From 53b9c6d854c93ea1ecebaa8cc743ec44a83eea2e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=D0=94=D0=B0=D0=BD=D0=B8=D0=B8=D0=BB=20=D0=93=D1=80=D0=B0?=
 =?UTF-8?q?=D0=B1=D0=B0=D1=80=D1=8C?= <grabardm@ml-dev.ru>
Date: Fri, 18 Sep 2026 18:08:55 +1000
Subject: [PATCH] =?UTF-8?q?=D0=94=D0=BE=D0=B1=D0=B0=D0=B2=D0=BB=D0=B5?=
 =?UTF-8?q?=D0=BD=20AlexNet?=
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Content-Transfer-Encoding: 8bit

---
 BaseCNNArhitecture/AlexNetCustom.py | 277 ++++++++++++++++++++++------
 environment.yml                     |  37 +++-
 environment_win.yml                 |  13 ++
 3 files changed, 265 insertions(+), 62 deletions(-)

diff --git a/BaseCNNArhitecture/AlexNetCustom.py b/BaseCNNArhitecture/AlexNetCustom.py
index 2796800..4a1bff2 100644
--- a/BaseCNNArhitecture/AlexNetCustom.py
+++ b/BaseCNNArhitecture/AlexNetCustom.py
@@ -3,15 +3,21 @@
 import time
 import torch
 import kagglehub
+import pandas as pd
 import torch.nn as nn
 import pytorch_lightning as pl
 
+from PIL import Image
 from torch import optim
+from pathlib import Path
 from torchmetrics import MetricCollection
+from typing import Optional, Callable, Any
 from torchvision import datasets, transforms
 from torchmetrics.classification import Accuracy
-from torch.utils.data import DataLoader, random_split
+from torch.utils.data import DataLoader, Dataset
 
+# Логирование данных
+from pytorch_lightning.loggers import MLFlowLogger
 # автоматическое отслеживание lr
 from pytorch_lightning.callbacks import LearningRateMonitor
 # ранние остановки
@@ -20,10 +26,11 @@ from pytorch_lightning.callbacks.early_stopping import EarlyStopping
 from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
 # %% Параметры
 
-dataset_root = "./Datasets/ImageNet"
-batch_size = 64
+dataset_root = "/media/daniel/developments/Datasets/ImageNet"
+batch_size = 128
 num_workers = 8
-num_epoch = 2
+num_epoch = 90
+learning_rate = 3e-4
 
 # %% Трансформации
 
@@ -59,62 +66,136 @@ path = kagglehub.competition_download('imagenet-object-localization-challenge',
 print("Path to competition files:", path)
 # %% Датасет
 
-full_train_dataset = datasets.ImageNet(
-    root=dataset_root,
-    split="train",
-    transform=train_transform
-)
+TransformType = Optional[Callable[..., Any]]
 
-train_size = int(0.9 * len(full_train_dataset))
-val_size = len(full_train_dataset) - train_size
+class ImageNetValDataset(Dataset):
+    def __init__(
+        self,
+        image_dir,
+        labels_file,
+        class_to_idx,
+        classes,
+        transform=None
+    ):
+        self.image_dir = Path(image_dir)
+        self.transform = transform
 
-train_dataset, val_dataset = random_split(
-    full_train_dataset,
-    [train_size, val_size],
-    generator=torch.Generator().manual_seed(42)
-)
+        self.class_to_idx = class_to_idx
+        self.classes = classes
+
+        df = pd.read_csv(labels_file)
+
+        self.samples = []
+
+        for _, row in df.iterrows():
+            image_id = row["ImageId"]
+            synset = row["PredictionString"].split()[0]
+            label = self.class_to_idx[synset]
+            image_path = self.image_dir / f"{image_id}.JPEG"
+            self.samples.append(
+                (image_path, label)
+            )
+
+    def __len__(self):
+        return len(self.samples)
+
+    def __getitem__(self, idx):
+        image_path, label = self.samples[idx]
+
+        image = Image.open(image_path).convert("RGB")
+
+        if self.transform is not None:
+            image = self.transform(image)
+
+        return image, label
 
