other files

bestmodel-epoch=46-monitor-val_acc1=63.7760009765625.ckpt

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+size 307148520

imagenet_class_labels.csv

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+Index,ID,Labels
+n02119789,1,kit_fox
+n02100735,2,English_setter
+n02110185,3,Siberian_husky
+n02096294,4,Australian_terrier
+n02102040,5,English_springer
+n02066245,6,grey_whale
+n02509815,7,lesser_panda
+n02124075,8,Egyptian_cat
+n02417914,9,ibex
+n02123394,10,Persian_cat
+n02125311,11,cougar
+n02423022,12,gazelle
+n02346627,13,porcupine
+n02077923,14,sea_lion
+n02110063,15,malamute
+n02447366,16,badger
+n02109047,17,Great_Dane
+n02089867,18,Walker_hound
+n02102177,19,Welsh_springer_spaniel
+n02091134,20,whippet
+n02092002,21,Scottish_deerhound
+n02071294,22,killer_whale
+n02442845,23,mink
+n02504458,24,African_elephant
+n02092339,25,Weimaraner
+n02098105,26,soft-coated_wheaten_terrier
+n02096437,27,Dandie_Dinmont
+n02114712,28,red_wolf
+n02105641,29,Old_English_sheepdog
+n02128925,30,jaguar
+n02091635,31,otterhound
+n02088466,32,bloodhound
+n02096051,33,Airedale
+n02117135,34,hyena
+n02138441,35,meerkat
+n02097130,36,giant_schnauzer
+n02493509,37,titi
+n02457408,38,three-toed_sloth
+n02389026,39,sorrel
+n02443484,40,black-footed_ferret
+n02110341,41,dalmatian
+n02089078,42,black-and-tan_coonhound
+n02086910,43,papillon
+n02445715,44,skunk
+n02093256,45,Staffordshire_bullterrier
+n02113978,46,Mexican_hairless
+n02106382,47,Bouvier_des_Flandres
+n02441942,48,weasel
+n02113712,49,miniature_poodle
+n02113186,50,Cardigan
+n02105162,51,malinois
+n02415577,52,bighorn
+n02356798,53,fox_squirrel
+n02488702,54,colobus
+n02123159,55,tiger_cat
+n02098413,56,Lhasa
+n02422699,57,impala
+n02114855,58,coyote
+n02094433,59,Yorkshire_terrier
+n02111277,60,Newfoundland
+n02132136,61,brown_bear
+n02119022,62,red_fox
+n02091467,63,Norwegian_elkhound
+n02106550,64,Rottweiler
+n02422106,65,hartebeest
+n02091831,66,Saluki
+n02120505,67,grey_fox
+n02104365,68,schipperke
+n02086079,69,Pekinese
+n02112706,70,Brabancon_griffon
+n02098286,71,West_Highland_white_terrier
+n02095889,72,Sealyham_terrier
+n02484975,73,guenon
+n02137549,74,mongoose
+n02500267,75,indri
+n02129604,76,tiger
+n02090721,77,Irish_wolfhound
+n02396427,78,wild_boar
+n02108000,79,EntleBucher
+n02391049,80,zebra
+n02412080,81,ram
+n02108915,82,French_bulldog
+n02480495,83,orangutan
+n02110806,84,basenji
+n02128385,85,leopard
+n02107683,86,Bernese_mountain_dog
+n02085936,87,Maltese_dog
+n02094114,88,Norfolk_terrier
+n02087046,89,toy_terrier
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+n02096177,91,cairn
+n02494079,92,squirrel_monkey
+n02105056,93,groenendael
+n02101556,94,clumber
+n02123597,95,Siamese_cat
+n02481823,96,chimpanzee
+n02105505,97,komondor
+n02088094,98,Afghan_hound
+n02085782,99,Japanese_spaniel
+n02489166,100,proboscis_monkey
+n02364673,101,guinea_pig
+n02114548,102,white_wolf
+n02134084,103,ice_bear
+n02480855,104,gorilla
+n02090622,105,borzoi
+n02113624,106,toy_poodle
+n02093859,107,Kerry_blue_terrier
+n02403003,108,ox
+n02097298,109,Scotch_terrier
+n02108551,110,Tibetan_mastiff
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+n02107908,115,Appenzeller
+n02086240,116,Shih-Tzu
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+n02093647,119,Bedlington_terrier
+n02397096,120,warthog
+n02437312,121,Arabian_camel
+n02483708,122,siamang
+n02097047,123,miniature_schnauzer
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+n02110627,127,affenpinscher
+n02106166,128,Border_collie
+n02326432,129,hare
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+n02097658,131,silky_terrier
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+n02111129,133,Leonberg
+n02100236,134,German_short-haired_pointer
+n02486261,135,patas
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+n02099849,139,Chesapeake_Bay_retriever
+n02108422,140,bull_mastiff
+n02104029,141,kuvasz
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+n02110958,143,pug
+n02099429,144,curly-coated_retriever
+n02094258,145,Norwich_terrier
+n02099267,146,flat-coated_retriever
+n02395406,147,hog
+n02112350,148,keeshond
+n02109961,149,Eskimo_dog
+n02101388,150,Brittany_spaniel
+n02113799,151,standard_poodle
+n02095570,152,Lakeland_terrier
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+n02101006,154,Gordon_setter
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+n02097209,156,standard_schnauzer
+n02342885,157,hamster
+n02097474,158,Tibetan_terrier
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+n02504013,194,Indian_elephant
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lightning_imagenet_classification.py

