More ExtensibleTrainer work

It runs now, just need to debug it to reach performance parity with SRGAN. Sweet.

codes/data/weight_scheduler.py

@@ -48,6 +48,8 @@ def get_scheduler_for_opt(opt):
         return LinearDecayWeightScheduler(opt['initial_weight'], opt['steps'], opt['lower_bound'], opt['start_step'])
     elif opt['type'] == 'sinusoidal':
         return SinusoidalWeightScheduler(opt['upper_weight'], opt['lower_weight'], opt['period'], opt['start_step'])
+    else:
+        raise NotImplementedError
 
 
 # Do some testing.

codes/models/ExtensibleTrainer.py

@@ -23,18 +23,16 @@ class ExtensibleTrainer(BaseModel):
         else:
             self.rank = -1  # non dist training
         train_opt = opt['train']
-        self.mega_batch_factor = 1
 
         # env is used as a global state to store things that subcomponents might need.
-        env = {'device': self.device,
+        self.env = {'device': self.device,
                'rank': self.rank,
-               'opt': opt}
+               'opt': opt,
+               'step': 0}
 
         self.netsG = {}
         self.netsD = {}
         self.netF = networks.define_F().to(self.device)  # Used to compute feature loss.
-        self.networks = []
-        self.visuals = {}
         for name, net in opt['networks'].items():
             if net['type'] == 'generator':
                 new_net = networks.define_G(net, None, opt['scale']).to(self.device)
@@ -44,18 +42,45 @@ class ExtensibleTrainer(BaseModel):
                 self.netsD[name] = new_net
             else:
                 raise NotImplementedError("Can only handle generators and discriminators")
-            self.networks.append(new_net)
+
+        # Initialize the train/eval steps
+        self.steps = []
+        for step_name, step in opt['steps'].items():
+            step = ConfigurableStep(step, self.env)
+            self.steps.append(step)
 
         if self.is_train:
             self.mega_batch_factor = train_opt['mega_batch_factor']
             if self.mega_batch_factor is None:
                 self.mega_batch_factor = 1
+            self.env['mega_batch_factor'] = self.mega_batch_factor
+
+            # The steps rely on the networks being placed in the env, so put them there. Even though they arent wrapped
+            # yet.
+            self.env['generators'] = self.netsG
+            self.env['discriminators'] = self.netsD
+
+            # Define the optimizers from the steps
+            for s in self.steps:
+                s.define_optimizers()
+                self.optimizers.extend(s.get_optimizers())
+
+            # Find the optimizers that are using the default scheduler, then build them.
+            def_opt = []
+            for s in self.steps:
+                def_opt.extend(s.get_optimizers_with_default_scheduler())
+            self.schedulers = lr_scheduler.get_scheduler_for_name(train_opt['default_lr_scheme'], def_opt, train_opt)
 
             # Initialize amp.
-            amp_nets, amp_opts = amp.initialize(self.networks, self.optimizers, opt_level=opt['amp_opt_level'], num_losses=len(opt['steps']))
-            # self.networks is stored unwrapped. It should never be used for forward() or backward() passes, instead use
-            # self.netG and self.netD for that.
-            self.networks = amp_nets
+            total_nets = [g for g in self.netsG.values()] + [d for d in self.netsD.values()]
+            amp_nets, amp_opts = amp.initialize(total_nets + [self.netF] + self.steps,
+                                                self.optimizers, opt_level=opt['amp_opt_level'], num_losses=len(opt['steps']))
+
+            # Unwrap steps & netF
+            self.netF = amp_nets[len(total_nets)]
+            assert(len(self.steps) == len(amp_nets[len(total_nets)+1:]))
+            self.steps = amp_nets[len(total_nets)+1:]
+            amp_nets = amp_nets[:len(total_nets)]
 
             # DataParallel
             dnets = []
@@ -71,32 +96,24 @@ class ExtensibleTrainer(BaseModel):
                 else:
                     dnet.eval()
                 dnets.append(dnet)
+            if not opt['dist']:
+                self.netF = DataParallel(self.netF)
 
