| import torch.nn as nn | |
| from . import csn | |
| from . import r2plus1d | |
| def ir_csn_152(pretrained=True, **kwargs): | |
| model = csn.ir_csn_152(pretraining='ig65m_32frms' if pretrained else '', num_classes=359) | |
| model.avgpool = nn.Identity() | |
| model.fc = nn.Identity() | |
| return model | |
| def ir_csn_101(pretrained=True, **kwargs): | |
| model = ir_csn_152(pretrained=pretrained, **kwargs) | |
| model.layer2 = model.layer2[:4] | |
| model.layer3 = model.layer3[:23] | |
| return model | |
| def ir_csn_50(pretrained=True, **kwargs): | |
| model = ir_csn_152(pretrained=pretrained, **kwargs) | |
| model.layer2 = model.layer2[:4] | |
| model.layer3 = model.layer3[:6] | |
| return model | |
| def ip_csn_152(pretrained=True, **kwargs): | |
| model = csn.ip_csn_152(pretraining='ig65m_32frms' if pretrained else '', num_classes=359) | |
| model.avgpool = nn.Identity() | |
| model.fc = nn.Identity() | |
| return model | |
| def ip_csn_101(pretrained=True, **kwargs): | |
| model = ip_csn_152(pretrained=pretrained, **kwargs) | |
| model.layer2 = model.layer2[:4] | |
| model.layer3 = model.layer3[:23] | |
| return model | |
| def ip_csn_50(pretrained=True, **kwargs): | |
| model = ip_csn_152(pretrained=pretrained, **kwargs) | |
| model.layer2 = model.layer2[:4] | |
| model.layer3 = model.layer3[:6] | |
| return model | |
| def r2plus1d_34(pretrained=True, **kwargs): | |
| model = r2plus1d.r2plus1d_34(pretraining='32_ig65m' if pretrained else '', num_classes=359) | |
| model.avgpool = nn.Identity() | |
| model.fc = nn.Identity() | |
| return model |