DCASE2018Task2/networks/network.py at master · Cocoxili/DCASE2018Task2 · GitHub

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import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models
import math


def resnet50_mfcc(pretrained=False, **kwargs):
    model = models.resnet50(pretrained=pretrained)
    # model.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,
    #                            bias=False)
    model.avgpool = nn.AvgPool2d((2, 5), stride=(2, 5))
    model.fc = nn.Linear(512 * 4, 41)
    return model


def resnet101_mfcc(pretrained=False, **kwargs):
    model = models.resnet101(pretrained=pretrained)
    # model.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,
    #                            bias=False)
    model.avgpool = nn.AvgPool2d((2, 5), stride=(2, 5))
    model.fc = nn.Linear(512 * 4, 41)
    return model


def resnet50_logmel(pretrained=False, **kwargs):
    model = models.resnet50(pretrained=pretrained)
    model.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
    model.avgpool = nn.AvgPool2d((2, 5), stride=(2, 5))
    model.fc = nn.Linear(512 * 4, 41)
    return model


def resnet101_logmel(pretrained=False, **kwargs):
    model = models.resnet101(pretrained=pretrained)
    model.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
    model.avgpool = nn.AvgPool2d((2, 5), stride=(2, 5))
    model.fc = nn.Linear(512 * 4, 41)
    return model