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"""
Copyright (c) 2022, salesforce.com, inc.
All rights reserved.
SPDX-License-Identifier: BSD-3-Clause
For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""
from copy import deepcopy
import torch
import torch.nn.functional as F
from lavis.common.registry import registry
from lavis.common.utils import get_abs_path
from lavis.models.albef_models import AlbefBase
from lavis.models.albef_models.albef_outputs import AlbefIntermediateOutput, AlbefOutput
from lavis.models.base_model import MomentumDistilationMixin
from lavis.models.med import BertModel
from lavis.models.vit import VisionTransformerEncoder
from torch import nn
from transformers import BertConfig
@registry.register_model("albef_nlvr")
class AlbefNLVR(AlbefBase, MomentumDistilationMixin):
PRETRAINED_MODEL_CONFIG_DICT = {
"nlvr": "configs/models/albef_nlvr.yaml",
}
def __init__(
self,
image_encoder,
text_encoder,
num_classes,
momentum=0.995,
alpha=0.4,
use_distill=True,
max_txt_len=40,
):
super().__init__()
self.tokenizer = self.init_tokenizer()
self.max_txt_len = max_txt_len
self.use_distill = use_distill
self.visual_encoder = image_encoder
self.text_encoder = text_encoder
hidden_size = text_encoder.config.hidden_size
self.cls_head = nn.Sequential(
nn.Linear(hidden_size, hidden_size),
nn.ReLU(),
nn.Linear(hidden_size, num_classes),
)
self.share_cross_attention(self.text_encoder.encoder)
if self.use_distill:
self.visual_encoder_m = deepcopy(self.visual_encoder)
self.text_encoder_m = deepcopy(self.text_encoder)
self.cls_head_m = deepcopy(self.cls_head)
self.share_cross_attention(self.text_encoder_m.encoder)
self.momentum = momentum
self.alpha = alpha
self.model_pairs = [
[self.visual_encoder, self.visual_encoder_m],
[self.text_encoder, self.text_encoder_m],
[self.cls_head, self.cls_head_m],
]
self.copy_params()
def _rampup_factor(self, epoch, iters, num_iters_per_epoch):
return min(1, (epoch * num_iters_per_epoch + iters) / (2 * num_iters_per_epoch))
def forward(self, samples, is_train=True):
"""
Forward function for training and evaluation.
Args:
samples (dict): a dict of input samples, which contains the following keys:
- image0 (torch.Tensor): input image 0, shape (batch_size, 3, H, W), default H=384, W=384.
- image1 (torch.Tensor): input image 1, shape (batch_size, 3, H, W), default H=384, W=384.
- text_input (list): list of strings, each string is a natural language sentence.
- label (torch.LongTensor): ground truth label with shape (batch_size,).
is_train (bool): whether the model is in training mode.
If True, the model will return the loss;
If False, the model will return the prediction.
