large refactor
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@@ -1,3 +1,4 @@
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from faulthandler import disable
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import numpy as np
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import tensorflow as tf
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import plotly.graph_objects as go
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@@ -135,14 +136,14 @@ def maskFacesBatch(V, F, mask):
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def plot_3d(mesh_gt, mesh_pred_unposed, faces, collision, collision_faces, collision_color):
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mesh_pred_unposed[:,1] += 1
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collision[:,1] += 2
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mesh_pred_unposed[:,2] += 1
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collision[:,2] += 2
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fig = go.Figure(data=[
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go.Mesh3d(
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# 8 vertices of a cube
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x=mesh_gt[:,0],
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y=mesh_gt[:,1],
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z=mesh_gt[:,2],
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y=mesh_gt[:,2],
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z=mesh_gt[:,1],
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# i, j and k give the vertices of triangles
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i = faces[:,0],
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j = faces[:,1],
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@@ -151,8 +152,8 @@ def plot_3d(mesh_gt, mesh_pred_unposed, faces, collision, collision_faces, colli
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go.Mesh3d(
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# 8 vertices of a cube
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x=mesh_pred_unposed[:,0],
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y=mesh_pred_unposed[:,1],
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z=mesh_pred_unposed[:,2],
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y=mesh_pred_unposed[:,2],
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z=mesh_pred_unposed[:,1],
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# i, j and k give the vertices of triangles
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i = faces[:,0],
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j = faces[:,1],
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@@ -161,8 +162,8 @@ def plot_3d(mesh_gt, mesh_pred_unposed, faces, collision, collision_faces, colli
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go.Mesh3d(
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# 8 vertices of a cube
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x=collision[:,0],
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y=collision[:,1],
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z=collision[:,2],
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y=collision[:,2],
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z=collision[:,1],
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# i, j and k give the vertices of triangles
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i = collision_faces[:,0],
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j = collision_faces[:,1],
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@@ -202,7 +203,10 @@ def sparse_to_tensor(L):
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return tf.SparseTensor(idx, data, shape)
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def load_model(model, checkpoint):
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def load_model(model, checkpoint, disable_assert=False):
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ckpt = tf.train.Checkpoint(model=model)
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manager = tf.train.CheckpointManager(ckpt, checkpoint, max_to_keep=1)
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ckpt.restore(manager.latest_checkpoint)
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if not disable_assert:
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assert manager.latest_checkpoint is not None
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assert manager.latest_checkpoint.split('/')[-1] != 'ckpt-0'
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ckpt.restore(manager.latest_checkpoint).assert_existing_objects_matched()#.assert_consumed()
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