!pip install -U aicrowd-cli
%load_ext aicrowd.magic

%aicrowd login

!pip install torch==1.10.2 torchvision==0.11.3 simclr

import os
import cv2
import numpy as np
from tqdm.auto import tqdm

import torch
import torchvision
import torchvision.transforms as T

from simclr import SimCLR
from simclr.modules import NT_Xent
from simclr.modules import LARS

aicrowd_challenge_name = "mabe-2022-ant-beetles-video-data"
if not os.path.exists('data'):
    os.mkdir('data')

datafolder = 'data/'

## If data is already downloaded and stored on google drive, skip the download and point to the prepared directory
# datafolder = '/content/drive/MyDrive/mabe-2022-ant-beetle/data/'

video_folder = f'{datafolder}video_clips/'

## The download might take a while, recommend to move to Google Drive if you want to run multiple times.
%aicrowd ds dl -c {aicrowd_challenge_name} -o data *.npy* # Download all files
# We'll download the 224x224 videos since they're fast on the dataloader, but you can use the full sized videos if you want
%aicrowd ds dl -c {aicrowd_challenge_name} -o data *resized_224* # Download all file
# %aicrowd ds dl -c {aicrowd_challenge_name} -o data *videos.zip* # Download the 512x512 videos

!unzip -q data/submission_videos_resized_224.zip  -d {video_folder}
!unzip -q data/userTrain_videos_resized_224.zip -d {video_folder}

## Careful when running the below commands - For copying to Google Drive
# !rm data/submission_videos.zip data/userTrain_videos.zip 
# !cp -r data/ '/content/drive/MyDrive/mabe-2022-ant-beetle/data/'

class AntBeetleVideoDataset(torch.utils.data.Dataset):
    """
    Reads frames from video files
    """
    def __init__(self, 
                 datafolder, 
                 frame_number_map,
                 keypoints,
                 frame_skip,
                 num_prev_frames, 
                 num_next_frames,
                 frame_size=(224, 224),
                 transform=None):
        """
        Initializing the dataset with images and labels
        """
        self.datafolder = datafolder
        self.transform = transform
        self.frame_number_map = frame_number_map
        self.num_prev_frames = num_prev_frames
        self.num_next_frames = num_next_frames
        self.frame_skip = frame_skip
        self.frame_size = frame_size
        self.keypoints = keypoints

        self._setup_frame_map()

    def set_transform(self, transform):
        self.transform = transform

    def _setup_frame_map(self):
        self._video_names = np.array(list(self.frame_number_map.keys()))
        # IMPORTANT: the frame number map should be sorted for self.get_video_name to work
        frame_nums = np.array([self.frame_number_map[k] for k in self._video_names])
        self._frame_numbers = frame_nums[:, 0] - 1 # start values
        assert np.all(np.diff(self._frame_numbers) > 0), "Frame number map is not sorted"

        self.length = frame_nums[-1, 1] # last value is the total number of frames

    def get_frame_info(self, global_index):
        """ Returns corresponding video name and frame number"""
        video_idx = np.searchsorted(self._frame_numbers, global_index) - 1
        frame_index = global_index - (self._frame_numbers[video_idx] + 1)
        return self._video_names[video_idx], frame_index
    
    def __len__(self):
        return self.length
    
    def __getitem__(self, idx):
        video_name, frame_index = self.get_frame_info(idx)

        video_path = os.path.join(self.datafolder, video_name + '.avi')
        
        nf = self.num_next_frames + self.num_prev_frames + 1
        frames_array = np.zeros((*self.frame_size, nf), dtype=np.float32)
        
        if not os.path.exists(video_path):
#             raise FileNotFoundError(video_path)
            if self.transform is not None:
                frames_array = self.transform(frames_array)
            return { "idx": idx,
                     "image": frames_array,
                    } 
        
