Tiny Imagenet

Author

Benedict Thekkel 

import os
os.environ['CUDA_VISIBLE_DEVICES']='1'
import shutil,timm,os,torch,random,datasets,math
import fastcore.all as fc, numpy as np, matplotlib as mpl, matplotlib.pyplot as plt
import k_diffusion as K, torchvision.transforms as T
import torchvision.transforms.functional as TF,torch.nn.functional as F

from torch.utils.data import DataLoader,default_collate
from pathlib import Path
from torch.nn import init
from fastcore.foundation import L
from torch import nn,tensor
from operator import itemgetter
from torcheval.metrics import MulticlassAccuracy
from functools import partial
from torch.optim import lr_scheduler
from torch import optim
from torchvision.io import read_image,ImageReadMode
from glob import glob

from fastAIcourse.datasets import *
from fastAIcourse.conv import *
from fastAIcourse.learner import *
from fastAIcourse.activations import *
from fastAIcourse.init import *
from fastAIcourse.sgd import *
from fastAIcourse.resnet import *
from fastAIcourse.augment import *
from fastAIcourse.accel import *
from fastAIcourse.training import *
from fastprogress import progress_bar
torch.set_printoptions(precision=5, linewidth=140, sci_mode=False)
torch.manual_seed(1)
mpl.rcParams['figure.dpi'] = 70

set_seed(42)
if fc.defaults.cpus>8: fc.defaults.cpus=8

Data processing

path_data = Path('data')
path_data.mkdir(exist_ok=True)
path = path_data/'tiny-imagenet-200'
url = 'http://cs231n.stanford.edu/tiny-imagenet-200.zip'
if not path.exists():
    path_zip = fc.urlsave(url, path_data)
    shutil.unpack_archive('data/tiny-imagenet-200.zip', 'data')
bs = 512
class TinyDS:
    def __init__(self, path):
        self.path = Path(path)
        self.files = glob(str(path/'**/*.JPEG'), recursive=True)
    def __len__(self): return len(self.files)
    def __getitem__(self, i): return self.files[i],Path(self.files[i]).parent.parent.name
tds = TinyDS(path/'train')
path_anno = path/'val'/'val_annotations.txt'
anno = dict(o.split('\t')[:2] for o in path_anno.read_text().splitlines())
class TinyValDS(TinyDS):
    def __getitem__(self, i): return self.files[i],anno[os.path.basename(self.files[i])]
vds = TinyValDS(path/'val')
class TfmDS:
    def __init__(self, ds, tfmx=fc.noop, tfmy=fc.noop): self.ds,self.tfmx,self.tfmy = ds,tfmx,tfmy
    def __len__(self): return len(self.ds)
    def __getitem__(self, i):
        x,y = self.ds[i]
        return self.tfmx(x),self.tfmy(y)
id2str = (path/'wnids.txt').read_text().splitlines()
str2id = {v:k for k,v in enumerate(id2str)}
xmean,xstd = (tensor([0.47565, 0.40303, 0.31555]), tensor([0.28858, 0.24402, 0.26615]))
def tfmy(y): return tensor(str2id[y])
def denorm(x): return (x*xstd[:,None,None]+xmean[:,None,None]).clip(0,1)
all_synsets = [o.split('\t') for o in (path/'words.txt').read_text().splitlines()]
synsets = {k:v.split(',', maxsplit=1)[0] for k,v in all_synsets if k in id2str}
def tfm_batch(b, tfm_x=fc.noop, tfm_y = fc.noop): return tfm_x(b[0]),tfm_y(b[1])
act_gr = partial(GeneralRelu, leak=0.1, sub=0.4)
iw = partial(init_weights, leaky=0.1)
nfs = (32,64,128,256,512,1024)
opt_func = partial(optim.AdamW, eps=1e-5)
nbks=(3,3,2,2,1)
def conv(ni, nf, ks=3, stride=1, act=nn.ReLU, norm=None, bias=True):
    layers = []
    if norm: layers.append(norm(ni))
    if act : layers.append(act())
    layers.append(nn.Conv2d(ni, nf, stride=stride, kernel_size=ks, padding=ks//2, bias=bias))
    return nn.Sequential(*layers)

