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import argparse
import os
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from .pr_datasets_all import FUNC_DICT
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (20, 3)
plt.rcParams['figure.dpi'] = 300
plt.rcParams['savefig.dpi'] = 300
def model_fn(x, t, y=None, rule=None,
model=nn.Identity(), num_classes=3, class_cond=True, cfg=False, w=0.):
# y has to be composer, rule is a dummy input
y_null = th.tensor([num_classes] * x.shape[0], device=x.device)
if class_cond:
if cfg:
return (1 + w) * model(x, t, y) - w * model(x, t, y_null)
else:
return model(x, t, y)
else:
return model(x, t, y_null)
def dc_model_fn(x, t, y=None, rule=None,
model=nn.Identity(), num_classes=3, class_cond=True, cfg=False, w=0.):
# diffcollage score function takes in 4 x pitch x time
x = x.permute(0, 1, 3, 2)
y_null = th.tensor([num_classes] * x.shape[0], device=x.device)
if class_cond:
if cfg:
eps = (1 + w) * model(x, t, y) - w * model(x, t, y_null)
return eps.permute(0, 1, 3, 2) # need to return 4 x time x pitch
else:
return model(x, t, y).permute(0, 1, 3, 2)
else:
return model(x, t, y_null).permute(0, 1, 3, 2)
# y is a dummy input for cond_fn, rule is the real input
def grad_nn_zt_xentropy(x, y=None, rule=None, classifier=nn.Identity()):
# Xentropy cond_fn
assert rule is not None
t = th.zeros(x.shape[0], device=x.device)
with th.enable_grad():
x_in = x.detach().requires_grad_(True)
logits = classifier(x_in, t)
log_probs = F.log_softmax(logits, dim=-1)
selected = log_probs[range(len(logits)), rule.view(-1)]
return th.autograd.grad(selected.sum(), x_in)[0]
def grad_nn_zt_mse(x, t, y=None, rule=None, classifier_scale=10., classifier=nn.Identity()):
assert rule is not None
with th.enable_grad():
x_in = x.detach().requires_grad_(True)
logits = classifier(x_in, t)
log_probs = - F.mse_loss(logits, rule, reduction="none").sum(dim=-1)
return th.autograd.grad(log_probs.sum(), x_in)[0] * classifier_scale
def grad_nn_zt_chord(x, t, y=None, rule=None, classifier_scale=10., classifier=nn.Identity(), both=False):
assert rule is not None
with th.enable_grad():
x_in = x.detach().requires_grad_(True)
key_logits, chord_logits = classifier(x_in, t)
if both:
rule_key = rule[:, :1]
rule_chord = rule[:, 1:]
rule_chord = rule_chord.reshape(-1)
chord_logits = chord_logits.reshape(-1, chord_logits.shape[-1])
key_log_probs = - F.cross_entropy(key_logits, rule_key, reduction="none")
chord_log_probs = - F.cross_entropy(chord_logits, rule_chord, reduction="none")
chord_log_probs = chord_log_probs.reshape(x_in.shape[0], -1).mean(dim=-1)
log_probs = key_log_probs + chord_log_probs
else:
rule = rule.reshape(-1)
chord_logits = chord_logits.reshape(-1, chord_logits.shape[-1])
log_probs = - F.cross_entropy(chord_logits, rule, reduction="none")
return th.autograd.grad(log_probs.sum(), x_in)[0] * classifier_scale
def nn_z0_chord_dummy(x, t, y=None, rule=None, classifier_scale=0.1, classifier=nn.Identity(), both=False):
# classifier_scale is equivalent to step_size
t = th.zeros(x.shape[0], device=x.device)
key_logits, chord_logits = classifier(x, t)
if both:
rule_key = rule[:, :1]
rule_chord = rule[:, 1:]
rule_chord = rule_chord.reshape(-1)
chord_logits = chord_logits.reshape(-1, chord_logits.shape[-1])
key_log_probs = - F.cross_entropy(key_logits, rule_key, reduction="none")
chord_log_probs = - F.cross_entropy(chord_logits, rule_chord, reduction="none")
chord_log_probs = chord_log_probs.reshape(x.shape[0], -1).mean(dim=-1)
log_probs = key_log_probs + chord_log_probs
else:
rule = rule.reshape(-1)
chord_logits = chord_logits.reshape(-1, chord_logits.shape[-1])
log_probs = - F.cross_entropy(chord_logits, rule, reduction="none")
log_probs = log_probs.reshape(x.shape[0], -1).mean(dim=-1)
return log_probs * classifier_scale
def nn_z0_mse_dummy(x, t, y=None, rule=None, classifier_scale=0.1, classifier=nn.Identity()):
# mse cond_fn, t is a dummy variable b/c wrap_model in respace
assert rule is not None
t = th.zeros(x.shape[0], device=x.device)
logits = classifier(x, t)
log_probs = - F.mse_loss(logits, rule, reduction="none").sum(dim=-1)
return log_probs * classifier_scale
def nn_z0_mse(x, rule=None, classifier=nn.Identity()):
# mse cond_fn, t is a dummy variable b/c wrap_model in respace
t = th.zeros(x.shape[0], device=x.device)
logits = classifier(x, t)
log_probs = - F.mse_loss(logits, rule, reduction="none").sum(dim=-1)
return log_probs
def rule_x0_mse_dummy(x, t, y=None, rule=None, rule_name='pitch_hist'):
# use differentiable rule to differentiate through rule(x_0), t is a dummy variable b/c wrap_model in respace
logits = FUNC_DICT[rule_name](x)
log_probs = - F.mse_loss(logits, rule, reduction="none").sum(dim=-1)
return log_probs
def rule_x0_mse(x, rule=None, rule_name='pitch_hist', soft=False):
# soften non-differentiable rule to differentiate through rule(x_0)
# soften doesn't seem to work so didn't actually take in soft as input, always set to False
logits = FUNC_DICT[rule_name](x, soft=soft)
log_probs = - F.mse_loss(logits, rule, reduction="none").sum(dim=-1)
return log_probs
class _WrappedFn:
def __init__(self, fn):
self.fn = fn
def __call__(self, x, t, y=None, rule=None):
return self.fn(x, t, y, rule)
function_map = {
"grad_nn_zt_xentropy": grad_nn_zt_xentropy,
"grad_nn_zt_mse": grad_nn_zt_mse,
"grad_nn_zt_chord": grad_nn_zt_chord,
"nn_z0_chord_dummy": nn_z0_chord_dummy,
"nn_z0_mse_dummy": nn_z0_mse_dummy,
"nn_z0_mse": nn_z0_mse,
"rule_x0_mse_dummy": rule_x0_mse_dummy,
"rule_x0_mse": rule_x0_mse
}
def composite_nn_zt(x, t, y=None, rule=None, fns=None, classifier_scales=None, classifiers=None, rule_names=None):
num_classifiers = len(classifiers)
out = 0
for i in range(num_classifiers):
out += function_map[fns[i]](x, t, y=y, rule=rule[rule_names[i]],
classifier_scale=classifier_scales[i], classifier=classifiers[i])
return out
def composite_rule(x, t, y=None, rule=None, fns=None, classifier_scales=None, rule_names=None):
out = 0
for i in range(len(fns)):
out += function_map[fns[i]](x, t, y=y, rule=rule[rule_names[i]], rule_name=rule_names[i]) * classifier_scales[i]
return out
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