             # Backpush the wrapped networks into the network dicts..
+            self.networks = {}
             found = 0
             for dnet in dnets:
                 for net_dict in [self.netsD, self.netsG]:
                     for k, v in net_dict.items():
                         if v == dnet.module:
                             net_dict[k] = dnet
+                            self.networks[k] = dnet
                             found += 1
-            assert found == len(self.networks)
+            assert found == len(self.netsG) + len(self.netsD)
 
-            env['generators'] = self.netsG
-            env['discriminators'] = self.netsD
-
-            # Initialize the training steps
-            self.steps = []
-            for step_name, step in opt['steps'].items():
-                step = ConfigurableStep(step, env)
-                self.steps.append(step)
-                self.optimizers.extend(step.get_optimizers())
-
-            # Find the optimizers that are using the default scheduler, then build them.
-            def_opt = []
-            for s in self.steps:
-                def_opt.extend(s.get_optimizers_with_default_scheduler())
-            lr_scheduler.get_scheduler_for_name(train_opt['default_lr_scheme'], def_opt, train_opt)
+            # Replace the env networks with the wrapped networks
+            self.env['generators'] = self.netsG
+            self.env['discriminators'] = self.netsD
 
         self.print_network()  # print network
         self.load()  # load G and D if needed
@@ -105,30 +122,38 @@ class ExtensibleTrainer(BaseModel):
         self.updated = True
 
     def feed_data(self, data):
-        self.lq = torch.chunk(corrupted_L, chunks=self.mega_batch_factor, dim=0)
+        self.lq = torch.chunk(data['LQ'].to(self.device), chunks=self.mega_batch_factor, dim=0)
         self.hq = [t.to(self.device) for t in torch.chunk(data['GT'], chunks=self.mega_batch_factor, dim=0)]
         input_ref = data['ref'] if 'ref' in data else data['GT']
         self.ref = [t.to(self.device) for t in torch.chunk(input_ref, chunks=self.mega_batch_factor, dim=0)]
 
     def optimize_parameters(self, step):
+        self.env['step'] = step
+
         # Some models need to make parametric adjustments per-step. Do that here.
         for net in self.networks.values():
             if hasattr(net, "update_for_step"):
                 net.update_for_step(step, os.path.join(self.opt['path']['models'], ".."))
 
         # Iterate through the steps, performing them one at a time.
-        self.visuals = {}
         state = {'lq': self.lq, 'hq': self.hq, 'ref': self.ref}
         for step_num, s in enumerate(self.steps):
             # Only set requires_grad=True for the network being trained.
             nets_to_train = s.get_networks_trained()
+            enabled = 0
             for name, net in self.networks.items():
                 net_enabled = name in nets_to_train
-                for p in self.netsG.parameters():
+                if net_enabled:
+                    enabled += 1
+                for p in net.parameters():
                     if p.dtype != torch.int64 and p.dtype != torch.bool:
                         p.requires_grad = net_enabled
                     else:
                         p.requires_grad = False
+            assert enabled == len(nets_to_train)
+
+            for o in s.get_optimizers():
+                o.zero_grad()
 
             # Now do a forward and backward pass for each gradient accumulation step.
             new_states = {}
@@ -136,13 +161,13 @@ class ExtensibleTrainer(BaseModel):
                 ns = s.do_forward_backward(state, m, step_num)
                 for k, v in ns.items():
                     if k not in new_states.keys():
-                        new_states[k] = [v.detach()]
+                        new_states[k] = [v]
                     else:
-                        new_states[k].append(v.detach())
+                        new_states[k].append(v)
 
             # Push the detached new state tensors into the state map for use with the next step.
             for k, v in new_states.items():
-                # Overwriting existing state keys is not supported.
+                # State is immutable to reduce complexity. Overwriting existing state keys is not supported.
                 assert k not in state.keys()
                 state[k] = v
 
@@ -150,17 +175,14 @@ class ExtensibleTrainer(BaseModel):
             s.do_step()
 
