提交 efdcf7c8 编写于 作者: L lubin

make code short;modify cifar10 data path

上级 4ea2f449
......@@ -63,8 +63,8 @@ DataLoader:
Train:
dataset:
name: ImageNetDataset
image_root: ./dataset/
cls_label_path: ./dataset/train.txt
image_root: ./dataset/CIFAR10/
cls_label_path: ./dataset/CIFAR10/train_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
......@@ -88,8 +88,8 @@ DataLoader:
Query:
dataset:
name: ImageNetDataset
image_root: ./dataset/
cls_label_path: ./dataset/test.txt
image_root: ./dataset/CIFAR10/
cls_label_path: ./dataset/CIFAR10/test_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
......@@ -112,8 +112,8 @@ DataLoader:
Gallery:
dataset:
name: ImageNetDataset
image_root: ./dataset/
cls_label_path: ./dataset/database.txt
image_root: ./dataset/CIFAR10/
cls_label_path: ./dataset/CIFAR10/train_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
......
......@@ -64,8 +64,8 @@ DataLoader:
Train:
dataset:
name: ImageNetDataset
image_root: ./dataset/
cls_label_path: ./dataset/train.txt
image_root: ./dataset/CIFAR10/
cls_label_path: ./dataset/CIFAR10/train_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
......@@ -89,8 +89,8 @@ DataLoader:
Query:
dataset:
name: ImageNetDataset
image_root: ./dataset/
cls_label_path: ./dataset/test.txt
image_root: ./dataset/CIFAR10/
cls_label_path: ./dataset/CIFAR10/test_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
......@@ -113,8 +113,8 @@ DataLoader:
Gallery:
dataset:
name: ImageNetDataset
image_root: ./dataset/
cls_label_path: ./dataset/database.txt
image_root: ./dataset/CIFAR10/
cls_label_path: ./dataset/CIFAR10/train_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
......
......@@ -60,8 +60,8 @@ DataLoader:
Train:
dataset:
name: ImageNetDataset
image_root: ./dataset/
cls_label_path: ./dataset/train.txt
image_root: ./dataset/CIFAR10/
cls_label_path: ./dataset/CIFAR10/train_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
......@@ -85,8 +85,8 @@ DataLoader:
Query:
dataset:
name: ImageNetDataset
image_root: ./dataset/
cls_label_path: ./dataset/test.txt
image_root: ./dataset/CIFAR10/
cls_label_path: ./dataset/CIFAR10/test_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
......@@ -109,8 +109,8 @@ DataLoader:
Gallery:
dataset:
name: ImageNetDataset
image_root: ./dataset/
cls_label_path: ./dataset/database.txt
image_root: ./dataset/CIFAR10/
cls_label_path: ./dataset/CIFAR10/train_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
......
......@@ -28,34 +28,35 @@ class DSHSDLoss(nn.Layer):
self.multi_label = multi_label
def forward(self, input, label):
feature = input["features"]
features = input["features"]
logits = input["logits"]
dist = paddle.sum(paddle.square(
(paddle.unsqueeze(feature, 1) - paddle.unsqueeze(feature, 0))),
axis=2)
features_temp1 = paddle.unsqueeze(features, 1)
features_temp2 = paddle.unsqueeze(features, 0)
dist = features_temp1 - features_temp2
dist = paddle.square(dist)
dist = paddle.sum(dist, axis=2)
# label to ont-hot
n_class = logits.shape[1]
label = paddle.nn.functional.one_hot(
label, n_class).astype("float32").squeeze()
labels = paddle.nn.functional.one_hot(label, n_class)
labels = labels.squeeze().astype("float32")
s = (paddle.matmul(
label, label, transpose_y=True) == 0).astype("float32")
margin = 2 * feature.shape[1]
s = paddle.matmul(labels, labels, transpose_y=True)
s = (s == 0).astype("float32")
margin = 2 * features.shape[1]
Ld = (1 - s) / 2 * dist + s / 2 * (margin - dist).clip(min=0)
Ld = Ld.mean()
if self.multi_label:
# multiple labels classification loss
Lc = (logits - label * logits + (