@@ -0,0 +1,294 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+### config
+
+total_epochs = 100
+batch_size = 256
+num_processes = 2
+image_size = 224
+drop_path = 0.05
+## Loss Function - CE (but try BCE)
+# Always choose "SGD" for CNNs and AdamW for ViTs - SGD is Difficult to Converge || We should use LAMB with Cosine LR
+## Multi-label --> Mixup and CutMix
+LR = 5e-3
+weight_decay = 0.05
+warmup_epoch = 5
+dropout = 0
+drop_path = 0.05
+
+
+# In[5]:
+
+
+import wandb
+
+wandb_token = "e653df8526c77d083379de033d13342620583fdf"
+
+wandb.login(key=wandb_token)
+
+
+# In[7]:
+
+
+import torch
+import torch.nn as nn
+from PIL import Image
+import numpy as np
+import pandas as pd
+
+
+import albumentations
+
+train_aug = albumentations.Compose(
+    [
+        albumentations.Resize(image_size, image_size, p=1),
+        albumentations.ShiftScaleRotate(
+            shift_limit=0.0625, scale_limit=0.1, rotate_limit=10, p=0.8
+        ),
+        albumentations.OneOf(
+            [
+                albumentations.RandomGamma(gamma_limit=(90, 110)),
+                albumentations.RandomBrightnessContrast(
+                    brightness_limit=0.1, contrast_limit=0.1
+                ),
+            ],
+            p=0.5,
+        ),
+        albumentations.HorizontalFlip(),
+        albumentations.Normalize(
+            mean=[0.485, 0.456, 0.406],
+            std=[0.229, 0.224, 0.225],
+            max_pixel_value=255.0,
+            p=1.0,
+        ),
+    ],
+    p=1.0,
+)
+
+valid_aug = albumentations.Compose(
+    [
+        albumentations.Resize(image_size, image_size, p=1),
+        albumentations.Normalize(
+            mean=[0.485, 0.456, 0.406],
+            std=[0.229, 0.224, 0.225],
+            max_pixel_value=255.0,
+            p=1.0,
+        ),
+    ],
+    p=1.0,
+)
+
+
+class ImageNetDataset(torch.utils.data.Dataset):
+    def __init__(self, image_path, augmentations=None, train=True):
+        self.image_path = image_path
+        self.augmentations = augmentations
+        self.df = pd.read_csv(
+            "/home/ubuntu/training/training/imagenet_class_labels.csv"
+        )
+        self.valid_df = pd.read_csv(
+            "/home/ubuntu/training/training/validation_classes.csv"
+        )
+        self.train = train
+
+    def __len__(self):
+        return len(self.image_path)
+
+    def __getitem__(self, item):
+        image_path = self.image_path[item]
+        with Image.open(image_path) as img:
+            image = img.convert("RGB")
+            image = np.asarray(image)
+
+        ## center crop 95% area
+        H, W, C = image.shape
+        image = image[int(0.04 * H) : int(0.96 * H), int(0.04 * W) : int(0.96 * W), :]
+
+        if self.train:
+            class_id = str(self.image_path[item].split("/")[-2])
+            targets = self.df[self.df["Index"] == class_id]["ID"].values[0] - 1
+        else:
+            class_id = str(self.image_path[item].split("/")[-1][:-5])
+            targets = (
+                self.valid_df[self.valid_df["ImageId"] == class_id]["LabelId"].values[0]
+                - 1
+            )
+
+        if self.augmentations is not None:
+            augmented = self.augmentations(image=image)
+            image = augmented["image"]
+
+        image = np.transpose(image, (2, 0, 1)).astype(np.float32)
+
+        return {
+            "image": torch.tensor(image, dtype=torch.float),
+            "targets": torch.tensor(targets, dtype=torch.long),
+        }
+
+
+from timm.data.mixup import Mixup
+
+mixup_args = {
+    "mixup_alpha": 0.1,
+    "cutmix_alpha": 1.0,
+    "cutmix_minmax": None,
+    "prob": 0.7,
+    "switch_prob": 0,
+    "mode": "batch",
+    "label_smoothing": 0.1,
+    "num_classes": 1000,
+}
+mixup_fn = Mixup(**mixup_args)
+
+
+import glob
+import random
+
+train_paths = glob.glob(
+    "/home/ubuntu/training/Imagenet/ILSVRC/Data/ImageNet/train/*/*.JPEG"
+)
+valid_paths = glob.glob(
+    "/home/ubuntu/training/Imagenet/ILSVRC/Data/ImageNet/val/*.JPEG"
+)
+
+
+import pytorch_lightning as pl
+from pytorch_lightning.loggers import WandbLogger
+
+
+import torch
+from timm import create_model
+from torchvision import transforms, datasets
+import pytorch_lightning as L
+
+# from timm.scheduler.cosine_lr import CosineLRScheduler
+
+
