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import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
from os.path import join
from models.networks.utils import NormGPS
class L1(nn.Module):
def __init__(self):
super(L1, self).__init__()
def forward(self, x, y):
"""
Args:
x: dict that contains "gps": torch.Tensor Bx2
y: dict that contains "gps": torch.Tensor Bx2
Returns:
torch.Tensor: L1 loss between x and y: torch.Tensor([B])
"""
return {"L1_loss": torch.abs(x["gps"] - y["gps"]).mean(dim=-1)}
class L2(nn.Module):
def __init__(self):
super(L2, self).__init__()
def forward(self, x, y):
"""
Args:
x: dict that contains "gps": torch.Tensor Bx2
y: dict that contains "gps": torch.Tensor Bx2
Returns:
torch.Tensor: L2 loss between x and y: torch.Tensor([B])
"""
return {"L2_loss": ((x["gps"] - y["gps"]) ** 2).mean(dim=-1)}
class L2Hybrid(nn.Module):
def __init__(self):
super(L2Hybrid, self).__init__()
self.norm = NormGPS()
def forward(self, x, y):
"""
Args:
x: dict that contains "gps": torch.Tensor Bx2
y: dict that contains "gps": torch.Tensor Bx2
Returns:
torch.Tensor: L2 loss between x and y: torch.Tensor([B])
"""
return {
"L2_loss": (
(x["reg"] - (self.norm(y["gps"]) - x["center"]) * x["size"]) ** 2
).mean(dim=-1)
}
class CrossEntropy(nn.Module):
def __init__(self):
super(CrossEntropy, self).__init__()
self.loss = nn.CrossEntropyLoss(reduction="none")
def forward(self, x, y):
"""
Args:
x: dict that contains "label": torch.Tensor BxN
y: dict that contains "label": torch.Tensor BxN
Returns:
torch.Tensor: CrossEntropy loss between x and y: torch.Tensor([B])
"""
return {"cross_entropy_loss": self.loss(x["label"], y["label"])}
class HierarchicalCrossEntropyQuad(nn.Module):
def __init__(self, data_path=""):
super(HierarchicalCrossEntropyQuad, self).__init__()
self.dict_losses = {"classif_loss": nn.CrossEntropyLoss(reduction="none")}
for i in range(1, 10):
self.dict_losses[f"quadtree_{i}_loss"] = nn.NLLLoss()
self.matrixes = torch.load(join(data_path, "quadtree_matrixes.pt"))
self.dicts = torch.load(join(data_path, "quadtree_dicts.pt"))
self.id_to_quad = torch.load(join(data_path, "id_to_quad_10_1000.pt"))
def forward(self, x, y):
"""
Args:
x: dict that contains "label": torch.Tensor BxN
y: dict that contains "label": torch.Tensor BxN
Returns:
torch.Tensor: Hierarchical CrossEntropy for Quadtrees loss between x and y: torch.Tensor([B])
"""
out = {"classif_loss": self.dict_losses["classif_loss"](x["label"], y["label"])}
probas = nn.functional.softmax(x["label"], dim=1)
device = x["label"].device
gt = self.id_to_quad[y["label"].cpu()]
for i in range(9):
logits = torch.log(torch.mm(probas, self.matrixes[i].to(device)) + 1e-10)
l = [s[: 9 - i] if len(s) >= 10 - i else s for s in gt]
out[f"quadtree_{i+1}_loss"] = self.dict_losses[f"quadtree_{i+1}_loss"](
logits, torch.tensor([self.dicts[i][item] for item in l]).to(device)
)
return out
class HierarchicalCrossEntropy(nn.Module):
def __init__(self, path=""):
super(HierarchicalCrossEntropy, self).__init__()
self.city_loss = nn.CrossEntropyLoss(reduction="none")
self.country_loss = nn.NLLLoss()
self.area_loss = nn.NLLLoss()
self.region_loss = nn.NLLLoss()
self.city_to_country = torch.load(path + "city_to_country.pt")
self.city_to_region = torch.load(path + "city_to_region.pt")
self.city_to_area = torch.load(path + "city_to_area.pt")
