I wrote a snippet for it.
import torchvision.models as models
from types import FunctionType
def calculate_num_of_learned_params(model):
    cnt = 0
    for param in model.parameters():
        if param.requires_grad:
            cnt += param.numel()
    return cnt

for model_name in dir(models):
    if model_name[0].islower():
        attr = getattr(models, model_name)
        if isinstance(attr, FunctionType):
            print(model_name, '\t\t\t', calculate_num_of_learned_params(attr()))


Results:
alexnet 			 61100840
densenet121 			 7978856
densenet161 			 28681000
densenet169 			 14149480
densenet201 			 20013928
googlenet 			 13004888
inception_v3 			 27161264
mobilenet_v2 			 3504872
resnet101 			 44549160
resnet152 			 60192808
resnet18 			 11689512
resnet34 			 21797672
resnet50 			 25557032
resnext101_32x8d 			 88791336
resnext50_32x4d 			 25028904
shufflenet_v2_x0_5 			 1366792
shufflenet_v2_x1_0 			 2278604
shufflenet_v2_x1_5 			 3503624
shufflenet_v2_x2_0 			 7393996
squeezenet1_0 			 1248424
squeezenet1_1 			 1235496
vgg11 			 132863336
vgg11_bn 			 132868840
vgg13 			 133047848
vgg13_bn 			 133053736
vgg16 			 138357544
vgg16_bn 			 138365992
vgg19 			 143667240
vgg19_bn 			 143678248