+class LitClassification(L.LightningModule):
+    def __init__(self):
+        super().__init__()
+        self.model = create_model(
+            "resnet50", pretrained=False, drop_path_rate=drop_path
+        )
+        # model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
+
+        self.loss_fn = torch.nn.CrossEntropyLoss()
+
+    def forward(self, x):
+        return self.model(x)
+
+    def training_step(self, batch):
+        images, targets = batch["image"], batch["targets"]
+        outputs = self.model(images)
+        loss = self.loss_fn(outputs, targets)
+        acc1, acc5 = self.__accuracy(outputs, targets, topk=(1, 5))
+        self.log("train_loss", loss)
+        self.log(
+            "train_acc1", acc1, on_step=True, prog_bar=True, on_epoch=True, logger=True
+        )
+        self.log("train_acc5", acc5, on_step=True, on_epoch=True, logger=True)
+        return loss
+
+    def validation_step(self, batch):
+        images, targets = batch["image"], batch["targets"]
+        outputs = self(images)
+        loss = self.loss_fn(outputs, targets)
+
+        acc1, acc5 = self.__accuracy(outputs, targets, topk=(1, 5))
+        self.log("valid_loss", loss)
+        self.log("val_acc1", acc1, on_step=True, prog_bar=True, on_epoch=True)
+        self.log("val_acc5", acc5, on_step=True, on_epoch=True)
+
+    @staticmethod
+    def __accuracy(output, target, topk=(1,)):
+        """Computes the accuracy over the k top predictions for the specified values of k."""
+        with torch.no_grad():
+            maxk = max(topk)
+            batch_size = target.size(0)
+
+            _, pred = output.topk(maxk, 1, True, True)
+            pred = pred.t()
+            correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+            res = []
+            for k in topk:
+                correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
+                res.append(correct_k.mul_(100.0 / batch_size))
+            return res
+
+    def configure_optimizers(self):
+        optimizer = torch.optim.AdamW(
+            self.parameters(), lr=LR, weight_decay=weight_decay
+        )
+
+        scheduler = torch.optim.lr_scheduler.OneCycleLR(
+            optimizer,
+            max_lr=LR,
+            total_steps=self.trainer.estimated_stepping_batches,
+            epochs=warmup_epoch,
+            steps_per_epoch=None,
+            pct_start=0.3,
+            anneal_strategy="cos",
+            cycle_momentum=True,
+            base_momentum=0.85,
+            max_momentum=0.95,
+            div_factor=25.0,
+            final_div_factor=10000.0,
+            three_phase=False,
+            last_epoch=-1,
+            verbose="deprecated",
+        )
+        return [optimizer], [scheduler]
+
+    def train_dataloader(self):
+        train_dataset = ImageNetDataset(train_paths, train_aug, train=True)
+        train_loader = torch.utils.data.DataLoader(
+            train_dataset,
+            batch_size=batch_size,
+            shuffle=True,
+            num_workers=num_processes,
+            pin_memory=True,
+        )
+        return train_loader
+
+    def val_dataloader(self):
+        valid_dataset = ImageNetDataset(valid_paths, valid_aug, train=False)
+        valid_loader = torch.utils.data.DataLoader(
+            valid_dataset,
+            batch_size=batch_size,
+            shuffle=False,
+        )
+        return valid_loader
+
+
+L.seed_everything(879246)
+
+
+wandb_logger = WandbLogger(log_model="all", project="ImageNet_Lightning")
+
+
+# Initialize a trainer
+best_checkpoint_callback = L.callbacks.ModelCheckpoint(
+    filename="bestmodel-{epoch}-monitor-{val_acc1}", mode="max"
+)
+every_epoch_checkpoint_callback = L.callbacks.ModelCheckpoint(
+    filename="{epoch}_{val_acc1}", every_n_epochs=10
+)
+
+trainer = L.Trainer(
+    max_epochs=total_epochs,
+    devices=torch.cuda.device_count(),
+    accelerator="gpu",
+    logger=wandb_logger,
+    # callbacks=[early_stop_callback],
+    precision=16,
+    callbacks=[best_checkpoint_callback, every_epoch_checkpoint_callback],
+)
+
+model = LitClassification()
+
+trainer.fit(
+    model,
+    ckpt_path="/home/ubuntu/training/training/ImageNet_Lightning/h94dnl2b/checkpoints/bestmodel-epoch=32-monitor-val_acc1=62.54399871826172.ckpt",
+)

