remi-or

    def aug_test(self, 
                 imgs: List[Tensor], 
                 img_metas: List[dict],
                 rescale: bool = False) -> Tensor:
        acc_boxes = np.zeros((0, 5))
        acc_score = np.zeros((0, self.roi_head.bbox_head.num_classes))
        for img, img_meta in zip(imgs, img_metas):
            for label, dets in enumerate(self.simple_test(img, img_meta, None, rescale)[0]):
                boxes, scores = dets[:, :-1], dets[:, -1]
                acc_boxes = np.vstack((acc_boxes, boxes))

remi-or

remi-or

## Imports
from typing import Tuple
import torch
from torch import Module, Tensor
from transformers.models.roberta.modeling_roberta import RobertaPreTrainedModel, RobertaConfig, RobertaModel, RobertaEncoder
from torch.nn import CrossEntropyLoss, CosineEmbeddingLoss



## Function

remi-or

import torch
from torch.nn import CrossEntropyLoss, CosineEmbeddingLoss

def distillation_loss(
    teacher_logits : Tensor,
    student_logits : Tensor,
    labels : Tensor,
    temperature : float = 1.0,
) -> Tensor:
    """

remi-or

from torch import Tensor

def get_logits(
    model : RobertaPreTrainedModel, 
    input_ids : Tensor,
    attention_mask : Tensor,
) -> Tensor:
    """
    Given a RoBERTa (model) for classification and the couple of (input_ids) and (attention_mask),
    returns the logits corresponding to the prediction.

remi-or

from transformers.models.roberta.modeling_roberta import RobertaEncoder, RobertaModel
from torch.nn import Module

def distill_roberta_weights(
    teacher : Module,
    student : Module,
) -> None:
    """
    Recursively copies the weights of the (teacher) to the (student).
    This function is meant to be first called on a RobertaFor... model, but is then called on every children of that model recursively.

remi-or

from transformers.models.roberta.modeling_roberta import RobertaPreTrainedModel, RobertaConfig

def distill_roberta(
    teacher_model : RobertaPreTrainedModel,
) -> RobertaPreTrainedModel:
    """
    Distilates a RoBERTa (teacher_model) like would DistilBERT for a BERT model.
    The student model has the same configuration, except for the number of hidden layers, which is // by 2.
    The student layers are initilized by copying one out of two layers of the teacher, starting with layer 0.
    The head of the teacher is also copied.

remi-or

from typing import Any
from transformers import AutoModelForMaskedLM

roberta = AutoModelForMaskedLM.from_pretrained("roberta-large")

def visualize_children(
    object : Any,
    level : int = 0,
) -> None:
    """

remi-or

from transformers import AutoModelForMaskedLM

roberta = AutoModelForMaskedLM.from_pretrained("roberta-large")

print(roberta)

remi-or

import matplotlib.pyplot as plt
import seaborn as sns

def average_word_count(list_of_texts):
    """
    Returns the average word count of a list of texts.
    """
    total_count = 0
    for text in list_of_texts:
        text = text.replace("'", ' ')