 
-val_base_dataset = datasets.ImageNet(
-    root=dataset_root,
-    split="train",
-    transform=eval_transform
-)
+class ImageNetDataModule(pl.LightningDataModule):
+    def __init__(self, 
+                 dataset_root: Path,
+                 train_transform: TransformType,
+                 eval_transform: TransformType,
+                 batch_size: int = 64,
+                 num_workers:int = 8
+                 ):
+        super().__init__()
+        
+        self.dataset_root = Path(dataset_root)
+        
+        self.train_transform = train_transform
+        self.eval_transform = eval_transform
+        self.num_workers = num_workers
+        self.batch_size = batch_size
+        
+        self.imagenet_root = Path(dataset_root) / "ILSVRC"
+        
+        self.train_dir = self.imagenet_root / "Data" / "CLS-LOC" / "train"
+        self.val_dir = self.imagenet_root / "Data" / "CLS-LOC" / "val"
+        
+        self.mapping_file = Path(dataset_root) / "LOC_synset_mapping.txt"
+        self.val_labels_file = Path(dataset_root) / "LOC_val_solution.csv"
 
-val_dataset.dataset = val_base_dataset
-
-test_dataset = datasets.ImageNet(
-    root=dataset_root,
-    split="val",
-    transform=eval_transform
-)
-
-train_loader = DataLoader(
-    dataset=train_dataset,
-    batch_size=batch_size,
-    shuffle=True,
-    num_workers=num_workers,
-    pin_memory=True,
-    persistent_workers=True
-)
-
-val_loader = DataLoader(
-    dataset=val_dataset,
-    batch_size=batch_size,
-    shuffle=False,
-    num_workers=num_workers,
-    pin_memory=True,
-    persistent_workers=True
-)
-
-test_loader = DataLoader(
-    dataset=test_dataset,
-    batch_size=batch_size,
-    shuffle=False,
-    num_workers=num_workers,
-    pin_memory=True,
-    persistent_workers=True
-)
+        self.train_dataset = None
+        self.val_dataset = None
+        
+        self.class_to_idx = None
+        self.classes = None
+        
+    def setup(self, stage=None):
+        synset_to_name = {}
+        with open(self.mapping_file, "r", encoding="utf-8") as f:
+            for line in f:
+                synset, class_name = line.strip().split(" ", 1)
+                synset_to_name[synset] = class_name
+        
+        train_dataset = datasets.ImageFolder(root=self.train_dir, 
+                                             transform=self.train_transform)
+    
+        self.class_to_idx = train_dataset.class_to_idx.copy()
+        self.classes = [synset_to_name[synset] 
+                        for synset in train_dataset.classes]
+        train_dataset.classes = self.classes
+        self.train_dataset = train_dataset
+        
+        self.val_dataset = ImageNetValDataset(
+            image_dir=self.val_dir,
+            labels_file=self.val_labels_file,
+            class_to_idx=self.class_to_idx,
+            classes=self.classes,
+            transform=self.eval_transform
+        )
+        
+    def train_dataloader(self):
+        return DataLoader(
+            dataset=self.train_dataset,
+            batch_size=self.batch_size,
+            shuffle=True,
+            num_workers=self.num_workers,
+            pin_memory=True,
+            persistent_workers=self.num_workers > 0,
+            prefetch_factor=4 if self.num_workers > 0 else None
+        )
+    
+    def val_dataloader(self):
+        return DataLoader(
+            dataset=self.val_dataset,
+            batch_size=self.batch_size,
+            shuffle=False,
+            num_workers=self.num_workers,
+            pin_memory=True,
+            persistent_workers=self.num_workers > 0,
+            prefetch_factor=4 if self.num_workers > 0 else None
+        )
+    
+    def test_dataloader(self):
+        return DataLoader(
+            dataset=self.val_dataset,
+            batch_size=self.batch_size,
+            shuffle=False,
+            num_workers=self.num_workers,
+            pin_memory=True,
+            persistent_workers=self.num_workers > 0    
+        )
 
 # %% AlexNet
 
@@ -163,7 +244,7 @@ class AlexNetCustom(nn.Module):
         
     def forward(self, image):
         x = self.feature(image)
-        # x = self.avgpool(x)
+        x = self.avgpool(x)
         x = torch.flatten(x, 1)
         x = self.mlp(x)
         
