noble/mmp-osp1
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Refactored for portability

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Oscar Blue 2022-03-21 10:54:29 +00:00
parent 9cfdb0b91f
commit eccc80425a
4 changed files with 255518 additions and 8 deletions
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2
src/autophotographer/config.py
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@ -10,11 +10,9 @@ VAL_SPLIT = 0.1
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
FEATURE_EXTRACTION_BATCH_SIZE = 256
FINETUNE_BATCH_SIZE = 64
PRED_BATCH_SIZE = 4
EPOCHS = 20
LR = 0.001
LR_FINETUNE = 0.0005 # REMOVE
IMAGE_SIZE = 32
WARMUP_PLOT = os.path.join("output", "plot.png")

255509
src/autophotographer/dataframe.csv Normal file
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5
src/autophotographer/dataset.py
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@ -24,10 +24,10 @@ projectRoot = abspath(os.path.join(script_directory, "../.."))
#projectRoot = "/src/"
print(projectRoot)
tensorImagesPath = os.path.join(projectRoot, "src/autophotographer/tensorImages.pt")
tensorImagesPath = os.path.join(projectRoot, "src/autophotographer/tensorImages.pt")
tensorRatingsPath = os.path.join(projectRoot, "src/autophotographer/tensorRatings.pt")
tensorArrayPath = os.path.join(projectRoot, "src/autophotographer/tensorArray.pt")
filePathRatings = os.path.join(projectRoot, "data/ratings.txt")
dataframePath = os.path.join(projectRoot, "src/autophotographer/dataframe.csv")
if not datasetDir == "":
filePathStyle = datasetDir + "AVA/style_image_lists/test.multilab"
@ -151,7 +151,8 @@ def build_dataframe(df, imgPath):
df['path'] = imagePaths
return df
df = build_dataframe(remove_entries_for_missing_images(load_image_ratings(), imgPath), imgPath)
#df = build_dataframe(remove_entries_for_missing_images(load_image_ratings(), imgPath), imgPath)
df = pd.read_csv(dataframePath, index_col = 0)
def create_tensor_array():
tensorArray = []

10
src/autophotographer/model.py
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@ -18,8 +18,10 @@ import dataset
script_directory = os.path.dirname(__file__)
projectRoot = abspath(os.path.join(script_directory, "../.."))
print("Project root: " + projectRoot)
INITIAL_PLOT_PATH = projectRoot + "/src/output/plot.png"
INTIIAL_MODEL_PATH = projectRoot + "/src/output/model.pth"
INITIAL_PLOT_PATH = os.path.join(projectRoot, "src/output/plot.png")
INTIIAL_MODEL_PATH = os.path.join(projectRoot, "src/output/model.pth")
valDatasetPath = os.path.join(projectRoot, "src/autophotographer/valDataset.pt")
trainDatasetPath = os.path.join(projectRoot, "src/autophotographer/trainDataset.pt")
# define transformations
trainTransform = transforms.Compose([
@ -50,10 +52,10 @@ print("Getting dataloaders...")
#transforms=valTransform, batchSize=config.FEATURE_EXTRACTION_BATCH_SIZE, shuffle=False)
#torch.save(valDataset, 'valDataset.pt')
valDataset = torch.load("/src/src/autophotographer/valDataset.pt")
valDataset = torch.load(valDatasetPath)
valLoader = DataLoader(valDataset, batch_size=config.FEATURE_EXTRACTION_BATCH_SIZE, shuffle=False, num_workers=os.cpu_count(),
pin_memory=True if config.DEVICE == "cuda" else False)
trainDataset = torch.load("/src/src/autophotographer/trainDataset.pt")
trainDataset = torch.load(trainDatasetPath)
trainLoader = DataLoader(trainDataset, batch_size=config.FEATURE_EXTRACTION_BATCH_SIZE, shuffle=True, num_workers=os.cpu_count(),
pin_memory=True if config.DEVICE == "cuda" else False)