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import_models.py
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import_models.py
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import torchvision.models as models
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader
import torch
#from models_dir.p_s_s.models import duc_hdc, fcn8s, fcn16s, fcn32s, gcn, psp_net, seg_net, u_net
from efficientnet_pytorch import EfficientNet
def import_models(download):
"""
Commented out models do not have pre-trained variants available.
This function will load and cache all of these models if they are not already cached.
On CRC we can just run this initially as its own python script to download and cache the models.
Afterwards, this function will be implemented into some other testing code that will call it in
a similar fashion to what is shown below.
"""
print("Loading or checking if models are cached...\n")
print("This may take a while. Progress will be shown if models are being downloaded.\n ", flush=True)
############################################################################################
# Image Classification
alexnet = models.alexnet(pretrained=download, progress=True)
squeezenet1_0 = models.squeezenet1_0(pretrained=download, progress=True)
squeezenet1_1 = models.squeezenet1_1(pretrained=download, progress=True)
vgg16 = models.vgg16_bn(pretrained=download, progress=True)
vgg19 = models.vgg19_bn(pretrained=download, progress=True)
resnet18 = models.resnet18(pretrained=download, progress=True)
resnet34 = models.resnet34(pretrained=download, progress=True)
resnet50 = models.resnet50(pretrained=download, progress=True)
resnet101 = models.resnet101(pretrained=download, progress=True)
resnet152 = models.resnet152(pretrained=download, progress=True)
densenet121 = models.densenet121(pretrained=download, progress=True, memory_efficient=False)
densenet161 = models.densenet161(pretrained=download, progress=True, memory_efficient=False)
densenet201 = models.densenet201(pretrained=download, progress=True, memory_efficient=False)
densenet121_efficient = models.densenet121(pretrained=download, progress=True, memory_efficient=True)
densenet161_efficient = models.densenet161(pretrained=download, progress=True, memory_efficient=True)
densenet201_efficient = models.densenet201(pretrained=download, progress=True, memory_efficient=True)
googlenet = models.googlenet(pretrained=download, progress=True)
shufflenet_v2_1 = models.shufflenet_v2_x1_0(pretrained=download, progress=True)
shufflenet_v2_0_5 = models.shufflenet_v2_x0_5(pretrained=download, progress=True)
#shufflenet_v2_1_5 = models.shufflenet_v2_x1_5(pretrained=download, progress=True)
#shufflenet_v2_2 = models.shufflenet_v2_x2_0(pretrained=download, progress=True)
mobilenet_v2 = models.mobilenet_v2(pretrained=download, progress=True)
resnext50_32x4d = models.resnext50_32x4d(pretrained=download, progress=True)
resnext101_32x8d = models.resnext101_32x8d(pretrained=download, progress=True)
wide_resnet50_2 = models.wide_resnet50_2(pretrained=download, progress=True)
wide_resnet101_2 = models.wide_resnet101_2(pretrained=download, progress=True)
mnasnet1_0 = models.mnasnet1_0(pretrained=download, progress=True)
mnasnet0_5 = models.mnasnet0_5(pretrained=download, progress=True)
#mnasnet0_75 = models.mnasnet0_75(pretrained=download, progress=True)
#mnasnet1_3 = models.mnasnet1_3(pretrained=download, progress=True)
print("Checking efficientnet", flush=True)
efficientnet_b0 = EfficientNet.from_pretrained('efficientnet-b0')
efficientnet_b1 = EfficientNet.from_pretrained('efficientnet-b1')
efficientnet_b2 = EfficientNet.from_pretrained('efficientnet-b2')
efficientnet_b3 = EfficientNet.from_pretrained('efficientnet-b3')
efficientnet_b4 = EfficientNet.from_pretrained('efficientnet-b4')
efficientnet_b5 = EfficientNet.from_pretrained('efficientnet-b5')
efficientnet_b6 = EfficientNet.from_pretrained('efficientnet-b6')
efficientnet_b7 = EfficientNet.from_pretrained('efficientnet-b7')
###########################################################################################
