def get_params(example_index):
"""
Gets used variables for almost all visualizations, like the image, model etc.
Args:
example_index (int): Image id to use from examples
returns:
original_image (numpy arr): Original image read from the file
prep_img (numpy_arr): Processed image
target_class (int): Target class for the image
file_name_to_export (string): File name to export the visualizations
pretrained_model(Pytorch model): Model to use for the operations
"""
# Pick one of the examples
example_list = [['../input_images/snake.jpg', 56],
['../input_images/cat_dog.png', 243],
['../input_images/spider.png', 72]]
selected_example = example_index
img_path = example_list[selected_example][0]
target_class = example_list[selected_example][1]
file_name_to_export = img_path[img_path.rfind('/')+1:img_path.rfind('.')]
# Read image
original_image = cv2.imread(img_path, 1)
# Process image
prep_img = preprocess_image(original_image)
# Define model
pretrained_model = models.alexnet(pretrained=True)
return (original_image,
prep_img,
target_class,
file_name_to_export,
pretrained_model)
def __init__(self):
super(AlexNetFc, self).__init__()
model_alexnet = models.alexnet(pretrained=True)
self.features = model_alexnet.features
self.classifier = nn.Sequential()
for i in range(6):
self.classifier.add_module("classifier"+str(i), model_alexnet.classifier[i])
self.__in_features = model_alexnet.classifier[6].in_features
def alexnet(num_classes=1000, pretrained='imagenet'):
r"""AlexNet model architecture from the
`"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.
"""
model = models.alexnet(pretrained=False)
if pretrained is not None:
settings = pretrained_settings['alexnet'][pretrained]
model = load_pretrained(model, num_classes, settings)
return model
def __init__(self, pretrained_models = models.alexnet(pretrained=True) , input_shape = (3, 224, 224), num_class = 200, freeze_layers = range(5), replace_clf=True):
super(pretrainedNetwork, self).__init__()
self.features, self.classifier = pretrained_models.features, pretrained_models.classifier
self.flat_fts = self.get_flat_fts(input_shape, self.features)
if replace_clf: self.classifier = nn.Sequential(nn.Linear(self.flat_fts, 100),nn.Dropout(p=0.2),nn.ReLU(),nn.Linear(100, num_class))
if freeze_layers is not None:
for idx, layer in enumerate(chain(self.features.children(), self.classifier.children())):
if idx in freeze_layers:
for p in layer.parameters(): p.requires_grad = False
self.input_shape = input_shape
def alexnet(num_classes=1000, pretrained='imagenet'):
r"""AlexNet model architecture from the
`"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.
"""
# https://github.com/pytorch/vision/blob/master/torchvision/models/alexnet.py
model = models.alexnet(pretrained=False)
if pretrained is not None:
settings = pretrained_settings['alexnet'][pretrained]
model = load_pretrained(model, num_classes, settings)
model = modify_alexnet(model)
return model
def select_model(name='vgg19'):
if name == 'vgg19':
model, feature_count = models.vgg19_bn(pretrained=True), 25088
elif name == 'densenet161':
model, feature_count = models.densenet161(pretrained=True), 2208
elif name == 'densenet121':
model, feature_count = models.densenet121(pretrained=True), 1024
else:
model, feature_count = models.alexnet(pretrained=True), 9216
return model, feature_count
def test_fast_initialization_without_orthonormal(image_data):
alexnet = models.alexnet(pretrained=False)
pre_init_var = run_with_capture(alexnet, image_data)
assert pre_init_var[0] >= 1000 # the first few pre-init variances are huge,
assert pre_init_var[1] >= 100 # even larger than these conservative tests.
