2D(filters=filter_num * 4, + kernel_size=(1, 1), + strides=1, + padding='same') + self.bn3 = tf.keras.layers.BatchNormalization() + + self.downsample = tf.keras.Sequential() + self.downsample.add(tf.keras.layers.Conv2D(filters=filter_num * 4, + kernel_size=(1, 1), + strides=stride)) + self.downsample.add(tf.keras.layers.BatchNormalization()) + + def call(self, inputs, training=None, **kwargs): + residual = self.downsample(inputs) + + x = self.conv1(inputs) + x = self.bn1(x, training=training) + x = tf.nn.relu(x) + x = self.conv2(x) + x = self.bn2(x, training=training) + x = tf.nn.relu(x) + x = self.conv3(x) + x = self.bn3(x, training=training) + + output = tf.nn.relu(tf.keras.layers.add([residual, x])) + + return output + + +def make_basic_block_layer(filter_num, blocks, stride=1, upsample=False): + res_block = tf.keras.Sequential() + res_block.add(BasicBlock(filter_num, stride=stride, upsample=upsample)) + + for _ in range(1, blocks): + res_block.add(BasicBlock(filter_num, stride=1)) + + return res_block + + +def make_bottleneck_layer(filter_num, blocks, stride=1): + res_block = tf.keras.Sequential() + res_block.add(BottleNeck(filter_num, stride=stride)) + + for _ in range(1, blocks): + res_block.add(BottleNeck(filter_num, stride=1)) + + return res_block + + +class ResNetTypeI(tf.keras.Model): + def __init__(self, layer_params, features, reversed=False, labels=None, is_cvae=False): + super(ResNetTypeI, self).__init__() + self.reversed = reversed + self.labels = labels + + if not reversed: + self.conv1 = tf.keras.layers.Conv2D(filters=64, + kernel_size=(7, 7), + strides=2, + padding="same") + self.bn1 = tf.keras.layers.BatchNormalization() + self.pool1 = tf.keras.layers.MaxPool2D(pool_size=(3, 3), + strides=2, + padding="same") + else: + self.conv1 = tf.keras.layers.Conv2DTranspose(filters=3 if labels is None else labels, + kernel_size=(7, 7), + strides=2, + padding="same") + self.bn1 = tf.keras.layers.BatchNormalization() + # self.pool1 = tf.keras.layers.MaxPool2D(pool_size=(3, 3), + # strides=2, + # padding="same") + self.pool1 = tf.keras.layers.UpSampling2D() + + self.layer1 = make_basic_block_layer(filter_num=64, + blocks=layer_params[0], + stride=1 if not reversed else 2, + upsample=reversed) + self.layer2 = make_basic_block_layer(filter_num=64, + blocks=layer_params[1], + stride=2, + upsample=reversed) + self.layer3 = make_basic_block_layer(filter_num=64, + blocks=layer_params[2], + stride=2, + upsample=reversed) + self.layer4 = make_basic_block_layer(filter_num=128, + blocks=layer_params[3], + stride=2, + upsample=reversed) + self.layer5 = make_basic_block_layer(filter_num=features if not is_cvae else features*2, + blocks=layer_params[4], + stride=2 if not reversed else 1, + upsample=reversed) + + def call(self, inputs, training=None, mask=None): + if not self.reversed: + x = self.conv1(inputs) + x = self.bn1(x, training=training) + x = tf.nn.relu(x) + x = self.pool1(x) + x = self.layer1(x, training=training) + x = self.layer2(x, training=training) + x = self.layer3(x, training=training) + x = self.layer4(x, training=training) + x = self.layer5(x, training=training) + else: + x = self.layer5(inputs, training=training) + x = self.layer4(x, training=training) + x = self.layer3(x, training=training) + x = self.layer2(x, training=training) + x = self.layer1(x, training=training) + + x = self.pool1(x) + x = self.bn1(x, training=training) + # x = tf.nn.relu(x) + x = self.conv1(x) + if self.labels is None: + x = tf.nn.tanh(x) + # else: + # x = tf.nn.softmax(x) + + return x + + +class ResNetTypeII(tf.keras.Model): + def __init__(self, layer_params): + super(ResNetTypeII, self).__init__() + self.conv1 = tf.keras.layers.Conv2D(filters=64, + kernel_size=(7, 7), + strides=2, + padding="same") + self.bn1 = tf.keras.layers.BatchNormalization() + self.pool1 = tf.keras.layers.MaxPool2D(pool_size=(3, 3), + strides=2, + padding="same") + + self.layer1 = make_bottleneck_layer(filter_num=64, + blocks=layer_params[0]) + self.layer2 = make_bottleneck_layer(filter_num=128, + blocks=layer_params[1], + stride=2) + self.layer3 = make_bottleneck_layer(filter_num=256, + blocks=layer_params[2], + stride=2) + self.layer4 = make_bottleneck_layer(filter_num=512, + blocks=layer_params[3], + stride=2) + + self.avgpool = tf.keras.layers.GlobalAveragePooling2D() + self.fc = tf.keras.layers.Dense(units=NUM_CLASSES, activation=tf.keras.activations.softmax) + + def call(self, inputs, training=None, mask=None): + x = self.conv1(inputs) + x = self.bn1(x, training=training) + x = tf.nn.relu(x) + x = self.pool1(x) + x = self.layer1(x, training=training) + x = self.layer2(x, training=training) + x = self.layer3(x, training=training) + x = self.layer4(x, training=training) + x = self.avgpool(x) + output = self.fc(x) + + return output + + +def resnet_18(features, reversed=False, labels=None, is_cvae=False): + return ResNetTypeI(layer_params=[2, 2, 2, 2, 2], features=features, reversed=reversed, labels=labels, is_cvae=is_cvae) + + +def resnet_34(): + return ResNetTypeI(layer_params=[3, 4, 6, 3]) + + +def resnet_50(): + return ResNetTypeII(layer_params=[3, 4, 6, 3]) + + +def resnet_101(): + return ResNetTypeII(layer_params=[3, 4, 23, 3]) + + +def resnet_152(): + return ResNetTypeII(layer_params=[3, 8, 36, 3]) \ No newline at end of file