def _conv_block(ni, nf, stride, act=act_gr, norm=None, ks=3):
    return nn.Sequential(conv(ni, nf, stride=1     , act=act, norm=norm, ks=ks),
                         conv(nf, nf, stride=stride, act=act, norm=norm, ks=ks))

class ResBlock(nn.Module):
    def __init__(self, ni, nf, stride=1, ks=3, act=act_gr, norm=None):
        super().__init__()
        self.convs = _conv_block(ni, nf, stride, act=act, ks=ks, norm=norm)
        self.idconv = fc.noop if ni==nf else conv(ni, nf, ks=1, stride=1, act=None, norm=norm)
        self.pool = fc.noop if stride==1 else nn.AvgPool2d(2, ceil_mode=True)

    def forward(self, x): return self.convs(x) + self.idconv(self.pool(x))

def res_blocks(n_bk, ni, nf, stride=1, ks=3, act=act_gr, norm=None):
    return nn.Sequential(*[
        ResBlock(ni if i==0 else nf, nf, stride=stride if i==n_bk-1 else 1, ks=ks, act=act, norm=norm)
        for i in range(n_bk)])

def get_dropmodel(act=act_gr, nfs=nfs, nbks=nbks, norm=nn.BatchNorm2d, drop=0.2):
    layers = [nn.Conv2d(3, nfs[0], 5, padding=2)]
    layers += [res_blocks(nbks[i], nfs[i], nfs[i+1], act=act, norm=norm, stride=2)
               for i in range(len(nfs)-1)]
    layers += [act_gr(), norm(nfs[-1]), nn.AdaptiveAvgPool2d(1), nn.Flatten(), nn.Dropout(drop)]
    layers += [nn.Linear(nfs[-1], 200, bias=False), nn.BatchNorm1d(200)]
    return nn.Sequential(*layers).apply(iw)
metrics = MetricsCB(accuracy=MulticlassAccuracy())
cbs = [DeviceCB(), metrics, ProgressCB(plot=True), MixedPrecision()]
aug_tfms = nn.Sequential(T.Pad(4), T.RandomCrop(64),
                     T.RandomHorizontalFlip(),
                     T.TrivialAugmentWide())

norm_tfm = T.Normalize(xmean, xstd)
erase_tfm = RandErase()
from PIL import Image
def tfmx(x, aug=False):
    x = Image.open(x).convert('RGB')
    if aug: x = aug_tfms(x)
    x = TF.to_tensor(x)
    x = norm_tfm(x)
    if aug: x = erase_tfm(x[None])[0]
    return x
tfm_tds = TfmDS(tds, partial(tfmx, aug=True), tfmy)
tfm_vds = TfmDS(vds, tfmx, tfmy)
dls = DataLoaders(*get_dls(tfm_tds, tfm_vds, bs=bs, num_workers=8))
def get_model(): return get_dropmodel(drop=0.1)
learn = TrainLearner(get_model(), dls, F.cross_entropy, cbs=[SingleBatchCB(), DeviceCB()])
learn.fit(1)
xb,yb = learn.batch
show_images(denorm(xb.cpu())[:9], imsize=2.5)