(1 + (-logits).exp()).log())).sum(axis=1).mean()
Lc_temp = (1 + (-logits).exp()).log()
Lc = (logits - labels * logits + Lc_temp).sum(axis=1)
else:
# single labels classification loss
Lc = (-paddle.nn.functional.softmax(logits).log() * label).sum(
axis=1).mean()
probs = paddle.nn.functional.softmax(logits)
Lc = (-probs.log() * labels).sum(axis=1)
Lc = Lc.mean()
return {"dshsdloss": Lc + Ld * self.alpha}
loss = Lc + Ld * self.alpha
return {"dshsdloss": loss}
class LCDSHLoss(nn.Layer):
......@@ -69,24 +70,30 @@ class LCDSHLoss(nn.Layer):
self.n_class = n_class
def forward(self, input, label):
feature = input["features"]
features = input["features"]
labels = paddle.nn.functional.one_hot(label, self.n_class)
labels = labels.squeeze().astype("float32")
label = paddle.nn.functional.one_hot(
label, self.n_class).astype("float32").squeeze()
s = 2 * (paddle.matmul(
label, label, transpose_y=True) > 0).astype("float32") - 1
inner_product = paddle.matmul(feature, feature, transpose_y=True) * 0.5
s = paddle.matmul(labels, labels, transpose_y=True)
s = 2 * (s > 0).astype("float32") - 1
inner_product = paddle.matmul(features, features, transpose_y=True)
inner_product = inner_product * 0.5
inner_product = inner_product.clip(min=-50, max=50)
L1 = paddle.log(1 + paddle.exp(-s * inner_product)).mean()
L1 = paddle.log(1 + paddle.exp(-s * inner_product))
L1 = L1.mean()
binary_features = features.sign()
b = feature.sign()
inner_product_ = paddle.matmul(b, b, transpose_y=True) * 0.5
inner_product_ = paddle.matmul(
binary_features, binary_features, transpose_y=True)
inner_product_ = inner_product_ * 0.5
sigmoid = paddle.nn.Sigmoid()
L2 = (sigmoid(inner_product) - sigmoid(inner_product_)).pow(2).mean()
L2 = (sigmoid(inner_product) - sigmoid(inner_product_)).pow(2)
L2 = L2.mean()
return {"lcdshloss": L1 + self._lambda * L2}
loss = L1 + self._lambda * L2
return {"lcdshloss": loss}
class DCHLoss(paddle.nn.Layer):
......@@ -111,14 +118,15 @@ class DCHLoss(paddle.nn.Layer):
len_j = feature_j.pow(2).sum(axis=1, keepdim=True).pow(0.5)
norm = paddle.matmul(len_i, len_j, transpose_y=True)
cos = inner_product / norm.clip(min=0.0001)
return (1 - cos.clip(max=0.99)) * K / 2
dist = (1 - cos.clip(max=0.99)) * K / 2
return dist
def forward(self, input, label):
u = input["features"]
y = paddle.nn.functional.one_hot(
label, self.n_class).astype("float32").squeeze()
features = input["features"]
labels = paddle.nn.functional.one_hot(label, self.n_class)
labels = labels.squeeze().astype("float32")
s = paddle.matmul(y, y, transpose_y=True).astype("float32")
s = paddle.matmul(labels, labels, transpose_y=True).astype("float32")
if (1 - s).sum() != 0 and s.sum() != 0:
positive_w = s * s.numel() / s.sum()
negative_w = (1 - s) * s.numel() / (1 - s).sum()
......@@ -126,15 +134,13 @@ class DCHLoss(paddle.nn.Layer):
else:
w = 1
d_hi_hj = self.distance(u, u)
dist_matric = self.distance(features, features)
cauchy_loss = w * (s * paddle.log(dist_matric / self.gamma) +
paddle.log(1 + self.gamma / dist_matric))
cauchy_loss = w * (s * paddle.log(d_hi_hj / self.gamma) +
paddle.log(1 + self.gamma / d_hi_hj))
all_one = paddle.ones_like(u, dtype="float32")
quantization_loss = paddle.log(1 + self.distance(u.abs(), all_one) /
self.gamma)
all_one = paddle.ones_like(features, dtype="float32")
dist_to_one = self.distance(features.abs(), all_one)
quantization_loss = paddle.log(1 + dist_to_one / self.gamma)
loss = cauchy_loss.mean() + self._lambda * quantization_loss.mean()
return {"dchloss": loss}
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