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matching_networks.py
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matching_networks.py
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"""
See original implementation at
https://github.com/facebookresearch/low-shot-shrink-hallucinate
"""
from typing import Optional
import torch
from torch import Tensor, nn
from easyfsl.methods import FewShotClassifier
from easyfsl.modules.predesigned_modules import (
default_matching_networks_query_encoder,
default_matching_networks_support_encoder,
)
class MatchingNetworks(FewShotClassifier):
"""
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, and Daan Wierstra.
"Matching networks for one shot learning." (2016)
https://arxiv.org/pdf/1606.04080.pdf
Matching networks extract feature vectors for both support and query images. Then they refine
these feature by using the context of the whole support set, using LSTMs. Finally they compute
query labels using their cosine similarity to support images.
Be careful: while some methods use Cross Entropy Loss for episodic training, Matching Networks
output log-probabilities, so you'll want to use Negative Log Likelihood Loss.
"""
def __init__(
self,
*args,
support_encoder: Optional[nn.Module] = None,
query_encoder: Optional[nn.Module] = None,
**kwargs
):
"""
Build Matching Networks by calling the constructor of FewShotClassifier.
Args:
support_encoder: module encoding support features. If none is specific, we use
the default encoder from the original paper.
query_encoder: module encoding query features. If none is specific, we use
the default encoder from the original paper.
Raises:
ValueError: if the backbone is not a feature extractor,
i.e. if its output for a given image is not a 1-dim tensor.
"""
super().__init__(*args, **kwargs)
if len(self.backbone_output_shape) != 1:
raise ValueError(
"Illegal backbone for Matching Networks. "
"Expected output for an image is a 1-dim tensor."
)
# These modules refine support and query feature vectors
# using information from the whole support set
self.support_features_encoder = (
support_encoder
if support_encoder
else default_matching_networks_support_encoder(self.feature_dimension)
)
self.query_features_encoding_cell = (
query_encoder
if query_encoder
else default_matching_networks_query_encoder(self.feature_dimension)
)
self.softmax = nn.Softmax(dim=1)
# Here we create the fields so that the model can store
# the computed information from one support set
self.contextualized_support_features = torch.tensor(())
self.one_hot_support_labels = torch.tensor(())
def process_support_set(
self,
support_images: Tensor,
support_labels: Tensor,
):
"""
Overrides process_support_set of FewShotClassifier.
Extract features from the support set with full context embedding.
Store contextualized feature vectors, as well as support labels in the one hot format.
Args:
support_images: images of the support set
support_labels: labels of support set images
"""
support_features = self.backbone(support_images)
self.contextualized_support_features = self.encode_support_features(
support_features
)
self.one_hot_support_labels = nn.functional.one_hot(support_labels).float()
def forward(self, query_images: Tensor) -> Tensor:
"""
Overrides method forward in FewShotClassifier.
Predict query labels based on their cosine similarity to support set features.
Classification scores are log-probabilities.
Args:
query_images: images of the query set
Returns:
a prediction of classification scores for query images
"""
# Refine query features using the context of the whole support set
contextualized_query_features = self.encode_query_features(
self.backbone(query_images)
)
# Compute the matrix of cosine similarities between all query images
# and normalized support images
# Following the original implementation, we don't normalize query features to keep
# "sharp" vectors after softmax (if normalized, all values tend to be the same)
similarity_matrix = self.softmax(
contextualized_query_features.mm(
nn.functional.normalize(self.contextualized_support_features).T
)
)
# Compute query log probabilities based on cosine similarity to support instances
# and support labels
log_probabilities = (
similarity_matrix.mm(self.one_hot_support_labels) + 1e-6
).log()
return self.softmax_if_specified(log_probabilities)
def encode_support_features(
self,
support_features: Tensor,
) -> Tensor:
"""
Refine support set features by putting them in the context of the whole support set,
using a bidirectional LSTM.
Args:
support_features: output of the backbone
Returns:
contextualised support features, with the same shape as input features
"""
# Since the LSTM is bidirectional, hidden_state is of the shape
# [number_of_support_images, 2 * feature_dimension]
hidden_state = self.support_features_encoder(support_features.unsqueeze(0))[
0
].squeeze(0)
# Following the paper, contextualized features are computed by adding original features, and
# hidden state of both directions of the bidirectional LSTM.
contextualized_support_features = (
support_features
+ hidden_state[:, : self.feature_dimension]
+ hidden_state[:, self.feature_dimension :]
)
return contextualized_support_features
def encode_query_features(self, query_features: Tensor) -> Tensor:
"""
Refine query set features by putting them in the context of the whole support set,
using attention over support set features.
Args:
query_features: output of the backbone
Returns:
contextualized query features, with the same shape as input features
"""
hidden_state = query_features
cell_state = torch.zeros_like(query_features)
# We do as many iterations through the LSTM cell as there are query instances
# Check out the paper for more details about this!
for _ in range(len(self.contextualized_support_features)):
attention = self.softmax(
hidden_state.mm(self.contextualized_support_features.T)
)
read_out = attention.mm(self.contextualized_support_features)
lstm_input = torch.cat((query_features, read_out), 1)
hidden_state, cell_state = self.query_features_encoding_cell(
lstm_input, (hidden_state, cell_state)
)
hidden_state = hidden_state + query_features
return hidden_state
@staticmethod
def is_transductive() -> bool:
return False