From ac273198602ee7a2a046833cf55b920d3b9b6b02 Mon Sep 17 00:00:00 2001 From: Hunor Laczko Date: Tue, 21 Feb 2023 21:08:05 +0000 Subject: [PATCH] fixed changed reverse? --- configs/config_abl_all_post.py | 14 +++++++------- convnext/models/convnext_tf.py | 29 ++++++++++++++++------------- 2 files changed, 23 insertions(+), 20 deletions(-) diff --git a/configs/config_abl_all_post.py b/configs/config_abl_all_post.py index 954a55f..2f3373a 100644 --- a/configs/config_abl_all_post.py +++ b/configs/config_abl_all_post.py @@ -19,9 +19,9 @@ 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'] = 'iccv_weights_test_6' -config['notes'] = 'smaller weight loss coeff, 1e-6, convolutional weight learning, continued from _5' +config['gpu'] = '5' +config['name'] = 'iccv_weights_test_8' +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() diff --git a/convnext/models/convnext_tf.py b/convnext/models/convnext_tf.py index e0fc3a4..f9380bf 100644 --- a/convnext/models/convnext_tf.py +++ b/convnext/models/convnext_tf.py @@ -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')