lightning_model.py

@@ -0,0 +1,53 @@
+import torch
+import pytorch_lightning as L
+from timm import create_model
+
+class LitClassification(L.LightningModule):
+    def __init__(self, drop_path=0.05):
+        super().__init__()
+        self.model = create_model(
+            "resnet50", pretrained=False, drop_path_rate=drop_path
+        )
+        self.loss_fn = torch.nn.CrossEntropyLoss()
+
+    def forward(self, x):
+        return self.model(x)
+
+    def training_step(self, batch, batch_idx):
+        images, targets = batch["image"], batch["targets"]
+        outputs = self.model(images)
+        loss = self.loss_fn(outputs, targets)
+        acc1, acc5 = self.__accuracy(outputs, targets, topk=(1, 5))
+        self.log("train_loss", loss)
+        self.log(
+            "train_acc1", acc1, on_step=True, prog_bar=True, on_epoch=True, logger=True
+        )
+        self.log("train_acc5", acc5, on_step=True, on_epoch=True, logger=True)
+        return loss
+
+    def validation_step(self, batch, batch_idx):
+        images, targets = batch["image"], batch["targets"]
+        outputs = self(images)
+        loss = self.loss_fn(outputs, targets)
+
+        acc1, acc5 = self.__accuracy(outputs, targets, topk=(1, 5))
+        self.log("valid_loss", loss)
+        self.log("val_acc1", acc1, on_step=True, prog_bar=True, on_epoch=True)
+        self.log("val_acc5", acc5, on_step=True, on_epoch=True)
+
+    @staticmethod
+    def __accuracy(output, target, topk=(1,)):
+        """Computes the accuracy over the k top predictions for the specified values of k."""
+        with torch.no_grad():
+            maxk = max(topk)
+            batch_size = target.size(0)
+
+            _, pred = output.topk(maxk, 1, True, True)
+            pred = pred.t()
+            correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+            res = []
+            for k in topk:
+                correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
+                res.append(correct_k.mul_(100.0 / batch_size))
+            return res