From 896a09a4a3c3d22181ea351d4897c215d4c70fa7 Mon Sep 17 00:00:00 2001 From: Thomas Bernard Date: Thu, 18 Jan 2024 18:42:59 +0100 Subject: [PATCH] Use new ML model --- backend/Dockerfile | 6 +- backend/model.pt | Bin 0 -> 10284991 bytes backend/requirements.txt | 1 + backend/src/main.py | 6 +- backend/src/model.py | 146 +++--------------------------------- backend/tests/test_api.py | 2 +- backend/tests/test_model.py | 43 +---------- 7 files changed, 19 insertions(+), 185 deletions(-) create mode 100644 backend/model.pt diff --git a/backend/Dockerfile b/backend/Dockerfile index d46c3845..cbe18cbb 100644 --- a/backend/Dockerfile +++ b/backend/Dockerfile @@ -21,13 +21,9 @@ RUN pip --default-timeout=300 install --upgrade pip \ && rm -r /root/.cache ARG VERSION -ARG MODEL="EffB7_2023-03-06_08" ENV SSL_CERT_FILE=$CACERT_LOCATION -RUN curl -o model.pth https://storage.gra.cloud.ovh.net/v1/AUTH_df731a99a3264215b973b3dee70a57af/basegun-models/${MODEL}.pth COPY src/ src/ -RUN mkdir -p src/weights \ - && mv model.pth src/weights/model.pth \ - && echo '{"app": "'${VERSION}'", "model": "'${MODEL}'"}' > versions.json +COPY model.pt . 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z?yyXqdmgoTSUFtY*d3OM<X^1a)E$<ovtZc=OXcdk<POW&m?C|v|E?TXbGt3do4Hlo zLZ;3oF<uiZzvRT`=88SlLZ;4kU&sERot%m{WO++>Sf<XjJCD&H)Ew&$%hma%VdKi- z@_2Vxrp|xqsn+hWOr8Jqd#!lQmt?R^o&R*&tOzF_A>FJkQ|CWD4lBatN8MqWI{)b| zR}oG;?heZo_D@f@ig5Xf{|o<b|GLC3HEM)`LHhekTQ&T@Z@}-||9<!P&)SCne$eYA k@R0*Az}5WzbI9}m{uxZ3z-QHKeE*CCzrO$b|Nh(m1yb69iU0rr literal 0 HcmV?d00001 diff --git a/backend/requirements.txt b/backend/requirements.txt index d3eb7fdd..45e71390 100644 --- a/backend/requirements.txt +++ b/backend/requirements.txt @@ -10,6 +10,7 @@ user-agents==2.2.0 boto3==1.28.39 torch==2.1.1 torchvision==0.16.1 +ultralytics==8.1.2 # Dev pytest==7.4.3 coverage==7.3.2 \ No newline at end of file diff --git a/backend/src/main.py b/backend/src/main.py index 09ffbc0d..389a227e 100644 --- a/backend/src/main.py +++ b/backend/src/main.py @@ -159,7 +159,7 @@ async def add_owasp_middleware(request: Request, call_next): logger = setup_logs(PATH_LOGS) # Load model -MODEL_PATH = os.path.join(CURRENT_DIR, "weights/model.pth") +MODEL_PATH = os.path.join(CURRENT_DIR, "../model.pt") model = None if os.path.exists(MODEL_PATH): model = load_model_inference(MODEL_PATH) @@ -242,9 +242,9 @@ async def imageupload( extras_logging["bg_label"] = label extras_logging["bg_confidence"] = confidence extras_logging["bg_model_time"] = round(time.time() - start, 2) - if confidence < 46: + if confidence < 0.46: extras_logging["bg_confidence_level"] = "low" - elif confidence < 76: + elif confidence < 0.76: extras_logging["bg_confidence_level"] = "medium" else: extras_logging["bg_confidence_level"] = "high" diff --git a/backend/src/model.py b/backend/src/model.py index 2edd4dc5..293b9865 100644 --- a/backend/src/model.py +++ b/backend/src/model.py @@ -2,10 +2,8 @@ from typing import Union import numpy as np -import torch -import torchvision.models as Model from PIL import Image -from torchvision import transforms +from ultralytics import YOLO CLASSES = [ "autre_pistolet", @@ -22,144 +20,21 @@ "semi_auto_style_militaire_autre", ] -MODEL_TORCH = Model.efficientnet_b7 -INPUT_SIZE = 600 -device = torch.device("cpu") - -class ConvertRgb(object): - """Converts an image to RGB""" - - def __init__(self): - pass - - def __call__(self, image): - if image.mode != "RGB": - image = image.convert("RGB") - return image - - -class Rescale(object): - """Rescale the image in a sample to a given size while keeping ratio - - Args: - output_size (int): Desired output size. The largest of image edges is matched - to output_size keeping aspect ratio the same. - """ - - def __init__(self, output_size): - assert isinstance(output_size, int) - self.output_size = output_size - - def __call__(self, image): - w, h = image.size - if w > h: - new_h, new_w = self.output_size * h / w, self.output_size - else: - new_h, new_w = self.output_size, self.output_size * w / h - new_h, new_w = int(new_h), int(new_w) - return transforms.functional.resize(image, (new_h, new_w)) - - -class RandomPad(object): - """Pad an image to reach a given size - - Args: - output_size (int): Desired output size. We pad all edges - symmetrically to reach a size x size image. - """ - - def __init__(self, output_size): - assert isinstance(output_size, int) - self.output_size = output_size - - def __call__(self, image): - w, h = image.size - pads = { - "horiz": [self.output_size - w, 0, 0], - "vert": [self.output_size - h, 0, 0], - } - if pads["horiz"][0] >= 0 and pads["vert"][0] >= 0: - for direction in ["horiz", "vert"]: - pads[direction][1] = pads[direction][0] // 2 - if ( - pads[direction][0] % 2 == 1 - ): # if the size to pad is odd, add a random +1 on one side - pads[direction][1] += np.random.randint(0, 1) - pads[direction][2] = pads[direction][0] - pads[direction][1] - - return transforms.functional.pad( - image, - [pads["horiz"][1], pads["vert"][1], pads["horiz"][2], pads["vert"][2]], - fill=int(np.random.choice([0, 255])), # border randomly white or black - ) - else: - return image - - -def build_model(model: Model) -> Model: - """Create the model structure - - Args: - model (Model): raw torchvision model - - Returns: - Model: modified model with classification layer size len(CLASSES) - """ - # freeze all layers except classification - very important - for param in model.parameters(): - param.requires_grad = False - # replace last layer of model for our number of classes - num_ftrs = model.classifier[1].in_features - model.classifier[1] = torch.nn.Linear(num_ftrs, len(CLASSES)) - model = model.to(device) - return model - - -def load_model_inference(state_dict_path: str) -> Model: +def load_model_inference(model_path: str): """Load model structure and weights Args: - state_dict_path (str): path to model (.pth file) + model_path (str): path to model (.pt file) Returns: Model: loaded model ready for prediction """ - model = build_model(MODEL_TORCH()) - # Initialize model with the pretrained weights - model.load_state_dict( - torch.load(state_dict_path, map_location=device)["model_state_dict"] - ) - model.to(device) - # set the model to inference mode - model.eval() + model = YOLO(model_path) return model -def prepare_input(image: Image) -> torch.Tensor: - """Convert a PIL Image to model-compatible input - - Args: - image (Image): input image - - Returns: - torch.Tensor: converted image - (shape (1, 3, size, size), normalized on ImageNet) - """ - loader = transforms.Compose( - [ - ConvertRgb(), - Rescale(INPUT_SIZE), - RandomPad(INPUT_SIZE), - transforms.ToTensor(), - transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), - ] - ) - image = loader(image).float() - return image.unsqueeze(0).to(device) - - -def predict_image(model: Model, img: bytes) -> Union[str, float]: +def predict_image(model, img: bytes) -> Union[str, float]: """Run the model prediction on an image Args: @@ -170,9 +45,8 @@ def predict_image(model: Model, img: bytes) -> Union[str, float]: Union[str, float]: (label, confidence) of best class predicted """ im = Image.open(BytesIO(img)) - image = prepare_input(im) - output = model(image) - probs = torch.nn.functional.softmax(output, dim=1).detach().numpy()[0] - res = [(CLASSES[i], round(probs[i] * 100, 2)) for i in range(len(CLASSES))] - res.sort(key=lambda x: x[1], reverse=True) - return res[0] + results = model(im, verbose=False) + predicted_class = results[0].probs.top5[0] + label = CLASSES[predicted_class] + confidence = float(results[0].probs.top5conf[0]) + return (label, confidence) diff --git a/backend/tests/test_api.py b/backend/tests/test_api.py index 05a5541b..750dda0a 100644 --- a/backend/tests/test_api.py +++ b/backend/tests/test_api.py @@ -81,7 +81,7 @@ def test_upload(self): # checks that the json result is as expected assert res["label"] == "revolver" - assert res["confidence"] == pytest.approx(98.43, 0.1) + assert res["confidence"] == pytest.approx(1, 0.1) assert res["confidence_level"] == "high" def test_feedback_and_logs(self): diff --git a/backend/tests/test_model.py b/backend/tests/test_model.py index ef694be6..b485032d 100644 --- a/backend/tests/test_model.py +++ b/backend/tests/test_model.py @@ -1,55 +1,18 @@ import os -import numpy as np import pytest -from PIL import Image -from src.model import ( - CLASSES, - INPUT_SIZE, - load_model_inference, - predict_image, - prepare_input, -) -from torch import Tensor +from src.model import CLASSES, load_model_inference, predict_image class TestModel: - model_path = os.path.join( - os.path.dirname(os.path.abspath(__file__)), "../src/weights/model.pth" - ) + model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "../model.pt") assert os.path.exists(model_path) model = load_model_inference(model_path) - def test_is_efficientnet(self): - """Checks that the loaded model is an efficientnet""" - assert "efficientnet" in self.model.__class__.__name__.lower() - assert len(self.model.features) == 9 - assert len(self.model.classifier) == 2 - - def test_model_correctly_built(self): - """Checks that the model is correctly built for prediction""" - # check number of classes - assert self.model.classifier[1].out_features == len(CLASSES) - # check model in "eval" mode - assert self.model.training is False - # check model on cpu - assert next(self.model.parameters()).is_cuda is False - - def test_prepare_input(self): - """Checks prepare_input works properly""" - # create random RGBA image - image = Image.fromarray((np.random.rand(100, 200, 4) * 255).astype("uint8")) - image = prepare_input(image) - assert type(image) == Tensor - # checks converted to 3 channels - assert image.size(dim=1) == 3 - # checks image resized to INPUT_SIZE x INPUT_SIZE - assert image.size() == (1, 3, INPUT_SIZE, INPUT_SIZE) - def test_predict_image(self): """Checks the prediction of an image by the model""" path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "revolver.jpg") with open(path, "rb") as f: res = predict_image(self.model, f.read()) assert res[0] == "revolver" - assert res[1] == pytest.approx(98.43, 0.1) + assert res[1] == pytest.approx(1, 0.1)

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