         # Record visual outputs for usage in debugging and testing.
-        if 'visuals' in self.opt['train'].keys():
+        if 'visuals' in self.opt['logger'].keys():
             sample_save_path = os.path.join(self.opt['path']['models'], "..", "visual_dbg")
-            for v in self.opt['train']['visuals']:
-                self.visuals[v] = state[v].detach().cpu()
-                if step % self.opt['train']['visual_debug_rate'] == 0:
-                    for i, dbgv in enumerate(self.visuals[v]):
+            for v in self.opt['logger']['visuals']:
+                if step % self.opt['logger']['visual_debug_rate'] == 0:
+                    for i, dbgv in enumerate(state[v]):
                         os.makedirs(os.path.join(sample_save_path, v), exist_ok=True)
                         utils.save_image(dbgv, os.path.join(sample_save_path, v, "%05i_%02i.png" % (step, i)))
 
-        # TODO: Do logging and image dumps
-
     def compute_fea_loss(self, real, fake):
         with torch.no_grad():
             logits_real = self.netF(real)
@@ -173,12 +195,11 @@ class ExtensibleTrainer(BaseModel):
 
         with torch.no_grad():
             # Iterate through the steps, performing them one at a time.
-            self.visuals = {}
             state = {'lq': self.lq, 'hq': self.hq, 'ref': self.ref}
             for step_num, s in enumerate(self.steps):
                 ns = s.do_forward_backward(state, 0, step_num, backward=False)
                 for k, v in ns.items():
-                    state[k] = [v.detach()]
+                    state[k] = [v]
 
             self.eval_state = state
 
@@ -192,7 +213,7 @@ class ExtensibleTrainer(BaseModel):
             log.update(s.get_metrics())
 
         # Some generators can do their own metric logging.
-        for net in self.networks:
+        for net in self.networks.values():
             if hasattr(net.module, "get_debug_values"):
                 log.update(net.module.get_debug_values(step))
         return log
@@ -204,17 +225,17 @@ class ExtensibleTrainer(BaseModel):
                 'rlt': self.eval_state[self.opt['eval']['output_state']][0].float().cpu()}
 
     def print_network(self):
-        for net in self.networks:
+        for name, net in self.networks.items():
             s, n = self.get_network_description(net)
             net_struc_str = '{}'.format(net.__class__.__name__)
             if self.rank <= 0:
-                logger.info('Network structure: {}, with parameters: {:,d}'.format(net_struc_str, n))
+                logger.info('Network {} structure: {}, with parameters: {:,d}'.format(name, net_struc_str, n))
                 logger.info(s)
 
     def load(self):
         for netdict in [self.netsG, self.netsD]:
             for name, net in netdict.items():
-                load_path = self.opt['path'][name]
+                load_path = self.opt['path']['pretrain_model_%s' % (name,)]
                 if load_path is not None:
                     logger.info('Loading model for [%s]' % (load_path))
                     self.load_network(load_path, net)
@@ -222,3 +243,7 @@ class ExtensibleTrainer(BaseModel):
     def save(self, iter_step):
         for name, net in self.networks.items():
             self.save_network(net, name, iter_step)
+
+    def force_restore_swapout(self):
+        # Legacy method. Do nothing.
+        pass

codes/models/SRGAN_model.py

@@ -385,7 +385,7 @@ class SRGANModel(BaseModel):
             print("Misc setup %f" % (time() - _t,))
             _t = time()
 
-        if step >= self.D_init_iters:
+        if step >= self.init_iters:
             self.optimizer_G.zero_grad()
         self.fake_GenOut = []
         self.fea_GenOut = []

codes/models/networks.py

@@ -223,10 +223,10 @@ def define_F(which_model='vgg', use_bn=False, for_training=False, load_path=None
                 load_net_clean[k] = v
         netF.load_state_dict(load_net_clean)
 
+    if not for_training:
         # Put into eval mode, freeze the parameters and set the 'weight' field.
         netF.eval()
         for k, v in netF.named_parameters():
             v.requires_grad = False
-        netF.fdisc_weight = opt['weight']
 
     return netF

codes/models/steps/injectors.py

@@ -1,10 +1,15 @@
 import torch.nn
 from models.archs.SPSR_arch import ImageGradientNoPadding
+from data.weight_scheduler import get_scheduler_for_opt
 