tol = 0.1
alexnet = apply_lsuv_init(alexnet, image_data, std_tol=tol, do_orthonorm=False, cuda=False)
*post_init_var, final_var = run_with_capture(alexnet, image_data)
for var in post_init_var:
assert 2 <= var <= 4
assert final_var == pytest.approx(1, tol**2)
def __init__(self, model=None):
super(ModifiedVGG16Model, self).__init__()
# model = models.vgg16(pretrained=True)
model = models.alexnet(pretrained=True)
self.features = model.features
self.classifier = nn.Sequential(
nn.Dropout(),
nn.Linear(25088, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
nn.Linear(4096, 2))
def load_model(arch='vgg19', num_labels=102, hidden_units=4096):
# Load a pre-trained model
if arch=='vgg19':
# Load a pre-trained model
model = models.vgg19(pretrained=True)
elif arch=='alexnet':
model = models.alexnet(pretrained=True)
else:
raise ValueError('Unexpected network architecture', arch)
# Freeze its parameters
for param in model.parameters():
param.requires_grad = False
# Features, removing the last layer
features = list(model.classifier.children())[:-1]
# Number of filters in the bottleneck layer
num_filters = model.classifier[len(features)].in_features
# Extend the existing architecture with new layers
features.extend([
nn.Dropout(),
nn.Linear(num_filters, hidden_units),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(hidden_units, hidden_units),
nn.ReLU(True),
nn.Linear(hidden_units, num_labels),
##nn.Softmax(dim=1)
# Please, notice Softmax layer has not been added as per Pytorch answer:
# https://github.com/pytorch/vision/issues/432#issuecomment-368330817
# It is not either included in its transfer learning tutorial:
# https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
])
model.classifier = nn.Sequential(*features)
return model
def __init__(self, requires_grad=False, pretrained=True):
super(alexnet, self).__init__()
alexnet_pretrained_features = models.alexnet(pretrained=pretrained).features
self.slice1 = torch.nn.Sequential()
self.slice2 = torch.nn.Sequential()
self.slice3 = torch.nn.Sequential()
self.slice4 = torch.nn.Sequential()
self.slice5 = torch.nn.Sequential()
self.N_slices = 5
for x in range(2):
self.slice1.add_module(str(x), alexnet_pretrained_features[x])
for x in range(2, 5):
self.slice2.add_module(str(x), alexnet_pretrained_features[x])
for x in range(5, 8):
self.slice3.add_module(str(x), alexnet_pretrained_features[x])
for x in range(8, 10):
self.slice4.add_module(str(x), alexnet_pretrained_features[x])
for x in range(10, 12):
self.slice5.add_module(str(x), alexnet_pretrained_features[x])
if not requires_grad:
for param in self.parameters():
param.requires_grad = False
def load_arch(arch):
"""
Load a pretrained network
"""
if arch == 'vgg16':
model = models.vgg16(pretrained=True)
input_size = 25088
elif arch == 'alexnet':
model = models.alexnet(pretrained=True)
input_size = 9216
elif arch == 'resnet18':
model = models.resnet18(pretrained=True)
input_size = 512
elif arch == 'densenet121':
model = models.densenet121(pretrained=True)
input_size = 1024
else:
raise ValueError('Please choose one of \'vgg16\', \'alexnet\', \'resnet18\' or , \'densenet121\' for parameter arch.')
for param in model.parameters():
param.requires_grad = False
return model, input_size
def __init__(self,
image_size,
image_features_size,
word_embedding,
words2ids,
ids2words,
lstm_hidden_size=256,
word_embedding_size=300,
cnn=models.alexnet(pretrained=True).features,
start_symbol=DEF_START,
end_symbol=DEF_SEND):
"""Init NN
image_size - size of input image.
lstm_hidden_size - size of cnn features output
image_features_size - size of image features vector
word_embedding - pretrained word embedding model
words2ids - dictionary word -> id
ids2words - dictionary id -> word
cnn - pretrained cnn net (alexnet, vgg and other)
start_symbol - symbol starting sequence
end_symbol - symbol ending sequence
"""
super(LSTM_W2V_Net_Cnn_Preload, self).__init__()
self.image_size = image_size
self.image_features_size = image_features_size
#self.cnn = cnn
# self.cnn_comp_features = cnn_comp_features
self.vocab_size = len(words2ids)
print(self.vocab_size)
self.word_embedding_size = word_embedding_size
self.word_embedding = word_embedding
self.words2ids = words2ids
self.ids2words = ids2words
self.start_symbol = start_symbol
self.start_symbol_embed = torch.from_numpy(
self.word_embedding[self.start_symbol])
self.end_symbol = end_symbol
self.end_symbol_embed = torch.from_numpy(
self.word_embedding[self.end_symbol])
