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model.py
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model.py
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import torch
from torch import nn
from models import c3d, squeezenet, mobilenet, shufflenet, mobilenetv2, shufflenetv2, resnext, resnet, resnetl
import pdb
def generate_model(opt):
assert opt.model in ['c3d', 'squeezenet', 'mobilenet', 'resnext', 'resnet', 'resnetl',
'shufflenet', 'mobilenetv2', 'shufflenetv2']
if opt.model == 'c3d':
from models.c3d import get_fine_tuning_parameters
model = c3d.get_model(
num_classes=opt.n_classes,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model == 'squeezenet':
from models.squeezenet import get_fine_tuning_parameters
model = squeezenet.get_model(
version=opt.version,
num_classes=opt.n_classes,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model == 'shufflenet':
from models.shufflenet import get_fine_tuning_parameters
model = shufflenet.get_model(
groups=opt.groups,
width_mult=opt.width_mult,
num_classes=opt.n_classes)
elif opt.model == 'shufflenetv2':
from models.shufflenetv2 import get_fine_tuning_parameters
model = shufflenetv2.get_model(
num_classes=opt.n_classes,
sample_size=opt.sample_size,
width_mult=opt.width_mult)
elif opt.model == 'mobilenet':
from models.mobilenet import get_fine_tuning_parameters
model = mobilenet.get_model(
num_classes=opt.n_classes,
sample_size=opt.sample_size,
width_mult=opt.width_mult)
elif opt.model == 'mobilenetv2':
from models.mobilenetv2 import get_fine_tuning_parameters
model = mobilenetv2.get_model(
num_classes=opt.n_classes,
sample_size=opt.sample_size,
width_mult=opt.width_mult)
elif opt.model == 'resnext':
assert opt.model_depth in [50, 101, 152]
from models.resnext import get_fine_tuning_parameters
if opt.model_depth == 50:
model = resnext.resnext50(
num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
cardinality=opt.resnext_cardinality,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 101:
model = resnext.resnext101(
num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
cardinality=opt.resnext_cardinality,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 152:
model = resnext.resnext152(
num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
cardinality=opt.resnext_cardinality,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model == 'resnetl':
assert opt.model_depth in [10]
from models.resnetl import get_fine_tuning_parameters
if opt.model_depth == 10:
model = resnetl.resnetl10(
num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model == 'resnet':
assert opt.model_depth in [10, 18, 34, 50, 101, 152, 200]
from models.resnet import get_fine_tuning_parameters
if opt.model_depth == 10:
model = resnet.resnet10(
num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 18:
model = resnet.resnet18(
num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 34:
model = resnet.resnet34(
num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 50:
model = resnet.resnet50(
num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 101:
model = resnet.resnet101(
num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 152:
model = resnet.resnet152(
num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 200:
model = resnet.resnet200(
num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
if not opt.no_cuda:
model = model.cuda()
model = nn.DataParallel(model, device_ids=None)
pytorch_total_params = sum(p.numel() for p in model.parameters() if
p.requires_grad)
print("Total number of trainable parameters: ", pytorch_total_params)
if opt.pretrain_path:
print('loading pretrained model {}'.format(opt.pretrain_path))
pretrain = torch.load(opt.pretrain_path, map_location=torch.device('cpu'))
# print(opt.arch)
# print(pretrain['arch'])
# assert opt.arch == pretrain['arch']
model = modify_kernels(opt, model, opt.pretrain_modality)
model.load_state_dict(pretrain['state_dict'])
if opt.model in ['mobilenet', 'mobilenetv2', 'shufflenet', 'shufflenetv2']:
model.module.classifier = nn.Sequential(
nn.Dropout(0.5),
nn.Linear(model.module.classifier[1].in_features, opt.n_finetune_classes))
model.module.classifier = model.module.classifier.cuda()
elif opt.model == 'squeezenet':
model.module.classifier = nn.Sequential(
nn.Dropout(p=0.5),
nn.Conv3d(model.module.classifier[1].in_channels, opt.n_finetune_classes, kernel_size=1),
nn.ReLU(inplace=True),
nn.AvgPool3d((1,4,4), stride=1))
model.module.classifier = model.module.classifier.cuda()
else:
model.module.fc = nn.Linear(model.module.fc.in_features, opt.n_finetune_classes)
model.module.fc = model.module.fc.cuda()
model = modify_kernels(opt, model, opt.modality)
else:
model = modify_kernels(opt, model, opt.modality)
parameters = get_fine_tuning_parameters(model, opt.ft_portion)
return model, parameters
else:
if opt.pretrain_path:
