apply PyTorch AMP

.drone.yml

@@ -24,4 +24,4 @@ steps:
       - mkdir -p data
       - $mount_command
       - ls data/
-      - # CI=True python finetune/facenet_transfer_learning.py
+      - CI=True python finetune/facenet_transfer_learning.py

finetune/facenet_transfer_learning.py

@@ -13,12 +13,13 @@ from numpy import arange, ndarray, ceil, full, uint8
 from torch.optim import SGD, Adam, lr_scheduler
 from torchvision.datasets import ImageFolder
 from torch.utils.data import DataLoader
+from torch.cuda.amp import GradScaler, autocast
 from tqdm import tqdm
 from PIL import Image, ImageDraw, ImageFont
 from settings import datadir
 from os.path import join
 from torch.cuda import is_available
-from torch import no_grad, save, Tensor, load, device
+from torch import no_grad, save, Tensor, load, device, float16
 from datetime import datetime
 from distutils.util import strtobool
 
@@ -125,6 +126,8 @@ optimizer = Adam(params=[
     {'params': model_gpu[1].parameters(), 'lr': 1e-3},
 ])
 
+scaler = GradScaler()
+
 # model, optimizer = optimize(model=model, optimizer=optimizer)
 scheduler = lr_scheduler.StepLR(optimizer=optimizer, step_size=10, gamma=0.9)
 epochs = 100
@@ -155,6 +158,7 @@ for epoch in range(epochs):
                                          label_text=image_folder['train'].classes)
             image_pallets.save(join(save_dir, 'pallets', str(epoch) + '_train.jpg'))
         optimizer.zero_grad()
+        with autocast(dtype=float16):
             images = images.to(device)
         labels = labels.to(device)
 
@@ -163,8 +167,12 @@ for epoch in range(epochs):
         loss = criterion(outputs, labels)
         train_loss += loss.item()
 
-        loss.backward()
-        optimizer.step()
+        scaler.scale(loss).backward()
+        scaler.unscale_(optimizer=optimizer)
+        # loss.backward()
+        scaler.step(optimizer=optimizer)
+        scaler.update()
+        # optimizer.step()
 
         predicted = outputs.max(1)[1]
         train_acc += (predicted == labels).sum()

inference_all.py

@@ -0,0 +1,84 @@
+import time
+from os import makedirs
+from os.path import join, exists, basename
+from shutil import rmtree, copyfile
+from more_itertools import chunked
+from torch import load, no_grad, device, randn, jit, float64, float32, float16
+from torch.cuda import is_available
+from torch.utils.data import DataLoader
+from torchvision.datasets import ImageFolder
+from torchvision.transforms import Compose, ToTensor, Resize, CenterCrop
+from torchinfo import summary
+from tqdm import tqdm
+from settings import datadir
+from concurrent.futures import ThreadPoolExecutor
+from pandas import DataFrame
+from seaborn import heatmap, color_palette, set_palette
+from matplotlib import pyplot
+from japanize_matplotlib import japanize
+from torch_tensorrt import compile
+
+device = device('cuda' if is_available() else 'cpu')
+# device = 'cpu'
+print(f'device: {device}')
+model_path: str = join(datadir(), 'artifact', 'facenet-tl_2023-10-15 07:08:44.537055', 'model.pth')
+print(f'model path: {model_path}')
+input_shape: int = 256
+batch_size = 64
+source_dir = join(datadir(), 'face_cropped')
+print(f'judge file: {source_dir}')
+dest_dir = join(datadir(), 'infer_all')
+image_class = ImageFolder(root=join(datadir(), 'dataset', 'train')).classes
+with open(join(datadir(), 'class_text'), mode='w') as f:
+    f.write(str(image_class))
+rmtree(dest_dir, ignore_errors=True)
+makedirs(dest_dir)
+
+transform = Compose([Resize(size=256), ToTensor()])
+image_folder = ImageFolder(root=source_dir, transform=transform)
+dataloader = DataLoader(image_folder, batch_size=batch_size, shuffle=False, num_workers=8)
+
+model = load(f=model_path)
+model = model.to(device)
+model.eval()
+for layer in model.parameters():
+    layer.requires_grad = False
+
+# summary(model=model, input_size=(batch_size, 3, input_shape, input_shape), device=device)
+
+if exists(join(datadir(), 'infer_all_torch_trt.ts')):
+    trt_model = jit.load(join(datadir(), 'infer_all_torch_trt.ts'))
+else:
+
+    example_input = randn(size=[batch_size, 3, 256, 256]).float().cuda()
+    traced_script_module = jit.trace(model, example_inputs=[example_input])
+    trt_model = compile(module=traced_script_module, inputs=[example_input],
+                        enabled_precisions={float32, float16},
+                        truncate_long_and_double=True)
+    jit.save(trt_model, join(datadir(), 'infer_all_torch_trt.ts'))
+
+# heatmap_df = DataFrame(index=image_class, columns=image_folder.classes).fillna(0)
+begin = time.time()
+with ThreadPoolExecutor(max_workers=60) as executor, no_grad():
+    for (images, labels), fileinfo in zip(tqdm(dataloader), chunked(image_folder.imgs, n=batch_size)):
+        # print(labels, fileinfo)
+        res = trt_model(images.to(device))
+        for name, (filename, person) in zip(res.to(device).max(1).indices.tolist(), fileinfo):
+            if not exists(join(dest_dir, image_class[name])):
+                makedirs(join(dest_dir, image_class[name]), exist_ok=True)
+            # print(name, filename, person)
+            # copyfile(src=filename,
+            #          dst=join(dest_dir, image_folder.classes[name], basename(filename)))
+            # if image_class[name] != image_folder.classes[person]:
+            #     heatmap_df[image_folder.classes[person]][image_class[name]] += 1
+            executor.submit(copyfile, filename, join(dest_dir, image_class[name], basename(filename)))
+print(f"{time.time() - begin:5f}sec")
+# print(heatmap_df)
+# set_palette('Blues')
+# pyplot.figure(figsize=(40, 40))
+# heat_img = heatmap(heatmap_df, cmap='Blues', linewidths=1)
+# japanize()
+# heatmap_df.max()
+# pyplot.savefig(join(dest_dir, 'confusion_matrix.png'))
+# print(f'acc: {1 - heatmap_df.to_numpy().flatten().sum() / image_folder.__len__()}')
+print(image_folder.classes)