if not self.Linear:
self.bn = nn.BatchNorm2d(input_channels, eps=1e-4, momentum=0.1, affine=True)
self.conv = nn.Conv2d(input_channels, output_channels,
kernel_size=kernel_size, stride=stride, padding=padding, groups=groups)
the weights which i printed using self.conv.weight and the weights which i obtained from model.named_parameters() are totally different can anyone please provide the difference between them and also which is the correct weight values
Could you post a minimal and executable code snippet, which shows the issue you are seeing?
It’s unclear how and when you are printing the weights based on your code snippet.
loading the pretrained model here:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
del checkpoint
else:
print("=> no checkpoint found at '{}'".format(args.resume))
#print("before fusing model",model)
fuse_module(model)
cudnn.benchmark = True
for key, value in model.named_parameters():
this is where i am printing weight
class BinConv2d(nn.Module): # change the name of BinConv2d
def __init__(self, input_channels, output_channels,
kernel_size=-1, stride=-1, padding=-1, groups=1, dropout=0,
Linear=False):
super(BinConv2d, self).__init__()
self.layer_type = 'BinConv2d'
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.dropout_ratio = dropout
if dropout!=0:
self.dropout = nn.Dropout(dropout)
self.Linear = Linear
if not self.Linear:
self.bn = nn.BatchNorm2d(input_channels, eps=1e-4, momentum=0.1, affine=True)
self.conv = nn.Conv2d(input_channels, output_channels,
and this place where i am printimg weight
kernel_size=kernel_size, stride=stride, padding=padding, groups=groups)
else:
self.bn = nn.BatchNorm1d(input_channels, eps=1e-4, momentum=0.1, affine=True)
self.linear = nn.Linear(input_channels, output_channels)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.bn(x)
x = BinActive()(x)
if self.dropout_ratio!=0:
x = self.dropout(x)
if not self.Linear:
x = self.conv(x)
else:
x = self.linear(x)
x = self.relu(x)
return x
class AlexNet(nn.Module):
def __init__(self, num_classes=1000):
super(AlexNet, self).__init__()
self.num_classes = num_classes
self.features = nn.Sequential(
nn.Conv2d(3, 96, kernel_size=11, stride=4, padding=0),
nn.BatchNorm2d(96, eps=1e-4, momentum=0.1, affine=True),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
BinConv2d(96, 256, kernel_size=5, stride=1, padding=2, groups=1),
nn.MaxPool2d(kernel_size=3, stride=2),
BinConv2d(256, 384, kernel_size=3, stride=1, padding=1),
BinConv2d(384, 384, kernel_size=3, stride=1, padding=1, groups=1),
BinConv2d(384, 256, kernel_size=3, stride=1, padding=1, groups=1),
nn.MaxPool2d(kernel_size=3, stride=2),
)
self.classifier = nn.Sequential(
BinConv2d(256 * 6 * 6, 4096, Linear=True),
BinConv2d(4096, 4096, dropout=0.5, Linear=True),
nn.BatchNorm1d(4096, eps=1e-3, momentum=0.1, affine=True),
nn.Dropout(),
nn.Linear(4096, num_classes),
)
def forward(self, x):
x = self.features(x)
x = x.view(x.size(0), 256 * 6 * 6)
x = self.classifier(x)
return xi am loading the pretrained model and i am printing the weight from model.named_parameter()
and also i am printing the weights from self.conv.weight
but the weight i am getting from these two are different
class BinConv2d(nn.Module): # change the name of BinConv2d
def __init__(self, input_channels, output_channels,
kernel_size=-1, stride=-1, padding=-1, groups=1, dropout=0,
Linear=False):
super(BinConv2d, self).__init__()
self.layer_type = 'BinConv2d'
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.dropout_ratio = dropout
if dropout!=0:
self.dropout = nn.Dropout(dropout)
self.Linear = Linear
if not self.Linear:
self.bn = nn.BatchNorm2d(input_channels, eps=1e-4, momentum=0.1, affine=True)
self.conv = nn.Conv2d(input_channels, output_channels,
kernel_size=kernel_size, stride=stride, padding=padding, groups=groups)
else:
self.bn = nn.BatchNorm1d(input_channels, eps=1e-4, momentum=0.1, affine=True)
self.linear = nn.Linear(input_channels, output_channels)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.bn(x)
x = BinActive()(x)
if self.dropout_ratio!=0:
x = self.dropout(x)
if not self.Linear:
x = self.conv(x)
else:
x = self.linear(x)
x = self.relu(x)
return x
class AlexNet(nn.Module):
def __init__(self, num_classes=1000):
super(AlexNet, self).__init__()
self.num_classes = num_classes
self.features = nn.Sequential(
nn.Conv2d(3, 96, kernel_size=11, stride=4, padding=0),
nn.BatchNorm2d(96, eps=1e-4, momentum=0.1, affine=True),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
BinConv2d(96, 256, kernel_size=5, stride=1, padding=2, groups=1),
nn.MaxPool2d(kernel_size=3, stride=2),
BinConv2d(256, 384, kernel_size=3, stride=1, padding=1),
BinConv2d(384, 384, kernel_size=3, stride=1, padding=1, groups=1),
BinConv2d(384, 256, kernel_size=3, stride=1, padding=1, groups=1),
nn.MaxPool2d(kernel_size=3, stride=2),
)I cannot reproduce it, as I’m getting the same values for the direct access and the state_dict:
model = AlexNet()
state_dict = model.state_dict()
print((model.features[4].conv.weight == state_dict['features.4.conv.weight']).all())
> tensor(True)
PS: you can post code snippets by wrapping them into three backticks ```, which makes debugging easier. 