 # Injectors are a way to sythesize data within a step that can then be used (and reused) by loss functions.
 def create_injector(opt_inject, env):
     type = opt_inject['type']
-    if type == 'img_grad':
+    if type == 'generator':
+        return ImageGeneratorInjector(opt_inject, env)
+    elif type == 'scheduled_scalar':
+        return ScheduledScalarInjector(opt_inject, env)
+    elif type == 'img_grad':
         return ImageGradientInjector(opt_inject, env)
     elif type == 'add_noise':
         return AddNoiseInjector(opt_inject, env)
@@ -19,7 +24,8 @@ class Injector(torch.nn.Module):
         super(Injector, self).__init__()
         self.opt = opt
         self.env = env
-        self.input = opt['in']
+        if 'in' in opt.keys():
+            self.input = opt['in']
         self.output = opt['out']
 
     # This should return a dict of new state variables.
@@ -27,23 +33,59 @@ class Injector(torch.nn.Module):
         raise NotImplementedError
 
 
+# Uses a generator to synthesize an image from [in] and injects the results into [out]
+# Note that results are *not* detached.
+class ImageGeneratorInjector(Injector):
+    def __init__(self, opt, env):
+        super(ImageGeneratorInjector, self).__init__(opt, env)
+
+    def forward(self, state):
+        gen = self.env['generators'][self.opt['generator']]
+        results = gen(state[self.input])
+        new_state = {}
+        if isinstance(self.output, list):
+            for i, k in enumerate(self.output):
+                new_state[k] = results[i]
+        else:
+            new_state[self.output] = results
+
+        return new_state
+
+
 # Creates an image gradient from [in] and injects it into [out]
 class ImageGradientInjector(Injector):
     def __init__(self, opt, env):
         super(ImageGradientInjector, self).__init__(opt, env)
-        self.img_grad_fn = ImageGradientNoPadding()
+        self.img_grad_fn = ImageGradientNoPadding().to(env['device'])
 
     def forward(self, state):
         return {self.opt['out']: self.img_grad_fn(state[self.opt['in']])}
 
 
+# Injects a scalar that is modulated with a specified schedule. Useful for increasing or decreasing the influence
+# of something over time.
+class ScheduledScalarInjector(Injector):
+    def __init__(self, opt, env):
+        super(ScheduledScalarInjector, self).__init__(opt, env)
+        self.scheduler = get_scheduler_for_opt(opt['scheduler'])
+
+    def forward(self, state):
+        return {self.opt['out']: self.scheduler.get_weight_for_step(self.env['step'])}
+
+
 # Adds gaussian noise to [in], scales it to [0,[scale]] and injects into [out]
 class AddNoiseInjector(Injector):
     def __init__(self, opt, env):
         super(AddNoiseInjector, self).__init__(opt, env)
 
     def forward(self, state):
-        noise = torch.randn_like(state[self.opt['in']]) * self.opt['scale']
+        # Scale can be a fixed float, or a state key (e.g. from ScheduledScalarInjector).
+        if isinstance(self.opt['scale'], str):
+            scale = state[self.opt['scale']]
+        else:
+            scale = self.opt['scale']
+
+        noise = torch.randn_like(state[self.opt['in']], device=self.env['device']) * scale
         return {self.opt['out']: state[self.opt['in']] + noise}
 
