johschmidt42

import torch
from skimage.io import imread
from torch.utils import data


class SegmentationDataSet(data.Dataset):
    def __init__(self,
                 inputs: list,
                 targets: list,
                 transform=None

johschmidt42

from typing import List, Callable, Tuple

import numpy as np
import albumentations as A
from sklearn.externals._pilutil import bytescale
from skimage.util import crop


def normalize_01(inp: np.ndarray):
    """Squash image input to the value range [0, 1] (no clipping)"""

johschmidt42

import numpy as np
import napari
from transformations import re_normalize


def enable_gui_qt():
    """Performs the magic command %gui qt"""
    from IPython import get_ipython

    ipython = get_ipython()

johschmidt42

from torch import nn
import torch


@torch.jit.script
def autocrop(encoder_layer: torch.Tensor, decoder_layer: torch.Tensor):
    """
    Center-crops the encoder_layer to the size of the decoder_layer,
    so that merging (concatenation) between levels/blocks is possible.
    This is only necessary for input sizes != 2**n for 'same' padding and always required for 'valid' padding.

johschmidt42

==========================================================================================
Layer (type:depth-idx)                   Output Shape              Param #
==========================================================================================
├─ModuleList: 1                          []                        --
|    └─DownBlock: 2-1                    [-1, 32, 256, 256]        --
|    |    └─Conv2d: 3-1                  [-1, 32, 512, 512]        320
|    |    └─ReLU: 3-2                    [-1, 32, 512, 512]        --
|    |    └─BatchNorm2d: 3-3             [-1, 32, 512, 512]        64
|    |    └─Conv2d: 3-4                  [-1, 32, 512, 512]        9,248
|    |    └─ReLU: 3-5                    [-1, 32, 512, 512]        --

johschmidt42

import numpy as np
import torch


class Trainer:
    def __init__(self,
                 model: torch.nn.Module,
                 device: torch.device,
                 criterion: torch.nn.Module,
                 optimizer: torch.optim.Optimizer,

johschmidt42

import torch
from skimage.io import imread
from torch.utils import data
from tqdm import tqdm


class SegmentationDataSet2(data.Dataset):
    """Image segmentation dataset with caching and pretransforms."""
    def __init__(self,
                 inputs: list,

johschmidt42

import pandas as pd
import torch
from torch import nn
from matplotlib import pyplot as plt
from tqdm import tqdm, trange
import math


class LearningRateFinder:
    """

johschmidt42

def plot_training(training_losses,
                  validation_losses,
                  learning_rate,
                  gaussian=True,
                  sigma=2,
                  figsize=(8, 6)
                  ):
    """
    Returns a loss plot with training loss, validation loss and learning rate.
    """

johschmidt42

import torch


def predict(img,
            model,
            preprocess,
            postprocess,
            device,
            ):
    model.eval()