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train_one_gpu.py
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train_one_gpu.py
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import os
import cv2
import numpy as np
import torch
from torchmetrics import Dice
from PIL import Image
from torch.nn import Conv2d, LayerNorm, ReLU, Upsample
from torch.optim import AdamW
from torch.optim.lr_scheduler import LinearLR
from torch.utils.data.dataloader import DataLoader
from vit import load_model_and_transform
os.makedirs("output", exist_ok=True)
def main():
my_model, transform = load_model_and_transform("ViT-B/32")
my_model.cuda().train()
my_model: torch.nn.Module
if os.path.exists("FP16-ViT-B-32.pt"):
my_model.load_state_dict(torch.load("FP16-ViT-B-32.pt", "cpu"), strict=True)
my_dataset = MyDataset(transform)
my_dataloader = DataLoader(my_dataset, 32, True, num_workers=8, drop_last=True)
my_model_seg = MyModelSeg().cuda().train()
my_optimizer = AdamW([
{"params": my_model_seg.parameters(), "lr": 0.0005},
{"params": my_model.parameters(), "lr": 0.00001},
], weight_decay=0.1)
my_linear_lr = LinearLR(my_optimizer, start_factor=1, end_factor=0, total_iters=20000)
my_loss = torch.nn.CrossEntropyLoss().cuda()
step = 0
while True:
for image, label in my_dataloader:
image = image.cuda()
label = label.cuda()
predict = my_model_seg(my_model(image))
loss = my_loss(predict.reshape(-1, 5), label.reshape(-1))
loss.backward()
my_optimizer.step()
my_linear_lr.step()
my_optimizer.zero_grad()
step += 1
if step % 100 == 0:
output, metric = evaluate(my_model, my_model_seg)
output = output.cpu().numpy()
color = np.ones([output.shape[0], output.shape[1], 3])
color[output==0] = [255, 255, 255] #其他，白色，0
color[output==1] = [0, 255, 0] #植被，绿色，1
color[output==2] = [0, 0, 0] #道路，黑色，2
color[output==3] = [131, 139, 139] #建筑，黄色，3
color[output==4] = [139, 69, 19] #水体，蓝色，4
cv2.imwrite(f"output/{step :07d}.jpg", color)
with torch.no_grad():
print(f"step: {step :07d} loss: {loss.item() :.4f} dice: {metric.item() :.4f}")
if step > 20000:
torch.save(my_model_seg.state_dict(), "model.pt")
break
class MyDataset(torch.utils.data.Dataset):
def __init__(self, transform):
if os.path.exists("dataset/path_list.csv"):
with open("dataset/path_list.csv", "r") as f:
lines = f.readlines()
lines = [x.strip() for x in lines]
lines = lines[1:]
self.lines = lines
else:
raise "Please run <python preprocess.py first!>"
self.transform = transform
def __getitem__(self, index):
path_image, path_label = self.lines[index].split(",")
image = self.transform(Image.open(path_image))
return image, torch.from_numpy(cv2.imread(path_label, cv2.IMREAD_GRAYSCALE))
def __len__(self):
return len(self.lines)
class MyModelSeg(torch.nn.Module):
def __init__(self,) -> None:
super().__init__()
self.layers = torch.nn.Sequential(
# B x 768 x 7 x 7
Upsample(scale_factor=2),
Conv2d(768, 768, (1, 1), 1, 0, bias=False),
ReLU(),
Conv2d(768, 768, (3, 3), 1, (1, 1), bias=False),
LayerNorm([768, 14, 14]),
ReLU(),
# B x 768 x 14 x 14
Upsample(scale_factor=2),
Conv2d(768, 384, (1, 1), 1, 0, bias=False),
ReLU(),
Conv2d(384, 384, (3, 3), 1, (1, 1), bias=False),
LayerNorm([384, 28, 28]),
ReLU(),
# B x 384 x 28 x 28
Upsample(scale_factor=2),
Conv2d(384, 192, (1, 1), 1, 0, bias=False),
ReLU(),
Conv2d(192, 192, (3, 3), 1, (1, 1), bias=False),
LayerNorm([192, 56, 56]),
ReLU(),
# B x 192 x 56 x 56
Upsample(scale_factor=2),
Conv2d(192, 96, (1, 1), 1, 0, bias=False),
ReLU(),
Conv2d(96, 96, (3, 3), 1, (1, 1), bias=False),
LayerNorm([96, 112, 112]),
ReLU(),
# B x 96 x 112 x 112
Upsample(scale_factor=2),
Conv2d(96, 48, (1, 1), 1, 0, bias=False),
ReLU(),
Conv2d(48, 48, (3, 3), 1, (1, 1), bias=False),
LayerNorm([48, 224, 224]),
ReLU(),
# B x 48 x 224 x 224
Conv2d(48, 5, (1, 1), 1, 0, bias=False))
def forward(self, x):
B, S, D = x.size()
x = torch.reshape(x, (B, 7, 7, 768))
x: torch.Tensor
x = x.permute(0, 3, 1, 2)
x = self.layers(x)
x = x.permute(0, 2, 3, 1)
return x
def evaluate(model, model_seg, image_size=224):
my_dice = Dice().cuda()
model.eval()
model_seg.eval()
image = cv2.imread("dataset/origin/5.png")
gt = torch.from_numpy(cv2.imread("dataset/origin/5_class.png", cv2.IMREAD_GRAYSCALE))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
h, w, c = image.shape
image = torch.from_numpy(image).float()
image = image.permute(2, 0, 1).unsqueeze(0)
output = torch.zeros([h, w]).cuda().long()
idx_h = 0
while idx_h * image_size < h:
h_e = min(h, idx_h * image_size + image_size)
h_s = h_e - image_size
idx_w = 0
while idx_w * image_size < w:
w_e = min(w, idx_w * image_size + image_size)
w_s = w_e - image_size
idx_w += 1
img = image[:, :, h_s: h_s + image_size, w_s: w_s + image_size].cuda()
img = img / 255
img[:, 0, :, :] -= 0.485
img[:, 1, :, :] -= 0.456
img[:, 2, :, :] -= 0.406
img[:, 0, :, :] /= 0.229
img[:, 1, :, :] /= 0.224
img[:, 2, :, :] /= 0.225
predict = model_seg(model(img))
predict = torch.argmax(predict, dim=3).squeeze()
output[h_s: h_s + image_size, w_s: w_s + image_size] = predict
idx_h += 1
model.train()
model_seg.train()
metric = my_dice(output, gt.cuda())
return output, metric
if __name__ == "__main__":
main()