 
@@ -56,4 +98,4 @@ class GreyInjector(Injector):
     def forward(self, state):
         mean = torch.mean(state[self.opt['in']], dim=1, keepdim=True)
         mean = torch.repeat(mean, (-1, 3, -1, -1))
-        return {self.opt['out']: mean}
+        return {self.opt['out']: mean}

codes/models/steps/losses.py

@@ -2,6 +2,7 @@ import torch
 import torch.nn as nn
 from models.networks import define_F
 from models.loss import GANLoss
+from torchvision.utils import save_image
 
 
 def create_generator_loss(opt_loss, env):
@@ -23,10 +24,14 @@ class ConfigurableLoss(nn.Module):
         super(ConfigurableLoss, self).__init__()
         self.opt = opt
         self.env = env
+        self.metrics = []
 
     def forward(self, net, state):
         raise NotImplementedError
 
+    def extra_metrics(self):
+        return self.metrics
+
 
 def get_basic_criterion_for_name(name, device):
     if name == 'l1':
@@ -53,6 +58,8 @@ class FeatureLoss(ConfigurableLoss):
         self.opt = opt
         self.criterion = get_basic_criterion_for_name(opt['criterion'], env['device'])
         self.netF = define_F(which_model=opt['which_model_F']).to(self.env['device'])
+        if not env['opt']['dist']:
+            self.netF = torch.nn.parallel.DataParallel(self.netF)
 
     def forward(self, net, state):
         with torch.no_grad():
@@ -66,18 +73,18 @@ class GeneratorGanLoss(ConfigurableLoss):
         super(GeneratorGanLoss, self).__init__(opt, env)
         self.opt = opt
         self.criterion = GANLoss(opt['gan_type'], 1.0, 0.0).to(env['device'])
-        self.netD = env['discriminators'][opt['discriminator']]
 
     def forward(self, net, state):
+        netD = self.env['discriminators'][self.opt['discriminator']]
         if self.opt['gan_type'] in ['gan', 'pixgan', 'pixgan_fea', 'crossgan']:
             if self.opt['gan_type'] == 'crossgan':
-                pred_g_fake = self.netD(state[self.opt['fake']], state['lq'])
+                pred_g_fake = netD(state[self.opt['fake']], state['lq'])
             else:
-                pred_g_fake = self.netD(state[self.opt['fake']])
+                pred_g_fake = netD(state[self.opt['fake']])
             return self.criterion(pred_g_fake, True)
         elif self.opt['gan_type'] == 'ragan':
-            pred_d_real = self.netD(state[self.opt['real']]).detach()
-            pred_g_fake = self.netD(state[self.opt['fake']])
+            pred_d_real = netD(state[self.opt['real']]).detach()
+            pred_g_fake = netD(state[self.opt['fake']])
             return (self.cri_gan(pred_d_real - torch.mean(pred_g_fake), False) +
                     self.cri_gan(pred_g_fake - torch.mean(pred_d_real), True)) / 2
         else:
@@ -91,16 +98,33 @@ class DiscriminatorGanLoss(ConfigurableLoss):
         self.criterion = GANLoss(opt['gan_type'], 1.0, 0.0).to(env['device'])
 
     def forward(self, net, state):
-        if self.opt['gan_type'] in ['gan', 'pixgan', 'pixgan_fea', 'crossgan']:
+        self.metrics = []
+
+        if self.opt['gan_type'] == 'crossgan':
+            d_real = net(state[self.opt['real']], state['lq'])
+            d_fake = net(state[self.opt['fake']].detach(), state['lq'])
+            mismatched_lq = torch.roll(state['lq'], shifts=1, dims=0)
+            d_mismatch_real = net(state[self.opt['real']], mismatched_lq)
+            d_mismatch_fake = net(state[self.opt['fake']].detach(), mismatched_lq)
+        else:
+            d_real = net(state[self.opt['real']])
+            d_fake = net(state[self.opt['fake']].detach())
+        self.metrics.append(("d_fake", torch.mean(d_fake)))
+
+        if self.opt['gan_type'] in ['gan', 'pixgan', 'crossgan']:
+            l_real = self.criterion(d_real, True)
+            l_fake = self.criterion(d_fake, False)
+            l_total = l_real + l_fake
             if self.opt['gan_type'] == 'crossgan':
-                pred_g_fake = net(state[self.opt['fake']].detach(), state['lq'])
-            else:
-                pred_g_fake = net(state[self.opt['fake']].detach())
-            return self.criterion(pred_g_fake, False)
+                l_mreal = self.criterion(d_mismatch_real, False)
+                l_mfake = self.criterion(d_mismatch_fake, False)
+                l_total += l_mreal + l_mfake
+                self.metrics.append(("l_mismatch", l_mfake + l_mreal))
+            self.metrics.append(("l_fake", l_fake))
+            return l_total
         elif self.opt['gan_type'] == 'ragan':
-            pred_d_real = self.netD(state[self.opt['real']])
-            pred_g_fake = self.netD(state[self.opt['fake']].detach())
-            return (self.cri_gan(pred_d_real - torch.mean(pred_g_fake), True) +
-                    self.cri_gan(pred_g_fake - torch.mean(pred_d_real), False)) / 2
+            return (self.cri_gan(d_real - torch.mean(d_fake), True) +
+                    self.cri_gan(d_fake - torch.mean(d_real), False))
         else:
             raise NotImplementedError
+

