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import torch
import torchvision.transforms as transforms
from typing import NamedTuple, List, Callable, List, Tuple, Optional
from torch import nn
import torch.nn.functional as F
import numpy as np
class LinData(NamedTuple):
in_dim : int # input dimension
hidden_layers : List[int] # hidden layers including the output layer
activations : List[Optional[Callable[[torch.Tensor],torch.Tensor]]] # list of activations
bns : List[bool] # list of bools
dropouts : List[Optional[float]] # list of dropouts probas
class CNNData(NamedTuple):
in_dim : int # input dimension
n_f : List[int] # num filters
kernel_size : List[Tuple] # kernel size [(5,5,5), (3,3,3),(3,3,3)]
activations : List[Optional[Callable[[torch.Tensor],torch.Tensor]]] # activation list
bns : List[bool] # batch normialization [True, True, False]
dropouts : List[Optional[float]] # # list of dropouts probas [.5,0,0]
#dropouts_ps : list # [0.5,.7, 0]
paddings : List[Optional[Tuple]] #[(0,0,0),(0,0,0), (0,0,0)]
strides : List[Optional[Tuple]] #[(1,1,1),(1,1,1),(1,1,1)]
class NetData(NamedTuple):
cnn3d : CNNData
lin : LinData
def conv3D_output_size(args, img_size):
if not isinstance(args, CNNData):
raise TypeError("input must be a ParserClass")
(cin, h , w) = img_size
# compute output shape of conv3D
for idx , chan in enumerate(args.kernel_size):
padding = args.paddings[idx]
stride = args.strides[idx]
(cin, h , w) = (np.floor((cin + 2 * padding[0] - chan[0] ) / stride[0] + 1).astype(int),
np.floor((h + 2 * padding[1] - chan[1] ) / stride[1] + 1).astype(int),
np.floor((w + 2 * padding[2] - chan[2] ) / stride[2] + 1).astype(int))
final_dim = int(args.n_f[-1] * cin * h * w)
return final_dim
class CNN3D_Mike(nn.Module):
def __init__(self, t_dim=30, img_x=256 , img_y=342, drop_p=0, fc_hidden1=256, fc_hidden2=256):
super(CNN3D_Mike, self).__init__() # set video dimension
self.t_dim = t_dim
self.img_x = img_x
self.img_y = img_y
# fully connected layer hidden nodes
self.fc_hidden1, self.fc_hidden2 = fc_hidden1, fc_hidden2
self.drop_p = drop_p
#self.num_classes = num_classes
self.ch1, self.ch2 = 32, 48
self.k1, self.k2 = (5, 5, 5), (3, 3, 3) # 3d kernel size
self.s1, self.s2 = (2, 2, 2), (2, 2, 2) # 3d strides
self.pd1, self.pd2 = (0, 0, 0), (0, 0, 0) # 3d padding # compute conv1 & conv2 output shape
self.conv1_outshape = conv3D_output_size((self.t_dim, self.img_x, self.img_y), self.pd1, self.k1, self.s1)
self.conv2_outshape = conv3D_output_size(self.conv1_outshape, self.pd2, self.k2, self.s2)
self.conv1 = nn.Conv3d(in_channels=1, out_channels=self.ch1, kernel_size=self.k1, stride=self.s1,
padding=self.pd1)
self.bn1 = nn.BatchNorm3d(self.ch1)
self.conv2 = nn.Conv3d(in_channels=self.ch1, out_channels=self.ch2, kernel_size=self.k2, stride=self.s2,
padding=self.pd2)
self.bn2 = nn.BatchNorm3d(self.ch2)
self.relu = nn.ReLU(inplace=True)
self.drop = nn.Dropout3d(self.drop_p)
self.pool = nn.MaxPool3d(2)
self.fc1 = nn.Linear(self.ch2*self.conv2_outshape[0]*self.conv2_outshape[1]*self.conv2_outshape[2],
self.fc_hidden1) # fully connected hidden layer
self.fc2 = nn.Linear(self.fc_hidden1, self.fc_hidden2)
self.fc3 = nn.Linear(self.fc_hidden2,1) # fully connected layer, output = multi-classes
def forward(self, x_3d):
# Conv 1
x = self.conv1(x_3d)
x = self.bn1(x)
x = self.relu(x)
x = self.drop(x)
# Conv 2
x = self.conv2(x)
x = self.bn2(x)
x = self.relu(x)
x = self.drop(x)
# FC 1 and 2
x = x.view(x.size(0), -1)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
#x = F.relu(self.fc3(x))
#x = F.relu(self.fc3(x))
x = F.dropout(x, p=self.drop_p, training=self.training)
#x = self.fc3(x)
#x = F.softmax(self.fc2(x))
x = self.fc3(x)
return x
class CNNLayers(nn.Module):
def __init__(self, args):
super(CNNLayers, self).__init__()
self.in_dim = args.in_dim# 1/3
self.n_f = args.n_f#[32,64]
self.kernel_size = args.kernel_size # [(5,5,5), (3,3,3)]
self.activations = args.activations#['relu', 'relu']
self.bns = args.bns #[True, True],
self.dropouts = args.dropouts #[True, True]
#self.dropouts_ps = args.dropouts_ps#[0.5,.7]
self.paddings = args.paddings #[(0,0,0),(0,0,0)]
self.strides = args.strides # strides [(1,1,1),(1,1,1),(1,1,1)])
#self.poolings = args.poolings
assert len(self.n_f) == len(self.activations) == len(self.bns) == len(self.dropouts), 'dimensions mismatch : check dimensions!'