        
        cap = cv2.VideoCapture(video_path)
        num_video_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
        for arridx, fnum in enumerate(range(frame_index - self.num_prev_frames * self.frame_skip,
                                            frame_index + self.num_next_frames * self.frame_skip + 1,
                                            self.frame_skip + 1)):
            if fnum < 0 or fnum >= num_video_frames:
                continue
            cap.set(cv2.CAP_PROP_POS_FRAMES, fnum)
            success, frame = cap.read()
            # print(fnum, frame_index, success)
            if success:
                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
                frames_array[:, :, arridx] = frame

        if video_name in self.keypoints['sequences']:
            bbox = self.keypoints['sequences'][video_name]['bbox']
            if bbox.shape[0] > frame_index:
                bbox = bbox[frame_index]
                frames_array = frames_array[bbox[0]:bbox[2], bbox[1]:bbox[3]] # Crop the image so random crop is more useful
        
        if self.transform is not None:
            frames_array = self.transform(frames_array)
          

        return { "idx": idx,
                 "image": frames_array,
               }

def load_optimizer(optimizer, epochs, weight_decay, batch_size, model):

    scheduler = None
    if optimizer == "Adam":
        optimizer = torch.optim.Adam(model.parameters(), lr=3e-4)  # TODO: LARS
    elif optimizer == "LARS":
        # optimized using LARS with linear learning rate scaling
        # (i.e. LearningRate = 0.3 × BatchSize/256) and weight decay of 10−6.
        learning_rate = 0.3 * batch_size / 256
        optimizer = LARS(
            model.parameters(),
            lr=learning_rate,
            weight_decay=weight_decay,
            exclude_from_weight_decay=["batch_normalization", "bias"],
        )

        # "decay the learning rate with the cosine decay schedule without restarts"
        scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
            optimizer, epochs, eta_min=0, last_epoch=-1
        )
    else:
        raise NotImplementedError

    return optimizer, scheduler

def save_model(epoch, model_path, model, optimizer):
    out = os.path.join(model_path, "checkpoint_{}.tar".format(epoch))

    # To save a DataParallel model generically, save the model.module.state_dict().
    # This way, you have the flexibility to load the model any way you want to any device you want.
    if isinstance(model, torch.nn.DataParallel):
        torch.save(model.module.state_dict(), out)
    else:
        torch.save(model.state_dict(), out)

    return out

class TransformsSimCLR:
    def __init__(self, size, pretrained=True, n_channel=3, validation=False) -> None:
        self.train_transforms = T.Compose([
            T.ToTensor(),
            T.RandomResizedCrop(size=size, scale=(0.25, 1.0)),
            T.RandomHorizontalFlip(),
            T.RandomVerticalFlip(),
            # Taking the means of the normal distributions of the 3 channels
            # since we are moving to grayscale
            T.Normalize(mean=np.mean([0.485, 0.456, 0.406]).repeat(n_channel),
                        std=np.sqrt(
                            (np.array([0.229, 0.224, 0.225])**2).sum()/9).repeat(n_channel)
                        ) if pretrained is True else T.Lambda(lambda x: x)
        ])

        self.validation_transforms = T.Compose([
            T.ToTensor(),
            T.Resize(size=size),
            # Taking the means of the normal distributions of the 3 channels
            # since we are moving to grayscale
            T.Normalize(mean=np.mean([0.485, 0.456, 0.406]).repeat(n_channel),
                        std=np.sqrt(
                            (np.array([0.229, 0.224, 0.225])**2).sum()/9).repeat(n_channel)
                        ) if pretrained is True else T.Lambda(lambda x: x)
        ])
        
        self.validation = validation

    def __call__(self, x):
        if not self.validation:
            return self.train_transforms(x), self.train_transforms(x)
        else:
            return self.validation_transforms(x)

