diff options
| -rw-r--r-- | config.py | 71 | ||||
| -rw-r--r-- | models/model.py | 16 | ||||
| -rw-r--r-- | test/model.py | 29 | ||||
| -rw-r--r-- | utils/configuration.py | 0 |
4 files changed, 74 insertions, 42 deletions
@@ -39,35 +39,48 @@ config = { }, # Hyperparameter tuning 'hyperparameter': { - # Auto-encoder feature channels coefficient - 'ae_feature_channels': 64, - # Appearance, canonical and pose feature dimensions - 'f_a_c_p_dims': (128, 128, 64), - # HPM pyramid scales, of which sum is number of parts - 'hpm_scales': (1, 2, 4), - # Global pooling method - 'hpm_use_avg_pool': True, - 'hpm_use_max_pool': True, - # FConv feature channels coefficient - 'fpfe_feature_channels': 32, - # FConv blocks kernel sizes - 'fpfe_kernel_sizes': ((5, 3), (3, 3), (3, 3)), - # FConv blocks paddings - 'fpfe_paddings': ((2, 1), (1, 1), (1, 1)), - # FConv blocks halving - 'fpfe_halving': (0, 2, 3), - # Attention squeeze ratio - 'tfa_squeeze_ratio': 4, - # Number of parts after Part Net - 'tfa_num_parts': 16, - # Embedding dimension for each part - 'embedding_dims': 256, - # Triplet loss margin - 'triplet_margin': 0.2, - # Initial learning rate of Adam Optimizer - 'lr': 1e-4, - # Betas of Adam Optimizer - 'betas': (0.9, 0.999), + 'model': { + # Auto-encoder feature channels coefficient + 'ae_feature_channels': 64, + # Appearance, canonical and pose feature dimensions + 'f_a_c_p_dims': (128, 128, 64), + # HPM pyramid scales, of which sum is number of parts + 'hpm_scales': (1, 2, 4), + # Global pooling method + 'hpm_use_avg_pool': True, + 'hpm_use_max_pool': True, + # FConv feature channels coefficient + 'fpfe_feature_channels': 32, + # FConv blocks kernel sizes + 'fpfe_kernel_sizes': ((5, 3), (3, 3), (3, 3)), + # FConv blocks paddings + 'fpfe_paddings': ((2, 1), (1, 1), (1, 1)), + # FConv blocks halving + 'fpfe_halving': (0, 2, 3), + # Attention squeeze ratio + 'tfa_squeeze_ratio': 4, + # Number of parts after Part Net + 'tfa_num_parts': 16, + # Embedding dimension for each part + 'embedding_dims': 256, + # Triplet loss margin + 'triplet_margin': 0.2, + }, + 'optimizer': { + # Initial learning rate of Adam Optimizer + 'lr': 1e-4, + # Coefficients used for computing running averages of + # gradient and its square + 'betas': (0.9, 0.999), + # Weight decay (L2 penalty) + 'weight_decay': 0.001, + }, + 'scheduler': { + # Period of learning rate decay + 'step_size': 500, + # Multiplicative factor of decay + 'gamma': 0.9, + } }, # Model metadata 'model': { diff --git a/models/model.py b/models/model.py index 7e4edaf..617966f 100644 --- a/models/model.py +++ b/models/model.py @@ -108,11 +108,12 @@ class Model: dataset = self._parse_dataset_config(dataset_config) dataloader = self._parse_dataloader_config(dataset, dataloader_config) # Prepare for model, optimizer and scheduler - hp = self.hp.copy() - lr, betas = hp.pop('lr', 1e-4), hp.pop('betas', (0.9, 0.999)) - self.rgb_pn = RGBPartNet(self.train_size, self.in_channels, **hp) - self.optimizer = optim.Adam(self.rgb_pn.parameters(), lr, betas) - self.scheduler = optim.lr_scheduler.StepLR(self.optimizer, 500, 0.9) + model_hp = self.hp.get('model', {}) + optim_hp = self.hp.get('optimizer', {}) + sched_hp = self.hp.get('scheduler', {}) + self.rgb_pn = RGBPartNet(self.train_size, self.in_channels, **model_hp) + self.optimizer = optim.Adam(self.rgb_pn.parameters(), **optim_hp) + self.scheduler = optim.lr_scheduler.StepLR(self.optimizer, **sched_hp) self.writer = SummaryWriter(self._log_name) # Try to accelerate computation using CUDA or others self._accelerate() @@ -197,9 +198,8 @@ class Model: iter_, dataset_config, dataset_selectors ) # Init models - hp = self.hp.copy() - hp.pop('lr'), hp.pop('betas') - self.rgb_pn = RGBPartNet(ae_in_channels=self.in_channels, **hp) + model_hp = self.hp.get('model', {}) + self.rgb_pn = RGBPartNet(ae_in_channels=self.in_channels, **model_hp) # Try to accelerate computation using CUDA or others self._accelerate() diff --git a/test/model.py b/test/model.py index f679908..5d60475 100644 --- a/test/model.py +++ b/test/model.py @@ -2,20 +2,39 @@ import os from config import config from models.model import Model +from utils.dataset import ClipConditions conf = config +os.chdir('..') def test_default_signature(): - os.chdir('..') model = Model(conf['system'], conf['model'], conf['hyperparameter']) casiab = model._parse_dataset_config(conf['dataset']) model._parse_dataloader_config(casiab, conf['dataloader']) assert model._log_name == os.path.join( 'runs', 'logs', 'RGB-GaitPart_80000_64_128_128_64_1_2_4_True_True_32_5_' '3_3_3_3_3_2_1_1_1_1_1_0_2_3_4_16_256_0.2_0.0001_0.9_' - '0.999_CASIA-B_74_30_15_3_64_32_8_16') + '0.999_0.001_500_0.9_CASIA-B_74_30_15_3_64_32_8_16') assert model._signature == ('RGB-GaitPart_80000_0_64_128_128_64_1_2_4_True_' - 'True_32_5_3_3_3_3_3_2_1_1_1_1_1_0_2_3_4_16_256_' - '0.2_0.0001_0.9_0.999_CASIA-B_74_30_15_3_64_32_' - '8_16') + 'True_32_5_3_3_3_3_3_2_1_1_1_1_1_0_2_3_4_16_' + '256_0.2_0.0001_0.9_0.999_0.001_500_0.9_CASIA-B' + '_74_30_15_3_64_32_8_16') + + +def test_default_signature_with_selector(): + model = Model(conf['system'], conf['model'], conf['hyperparameter']) + casiab = model._parse_dataset_config(dict( + **conf['dataset'], + **{'selector': {'conditions': ClipConditions({r'nm-0\d', r'bg-0\d'})}} + )) + model._parse_dataloader_config(casiab, conf['dataloader']) + assert model._log_name == os.path.join( + 'runs', 'logs', 'RGB-GaitPart_80000_64_128_128_64_1_2_4_True_True_32_5_' + '3_3_3_3_3_2_1_1_1_1_1_0_2_3_4_16_256_0.2_0.0001_0.9_' + '0.999_0.001_500_0.9_CASIA-B_74_30_15_3_64_32_bg-0\\d_' + 'nm-0\\d_8_16') + assert model._signature == ('RGB-GaitPart_80000_0_64_128_128_64_1_2_4_True_' + 'True_32_5_3_3_3_3_3_2_1_1_1_1_1_0_2_3_4_16_' + '256_0.2_0.0001_0.9_0.999_0.001_500_0.9_CASIA-B' + '_74_30_15_3_64_32_bg-0\\d_nm-0\\d_8_16') diff --git a/utils/configuration.py b/utils/configuration.py new file mode 100644 index 0000000..e69de29 --- /dev/null +++ b/utils/configuration.py |