136 lines
3.0 KiB
Python
136 lines
3.0 KiB
Python
import torch
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from torch.utils.data import DataLoader
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from sklearn.metrics import classification_report
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import SetUp
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from Data import *
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from IdentitySubset import IdentitySubset
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# models
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from architectures.Model import Model, Architecture
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# numbre of classes
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CLASS_SIZE = 20
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# batch
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BATCH_SIZE = 16
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# size of images per class trainset + testset
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# 30 works best, more than that and we dont have enough data
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SAMPLE_SIZE = 30
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# this is then (full_sample - test_sample)
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TRAINING_SMPLE = 28
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# learning rate
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LR_RATE = 0.0001
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EPOCHS = 16
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# depends on model architecture
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# ResNet, DenseNet = 224
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# Inception = 299
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RESOLUTION = 224
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# model architecture options are
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# - RESNET18
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# - RESNET50
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# - DENSENET121
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# - INCEPTION
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# - GOOGLENET
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# - EFFICIENTNET
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# - SHUFFLENET
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arch = Architecture.RESNET50
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# DATA PREPARATION
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# load data set and prepare
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dataset = get_set()
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# select identities for experiment
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selected_identities = select_ids(
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dataset = dataset,
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sample_size = SAMPLE_SIZE,
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class_size = CLASS_SIZE
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)
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print(f'> Selected {CLASS_SIZE} random identity classes from CelebA dataset.')
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print(f'> A class has {TRAINING_SMPLE} train and {SAMPLE_SIZE-TRAINING_SMPLE} test samples')
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# split class images to train/test indices
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train_indices, test_indices = get_indices(
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dataset = dataset,
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identities = selected_identities,
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split_at = TRAINING_SMPLE
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)
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# helps map class id to index
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id_map = {old_id: new_id for new_id, old_id in enumerate(selected_identities)}
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# we remap identities because crossEntropyLoss requires in indices 0 -> (n-1)
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# where n = class size.
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tr_transform = train_transform(res = RESOLUTION)
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train_data = IdentitySubset(
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dataset=dataset,
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indices=train_indices,
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id_mapping=id_map,
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transform=tr_transform)
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train_loader = DataLoader(
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train_data,
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batch_size = BATCH_SIZE,
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shuffle = True)
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print(f"> Total training images: {len(train_data)}")
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print(f'> Constants : Classes = {CLASS_SIZE}, Batch = {BATCH_SIZE}, epochs = {EPOCHS}')
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# MODEL PREPARATION
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# cuda if exists (it does here)
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device = SetUp.get_device()
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# Create model using Factory
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model = Model.create(
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arch = arch,
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device = device,
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size = CLASS_SIZE)
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# we may need to load existing model or finetune
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model.train(
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epochs = EPOCHS,
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loader = train_loader,
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rate = LR_RATE)
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# save.
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model.save(filename=arch.name.lower())
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# done tuning
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print('Model saved!')
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# EVALUATE
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te_transform = test_transform(RESOLUTION)
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# Testing
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test_data = IdentitySubset(
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dataset = dataset,
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indices=test_indices,
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id_mapping=id_map,
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transform=te_transform)
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test_loader = DataLoader(
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test_data,
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batch_size=BATCH_SIZE,
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shuffle=False)
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print(f"Total test images for these {CLASS_SIZE} classes: {len(test_data)}")
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# Evaluate
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model.evaluate(
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loader = test_loader)
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# test again
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reloaded = Model.create(
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arch=arch,
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device = device,
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size = CLASS_SIZE
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)
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reloaded.load(arch = arch)
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print("Evaluating loaded")
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reloaded.evaluate(
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loader = test_loader
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)
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