@@ -241,9 +322,6 @@ class ClassificationLightning(pl.LightningModule):
                     stage,
                     metrics):
         x, y = sample
-        x = x.to(torch.float32)
-        y = y.to(torch.int64)
-        
         
         logits = self(x)
         loss = self.criterion(logits, y)
@@ -327,3 +405,84 @@ class ClassificationLightning(pl.LightningModule):
             "optimizer": optimizer,
             "lr_scheduler": scheduler_config
         }
+
+# %% Обучение
+
+pl.seed_everything(42, workers=True)
+torch.set_float32_matmul_precision("high")
+
+imagenet_dm = ImageNetDataModule(
+    dataset_root=dataset_root,
+    train_transform=train_transform,
+    eval_transform=eval_transform,
+    batch_size=batch_size,
+    num_workers=num_workers    
+)
+
+
+optimizer_Adam = optim.Adam
+optimizer_Adam_kwargs = {
+    "lr": 3e-4,
+    "weight_decay": 1e-4
+}
+
+scheduler_ReduceLR = optim.lr_scheduler.ReduceLROnPlateau
+scheduler_ReduceLR_kwargs={
+    "mode": "min",
+    "factor": 0.1,
+    "patience": 5
+}
+
+callbacks = [
+    ModelCheckpoint(
+        dirpath="models",
+        filename="epoch={epoch}",
+        auto_insert_metric_name=False,
+        save_last=True,
+        save_top_k=1,
+        monitor="val/acc_top1",
+        mode="max"
+    ),
+
+    LearningRateMonitor(
+        logging_interval="epoch"
+    ),
+
+    EarlyStopping(
+        monitor="val/loss",
+        min_delta=2e-4,
+        patience=10,
+        verbose=False,
+        mode="min"
+    )
+]
+
+logger = MLFlowLogger(
+    experiment_name="ImageNetMyAlgoritms",
+    run_name="AlexNetCustom",
+    tracking_uri="http://10.16.18.1:8055",
+    log_model=True,
+) 
+
+alexnet = AlexNetCustom(in_channels=3, out_classes=1000)
+model_adam = ClassificationLightning(
+    model=alexnet,
+    num_classes=1000,
+    optimizer_class=optimizer_Adam,
+    optimizer_kwargs=optimizer_Adam_kwargs,
+    scheduler_class=scheduler_ReduceLR,
+    scheduler_kwargs=scheduler_ReduceLR_kwargs,
+    scheduler_monitor="val/loss"    
+)
+
+trainer = pl.Trainer(
+    max_epochs=num_epoch,
+    accelerator="gpu",
+    devices=1,
+    logger=logger,
+    # precision="16-mixed",
+    callbacks=callbacks
+)
+
+trainer.fit(model=model_adam,
+            datamodule=imagenet_dm)
diff --git a/environment.yml b/environment.yml
index c9487ab..8b3c662 100644
--- a/environment.yml
+++ b/environment.yml
@@ -11,6 +11,7 @@ dependencies:
   - aiosignal=1.4.0=py312h06a4308_0
   - alabaster=1.0.0=py312h06a4308_0
   - alsa-lib=1.2.15.3=h47b2149_0
+  - annotated-types=0.7.0=py312h06a4308_0
   - anyio=4.12.1=py312h06a4308_0
   - aom=3.13.2=h664349e_0
   - argon2-cffi=25.1.0=py312h06a4308_0
@@ -42,6 +43,7 @@ dependencies:
   - charset-normalizer=3.4.7=py312h06a4308_0
   - click=8.5.0=py312h06a4308_0
