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TEST: train on gt_count instead of kpoint

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Zhengyi Chen 2024-03-04 01:49:09 +00:00
parent ee50e84946
commit 83fcc43f0b
3 changed files with 26 additions and 39 deletions
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37
dataset.py
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@ -56,14 +56,16 @@ def convert_data(train_list, args: Namespace, train: bool):
try:
gt_file = h5py.File(gt_path)
kpoint = np.asarray(gt_file["kpoint"])
gt_count = np.asarray(gt_file["gt_count"])
break
except OSError:
print("[dataset] Load error on \'{}\'", img_path)
img = img.copy()
kpoint = kpoint.copy()
gt_count = gt_count.copy()
return img, kpoint
return img, kpoint, gt_count
print("[dataset] Pre-loading dataset...\n{}".format("-" * 50))
@ -71,11 +73,12 @@ def convert_data(train_list, args: Namespace, train: bool):
for i in range(len(train_list)):
img_path = train_list[i]
fname = os.path.basename(img_path)
img, kpoint = _load_data(img_path, train, args)
img, kpoint, gt_count = _load_data(img_path, train, args)
pack = {
"img": img,
"kpoint": kpoint,
"gt_count": gt_count,
"fname": fname,
}
data_keys.append(pack)
@ -120,6 +123,7 @@ class ListDataset(Dataset):
fname = self.lines[index]["fname"]
img = self.lines[index]["img"]
kpoint = self.lines[index]["kpoint"]
gt_count = self.lines[index]["gt_count"]
# Data augmentation
if self.train:
@ -129,35 +133,10 @@ class ListDataset(Dataset):
kpoint = kpoint.copy()
img = img.copy()
gt_count = gt_count.copy()
# Custom transform
if self.transform is not None:
img = self.transform(img)
return fname, img, kpoint
# if self.train:
# return fname, img, gt_count
# else:
# device = args.device
# height, width = img.shape[1], img.shape[2]
# m = int(width / 384)
# n = int(height / 384)
# for i in range(m):
# for j in range(n):
# if i == 0 and j == 0:
# img_ret = img[
# :, # C
# j * 384 : 384 * (j + 1), # H
# i * 384 : 384 * (i + 1), # W
# ].to(device).unsqueeze(0)
# else:
# cropped = img[
# :, # C
# j * 384 : 384 * (j + 1), # H
# i * 384 : 384 * (i + 1), # W
# ].to(device).unsqueeze(0)
# img_ret = torch.cat([img_ret, cropped], 0).to(device)
# return fname, img_ret, gt_count
return fname, img, kpoint, gt_count

4
preprocess_data.py
View file

@ -96,6 +96,9 @@ def pre_dataset_sh():
) # To same shape as image, so i, j flipped wrt. coordinates
kpoint = sparse_mat.toarray()
# Sum count as ground truth (we need to train STN, remember?)
gt_count = sparse_mat.nnz
fname = img_path.split("/")[-1]
root_path = img_path.split("IMG_")[0].replace("images", "images_crop")
@ -108,6 +111,7 @@ def pre_dataset_sh():
mode='w'
) as hf:
hf["kpoint"] = kpoint
hf["gt_count"] = gt_count
def make_npydata():

24
train.py
View file

@ -239,16 +239,19 @@ def train_one_epoch(
model.train()
# In one epoch, for each training sample
for i, (fname, img, kpoint) in enumerate(train_loader):
for i, (fname, img, kpoint, gt_count) in enumerate(train_loader):
kpoint = kpoint.type(torch.FloatTensor)
gt_count = gt_count.type(torch.FloatTensor)
# fpass
if device is not None:
img = img.to(device)
kpoint = kpoint.to(device)
gt_count = gt_count.to(device)
elif torch.cuda.is_available():
img = img.cuda()
kpoint = kpoint.cuda()
out, gt_count = model(img, kpoint)
gt_count = gt_count.cuda()
out, _ = model(img, kpoint)
# loss
loss = criterion(out, gt_count)
@ -282,27 +285,30 @@ def valid_one_epoch(test_loader, model, device, args):
visi = []
index = 0
for i, (fname, img, kpoint) in enumerate(test_loader):
for i, (fname, img, kpoint, gt_count) in enumerate(test_loader):
kpoint = kpoint.type(torch.FloatTensor)
gt_count = gt_count.type(torch.FloatTensor)
if device is not None:
img = img.to(device)
kpoint = kpoint.to(device)
gt_count = gt_count.to(device)
elif torch.cuda.is_available():
img = img.cuda()
kpoint = kpoint.cuda()
gt_count = gt_count.cuda()
# XXX: what do this do
# XXX: do this even happen?
if len(img.shape) == 5:
img = img.squeeze(0)
if len(img.shape) == 3:
img = img.unsqueeze(0)
with torch.no_grad():
out, gt_count = model(img, kpoint)
out, _ = model(img, kpoint)
pred_count = torch.squeeze(out, 1)
gt_count = torch.squeeze(gt_count, 1)
# gt_count = torch.squeeze(gt_count, 1)
diff = torch.sum(torch.abs(gt_count - pred_count)).item()
diff = torch.abs(gt_count - torch.sum(pred_count)).item()
mae += diff
mse += diff ** 2
mae = mae * 1.0 / (len(test_loader) * batch_size)
@ -325,12 +331,10 @@ if __name__ == "__main__":
tuner_params = nni.get_next_parameter()
logger.debug("Generated hyperparameters: {}", tuner_params)
combined_params = nni.utils.merge_parameter(ret_args, tuner_params)
if args.debug:
os.nice(15)
#combined_params = args
#logger.debug("Parameters: {}", combined_params)
if combined_params.use_ddp:
# Use DDP, spawn threads
torch_mp.spawn(