# Video Classification
# resnet_3d = models.video.r3d_18(pretrained=download, progress=True)
# resnet_mixed_conv = models.video.mc3_18(pretrained=download, progress=True)
# resnet_2_1D = models.video.r2plus1d_18(pretrained=download, progress=True)
###########################################################################################
# Object Detection
fasterrcnn_resnet50 = models.detection.fasterrcnn_resnet50_fpn(pretrained=download, progress=True, num_classes=91, pretrained_backbone=True)
maskcnn_resnet50 = models.detection.maskrcnn_resnet50_fpn(pretrained=download, progress=True, num_classes=91, pretrained_backbone=True)
keypointrcnn_resnet50 = models.detection.keypointrcnn_resnet50_fpn(pretrained=download, progress=True, num_classes=2, num_keypoints=17, pretrained_backbone=True)
###########################################################################################
# Semantic Segmentation
fcn_resnet50 = models.segmentation.fcn_resnet50(pretrained=False, progress=True, num_classes=21, aux_loss=None)
fcn_resnet101 = models.segmentation.fcn_resnet101(pretrained=download, progress=True, num_classes=21, aux_loss=None)
deeplabv3_resnet50 = models.segmentation.deeplabv3_resnet50(pretrained=False, progress=True, num_classes=21, aux_loss=None)
deeplabv3_resnet101 = models.segmentation.deeplabv3_resnet101(pretrained=False, progress=True, num_classes=21, aux_loss=None)
###########################################################################################
# Generative Adversarial Networks
###########################################################################################
checking_input = True
while (checking_input):
model_type = int(input("Choose the type of model you want:\n1 (Image Classification)\n2 (Video Classification)\n3 (Object Detection)\n4 (Semantic Segmentation)\n5 (GAN)\nInput: "))
print(model_type)
# Convolutional Neual Networks
if model_type == 1:
models_dict = {
"alexnet" : alexnet,
"squeezenet1_0" : squeezenet1_0,
"squeezenet1_1": squeezenet1_1,
"vgg16" : vgg16,
"vgg19" : vgg19,
"resnet18" : resnet18,
"resnet34" : resnet34,
"resnet50" : resnet50,
"resnet101" : resnet101,
"resnet152" : resnet152,
"densenet121" : densenet121,
"densenet161" : densenet161,
"densenet201" : densenet201,
"densenet121_efficient": densenet121_efficient,
"densenet161_efficient": densenet161_efficient,
"densenet201_efficient": densenet201_efficient,
"googlenet" : googlenet,
"shufflenet_v2_1" : shufflenet_v2_1,
"shufflenet_v2_0_5": shufflenet_v2_0_5,
#"shufflenet_v2_1_5": shufflenet_v2_1_5,
#"shufflenet_v2_2": shufflenet_v2_2,
"mobilenet_v2" : mobilenet_v2,
"resnext50_32x4d" : resnext50_32x4d,
"resnext101_32x4d": resnext101_32x8d,
"wide_resnet50_2" : wide_resnet50_2,
"wide_resnet101_2" : wide_resnet101_2,
"mnasnet1_0" : mnasnet1_0,
#"mnasnet1_3": mnasnet1_3,
"mnasnet0_5": mnasnet0_5,
#"mnasnet0_75": mnasnet0_75,
"efficientnet_b0" : efficientnet_b0,
"efficientnet_b1" : efficientnet_b1,
"efficientnet_b2" : efficientnet_b2,
"efficientnet_b3" : efficientnet_b3,
"efficientnet_b4" : efficientnet_b4,
"efficientnet_b5" : efficientnet_b5,
"efficientnet_b6" : efficientnet_b6,
"efficientnet_b7" : efficientnet_b7
}
checking_input = False
return models_dict
# Video Classification
elif model_type == 2:
checking_input = False
models_dict = {
"resnet_3d" : resnet_3d,
"resnet_mixed_conv" : resnet_mixed_conv,
"resnet_2_1D" : resnet_2_1D
}
return models_dict
# Object Detection
elif model_type == 3:
checking_input = False
models_dict = {
"fasterrcnn_resnet50" : fasterrcnn_resnet50,
"maskcnn_resnet50" : maskcnn_resnet50,
"keypointrcnn_resnet50" : keypointrcnn_resnet50
}
return models_dict
# Semantic Segmentation
elif model_type == 4:
checking_input = False
models_dict = {
"fcn_resnet50" : fcn_resnet50,
"fcn_resnet101" : fcn_resnet101,
"deeplabv3_resnet50" : deeplabv3_resnet50,
"deeplabv3_resnet101" : deeplabv3_resnet101
}
return models_dict
# Generative Adversarial Networks
elif model_type == 5:
checking_input = False
models_dict = {
}
return models_dict
else:
print("You did not choose a valid input...")
def import_all(download):
"""
Commented out models do not have pre-trained variants available.
