197 lines
6.9 KiB
Python
197 lines
6.9 KiB
Python
import numpy as np
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import tensorflow_graphics as tfg
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import tensorflow as tf
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import scipy as sp
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import scipy.io as sio
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from scipy.spatial import cKDTree
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from tensorflow_graphics.geometry.representation.mesh.normals import vertex_normals
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def d_logistic(real_images, generator, discriminator, z_dim, conditions):
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batch_size = tf.shape(real_images)[0]
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z = tf.random.normal(shape=[batch_size, z_dim], dtype=tf.float32)
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# forward pass
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fake_images = generator([z, conditions], training=True)
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real_scores = discriminator(real_images, training=True)
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fake_scores = discriminator(fake_images, training=True)
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# gan loss
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d_loss = tf.math.softplus(fake_scores)
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d_loss += tf.math.softplus(-real_scores)
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return d_loss
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def d_logistic_r1_reg(real_images, generator, discriminator, z_dim, conditions):
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batch_size = tf.shape(real_images)[0]
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z = tf.random.normal(shape=[batch_size, z_dim], dtype=tf.float32)
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# forward pass
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fake_images = generator([z, conditions], training=True)
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real_scores = discriminator(real_images, training=True)
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fake_scores = discriminator(fake_images, training=True)
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# gan loss
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d_loss = tf.math.softplus(fake_scores)
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d_loss = d_loss + tf.math.softplus(-real_scores)
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# gradient penalty
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with tf.GradientTape() as r1_tape:
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r1_tape.watch([real_images, conditions])
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real_scores = discriminator(real_images, training=True)
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real_loss = tf.reduce_sum(real_scores)
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real_grads = r1_tape.gradient(real_loss, real_images)
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r1_penalty = tf.reduce_sum(tf.math.square(real_grads), axis=[1, 2, 3])
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r1_penalty = tf.expand_dims(r1_penalty, axis=1)
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return d_loss, r1_penalty
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def g_logistic_non_saturating(real_images, generator, discriminator, z_dim, conditions):
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batch_size = tf.shape(real_images)[0]
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z = tf.random.normal(shape=[batch_size, z_dim], dtype=tf.float32)
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# forward pass
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fake_images = generator([z, conditions], training=True)
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fake_scores = discriminator(fake_images, training=True)
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# gan loss
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g_loss = tf.math.softplus(-fake_scores)
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return fake_images, g_loss
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def g_logistic_ns_pathreg(real_images, generator, discriminator, z_dim,
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pl_mean, pl_minibatch_shrink, pl_denorm, pl_decay,
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conditions):
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batch_size = tf.shape(real_images)[0]
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z = tf.random.normal(shape=[batch_size, z_dim], dtype=tf.float32)
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pl_minibatch = tf.maximum(1, tf.math.floordiv(batch_size, pl_minibatch_shrink))
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pl_z = tf.random.normal(shape=[pl_minibatch, z_dim], dtype=tf.float32)
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pl_conditions = (conditions[0][:pl_minibatch], conditions[1][:pl_minibatch], conditions[2][:pl_minibatch])
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# forward pass
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fake_images = generator([z, conditions], ret_w_broadcasted=False, training=True)
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fake_scores = discriminator(fake_images, training=True)
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g_loss = tf.math.softplus(-fake_scores)
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# Evaluate the regularization term using a smaller minibatch to conserve memory.
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with tf.GradientTape() as pl_tape:
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pl_tape.watch([pl_z, pl_conditions])
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pl_fake_images, pl_w_broadcasted = generator([pl_z, pl_conditions], ret_w_broadcasted=True, training=True)
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pl_noise = tf.random.normal(tf.shape(pl_fake_images)) * pl_denorm
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pl_noise_applied = tf.reduce_sum(pl_fake_images * pl_noise)
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pl_grads = pl_tape.gradient(pl_noise_applied, pl_w_broadcasted)
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pl_lengths = tf.math.sqrt(tf.reduce_mean(tf.reduce_sum(tf.math.square(pl_grads), axis=2), axis=1))
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# Track exponential moving average of |J*y|.
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pl_mean_val = pl_mean + pl_decay * (tf.reduce_mean(pl_lengths) - pl_mean)
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pl_mean.assign(pl_mean_val)
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# Calculate (|J*y|-a)^2.
