Hi everyone,
A newbie PyTorch (from Keras) convert here. I’m a confused by the behavior of the code below. It seems that AdaptiveAvgPool2D should have returned [BxC] shaped vector but instead, it returns [B*C,1]
I’m trying to take a pretrained model and slap a completely different head to it.
The second piece of code gives me, where 32768=Batch size * number of channels before AdaptiveAvfPool2D
RuntimeError: size mismatch, m1: [32768 x 1], m2: [2048 x 5005] at c:\a\w\1\s\tmp_conda_3.5_091434\conda\conda-bld\pytorch_1544087939577\work\aten\src\th\generic/THTensorMath.cpp:940
Can you please help me understand the logic of what’s going on?
import torch
import torch.nn as nn
import torchvision.models as models
from torch.autograd import Variable


#This part works
resnet50 = models.resnet50(pretrained=True)
modules=list(resnet50.children())[:-2]
resnet50=nn.Sequential(*modules)
for p in resnet50.parameters():
    p.requires_grad = True

img = torch.Tensor(16, 3, 256, 512).normal_() # random image
img_var = Variable(img) # assign it to a variable
features_var = resnet50(img_var) # get the output from the last hidden layer of the pretrained resnet
features = features_var.data # get the tensor out of the variable
print(features.shape)

#This part doesn't
resnet50 = models.resnet50(pretrained=True)
modules=list(resnet50.children())[:-2]
resnet50=nn.Sequential(*modules)
resnet50=nn.Sequential(resnet50,
                    nn.AdaptiveAvgPool2d(1),
                    nn.Linear(2048, 5005))

for p in resnet50.parameters():
    p.requires_grad = True
print(img_var.shape)
features_var = resnet50(img_var) # get the output from the last hidden layer of the pretrained resnet
features = features_var.data # get the tensor out of the variable
print(features.shape)

Or should I approach this task differently?
PS. I know about this link
https://pytorch.org/tutorials/beginner/finetuning_torchvision_models_tutorial.html 7
Thanks!