self.country_to_idx = torch.load(path + "country_to_idx.pt")
self.region_to_idx = torch.load(path + "region_to_idx.pt")
self.area_to_idx = torch.load(path + "area_to_idx.pt")
def forward(self, x, y):
"""
Args:
x: dict that contains "label": torch.Tensor BxN
y: dict that contains "label": torch.Tensor BxN
Returns:
torch.Tensor: Hierarchical CrossEntropy loss between x and y: torch.Tensor([B])
"""
country_mask = np.array(y["unique_country"]) != "NaN"
self.city_to_country = self.city_to_country.to(x["label"].device)
countries_probas = nn.functional.softmax(x["label"][country_mask], dim=1)
countries_logits = torch.log(
torch.mm(countries_probas, self.city_to_country) + 1e-10
)
country_gt = torch.tensor(
[
self.country_to_idx[item]
for item in np.array(y["unique_country"])[country_mask]
]
).to(x["label"].device)
region_mask = np.array(y["unique_region"]) != "NaN"
self.city_to_region = self.city_to_region.to(x["label"].device)
regions_probas = nn.functional.softmax(x["label"][region_mask], dim=1)
regions_logits = torch.log(
torch.mm(regions_probas, self.city_to_region) + 1e-10
)
region_gt = torch.tensor(
[
self.region_to_idx[item]
for item in np.array(y["unique_region"])[region_mask]
]
).to(x["label"].device)
area_mask = np.array(y["unique_sub-region"]) != "NaN"
self.city_to_area = self.city_to_area.to(x["label"].device)
areas_probas = nn.functional.softmax(x["label"][area_mask], dim=1)
areas_logits = torch.log(torch.mm(areas_probas, self.city_to_area) + 1e-10)
area_gt = torch.tensor(
[
self.area_to_idx[item]
for item in np.array(y["unique_sub-region"])[area_mask]
]
).to(x["label"].device)
return {
"cross_entropy_country_loss": self.country_loss(
countries_logits, country_gt
),
"cross_entropy_city_loss": self.city_loss(x["label"], y["label"]),
"cross_entropy_area_loss": self.area_loss(areas_logits, area_gt),
"cross_entropy_region_loss": self.region_loss(regions_logits, region_gt),
}
class LandCoverLoss(nn.Module):
def __init__(self):
super(LandCoverLoss, self).__init__()
self.loss = nn.CrossEntropyLoss()
def forward(self, x, y):
"""
Args:
x: dict that contains "land_cover": torch.Tensor BxN
y: dict that contains "land_cover": torch.Tensor BxN
Returns:
torch.Tensor: CrossEntropy loss between x and y: torch.Tensor([B])
"""
return {
"land_cover_cross_entropy_loss": self.loss(x["land_cover"], y["land_cover"])
}
class RoadIndexLoss(nn.Module):
def __init__(self):
super(RoadIndexLoss, self).__init__()
self.loss = nn.MSELoss()
def forward(self, x, y):
"""
Args:
x: dict that contains "road_index": torch.Tensor BxN
y: dict that contains "road_index": torch.Tensor BxN
Returns:
torch.Tensor: CrossEntropy loss between x and y: torch.Tensor([B])
"""
return {"road_index_mse_loss": self.loss(x["road_index"], y["road_index"])}
class DriveSideLoss(nn.Module):
def __init__(self):
super(DriveSideLoss, self).__init__()
self.loss = nn.BCELoss()
def forward(self, x, y):
"""
Args:
x: dict that contains "drive_side": torch.Tensor BxN
y: dict that contains "drive_side": torch.Tensor BxN
Returns:
torch.Tensor: CrossEntropy loss between x and y: torch.Tensor([B])
"""
return {"drive_side_bce_loss": self.loss(x["drive_side"], y["drive_side"])}
class ClimateLoss(nn.Module):
def __init__(self):
super(ClimateLoss, self).__init__()
self.loss = nn.CrossEntropyLoss()
def forward(self, x, y):
"""
Args:
x: dict that contains "climate": torch.Tensor BxN
