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models.py
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models.py
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from torch import nn
from ops.basic_ops import ConsensusModule, Identity
from transforms import *
from torch.nn.init import normal, constant
class TSN(nn.Module):
def __init__(self, num_class, num_segments, modality,
base_model='resnet101', new_length=None,
consensus_type='avg', before_softmax=True,
dropout=0.8,
crop_num=1, partial_bn=True):
super(TSN, self).__init__()
self.modality = modality
self.num_segments = num_segments
self.reshape = True
self.before_softmax = before_softmax
self.dropout = dropout
self.crop_num = crop_num
self.consensus_type = consensus_type
if not before_softmax and consensus_type != 'avg':
raise ValueError("Only avg consensus can be used after Softmax")
if new_length is None:
self.new_length = 1 if modality == "RGB" else 5
else:
self.new_length = new_length
print(("""
Initializing TSN with base model: {}.
TSN Configurations:
input_modality: {}
num_segments: {}
new_length: {}
consensus_module: {}
dropout_ratio: {}
""".format(base_model, self.modality, self.num_segments, self.new_length, consensus_type, self.dropout)))
self._prepare_base_model(base_model)
feature_dim = self._prepare_tsn(num_class)
if self.modality == 'Flow':
print("Converting the ImageNet model to a flow init model")
self.base_model = self._construct_flow_model(self.base_model)
print("Done. Flow model ready...")
elif self.modality == 'RGBDiff':
print("Converting the ImageNet model to RGB+Diff init model")
self.base_model = self._construct_diff_model(self.base_model)
print("Done. RGBDiff model ready.")
self.consensus = ConsensusModule(consensus_type)
if not self.before_softmax:
self.softmax = nn.Softmax()
self._enable_pbn = partial_bn
if partial_bn:
self.partialBN(True)
def _prepare_tsn(self, num_class):
feature_dim = getattr(self.base_model, self.base_model.last_layer_name).in_features
if self.dropout == 0:
setattr(self.base_model, self.base_model.last_layer_name, nn.Linear(feature_dim, num_class))
self.new_fc = None
else:
setattr(self.base_model, self.base_model.last_layer_name, nn.Dropout(p=self.dropout))
self.new_fc = nn.Linear(feature_dim, num_class)
std = 0.001
if self.new_fc is None:
normal(getattr(self.base_model, self.base_model.last_layer_name).weight, 0, std)
constant(getattr(self.base_model, self.base_model.last_layer_name).bias, 0)
else:
normal(self.new_fc.weight, 0, std)
constant(self.new_fc.bias, 0)
return feature_dim
def _prepare_base_model(self, base_model):
if 'resnet' in base_model or 'vgg' in base_model:
self.base_model = getattr(torchvision.models, base_model)(True)
self.base_model.last_layer_name = 'fc'
self.input_size = 224
self.input_mean = [0.485, 0.456, 0.406]
self.input_std = [0.229, 0.224, 0.225]
if self.modality == 'Flow':
self.input_mean = [0.5]
self.input_std = [np.mean(self.input_std)]
elif self.modality == 'RGBDiff':
self.input_mean = [0.485, 0.456, 0.406] + [0] * 3 * self.new_length
self.input_std = self.input_std + [np.mean(self.input_std) * 2] * 3 * self.new_length
elif base_model == 'BNInception':
import tf_model_zoo
self.base_model = getattr(tf_model_zoo, base_model)()
self.base_model.last_layer_name = 'fc'
self.input_size = 224
self.input_mean = [104, 117, 128]
self.input_std = [1]
if self.modality == 'Flow':
self.input_mean = [128]
elif self.modality == 'RGBDiff':
self.input_mean = self.input_mean * (1 + self.new_length)
elif 'inception' in base_model:
import tf_model_zoo
self.base_model = getattr(tf_model_zoo, base_model)()
self.base_model.last_layer_name = 'classif'
self.input_size = 299
self.input_mean = [0.5]
self.input_std = [0.5]
else:
raise ValueError('Unknown base model: {}'.format(base_model))
def train(self, mode=True):
"""
Override the default train() to freeze the BN parameters
:return:
"""
super(TSN, self).train(mode)
count = 0
if self._enable_pbn:
print("Freezing BatchNorm2D except the first one.")