######## Prepare bounding boxes from keypoints ##########

# Preparing some bounding box information to be used for cropping frames during training
keypoints = np.load(os.path.join(datafolder, 'submission_keypoints.npy'), allow_pickle=True).item()

for sk in tqdm(keypoints['sequences'].keys()):
    kp = keypoints['sequences'][sk]['keypoints']
    bboxes = []
    for frame_idx in range(len(kp)):
        allcoords = np.int32(kp[frame_idx].reshape(-1, 2) * 224)
        padbbox = 50
        crop_size = 224
        minvals = max(np.min(allcoords[:, 0]) - padbbox, 0), max(np.min(allcoords[:, 1]) - padbbox, 0)
        maxvals = min(np.max(allcoords[:, 0]) + padbbox, crop_size), min(np.max(allcoords[:, 1]) + padbbox, crop_size)
        bbox = (*minvals, *maxvals)
        bboxes.append(list(bbox))
    keypoints['sequences'][sk]['bbox'] = np.array(bboxes)

# Can save it you want and load later
# keypoints = np.save(os.path.join(datafolder, 'submission_keypoints_bbox.npy'), keypoints)

# keypoints = np.load(os.path.join(datafolder, 'submission_keypoints_bbox.npy'), allow_pickle=True).item()

################### CONFIG #########################
IS_PRETRAINED = True
batch_size = int(32*1.5)
epochs = 10

# Stack frames with frame skip from the video sequences
LEFT_WINDOW = 3
RIGHT_WINDOW = 3
IMG_SIZE = 224
FRAME_SKIP = 5
n_channel = LEFT_WINDOW + RIGHT_WINDOW + 1

# Check batch size that fits when changing this
embedding_size = 128

# Full Dataset is huge, he're we limiting to steps per epoch
steps_per_epoch = 1000

videos_folder = os.path.join(datafolder, 'video_clips') # TODO: Change this to combined folder
frame_number_map = np.load(os.path.join(datafolder, 'frame_number_map.npy'), allow_pickle=True).item()

checkpoint_folder = "antbeetle_checkpoints/" # Can change this to a Google drive folder
if not os.path.exists(checkpoint_folder): 
    os.mkdir(checkpoint_folder)

train_dataset = AntBeetleVideoDataset(datafolder=videos_folder, 
                      frame_number_map=frame_number_map,
                      keypoints=keypoints,
                      frame_skip=FRAME_SKIP, 
                      num_prev_frames=LEFT_WINDOW, 
                      num_next_frames=RIGHT_WINDOW,
                      frame_size=(224, 224),
                      transform=TransformsSimCLR(size=(IMG_SIZE, IMG_SIZE), 
                                                 pretrained=IS_PRETRAINED, 
                                                 n_channel=n_channel))


train_loader = torch.utils.data.DataLoader(
    train_dataset,
    batch_size=batch_size,
    shuffle=True,
    drop_last=True,
    pin_memory=True,
    num_workers=2,
)

device = 'cuda' if torch.cuda.is_available() else 'cpu'

##################### MODEL ############################
def get_simclr_model():
    resnet_encoder = torchvision.models.resnet50(pretrained=IS_PRETRAINED)

    ## Experimental setup for multiplying the grayscale channel
    ## https://stackoverflow.com/a/54777347
    weight = resnet_encoder.conv1.weight.clone()
    resnet_encoder.conv1 = torch.nn.Conv2d(n_channel, 64, kernel_size=7, stride=2, padding=3, bias=False)
    # normalize back by n_channels after tiling
    resnet_encoder.conv1.weight.data = weight.sum(dim=1, keepdim=True).tile(1, n_channel, 1, 1)/n_channel


    n_features = resnet_encoder.fc.in_features
    model = SimCLR(resnet_encoder, embedding_size, n_features)
    model = model.to(device)
    
    return model

model = get_simclr_model()

##################### UTILS ############################

optimizer_type = 'Adam'
weight_decay = 1e-6

optimizer, scheduler = load_optimizer(optimizer_type, epochs, weight_decay, batch_size, model)

world_size = 1
temperature = 0.5
criterion = NT_Xent(batch_size, temperature, world_size)

# Basic Training loop
def train(epoch, train_loader, model, criterion, optimizer):
    loss_epoch = 0
    
    # tqdm_iter = tqdm(train_loader, total=len(train_loader)) # Total train loader is huge, he're we limiting to steps per epoch
    tqdm_iter = tqdm(train_loader, total=steps_per_epoch)

    tqdm_iter.set_description(f"Epoch {epoch}")
    for step, batch in enumerate(tqdm_iter):
        optimizer.zero_grad()
        x_i = batch['image'][0].cuda(non_blocking=True)
        x_j = batch['image'][1].cuda(non_blocking=True)

