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import os |
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import numpy as np |
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import pandas as pd |
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import timm |
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import torch |
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import torch.nn as nn |
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from PIL import Image |
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from timm.models.metaformer import MlpHead |
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from torch.utils.data import DataLoader, Dataset |
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from tqdm import tqdm |
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from albumentations import Compose, Normalize, Resize |
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from albumentations.pytorch import ToTensorV2 |
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import cv2 |
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DIM = 518 |
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DATE_SIZE = 4 |
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GEO_SIZE = 7 |
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SUBSTRATE_SIZE = 73 |
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NUM_CLASSES = 1717 |
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TIME = ["m0", "m1", "d0", "d1"] |
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GEO = ["g0", "g1", "g2", "g3", "g4", "g5", "g_float"] |
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SUBSTRATE = [ |
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"substrate_0", |
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"substrate_1", |
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"substrate_2", |
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"substrate_3", |
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"substrate_4", |
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"substrate_5", |
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"substrate_6", |
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"substrate_7", |
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"substrate_8", |
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"substrate_9", |
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"substrate_10", |
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"substrate_11", |
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"substrate_12", |
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"substrate_13", |
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"substrate_14", |
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"substrate_15", |
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"substrate_16", |
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"substrate_17", |
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"substrate_18", |
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"substrate_19", |
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"substrate_20", |
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"substrate_21", |
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"substrate_22", |
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"substrate_23", |
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"substrate_24", |
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"substrate_25", |
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"substrate_26", |
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"substrate_27", |
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"substrate_28", |
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"substrate_29", |
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"substrate_30", |
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"metasubstrate_0", |
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"metasubstrate_1", |
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"metasubstrate_2", |
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"metasubstrate_3", |
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"metasubstrate_4", |
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"metasubstrate_5", |
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"metasubstrate_6", |
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"metasubstrate_7", |
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"metasubstrate_8", |
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"metasubstrate_9", |
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"habitat_0", |
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"habitat_1", |
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"habitat_2", |
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"habitat_3", |
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"habitat_4", |
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"habitat_5", |
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"habitat_6", |
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"habitat_7", |
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"habitat_8", |
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"habitat_9", |
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"habitat_10", |
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"habitat_11", |
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"habitat_12", |
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"habitat_13", |
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"habitat_14", |
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"habitat_15", |
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"habitat_16", |
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"habitat_17", |
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"habitat_18", |
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"habitat_19", |
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"habitat_20", |
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"habitat_21", |
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"habitat_22", |
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"habitat_23", |
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"habitat_24", |
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"habitat_25", |
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"habitat_26", |
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"habitat_27", |
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"habitat_28", |
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"habitat_29", |
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"habitat_30", |
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"habitat_31", |
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] |
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class ImageDataset(Dataset): |
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def __init__(self, df, local_filepath): |
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self.df = df |
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self.transform = Compose( |
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[ |
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Resize(DIM, DIM), |
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Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), |
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ToTensorV2(), |
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] |
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) |
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self.local_filepath = local_filepath |
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self.filepaths = df["image_path"].to_list() |
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def __len__(self): |
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return len(self.df) |
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def __getitem__(self, idx): |
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image_path = os.path.join(self.local_filepath, self.filepaths[idx]) |
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image = cv2.imread(image_path) |
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image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) |
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return self.transform(image=image)["image"] |
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class EmbeddingMetadataDataset(Dataset): |
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def __init__(self, df): |
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self.df = df |
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self.emb = df["embedding"] |
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self.metadata_date = df[TIME].to_numpy() |
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self.metadata_geo = df[GEO].to_numpy() |
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self.metadata_substrate = df[SUBSTRATE].to_numpy() |
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def __len__(self): |
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return len(self.df) |
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def __getitem__(self, idx): |
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embedding = torch.Tensor(self.emb[idx].copy()).type(torch.float) |
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metadata = { |
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"date": torch.from_numpy(self.metadata_date[idx, :]).type(torch.float), |
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"geo": torch.from_numpy(self.metadata_geo[idx, :]).type(torch.float), |
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"substr": torch.from_numpy(self.metadata_substrate[idx, :]).type( |
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torch.float |
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), |
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} |
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return embedding, metadata |
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def generate_embeddings(metadata_file_path, root_dir): |
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DINOV2_CKPT = "./checkpoints/dinov2.bin" |
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metadata_df = pd.read_csv(metadata_file_path) |
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test_dataset = ImageDataset(metadata_df, local_filepath=root_dir) |
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loader = DataLoader(test_dataset, batch_size=3, shuffle=False) |
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device = torch.device('cpu') |
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model = timm.create_model( |
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"timm/vit_large_patch14_reg4_dinov2.lvd142m", pretrained=False |
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) |
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weights = torch.load(DINOV2_CKPT) |
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model.load_state_dict(weights) |
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model = model.to(device) |
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model.eval() |
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all_embs = [] |
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for img in tqdm(loader): |
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img = img.to(device) |
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emb = model.forward(img) |
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all_embs.append(emb.detach().cpu().numpy()) |
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all_embs = np.vstack(all_embs) |
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embs_list = [x for x in all_embs] |
