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MultiHeadAlexNet.py
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MultiHeadAlexNet.py
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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from pdb import set_trace as breakpoint
class Flatten(nn.Module):
def __init__(self):
super(Flatten, self).__init__()
def forward(self, feat):
return feat.view(feat.size(0), -1) #torch.Size([1, 9216])
class MHAlexNet(nn.Module):
def __init__(self, opt):
super(MHAlexNet, self).__init__()
num_heads = opt['num_heads']
num_classes = opt['num_classes']
out_feat_keys = opt['out_feat_keys']
conv1 = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
)
pool1 = nn.MaxPool2d(kernel_size=3, stride=2)
conv2 = nn.Sequential(
nn.Conv2d(64, 192, kernel_size=5, padding=2),
nn.BatchNorm2d(192),
nn.ReLU(inplace=True),
)
pool2 = nn.MaxPool2d(kernel_size=3, stride=2)
conv3 = nn.Sequential(
nn.Conv2d(192, 384, kernel_size=3, padding=1),
nn.BatchNorm2d(384),
nn.ReLU(inplace=True),
)
conv4 = nn.Sequential(
nn.Conv2d(384, 256, kernel_size=3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
)
conv5 = nn.Sequential(
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
)
pool5 = nn.MaxPool2d(kernel_size=3, stride=2) # [1, 256, 11, 11] (size 400 --> 11, size 224 --> 6)
num_pool5_feats = 11 * 11 * 256
fc_block = nn.Sequential(
Flatten(),
nn.Linear(num_pool5_feats, 4096, bias=False),
nn.BatchNorm1d(4096),
nn.ReLU(inplace=True),
nn.Linear(4096, 4096, bias=False),
nn.BatchNorm1d(4096),
nn.ReLU(inplace=True),
)
pathology_head = nn.Sequential(
nn.Linear(4096, num_classes[0]),
)
distortion1_head = nn.Sequential(
nn.Linear(4096, num_classes[1]),
)
# distortion2_head = nn.Sequential(
# nn.Linear(4096, num_classes[2]),
# )
self._feature_blocks = nn.ModuleList([
conv1,
pool1,
conv2,
pool2,
conv3,
conv4,
conv5,
pool5,
fc_block,
# distortion2_head,
distortion1_head,
pathology_head,
])
self.all_feat_names = [
'conv1',
'pool1',
'conv2',
'pool2',
'conv3',
'conv4',
'conv5',
'pool5',
'fc_block',
# 'distortion2_head',
'distortion1_head',
'pathology_head',
]
assert(len(self.all_feat_names) == len(self._feature_blocks))
def _parse_out_keys_arg(self, out_feat_keys):
# By default return the features of the last layer / module.
out_feat_keys = [self.all_feat_names[-1],] if out_feat_keys is None else out_feat_keys
if len(out_feat_keys) == 0:
raise ValueError('Empty list of output feature keys.')
for f, key in enumerate(out_feat_keys):
if key not in self.all_feat_names:
raise ValueError('Feature with name {0} does not exist. Existing features: {1}.'.format(key, self.all_feat_names))
elif key in out_feat_keys[:f]:
raise ValueError('Duplicate output feature key: {0}.'.format(key))
# Find the highest output feature in `out_feat_keys
max_out_feat = max([self.all_feat_names.index(key) for key in out_feat_keys])
return out_feat_keys, max_out_feat
def forward(self, x, out_feat_keys=None, num_heads=2):
""" Forward an image `x` through the network and return the asked output features.
Args:
x: input image.
out_feat_keys: a list/tuple with the feature names of the features
that the function should return. By default the last feature of
the network is returned.
Return:
out_feats: If multiple output features were asked then `out_feats`
is a list with the asked output features placed in the same
order as in `out_feat_keys`. If a single output feature was
asked then `out_feats` is that output feature (and not a list)
"""
out_feat_keys, max_out_feat = self._parse_out_keys_arg(out_feat_keys)
out_feats = [None] * len(out_feat_keys)
feat = x
for f in range(max_out_feat+1): # shd be (max_out_feat-num_heads)
feat = self._feature_blocks[f](feat)
key = self.all_feat_names[f]
if f <= (max_out_feat-1):
if key in out_feat_keys:
# print(key)
out_feats[out_feat_keys.index(key)] = feat
else: #'fc_block':
assert(key == 'fc_block')
if key in out_feat_keys:
out_feats[out_feat_keys.index(key)] = feat
# print('fc_feat evaluated')
out_head_p = self._feature_blocks[-1](feat)
out_head_d1 = self._feature_blocks[-2](feat)
# out_head_d2 = self._feature_blocks[-3](feat)
out_feats = out_feats[0] if len(out_feats)==1 else out_feats
out_yhats = {
'pathology': out_head_p,
'distortion_1': out_head_d1,
# 'distortion_2': out_head_d2
}
return out_feats, out_yhats
def get_L1filters(self):
convlayer = self._feature_blocks[0][0]
batchnorm = self._feature_blocks[0][1]
filters = convlayer.weight.data
scalars = (batchnorm.weight.data / torch.sqrt(batchnorm.running_var + 1e-05))
filters = (filters * scalars.view(-1, 1, 1, 1).expand_as(filters)).cpu().clone()
return filters
def create_model(opt):
return MHAlexNet(opt)
if __name__ == '__main__':
size = 224
opt = {'num_heads':3, 'num_classes':[3, 4]}
net = create_model(opt)
net.eval()
x = torch.autograd.Variable(torch.FloatTensor(1,3,size,size).uniform_(-1,1))
feat, out_dict = net(x, out_feat_keys=net.all_feat_names[:-opt['num_heads']], num_heads=opt['num_heads']) # till fc block
out_1, out_2 = out_dict['pathology'], out_dict['distortion_1']
for f in range(len(out_1)):
print('Output feature {0} - size {1}'.format(
net.all_feat_names[f], out_1[f].size()))
filters = net.get_L1filters()
print('First layer filter shape: {0}'.format(filters.size()))