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run-35

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luni
2022-06-27 12:27:55 +00:00
parent 5e00b395ad
commit cd498e79f3
11 changed files with 301 additions and 109 deletions
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losses.py
+6 -3
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@@ -70,7 +70,10 @@ def g_logistic_ns_pathreg(real_images, generator, discriminator, z_dim,
pl_minibatch = tf.maximum(1, tf.math.floordiv(batch_size, pl_minibatch_shrink))
pl_z = tf.random.normal(shape=[pl_minibatch, z_dim], dtype=tf.float32)
pl_conditions = (conditions[0][:pl_minibatch], conditions[1][:pl_minibatch], conditions[2][:pl_minibatch], conditions[3][:pl_minibatch], conditions[4][:pl_minibatch])
pl_conditions = { key: conditions[key][:pl_minibatch] for key in conditions.keys() }
# for key in conditions.keys():
# pl_conditions[key] = conditions[key][:pl_minibatch]
# pl_conditions = (conditions[0][:pl_minibatch], conditions[1][:pl_minibatch], conditions[2][:pl_minibatch], conditions[3][:pl_minibatch], conditions[4][:pl_minibatch])
# forward pass
fake_images = generator([z, conditions], ret_w_broadcasted=False, training=True)
@@ -124,7 +127,7 @@ def collision_loss(V, B, mask, thr=.004):
D = V - tf.gather_nd(B, idx)
b_vn = tf.gather_nd(B_vn, idx)
dot = tf.einsum('abc,abc->ab', D, b_vn)
loss = tf.reduce_sum((tf.minimum(dot - thr, 0) * mask) ** 2) / tf.reduce_sum(mask)
loss = tf.reduce_sum((tf.minimum(dot - thr, 0) * mask) ** 2, axis=-1) # / tf.reduce_sum(mask, axis=-1)
return loss
@@ -147,7 +150,7 @@ def edge_loss(x, y, mask):
y_e = tf.sqrt(y_e + 1e-9)
d_e = y_e - x_e
d_e = d_e * e_mask
loss = tf.reduce_sum(d_e ** 2) / tf.reduce_sum(e_mask)
loss = tf.reduce_sum(d_e ** 2, axis=-1)# / tf.reduce_sum(e_mask, axis=-1)
return loss