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metadata_df["embedding"] = embs_list |
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return metadata_df |
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class StarReLU(nn.Module): |
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""" |
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StarReLU: s * relu(x) ** 2 + b |
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""" |
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def __init__( |
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self, |
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scale_value=1.0, |
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bias_value=0.0, |
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scale_learnable=True, |
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bias_learnable=True, |
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mode=None, |
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inplace=False, |
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): |
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super().__init__() |
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self.inplace = inplace |
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self.relu = nn.ReLU(inplace=inplace) |
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self.scale = nn.Parameter( |
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scale_value * torch.ones(1), requires_grad=scale_learnable |
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) |
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self.bias = nn.Parameter( |
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bias_value * torch.ones(1), requires_grad=bias_learnable |
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) |
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def forward(self, x): |
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return self.scale * self.relu(x) ** 2 + self.bias |
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class FungiMEEModel(nn.Module): |
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def __init__( |
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self, |
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num_classes=NUM_CLASSES, |
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dim=1024, |
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): |
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super().__init__() |
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print("Setting up Pytorch Model") |
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self.device = torch.device('cpu') |
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print(f"Using devide: {self.device}") |
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self.date_embedding = MlpHead( |
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dim=DATE_SIZE, num_classes=dim, mlp_ratio=128, act_layer=StarReLU |
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) |
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self.geo_embedding = MlpHead( |
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dim=GEO_SIZE, num_classes=dim, mlp_ratio=128, act_layer=StarReLU |
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) |
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self.substr_embedding = MlpHead( |
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dim=SUBSTRATE_SIZE, |
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num_classes=dim, |
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mlp_ratio=8, |
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act_layer=StarReLU, |
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) |
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self.encoder = nn.TransformerEncoder( |
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nn.TransformerEncoderLayer(d_model=dim, nhead=8, batch_first=True), |
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num_layers=4, |
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) |
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self.head = MlpHead(dim=dim, num_classes=num_classes, drop_rate=0) |
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for param in self.parameters(): |
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if param.dim() > 1: |
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nn.init.kaiming_normal_(param) |
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def forward(self, img_emb, metadata): |
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img_emb = img_emb.to(self.device) |
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date_emb = self.date_embedding.forward(metadata["date"].to(self.device)) |
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geo_emb = self.geo_embedding.forward(metadata["geo"].to(self.device)) |
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substr_emb = self.substr_embedding.forward(metadata["substr"].to(self.device)) |
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full_emb = torch.stack((img_emb, date_emb, geo_emb, substr_emb), dim=1) |
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cls_emb = self.encoder.forward(full_emb)[:, 0, :].squeeze(1) |
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return self.head.forward(cls_emb) |
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def predict(self, img_emb, metadata): |
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logits = self.forward(img_emb, metadata) |
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return logits.argmax(1).tolist() |
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class FungiEnsembleModel(nn.Module): |
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def __init__(self, models) -> None: |
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super().__init__() |
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self.models = nn.ModuleList() |
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self.device = torch.device('cpu') |
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for model in models: |
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model = model.to(self.device) |
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model.eval() |
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self.models.append(model) |
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def forward(self, img_emb, metadata): |
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img_emb = img_emb.to(self.device) |
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probs = [] |
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for model in self.models: |
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logits = model.forward(img_emb, metadata) |
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p = logits.softmax(dim=1).detach().cpu() |
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probs.append(p) |
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return torch.stack(probs).mean(dim=0) |
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def predict(self, img_emb, metadata): |
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logits = self.forward(img_emb, metadata) |
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return logits.argmax(1).tolist() |
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def make_submission(metadata_df): |
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OUTPUT_CSV_PATH = "./submission.csv" |
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BASE_CKPT_PATH = "./checkpoints" |
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model_names = [ |
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"dino_2_optuna_05242231.ckpt", |
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"dino_optuna_05241449.ckpt", |
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"dino_optuna_05241257.ckpt", |
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"dino_optuna_05241222.ckpt", |
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"dino_2_optuna_05242055.ckpt", |
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"dino_2_optuna_05242156.ckpt", |
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"dino_2_optuna_05242344.ckpt", |
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] |
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models = [] |
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for model_path in model_names: |
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print("loading ", model_path) |
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ckpt_path = os.path.join(BASE_CKPT_PATH, model_path) |
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ckpt = torch.load(ckpt_path) |
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model = FungiMEEModel() |
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model.load_state_dict( |
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{w: ckpt["model." + w] for w in model.state_dict().keys()} |
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) |
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model.eval() |
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models.append(model) |
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fungi_model = FungiEnsembleModel(models) |
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embedding_dataset = EmbeddingMetadataDataset(metadata_df) |
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loader = DataLoader(embedding_dataset, batch_size=128, shuffle=False) |
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preds = [] |
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for data in tqdm(loader): |
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emb, metadata = data |
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pred = fungi_model.forward(emb, metadata) |
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preds.append(pred) |
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all_preds = torch.vstack(preds).numpy() |
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preds_df = metadata_df[["observation_id", "image_path"]] |
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preds_df["preds"] = [i for i in all_preds] |
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preds_df = ( |
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preds_df[["observation_id", "preds"]] |
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.groupby("observation_id") |
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.mean() |
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.reset_index() |
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) |
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preds_df["class_id"] = preds_df["preds"].apply( |
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lambda x: x.argmax() if x.argmax() <= 1603 else -1 |
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) |
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preds_df[["observation_id", "class_id"]].to_csv(OUTPUT_CSV_PATH, index=None) |
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print("Submission complete") |
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if __name__ == "__main__": |
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import zipfile |
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with zipfile.ZipFile("/tmp/data/private_testset.zip", "r") as zip_ref: |
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zip_ref.extractall("/tmp/data/") |
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metadata_file_path = "./_test_preprocessed.csv" |
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root_dir = "/tmp/data/private_testset" |
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metadata_df = generate_embeddings(metadata_file_path, root_dir) |
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make_submission(metadata_df) |
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