y: dict that contains "climate": torch.Tensor BxN
Returns:
torch.Tensor: CrossEntropy loss between x and y: torch.Tensor([B])
"""
return {"climate_cross_entropy_loss": self.loss(x["climate"], y["climate"])}
class SoilLoss(nn.Module):
def __init__(self):
super(SoilLoss, self).__init__()
self.loss = nn.CrossEntropyLoss()
def forward(self, x, y):
"""
Args:
x: dict that contains "soil": torch.Tensor BxN
y: dict that contains "soil": torch.Tensor BxN
Returns:
torch.Tensor: CrossEntropy loss between x and y: torch.Tensor([B])
"""
return {"soil_cross_entropy_loss": self.loss(x["soil"], y["soil"])}
class DistSeaLoss(nn.Module):
def __init__(self):
super(DistSeaLoss, self).__init__()
self.loss = nn.MSELoss()
def forward(self, x, y):
"""
Args:
x: dict that contains "dist_sea": torch.Tensor BxN
y: dict that contains "dist_sea": torch.Tensor BxN
Returns:
torch.Tensor: CrossEntropy loss between x and y: torch.Tensor([B])
"""
return {"dist_sea_mse_loss": self.loss(x["dist_sea"], y["dist_sea"])}
class Haversine(nn.Module):
def __init__(self):
super(Haversine, self).__init__()
def forward(self, x, y):
"""
Args:
x: dict that contains "gps": torch.Tensor Bx2
y: dict that contains "gps": torch.Tensor Bx2
Returns:
torch.Tensor: Haversine loss between x and y: torch.Tensor([B])
Note:
Haversine distance doesn't contain the 2 * 6371 constant.
"""
x, y = x["gps"], y["gps"]
lhs = torch.sin((x[:, 0] - y[:, 0]) / 2) ** 2
rhs = (
torch.cos(x[:, 0])
* torch.cos(y[:, 0])
* torch.sin((x[:, 1] - y[:, 1]) / 2) ** 2
)
a = lhs + rhs
return {
"haversine_loss": torch.arctan2(torch.sqrt(a), torch.sqrt(1 - a))
} # ommitting 2 * 6371 as both are a constant
class GeoguessrLoss(Haversine):
def __init__(self):
super(GeoguessrLoss, self).__init__()
def forward(self, x, y):
distance = super().forward(x, y)["haversine_loss"]
loss = torch.exp(-distance / 1852)
return {"geoguessr_loss": loss}
class InfoNCE(nn.Module):
def __init__(self, tau=0.1):
super(InfoNCE, self).__init__()
self.tau = tau
def cosine_similarity(self, a, b, normalize=True):
if normalize:
w1 = a.norm(p=2, dim=1, keepdim=True)
w2 = b.norm(p=2, dim=1, keepdim=True)
sim_matrix = torch.mm(a, b.t()) / (w1 * w2.t()).clamp(min=1e-8)
else:
sim_matrix = torch.mm(a, b.t())
return sim_matrix
def forward(self, x, y=None):
"""
neg_sim: BxB
pos_sim: Bx1
"""
features = x["features"]
positive_features = x["pos_features"]
pos_sim = F.cosine_similarity(
features, positive_features, dim=1, eps=1e-8
).unsqueeze(1)
neg_sim = self.cosine_similarity(features, features, normalize=True)
b = neg_sim.shape[0]
logits = (1 - torch.eye(b)).type_as(neg_sim) * neg_sim + torch.eye(b).type_as(
pos_sim
) * pos_sim
logits = logits / self.tau
labels = torch.arange(b, dtype=torch.long).cuda()
loss = F.cross_entropy(logits, labels)
return {
"contrastive_loss": loss,
}
class TextNCE(nn.Module):
def __init__(self, tau=0.1, num_devices=1):
super(TextNCE, self).__init__()
self.distributed = num_devices > 1
self.tau = tau
def cosine_similarity(self, a, b, normalize=True):
if normalize:
w1 = a.norm(p=2, dim=1, keepdim=True)
w2 = b.norm(p=2, dim=1, keepdim=True)
sim_matrix = torch.mm(a, b.t()) / (w1 * w2.t()).clamp(min=1e-8)
else:
sim_matrix = torch.mm(a, b.t())
return sim_matrix
def forward(self, x, y=None):
"""
neg_sim: BxB
pos_sim: Bx1
"""
if self.distributed:
all_image_features = torch.cat(