# self.sentence_end_symbol = sentence_end_symbol
# self.sentence_end_symbol_id = self.words2ids[self.sentence_end_symbol]
# if sentence_end_embed is not None:
# self.sentence_end_embed = sentence_end_embed
# else:
# self.sentence_end_embed = word_embeding['.']
#self.max_sentence_len = max_sentence_len
self.lstm_hidden_size = lstm_hidden_size
# self.fc1 = nn.Sequential( nn.BatchNorm1d(self.image_features_size),
# nn.Linear(self.image_features_size, int(self.image_features_size/2)),
# nn.Dropout(0.001),
# nn.ReLU(),
# nn.Linear(int(self.image_features_size/2), int(self.image_features_size/4) ),
# nn.Dropout(0.001),
# nn.ReLU(),
# nn.Linear(int(self.image_features_size/4), self.lstm_hidden_size),
# nn.BatchNorm1d(self.lstm_hidden_size)
# ).cuda()
self.fc1 = nn.Sequential(
nn.Linear(self.image_features_size, self.lstm_hidden_size)).cuda()
self.lstm_cell = nn.LSTMCell(
self.lstm_hidden_size + self.word_embedding_size,
self.lstm_hidden_size).cuda()
self.fc2 = nn.Sequential(
nn.Linear(self.lstm_hidden_size, self.vocab_size),
nn.LogSoftmax()).cuda()
from torchvision.models import alexnet net = alexnet() print(net)
def get_model(architecture_name, freeze_parameters):
print("Initiating the Neural Network Model...\n")
trained_model = None
cleared_for_training = False
#Load a Pretrained Network - Default vgg16
if architecture_name == 'vgg16':
trained_model = models.vgg16(pretrained=True)
trained_model.name = 'vgg16'
cleared_for_training = True
elif architecture_name == 'resnet18':
trained_model = models.resnet18(pretrained=True)
trained_model.name = 'resnet18'
cleared_for_training = True
elif architecture_name == 'alexnet':
trained_model = models.alexnet(pretrained=True)
trained_model.name = 'alexnet'
cleared_for_training = True
elif architecture_name == 'squeezenet1':
trained_model = models.squeezenet1_0(pretrained=True)
trained_model.name = 'squeezenet1'
cleared_for_training = False
elif architecture_name == 'densenet161':
trained_model = models.densenet161(pretrained=True)
trained_model.name = 'densenet161'
cleared_for_training = False
elif architecture_name == 'inception':
trained_model = models.inception_v3(pretrained=True)
trained_model.name = 'inception_v3'
cleared_for_training = False
elif architecture_name == 'googlenet':
trained_model = models.googlenet(pretrained=True)
trained_model.name = 'googlenet'
cleared_for_training = False
elif architecture_name == 'shufflenet':
trained_model = models.shufflenet_v2_x1_0(pretrained=True)
trained_model.name = 'shufflenet_v2_x1_0'
cleared_for_training = False
elif architecture_name == 'mobilenet':
trained_model = models.mobilenet_v2(pretrained=True)
trained_model.name = 'mobilenet_v2'
cleared_for_training = False
elif architecture_name == 'resnext50':
trained_model = models.resnext50_32x4d(pretrained=True)
trained_model.name = 'resnext50_32x4d'
cleared_for_training = False
elif architecture_name == 'wide_resnet50':
trained_model = models.wide_resnet50_2(pretrained=True)
trained_model.name = 'wide_resnet50_2'
cleared_for_training = False
elif architecture_name == 'mnasnet1':
trained_model = models.mnasnet1_0(pretrained=True)
trained_model.name = 'mnasnet1_0'
cleared_for_training = False
else:
print(
"train.py - Function: get_model\nERROR: Unknown model passed at the command line..."
