import numpy as np
import pandas as pd
import PIL.Image
import matplotlib.pyplot as plt
from catalyst.utils import get_one_hot
from imageio import imread
import pretrainedmodels
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from visdom import Visdom
jupytools.syspath.add(join(dirname(os.getcwd()), 'protein_project'))
jupytools.syspath.add('rxrx1-utils')
if jupytools.is_notebook():
from tqdm import tqdm_notebook as tqdm
else:
from tqdm import tqdm as tdqm
from basedir import ROOT, NUM_CLASSES
from dataset import build_stats_index
torch.set_default_tensor_type(torch.FloatTensor)
from augmentation import JoinChannels, SwapChannels, Resize, ToFloat, Rescale
from augmentation import VerticalFlip, HorizontalFlip, PixelStatsNorm, composer
from augmentation import AugmentedImages, bernoulli
default_open_fn = imread # PIL.Image.open
bar.update(1)
vis.line(X=[iteration], Y=[avg_loss],
win='loss', name='avg_loss', update='append')
val_dl = dataset['valid']
n = len(val_dl)
model.eval()
with torch.no_grad():
matches = []
with tqdm(total=n) as bar:
for batch in val_dl:
x = batch['features'].to(device)
y = batch['targets'].to(device)
out = model(x)
y_pred = out.softmax(dim=1).argmax(dim=1)
matched = (y == y_pred).detach().cpu().numpy().tolist()
matches.extend(matched)
bar.update(1)
acc = np.mean(matches)
vis.line(X=[epoch], Y=[acc], win='acc', name='val_acc', update='append')
print(f'validation accuracy: {acc:2.2%}')
acc_str = str(int(round(acc * 10_000, 0)))
path = os.path.join(logdir, f'train.{epoch}.{acc_str}.pth')
torch.save(model.state_dict(), path)
if __name__ == '__main__':
if not is_notebook():
ancli.make_cli(train)
