Extracting Features from learn.model (resnext50)


(Vibhor Kashyap) #1

I am trying to replicate this experiment - https://becominghuman.ai/extract-a-feature-vector-for-any-image-with-pytorch-9717561d1d4c

but, with a model I trained on resnext50. Here is how I proceed

# to get automatic reloading and inline plotting
%reload_ext autoreload
%autoreload 2
%matplotlib inline

# all the main external libs we'll use
from fastai.imports import *

from fastai.transforms import *
from fastai.conv_learner import *
from fastai.model import *
from fastai.dataset import *
from fastai.sgdr import *
from fastai.plots import *

PATH = "/home/ubuntu/datadrive/SLEEVE_LENGTH_CROPPED_PREPARED_SQUARED/"
sz=299
arch=resnext50
bs=28

tfms = tfms_from_model(arch, sz, aug_tfms=transforms_side_on, max_zoom=1.1)
#tfms = tfms_from_model(arch, sz)
data = ImageClassifierData.from_paths(PATH, tfms=tfms, val_name="valid", bs=bs, num_workers=4)

learn = ConvLearner.pretrained(arch, data, precompute=True, ps=0.5)

learn.precompute=False

learn.load('rxt50_sleeve_length')

import torch
import torch.nn as nn
import torchvision.models as models
import torchvision.transforms as transforms
from torch.autograd import Variable
from PIL import Image

scaler = transforms.Resize((299, 299))
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                 std=[0.229, 0.224, 0.225])
to_tensor = transforms.ToTensor()
model = learn.model
layer = model._modules.get('10')

def get_vector(image_name):
    # 1. Load the image with Pillow library
    img = Image.open(image_name)
    # 2. Create a PyTorch Variable with the transformed image
    t_img = Variable(normalize(to_tensor(scaler(img))).unsqueeze(0))
    # 3. Create a vector of zeros that will hold our feature vector
    
    my_embedding = torch.zeros(1000)
    # 4. Define a function that will copy the output of a layer
    def copy_data(m, i, o):
        my_embedding.copy_(o.data)
    # 5. Attach that function to our selected layer
    h = layer.register_forward_hook(copy_data)
    # 6. Run the model on our transformed image
    model(t_img)
    # 7. Detach our copy function from the layer
    h.remove()
    # 8. Return the feature vector
    return my_embedding

Here, I try to extract features from (10): BatchNorm1d(4096, eps=1e-05, momentum=0.1, affine=True) layer of the net.
The function get_vector() runs fine on ResNet18 as mentioned in the article but when I try to run it on my model, I get the following error -

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-150-679a3b08470c> in <module>()
----> 1 p = get_vector('/home/ubuntu/datadrive/SLEEVE_LENGTH_CROPPED_PREPARED_SQUARED/train/cap_sleeves/11487246404239-Roadster-Women-Blue-Checked-A-Line-Dress-2141487246403875-1-148921074751.jpg.jpg')
      2 p

<ipython-input-149-2e49da99815b> in get_vector(image_name)
     27     h = layer.register_forward_hook(copy_data)
     28     # 6. Run the model on our transformed image
---> 29     model(t_img)
     30     # 7. Detach our copy function from the layer
     31     h.remove()

~/.conda/envs/fastai/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    355             result = self._slow_forward(*input, **kwargs)
    356         else:
--> 357             result = self.forward(*input, **kwargs)
    358         for hook in self._forward_hooks.values():
    359             hook_result = hook(self, input, result)

~/.conda/envs/fastai/lib/python3.6/site-packages/torch/nn/modules/container.py in forward(self, input)
     65     def forward(self, input):
     66         for module in self._modules.values():
---> 67             input = module(input)
     68         return input
     69 

~/.conda/envs/fastai/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    355             result = self._slow_forward(*input, **kwargs)
    356         else:
--> 357             result = self.forward(*input, **kwargs)
    358         for hook in self._forward_hooks.values():
    359             hook_result = hook(self, input, result)

~/.conda/envs/fastai/lib/python3.6/site-packages/torch/nn/modules/conv.py in forward(self, input)
    280     def forward(self, input):
    281         return F.conv2d(input, self.weight, self.bias, self.stride,
--> 282                         self.padding, self.dilation, self.groups)
    283 
    284 

~/.conda/envs/fastai/lib/python3.6/site-packages/torch/nn/functional.py in conv2d(input, weight, bias, stride, padding, dilation, groups)
     88                 _pair(0), groups, torch.backends.cudnn.benchmark,
     89                 torch.backends.cudnn.deterministic, torch.backends.cudnn.enabled)
---> 90     return f(input, weight, bias)
     91 
     92 

RuntimeError: Expected object of type torch.FloatTensor but found type torch.cuda.FloatTensor for argument #2 'weight'

How do I rectify it ?


#2

I have another error, and I solve it by change ‘resnext50’ to ‘resnet50’. I think the name is wrong. So you can try it.


(Vibhor Kashyap) #3

‘resnext50’ is a valid architecture name, and I can extract features from the pretrained network. However, as shown in my post, I am experiencing an issue with the same, when I load weights after training a resnext50 model.


(geethasaikrishna) #4

RuntimeError: Expected object of type torch.FloatTensor but found type torch.cuda.FloatTensor for argument #2 'weight.

This error seems to be due to the CPU & GPU, as it says "torch.cuda.FloatTensor " & “torch.FloatTensor”


(Vibhor Kashyap) #5

Could figure that out, but why ? normally weights are stored as GPU tensor.

RuntimeError: Expected object of type torch.FloatTensor but found type torch.cuda.FloatTensor for argument #2 ‘weight’

weights are supposed to be gpu tensors right ?


(Shivendra singh) #6

I think you have trained your model on CPU and you are checking using GPU so try these two things one of them definitely work:

  1. change the input “t_img” to t_img=Variable(normalize(to_tensor(scaler(img)).to(torch.device(“cpu”))).unsqueeze(0)) or t_img=Variable(normalize(to_tensor(scaler(img)).to(torch.device(“cuda”))).unsqueeze(0)) as your requirement.

  2. Try calling model.cuda() before loading weight to convert your model to GPU compatible.