diff options
Diffstat (limited to 'models')
| -rw-r--r-- | models/model.py | 67 |
1 files changed, 51 insertions, 16 deletions
diff --git a/models/model.py b/models/model.py index 456c2f1..b343f86 100644 --- a/models/model.py +++ b/models/model.py @@ -1,4 +1,5 @@ import os +from datetime import datetime from typing import Union, Optional import numpy as np @@ -86,6 +87,21 @@ class Model: def _checkpoint_name(self) -> str: return os.path.join(self.checkpoint_dir, self._signature) + def fit_all( + self, + dataset_config: DatasetConfiguration, + dataset_selectors: dict[ + str, dict[str, Union[ClipClasses, ClipConditions, ClipViews]] + ], + dataloader_config: DataloaderConfiguration, + ): + for (condition, selector) in dataset_selectors.items(): + print(f'Training model {condition} ...') + self.fit( + dict(**dataset_config, **{'selector': selector}), + dataloader_config + ) + def fit( self, dataset_config: DatasetConfiguration, @@ -115,6 +131,8 @@ class Model: self.rgb_pn.load_state_dict(checkpoint['model_state_dict']) self.optimizer.load_state_dict(checkpoint['optim_state_dict']) + # Training start + start_time = datetime.now() for (batch_c1, batch_c2) in dataloader: self.curr_iter += 1 # Zero the parameter gradients @@ -137,7 +155,7 @@ class Model: ], metrics)), self.curr_iter) if self.curr_iter % 100 == 0: - print('{0:5d} loss: {1:.3f}'.format(self.curr_iter, loss), + print('{0:5d} loss: {1:6.3f}'.format(self.curr_iter, loss), '(xrecon = {:f}, pose_sim = {:f},' ' cano_cons = {:f}, ba_trip = {:f})'.format(*metrics), 'lr:', self.scheduler.get_last_lr()[0]) @@ -149,8 +167,11 @@ class Model: 'optim_state_dict': self.optimizer.state_dict(), 'loss': loss, }, self._checkpoint_name) + print(datetime.now() - start_time, 'used') + start_time = datetime.now() if self.curr_iter == self.total_iter: + self.curr_iter = 0 self.writer.close() break @@ -160,7 +181,7 @@ class Model: self.rgb_pn = nn.DataParallel(self.rgb_pn) self.rgb_pn = self.rgb_pn.to(self.device) - def predict( + def predict_all( self, iter_: int, dataset_config: DatasetConfiguration, @@ -189,23 +210,36 @@ class Model: gallery_samples, probe_samples = [], {} # Gallery - self.rgb_pn.load_state_dict(torch.load(list(checkpoints.values())[0])) + checkpoint = torch.load(list(checkpoints.values())[0]) + self.rgb_pn.load_state_dict(checkpoint['model_state_dict']) for sample in tqdm(gallery_dataloader, desc='Transforming gallery', unit='clips'): + label = sample.pop('label').item() clip = sample.pop('clip').to(self.device) feature = self.rgb_pn(clip).detach() - gallery_samples.append({**sample, **{'feature': feature}}) + gallery_samples.append({ + **{'label': label}, + **sample, + **{'feature': feature} + }) gallery_samples = default_collate(gallery_samples) # Probe - for (name, dataloader) in probe_dataloaders.items(): - self.rgb_pn.load_state_dict(torch.load(checkpoints[name])) - probe_samples[name] = [] + for (condition, dataloader) in probe_dataloaders.items(): + checkpoint = torch.load(checkpoints[condition]) + self.rgb_pn.load_state_dict(checkpoint['model_state_dict']) + probe_samples[condition] = [] for sample in tqdm(dataloader, - desc=f'Transforming probe {name}', unit='clips'): + desc=f'Transforming probe {condition}', + unit='clips'): + label = sample.pop('label').item() clip = sample.pop('clip').to(self.device) feature = self.rgb_pn(clip).detach() - probe_samples[name].append({**sample, **{'feature': feature}}) + probe_samples[condition].append({ + **{'label': label}, + **sample, + **{'feature': feature} + }) for (k, v) in probe_samples.items(): probe_samples[k] = default_collate(v) @@ -243,11 +277,11 @@ class Model: f_p = features_p[probe_view_mask] y_p = labels_p[probe_view_mask] # Euclidean distance - f_g_squared_sum = torch.sum(f_g ** 2, dim=1).unsqueeze(1) - f_p_squared_sum = torch.sum(f_p ** 2, dim=1).unsqueeze(0) - f_g_times_f_p_sum = f_g @ f_p.T + f_p_squared_sum = torch.sum(f_p ** 2, dim=1).unsqueeze(1) + f_g_squared_sum = torch.sum(f_g ** 2, dim=1).unsqueeze(0) + f_p_times_f_g_sum = f_p @ f_g.T dist = torch.sqrt(F.relu( - f_g_squared_sum - 2*f_g_times_f_p_sum + f_p_squared_sum + f_p_squared_sum - 2*f_p_times_f_g_sum + f_g_squared_sum )) # Ranked accuracy rank_mask = dist.argsort(1)[:, :num_ranks] @@ -354,8 +388,8 @@ class Model: dataloader_config: DataloaderConfiguration ) -> DataLoader: config: dict = dataloader_config.copy() + (self.pr, self.k) = config.pop('batch_size') if self.is_train: - (self.pr, self.k) = config.pop('batch_size') self._log_name = '_'.join( (self._log_name, str(self.pr), str(self.k))) triplet_sampler = TripletSampler(dataset, (self.pr, self.k)) @@ -364,7 +398,6 @@ class Model: collate_fn=self._batch_splitter, **config) else: # is_test - config.pop('batch_size') return DataLoader(dataset, **config) def _batch_splitter( @@ -399,8 +432,10 @@ class Model: for v in values: if isinstance(v, str): strings.append(v) - elif isinstance(v, (tuple, list, set)): + elif isinstance(v, (tuple, list)): strings.append(self._gen_sig(v)) + elif isinstance(v, set): + strings.append(self._gen_sig(sorted(list(v)))) elif isinstance(v, dict): strings.append(self._gen_sig(list(v.values()))) else: |