DL-Art-School/codes/models/steps/steps.py

from utils.loss_accumulator import LossAccumulator
from torch.nn import Module
import logging
from models.steps.losses import create_loss
import torch
from apex import amp
from collections import OrderedDict
from .injectors import create_injector
from utils.util import recursively_detach

logger = logging.getLogger('base')


# Defines the expected API for a single training step
class ConfigurableStep(Module):

    def __init__(self, opt_step, env):
        super(ConfigurableStep, self).__init__()

        self.step_opt = opt_step
        self.env = env
        self.opt = env['opt']
        self.gen_outputs = opt_step['generator_outputs']
        self.loss_accumulator = LossAccumulator()
        self.optimizers = None

        self.injectors = []
        if 'injectors' in self.step_opt.keys():
            injector_names = []
            for inj_name, injector in self.step_opt['injectors'].items():
                assert inj_name not in injector_names  # Repeated names are always an error case.
                injector_names.append(inj_name)
                self.injectors.append(create_injector(injector, env))

        losses = []
        self.weights = {}
        if 'losses' in self.step_opt.keys():
            for loss_name, loss in self.step_opt['losses'].items():
                assert loss_name not in self.weights.keys()  # Repeated names are always an error case.
                losses.append((loss_name, create_loss(loss, env)))
                self.weights[loss_name] = loss['weight']
        self.losses = OrderedDict(losses)

    def get_network_for_name(self, name):
        return self.env['generators'][name] if name in self.env['generators'].keys() \
                else self.env['discriminators'][name]

    # Subclasses should override this to define individual optimizers. They should all go into self.optimizers.
    #  This default implementation defines a single optimizer for all Generator parameters.
    #  Must be called after networks are initialized and wrapped.
    def define_optimizers(self):
        training = self.step_opt['training']
        if isinstance(training, list):
            self.training_net = [self.get_network_for_name(t) for t in training]
            opt_configs = [self.step_opt['optimizer_params'][t] for t in training]
            nets = self.training_net
        else:
            self.training_net = self.get_network_for_name(training)
            # When only training one network, optimizer params can just embedded in the step params.
            if 'optimizer_params' not in self.step_opt.keys():
                opt_configs = [self.step_opt]
            else:
                opt_configs = [self.step_opt['optimizer_params']]
            nets = [self.training_net]
        self.optimizers = []
        for net, opt_config in zip(nets, opt_configs):
            optim_params = []
            for k, v in net.named_parameters():  # can optimize for a part of the model
                if v.requires_grad:
                    optim_params.append(v)
                else:
                    if self.env['rank'] <= 0:
                        logger.warning('Params [{:s}] will not optimize.'.format(k))

            if 'optimizer' not in self.step_opt.keys() or self.step_opt['optimizer'] == 'adam':
                opt = torch.optim.Adam(optim_params, lr=opt_config['lr'],
                                       weight_decay=opt_config['weight_decay'],
                                       betas=(opt_config['beta1'], opt_config['beta2']))
            elif self.step_opt['optimizer'] == 'novograd':
                opt = NovoGrad(optim_params, lr=opt_config['lr'], weight_decay=opt_config['weight_decay'],
                                       betas=(opt_config['beta1'], opt_config['beta2']))
            opt._config = opt_config  # This is a bit seedy, but we will need these configs later.
            self.optimizers.append(opt)

    # Returns all optimizers used in this step.
    def get_optimizers(self):
        assert self.optimizers is not None
        return self.optimizers

    # Returns optimizers which are opting in for default LR scheduling.
    def get_optimizers_with_default_scheduler(self):
        assert self.optimizers is not None
        return self.optimizers

    # Returns the names of the networks this step will train. Other networks will be frozen.
    def get_networks_trained(self):
        if isinstance(self.step_opt['training'], list):
            return self.step_opt['training']
        else:
            return [self.step_opt['training']]

    def get_training_network_name(self):
        if isinstance(self.step_opt['training'], list):
            return self.step_opt['training'][0]
        else:
            return self.step_opt['training']

    # Performs all forward and backward passes for this step given an input state. All input states are lists of
    # chunked tensors. Use grad_accum_step to dereference these steps. Should return a dict of tensors that later
    # steps might use. These tensors are automatically detached and accumulated into chunks.
    def do_forward_backward(self, state, grad_accum_step, amp_loss_id, train=True):
        new_state = {}

        # Prepare a de-chunked state dict which will be used for the injectors & losses.
        local_state = {}
        for k, v in state.items():
            local_state[k] = v[grad_accum_step]
        local_state.update(new_state)
        local_state['train_nets'] = str(self.get_networks_trained())

        # Some losses compute backward() internally. Accomodate this by stashing the amp_loss_id in env.
        self.env['amp_loss_id'] = amp_loss_id
        self.env['current_step_optimizers'] = self.optimizers
        self.env['training'] = train

        # Inject in any extra dependencies.
        for inj in self.injectors:
            # Don't do injections tagged with eval unless we are not in train mode.
            if train and 'eval' in inj.opt.keys() and inj.opt['eval']:
                continue
            # Likewise, don't do injections tagged with train unless we are not in eval.
            if not train and 'train' in inj.opt.keys() and inj.opt['train']:
                continue
            # Don't do injections tagged with 'after' or 'before' when we are out of spec.
            if 'after' in inj.opt.keys() and self.env['step'] < inj.opt['after'] or \
                'before' in inj.opt.keys() and self.env['step'] > inj.opt['before']:
                continue
            injected = inj(local_state)
            local_state.update(injected)
            new_state.update(injected)

        if train and len(self.losses) > 0:
            # Finally, compute the losses.
            total_loss = 0
            for loss_name, loss in self.losses.items():
                # Some losses only activate after a set number of steps. For example, proto-discriminator losses can
                # be very disruptive to a generator.
                if 'after' in loss.opt.keys() and loss.opt['after'] > self.env['step']:
                    continue

                l = loss(self.training_net, local_state)
                total_loss += l * self.weights[loss_name]
                # Record metrics.
                if isinstance(l, torch.Tensor):
                    self.loss_accumulator.add_loss(loss_name, l)
                for n, v in loss.extra_metrics():
                    self.loss_accumulator.add_loss("%s_%s" % (loss_name, n), v)
                loss.clear_metrics()

            # In some cases, the loss could not be set (e.g. all losses have 'after'
            if isinstance(total_loss, torch.Tensor):
                self.loss_accumulator.add_loss("%s_total" % (self.get_training_network_name(),), total_loss)
                # Scale the loss down by the accumulation factor.
                total_loss = total_loss / self.env['mega_batch_factor']

                # Get dem grads!
                if self.env['amp']:
                    with amp.scale_loss(total_loss, self.optimizers, amp_loss_id) as scaled_loss:
                        scaled_loss.backward()
                else:
                    total_loss.backward()

        # Detach all state variables. Within the step, gradients can flow. Once these variables leave the step
        # we must release the gradients.
        new_state = recursively_detach(new_state)
        return new_state

    # Performs the optimizer step after all gradient accumulation is completed. Default implementation simply steps()
    # all self.optimizers.
    def do_step(self):
        for opt in self.optimizers:
            # Optimizers can be opted out in the early stages of training.
            after = opt._config['after'] if 'after' in opt._config.keys() else 0
            if self.env['step'] < after:
                continue
            before = opt._config['before'] if 'before' in opt._config.keys() else -1
            if before != -1 and self.env['step'] > before:
                continue
            opt.step()

    def get_metrics(self):
        return self.loss_accumulator.as_dict()