epochs = 200
lr = 0.1
tmax = epochs * len(dls.train)
sched = partial(lr_scheduler.OneCycleLR, max_lr=lr, total_steps=tmax)
xtra = [BatchSchedCB(sched)]
learn = Learner(get_model(), dls, F.cross_entropy, lr=lr, cbs=cbs+xtra, opt_func=opt_func)
learn.fit(epochs)
accuracy loss epoch train
0.025 5.057 0 train
0.043 4.807 0 eval
0.049 4.768 1 train
0.065 4.521 1 eval
0.079 4.500 2 train
0.111 4.182 2 eval
0.116 4.207 3 train
0.159 3.827 3 eval
0.152 3.950 4 train
0.193 3.615 4 eval
0.185 3.721 5 train
0.242 3.316 5 eval
0.220 3.516 6 train
0.286 3.062 6 eval
0.246 3.361 7 train
0.306 2.962 7 eval
0.270 3.209 8 train
0.309 3.030 8 eval
0.293 3.089 9 train
0.344 2.780 9 eval
0.312 2.971 10 train
0.355 2.753 10 eval
0.335 2.863 11 train
0.384 2.627 11 eval
0.350 2.779 12 train
0.381 2.646 12 eval
0.365 2.699 13 train
0.388 2.619 13 eval
0.379 2.624 14 train
0.409 2.542 14 eval
0.390 2.571 15 train
0.410 2.514 15 eval
0.405 2.504 16 train
0.405 2.563 16 eval
0.410 2.461 17 train
0.426 2.462 17 eval
0.419 2.422 18 train
0.442 2.390 18 eval
0.427 2.378 19 train
0.451 2.348 19 eval
0.434 2.346 20 train
0.413 2.565 20 eval
0.439 2.311 21 train
0.371 2.858 21 eval
0.447 2.283 22 train
0.390 2.787 22 eval
0.449 2.278 23 train
0.434 2.446 23 eval
0.452 2.255 24 train
0.444 2.351 24 eval
0.455 2.235 25 train
0.438 2.416 25 eval
0.458 2.231 26 train
0.457 2.326 26 eval
0.459 2.215 27 train
0.455 2.349 27 eval
0.464 2.204 28 train
0.442 2.400 28 eval
0.464 2.200 29 train
0.344 3.163 29 eval
0.467 2.188 30 train
0.403 2.749 30 eval
0.463 2.205 31 train
0.407 2.692 31 eval
0.465 2.196 32 train
0.400 2.628 32 eval
0.462 2.206 33 train
0.467 2.278 33 eval
0.465 2.202 34 train
0.452 2.303 34 eval
0.462 2.203 35 train
0.452 2.377 35 eval
0.464 2.200 36 train
0.437 2.443 36 eval
0.465 2.207 37 train
0.436 2.428 37 eval
0.462 2.200 38 train
0.423 2.565 38 eval
0.463 2.210 39 train
0.357 3.034 39 eval
0.460 2.221 40 train
0.396 2.672 40 eval
0.459 2.225 41 train
0.450 2.317 41 eval
0.459 2.230 42 train
0.446 2.407 42 eval
0.458 2.237 43 train
0.397 2.638 43 eval
0.459 2.236 44 train
0.439 2.424 44 eval
0.456 2.243 45 train
0.445 2.336 45 eval
0.455 2.253 46 train
0.412 2.652 46 eval
0.453 2.254 47 train
0.380 2.760 47 eval
0.456 2.248 48 train
0.400 2.701 48 eval
0.453 2.256 49 train
0.409 2.691 49 eval
0.451 2.264 50 train
0.354 3.238 50 eval
0.452 2.267 51 train
0.434 2.413 51 eval
0.450 2.265 52 train
0.431 2.499 52 eval
0.452 2.260 53 train
0.447 2.352 53 eval
0.452 2.267 54 train
0.442 2.383 54 eval
0.447 2.271 55 train
0.405 2.598 55 eval
0.451 2.266 56 train
0.418 2.584 56 eval
0.450 2.268 57 train
0.419 2.531 57 eval
0.452 2.263 58 train
0.457 2.286 58 eval
0.451 2.262 59 train
0.351 3.086 59 eval
0.449 2.270 60 train
0.387 2.717 60 eval
0.456 2.243 61 train
0.427 2.532 61 eval
0.456 2.241 62 train
0.408 2.488 62 eval
0.455 2.249 63 train
0.405 2.625 63 eval
0.456 2.239 64 train
0.420 2.485 64 eval
