How can I stop pytorch model from downloading vgg .pth pretrained file every time I do inference?

hedeya1980: <p>I’m trying to use the u-net segmentation model at <a href="https://github.com/khanhha/crack_segmentation" class="inline-onebox" rel="noopener nofollow ugc">GitHub - khanhha/crack_segmentation: This repository contains code and dataset for the task crack segmentation using two architectures UNet_VGG16, UNet_Resnet and DenseNet-Tiramusu</a>, and incorporate it into my pipeline. However, I noticed that whenever I use ‘inference_unet.py’, for the first time in the session, it downloads a .pth file for vgg.</p>
<pre><code class="lang-auto">Downloading: "https://download.pytorch.org/models/vgg16-397923af.pth" to C:\Users\hedey/.cache\torch\hub\checkpoints\vgg16-397923af.pth
</code></pre>
<p>It’s not practical to download that file every time I make an inference, especially that this will be a part of an application. How can I avoid having to download that file every time?</p>
<p>Here is the code at ‘inference_unet.py’:</p>
<pre><code class="lang-auto">import sys
import os
import numpy as np
from pathlib import Path
import cv2 as cv
import torch
import torch.nn.functional as F
from torch.autograd import Variable
import torchvision.transforms as transforms
from unet.unet_transfer import UNet16, input_size
import matplotlib.pyplot as plt
import argparse
from os.path import join
from PIL import Image
import gc
from utils import load_unet_vgg16, load_unet_resnet_101, load_unet_resnet_34
from tqdm import tqdm

def evaluate_img(model, img):
    input_width, input_height = input_size[0], input_size[1]

    img_1 = cv.resize(img, (input_width, input_height), cv.INTER_AREA)
    X = train_tfms(Image.fromarray(img_1))
    X = Variable(X.unsqueeze(0)).cuda()  # [N, 1, H, W]

    mask = model(X)

    mask = F.sigmoid(mask[0, 0]).data.cpu().numpy()
    mask = cv.resize(mask, (img_width, img_height), cv.INTER_AREA)
    return mask

def evaluate_img_patch(model, img):
    input_width, input_height = input_size[0], input_size[1]

    img_height, img_width, img_channels = img.shape

    if img_width &lt; input_width or img_height &lt; input_height:
        return evaluate_img(model, img)

    stride_ratio = 0.1
    stride = int(input_width * stride_ratio)

    normalization_map = np.zeros((img_height, img_width), dtype=np.int16)

    patches = []
    patch_locs = []
    for y in range(0, img_height - input_height + 1, stride):
        for x in range(0, img_width - input_width + 1, stride):
            segment = img[y:y + input_height, x:x + input_width]
            normalization_map[y:y + input_height, x:x + input_width] += 1
            patches.append(segment)
            patch_locs.append((x, y))

    patches = np.array(patches)
    if len(patch_locs) &lt;= 0:
        return None

    preds = []
    for i, patch in enumerate(patches):
        patch_n = train_tfms(Image.fromarray(patch))
        X = Variable(patch_n.unsqueeze(0)).cuda()  # [N, 1, H, W]
        masks_pred = model(X)
        mask = F.sigmoid(masks_pred[0, 0]).data.cpu().numpy()
        preds.append(mask)

    probability_map = np.zeros((img_height, img_width), dtype=float)
    for i, response in enumerate(preds):
        coords = patch_locs[i]
        probability_map[coords[1]:coords[1] + input_height, coords[0]:coords[0] + input_width] += response

    return probability_map

def disable_axis():
    plt.axis('off')
    plt.gca().axes.get_xaxis().set_visible(False)
    plt.gca().axes.get_yaxis().set_visible(False)
    plt.gca().axes.get_xaxis().set_ticklabels([])
    plt.gca().axes.get_yaxis().set_ticklabels([])

