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import importlib |
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import torch |
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from torch import optim |
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import numpy as np |
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from inspect import isfunction |
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from PIL import Image, ImageDraw, ImageFont |
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def log_txt_as_img(wh, xc, size=10): |
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b = len(xc) |
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txts = list() |
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for bi in range(b): |
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txt = Image.new("RGB", wh, color="white") |
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draw = ImageDraw.Draw(txt) |
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font = ImageFont.truetype('font/DejaVuSans.ttf', size=size) |
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nc = int(40 * (wh[0] / 256)) |
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lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc)) |
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try: |
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draw.text((0, 0), lines, fill="black", font=font) |
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except UnicodeEncodeError: |
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print("Cant encode string for logging. Skipping.") |
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txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 |
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txts.append(txt) |
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txts = np.stack(txts) |
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txts = torch.tensor(txts) |
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return txts |
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def ismap(x): |
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if not isinstance(x, torch.Tensor): |
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return False |
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return (len(x.shape) == 4) and (x.shape[1] > 3) |
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def isimage(x): |
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if not isinstance(x,torch.Tensor): |
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return False |
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return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) |
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def exists(x): |
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return x is not None |
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def default(val, d): |
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if exists(val): |
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return val |
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return d() if isfunction(d) else d |
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def mean_flat(tensor): |
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""" |
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https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 |
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Take the mean over all non-batch dimensions. |
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""" |
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return tensor.mean(dim=list(range(1, len(tensor.shape)))) |
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def count_params(model, verbose=False): |
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total_params = sum(p.numel() for p in model.parameters()) |
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if verbose: |
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print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.") |
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return total_params |
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def instantiate_from_config(config): |
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if not "target" in config: |
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if config == '__is_first_stage__': |
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return None |
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elif config == "__is_unconditional__": |
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return None |
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raise KeyError("Expected key `target` to instantiate.") |
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return get_obj_from_str(config["target"])(**config.get("params", dict())) |
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def get_obj_from_str(string, reload=False): |
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module, cls = string.rsplit(".", 1) |
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if reload: |
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module_imp = importlib.import_module(module) |
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importlib.reload(module_imp) |
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return getattr(importlib.import_module(module, package=None), cls) |
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class AdamWwithEMAandWings(optim.Optimizer): |
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def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8, |
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weight_decay=1.e-2, amsgrad=False, ema_decay=0.9999, |
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ema_power=1., param_names=()): |
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"""AdamW that saves EMA versions of the parameters.""" |
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if not 0.0 <= lr: |
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raise ValueError("Invalid learning rate: {}".format(lr)) |
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if not 0.0 <= eps: |
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raise ValueError("Invalid epsilon value: {}".format(eps)) |
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if not 0.0 <= betas[0] < 1.0: |
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raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) |
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if not 0.0 <= betas[1] < 1.0: |
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raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) |
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if not 0.0 <= weight_decay: |
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raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) |
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if not 0.0 <= ema_decay <= 1.0: |
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raise ValueError("Invalid ema_decay value: {}".format(ema_decay)) |
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defaults = dict(lr=lr, betas=betas, eps=eps, |
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weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay, |
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ema_power=ema_power, param_names=param_names) |
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super().__init__(params, defaults) |
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def __setstate__(self, state): |
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super().__setstate__(state) |
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for group in self.param_groups: |
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group.setdefault('amsgrad', False) |
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@torch.no_grad() |
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def step(self, closure=None): |
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"""Performs a single optimization step. |
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Args: |
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closure (callable, optional): A closure that reevaluates the model |
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and returns the loss. |
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""" |
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loss = None |
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if closure is not None: |
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with torch.enable_grad(): |
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loss = closure() |
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for group in self.param_groups: |
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params_with_grad = [] |
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grads = [] |
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exp_avgs = [] |
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exp_avg_sqs = [] |
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ema_params_with_grad = [] |
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state_sums = [] |
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max_exp_avg_sqs = [] |
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state_steps = [] |
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amsgrad = group['amsgrad'] |
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beta1, beta2 = group['betas'] |
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ema_decay = group['ema_decay'] |
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ema_power = group['ema_power'] |
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for p in group['params']: |
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if p.grad is None: |
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continue |
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params_with_grad.append(p) |
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if p.grad.is_sparse: |
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raise RuntimeError('AdamW does not support sparse gradients') |
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grads.append(p.grad) |
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state = self.state[p] |
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if len(state) == 0: |
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state['step'] = 0 |
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state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format) |
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state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) |
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if amsgrad: |
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state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) |
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state['param_exp_avg'] = p.detach().float().clone() |
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exp_avgs.append(state['exp_avg']) |
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exp_avg_sqs.append(state['exp_avg_sq']) |
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ema_params_with_grad.append(state['param_exp_avg']) |
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if amsgrad: |
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max_exp_avg_sqs.append(state['max_exp_avg_sq']) |
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state['step'] += 1 |
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state_steps.append(state['step']) |
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optim._functional.adamw(params_with_grad, |
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grads, |
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exp_avgs, |
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exp_avg_sqs, |
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max_exp_avg_sqs, |
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state_steps, |
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amsgrad=amsgrad, |
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beta1=beta1, |
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beta2=beta2, |
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lr=group['lr'], |
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weight_decay=group['weight_decay'], |
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eps=group['eps'], |
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maximize=False) |
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cur_ema_decay = min(ema_decay, 1 - state['step'] ** -ema_power) |
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for param, ema_param in zip(params_with_grad, ema_params_with_grad): |
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ema_param.mul_(cur_ema_decay).add_(param.float(), alpha=1 - cur_ema_decay) |
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return loss |