This function will load and cache all of these models if they are not already cached.
On CRC we can just run this initially as its own python script to download and cache the models.
Afterwards, this function will be implemented into some other testing code that will call it in
a similar fashion to what is shown below.
"""
print("Loading or checking if models are cached...\n")
print("This may take a while. Progress will be shown if models are being downloaded.\n ")
############################################################################################
# Image Classification
alexnet = models.alexnet(pretrained=download, progress=True)
squeezenet1_0 = models.squeezenet1_0(pretrained=download, progress=True)
squeezenet1_1 = models.squeezenet1_1(pretrained=download, progress=True)
vgg16 = models.vgg16_bn(pretrained=download, progress=True)
vgg19 = models.vgg19_bn(pretrained=download, progress=True)
resnet18 = models.resnet18(pretrained=download, progress=True)
resnet34 = models.resnet34(pretrained=download, progress=True)
resnet50 = models.resnet50(pretrained=download, progress=True)
resnet101 = models.resnet101(pretrained=download, progress=True)
resnet152 = models.resnet152(pretrained=download, progress=True)
densenet121 = models.densenet121(pretrained=download, progress=True, memory_efficient=False)
densenet161 = models.densenet161(pretrained=download, progress=True, memory_efficient=False)
densenet201 = models.densenet201(pretrained=download, progress=True, memory_efficient=False)
densenet121_efficient = models.densenet121(pretrained=download, progress=True, memory_efficient=True)
densenet161_efficient = models.densenet161(pretrained=download, progress=True, memory_efficient=True)
densenet201_efficient = models.densenet201(pretrained=download, progress=True, memory_efficient=True)
googlenet = models.googlenet(pretrained=download, progress=True)
shufflenet_v2_1 = models.shufflenet_v2_x1_0(pretrained=download, progress=True)
shufflenet_v2_0_5 = models.shufflenet_v2_x0_5(pretrained=download, progress=True)
#shufflenet_v2_1_5 = models.shufflenet_v2_x1_5(pretrained=download, progress=True)
#shufflenet_v2_2 = models.shufflenet_v2_x2_0(pretrained=download, progress=True)
mobilenet_v2 = models.mobilenet_v2(pretrained=download, progress=True)
resnext50_32x4d = models.resnext50_32x4d(pretrained=download, progress=True)
resnext101_32x8d = models.resnext101_32x8d(pretrained=download, progress=True)
wide_resnet50_2 = models.wide_resnet50_2(pretrained=download, progress=True)
wide_resnet101_2 = models.wide_resnet101_2(pretrained=download, progress=True)
mnasnet1_0 = models.mnasnet1_0(pretrained=download, progress=True)
mnasnet0_5 = models.mnasnet0_5(pretrained=download, progress=True)
#mnasnet0_75 = models.mnasnet0_75(pretrained=download, progress=True)
#mnasnet1_3 = models.mnasnet1_3(pretrained=download, progress=True)
efficientnet_b0 = EfficientNet.from_pretrained('efficientnet-b0')
efficientnet_b1 = EfficientNet.from_pretrained('efficientnet-b1')
efficientnet_b2 = EfficientNet.from_pretrained('efficientnet-b2')
efficientnet_b3 = EfficientNet.from_pretrained('efficientnet-b3')
efficientnet_b4 = EfficientNet.from_pretrained('efficientnet-b4')
efficientnet_b5 = EfficientNet.from_pretrained('efficientnet-b5')
efficientnet_b6 = EfficientNet.from_pretrained('efficientnet-b6')
efficientnet_b7 = EfficientNet.from_pretrained('efficientnet-b7')
