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tracing.py
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tracing.py
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from typing import List, TYPE_CHECKING, Union
from pathlib import Path
import torch
from catalyst.core import Callback, CallbackNode, CallbackOrder
from catalyst.utils.torch import any2device
from catalyst.utils.tracing import trace_model
if TYPE_CHECKING:
from catalyst.core import IRunner
class TracingCallback(Callback):
"""
Callback for model tracing.
Args:
input_key: input key from ``runner.batch`` to use for model tracing
logdir: path to folder for saving
filename: filename
method_name: Model's method name that will be used as entrypoint during tracing
Example:
.. code-block:: python
import os
import torch
from torch import nn
from torch.utils.data import DataLoader
from catalyst import dl
from catalyst.data.transforms import ToTensor
from catalyst.contrib.datasets import MNIST
from catalyst.contrib.nn.modules import Flatten
loaders = {
"train": DataLoader(
MNIST(
os.getcwd(), train=False, download=True, transform=ToTensor()
),
batch_size=32,
),
"valid": DataLoader(
MNIST(
os.getcwd(), train=False, download=True, transform=ToTensor()
),
batch_size=32,
),
}
model = nn.Sequential(
Flatten(), nn.Linear(784, 512), nn.ReLU(), nn.Linear(512, 10)
)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-2)
runner = dl.SupervisedRunner()
runner.train(
model=model,
callbacks=[dl.TracingCallback(input_key="features", logdir="./logs")],
loaders=loaders,
criterion=criterion,
optimizer=optimizer,
num_epochs=1,
logdir="./logs",
)
"""
def __init__(
self,
input_key: Union[str, List[str]],
logdir: Union[str, Path] = None,
filename: str = "traced_model.pth",
method_name: str = "forward",
):
"""
Callback for model tracing.
Args:
input_key: input key from ``runner.batch`` to use for model tracing
logdir: path to folder for saving
filename: filename
method_name: Model's method name that will be used as entrypoint during tracing
Example:
.. code-block:: python
import os
import torch
from torch import nn
from torch.utils.data import DataLoader
from catalyst import dl
from catalyst.data.transforms import ToTensor
from catalyst.contrib.datasets import MNIST
from catalyst.contrib.nn.modules import Flatten
loaders = {
"train": DataLoader(
MNIST(
os.getcwd(), train=False, download=True, transform=ToTensor()
),
batch_size=32,
),
"valid": DataLoader(
MNIST(
os.getcwd(), train=False, download=True, transform=ToTensor()
),
batch_size=32,
),
}
model = nn.Sequential(
Flatten(), nn.Linear(784, 512), nn.ReLU(), nn.Linear(512, 10)
)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-2)
runner = dl.SupervisedRunner()
runner.train(
model=model,
callbacks=[dl.TracingCallback(input_key="features", logdir="./logs")],
loaders=loaders,
criterion=criterion,
optimizer=optimizer,
num_epochs=1,
logdir="./logs",
)
"""
super().__init__(order=CallbackOrder.ExternalExtra, node=CallbackNode.Master)
if logdir is not None:
self.filename = str(Path(logdir) / filename)
else:
self.filename = filename
self.method_name = method_name
self.input_key = [input_key] if isinstance(input_key, str) else input_key
def on_stage_end(self, runner: "IRunner") -> None:
"""
On stage end action.
Args:
runner: runner for experiment
"""
model = runner.model
batch = tuple(runner.batch[key] for key in self.input_key)
batch = any2device(batch, "cpu")
traced_model = trace_model(model=model, batch=batch, method_name=self.method_name)
torch.jit.save(traced_model, self.filename)
__all__ = ["TracingCallback"]