)
if (cleared_for_training == True) and (freeze_parameters == True):
#Freeze parameters to prevent training
for param in trained_model.parameters():
param.requires_grad = False
return trained_model, cleared_for_training
def initialize_model(model_name,
num_classes,
feature_extract,
use_pretrained=True):
model_ft = None
input_size = 0
if model_name == 'resnet':
model_ft = models.resnet152(pretrained=use_pretrained) #50 or 152
'''
in case, feature_extract is true, set_parameter_requires_grad will set all grad parameters to false
and because after this operation a new layer (the output one) is added, these new parameters will have grad as true
'''
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, num_classes)
input_size = 224
elif model_name == 'alexnet':
model_ft = models.alexnet(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.classifier[6].in_features
model_ft.classifier[6] = nn.Linear(num_ftrs, num_classes)
input_size = 224
elif model_name == 'vgg':
model_ft = models.vgg11_bn(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.classifier[6].in_features
model_ft.classifier[6] = nn.Linear(num_ftrs, num_classes)
input_size = 224
elif model_name == 'squeezenet':
model_ft = models.squeezenet1_0(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
model_ft.classifier[1] = nn.Conv2d(512,
num_classes,
kernel_size=(1, 1),
stride=(1, 1))
model_ft.num_classes = num_classes
input_size = 224
elif model_name == 'densenet':
model_ft = models.densenet121(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.classifier.in_features
model_ft.classifier = nn.Linear(num_ftrs, num_classes)
input_size = 224
elif model_name == 'inception':
#inception v3
model_ft = models.inception_v3(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
#Aux net
num_ftrs = model_ft.AuxLogits.fc.in_features
model_ft.AuxLogits.fc = nn.Linear(num_ftrs, num_classes)
#Primary Net
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, num_classes)
input_size = 299
else:
print('invalid model name')
exit()
return model_ft, input_size
batch_size = batch_size,
device = device,
LE = LE,
checkpoint_path = None,
verbose = True
)
training.fit(epochs = 500, train_data = data_loaders['train'], val_data = data_loaders['val'], \
callbacks = [
MetricTracker(metrics = [
('log_loss', metrics.log_loss),
('accuracy_score', metrics.accuracy_score),
('f1_score', partial(metrics.fbeta_score, beta = 2)),
# ('sk_accuracy_score', metrics.sk_accuracy_score),
# ('sk_f1_weighted', partial(metrics.sk_f1_score, average = 'weighted')),
# ('sk_f1_macro', partial(metrics.sk_f1_score, average='macro')),
], save_folder_path = r'/data/PyTorch-Pipeline/logs/{}/MetricTracker/'.format(experiment_name)),
ProgressBar(show_batch_metrics = ['log_loss']),
# ModelCheckpoint(save_folder_path = r'/data/PyTorch-Pipeline/models/{}/'.format(experiment_name), metric = 'log_loss', best_metric_highest = False, best_only = False, write_frequency = 2, verbose = True),
TensorBoard(log_dir = r'/data/PyTorch-Pipeline/logs/{}/TensorBoard/'.format(experiment_name), update_frequency = 1),
EarlyStopping(save_folder_path=r'/data/PyTorch-Pipeline/models/{}/'.format(experiment_name), metric='accuracy_score', best_metric_highest=True, best_only=True, write_frequency=2, patience=10, min_delta =0, verbose=True),
])
return
if __name__ == '__main__':
model = pretrainedNetwork(pretrained_models = models.alexnet(pretrained=True), input_shape = (3, 224, 224), freeze_layers=range(15), replace_clf=True, num_class=2)
model_evaluation(path = '/data/DreamPhant/datasets', model = model, experiment_name = r'test7')
print 'done'
import torch
from torchvision import models
from torchvision import transforms
from PIL import Image
# Change pretrained from false-true to pretrain or vice-versa
alexnet = models.alexnet(pretrained=True)
# Transform for image to fit into alexnet
transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
img = Image.open("WelshCorgi.jpeg")
img_t = transform(img)
batch_t = torch.unsqueeze(img_t, 0)
# Uncomment to show the neuralnet details
# print(alexnet)
# Code to run Alexnet in eval
alexnet.eval()
output = alexnet(batch_t)
# Get the classes from the imagenet file to be used
with open('classes.txt') as f:
labels = [line.strip() for line in f.readlines()]
def initialize_model(model_name, num_classes, feature_extract, use_pretrained=True):
