zjc
/
yolov5
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

There is no need to download extra packages, official bring it with you 

I submitted it once in your yolov3 project, you seem to accept it? I'm not sure. I'll submit PR again.
This commit is contained in:
Lornatang 2020-06-16 08:28:52 +08:00
parent 0c4b4b8817
commit 8b8b792802
1 changed files with 12 additions and 7 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

19
utils/torch_utils.py
View File

@ -7,6 +7,7 @@ import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
def init_seeds(seed=0):
@ -120,18 +121,22 @@ def model_info(model, verbose=False):
def load_classifier(name='resnet101', n=2):
# Loads a pretrained model reshaped to n-class output
import pretrainedmodels # https://github.com/Cadene/pretrained-models.pytorch#torchvision
model = pretrainedmodels.__dict__[name](num_classes=1000, pretrained='imagenet')
model = models.__dict__[name](pretrained=True)
# Display model properties
for x in ['model.input_size', 'model.input_space', 'model.input_range', 'model.mean', 'model.std']:
input_size = [3, 224, 224]
input_space = 'RGB'
input_range = [0, 1]
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
for x in [input_size, input_space, input_range, mean, std]:
print(x + ' =', eval(x))
# Reshape output to n classes
filters = model.last_linear.weight.shape[1]
model.last_linear.bias = torch.nn.Parameter(torch.zeros(n))
model.last_linear.weight = torch.nn.Parameter(torch.zeros(n, filters))
model.last_linear.out_features = n
filters = model.fc.weight.shape[1]
model.fc.bias = torch.nn.Parameter(torch.zeros(n), requires_grad=True)
model.fc.weight = torch.nn.Parameter(torch.zeros(n, filters), requires_grad=True)
model.fc.out_features = n
return model