lombardata
commited on
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fae65ac
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Parent(s):
024e019
Update app.py
Browse files
app.py
CHANGED
@@ -3,60 +3,43 @@ from tensorflow import keras
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import numpy as np
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import gradio as gr
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"Gmelina arborea Roxb",
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"Hevea brasiliensis",
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"Hopea",
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"Khaya senegalensis",
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"Khaya senegalensis A.Juss",
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"Lagerstroemia speciosa",
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"Magnolia alba",
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"Mangifera",
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"Melaleuca",
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"Melia azedarach",
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"Musa",
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"Nephelium lappaceum",
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"Persea",
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"Polyalthia longifolia",
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"Prunnus",
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"Prunus salicina",
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"Psidium guajava",
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"Pterocarpus macrocarpus",
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"Senna siamea",
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"Spondias mombin L",
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"Syzygium nervosum",
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"Tamarindus indica",
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"Tectona grandis",
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"Terminalia catappa",
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"Veitchia merrilli",
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"Wrightia",
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"Wrightia religiosa",
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def predict(path):
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@@ -65,7 +48,7 @@ def predict(path):
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image = np.expand_dims(image, axis=0)
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pred = model.predict(image, verbose=0)
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pred = pred[0]
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confidences = {
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return confidences
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import numpy as np
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import gradio as gr
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# Load PyTorch model
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def create_head(num_features , number_classes ,dropout_prob=0.5 ,activation_func =nn.ReLU):
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features_lst = [num_features , num_features//2 , num_features//4]
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layers = []
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for in_f ,out_f in zip(features_lst[:-1] , features_lst[1:]):
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layers.append(nn.Linear(in_f , out_f))
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layers.append(activation_func())
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layers.append(nn.BatchNorm1d(out_f))
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if dropout_prob !=0 : layers.append(nn.Dropout(dropout_prob))
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layers.append(nn.Linear(features_lst[-1] , number_classes))
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return nn.Sequential(*layers)
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from transformers import Dinov2Config, Dinov2Model
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class NewheadDinov2ForImageClassification(Dinov2ForImageClassification):
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def __init__(self, config: Dinov2Config) -> None:
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super().__init__(config)
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self.num_labels = config.num_labels
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self.dinov2 = Dinov2Model(config)
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# Classifier head
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self.classifier = create_head(config.hidden_size * 2, config.num_labels)
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# IMPORT CLASSIFICATION MODEL
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checkpoint_name = "lombardata/dino-base-2023_11_27-with_custom_head"
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# import labels
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classes_names = ["Acropore_branched", "Acropore_digitised", "Acropore_tabular", "Algae_assembly",
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"Algae_limestone", "Algae_sodding", "Dead_coral", "Fish", "Human_object",
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"Living_coral", "Millepore", "No_acropore_encrusting", "No_acropore_massive",
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"No_acropore_sub_massive", "Rock", "Sand",
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"Scrap", "Sea_cucumber", "Syringodium_isoetifolium",
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"Thalassodendron_ciliatum", "Useless"]
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classes_nb = list(np.arange(len(classes_names)))
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id2label = {int(classes_nb[i]): classes_names[i] for i in range(len(classes_nb))}
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label2id = {v: k for k, v in id2label.items()}
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model = NewheadDinov2ForImageClassification.from_pretrained(checkpoint_name)
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def predict(path):
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image = np.expand_dims(image, axis=0)
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pred = model.predict(image, verbose=0)
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pred = pred[0]
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confidences = {classes_names[i]: round(float(pred[i]), 2) for i in range(50)}
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return confidences
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