File size: 3,482 Bytes
e8e50c3 1ca266a 7fb7cd0 29136f2 e8e50c3 7fb7cd0 1ca266a e8e50c3 1ca266a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | from typing import Dict, List, Any
from PIL import Image
import os
import json
import numpy as np
from fastai.learner import load_learner
class OrdinalRegressionMetric(Metric):
def __init__(self):
super().__init__()
self.total = 0
self.count = 0
def accumulate(self, learn):
# Get predictions and targets
preds, targs = learn.pred, learn.y
# Your custom logic to convert predictions and targets to numeric values
preds_numeric = torch.argmax(preds, dim=1)
targs_numeric = targs
#print("preds_numeric: ",preds_numeric)
#print("targs_numeric: ",targs_numeric)
# Calculate the metric (modify this based on your specific needs)
squared_diff = torch.sum(torch.sqrt((preds_numeric - targs_numeric)**2))
# Normalize by the maximum possible difference
max_diff = torch.sqrt((torch.max(targs_numeric) - torch.min(targs_numeric))**2)
#print("squared_diff: ",squared_diff)
#print("max_diff: ",max_diff)
# Update the metric value
self.total += squared_diff
#print("self.total: ",self.total)
self.count += max_diff
#print("self.count: ",self.count)
@property
def value(self):
if self.count == 0:
return 0.0 # or handle this case appropriately
#print("self.total / self.count: ", (self.total / self.count))
# Calculate the normalized metric value
metric_value = 1/(self.total / self.count)
return metric_value
class PreTrainedPipeline():
def __init__(self, path=""):
# IMPLEMENT_THIS
# Preload all the elements you are going to need at inference.
# For instance your model, processors, tokenizer that might be needed.
# This function is only called once, so do all the heavy processing I/O here"""
self.metric = OrdinalRegressionMetric()
self.model = load_learner(os.path.join(path, "textfile3-2.pk1"))
with open(os.path.join(path, "config.json")) as config:
config = json.load(config)
self.id2label = config["id2label"]
def __call__(self, inputs: "Image.Image") -> List[Dict[str, Any]]:
"""
Args:
inputs (:obj:`PIL.Image`):
The raw image representation as PIL.
No transformation made whatsoever from the input. Make all necessary transformations here.
Return:
A :obj:`list`:. The list contains items that are dicts should be liked {"label": "XXX", "score": 0.82}
It is preferred if the returned list is in decreasing `score` order
"""
# IMPLEMENT_THIS
# FastAI expects a np array, not a PIL Image.
_, _, preds = self.model.predict(np.array(inputs))
preds = preds.tolist()
labels = [
{"label": str(self.id2label["0"]), "score": preds[0]},
{"label": str(self.id2label["1"]), "score": preds[1]},
{"label": str(self.id2label["2"]), "score": preds[2]},
{"label": str(self.id2label["3"]), "score": preds[3]},
{"label": str(self.id2label["4"]), "score": preds[4]},
{"label": str(self.id2label["5"]), "score": preds[5]},
{"label": str(self.id2label["6"]), "score": preds[6]},
{"label": str(self.id2label["7"]), "score": preds[7]},
{"label": str(self.id2label["8"]), "score": preds[8]},
]
return labels
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