forked from mrq/DL-Art-School
122 lines
5.1 KiB
Python
122 lines
5.1 KiB
Python
from utils.loss_accumulator import LossAccumulator
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from torch.nn import Module
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import logging
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from models.steps.losses import create_generator_loss
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import torch
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from apex import amp
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from collections import OrderedDict
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from .injectors import create_injector
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logger = logging.getLogger('base')
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# Defines the expected API for a single training step
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class ConfigurableStep(Module):
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def __init__(self, opt_step, env):
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super(ConfigurableStep, self).__init__()
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self.step_opt = opt_step
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self.env = env
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self.opt = env['opt']
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self.gen_outputs = opt_step['generator_outputs']
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self.loss_accumulator = LossAccumulator()
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self.optimizers = None
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self.injectors = []
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if 'injectors' in self.step_opt.keys():
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for inj_name, injector in self.step_opt['injectors'].items():
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self.injectors.append(create_injector(injector, env))
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losses = []
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self.weights = {}
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for loss_name, loss in self.step_opt['losses'].items():
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losses.append((loss_name, create_generator_loss(loss, env)))
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self.weights[loss_name] = loss['weight']
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self.losses = OrderedDict(losses)
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# Subclasses should override this to define individual optimizers. They should all go into self.optimizers.
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# This default implementation defines a single optimizer for all Generator parameters.
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# Must be called after networks are initialized and wrapped.
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def define_optimizers(self):
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self.training_net = self.env['generators'][self.step_opt['training']] \
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if self.step_opt['training'] in self.env['generators'].keys() \
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else self.env['discriminators'][self.step_opt['training']]
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optim_params = []
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for k, v in self.training_net.named_parameters(): # can optimize for a part of the model
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if v.requires_grad:
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optim_params.append(v)
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else:
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if self.env['rank'] <= 0:
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logger.warning('Params [{:s}] will not optimize.'.format(k))
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opt = torch.optim.Adam(optim_params, lr=self.step_opt['lr'],
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weight_decay=self.step_opt['weight_decay'],
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betas=(self.step_opt['beta1'], self.step_opt['beta2']))
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self.optimizers = [opt]
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# Returns all optimizers used in this step.
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def get_optimizers(self):
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assert self.optimizers is not None
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return self.optimizers
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# Returns optimizers which are opting in for default LR scheduling.
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def get_optimizers_with_default_scheduler(self):
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assert self.optimizers is not None
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return self.optimizers
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# Returns the names of the networks this step will train. Other networks will be frozen.
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def get_networks_trained(self):
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return [self.step_opt['training']]
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# Performs all forward and backward passes for this step given an input state. All input states are lists of
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# chunked tensors. Use grad_accum_step to dereference these steps. Should return a dict of tensors that later
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# steps might use. These tensors are automatically detached and accumulated into chunks.
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def do_forward_backward(self, state, grad_accum_step, amp_loss_id, backward=True):
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new_state = {}
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# Prepare a de-chunked state dict which will be used for the injectors & losses.
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local_state = {}
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for k, v in state.items():
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local_state[k] = v[grad_accum_step]
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local_state.update(new_state)
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# Inject in any extra dependencies.
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for inj in self.injectors:
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injected = inj(local_state)
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local_state.update(injected)
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new_state.update(injected)
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if backward:
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# Finally, compute the losses.
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total_loss = 0
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for loss_name, loss in self.losses.items():
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l = loss(self.training_net, local_state)
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total_loss += l * self.weights[loss_name]
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# Record metrics.
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self.loss_accumulator.add_loss(loss_name, l)
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for n, v in loss.extra_metrics():
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self.loss_accumulator.add_loss("%s_%s" % (loss_name, n), v)
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self.loss_accumulator.add_loss("%s_total" % (self.step_opt['training'],), total_loss)
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# Scale the loss down by the accumulation factor.
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total_loss = total_loss / self.env['mega_batch_factor']
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# Get dem grads!
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with amp.scale_loss(total_loss, self.optimizers, amp_loss_id) as scaled_loss:
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scaled_loss.backward()
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# Detach all state variables. Within the step, gradients can flow. Once these variables leave the step
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# we must release the gradients.
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for k, v in new_state.items():
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if isinstance(v, torch.Tensor):
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new_state[k] = v.detach()
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return new_state
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# Performs the optimizer step after all gradient accumulation is completed. Default implementation simply steps()
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# all self.optimizers.
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def do_step(self):
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for opt in self.optimizers:
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opt.step()
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def get_metrics(self):
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return self.loss_accumulator.as_dict()
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