codes/models/steps/steps.py

@@ -19,11 +19,9 @@ class ConfigurableStep(Module):
         self.step_opt = opt_step
         self.env = env
         self.opt = env['opt']
-        self.gen = env['generators'][opt_step['generator']]
-        self.discs = env['discriminators']
         self.gen_outputs = opt_step['generator_outputs']
-        self.training_net = env['generators'][opt_step['training']] if opt_step['training'] in env['generators'].keys() else env['discriminators'][opt_step['training']]
         self.loss_accumulator = LossAccumulator()
+        self.optimizers = None
 
         self.injectors = []
         if 'injectors' in self.step_opt.keys():
@@ -37,12 +35,13 @@ class ConfigurableStep(Module):
             self.weights[loss_name] = loss['weight']
         self.losses = OrderedDict(losses)
 
-        # Intentionally abstract so subclasses can have alternative optimizers.
-        self.define_optimizers()
-
     # 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):
+        self.training_net = self.env['generators'][self.step_opt['training']] \
+            if self.step_opt['training'] in self.env['generators'].keys() \
+            else self.env['discriminators'][self.step_opt['training']]
         optim_params = []
         for k, v in self.training_net.named_parameters():  # can optimize for a part of the model
             if v.requires_grad:
@@ -73,12 +72,7 @@ class ConfigurableStep(Module):
     # 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, backward=True):
-        # First, do a forward pass with the generator.
-        results = self.gen(state[self.step_opt['generator_input']][grad_accum_step])
-        # Extract the resultants into a "new_state" dict per the configuration.
         new_state = {}
-        for i, gen_out in enumerate(self.gen_outputs):
-            new_state[gen_out] = results[i]
 
         # Prepare a de-chunked state dict which will be used for the injectors & losses.
         local_state = {}
@@ -97,17 +91,26 @@ class ConfigurableStep(Module):
             total_loss = 0
             for loss_name, loss in self.losses.items():
                 l = loss(self.training_net, local_state)
-                self.loss_accumulator.add_loss(loss_name, l)
                 total_loss += l * self.weights[loss_name]
-            self.loss_accumulator.add_loss("total", total_loss)
+                # 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.step_opt['training'],), total_loss)
+            # Scale the loss down by the accumulation factor.
+            total_loss = total_loss / self.env['mega_batch_factor']
 
             # Get dem grads!
             with amp.scale_loss(total_loss, self.optimizers, amp_loss_id) as scaled_loss:
                 scaled_loss.backward()
 
+        # Detach all state variables. Within the step, gradients can flow. Once these variables leave the step
+        # we must release the gradients.
+        for k, v in new_state.items():
+            if isinstance(v, torch.Tensor):
+                new_state[k] = v.detach()
         return new_state
 
-
     # Performs the optimizer step after all gradient accumulation is completed. Default implementation simply steps()
     # all self.optimizers.
     def do_step(self):

codes/options/options.py

@@ -112,14 +112,21 @@ def check_resume(opt, resume_iter):
                 'pretrain_model_D', None) is not None:
             logger.warning('pretrain_model path will be ignored when resuming training.')
 
