From eda75c9779c186f5043b610d9bb31cfd6da2560d Mon Sep 17 00:00:00 2001 From: James Betker Date: Thu, 15 Oct 2020 10:13:17 -0600 Subject: [PATCH] Cleanup fixes --- codes/models/steps/recurrent.py | 271 --------------------------- codes/models/steps/tecogan_losses.py | 3 +- codes/process_video.py | 4 +- 3 files changed, 3 insertions(+), 275 deletions(-) delete mode 100644 codes/models/steps/recurrent.py diff --git a/codes/models/steps/recurrent.py b/codes/models/steps/recurrent.py deleted file mode 100644 index 388bf524..00000000 --- a/codes/models/steps/recurrent.py +++ /dev/null @@ -1,271 +0,0 @@ -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 models.novograd import NovoGrad -from utils.util import recursively_detach - -logger = logging.getLogger('base') - - -def define_recurrent_controller(opt, env): - pass - - -class RecurrentController: - def __init__(self, opt, env): - self.opt = opt - self.env = env - - # This is the meat of the RecurrentController code. It is expected to return a recurrent_state which is fed into - # the injectors and losses, or None if the recurrent loop is to be exited. - # Note that on the first call, the recurrent_state parameter is set to None. - def get_next_step(self, state, recurrent_state): - return None - - -# This class implements the logic necessary to gather the gradients resulting from recurrent network passes. -class RecurrentStep(Module): - - def __init__(self, opt_step, env): - super(RecurrentStep, 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 - - # Recurrent steps must have a bespoke "controller". This is a snippet of code responsible for determining - # how many recurrent steps should be executed, and also compiles a "recurrent_state" which is passed to the - # injectors and losses within the recurrent loop. Note that the recurrent state does not persist past the - # recurrent loop. - self.controller = define_recurrent_controller(self.step_opt) - - # Unlike a "normal" step, recurrent steps have 2 injection sites: "initial" and "recurrent". Initial injectors - # are run once when the step is first executed. Recurrent injectors are run for every recurrent cycle and their - # outputs are appended to a list. - self.initial_injectors = [] - if 'initial_injectors' in self.step_opt.keys(): - for inj_name, injector in self.step_opt['initial_injectors'].items(): - self.initial_injectors.append(create_injector(injector, env)) - self.recurrent_injectors = [] - if 'recurrent_injectors' in self.step_opt.keys(): - for inj_name, injector in self.step_opt['recurrent_injectors'].items(): - self.recurrent_injectors.append(create_injector(injector, env)) - - # Recurrent detach points are a list of state variables that get detached on every iteration. Since recurrent - # injections are pushed into lists, detach points specify the exact tensor to detach by being a list of lists, - # e.g.: [['var1', -2], ['var2', -1], ['var3', 0]] - # The first element of the sublist is the state variable you want to detach. The second element is a list index - # into that state variable. - self.recurrent_detach_points = [] - if 'recurrent_detach_points' in self.step_opt.keys(): - for name, index in self.step_opt['recurrent_detach_points']: - self.recurrent_detach_points.append(name, index) - - # Recurrent steps also have two types of losses: 'recurrent' and 'final'. - # Similar to injection points, 'recurrent' losses are invoked every iteration. - # 'final' losses are invoked after all iterations have completed. - losses = [] - self.recurrent_weights = {} - if 'recurrent_losses' in self.step_opt.keys(): - for loss_name, loss in self.step_opt['recurrent_losses'].items(): - losses.append((loss_name, create_loss(loss, env))) - self.recurrent_weights[loss_name] = loss['weight'] - self.recurrent_losses = OrderedDict(losses) - self.final_weights = {} - if 'final_losses' in self.step_opt.keys(): - for loss_name, loss in self.step_opt['final_losses'].items(): - losses.append((loss_name, create_loss(loss, env))) - self.final_weights[loss_name] = loss['weight'] - self.final_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'] - - def do_injection(self, injectors, local_state, train=True): - injected_state = {} - for inj in 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_state.update(inj(local_state)) - return injected_state - - def compute_gradients(self, losses, weights, local_state, amp_loss_id): - total_loss = 0 - for loss_name, loss in 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 * weights[loss_name] - # Record metrics. - 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) - 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() - - # 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 initial tensors. - injected = self.do_injection(self.initial_injectors, local_state, train) - local_state.update(injected) - new_state.update(injected) - - recurrent_state = self.controller.get_next_step(state, None) - while recurrent_state: - # Detach items no longer needed from previous recursive loop. - for name, ind in self.recurrent_detach_points: - len_required = ind if ind > 0 else abs(ind)+1 - if len(local_state[name]) >= len_required: - local_state[name][ind] = local_state[name][ind].detach() - - # Recurrent injectors and losses rely on state variables from recurrent_state. Combine that with local_state. - combined_state = local_state - combined_state.update(recurrent_state) - - # Inject recurrent injections. - injected = self.do_injection(self.recurrent_injectors, combined_state, train) - for k, v in injected.items(): - if k not in local_state.keys(): - local_state[k] = [] - combined_state[k] = [] - new_state[k] = [] - local_state[k].append(v) - combined_state[k].append(v) - new_state[k].append(v.detach()) - - # Compute the recurrent losses. - if train: - self.compute_gradients(self.recurrent_losses, self.recurrent_weights, combined_state, amp_loss_id) - - # Zero out combined_state, it'll be repopulated in the next loop. - combined_state = {} - - # Compute the final losses - if train: - self.compute_gradients(self.final_losses, self.final_weights, local_state, amp_loss_id) - - # 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() diff --git a/codes/models/steps/tecogan_losses.py b/codes/models/steps/tecogan_losses.py index c1a57e3a..c3b88cf7 100644 --- a/codes/models/steps/tecogan_losses.py +++ b/codes/models/steps/tecogan_losses.py @@ -1,6 +1,5 @@ from models.steps.losses import ConfigurableLoss, GANLoss, extract_params_from_state, get_basic_criterion_for_name -from models.layers.resample2d_package.resample2d import Resample2d -from models.steps.recurrent import RecurrentController +from models.flownet2.networks.resample2d_package.resample2d import Resample2d from models.steps.injectors import Injector import torch import torch.nn.functional as F diff --git a/codes/process_video.py b/codes/process_video.py index 6f2279e9..056f57fa 100644 --- a/codes/process_video.py +++ b/codes/process_video.py @@ -11,10 +11,10 @@ import torchvision.transforms.functional as F from PIL import Image from tqdm import tqdm +from models.ExtensibleTrainer import ExtensibleTrainer from utils import options as option import utils.util as util from data import create_dataloader -from models import create_model class FfmpegBackedVideoDataset(data.Dataset): @@ -128,7 +128,7 @@ if __name__ == "__main__": logger.info('Number of test images in [{:s}]: {:d}'.format(opt['dataset']['name'], len(test_set))) test_loaders.append(test_loader) - model = create_model(opt) + model = ExtensibleTrainer(opt) test_set_name = test_loader.dataset.opt['name'] logger.info('\nTesting [{:s}]...'.format(test_set_name)) test_start_time = time.time()