# generate layers seq of seq
self._get_layers()
def _get_layers(self):
layers =nn.ModuleList()
in_channels = self.in_dim
for idx, chans in enumerate(self.n_f):
sub_layers = nn.ModuleList()
sub_layers.append(nn.Conv3d(in_channels = in_channels,
out_channels = chans, #self.n_f[idx],
kernel_size = self.kernel_size[idx],
stride = self.strides[idx],
padding = self.paddings[idx]
))
if self.bns[idx] : sub_layers.append(nn.BatchNorm3d(num_features = self.n_f[idx]))
#if self.dropouts[idx] : sub_layers.append(nn.Dropout3d(p = self.dropouts_ps[idx]))
if self.dropouts[idx] : sub_layers.append(nn.Dropout3d(p = self.dropouts[idx]))
#if self.activations[idx] : sub_layers.append(self.__class__.get_activation(self.activations[idx]))
if self.activations[idx] : sub_layers.append(self.activations[idx])
sub_layers = nn.Sequential(*sub_layers)
layers.append(sub_layers)
in_channels = self.n_f[idx]
self.layers = nn.Sequential(*layers)
@staticmethod
def get_activation(activation):
if activation == 'relu':
activation=nn.ReLU()
elif activation == 'leakyrelu':
activation=nn.LeakyReLU(negative_slope=0.1)
elif activation == 'selu':
activation=nn.SELU()
return activation
def forward(self, x):
x = self.layers(x)
return x
class CNN3D(nn.Module):
def __init__(self, args):
super(CNN3D,self).__init__()
# check datatype
if not isinstance(args, NetData):
raise TypeError("input must be a ParserClass")
self.cnn3d = CNNLayers(args.cnn3d)
self.lin = LinLayers(args.lin)
self.in_dim = args.lin.in_dim
def forward(self, x):
# cnn 3d
x = self.cnn3d(x)
x = x.view(-1, self.in_dim)
# feedforward
x = self.lin(x)
return x
class LinLayers(nn.Module):
def __init__(self, args):
super(LinLayers,self).__init__()
in_dim= args.in_dim #16,
hidden_layers= args.hidden_layers #[512,256,128,2],
activations=args.activations#[nn.LeakyReLU(0.2),nn.LeakyReLU(0.2),nn.LeakyReLU(0.2)],
batchnorms=args.bns#[True,True,True],
dropouts = args.dropouts#[None, 0.2, 0.2]
assert len(hidden_layers) == len(activations) == len(batchnorms) == len(dropouts), 'dimensions mismatch!'
layers=nn.ModuleList()
if hidden_layers:
old_dim=in_dim
for idx,layer in enumerate(hidden_layers):
sub_layers = nn.ModuleList()
sub_layers.append(nn.Linear(old_dim,layer))
if batchnorms[idx] : sub_layers.append(nn.BatchNorm1d(num_features=layer))
if activations[idx] : sub_layers.append(activations[idx])
if dropouts[idx] : sub_layers.append(nn.Dropout(p=dropouts[idx]))
old_dim = layer
sub_layers = nn.Sequential(*sub_layers)
layers.append(sub_layers)
else:# for single layer
layers.append(nn.Linear(in_dim,out_dim))
if batchnorms : layers.append(nn.BatchNorm1d(num_features=out_dim))
if activations : layers.append(activations)
if dropouts : layers.append(nn.Dropout(p=dropouts))
self.layers = nn.Sequential(*layers)
def forward(self,x):
x = self.layers(x)
return x
'''
def _check_dimensions(self):
if isinstance(self.hidden_layers,list) :
assert len(self.hidden_layers)==len(self.activations)
assert len(self.hidden_layers)==len(self.batchnorms)
assert len(self.hidden_layers)==len(self.dropouts)
'''
def load_model():
# CNN3D Layer's architecture
cnndata = CNNData(in_dim = 1,
n_f =[32,48],
kernel_size=[(5,5,5), (3,3,3)],
activations=[nn.ReLU(),nn.ReLU()],
bns = [True, True],
dropouts = [0, 0],
paddings = [(0,0,0),(0,0,0)],
strides = [(2,2,2),(2,2,2)])
# Feedforward layer's architecture
lindata = LinData(in_dim = conv3D_output_size(cnndata, [30, 256, 342]),
hidden_layers= [256,256,1],
activations=[nn.ReLU(),nn.ReLU(),None],
bns=[False,False,False],
dropouts =[0.2, 0, 0])
# combined architecture
args = NetData(cnndata, lindata)
# weight file
weight_file = 'cnn3d_epoch_300.pt'
# CNN3D model
# device = 'cuda' if torch.cuda.is_available() else 'cpu'
device = torch.device('cpu')
cnn3d = CNN3D(args).to(device)
cnn3d.load_state_dict(torch.load(weight_file, map_location=device))
cnn3d.eval()
#print(cnn3d)
return cnn3d |