separate sub models from their training (wip)
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+4
-117
@@ -5,7 +5,7 @@ import wandb
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import random
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import numpy as np
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import tensorflow as tf
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os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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os.environ["CUDA_VISIBLE_DEVICES"] = "1"
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from custom_utils.custom_utils import UV2Mesh, maskFacesBatch
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@@ -13,13 +13,13 @@ import plotly.graph_objects as go
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import plotly.express as ex
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from tf_utils import check_tf_version, allow_memory_growth
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from losses import collision_loss as collision_penalty
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from convnext.models.convnext_tf import ConvNeXt, ConvNeXtReverse, Block, ConvNeXtReverseLabels
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from dataset import DatasetFactory, StaticDataset
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from custom_utils.custom_utils import batch_to_dict
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from custom_utils.smpl import SMPL
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from models.ae_curv_model import AutoEncoder
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name='ae_curvature_7_2'
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name='ae_curvature_x_2'
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testing = False
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def init_wandb():
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if not testing:
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@@ -35,43 +35,6 @@ def init_wandb():
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uv2mesh = UV2Mesh(res=256)
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class DatasetSimple(tf.data.Dataset):
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def _generator(folder):
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# Opening the file
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folder = folder.decode("utf-8")
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uv_folder = os.path.join(folder, 'uv_posed')
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frames = os.listdir(uv_folder)
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random.shuffle(frames)
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for frame in frames:
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id, frame_nr = frame.split('.')[0].split('_')
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mesh_mask = np.load(os.path.join(folder, 'mesh_mask', id + '.npy')); mesh_mask = mesh_mask.astype(np.float32)
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uv_mask = np.load(os.path.join(folder, 'uv_mask', id + '.npz')); uv_mask=uv_mask['uv_mask']; uv_mask = uv_mask.astype(np.float32)
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mesh_unposed = np.load(os.path.join(folder, 'mesh_unposed', id + '_' + frame_nr + '.npz')); mesh_unposed = mesh_unposed['mesh_unposed']
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uv_unposed = np.load(os.path.join(folder, 'uv_unposed', id + '_' + frame_nr + '.npz')); uv_unposed = uv_unposed['uv_unposed']; uv_unposed = np.transpose(uv_unposed, [2, 0, 1])
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yield uv_unposed, mesh_unposed, uv_mask, mesh_mask
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def __new__(cls, folder, res=256):
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return tf.data.Dataset.from_generator(
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cls._generator,
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output_signature = (tf.TensorSpec(shape = (3, res * 2, res,), dtype = tf.float32),
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tf.TensorSpec(shape = (14475, 3,), dtype = tf.float32),
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tf.TensorSpec(shape = (res * 2, res,), dtype = tf.float32),
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tf.TensorSpec(shape = (14475,), dtype = tf.float32), ),
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args=(folder, )
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)
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def filter_resolutions_featuremaps(resolutions, featuremaps, res):
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index = resolutions.index(res)
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filtered_resolutions = resolutions[:index + 1]
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filtered_featuremaps = featuremaps[:index + 1]
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return filtered_resolutions, filtered_featuremaps
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def plot_3d(mesh_gt, mesh_pred, faces):
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mesh_pred[:,1] += 2
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fig = go.Figure(data=[
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@@ -109,82 +72,6 @@ def plot_3d(mesh_gt, mesh_pred, faces):
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return fig
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class Bottleneck(tf.keras.Model):
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def __init__(self, dim, latent_dim):
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super(Bottleneck, self).__init__()
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self.bottleneck = tf.keras.Sequential(
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[Block(dim=dim, drop_path=0,
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layer_scale_init_value=1e-6, prefix=f'bottleneck{j}') for j in range(5)]
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)
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self.flatten = tf.keras.layers.Flatten()
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self.dense = tf.keras.layers.Dense(latent_dim)
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def call(self, inputs, training=None):
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x = inputs
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x = self.bottleneck(x)
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x = self.flatten(x)
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x = self.dense(x)
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return x
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class Decoder(tf.keras.Model):
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def __init__(self, bottleneck_size, latent_dim):
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super(Decoder, self).__init__()
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self.dense = tf.keras.layers.Dense(16 * 8 * bottleneck_size)
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self.reshape = tf.keras.layers.Reshape((16, 8, bottleneck_size))
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self.decoder = ConvNeXtReverseLabels(depths=list(reversed([3, 3, 9, 3])), dims=list(reversed([bottleneck_size, 128, 64, 16])), nr_of_classes=7)
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def call(self, inputs, training=None):
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x = inputs
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x = self.dense(x)
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x = self.reshape(x)
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x = self.decoder(x)
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return x
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class AutoEncoder(tf.keras.Model):
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def __init__(self):
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super(AutoEncoder, self).__init__()
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bottleneck_size = 32
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latent_dim = 128
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self.decoder = Decoder(bottleneck_size, latent_dim)
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self.bottleneck = Bottleneck(bottleneck_size, latent_dim)
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self.encoder = ConvNeXt(depths=[3, 3, 9, 3], dims=[32, 64, 128, bottleneck_size], include_top=False)
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def call(self, inputs, training=None):
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x, uv_mask = inputs
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# x = tf.transpose(x, [0,2,3,1])
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x = self.encoder(x, training=training)
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x = self.bottleneck(x, training=training)
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x = self.decoder(x, training=training)
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# x = tf.transpose(x, [0,3,1,2])
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# x = x * uv_mask[:,:, tf.newaxis]
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# x = x * uv_mask[:,tf.newaxis,:,:]
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x = x * uv_mask[:,:,:,tf.newaxis]
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return x
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g_mean_iou = tf.keras.metrics.MeanIoU(num_classes=7)
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