0.456 2.237 65 train
0.411 2.518 65 eval
0.457 2.239 66 train
0.417 2.590 66 eval
0.457 2.230 67 train
0.354 2.970 67 eval
0.458 2.229 68 train
0.424 2.444 68 eval
0.460 2.219 69 train
0.347 2.992 69 eval
0.463 2.212 70 train
0.395 2.699 70 eval
0.463 2.207 71 train
0.339 3.148 71 eval
0.464 2.200 72 train
0.353 3.148 72 eval
0.464 2.201 73 train
0.434 2.422 73 eval
0.466 2.198 74 train
0.424 2.551 74 eval
0.471 2.179 75 train
0.393 2.710 75 eval
0.468 2.183 76 train
0.405 2.624 76 eval
0.465 2.194 77 train
0.417 2.578 77 eval
0.471 2.172 78 train
0.440 2.477 78 eval
0.471 2.164 79 train
0.392 2.752 79 eval
0.473 2.161 80 train
0.424 2.490 80 eval
0.473 2.164 81 train
0.420 2.547 81 eval
0.476 2.147 82 train
0.394 2.779 82 eval
0.475 2.155 83 train
0.400 2.747 83 eval
0.477 2.138 84 train
0.397 2.684 84 eval
0.481 2.134 85 train
0.442 2.428 85 eval
0.479 2.122 86 train
0.371 2.966 86 eval
0.482 2.123 87 train
0.410 2.634 87 eval
0.481 2.121 88 train
0.442 2.399 88 eval
0.482 2.111 89 train
0.461 2.331 89 eval
0.487 2.092 90 train
0.435 2.423 90 eval
0.487 2.092 91 train
0.414 2.691 91 eval
0.486 2.089 92 train
0.384 2.826 92 eval
0.493 2.062 93 train
0.462 2.385 93 eval
0.494 2.056 94 train
0.460 2.318 94 eval
0.493 2.056 95 train
0.418 2.625 95 eval
0.497 2.045 96 train
0.444 2.455 96 eval
0.495 2.043 97 train
0.484 2.159 97 eval
0.503 2.016 98 train
0.417 2.637 98 eval
0.504 2.016 99 train
0.384 2.846 99 eval
0.503 2.016 100 train
0.494 2.186 100 eval
0.504 2.008 101 train
0.436 2.522 101 eval
0.509 1.988 102 train
0.445 2.428 102 eval
0.508 1.986 103 train
0.488 2.131 103 eval
0.512 1.973 104 train
0.485 2.196 104 eval
0.515 1.959 105 train
0.486 2.231 105 eval
0.515 1.953 106 train
0.461 2.331 106 eval
0.522 1.924 107 train
0.477 2.287 107 eval
0.522 1.920 108 train
0.452 2.439 108 eval
0.522 1.921 109 train
0.493 2.176 109 eval
0.525 1.909 110 train
0.488 2.259 110 eval
0.528 1.894 111 train
0.477 2.305 111 eval
0.532 1.879 112 train
0.485 2.224 112 eval
0.536 1.862 113 train
0.485 2.244 113 eval
0.538 1.850 114 train
0.451 2.439 114 eval
0.540 1.842 115 train
0.482 2.231 115 eval
0.545 1.824 116 train
0.506 2.124 116 eval
0.548 1.803 117 train
0.461 2.427 117 eval
0.550 1.797 118 train
0.510 2.132 118 eval
0.555 1.774 119 train
0.504 2.124 119 eval
0.556 1.767 120 train
0.447 2.468 120 eval
0.560 1.748 121 train
0.472 2.329 121 eval
0.563 1.736 122 train
0.488 2.283 122 eval
0.568 1.714 123 train
0.466 2.340 123 eval
0.568 1.710 124 train
0.464 2.382 124 eval
0.574 1.686 125 train
0.451 2.466 125 eval
0.578 1.668 126 train
0.518 2.113 126 eval
0.584 1.647 127 train
0.495 2.331 127 eval
0.585 1.635 128 train
0.496 2.276 128 eval
0.589 1.619 129 train
0.452 2.528 129 eval
0.594 1.596 130 train
0.534 2.063 130 eval
0.596 1.587 131 train
0.522 2.098 131 eval
0.600 1.561 132 train
0.535 1.996 132 eval
0.606 1.536 133 train
0.539 1.994 133 eval
0.609 1.528 134 train
