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import glob
import json
import math
import os
from dataclasses import dataclass
import dateutil
import numpy as np
from src.display.formatting import make_hyperlink
from src.display.utils import AutoEvalColumn, ModelType, Tasks, Precision
from src.about import Model_Backbone, Training_Dataset, Testing_Type
@dataclass
class EvalResult:
"""
Represents one full evaluation. Built from a combination of the result and request file for a given run.
"""
eval_name: str # model_training_testing_precision (identifier for evaluations)
model_name: str
training_dataset_type: Training_Dataset
training_dataset: str
testing_type: Testing_Type
results: dict
paper_name: str = ""
model_link: str = ""
paper_link: str = ""
model_backbone_type: Model_Backbone = Model_Backbone.Other
model_backbone: str = ""
precision: Precision = Precision.Other
model_parameters: float = 0
model_type: ModelType = ModelType.Other # Pretrained, fine tuned, ...
date: str = "" # submission date of request file
@classmethod
def init_from_json_file(self, json_filepath):
"""Inits the result from the specific model result file"""
with open(json_filepath) as fp:
data = json.load(fp)
config = data.get("config")
# Extract evaluation config
model_name = config["model_name"]
training_dataset_type = Training_Dataset.from_str(config["training_dataset"])
if training_dataset_type.name != Training_Dataset.Other.name:
training_dataset = training_dataset_type.value
else:
training_dataset = config["training_dataset"]
testing_type = Testing_Type(config["testing_type"])
precision = Precision.from_str(config.get("model_dtype"))
eval_name = model_name + precision.value + training_dataset + testing_type.value
# Extract results
results = {}
for task in Tasks:
task = task.value
results[task.metric] = data["results"].get(task.metric, -1)
return self(
eval_name=eval_name,
model_name=model_name,
training_dataset_type=training_dataset_type,
training_dataset=training_dataset,
testing_type=testing_type,
precision=precision,
results=results,
)
def update_with_request_file(self, requests_path):
"""Finds the relevant request file for the current model and updates info with it"""
if self.training_dataset_type.name != Training_Dataset.Other.name:
training_dataset_request = self.training_dataset_type.name
else:
training_dataset_request = self.training_dataset
training_dataset_request = "_".join(training_dataset_request.split())
request_file = get_request_file_for_model(requests_path, self.model_name, self.precision.value, training_dataset_request, self.testing_type.value)
try:
with open(request_file, "r") as f:
request = json.load(f)
self.model_parameters = request.get("model_parameters", 0)
self.model_link = request.get("model_link", "None")
self.model_backbone = request.get("model_backbone", "Unknown")
self.model_backbone_type = Model_Backbone.from_str(self.model_backbone)
self.paper_name = request.get("paper_name", "None")
self.paper_link = request.get("paper_link", "None")
self.model_type = ModelType.from_str(request.get("model_type", ""))
self.date = request.get("submitted_time", "")
except Exception:
print(f"Could not find request file for {self.model_name} with precision {self.precision.value}, training dataset {self.training_dataset} and testing type {self.testing_type.value}")
def to_dict(self):
"""Converts the Eval Result to a dict compatible with our dataframe display"""
data_dict = {
"eval_name": self.eval_name, # not a column, just a save name,
AutoEvalColumn.precision.name: self.precision.value,
AutoEvalColumn.model_parameters.name: self.model_parameters,
AutoEvalColumn.model_name.name: self.model_name,
AutoEvalColumn.paper.name: make_hyperlink(self.paper_link, self.paper_name) if self.paper_link.startswith("http") else self.paper_name,
AutoEvalColumn.model_backbone_type.name: self.model_backbone_type.value,
AutoEvalColumn.model_backbone.name: self.model_backbone,
AutoEvalColumn.training_dataset_type.name: self.training_dataset_type.value,
AutoEvalColumn.training_dataset.name: self.training_dataset,
AutoEvalColumn.testing_type.name: self.testing_type.name,
AutoEvalColumn.model_link.name: self.model_link
}
for task in Tasks:
data_dict[task.value.col_name] = self.results[task.value.metric]
return data_dict
def get_request_file_for_model(requests_path, model_name, precision, training_dataset, testing_type):
"""Selects the correct request file for a given model if it's marked as FINISHED"""
request_filename = os.path.join(
requests_path,
model_name,
f"{model_name}_eval_request_{precision}_{training_dataset}_{testing_type}.json",
)
# check for request file
try:
with open(request_filename, "r") as file:
req_content = json.load(file)
if req_content["status"] not in ["FINISHED"]:
return None
except OSError:
return None
return request_filename
def get_raw_eval_results(results_path: str, requests_path: str) -> list[EvalResult]:
"""From the path of the results folder root, extract all needed info for results"""
model_result_filepaths = []
for root, _, files in os.walk(results_path):
# We should only have json files in model results
if len(files) == 0 or any([not f.endswith(".json") for f in files]):
continue
for file in files:
model_result_filepaths.append(os.path.join(root, file))
eval_results = {}
for model_result_filepath in model_result_filepaths:
# Creation of result
eval_result = EvalResult.init_from_json_file(model_result_filepath)
eval_result.update_with_request_file(requests_path)
eval_name = eval_result.eval_name
eval_results[eval_name] = eval_result
results = []
for v in eval_results.values():
try:
v.to_dict() # we test if the dict version is complete
results.append(v)
except KeyError: # not all eval values present
continue
return results