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aa.py
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def cm_training_losses_mutilStep(self, model,model_target,model_teacher, x_start, t,model_kwargs=None, noise=None,step=1):
"""
Compute training losses for a single timestep.
:param model: the model to evaluate loss on.
:param x_start: the [N x C x ...] tensor of inputs.
:param t: a batch of timestep indices.
:param model_kwargs: if not None, a dict of extra keyword arguments to
pass to the model. This can be used for conditioning.
:param noise: if specified, the specific Gaussian noise to try to remove.
:return: a dict with the key "loss" containing a tensor of shape [N].
Some mean or variance settings may also have other keys.
"""
t_target=t-1
t_target = th.where(t_target < 0, th.zeros_like(t), t_target)
# print("t is :",t)
map_tensor = th.tensor(self.timestep_map, device=t.device, dtype=t.dtype)
c_skip_start, c_out_start = self.scalings_for_boundary_conditions_timestep(
map_tensor[t], #timestep_scaling= 10000 #timestep_scaling=args.timestep_scaling_factor
)
c_skip_start, c_out_start = [self.append_dims(x, x_start.ndim) for x in [c_skip_start, c_out_start]]
c_skip, c_out = self.scalings_for_boundary_conditions_timestep(
map_tensor[t_target], #timestep_scaling= 10000 #timestep_scaling=args.timestep_scaling_factor
)
c_skip, c_out = [self.append_dims(x, x_start.ndim) for x in [c_skip, c_out]]
if model_kwargs is None:
model_kwargs = {}
if noise is None:
noise = th.randn_like(x_start)
x_t = self.q_sample(x_start, t, noise=noise)
terms = {}
model_output = model(x_t, t, **model_kwargs)
if self.model_var_type in [
ModelVarType.LEARNED,
ModelVarType.LEARNED_RANGE,
]:
B, C = x_t.shape[:2]
assert model_output.shape == (B, C * 2, *x_t.shape[2:])
model_output, model_var_values = th.split(model_output, C, dim=1)
pred_x_0=self._predict_xstart_from_eps(x_t,t,model_output) #因为αβ已经改了，所以这里直接用t
model_pred = c_skip_start * x_t + c_out_start * pred_x_0
with th.no_grad():
with th.autocast("cuda"):
#1 计算老师前进一步的结果
# z = th.cat([z, z], 0)
y_null = th.tensor([1000] * x_t.shape[0], device=x_t.device)
# print(model_kwargs)
y=model_kwargs['y']
teacher_kwargs=dict(**model_kwargs)
y = th.cat([y, y_null], 0)
teacher_kwargs['y']=y
teacher_kwargs['cfg_scale']=4.0
# x_prev=self.ddim_sample_cm(model_teacher,x_t,t,model_kwargs=model_kwargs)["sample"]
x_tmp=deepcopy(x_t)
eps_tmp=0
# for i in range(1):
# tmp_t=th.where(t-i<0, th.zeros_like(t),t-i)
# ddim_res=self.ddim_sample_cm(model_teacher,th.cat([x_tmp,x_tmp],0),th.cat([tmp_t,tmp_t],0),model_kwargs=teacher_kwargs)
# x_tmp=ddim_res['sample']
# x_tmp, x_tmp_nocfg = x_tmp.chunk(2, dim=0) # Remove null class samples
# eps_tmp+=ddim_res['eps']
# # eps_tmp=eps_tmp/step
# #使用eps计算下一步
# # eps_tmp, _ = eps_tmp.chunk(2, dim=0)
# # x_prev=self._predict_xnext_from_eps(x_t,t,eps_tmp)
# x_prev=x_tmp
x_prev=self.ddim_sample_cm(model_teacher,th.cat([x_t,x_t],0),th.cat([t,t],0),model_kwargs=teacher_kwargs)["sample"]
x_prev, x_prev_nocfg = x_prev.chunk(2, dim=0) # Remove null class samples
x_prev=self.ddim_sample_cm(model_teacher,th.cat([x_t,x_t],0),th.cat([t,t],0),model_kwargs=teacher_kwargs)["sample"]
