engine/mattingtrainer.py

from detectron2.engine import AMPTrainer
import torch
import time
import logging

logger = logging.getLogger("detectron2")

import typing
from collections import defaultdict
import tabulate
from torch import nn


def parameter_count(model: nn.Module, trainable_only: bool = False) -> typing.DefaultDict[str, int]:
    """
    Count parameters of a model and its submodules.

    Args:
        model: a torch module

    Returns:
        dict (str-> int): the key is either a parameter name or a module name.
        The value is the number of elements in the parameter, or in all
        parameters of the module. The key "" corresponds to the total
        number of parameters of the model.
    """
    r = defaultdict(int)
    for name, prm in model.named_parameters():
        if trainable_only:
            if not prm.requires_grad:
                continue
        size = prm.numel()
        name = name.split(".")
        for k in range(0, len(name) + 1):
            prefix = ".".join(name[:k])
            r[prefix] += size
    return r


def parameter_count_table(
    model: nn.Module, max_depth: int = 3, trainable_only: bool = False
) -> str:
    """
    Format the parameter count of the model (and its submodules or parameters)
    in a nice table. It looks like this:

    ::

        | name                            | #elements or shape   |
        |:--------------------------------|:---------------------|
        | model                           | 37.9M                |
        |  backbone                       |  31.5M               |
        |   backbone.fpn_lateral3         |   0.1M               |
        |    backbone.fpn_lateral3.weight |    (256, 512, 1, 1)  |
        |    backbone.fpn_lateral3.bias   |    (256,)            |
        |   backbone.fpn_output3          |   0.6M               |
        |    backbone.fpn_output3.weight  |    (256, 256, 3, 3)  |
        |    backbone.fpn_output3.bias    |    (256,)            |
        |   backbone.fpn_lateral4         |   0.3M               |
        |    backbone.fpn_lateral4.weight |    (256, 1024, 1, 1) |
        |    backbone.fpn_lateral4.bias   |    (256,)            |
        |   backbone.fpn_output4          |   0.6M               |
        |    backbone.fpn_output4.weight  |    (256, 256, 3, 3)  |
        |    backbone.fpn_output4.bias    |    (256,)            |
        |   backbone.fpn_lateral5         |   0.5M               |
        |    backbone.fpn_lateral5.weight |    (256, 2048, 1, 1) |
        |    backbone.fpn_lateral5.bias   |    (256,)            |
        |   backbone.fpn_output5          |   0.6M               |
        |    backbone.fpn_output5.weight  |    (256, 256, 3, 3)  |
        |    backbone.fpn_output5.bias    |    (256,)            |
        |   backbone.top_block            |   5.3M               |
        |    backbone.top_block.p6        |    4.7M              |
        |    backbone.top_block.p7        |    0.6M              |
        |   backbone.bottom_up            |   23.5M              |
        |    backbone.bottom_up.stem      |    9.4K              |
        |    backbone.bottom_up.res2      |    0.2M              |
        |    backbone.bottom_up.res3      |    1.2M              |
        |    backbone.bottom_up.res4      |    7.1M              |
        |    backbone.bottom_up.res5      |    14.9M             |
        |    ......                       |    .....             |

    Args:
        model: a torch module
        max_depth (int): maximum depth to recursively print submodules or
            parameters

    Returns:
        str: the table to be printed
    """
    count: typing.DefaultDict[str, int] = parameter_count(model, trainable_only)
    # pyre-fixme[24]: Generic type `tuple` expects at least 1 type parameter.
    param_shape: typing.Dict[str, typing.Tuple] = {
        k: tuple(v.shape) for k, v in model.named_parameters()
    }

    # pyre-fixme[24]: Generic type `tuple` expects at least 1 type parameter.
    table: typing.List[typing.Tuple] = []

    def format_size(x: int) -> str:
        if x > 1e8:
            return "{:.1f}G".format(x / 1e9)
        if x > 1e5:
            return "{:.1f}M".format(x / 1e6)
        if x > 1e2:
            return "{:.1f}K".format(x / 1e3)
        return str(x)

    def fill(lvl: int, prefix: str) -> None:
        if lvl >= max_depth:
            return
        for name, v in count.items():
            if name.count(".") == lvl and name.startswith(prefix):
                indent = " " * (lvl + 1)
                if name in param_shape:
                    table.append((indent + name, indent + str(param_shape[name])))
                else:
                    table.append((indent + name, indent + format_size(v)))
                    fill(lvl + 1, name + ".")

    table.append(("model", format_size(count.pop(""))))
    fill(0, "")

    old_ws = tabulate.PRESERVE_WHITESPACE
    tabulate.PRESERVE_WHITESPACE = True
    tab = tabulate.tabulate(table, headers=["name", "#elements or shape"], tablefmt="pipe")
    tabulate.PRESERVE_WHITESPACE = old_ws
    return tab


def cycle(iterable):
    while True:
        for x in iterable:
            yield x

class MattingTrainer(AMPTrainer):
    def __init__(self, model, data_loader, optimizer, grad_scaler=None):
        super().__init__(model, data_loader, optimizer, grad_scaler=None)
        self.data_loader_iter = iter(cycle(self.data_loader))

        # print model parameters
        logger.info("All parameters: \n" + parameter_count_table(model))
        logger.info("Trainable parameters: \n" + parameter_count_table(model, trainable_only=True, max_depth=8))

    def run_step(self):
        """
        Implement the AMP training logic.
        """
        assert self.model.training, "[AMPTrainer] model was changed to eval mode!"
        assert torch.cuda.is_available(), "[AMPTrainer] CUDA is required for AMP training!"
        from torch.cuda.amp import autocast

        #matting pass
        start = time.perf_counter()        
        data = next(self.data_loader_iter)
        data_time = time.perf_counter() - start

        with autocast():
            loss_dict = self.model(data)
            if isinstance(loss_dict, torch.Tensor):
                losses = loss_dict
                loss_dict = {"total_loss": loss_dict}
            else:
                losses = sum(loss_dict.values())

        self.optimizer.zero_grad()
        self.grad_scaler.scale(losses).backward()

        self._write_metrics(loss_dict, data_time)

        self.grad_scaler.step(self.optimizer)
        self.grad_scaler.update()