print((model.features[4].conv.weight == state_dict[‘features.4.conv.weight’]).all())
TypeError: ‘DataParallel’ object does not support indexing
can you please provide the solution ,since i am very new to python,pytorch
You might have wrapped model.features into nn.DataParallel.
If that’s the case, you could add the .module attribute to the access:
model.features.module[4]
If that doesn’t work, could you post the model architecture, so that we could have a look?
File “/content/XNOR-Net-PyTorch/ImageNet/networks/model_list/alexnet.py”, line 871, in alexnet
print((model.features.module[4] == state_dict[‘features.module.4.conv.weight’]).all())
AttributeError: ‘bool’ object has no attribute ‘all’
“”“(‘after fusing model’, AlexNet(
(features): DataParallel(
(module): Sequential(
(0): Conv2d(3, 96, kernel_size=(11, 11), stride=(4, 4))
(1): DummyModule()
(2): ReLU(inplace=True)
(3): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
(4): BinConv2d(
(bn): BatchNorm2d(96, eps=0.0001, momentum=0.1, affine=True, track_running_stats=True)
(conv): Conv2d(96, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(relu): ReLU(inplace=True)
)
(5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
(6): BinConv2d(
(bn): BatchNorm2d(256, eps=0.0001, momentum=0.1, affine=True, track_running_stats=True)
(conv): Conv2d(256, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(relu): ReLU(inplace=True)
)
(7): BinConv2d(
(bn): BatchNorm2d(384, eps=0.0001, momentum=0.1, affine=True, track_running_stats=True)
(conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(relu): ReLU(inplace=True)
)
(8): BinConv2d(
(bn): BatchNorm2d(384, eps=0.0001, momentum=0.1, affine=True, track_running_stats=True)
(conv): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(relu): ReLU(inplace=True)
)
(9): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
)
)
(classifier): Sequential(
(0): BinConv2d(
(bn): BatchNorm1d(9216, eps=0.0001, momentum=0.1, affine=True, track_running_stats=True)
(linear): Linear(in_features=9216, out_features=4096, bias=True)
(relu): ReLU(inplace=True)
)
(1): BinConv2d(
(dropout): Dropout(p=0.5, inplace=False)
(bn): BatchNorm1d(4096, eps=0.0001, momentum=0.1, affine=True, track_running_stats=True)
(linear): Linear(in_features=4096, out_features=4096, bias=True)
(relu): ReLU(inplace=True)
)
(2): BatchNorm1d(4096, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(3): Dropout(p=0.5, inplace=False)
(4): Linear(in_features=4096, out_features=1000, bias=True)
)
))”“”
Could you print the type and the shape of both tensors you are trying to compare, please?
print(model.features.module[4].conv.weight.size())
(256, 96, 5, 5)
print(type(model.features.module[4]))
<class ‘model_list.alexnet.BinConv2d’>
print(state_dict[‘features.module.4.conv.weight’].size())
(256,96,5,5)
print(type(state_dict[‘features.module.4.conv.weight’]))
<class ‘torch.Tensor’>
print((model.features.module[4].conv.weight == state_dict[‘features.module.4.conv.weight’]).all())
tensor(True, device=‘cuda:0’)
This line of code seems to work now:
print((model.features.module[4].conv.weight == state_dict[‘features.module.4.conv.weight’]).all())
> tensor(True, device=‘cuda:0’)
while it seemed to create the AttributeError before, which is strange.
Anyway, good to see it’s working now.
Thanks for your time