   - cloudpickle=3.1.2=py312h06a4308_0
+  - coloredlogs=15.0.1=py312h06a4308_3
   - comm=0.2.3=py312h06a4308_0
   - cookiecutter=2.7.1=py312h06a4308_0
   - cpython=3.12.14=py312hd8ed1ab_3
@@ -82,6 +84,8 @@ dependencies:
   - gettext=0.25.1=h92eb808_0
   - gettext-tools=0.25.1=h6a67909_0
   - giflib=6.1.3=h621bd71_1
+  - gitdb=4.0.12=py312h06a4308_0
+  - gitpython=3.1.59=py312h06a4308_0
   - gmp=6.3.0=hfd2156b_3
   - gmpy2=2.3.1=py312h04c1168_0
   - graphite2=1.3.15=h9ba177b_0
@@ -89,6 +93,7 @@ dependencies:
   - harfbuzz=12.3.0=h79d275a_1
   - httpcore=1.0.9=py312h06a4308_0
   - httpx=0.28.1=py312h06a4308_1
+  - humanfriendly=10.0=py312h06a4308_2
   - icu=73.1=h6a678d5_0
   - idna=3.18=py312h06a4308_0
   - imagesize=1.4.1=py312h06a4308_0
@@ -98,6 +103,7 @@ dependencies:
   - ipykernel=6.31.0=py312h7040dfc_0
   - ipython=9.15.0=py312h06a4308_0
   - ipython_pygments_lexers=1.1.1=py312h06a4308_0
+  - ipywidgets=8.1.9=py312h06a4308_0
   - isort=8.0.1=py312h06a4308_1
   - jansson=2.15.0=hbcba0ee_0
   - jaraco.classes=3.4.0=py312h06a4308_0
@@ -121,6 +127,7 @@ dependencies:
   - jupyterlab=4.6.2=py312h06a4308_1
   - jupyterlab_pygments=0.3.0=py312h06a4308_0
   - jupyterlab_server=2.28.0=py312h06a4308_1
+  - jupyterlab_widgets=3.0.17=pyhd3eb1b0_0
   - kagglehub=1.0.2=py312h06a4308_0
   - kagglesdk=0.1.35=py312h06a4308_0
   - keyring=25.7.0=py312h06a4308_0
@@ -219,6 +226,7 @@ dependencies:
   - mesalib=25.1.5=h3583ad3_5
   - minizip=4.2.1=h7e49174_0
   - mistune=3.3.3=py312h06a4308_0
+  - ml_dtypes=0.6.0=py312h86c3e14_0
   - more-itertools=11.0.2=py312h06a4308_0
   - mpc=1.4.0=ha2cb11d_1
   - mpfr=4.2.2=ha2cb11d_1
@@ -248,11 +256,16 @@ dependencies:
   - numpy=2.5.2=py312h35deafb_0
   - numpy-base=2.5.2=py312h4bc27c9_0
   - numpydoc=1.10.0=py312h06a4308_0
+  - onnx=1.22.0=py312hc50a595_0
+  - onnx-ir=0.2.1=py312h06a4308_0
+  - onnxruntime=1.24.4=py312h0197eaa_2_cpu
+  - onnxscript=0.7.1=py312h06a4308_0
   - openjpeg=2.5.4=h4e0627c_1
   - openldap=2.6.12=h007892f_1
   - openssl=3.6.4=h781a0a9_0
   - optree=0.20.0=py312ha5105d2_1
   - packaging=26.3=py312h06a4308_0
+  - pandas=3.0.5=py312h86c3e14_0
   - pandoc=3.8=h06a4308_0
   - pandocfilters=1.5.1=py312h06a4308_0
   - parso=0.8.7=py312h06a4308_0
@@ -280,6 +293,8 @@ dependencies:
   - pycares=4.10.0=py312ha137689_0
   - pycodestyle=2.12.1=py312h06a4308_0
   - pycparser=3.0=py312h06a4308_0
+  - pydantic=2.13.4=py312h06a4308_0
+  - pydantic-core=2.46.4=py312h6e1b9ff_0
   - pydocstyle=6.3.0=py312h06a4308_1
   - pyflakes=3.2.0=py312h06a4308_0
   - pygithub=2.9.1=py312h06a4308_0
@@ -296,6 +311,7 @@ dependencies:
   - python=3.12.14=hc5f7cf0_0
   - python-dateutil=2.9.0post0=py312h06a4308_2
   - python-fastjsonschema=2.22.1=py312h06a4308_0
+  - python-flatbuffers=25.9.23=py312h8034063_0
   - python-json-logger=4.1.0=py312h06a4308_0
