Remove python checkpoints

core/models/.ipynb_checkpoints/model_factory-checkpoint.py

-import os
-import torch
-import torch.nn as nn
-from torch.optim import Adam
-from core.models import predict
-
-
-class Model(object):
-    def __init__(self, configs):
-        self.configs = configs
-        self.num_hidden = [int(x) for x in configs.num_hidden.split(",")]
-        self.num_layers = len(self.num_hidden)
-        networks_map = {
-            "convlstm": predict.ConvLSTM,
-            "predrnn": predict.PredRNN,
-            "predrnn_plus": predict.PredRNN_Plus,
-            "interact_convlstm": predict.InteractionConvLSTM,
-            "interact_predrnn": predict.InteractionPredRNN,
-            "interact_predrnn_plus": predict.InteractionPredRNN_Plus,
-            "cst_predrnn": predict.CST_PredRNN,
-            "sst_predrnn": predict.SST_PredRNN,
-            "dst_predrnn": predict.DST_PredRNN,
-            "interact_dst_predrnn": predict.InteractionDST_PredRNN,
-        }
-
-        if not configs.model_name in networks_map:
-            raise ValueError("Name of network unknown %s" % configs.model_name)
-        Network = networks_map[configs.model_name]
-        self.network = Network(self.num_layers, self.num_hidden, configs).to(
-            configs.device
-        )
-        # self.network = Network(self.num_layers, self.num_hidden, configs).cuda()
-
-        if self.configs.is_parallel:
-            self.network = nn.DataParallel(self.network)
-        self.optimizer = Adam(self.network.parameters(), lr=configs.lr)
-        # TODO: size_average to sum
-        self.MSE_criterion = nn.MSELoss(size_average=False)
-        self.MAE_criterion = nn.L1Loss(size_average=False)
-
-    def save(self, ite=None):
-        stats = {}
-        stats["net_param"] = self.network.state_dict()
-        torch.save(stats, self.configs.save_dir)
-        print(f"Saving model to {self.configs.save_dir}")
-
-    def load(self):
-        if os.path.exists(self.configs.save_dir):
-            stats = torch.load(self.configs.save_dir)
-            self.network.load_state_dict(stats["net_param"])
-            print("Model loaded")
-        else:
-            print("Training from scratch")
-
-    def train(self, frames, mask):
-
-        frames_tensor = torch.FloatTensor(frames).to(self.configs.device)
-        mask_tensor = torch.FloatTensor(mask).to(self.configs.device)
-
-        # frames_tensor = torch.FloatTensor(frames).cuda()
-        # mask_tensor = torch.FloatTensor(mask).cuda()
-        self.optimizer.zero_grad()
-        next_frames = self.network(frames_tensor, mask_tensor)
-        loss = self.MSE_criterion(
-            next_frames, frames_tensor[:, 1:]
-        ) + self.MAE_criterion(next_frames, frames_tensor[:, 1:])
-        # 0.02*self.SSIM_criterion(next_frames, frames_tensor[:, 1:])
-        loss.backward()
-        self.optimizer.step()
-        return loss.detach().cpu().numpy()
-
-    def test(self, frames, mask):
-        # frames_tensor = torch.FloatTensor(frames).cuda()
-        # mask_tensor = torch.FloatTensor(mask).cuda()
-        frames_tensor = torch.FloatTensor(frames).to(self.configs.device)
-        mask_tensor = torch.FloatTensor(mask).to(self.configs.device)
-        next_frames = self.network(frames_tensor, mask_tensor)
-        loss = self.MSE_criterion(
-            next_frames, frames_tensor[:, 1:]
-        ) + self.MAE_criterion(next_frames, frames_tensor[:, 1:])
-        # + 0.02 * self.SSIM_criterion(next_frames, frames_tensor[:, 1:])
-
-        return next_frames.detach().cpu().numpy(), loss.detach().cpu().numpy()

core/utils/.ipynb_checkpoints/util-checkpoint.py

-
-import numpy as np
-import shutil
-import copy
-import os
-
-
-def nor(frames):
-    new_frames = frames.astype(np.float32)/255.0
-    return new_frames
-
-def de_nor(frames):
-    new_frames = copy.deepcopy(frames)
-    new_frames *= 255.0
-    new_frames = new_frames.astype(np.uint8)
-    return new_frames
-
-def normalization(frames,up=80):
-    new_frames = frames.astype(np.float32)
-    new_frames /= (up/2)
-    new_frames -= 1
-    return new_frames
-
-def denormalization(frames,up=80):
-    new_frames = copy.deepcopy(frames)
-    new_frames += 1
-    new_frames *= (up/2)
-    new_frames = new_frames.astype(np.uint8)
-    return new_frames
-
-def clean_fold(path):
-    if os.path.exists(path):
-        shutil.rmtree(path)
-        os.makedirs(path)
-    else:
-        os.makedirs(path)
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