Commit a40c6e95 authored by Alexandru Dura's avatar Alexandru Dura

Retrain MobileNetV2

parent 6eae1e34
import torch
from torchviz import make_dot
import urllib
url, filename = ("https://github.com/pytorch/hub/raw/master/dog.jpg", "dog.jpg")
try: urllib.URLopener().retrieve(url, filename)
except: urllib.request.urlretrieve(url, filename)
model = torch.hub.load('pytorch/vision:v0.5.0', 'mobilenet_v2', pretrained=True)
model.eval()
from PIL import Image
from torchvision import transforms
input_image = Image.open(filename)
# I think this resized the picture and does some processing on pixels.
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]),
])
input_tensor = preprocess(input_image)
input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
# move the input and model to GPU for speed if available
if torch.cuda.is_available():
input_batch = input_batch.to('cuda')
model.to('cuda')
with torch.no_grad():
output = model(input_batch)
# Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes
print(output[0])
# The output has unnormalized scores. To get probabilities, you can run a softmax on it.
print(torch.nn.functional.softmax(output[0], dim=0))
# We load the labels for the classes from a file
import json
class_idx = json.load(open("imagenet_class_index.json", 'r'))
idx2label = [class_idx[str(k)][1] for k in range(len(class_idx))]
values, indices = output[0].sort()
for idx in indices[-10:]:
print(idx2label[idx])
# This code is a mix-and-match from:
# https://pytorch.org/tutorials/beginner/finetuning_torchvision_models_tutorial.html
#
import torch
import numpy as np
import matplotlib.pyplot as plt
import torch.nn as nn
from torchviz import make_dot
import torchvision as tv
import urllib
import torch.optim as optim
from torch.optim import lr_scheduler
import time
import copy
IMAGE_FOLDER="../data/images"
# Visualize some images
def imshow(inp, title=None):
inp = inp.numpy().transpose((1, 2, 0))
# mean = np.array([mean_nums])
# std = np.array([std_nums])
# inp = std * inp + mean
# inp = np.clip(inp, 0, 1)
plt.imshow(inp)
if title is not None:
plt.title(title)
plt.pause(0.001) # Pause a bit so that plots are updated
plt.show(block=True)
def train_model(device, dataloaders, model, criterion, optimizer, scheduler, num_epochs=25):
since = time.time()
best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
# Each epoch has a training and validation phase
for phase in ['train', 'val']:
print (phase)
if phase == 'train':
model.train() # Set model to training mode
else:
model.eval() # Set model to evaluate mode
running_loss = 0.0
running_corrects = 0
# Iterate over data.
for inputs, labels in dataloaders[phase]:
inputs = inputs.to(device)
labels = labels.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward
# track history if only in train
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
# backward + optimize only if in training phase
if phase == 'train':
loss.backward()
optimizer.step()
# statistics
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
if phase == 'train':
scheduler.step()
epoch_loss = running_loss / dataset_sizes[phase]
epoch_acc = running_corrects.double() / dataset_sizes[phase]
print('{} Loss: {:.4f} Acc: {:.4f}'.format(
phase, epoch_loss, epoch_acc))
# deep copy the model
if phase == 'val' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = copy.deepcopy(model.state_dict())
print()
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))
# load best model weights
model.load_state_dict(best_model_wts)
return model
def main() :
preprocess = tv.transforms.Compose([
tv.transforms.Resize(256),
tv.transforms.CenterCrop(224),
tv.transforms.ToTensor(),
tv.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
# the folder where the images are
img_folder = tv.datasets.ImageFolder(IMAGE_FOLDER, preprocess)
# load the training set in random order
data_loader_train = torch.utils.data.DataLoader(img_folder, batch_size=8,
shuffle=True)
data_loader_eval = torch.utils.data.DataLoader(img_folder, batch_size=8,
shuffle=True)
dataloaders = {'train' : data_loader_train, 'eval' : data_loader_eval}
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# load the model from torch hub
model = torch.hub.load('pytorch/vision:v0.5.0', 'mobilenet_v2', pretrained=True)
# extract the class names
class_names = img_folder.classes
# model for training
model_ft = model
# num_ftrs = model_ft.fc.in_features
# Here the size of each output sample is set to 2.
# Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)).
# model_ft.fc = nn.Linear(num_ftrs, 2)
model_ft = model_ft.to(device)
criterion = nn.CrossEntropyLoss()
# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
# Train the model
model_ft = train_model(device, dataloaders, model_ft, criterion, optimizer_ft, exp_lr_scheduler,
num_epochs=25)
return
# Grab some of the training data to visualize
inputs, classes = next(iter(data_loader))
print (classes)
# Now we construct a grid from batch
out = tv.utils.make_grid(inputs)
imshow(out, title=[class_names[x] for x in classes])
if __name__ == "__main__":
main()
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