print('loading pretrained model {}'.format(opt.pretrain_path))
pretrain = torch.load(opt.pretrain_path)
model = modify_kernels(opt, model, opt.pretrain_modality)
model.load_state_dict(pretrain['state_dict'])
if opt.model in ['mobilenet', 'mobilenetv2', 'shufflenet', 'shufflenetv2']:
model.module.classifier = nn.Sequential(
nn.Dropout(0.9),
nn.Linear(model.module.classifier[1].in_features, opt.n_finetune_classes)
)
elif opt.model == 'squeezenet':
model.module.classifier = nn.Sequential(
nn.Dropout(p=0.5),
nn.Conv3d(model.module.classifier[1].in_channels, opt.n_finetune_classes, kernel_size=1),
nn.ReLU(inplace=True),
nn.AvgPool3d((1,4,4), stride=1))
else:
model.module.fc = nn.Linear(model.module.fc.in_features, opt.n_finetune_classes)
model = modify_kernels(opt, model, opt.modality)
parameters = get_fine_tuning_parameters(model, opt.ft_begin_index)
return model, parameters
else:
model = modify_kernels(opt, model, opt.modality)
return model, model.parameters()
def _construct_depth_model(base_model):
# modify the first convolution kernels for Depth input
modules = list(base_model.modules())
first_conv_idx = list(filter(lambda x: isinstance(modules[x], nn.Conv3d),
list(range(len(modules)))))[0]
conv_layer = modules[first_conv_idx]
container = modules[first_conv_idx - 1]
# modify parameters, assume the first blob contains the convolution kernels
motion_length = 1
params = [x.clone() for x in conv_layer.parameters()]
kernel_size = params[0].size()
new_kernel_size = kernel_size[:1] + (1*motion_length, ) + kernel_size[2:]
new_kernels = params[0].data.mean(dim=1, keepdim=True).expand(new_kernel_size).contiguous()
new_conv = nn.Conv3d(1, conv_layer.out_channels, conv_layer.kernel_size, conv_layer.stride,
conv_layer.padding, bias=True if len(params) == 2 else False)
new_conv.weight.data = new_kernels
if len(params) == 2:
new_conv.bias.data = params[1].data # add bias if neccessary
layer_name = list(container.state_dict().keys())[0][:-7] # remove .weight suffix to get the layer name
# replace the first convlution layer
setattr(container, layer_name, new_conv)
return base_model
def _construct_rgbdepth_model(base_model):
# modify the first convolution kernels for RGB-D input
modules = list(base_model.modules())
first_conv_idx = list(filter(lambda x: isinstance(modules[x], nn.Conv3d),
list(range(len(modules)))))[0]
conv_layer = modules[first_conv_idx]
container = modules[first_conv_idx - 1]
# modify parameters, assume the first blob contains the convolution kernels
motion_length = 1
params = [x.clone() for x in conv_layer.parameters()]
kernel_size = params[0].size()
new_kernel_size = kernel_size[:1] + (1 * motion_length,) + kernel_size[2:]
new_kernels = torch.mul(torch.cat((params[0].data, params[0].data.mean(dim=1,keepdim=True).expand(new_kernel_size).contiguous()), 1), 0.6)
new_kernel_size = kernel_size[:1] + (3 + 1 * motion_length,) + kernel_size[2:]
new_conv = nn.Conv3d(4, conv_layer.out_channels, conv_layer.kernel_size, conv_layer.stride,
conv_layer.padding, bias=True if len(params) == 2 else False)
new_conv.weight.data = new_kernels
if len(params) == 2:
new_conv.bias.data = params[1].data # add bias if neccessary
layer_name = list(container.state_dict().keys())[0][:-7] # remove .weight suffix to get the layer name
# replace the first convolution layer
setattr(container, layer_name, new_conv)
return base_model
def _modify_first_conv_layer(base_model, new_kernel_size1, new_filter_num):
modules = list(base_model.modules())
first_conv_idx = list(filter(lambda x: isinstance(modules[x], nn.Conv3d),
list(range(len(modules)))))[0]
conv_layer = modules[first_conv_idx]
container = modules[first_conv_idx - 1]
new_conv = nn.Conv3d(new_filter_num, conv_layer.out_channels, kernel_size=(new_kernel_size1,7,7),
stride=(1,2,2), padding=(1,3,3), bias=False)
layer_name = list(container.state_dict().keys())[0][:-7]
setattr(container, layer_name, new_conv)
return base_model
def modify_kernels(opt, model, modality):
if modality == 'RGB' and opt.model not in ['c3d', 'squeezenet', 'mobilenet','shufflenet', 'mobilenetv2', 'shufflenetv2']:
print("[INFO]: RGB model is used for init model")
model = _modify_first_conv_layer(model,3,3) ##### Check models trained (3,7,7) or (7,7,7)
elif modality == 'Depth':
print("[INFO]: Converting the pretrained model to Depth init model")
model = _construct_depth_model(model)
print("[INFO]: Done. Flow model ready.")
elif modality == 'RGB-D':
print("[INFO]: Converting the pretrained model to RGB+D init model")
model = _construct_rgbdepth_model(model)
print("[INFO]: Done. RGB-D model ready.")
modules = list(model.modules())
first_conv_idx = list(filter(lambda x: isinstance(modules[x], nn.Conv3d),
list(range(len(modules)))))[0]
#conv_layer = modules[first_conv_idx]
#if conv_layer.kernel_size[0]> opt.sample_duration:
# model = _modify_first_conv_layer(model,int(opt.sample_duration/2),1)
return model