# Initialize these variables which will be set in this if statement. Each of these variables is model specific.
model_ft = None
input_size = 0
if model_name == "resnet":
""" Resnet18
"""
model_ft = models.resnet18(pretrained=use_pretrained)
if feature_extract == True:
set_parameter_requires_grad(model_ft)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, num_classes)
input_size = 224
elif model_name == "alexnet":
""" Alexnet
"""
model_ft = models.alexnet(pretrained=use_pretrained)
if feature_extract == True:
set_parameter_requires_grad(model_ft)
num_ftrs = model_ft.classifier[6].in_features
model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)
input_size = 224
elif model_name == "vgg":
""" VGG11_bn
"""
model_ft = models.vgg11_bn(pretrained=use_pretrained)
if feature_extract == True:
set_parameter_requires_grad(model_ft)
num_ftrs = model_ft.classifier[6].in_features
model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)
input_size = 224
elif model_name == "squeezenet":
""" Squeezenet
"""
model_ft = models.squeezenet1_0(pretrained=use_pretrained)
if feature_extract == True:
set_parameter_requires_grad(model_ft)
model_ft.classifier[1] = nn.Conv2d(512, num_classes, kernel_size=(1,1), stride=(1,1))
model_ft.num_classes = num_classes
input_size = 224
elif model_name == "densenet":
""" Densenet
"""
model_ft = models.densenet121(pretrained=use_pretrained)
if feature_extract == True:
set_parameter_requires_grad(model_ft)
num_ftrs = model_ft.classifier.in_features
model_ft.classifier = nn.Linear(num_ftrs, num_classes)
input_size = 224
elif model_name == "inception":
""" Inception v3
Be careful, expects (299,299) sized images and has auxiliary output
"""
model_ft = models.inception_v3(pretrained=use_pretrained)
if feature_extract == True:
set_parameter_requires_grad(model_ft)
# Handle the auxilary net
num_ftrs = model_ft.AuxLogits.fc.in_features
model_ft.AuxLogits.fc = nn.Linear(num_ftrs, num_classes)
# Handle the primary net
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs,num_classes)
input_size = 299
else:
print("Invalid model name, exiting...")
exit()
return model_ft, input_size
import time
import torch
from torchvision import models
from torch import nn
import torchvision.transforms as transforms
from torch.autograd import Variable
import PIL
from PIL import Image
orignal_models = models.alexnet(pretrained=True)
class AlexnetConv4(nn.Module):
def __init__(self):
super(AlexnetConv4, self).__init__()
self.features = nn.Sequential(
*list(orignal_models.features.children())[:-3])
def forward(self, x):
x = self.features(x)
return (x)
model = AlexnetConv4()
print(model)
image_transforms = transforms.Compose([
transforms.Scale((256, 256), PIL.Image.NEAREST),
transforms.CenterCrop(224),
def initialize_model(model_name,
num_classes,
feature_extract,
use_pretrained=True):
model_ft = None
input_size = 0
# 其他网络要求输入为224，inception要求输入为299
if model_name == "resnet":
model_ft = models.resnet18(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, num_classes)
input_size = 224
elif model_name == "alexnet":
model_ft = models.alexnet(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.classifier[6].in_features
model_ft.classifier[6] = nn.Linear(num_ftrs, num_classes)
input_size = 224
elif model_name == "vgg":
model_ft = models.vgg11_bn(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.classifier[6].in_features
model_ft.classifier[6] = nn.Linear(num_ftrs, num_classes)
input_size = 224
elif model_name == "squeezenet":
model_ft = models.squeezenet1_0(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
model_ft.classifier[1] = nn.Conv2d(512,
num_classes,
kernel_size=(1, 1),
stride=(1, 1))
model_ft.num_classes = num_classes
input_size = 224
elif model_name == "densenet":
model_ft = models.densenet121(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.classifier.in_features
model_ft.classifier = nn.Linear(num_ftrs, num_classes)
input_size = 224
elif model_name == "inception":
model_ft = models.inception_v3(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
# 处理辅助网络
num_ftrs = model_ft.AuxLogits.fc.in_features
model_ft.AuxLogits.fc = nn.Linear(num_ftrs, num_classes)
# 处理主网络
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, num_classes)
input_size = 299
else:
print("Invalid model name, exiting...")