for m in self.base_model.modules():
if isinstance(m, nn.BatchNorm2d):
count += 1
if count >= (2 if self._enable_pbn else 1):
m.eval()
# shutdown update in frozen mode
m.weight.requires_grad = False
m.bias.requires_grad = False
def partialBN(self, enable):
self._enable_pbn = enable
def get_optim_policies(self):
first_conv_weight = []
first_conv_bias = []
normal_weight = []
normal_bias = []
bn = []
conv_cnt = 0
bn_cnt = 0
for m in self.modules():
if isinstance(m, torch.nn.Conv2d) or isinstance(m, torch.nn.Conv1d):
ps = list(m.parameters())
conv_cnt += 1
if conv_cnt == 1:
first_conv_weight.append(ps[0])
if len(ps) == 2:
first_conv_bias.append(ps[1])
else:
normal_weight.append(ps[0])
if len(ps) == 2:
normal_bias.append(ps[1])
elif isinstance(m, torch.nn.Linear):
ps = list(m.parameters())
normal_weight.append(ps[0])
if len(ps) == 2:
normal_bias.append(ps[1])
elif isinstance(m, torch.nn.BatchNorm1d):
bn.extend(list(m.parameters()))
elif isinstance(m, torch.nn.BatchNorm2d):
bn_cnt += 1
# later BN's are frozen
if not self._enable_pbn or bn_cnt == 1:
bn.extend(list(m.parameters()))
elif len(m._modules) == 0:
if len(list(m.parameters())) > 0:
raise ValueError("New atomic module type: {}. Need to give it a learning policy".format(type(m)))
return [
{'params': first_conv_weight, 'lr_mult': 5 if self.modality == 'Flow' else 1, 'decay_mult': 1,
'name': "first_conv_weight"},
{'params': first_conv_bias, 'lr_mult': 10 if self.modality == 'Flow' else 2, 'decay_mult': 0,
'name': "first_conv_bias"},
{'params': normal_weight, 'lr_mult': 1, 'decay_mult': 1,
'name': "normal_weight"},
{'params': normal_bias, 'lr_mult': 2, 'decay_mult': 0,
'name': "normal_bias"},
{'params': bn, 'lr_mult': 1, 'decay_mult': 0,
'name': "BN scale/shift"},
]
def forward(self, input):
sample_len = (3 if self.modality == "RGB" else 2) * self.new_length
if self.modality == 'RGBDiff':
sample_len = 3 * self.new_length
input = self._get_diff(input)
base_out = self.base_model(input.view((-1, sample_len) + input.size()[-2:]))
if self.dropout > 0:
base_out = self.new_fc(base_out)
if not self.before_softmax:
base_out = self.softmax(base_out)
if self.reshape:
base_out = base_out.view((-1, self.num_segments) + base_out.size()[1:])
output = self.consensus(base_out)
return output.squeeze(1)
def _get_diff(self, input, keep_rgb=False):
input_c = 3 if self.modality in ["RGB", "RGBDiff"] else 2
input_view = input.view((-1, self.num_segments, self.new_length + 1, input_c,) + input.size()[2:])
if keep_rgb:
new_data = input_view.clone()
else:
new_data = input_view[:, :, 1:, :, :, :].clone()
for x in reversed(list(range(1, self.new_length + 1))):
if keep_rgb:
new_data[:, :, x, :, :, :] = input_view[:, :, x, :, :, :] - input_view[:, :, x - 1, :, :, :]
else:
new_data[:, :, x - 1, :, :, :] = input_view[:, :, x, :, :, :] - input_view[:, :, x - 1, :, :, :]
return new_data
def _construct_flow_model(self, base_model):
# modify the convolution layers
# Torch models are usually defined in a hierarchical way.
# nn.modules.children() return all sub modules in a DFS manner
modules = list(self.base_model.modules())
first_conv_idx = list(filter(lambda x: isinstance(modules[x], nn.Conv2d), 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
params = [x.clone() for x in conv_layer.parameters()]
kernel_size = params[0].size()
new_kernel_size = kernel_size[:1] + (2 * self.new_length, ) + kernel_size[2:]
new_kernels = params[0].data.mean(dim=1, keepdim=True).expand(new_kernel_size).contiguous()
new_conv = nn.Conv2d(2 * self.new_length, 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_diff_model(self, base_model, keep_rgb=False):
# modify the convolution layers
# Torch models are usually defined in a hierarchical way.
# nn.modules.children() return all sub modules in a DFS manner
modules = list(self.base_model.modules())
first_conv_idx = filter(lambda x: isinstance(modules[x], nn.Conv2d), 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
params = [x.clone() for x in conv_layer.parameters()]
kernel_size = params[0].size()
if not keep_rgb:
new_kernel_size = kernel_size[:1] + (3 * self.new_length,) + kernel_size[2:]
new_kernels = params[0].data.mean(dim=1, keepdim=True).expand(new_kernel_size).contiguous()
else:
new_kernel_size = kernel_size[:1] + (3 * self.new_length,) + kernel_size[2:]
new_kernels = torch.cat((params[0].data, params[0].data.mean(dim=1, keepdim=True).expand(new_kernel_size).contiguous()),
1)
new_kernel_size = kernel_size[:1] + (3 + 3 * self.new_length,) + kernel_size[2:]
new_conv = nn.Conv2d(new_kernel_size[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 convolution layer
setattr(container, layer_name, new_conv)
return base_model
@property
def crop_size(self):
return self.input_size
@property
def scale_size(self):
return self.input_size * 256 // 224
def get_augmentation(self):
if self.modality == 'RGB':
return torchvision.transforms.Compose([GroupMultiScaleCrop(self.input_size, [1, .875, .75, .66]),
GroupRandomHorizontalFlip(is_flow=False)])
elif self.modality == 'Flow':
return torchvision.transforms.Compose([GroupMultiScaleCrop(self.input_size, [1, .875, .75]),
GroupRandomHorizontalFlip(is_flow=True)])
elif self.modality == 'RGBDiff':
return torchvision.transforms.Compose([GroupMultiScaleCrop(self.input_size, [1, .875, .75]),
GroupRandomHorizontalFlip(is_flow=False)])