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('-img_dir',type=str, help='input dataset directory')
    parser.add_argument('-model_path', type=str, help='trained model path')
    parser.add_argument('-model_type', type=str, choices=['vgg16', 'resnet101', 'resnet34'])
    parser.add_argument('-out_viz_dir', type=str, default='', required=False, help='visualization output dir')
    parser.add_argument('-out_pred_dir', type=str, default='', required=False,  help='prediction output dir')
    parser.add_argument('-threshold', type=float, default=0.2 , help='threshold to cut off crack response')
    args = parser.parse_args()

    if args.out_viz_dir != '':
        os.makedirs(args.out_viz_dir, exist_ok=True)
        for path in Path(args.out_viz_dir).glob('*.*'):
            os.remove(str(path))

    if args.out_pred_dir != '':
        os.makedirs(args.out_pred_dir, exist_ok=True)
        for path in Path(args.out_pred_dir).glob('*.*'):
            os.remove(str(path))

    if args.model_type == 'vgg16':
        model = load_unet_vgg16(args.model_path)
    elif args.model_type  == 'resnet101':
        model = load_unet_resnet_101(args.model_path)
    elif args.model_type  == 'resnet34':
        model = load_unet_resnet_34(args.model_path)
        print(model)
    else:
        print('undefind model name pattern')
        exit()

    channel_means = [0.485, 0.456, 0.406]
    channel_stds  = [0.229, 0.224, 0.225]

    paths = [path for path in Path(args.img_dir).glob('*.*')]
    for path in tqdm(paths):
        #print(str(path))

        #train_tfms = transforms.Compose([transforms.ToTensor(), transforms.Normalize(channel_means, channel_stds)])
        train_tfms = transforms.Compose([transforms.ToTensor()])

        img_0 = Image.open(str(path))
        img_0 = np.asarray(img_0)
        
        if len(img_0.shape) != 3:
            print(f'incorrect image shape: {path.name}{img_0.shape}')
            continue

        img_0 = img_0[:,:,:3]
        img_height, img_width, img_channels = img_0.shape
        
        #img_height, img_width = img_0.shape

        prob_map_full = evaluate_img(model, img_0)

        if args.out_pred_dir != '':
            #cv.imwrite(filename=join(args.out_pred_dir, f'{path.stem}.jpg'), img=(prob_map_full * 255).astype(np.uint8))
            cv.imwrite(filename=join(args.out_pred_dir, f'{path.stem}.jpg'), img=(prob_map_full).astype(np.uint8))

        if args.out_viz_dir != '':
            # plt.subplot(121)
            # plt.imshow(img_0), plt.title(f'{img_0.shape}')
            if img_0.shape[0] &gt; 2000 or img_0.shape[1] &gt; 2000:
                img_1 = cv.resize(img_0, None, fx=0.2, fy=0.2, interpolation=cv.INTER_AREA)
            else:
                img_1 = img_0

            # plt.subplot(122)
            # plt.imshow(img_0), plt.title(f'{img_0.shape}')
            # plt.show()

            prob_map_patch = evaluate_img_patch(model, img_1)

            #plt.title(f'name={path.stem}. \n cut-off threshold = {args.threshold}', fontsize=4)
            prob_map_viz_patch = prob_map_patch.copy()
            prob_map_viz_patch = prob_map_viz_patch/ prob_map_viz_patch.max()
            prob_map_viz_patch[prob_map_viz_patch &lt; args.threshold] = 0.0
            fig = plt.figure()
            st = fig.suptitle(f'name={path.stem} \n cut-off threshold = {args.threshold}', fontsize="x-large")
            ax = fig.add_subplot(231)
            ax.imshow(img_1)
            ax = fig.add_subplot(232)
            ax.imshow(prob_map_viz_patch)
            ax = fig.add_subplot(233)
            ax.imshow(img_1)
            ax.imshow(prob_map_viz_patch, alpha=0.4)

            prob_map_viz_full = prob_map_full.copy()
            prob_map_viz_full[prob_map_viz_full &lt; args.threshold] = 0.0

            ax = fig.add_subplot(234)
            ax.imshow(img_0)
            ax = fig.add_subplot(235)
            ax.imshow(prob_map_viz_full)
            ax = fig.add_subplot(236)
            ax.imshow(img_0)
            ax.imshow(prob_map_viz_full, alpha=0.4)

