learning/annotated_deep_learning_paper_implementations
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tracking hooks

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This commit is contained in:
Varuna Jayasiri
2020-09-26 21:21:46 +05:30
parent 32e7df1e54
commit 19bd8b8e67
1 changed files with 13 additions and 5 deletions
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18
labml_nn/gan/mnist.py
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@ -6,13 +6,14 @@ import torch.nn as nn
import torch.utils.data import torch.utils.data
from torchvision import transforms from torchvision import transforms
import labml.utils.pytorch as pytorch_utils
from labml import tracker, monit, experiment from labml import tracker, monit, experiment
from labml.configs import option, calculate from labml.configs import option, calculate
from labml_helpers.datasets.mnist import MNISTConfigs from labml_helpers.datasets.mnist import MNISTConfigs
from labml_helpers.device import DeviceConfigs from labml_helpers.device import DeviceConfigs
from labml_helpers.module import Module from labml_helpers.module import Module
from labml_helpers.optimizer import OptimizerConfigs from labml_helpers.optimizer import OptimizerConfigs
from labml_helpers.train_valid import MODE_STATE, BatchStepProtocol, TrainValidConfigs from labml_helpers.train_valid import MODE_STATE, BatchStepProtocol, TrainValidConfigs, hook_model_outputs, Mode
from labml_nn.gan import DiscriminatorLogitsLoss, GeneratorLogitsLoss from labml_nn.gan import DiscriminatorLogitsLoss, GeneratorLogitsLoss
plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.interpolation'] = 'nearest'
@ -71,6 +72,9 @@ class GANBatchStep(BatchStepProtocol):
self.discriminator_loss = discriminator_loss self.discriminator_loss = discriminator_loss
self.generator_optimizer = generator_optimizer self.generator_optimizer = generator_optimizer
self.discriminator_optimizer = discriminator_optimizer self.discriminator_optimizer = discriminator_optimizer
hook_model_outputs(self.generator, 'generator')
hook_model_outputs(self.discriminator, 'discriminator')
tracker.set_scalar("loss.generator.*", True) tracker.set_scalar("loss.generator.*", True)
tracker.set_scalar("loss.discriminator.*", True) tracker.set_scalar("loss.discriminator.*", True)
tracker.set_image("generated", True, 1 / 100) tracker.set_image("generated", True, 1 / 100)
@ -99,6 +103,8 @@ class GANBatchStep(BatchStepProtocol):
tracker.add("loss.generator.", loss) tracker.add("loss.generator.", loss)
if MODE_STATE.is_train: if MODE_STATE.is_train:
loss.backward() loss.backward()
if MODE_STATE.is_log_parameters:
pytorch_utils.store_model_indicators(self.generator, 'generator')
self.generator_optimizer.step() self.generator_optimizer.step()
with monit.section("discriminator"): with monit.section("discriminator"):
@ -114,6 +120,8 @@ class GANBatchStep(BatchStepProtocol):
tracker.add("loss.discriminator.", loss) tracker.add("loss.discriminator.", loss)
if MODE_STATE.is_train: if MODE_STATE.is_train:
loss.backward() loss.backward()
if MODE_STATE.is_log_parameters:
pytorch_utils.store_model_indicators(self.discriminator, 'discriminator')
self.discriminator_optimizer.step() self.discriminator_optimizer.step()
return {'samples': len(data)}, None return {'samples': len(data)}, None
@ -163,8 +171,8 @@ def _discriminator_optimizer(c: Configs):
opt_conf = OptimizerConfigs() opt_conf = OptimizerConfigs()
opt_conf.optimizer = 'Adam' opt_conf.optimizer = 'Adam'
opt_conf.parameters = c.discriminator.parameters() opt_conf.parameters = c.discriminator.parameters()
opt_conf.learning_rate = 2.5e-4 opt_conf.learning_rate = 2.5e-5
opt_conf.betas = (0.5, 0.999) # opt_conf.betas = (0.5, 0.999)
return opt_conf return opt_conf
@ -173,8 +181,8 @@ def _generator_optimizer(c: Configs):
opt_conf = OptimizerConfigs() opt_conf = OptimizerConfigs()
opt_conf.optimizer = 'Adam' opt_conf.optimizer = 'Adam'
opt_conf.parameters = c.generator.parameters() opt_conf.parameters = c.generator.parameters()
opt_conf.learning_rate = 2.5e-4 opt_conf.learning_rate = 2.5e-5
opt_conf.betas = (0.5, 0.999) # opt_conf.betas = (0.5, 0.999)
return opt_conf return opt_conf