        # positive pair, with encoding
        h_i, h_j, z_i, z_j = model(x_i, x_j)


        loss = criterion(z_i, z_j)
        loss.backward()

        optimizer.step()

        tqdm_iter.set_postfix(iter_loss=loss.item())

        loss_epoch += loss.item()
        if step >= steps_per_epoch:
            break

    return loss_epoch

# Baseline submission on the leaderboard is trained with 100 epochs, you can train according to your needs
for epoch in range(epochs):
    lr = optimizer.param_groups[0]['lr']
    loss_epoch = train(epoch, train_loader, model, criterion, optimizer)

    if scheduler:
        scheduler.step()

    if (epoch % 3) == 0:
        save_model(epoch, checkpoint_folder, model, optimizer)

checkpoint_file = save_model(epochs, checkpoint_folder, model, optimizer)

checkpoint_file = save_model(epochs, checkpoint_folder, model, optimizer)
print(checkpoint_file)

# Cleanup RAM
del model, optimizer
del train_loader, train_dataset

!du -h $checkpoint_folder

# Load latest model
model = get_simclr_model()
checkpoint_path = os.path.join(checkpoint_folder, 'checkpoint_10.tar') 
model.load_state_dict(torch.load(checkpoint_path))
model = model.to(device)
model.eval();

prediction_dataset = AntBeetleVideoDataset(datafolder=videos_folder, 
                                          frame_number_map=frame_number_map,
                                          keypoints=keypoints,
                                          frame_skip=FRAME_SKIP, 
                                          num_prev_frames=LEFT_WINDOW, 
                                          num_next_frames=RIGHT_WINDOW,
                                          frame_size=(224, 224),
                                          transform=TransformsSimCLR(size=(IMG_SIZE, IMG_SIZE), 
                                                                     pretrained=IS_PRETRAINED, 
                                                                     n_channel=n_channel,
                                                                     validation=True))

prediction_batch_size=128

prediction_loader = torch.utils.data.DataLoader(
    prediction_dataset,
    batch_size=prediction_batch_size,
    shuffle=False,
    drop_last=False,
    pin_memory=True,
    num_workers=2,
)

sample_submission = np.load(datafolder + 'sample_submission.npy')
submission = np.empty((sample_submission.shape[0], embedding_size), dtype=np.float32)
idx = 0
# This may take quite long, since predicting on all frames
for data in tqdm(prediction_loader, total=len(prediction_loader)):
    with torch.no_grad():
        images = data['image'].to(device)
        output = model.projector(model.encoder(images))
        output = output.cpu().numpy()
        submission[idx:idx+len(output)] = output
        idx += len(output)

print("Embedding shape:", submission.shape)

def validate_submission(submission, frame_number_map):
    if not isinstance(submission, np.ndarray):
        print("Embeddings should be a numpy array")
        return False
    elif not len(submission.shape) == 2:
        print("Embeddings should be 2D array")
        return False
    elif not submission.shape[1] <= 128:
        print("Embeddings too large, max allowed is 128")
        return False
    elif not isinstance(submission[0, 0], np.float32):
        print(f"Embeddings are not float32")
        return False

    
    total_clip_length = frame_number_map[list(frame_number_map.keys())[-1]][1]
            
    if not len(submission) == total_clip_length:
        print(f"Emebddings length doesn't match submission clips total length")
        return False

    if not np.isfinite(submission).all():
        print(f"Emebddings contains NaN or infinity")
        return False

    print("All checks passed")
    return True

validate_submission(submission, frame_number_map)

np.save('submission_ant_simclr.npy', submission)

## Uploads may take time, you can also rund aicrowd-cli on your local machines with he prepared submission file
%aicrowd submission create --description "Ant-Beetle SimCLR Baseline" -c {aicrowd_challenge_name} -f submission_ant_simclr.npy