x_prev, x_prev_nocfg = x_prev.chunk(2, dim=0) # Remove null class samples
#2 对该结果进行一次求解
# print(type(x_prev),type)
model_target_output,_ = th.split(model_target(x_prev, t_target, **model_kwargs), C, dim=1)
pred_target_x_0=self._predict_xstart_from_eps(x_prev,t_target,model_target_output)
model_target_pred = c_skip * x_prev + c_out * pred_target_x_0
# aa,_=ddim_res['pred_xstart'].chunk(2, dim=0)
# print(aa.shape,x_t.shape)
# print(aa.equal(self._predict_xstart_from_eps(x_t,t,eps_tmp)))
# print((aa-self._predict_xstart_from_eps(x_t,t,eps_tmp)).max())
# print((x_tmp-x_t).max())
# print(aa-self._predict_xstart_from_eps(x_t,t,eps_tmp))
# print(x_tmp.equal(x_prev))
# print((x_tmp-x_prev).max())
# x_prev, _ = x_prev.chunk(2, dim=0) # Remove null class samples
#2 对该结果进行一次求解
# print(type(x_prev),type)
model_target_output,_ = th.split(model_target(x_prev, t_target, **model_kwargs), C, dim=1)
pred_target_x_0=self._predict_xstart_from_eps(x_prev,t_target,model_target_output)
model_target_pred = c_skip * x_prev + c_out * pred_target_x_0
# print(alphas_cumprod.shape)
# print((model_pred.float() - model_target_pred.float())).shape
# alphas_cumprod =_extract_into_tensor(self.alphas_cumprod,t,model_pred)
# alphas_cumprod=th.tensor(self.alphas_cumprod,device=model_pred.device).sqrt()[t].view(model_pred.size(0), 1, 1, 1)
# # weight= th.clamp(1/alphas_cumprod,1,100)
# weight=1
# # terms["loss"] = F.mse_loss(model_output.float(), model_target_output.float(), reduction="mean")
# # terms["loss"] = th.mean(
# # th.sqrt((model_pred.float() - model_target_pred.float()) ** 2 + 0.001**2) - 0.001
# # ) #+terms["vb"]
# terms["loss"] = th.mean(
# weight*th.sqrt((model_pred.float() - model_target_pred.float()) ** 2 + 0.001**2) - 0.001
# ) #+terms["vb"]
# # loss = torch.mean(
# # torch.sqrt((model_pred.float() - model_target_pred.float()) ** 2 + args.huber_c**2) - args.huber_c
# # )
# return terms
alphas_cumprod_sqrt=th.tensor(self.alphas_cumprod,device=model_pred.device).sqrt()[t].view(model_pred.size(0), 1, 1, 1)
# alpha_bar_prev = _extract_into_tensor(self.alphas_cumprod_prev, t, x.shape)
alphas_cumprod_sqrt_prev=th.tensor(self.alphas_cumprod_prev,device=model_pred.device)[t].view(model_pred.size(0), 1, 1, 1)
# weight= th.clamp(1/alphas_cumprod,1,100)
weight=1
eps_conf=(1-alphas_cumprod_sqrt_prev).sqrt()/alphas_cumprod_sqrt_prev.sqrt()
# terms["loss"] = F.mse_loss(eps_conf*model_output.float(), eps_conf*model_target_output.float(), reduction="mean")
# terms["loss"] = th.mean(
# th.sqrt((model_pred.float() - model_target_pred.float()) ** 2 + 0.001**2) - 0.001
# ) #+terms["vb"]
# terms["loss"]=th.mean((model_output.float() - model_target_output.float()))
terms["loss"] = th.mean(
weight*th.sqrt((model_pred.float() - model_target_pred.float()) ** 2 + 0.001**2) - 0.001
)
# terms["loss"] = th.mean(
# weight*th.sqrt((eps_conf*model_output.float() - eps_conf*model_target_output.float()) ** 2 + 0.001**2) - 0.001
# ) #+terms["vb"]
# loss = torch.mean(
# torch.sqrt((model_pred.float() - model_target_pred.float()) ** 2 + args.huber_c**2) - args.huber_c
# )
return terms