《Kaggle Histopathologic Cancer Detection癌症图像分类比赛》之PyTorch实现
项目地址：https://www.kaggle.com/c/histopathologic-cancer-detection/overview
本文记录了自己使用Pytorch以及Pytorch标准的Dataset的准备方式对同一问题进行了实现：
其他实现版本:
Kaggle Histopathology Cancel Detection之Pyorch实现
Kaggle Histopathologic Cancer Detection之Keras/Generator实现
Kaggle Histopathologic Cancer Detection之Tensorflow2.0实现
# -*- coding: utf-8 -*-
import numpy as np
import os,sys,csv,math
import cv2 as cv
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import myimageutil as iu
import copy
import time
"""
====================================================================================
<<1.初步了解掌握数据的情况>>
====================================================================================
用pandas简单处理一下CSV并画出来看一下
这里我借用了kaggle的这篇kernel里的plot的代码，有兴趣的童鞋可以读一下，
https://www.kaggle.com/qitvision/a-complete-ml-pipeline-fast-ai
"""
ROOT_PATH = 'D:/ai_data/histopathologic-cancer-detection'
CSV_PATH = 'D:/ai_data/histopathologic-cancer-detection/train_labels.csv'
TRAIN_PATH = 'D:/ai_data/histopathologic-cancer-detection/train'
TEST_PATH = 'D:/ai_data/histopathologic-cancer-detection/test'
print(">>>看一下根目录下有哪些东西：")
print(os.listdir(ROOT_PATH))
df = pd.read_csv(CSV_PATH) #pandas里的数据集叫dataframe，和scala里的一样，我们简称df
# 接下来我们来看一下数据的情况
print(">>>这个数据集的大小：")
print(df.shape)
print(">>>这个数据集的样本分布：")
print(df['label'].value_counts())
print(">>>看一下数据：")
print(df.head())
# 这边我想说明一下，之前我们的第一篇walkthrough里是直接从csv中获得文件列表的，这边最好检查一下列表里的文件和文件夹里的是不是一一对应
print(">>>list一下训练图片文件夹里的图片：")
from glob import glob
train_file_paths = glob(TRAIN_PATH + '/*.tif')
test_file_paths = glob(TEST_PATH + '/*.tif')
print("train_file_paths size:", len(train_file_paths))
print("test_file_paths size:", len(test_file_paths))
import re
def check_valid():
assert len(train_file_paths) == len(df['id']),'图片数量不一致'
ids_from_filepath = list(map(lambda filepath:''.join(re.findall(r'[a-z0-9]{40}',filepath)), train_file_paths))
dif = list(set(ids_from_filepath)^set(df['id'])) #求两个list的差集，如果差集为0，那说明两个list相等
if len(dif) == 0:
print("文件名匹配正常")
else:
print("匹配异常,下列文件名有差异：")
print(dif)
exit()
check_valid()
# print(">>>数据没问题的话接下来看一下正负数据样例的图片：")
# iu.plotSamples(df,TRAIN_PATH) #要注意本次的图片数据是使用中间32X32像素的内容为基准进行标注的，所以画图把中间一块标注出来了，但实际分类的时候不一定要把中间裁出来
# print(">>>进入正题，我们拆分一下数据，把训练数据分成训练和测试2部分，比例为9：1")
train, val = train_test_split(train_file_paths, test_size=0.1, shuffle=True)
# train = train[:640]
# val = val[:64]
"""
====================================================================================
<<2.图片处理和扩增>>
====================================================================================
这边使用pytorch官方的pipline dataset来预处理图片
"""
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.utils.data import DataLoader
import torchvision
from torchvision import models
import pretrainedmodels
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
import inspect
from gpu_mem_track import MemTracker
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print('trian_device:{}'.format(device.type))
id_label_map = {k:v for k,v in zip(df.id.values, df.label.values)}
def get_id_from_file_path(file_path):
return file_path.split(os.path.sep)[-1].replace('.tif', '')
class MyDataset(Dataset):
def __init__(self, file_list,id_label_map,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.