#
#
# ###########################################################################################
# # Video Classification
# # resnet_3d = models.video.r3d_18(pretrained=download, progress=True)
# # resnet_mixed_conv = models.video.mc3_18(pretrained=download, progress=True)
# # resnet_2_1D = models.video.r2plus1d_18(pretrained=download, progress=True)
#
# ###########################################################################################
# # Object Detection
#
fasterrcnn_resnet50 = models.detection.fasterrcnn_resnet50_fpn(pretrained=download, progress=True, num_classes=91, pretrained_backbone=True)
maskcnn_resnet50 = models.detection.maskrcnn_resnet50_fpn(pretrained=download, progress=True, num_classes=91, pretrained_backbone=True)
keypointrcnn_resnet50 = models.detection.keypointrcnn_resnet50_fpn(pretrained=download, progress=True, num_classes=2, num_keypoints=17, pretrained_backbone=True)
#
# ###########################################################################################
# # Semantic Segmentation
#
fcn_resnet50 = models.segmentation.fcn_resnet50(pretrained=False, progress=True, num_classes=21, aux_loss=None)
fcn_resnet101 = models.segmentation.fcn_resnet101(pretrained=download, progress=True, num_classes=21, aux_loss=None)
deeplabv3_resnet50 = models.segmentation.deeplabv3_resnet50(pretrained=False, progress=True, num_classes=21, aux_loss=None)
deeplabv3_resnet101 = models.segmentation.deeplabv3_resnet101(pretrained=False, progress=True, num_classes=21, aux_loss=None)
###########################################################################################
# Generative Adversarial Networks
###########################################################################################
models_dict = {
"alexnet" : alexnet,
"fcn_resnet50" : fcn_resnet50,
"fcn_resnet101" : fcn_resnet101,
"deeplabv3_resnet50" : deeplabv3_resnet50,
"deeplabv3_resnet101" : deeplabv3_resnet101,
"squeezenet1_0" : squeezenet1_0,
"squeezenet1_1": squeezenet1_1,
"vgg16" : vgg16,
"vgg19" : vgg19,
"resnet18" : resnet18,
"resnet34" : resnet34,
"resnet50" : resnet50,
"resnet101" : resnet101,
"resnet152" : resnet152,
"densenet121" : densenet121,
"densenet161" : densenet161,
"densenet201" : densenet201,
"densenet121_efficient": densenet121_efficient,
"densenet161_efficient": densenet161_efficient,
"densenet201_efficient": densenet201_efficient,
"googlenet" : googlenet,
"shufflenet_v2_1" : shufflenet_v2_1,
"shufflenet_v2_0_5": shufflenet_v2_0_5,
#"shufflenet_v2_1_5": shufflenet_v2_1_5,
#"shufflenet_v2_2": shufflenet_v2_2,
"mobilenet_v2" : mobilenet_v2,
"resnext50_32x4d" : resnext50_32x4d,
"resnext101_32x4d": resnext101_32x8d,
"wide_resnet50_2" : wide_resnet50_2,
"wide_resnet101_2" : wide_resnet101_2,
"mnasnet1_0" : mnasnet1_0,
#"mnasnet1_3": mnasnet1_3,
"mnasnet0_5": mnasnet0_5,
#"mnasnet0_75": mnasnet0_75,
"efficientnet_b0" : efficientnet_b0,
"efficientnet_b1" : efficientnet_b1,
"efficientnet_b2" : efficientnet_b2,
"efficientnet_b3" : efficientnet_b3,
"efficientnet_b4" : efficientnet_b4,
"efficientnet_b5" : efficientnet_b5,
"efficientnet_b6" : efficientnet_b6,
"efficientnet_b7" : efficientnet_b7,
}
# "maskcnn_resnet50" : maskcnn_resnet50,
# "keypointrcnn_resnet50" : keypointrcnn_resnet50,
# "fasterrcnn_resnet50" : fasterrcnn_resnet50,
# "resnet_3d" : resnet_3d,
# "resnet_mixed_conv" : resnet_mixed_conv,
# "resnet_2_1D" : resnet_2_1D,
return models_dict