Examples:
>>> import torch
>>> from lavis.models import load_model
>>> model = load_model("albef_nlvr")
>>> samples = {
... "image0": torch.randn(2, 3, 384, 384),
... "image1": torch.randn(2, 3, 384, 384),
... "text_input": ["there is a ferret in tall grass", "there are lips in one of the images"],
... "label": torch.tensor([0, 1]),
... }
>>> output = model(samples)
>>> output.keys()
odict_keys(['intermediate_output', 'loss'])
"""
text = samples["text_input"]
text = self.tokenizer(
text,
padding="longest",
truncation=True,
max_length=self.max_txt_len,
return_tensors="pt",
).to(self.device)
targets = samples["label"]
image0 = samples["image0"]
image1 = samples["image1"]
images = torch.cat([image0, image1], dim=0)
image_embeds = self.visual_encoder.forward_features(images)
image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(
self.device
)
image0_embeds, image1_embeds = torch.split(image_embeds, targets.size(0))
encoder_output = self.text_encoder(
text.input_ids,
attention_mask=text.attention_mask,
encoder_hidden_states=[image0_embeds, image1_embeds],
encoder_attention_mask=[
image_atts[: image0_embeds.size(0)],
image_atts[image0_embeds.size(0) :],
],
return_dict=True,
)
prediction = self.cls_head(encoder_output.last_hidden_state[:, 0, :])
if is_train:
if self.use_distill:
with torch.no_grad():
self._momentum_update()
image_embeds_m = self.visual_encoder_m(images)
image0_embeds_m, image1_embeds_m = torch.split(
image_embeds_m, targets.size(0)
)
encoder_output_m = self.text_encoder(
text.input_ids,
attention_mask=text.attention_mask,
encoder_hidden_states=[image0_embeds_m, image1_embeds_m],
encoder_attention_mask=[
image_atts[: image0_embeds_m.size(0)],
image_atts[image0_embeds_m.size(0) :],
],
return_dict=True,
)
prediction_m = self.cls_head_m(
encoder_output_m.last_hidden_state[:, 0, :]
)
alpha = self.alpha * self._rampup_factor(
epoch=samples["epoch"],
iters=samples["iters"],
num_iters_per_epoch=samples["num_iters_per_epoch"],
)
loss = (1 - alpha) * F.cross_entropy(
prediction, targets
) - alpha * torch.sum(
F.log_softmax(prediction, dim=1) * F.softmax(prediction_m, dim=1),
dim=1,
).mean()
else:
loss = F.cross_entropy(prediction, targets)
encoder_output_m = None
image0_embeds_m, image1_embeds_m = None, None
# return {"loss": loss}
return AlbefOutput(
loss=loss,
intermediate_output=AlbefIntermediateOutput(
image_embeds=torch.stack([image0_embeds, image1_embeds], dim=0),
image_embeds_m=torch.stack(
[image0_embeds_m, image1_embeds_m], dim=0
),
encoder_output=encoder_output,
encoder_output_m=encoder_output_m,
),
)
else:
return {"predictions": prediction, "targets": targets}
def share_cross_attention(self, model):
for i in range(6):
layer_num = 6 + i * 2
modules_0 = model.layer[layer_num].crossattention.self._modules
modules_1 = model.layer[layer_num + 1].crossattention.self._modules
for name in modules_0.keys():
if "key" in name or "value" in name:
module_0 = modules_0[name]
module_1 = modules_1[name]
if hasattr(module_0, "weight"):
module_0.weight = module_1.weight
if hasattr(module_0, "bias"):
module_0.bias = module_1.bias
def predict(self, samples):
output = self.forward(samples, is_train=False)
return output
def load_from_pretrained(self, url_or_filename, use_distill=True):
_, msg = super().load_from_pretrained(url_or_filename)
if use_distill and any(["_m" in k for k in msg.missing_keys]):
# this is required when initializing the model from TA pre-trained weights
self.copy_params()
return msg
@classmethod
def from_config(cls, cfg=None):
image_encoder = VisionTransformerEncoder.from_config(cfg)
# text encoder + multimodal encoder
bert_config = BertConfig.from_json_file(get_abs_path(cfg["med_config_path"]))
bert_config.num_hidden_layers = 18
text_encoder = BertModel.from_pretrained(
"bert-base-uncased", config=bert_config, add_pooling_layer=False
)
alpha = cfg.get("alpha", 0.4)
momentum = cfg.get("momentum", 0.995)
use_distill = cfg.get("use_distill", True)
num_classes = cfg.get("num_classes", -1)
max_txt_len = cfg.get("max_txt_len", 40)
assert num_classes > 1, "Invalid number of classes provided, found {}".format(
num_classes
)
model = cls(
image_encoder=image_encoder,
text_encoder=text_encoder,
use_distill=use_distill,
alpha=alpha,
num_classes=num_classes,
momentum=momentum,
max_txt_len=max_txt_len,
)
model.load_checkpoint_from_config(cfg)
return model
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