"""
self.file_list = file_list
self.transform = transform
self.id_label_map = id_label_map
def __len__(self):
return len(self.file_list)
def __getitem__(self,index):
file_path = self.file_list[index]
label = self.id_label_map[get_id_from_file_path(file_path)]
image_bgr = cv.imread(file_path)
image_rgb = image_bgr[:,:,[2,1,0]]
# image_c_h_w = np.transpose(image_rgb,(2,0,1))
# label_one_hot = np.array([0,1],dtype=np.int64) if label == 1 else np.array([1,0],dtype=np.int64)
# label = np.array([label],dtype=np.int64)
# label = np.array([label_one_hot],dtype='float')
sample = {'image': image_rgb, 'label': label}
if self.transform:
sample = self.transform(sample)
return sample
# def show_labels(image,label):
# plt.imshow(image)
# plt.pause(0.001)
# myDataset = MyDataset(train,id_label_map)
# fig = plt.figure()
# for i in range(len(myDataset)):
# sample = myDataset[i]
# print(i, sample['image'].shape, sample['label'].shape)
# ax = plt.subplot(1, 4, i + 1)
# plt.tight_layout()
# ax.set_title('Sample #{}'.format(i))
# ax.axis('off')
# show_landmarks(**sample)
# if i == 3:
# plt.show()
# break
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):
assert isinstance(output_size, (int, tuple))
self.output_size = output_size
def __call__(self, sample):
image, label = sample['image'], sample['label']
h, w = image.shape[:2]
if isinstance(self.output_size, int):
if h > w:
new_h, new_w = self.output_size * h / w, self.output_size
else:
new_h, new_w = self.output_size, self.output_size * w / h
else:
new_h, new_w = self.output_size
new_h, new_w = int(new_h), int(new_w)
img = cv.resize(image, (new_w,new_h))
# print(img)
return {'image': img, 'label': label}
class RandomCrop(object):
"""Crop randomly the image in a sample.
Args:
output_size (tuple or int): Desired output size. If int, square crop
is made.
"""
def __init__(self, output_size):
assert isinstance(output_size, (int, tuple))
if isinstance(output_size, int):
self.output_size = (output_size, output_size)
else:
assert len(output_size) == 2
self.output_size = output_size
def __call__(self, sample):
image, label = sample['image'], sample['label']
h, w = image.shape[:2]
new_h, new_w = self.output_size
top = np.random.randint(0, h - new_h)
left = np.random.randint(0, w - new_w)
image = image[top: top + new_h,
left: left + new_w]
return {'image': image, 'label': label}
class ToTensor(object):
"""Convert ndarrays in sample to Tensors."""
def __call__(self, sample):
image, label = sample['image'], sample['label']
# swap color axis because
# numpy image: H x W x C
# torch image: C X H X W
image = image.transpose((2, 0, 1))
return {'image': torch.from_numpy(image).float().div(255),
'label': label}
class Normalize(object):
"""Normalize a tensor image with mean and standard deviation.
Given mean: ``(M1,...,Mn)`` and std: ``(S1,..,Sn)`` for ``n`` channels, this transform
will normalize each channel of the input ``torch.*Tensor`` i.e.
``input[channel] = (input[channel] - mean[channel]) / std[channel]``
.. note::
This transform acts out of place, i.e., it does not mutates the input tensor.
Args:
mean (sequence): Sequence of means for each channel.
std (sequence): Sequence of standard deviations for each channel.
"""
def __init__(self, mean, std):
self.mean = mean
self.std = std
def __call__(self, sample):
"""
Args:
tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
Returns:
Tensor: Normalized Tensor image.
