lib/utils/utils.py

# ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# ------------------------------------------------------------------------------

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import logging
import time
from collections import namedtuple
from pathlib import Path

import torch
import torch.optim as optim
import torch.nn as nn


def create_logger(cfg, cfg_name, phase='train'):
    root_output_dir = Path(cfg.OUTPUT_DIR)
    # set up logger
    if not root_output_dir.exists():
        print('=> creating {}'.format(root_output_dir))
        root_output_dir.mkdir()

    dataset = cfg.DATASET.DATASET + '_' + cfg.DATASET.HYBRID_JOINTS_TYPE \
        if cfg.DATASET.HYBRID_JOINTS_TYPE else cfg.DATASET.DATASET
    dataset = dataset.replace(':', '_')
    model = cfg.MODEL.NAME
    cfg_name = os.path.basename(cfg_name).split('.')[0]

    time_str = time.strftime('%Y-%m-%d-%H-%M')

    TAG = cfg.TAG if len(cfg.TAG) != 0 else time_str
    final_output_dir = root_output_dir / dataset / model / cfg_name / TAG

    print('=> creating {}'.format(final_output_dir))
    final_output_dir.mkdir(parents=True, exist_ok=True)

    log_file = '{}_{}_{}.log'.format(cfg_name, time_str, phase)
    final_log_file = final_output_dir / log_file
    head = '%(asctime)-15s %(message)s'
    logging.basicConfig(filename=str(final_log_file),
                        format=head)
    logger = logging.getLogger()
    logger.setLevel(logging.INFO)
    console = logging.StreamHandler()
    logging.getLogger('').addHandler(console)

    tensorboard_log_dir = Path(cfg.LOG_DIR) / dataset / model / \
        cfg_name / TAG

    print('=> creating {}'.format(tensorboard_log_dir))
    tensorboard_log_dir.mkdir(parents=True, exist_ok=True)

    return logger, str(final_output_dir), str(tensorboard_log_dir)


def get_optimizer(cfg, model):
    optimizer = None
    if cfg.TRAIN.OPTIMIZER == 'sgd':
        optimizer = optim.SGD(
            model.parameters(),
            lr=cfg.TRAIN.LR,
            momentum=cfg.TRAIN.MOMENTUM,
            weight_decay=cfg.TRAIN.WD,
            nesterov=cfg.TRAIN.NESTEROV
        )
    elif cfg.TRAIN.OPTIMIZER == 'adam':
        optimizer = optim.Adam(
            model.parameters(),
            lr=cfg.TRAIN.LR,
            weight_decay=cfg.TRAIN.WD
        )

    return optimizer


def save_checkpoint(states, is_best, output_dir,
                    filename='checkpoint.pth'):
    torch.save(states, os.path.join(output_dir, filename))
    if is_best and 'state_dict' in states:
        torch.save(states['best_state_dict'],
                   os.path.join(output_dir, 'model_best.pth'))


def get_model_summary(model, *input_tensors, item_length=26, verbose=False):
    """
    :param model:
    :param input_tensors:
    :param item_length:
    :return:
    """

    summary = []

    ModuleDetails = namedtuple(
        "Layer", ["name", "input_size", "output_size", "num_parameters", "multiply_adds"])
    hooks = []
    layer_instances = {}

    def add_hooks(module):

        def hook(module, input, output):
            class_name = str(module.__class__.__name__)

            instance_index = 1
            if class_name not in layer_instances:
                layer_instances[class_name] = instance_index
            else:
                instance_index = layer_instances[class_name] + 1
                layer_instances[class_name] = instance_index

            layer_name = class_name + "_" + str(instance_index)

            params = 0

            if class_name.find("Conv") != -1 or class_name.find("BatchNorm") != -1 or \
               class_name.find("Linear") != -1:
                for param_ in module.parameters():
                    params += param_.view(-1).size(0)

            flops = "Not Available"
            if class_name.find("Conv") != -1 and hasattr(module, "weight"):
                flops = (
                    torch.prod(
                        torch.LongTensor(list(module.weight.data.size()))) *
                    torch.prod(
                        torch.LongTensor(list(output.size())[2:]))).item()
            elif isinstance(module, nn.Linear):
                flops = (torch.prod(torch.LongTensor(list(output.size()))) \
                         * input[0].size(1)).item()

            if isinstance(input[0], list):
                input = input[0]
            if isinstance(output, list):
                output = output[0]

            summary.append(
                ModuleDetails(
                    name=layer_name,
                    input_size=list(input[0].size()),
                    output_size=list(output.size()),
                    num_parameters=params,
                    multiply_adds=flops)
            )

        if not isinstance(module, nn.ModuleList) \
           and not isinstance(module, nn.Sequential) \
           and module != model:
            hooks.append(module.register_forward_hook(hook))

    model.eval()
    model.apply(add_hooks)

    space_len = item_length

    model(*input_tensors)
    for hook in hooks:
        hook.remove()

    details = ''
    if verbose:
        details = "Model Summary" + \
            os.linesep + \
            "Name{}Input Size{}Output Size{}Parameters{}Multiply Adds (Flops){}".format(
                ' ' * (space_len - len("Name")),
                ' ' * (space_len - len("Input Size")),
                ' ' * (space_len - len("Output Size")),
                ' ' * (space_len - len("Parameters")),
                ' ' * (space_len - len("Multiply Adds (Flops)"))) \
                + os.linesep + '-' * space_len * 5 + os.linesep

    params_sum = 0
    flops_sum = 0
    for layer in summary:
        params_sum += layer.num_parameters
        if layer.multiply_adds != "Not Available":
            flops_sum += layer.multiply_adds
        if verbose:
            details += "{}{}{}{}{}{}{}{}{}{}".format(
                layer.name,
                ' ' * (space_len - len(layer.name)),
                layer.input_size,
                ' ' * (space_len - len(str(layer.input_size))),
                layer.output_size,
                ' ' * (space_len - len(str(layer.output_size))),
                layer.num_parameters,
                ' ' * (space_len - len(str(layer.num_parameters))),
                layer.multiply_adds,
                ' ' * (space_len - len(str(layer.multiply_adds)))) \
                + os.linesep + '-' * space_len * 5 + os.linesep

    details += os.linesep \
        + "Total Parameters: {:,}".format(params_sum) \
        + os.linesep + '-' * space_len * 5 + os.linesep
    details += "Total Multiply Adds (For Convolution and Linear Layers only): {:,} GFLOPs".format(flops_sum/(1024**3)) \
        + os.linesep + '-' * space_len * 5 + os.linesep
    details += "Number of Layers" + os.linesep
    for layer in layer_instances:
        details += "{} : {} layers   ".format(layer, layer_instances[layer])

    return details