exit()
return model_ft, input_size
import ast
from PIL import Image
import torchvision.transforms as transforms
from torch.autograd import Variable
import torchvision.models as models
from torch import __version__
resnet18 = models.resnet18(pretrained=True)
alexnet = models.alexnet(pretrained=True)
vgg16 = models.vgg16(pretrained=True)
models = {'resnet': resnet18, 'alexnet': alexnet, 'vgg': vgg16}
# obtain ImageNet labels
with open('imagenet1000_clsid_to_human.txt') as imagenet_classes_file:
imagenet_classes_dict = ast.literal_eval(imagenet_classes_file.read())
def classifier(img_path, model_name):
# load the image
img_pil = Image.open(img_path)
# define transforms
preprocess = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
# preprocess the image
img_tensor = preprocess(img_pil)
def display_net():
net_from_web = alexnet()
net_from_torchvision = models.alexnet()
print(net_from_web)
print(net_from_torchvision)
def pretrained_state_dict(cls):
weights = alexnet(pretrained=True).state_dict().values()
return dict(zip(cls().state_dict().keys(), weights))
def initialize_model(model_name, num_classes, feature_extract, use_pretrained=True):
# Initialize these variables which will be set in this if statement. Each of these
# variables is model specific.
model_ft = None
input_size = 0
if model_name == "resnet":
""" Resnet18
"""
model_ft = models.resnet18(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, num_classes)
input_size = 224
elif model_name == "alexnet":
""" Alexnet
"""
model_ft = models.alexnet(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.classifier[6].in_features
model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)
input_size = 224
elif model_name == "vgg":
""" VGG11_bn
"""
model_ft = models.vgg11_bn(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.classifier[6].in_features
model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)
input_size = 224
elif model_name == "squeezenet":
""" Squeezenet
"""
model_ft = models.squeezenet1_0(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
model_ft.classifier[1] = nn.Conv2d(512, num_classes, kernel_size=(1,1), stride=(1,1))
model_ft.num_classes = num_classes
input_size = 224
elif model_name == "densenet":
""" Densenet
"""
model_ft = models.densenet121(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.classifier.in_features
model_ft.classifier = nn.Linear(num_ftrs, num_classes)
input_size = 224
elif model_name == "inception":
""" Inception v3
Be careful, expects (299,299) sized images and has auxiliary output
"""
model_ft = models.inception_v3(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
# Handle the auxilary net
num_ftrs = model_ft.AuxLogits.fc.in_features
model_ft.AuxLogits.fc = nn.Linear(num_ftrs, num_classes)
# Handle the primary net
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs,num_classes)
input_size = 299
else:
print("Invalid model name, exiting...")
exit()
return model_ft, input_size
import torch
import torchvision.models as models
models = {
"alexnet": {
"model": models.alexnet(pretrained=True),
"path": "both"
},
"vgg16": {
"model": models.vgg16(pretrained=True),
"path": "both"
},
"squeezenet": {
"model": models.squeezenet1_0(pretrained=True),
"path": "both"
},
"densenet": {
"model": models.densenet161(pretrained=True),
"path": "both"
},
"inception_v3": {
"model": models.inception_v3(pretrained=True),
"path": "both"
},
#"googlenet": models.googlenet(pretrained=True),
"shufflenet": {
"model": models.shufflenet_v2_x1_0(pretrained=True),
"path": "both"
},
"mobilenet_v2": {
"model": models.mobilenet_v2(pretrained=True),
from pytorch_utils import *
# Test
import torchvision.models as models
model = models.alexnet()
print(model)
print('\n\n#### WITH torch_summarize: ####')
print(torch_summarize(model))
# # Output
# AlexNet (
# (features): Sequential (
# (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2)), weights=((64, 3, 11, 11), (64,)), parameters=23296
# (1): ReLU (inplace), weights=(), parameters=0
# (2): MaxPool2d (size=(3, 3), stride=(2, 2), dilation=(1, 1)), weights=(), parameters=0
# (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2)), weights=((192, 64, 5, 5), (192,)), parameters=307392
# (4): ReLU (inplace), weights=(), parameters=0
# (5): MaxPool2d (size=(3, 3), stride=(2, 2), dilation=(1, 1)), weights=(), parameters=0
# (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), weights=((384, 192, 3, 3), (384,)), parameters=663936
# (7): ReLU (inplace), weights=(), parameters=0
# (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), weights=((256, 384, 3, 3), (256,)), parameters=884992
# (9): ReLU (inplace), weights=(), parameters=0
# (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), weights=((256, 256, 3, 3), (256,)), parameters=590080
# (11): ReLU (inplace), weights=(), parameters=0
# (12): MaxPool2d (size=(3, 3), stride=(2, 2), dilation=(1, 1)), weights=(), parameters=0
# ), weights=((64, 3, 11, 11), (64,), (192, 64, 5, 5), (192,), (384, 192, 3, 3), (384,), (256, 384, 3, 3), (256,), (256, 256, 3, 3), (256,)), parameters=2469696
# (classifier): Sequential (
# (0): Dropout (p = 0.5), weights=(), parameters=0
# (1): Linear (9216 -> 4096), weights=((4096, 9216), (4096,)), parameters=37752832
# (2): ReLU (inplace), weights=(), parameters=0
# (3): Dropout (p = 0.5), weights=(), parameters=0
def fine_tune_l2transfer(dataset_path,
model_path,
exp_dir,
batch_size=100,
num_epochs=100,
lr=0.0004,
reg_lambda=100,
init_freeze=0,
weight_decay=0,
saving_freq=5):
"""
IMM pipeline, only using L2-transfer technique and weight transfer.
