Hanrui-Wang

import torch
import torch.nn as nn
import torch.nn.functional as F


class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        # 1 input image channel, 6 output channels, 3x3 square convolution

Hanrui-Wang

import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms


# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Hyper-parameters 

Hanrui-Wang

import matplotlib.pyplot as plt
import matplotlib

plt.figure(i, figsize=(5.5, 5))
plt.grid(True, linewidth=0.2)
plt.plot(step, reward, linestyle=line_style[methods[i][ii]], linewidth=line_width[methods[i][ii]], color=colors[methods[i][ii]])
plt.ylim(top=y_top[i], bottom=y_bottom[i])
plt.xlim(left=0, right=10000)

plt.rcParams.update({'font.size': 12})

Hanrui-Wang

isinstance(output_size, (int, tuple))

Hanrui-Wang

import numpy as np
np.random.randint(0, h - new_h)
np.random.randn(in_size, out_size)

Hanrui-Wang

class Rescale(object):
    """Rescale the image in a sample to a given size.

    Args:
        output_size (tuple or int): Desired output size. If tuple, output is
            matched to output_size. If int, smaller of image edges is matched
            to output_size keeping aspect ratio the same.
    """

    def __init__(self, output_size):

Hanrui-Wang

class FaceLandmarksDataset(Dataset):
    """Face Landmarks dataset."""

    def __init__(self, csv_file, root_dir, transform=None):
        """
        Args:
            csv_file (string): Path to the csv file with annotations.
            root_dir (string): Directory with all the images.
            transform (callable, optional): Optional transform to be applied
                on a sample.

Hanrui-Wang

grid = utils.make_grid(images_batch)
plt.imshow(grid.numpy().transpose((1, 2, 0)))

Hanrui-Wang

dataloader = DataLoader(transformed_dataset, batch_size=4,
                        shuffle=True, num_workers=4)

Hanrui-Wang

import torch
from torchvision import transforms, datasets

data_transform = transforms.Compose([
        transforms.RandomSizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406],
                             std=[0.229, 0.224, 0.225])
    ])