0.506 2.207 134 eval
0.610 1.513 135 train
0.515 2.208 135 eval
0.620 1.477 136 train
0.542 1.923 136 eval
0.625 1.460 137 train
0.521 2.127 137 eval
0.627 1.438 138 train
0.518 2.218 138 eval
0.635 1.417 139 train
0.515 2.151 139 eval
0.641 1.387 140 train
0.555 1.921 140 eval
0.646 1.364 141 train
0.526 2.154 141 eval
0.648 1.355 142 train
0.544 2.087 142 eval
0.654 1.325 143 train
0.558 1.904 143 eval
0.661 1.300 144 train
0.551 2.035 144 eval
0.667 1.281 145 train
0.537 2.157 145 eval
0.671 1.253 146 train
0.558 1.982 146 eval
0.678 1.233 147 train
0.553 2.052 147 eval
0.682 1.216 148 train
0.547 2.070 148 eval
0.690 1.180 149 train
0.571 1.968 149 eval
0.699 1.144 150 train
0.578 1.882 150 eval
0.702 1.136 151 train
0.574 1.972 151 eval
0.711 1.098 152 train
0.571 1.981 152 eval
0.716 1.073 153 train
0.589 1.899 153 eval
0.725 1.044 154 train
0.561 2.112 154 eval
0.728 1.036 155 train
0.592 1.967 155 eval
0.733 1.005 156 train
0.567 2.035 156 eval
0.741 0.978 157 train
0.573 2.003 157 eval
0.749 0.947 158 train
0.599 1.874 158 eval
0.755 0.929 159 train
0.596 1.894 159 eval
0.766 0.892 160 train
0.600 1.913 160 eval
0.770 0.866 161 train
0.606 1.850 161 eval
0.777 0.839 162 train
0.604 1.852 162 eval
0.786 0.814 163 train
0.592 1.922 163 eval
0.789 0.800 164 train
0.604 1.832 164 eval
0.796 0.769 165 train
0.613 1.781 165 eval
0.801 0.747 166 train
0.617 1.826 166 eval
0.808 0.731 167 train
0.609 1.871 167 eval
0.817 0.695 168 train
0.621 1.781 168 eval
0.825 0.669 169 train
0.622 1.788 169 eval
0.828 0.656 170 train
0.624 1.763 170 eval
0.835 0.631 171 train
0.635 1.761 171 eval
0.842 0.605 172 train
0.641 1.699 172 eval
0.845 0.589 173 train
0.629 1.740 173 eval
0.852 0.566 174 train
0.621 1.812 174 eval
0.859 0.542 175 train
0.637 1.716 175 eval
0.860 0.533 176 train
0.646 1.710 176 eval
0.866 0.509 177 train
0.652 1.685 177 eval
0.870 0.495 178 train
0.654 1.644 178 eval
0.876 0.472 179 train
0.652 1.638 179 eval
0.882 0.452 180 train
0.654 1.630 180 eval
0.884 0.443 181 train
0.653 1.651 181 eval
0.889 0.424 182 train
0.661 1.592 182 eval
0.893 0.412 183 train
0.661 1.584 183 eval
0.896 0.403 184 train
0.663 1.595 184 eval
0.899 0.391 185 train
0.666 1.572 185 eval
0.903 0.372 186 train
0.669 1.551 186 eval
0.905 0.366 187 train
0.668 1.561 187 eval
0.907 0.354 188 train
0.670 1.540 188 eval
0.909 0.346 189 train
0.672 1.526 189 eval
0.911 0.342 190 train
0.674 1.522 190 eval
0.912 0.335 191 train
0.676 1.521 191 eval
0.915 0.327 192 train
0.675 1.507 192 eval
0.915 0.323 193 train
0.675 1.504 193 eval
0.919 0.312 194 train
0.675 1.507 194 eval
0.917 0.318 195 train
0.676 1.505 195 eval
0.917 0.315 196 train
0.679 1.502 196 eval
0.918 0.311 197 train
0.677 1.504 197 eval
0.918 0.315 198 train
0.676 1.505 198 eval
0.919 0.309 199 train
0.675 1.502 199 eval 

torch.save(learn.model, 'models/inettiny-trivaug-200')
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