   - python-lsp-black=2.0.0=py312h06a4308_1
   - python-lsp-jsonrpc=1.1.2=pyhd3eb1b0_0
@@ -318,6 +334,7 @@ dependencies:
   - qtbase=6.11.0=h9201cad_0
   - qtconsole=5.7.2=py312h06a4308_0
   - qtdeclarative=6.11.0=h89ee561_1
+  - qtimageformats=6.11.0=h00148ef_1
   - qtpy=2.4.3=py312h06a4308_0
   - qtsvg=6.11.0=hbecefc2_1
   - qttools=6.11.0=h0af0a4d_1
@@ -338,12 +355,15 @@ dependencies:
   - rpds-py=0.28.0=py312h498d7c9_0
   - rtree=1.4.1=py312hd966e25_1
   - ruff=0.15.1=py312h6e1b9ff_0
+  - scipy=1.18.1=py312h35deafb_0
   - secretstorage=3.5.0=py312h3e8c6aa_0
   - send2trash=1.8.3=py312h06a4308_0
+  - sentry-sdk=2.67.1=py312h06a4308_0
   - setuptools=80.10.2=py312h06a4308_0
   - sip=6.15.1=py312he6c97cb_0
   - six=1.17.0=py312h06a4308_0
   - sleef=3.9.0=ha0421bc_0
+  - smmap=5.0.2=py312h06a4308_0
   - snappy=1.2.2=h4bcf44c_1
   - snowballstemmer=3.1.1=py312h06a4308_0
   - sortedcontainers=2.4.0=pyhd3eb1b0_0
@@ -377,11 +397,12 @@ dependencies:
   - traitlets=5.15.0=py312h06a4308_0
   - triton=3.7.0=cuda129py312h338dc61_1
   - typing-extensions=4.16.0=py312h06a4308_0
+  - typing-inspection=0.4.3=py312h06a4308_0
   - typing_extensions=4.16.0=py312h06a4308_0
   - tzdata=2026c=he532380_0
   - ujson=5.13.0=py312h7354ed3_0
-  - urllib3=2.7.0=py312h06a4308_0
   - uvloop=0.22.1=py312h939ff40_1
+  - wandb=0.27.2=py312h03481c5_1
   - watchdog=6.0.0=py312h06a4308_0
   - wayland=1.24.0=hdac8c69_0
   - wcwidth=0.8.2=py312h06a4308_0
@@ -389,9 +410,10 @@ dependencies:
   - websocket-client=1.8.0=py312h06a4308_0
   - whatthepatch=1.0.7=py312h06a4308_0
   - wheel=0.47.0=py312h06a4308_0
+  - widgetsnbextension=4.0.16=py312h06a4308_0
   - wurlitzer=3.1.1=py312h06a4308_0
   - xcb-util=0.4.1=h5eee18b_2
-  - xcb-util-cursor=0.1.5=h5eee18b_0
+  - xcb-util-cursor=0.1.6=hb03c661_0
   - xcb-util-image=0.4.0=h5eee18b_2
   - xcb-util-keysyms=0.4.1=h5eee18b_0
   - xcb-util-renderutil=0.3.10=h5eee18b_0
@@ -422,6 +444,15 @@ dependencies:
   - zlib=1.3.2=h47b2149_0
   - zstd=1.5.7=h11fc155_0
   - pip:
-      - pyqt6-qt6==6.11.2
+      - backoff==2.2.1
+      - blake3==1.0.9
+      - docker==7.2.0
+      - lightning-sdk==2026.9.15
+      - linkify-it-py==2.2.0
+      - litlogger==2026.8.28
+      - mdit-py-plugins==0.6.1
+      - rich-click==1.9.9
       - split-folders==0.6.1
+      - textual==8.2.8
+      - urllib3==2.5.0
 prefix: /home/daniel/anaconda3/envs/CV
diff --git a/environment_win.yml b/environment_win.yml
index 573374b..9576011 100644
--- a/environment_win.yml
+++ b/environment_win.yml
@@ -10,4 +10,17 @@ dependencies:
   - beartype
   - torchvision
   - kagglehub
+  - xcb-util-cursor
+  - pyqt
+  - pyqt6-sip
+  - pyqtwebengine
+  - qtbase
+  - qtimageformats
+  - onnx
+  - onnxruntime
+  - onnxscript
+  - scipy
+  - ipywidgets
+  - pandas
+  - wandb
 prefix: /home/daniel/anaconda3/envs/CV