-        opt['path']['pretrain_model_G'] = osp.join(opt['path']['models'],
-                                                   '{}_G.pth'.format(resume_iter))
-        logger.info('Set [pretrain_model_G] to ' + opt['path']['pretrain_model_G'])
-        if 'gan' in opt['model'] or 'spsr' in opt['model']:
-            opt['path']['pretrain_model_D'] = osp.join(opt['path']['models'],
-                                                       '{}_D.pth'.format(resume_iter))
-            logger.info('Set [pretrain_model_D] to ' + opt['path']['pretrain_model_D'])
-        if 'spsr' in opt['model']:
-            opt['path']['pretrain_model_D_grad'] = osp.join(opt['path']['models'],
-                                                       '{}_D_grad.pth'.format(resume_iter))
-            logger.info('Set [pretrain_model_D_grad] to ' + opt['path']['pretrain_model_D_grad'])
+        if opt['model'] == 'extensibletrainer':
+            for k in opt['networks'].keys():
+                pt_key = 'pretrain_model_%s' % (k,)
+                opt['path'][pt_key] = osp.join(opt['path']['models'],
+                                          '{}_{}.pth'.format(resume_iter, k))
+                logger.info('Set model [%s] to %s' % (k, opt['path'][pt_key]))
+        else:
+            opt['path']['pretrain_model_G'] = osp.join(opt['path']['models'],
+                                                       '{}_G.pth'.format(resume_iter))
+            logger.info('Set [pretrain_model_G] to ' + opt['path']['pretrain_model_G'])
+            if 'gan' in opt['model'] or 'spsr' in opt['model']:
+                opt['path']['pretrain_model_D'] = osp.join(opt['path']['models'],
+                                                           '{}_D.pth'.format(resume_iter))
+                logger.info('Set [pretrain_model_D] to ' + opt['path']['pretrain_model_D'])
+            if 'spsr' in opt['model']:
+                opt['path']['pretrain_model_D_grad'] = osp.join(opt['path']['models'],
+                                                           '{}_D_grad.pth'.format(resume_iter))
+                logger.info('Set [pretrain_model_D_grad] to ' + opt['path']['pretrain_model_D_grad'])

codes/train.py

@@ -32,7 +32,7 @@ def init_dist(backend='nccl', **kwargs):
 def main():
     #### options
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_feature_net.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_mi1_spsr_switched2.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none',
                         help='job launcher')
     parser.add_argument('--local_rank', type=int, default=0)

codes/train2.py

@@ -161,7 +161,7 @@ def main():
         current_step = resume_state['iter']
         model.resume_training(resume_state)  # handle optimizers and schedulers
     else:
-        current_step = -1 if 'start_step' not in opt.keys() else opt['start_step']
+        current_step = 0 if 'start_step' not in opt.keys() else opt['start_step']
         start_epoch = 0
 
     #### training
@@ -215,7 +215,7 @@ def main():
                     logger.info(message)
             #### validation
             if opt['datasets'].get('val', None) and current_step % opt['train']['val_freq'] == 0:
-                if opt['model'] in ['sr', 'srgan', 'corruptgan', 'spsrgan'] and rank <= 0:  # image restoration validation
+                if opt['model'] in ['sr', 'srgan', 'corruptgan', 'spsrgan', 'extensibletrainer'] and rank <= 0:  # image restoration validation
                     model.force_restore_swapout()
                     val_batch_sz = 1 if 'batch_size' not in opt['datasets']['val'].keys() else opt['datasets']['val']['batch_size']
                     # does not support multi-GPU validation

codes/utils/loss_accumulator.py

@@ -2,7 +2,7 @@ import torch
 
 # Utility class that stores detached, named losses in a rotating buffer for smooth metric outputting.
 class LossAccumulator:
-    def __init__(self, buffer_sz=10):
+    def __init__(self, buffer_sz=50):
         self.buffer_sz = buffer_sz
         self.buffers = {}
 
@@ -15,6 +15,6 @@ class LossAccumulator:
 
     def as_dict(self):
         result = {}
-        for k, v in self.buffers:
-            result["loss_" + k] = torch.mean(v)
+        for k, v in self.buffers.items():
+            result["loss_" + k] = torch.mean(v[1])
         return result