torch.distributed.nn.all_gather(x["features"]), dim=0
)
all_text_features = torch.cat(
torch.distributed.nn.all_gather(x["text_features"]), dim=0
)
all_labels = torch.cat(torch.distributed.nn.all_gather(y["label"]), dim=0)
else:
all_image_features = x["features"]
all_text_features = x["text_features"]
all_labels = y["label"]
labels_u = torch.unique(all_labels)
logits = self.cosine_similarity(
all_image_features, all_text_features, normalize=True
)
rows, cols = logits.size()
indices = torch.arange(0, rows, device=all_image_features.device)
loss = torch.sum(
torch.logsumexp(
logits[indices != indices.view(-1, 1)].view(rows, cols - 1) / self.tau,
dim=1,
)
)
for label in labels_u:
if not (label == "NaN"):
# Get the positive and negative examples
idx = all_labels == label
pos_logits = logits[idx][:, idx]
# Compute the MIL-NCE loss
loss += torch.sum(-torch.logsumexp(pos_logits / self.tau, dim=1))
return {
"contrastive_loss": loss,
}
class MILNCE(nn.Module):
def __init__(self, tau=0.1, num_devices=1):
super(MILNCE, self).__init__()
self.distributed = num_devices > 1
self.tau = tau
def cosine_similarity(self, a, b, normalize=True):
if normalize:
w1 = a.norm(p=2, dim=1, keepdim=True)
w2 = b.norm(p=2, dim=1, keepdim=True)
sim_matrix = torch.mm(a, b.t()) / (w1 * w2.t()).clamp(min=1e-8)
else:
sim_matrix = torch.mm(a, b.t())
return sim_matrix
def forward(self, x, y=None):
"""
COmpute MIL-NCE loss
"""
if self.distributed:
all_image_features = torch.cat(
torch.distributed.nn.all_gather(x["features"]), dim=0
)
all_pos_features = torch.cat(
torch.distributed.nn.all_gather(x["pos_features"]), dim=0
)
all_labels = torch.cat(torch.distributed.nn.all_gather(y["label"]), dim=0)
else:
all_image_features = x["features"]
all_pos_features = x["pos_features"]
all_labels = y["label"]
labels_u = torch.unique(all_labels)
features = torch.cat([all_image_features, all_pos_features])
labels = torch.cat([all_labels, all_labels])
logits = self.cosine_similarity(features, features, normalize=True)
rows, cols = logits.size()
indices = torch.arange(0, rows, device=features.device)
loss = torch.sum(
torch.logsumexp(
logits[indices != indices.view(-1, 1)].view(rows, cols - 1) / self.tau,
dim=1,
)
)
for label in labels_u:
if not (label == "NaN"):
# Get the positive and negative examples
idx = labels == label
pos_logits = logits[idx][:, idx]
rows, cols = pos_logits.size()
indices = torch.arange(0, rows, device=features.device)
pos_logits = pos_logits[indices != indices.view(-1, 1)].view(
rows, cols - 1
)
# Compute the MIL-NCE loss
loss += torch.sum(-torch.logsumexp(pos_logits / self.tau, dim=1))
return {
"contrastive_loss": loss,
}
class RegionMILNCE(nn.Module):
def __init__(self, tau=0.1, num_devices=1):
super(RegionMILNCE, self).__init__()
self.distributed = num_devices > 1
self.tau = tau
def cosine_similarity(self, a, b, normalize=True):
if normalize:
w1 = a.norm(p=2, dim=1, keepdim=True)
w2 = b.norm(p=2, dim=1, keepdim=True)
sim_matrix = torch.mm(a, b.t()) / (w1 * w2.t()).clamp(min=1e-8)
else:
sim_matrix = torch.mm(a, b.t())
return sim_matrix
def forward(self, x, y=None):
"""
neg_sim: BxB
pos_sim: Bx1
"""
if self.distributed:
all_image_features = torch.cat(
torch.distributed.nn.all_gather(x["features"]), dim=0
)
all_pos_features = torch.cat(
torch.distributed.nn.all_gather(x["pos_features"]), dim=0
)
all_labels = torch.cat(torch.distributed.nn.all_gather(y["label"]), dim=0)