reg_params is dictionary which looks like:
- param tensors
- param weights/backup: tensor(){omega=[one-vectors], init_val=[weights prev task net]}
- lambda = the regularization hyperparameter used
:param reg_lambda: reg hyperparam for the L2-transfer
"""
print('lr is ' + str(lr))
########################################
# DATASETS
########################################
dsets = torch.load(dataset_path)
dset_loaders = {
x: torch.utils.data.DataLoader(dsets[x],
batch_size=batch_size,
shuffle=True,
num_workers=8,
pin_memory=True)
for x in ['train', 'val']
}
dset_sizes = {x: len(dsets[x]) for x in ['train', 'val']}
dset_classes = dsets['train'].classes
########################################
# LOAD INIT MODEL
########################################
resume = os.path.join(exp_dir, 'epoch.pth.tar')
if os.path.isfile(resume):
checkpoint = torch.load(resume)
model_ft = checkpoint['model']
print("=> RESUMING FROM CHECKPOINTED MODEL: ", resume)
else:
if not os.path.isfile(model_path):
model_ft = models.alexnet(pretrained=True)
print("=> STARTING PRETRAINED ALEXNET")
else:
model_ft = torch.load(model_path)
print("=> STARTING FROM OTHER MODEL: ", model_path)
# Replace last layer classifier, for the amount of classes in the current dataset
if not init_freeze:
# Alexnet vs VGG
last_layer_index = len(model_ft.classifier) - 1
num_ftrs = model_ft.classifier[last_layer_index].in_features
model_ft.classifier._modules[str(last_layer_index)] = nn.Linear(
num_ftrs, len(dset_classes))
if not os.path.exists(exp_dir):
os.makedirs(exp_dir)
# If not resuming from an preempted IMM model, cleanup model
if not os.path.isfile(resume):
# Prev task model, last 2 hyperparam
parameters = list(model_ft.parameters())
parameter1 = parameters[-1]
parameter2 = parameters[-2]
# Try to remove them from the reg_params
try:
model_ft.reg_params.pop(parameter1, None)
model_ft.reg_params.pop(parameter2, None)
except:
print('nothing to remove')
# The regularization params are the parameters of the prev model (trying to)
reg_params = update_reg_params(model_ft)
print('update')
reg_params['lambda'] = reg_lambda # The regularization hyperparam
model_ft.reg_params = reg_params
# Only transfer here to CUDA, preventing non-cuda network adaptations
use_gpu = torch.cuda.is_available()
if use_gpu:
model_ft = model_ft.cuda()
########################################
# TRAIN
########################################
# Define Optimizer for IMM: extra loss term in step
optimizer_ft = train_L2transfer.Weight_Regularized_SGD(
model_ft.parameters(), lr, momentum=0.9, weight_decay=weight_decay)
criterion = nn.CrossEntropyLoss() # Loss
model_ft, acc = train_L2transfer.train_model(model_ft,
criterion,
optimizer_ft,
lr,
dset_loaders,
dset_sizes,
use_gpu,
num_epochs,
exp_dir,
resume,
saving_freq=saving_freq)
return model_ft, acc