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pl_penalty = tf.square(pl_lengths - pl_mean)
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return fake_images, g_loss, pl_penalty
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# from hbertiche/PBNS
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""" COLLISION FUNCTIONS START """
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F = np.load("custom_utils/smpl_faces.npy")
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def _nearest_neighbour(V, B):
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tree = cKDTree(B.numpy())
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return tree.query(V.numpy(), workers=-1)[1]
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@tf.function
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def _tf_nn_parallel(V, B):
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return tf.map_fn(fn=lambda elem: tf.py_function(func=_nearest_neighbour, inp=elem, Tout=tf.int32), elems=(V,B), fn_output_signature=tf.int32, parallel_iterations=V.shape[0])
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# @tf.function
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def collision_loss(V, B, mask, thr=.004):
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B_vn = vertex_normals(B, tf.tile(F[None], [B.shape[0], 1, 1]))
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# compute correspondences
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idx = _tf_nn_parallel(V, B)
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N, M = V.shape[:2]
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# _idx = tf.tile(np.array(list(range(N)), np.int32)[:,None], [1, M])
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_idx = tf.ones((N, M), dtype=tf.int32) * np.array(list(range(N)), np.int32)[:,None]
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idx = tf.stack((_idx, idx), axis=-1)
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# compute loss
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D = V - tf.gather_nd(B, idx)
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b_vn = tf.gather_nd(B_vn, idx)
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dot = tf.einsum('abc,abc->ab', D, b_vn)
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loss = tf.reduce_sum((tf.minimum(dot - thr, 0) * mask) ** 2)
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return loss
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""" COLLISION FUNCTIONS END """
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laplacian_loss = sio.loadmat("custom_utils/ssmpl0.mat")['L'].tocoo()
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edges = np.stack(np.nonzero(laplacian_loss.todense())).transpose()
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def get_edge_mask(masks):
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return tf.reduce_min(tf.gather(masks, edges, axis=1), axis=-1)
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def edge_loss(x, y, mask):
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e_mask = get_edge_mask(mask)
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x_e = tf.gather(x, edges[:,0], axis=1) - tf.gather(x, edges[:,1], axis=1)
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x_e = tf.reduce_sum(x_e ** 2, -1)
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x_e = tf.sqrt(x_e + 1e-9)
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y_e = tf.gather(y, edges[:,0], axis=1) - tf.gather(y, edges[:,1], axis=1)
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y_e = tf.reduce_sum(y_e ** 2, -1)
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y_e = tf.sqrt(y_e + 1e-9)
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d_e = y_e - x_e
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d_e = d_e * e_mask
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loss = tf.reduce_sum(d_e ** 2)
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return loss
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class PairwiseDistance(tf.keras.layers.Layer):
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def __init__(self, **kwargs):
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super(PairwiseDistance, self).__init__(**kwargs)
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def call(self, A, B):
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rA = tf.reduce_sum(tf.square(A), axis=-1)
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rB = tf.reduce_sum(tf.square(B), axis=-1)
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transpose_axes = [0, 2, 1]
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distances = - 2*tf.matmul(A, tf.transpose(B, transpose_axes)) + rA[:, :, tf.newaxis] + rB[:, tf.newaxis, :]
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return distances
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class NearestNeighbour(tf.keras.layers.Layer):
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def __init__(self, **kwargs):
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super(NearestNeighbour, self).__init__(**kwargs)
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def call(self, A, B):
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distances = PairwiseDistance(dtype=self.dtype)(A, B)
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nearest_neighbour = tf.argmin(distances, axis=-1)
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return tf.cast(nearest_neighbour, dtype=tf.int32)
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#
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# @tf.function
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def collision_penalty(va, vb, mask, eps=1e-3):
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nb = vertex_normals(vb, tf.tile(F[None], [vb.shape[0], 1, 1]))
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batch_size = tf.cast(tf.shape(va)[0], va.dtype)
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closest_vertices = NearestNeighbour(dtype=va.dtype)(va, vb)
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vb = tf.gather(vb, closest_vertices, batch_dims=1)
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nb = tf.gather(nb, closest_vertices, batch_dims=1)
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distance = tf.reduce_sum(nb*(va - vb), axis=-1)
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interpenetration = tf.maximum(eps - distance * mask, 0)
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return tf.reduce_sum(interpenetration**3) / batch_size
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if __name__ == '__main__':
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V = tf.ones((4, 14475, 3))
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B = tf.zeros((4, 14475, 3))
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m = tf.ones((4, 14475))
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loss_1 = collision_loss(V, B, m, thr=.004)
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print()
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