else:
all_image_features = x["features"]
all_pos_features = x["pos_features"]
all_labels = y["label"]
labels_u = torch.unique(all_labels)
features = torch.cat([all_image_features, all_pos_features])
labels = torch.cat([all_labels, all_labels])
logits = self.cosine_similarity(features, features, normalize=True)
rows, cols = logits.size()
indices = torch.arange(0, rows, device=features.device)
loss = torch.sum(
torch.logsumexp(
logits[indices != indices.view(-1, 1)].view(rows, cols - 1) / self.tau,
dim=1,
)
)
for label in labels_u:
if not (label == "NaN"):
# Get the positive and negative examples
idx = labels == label
pos_logits = logits[idx][:, idx]
rows, cols = pos_logits.size()
indices = torch.arange(0, rows, device=features.device)
pos_logits = pos_logits[indices != indices.view(-1, 1)].view(
rows, cols - 1
)
# Compute the MIL-NCE loss
loss += torch.sum(-torch.logsumexp(pos_logits / self.tau, dim=1))
return {
"contrastive_loss": loss / len(all_labels),
}
LOSSES = {
"l1": L1,
"l2": L2,
"l2_hybrid": L2Hybrid,
"haversine": Haversine,
"geoguessr": GeoguessrLoss,
"crossentropy": CrossEntropy,
"infonce": InfoNCE,
"mil-nce": MILNCE,
"text-nce": TextNCE,
"land_cover": LandCoverLoss,
"road_index": RoadIndexLoss,
"drive_side": DriveSideLoss,
"climate": ClimateLoss,
"soil": SoilLoss,
"dist_sea": DistSeaLoss,
"hierarchical": HierarchicalCrossEntropy,
"hier_quad": HierarchicalCrossEntropyQuad,
"region_mil": RegionMILNCE,
}
AVERAGE = {False: lambda x: x, True: lambda x: x.mean(dim=-1)}
class Losses(nn.Module):
"""The Losses meta-object that can take a mix of losses."""
def __init__(self, mix={}, aux_data=[], path="", num_devices=1):
"""Initializes the Losses object.
Args:
mix (dict): dictionary with keys "loss_name" and values weight
"""
super(Losses, self).__init__()
assert len(mix)
self.aux = len(aux_data) > 0
if self.aux:
self.aux_list = aux_data
total = ["land_cover", "drive_side", "climate", "soil", "dist_sea"]
for col in self.aux_list:
total.remove(col)
for col in total:
del mix[col]
self.init_losses(mix, path, num_devices)
def init_losses(self, mix, path="", num_devices=1):
"""Initializes the losses.
Args:
mix (dict): dictionary with keys "loss_name" and values weight
"""
self.loss = {}
for m, v in mix.items():
m = m.lower()
if m in ["hierarchical", "hier_quad"]:
try:
self.loss[m] = (LOSSES[m](path), v)
except KeyError:
raise KeyError(f"Loss {m} not found in {LOSSES.keys()}")
elif m in ["region_mil", "mil-nce", "text-nce"]:
try:
self.loss[m] = (LOSSES[m](num_devices=num_devices), v)
except KeyError:
raise KeyError(f"Loss {m} not found in {LOSSES.keys()}")
else:
try:
self.loss[m] = (LOSSES[m](), v)
except KeyError:
raise KeyError(f"Loss {m} not found in {LOSSES.keys()}")
def forward(self, x, y, average=True):
"""Computes the losses.
Args:
x: dict that contains "gps": torch.Tensor Bx2 or "label": torch.Tensor BxN
y: dict that contains "gps": torch.Tensor Bx2 or "label": torch.Tensor BxN
average (bool): whether to average the losses or not
Returns:
dict: dictionary with losses
"""
output = {"loss": 0}
for loss_name, (loss, weight) in self.loss.items():
loss_output = loss(x, y)
for k, v in loss_output.items():
v = AVERAGE[average](v)
if k.endswith("_loss"):
output["loss"] += weight * v
output[k] = v
return output
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