"""
tensor , label = sample['image'], sample['label']
mean = torch.as_tensor(self.mean, dtype=torch.float32, device=tensor.device)
std = torch.as_tensor(self.std, dtype=torch.float32, device=tensor.device)
# tensor.sub_(mean[:, None, None]).div_(std[:, None, None])
tensor.mul(std[:, None, None]).sub_(mean[:, None, None])
return {'image': tensor,'label': label}
# scale = Rescale(400)
# # crop = RandomCrop(331)
# composed = transforms.Compose([Rescale(400),RandomCrop(331)])
# fig = plt.figure()
# sample = myDataset[65]
# for i, tsfrm in enumerate([scale, composed]):
# transformed_sample = tsfrm(sample)
# ax = plt.subplot(1, 3, i + 1)
# plt.tight_layout()
# ax.set_title(type(tsfrm).__name__)
# show_labels(**transformed_sample)
# plt.show()
# exit()
def main():
myDataset_train = MyDataset(train,id_label_map,
transform=transforms.Compose([
Rescale(331),
ToTensor(),
# Normalize(mean=[0.5, 0.5, 0.5],std=[0.5, 0.5, 0.5])
]))
myDataset_val = MyDataset(val,id_label_map,
transform=transforms.Compose([
Rescale(331),
ToTensor(),
# Normalize(mean=[0.5, 0.5, 0.5],std=[0.5, 0.5, 0.5])
]))
dataloader_train = torch.utils.data.DataLoader(myDataset_train, batch_size= 4,
shuffle=True, num_workers=0)
dataloader_val = torch.utils.data.DataLoader(myDataset_val, batch_size=4,
shuffle=True, num_workers=0)
dataloaders = {'train':dataloader_train,'val':dataloader_val}
# def imshow(inp, title=None):
# """Imshow for Tensor."""
# inp = inp.numpy().transpose((1, 2, 0))
# # mean = np.array([0.5, 0.5, 0.5])
# # std = np.array([0.5, 0.5, 0.5])
# # inp = (inp + mean)/std
# 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()
# output = next(iter(dataloader_train))
# inputs = output['image']
# labels = output['label']
# # inputs,labels = [ x['image'] for x in output],[ x['label'] for x in output]
# out = torchvision.utils.make_grid(inputs)
# imshow(out,title=[x for x in labels])
# exit()
frame = inspect.currentframe()
gpu_tracker = MemTracker(frame)
# #GPU VRAM Track
# gpu_tracker.track()
model_name = 'nasnetalarge' # could be fbresnet152 or inceptionresnetv2
model = pretrainedmodels.__dict__[model_name](num_classes=1000, pretrained='imagenet')
model.eval()
model.last_linear = nn.Linear(4032,2)
# model = nn.Sequential(model,nn.Sigmoid())
model = model.to(device)
# GPU VRAM Track
# gpu_tracker.track()
loss_func = nn.CrossEntropyLoss()
# optimizer = optim.SGD(model.parameters(),lr=0.0001,momentum=0.9)
optimizer = optim.Adam(model.parameters(), lr=0.0001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False)
# exp_lr_scheduler = lr_scheduler.StepLR(optimizer=optimizer,step_size=10,gamma=0.5)
# print(model)
# exit()
def train_model(model, loss_func, 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']:
if phase == 'train':
if scheduler is not None:
scheduler.step()
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 x in dataloaders[phase]:
inputs = x['image'].to(device)
labels = x['label'].to(device)
# #GPU VRAM Track
# gpu_tracker.track()
# 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)
# print(outputs)
# print(labels)
# print(labels.squeeze())
loss = loss_func(outputs, labels)
# backward + optimize only if in training phase
if phase == 'train':
loss.backward()
optimizer.step()
# #GPU VRAM Track
# gpu_tracker.track()
# statistics
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
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
model_conv = train_model(model,loss_func,optimizer,None)
# print('training finish !')
# torch.save(model_conv.state_dict(), './model/model_4.pth')
if __name__=='__main__':
main()