            plt.savefig(join(args.out_viz_dir, f'{path.stem}.jpg'), dpi=500)
            plt.close('all')

        gc.collect()
</code></pre>
<p>Here is the code at ‘utils.py’:</p>
<pre><code class="lang-auto">import json
from datetime import datetime
from pathlib import Path

import random
import numpy as np

import torch
import tqdm
from unet.unet_transfer import UNet16, UNetResNet


class AverageMeter(object):
    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count

def cuda(x):
    #return x.cuda(async=True) if torch.cuda.is_available() else x
    return x.cuda(non_blocking=True) if torch.cuda.is_available() else x

def write_event(log, step, **data):
    data['step'] = step
    data['dt'] = datetime.now().isoformat()
    log.write(json.dumps(data, sort_keys=True))
    log.write('\n')
    log.flush()

def check_crop_size(image_height, image_width):
    """Checks if image size divisible by 32.
    Args:
        image_height:
        image_width:
    Returns:
        True if both height and width divisible by 32 and False otherwise.
    """
    return image_height % 32 == 0 and image_width % 32 == 0

def create_model(device, type ='vgg16'):
    assert type == 'vgg16' or type == 'resnet101'
    if type == 'vgg16':
        model = UNet16(pretrained=True)
    elif type == 'resnet101':
        model = UNetResNet(pretrained=True, encoder_depth=101, num_classes=1)
    else:
        assert False
    model.eval()
    return model.to(device)

def load_unet_vgg16(model_path):
    model = UNet16(pretrained=True)
    #model = UNet16(pretrained=False)
    checkpoint = torch.load(model_path)
    if 'model' in checkpoint:
        model.load_state_dict(checkpoint['model'])
    elif 'state_dict' in checkpoint:
        model.load_state_dict(checkpoint['check_point'])
    else:
        raise Exception('undefind model format')

    model.cuda()
    model.eval()

    return model

def load_unet_resnet_101(model_path):
    #model = UNetResNet(pretrained=True, encoder_depth=101, num_classes=1)
    model = UNetResNet(pretrained=True, encoder_depth=101, num_classes=8)
    checkpoint = torch.load(model_path)
    if 'model' in checkpoint:
        model.load_state_dict(checkpoint['model'])
    elif 'state_dict' in checkpoint:
        model.load_state_dict(checkpoint['check_point'])
    else:
        raise Exception('undefind model format')

    model.cuda()
    model.eval()

    return model

def load_unet_resnet_34(model_path):
    model = UNetResNet(pretrained=True, encoder_depth=34, num_classes=1)
    checkpoint = torch.load(model_path)
    if 'model' in checkpoint:
        model.load_state_dict(checkpoint['model'])
    elif 'state_dict' in checkpoint:
        model.load_state_dict(checkpoint['check_point'])
    else:
        raise Exception('undefind model format')

    model.cuda()
    model.eval()

    return model

def train(args, model, criterion, train_loader, valid_loader, validation, init_optimizer, n_epochs=None, fold=None,
          num_classes=None):
    lr = args.lr
    n_epochs = n_epochs or args.n_epochs
    optimizer = init_optimizer(lr)

    root = Path(args.model_path)
    model_path = root / 'model_{fold}.pt'.format(fold=fold)
    if model_path.exists():
        state = torch.load(str(model_path))
        epoch = state['epoch']
        step = state['step']
        model.load_state_dict(state['model'])
        print('Restored model, epoch {}, step {:,}'.format(epoch, step))
    else:
        epoch = 1
        step = 0

    save = lambda ep: torch.save({
        'model': model.state_dict(),
        'epoch': ep,
        'step': step,
    }, str(model_path))

    report_each = 10
    log = root.joinpath('train_{fold}.log'.format(fold=fold)).open('at', encoding='utf8')
    valid_losses = []
    for epoch in range(epoch, n_epochs + 1):
        model.train()
        random.seed()
        tq = tqdm.tqdm(total=(len(train_loader) * args.batch_size))
        tq.set_description('Epoch {}, lr {}'.format(epoch, lr))
        losses = []
        tl = train_loader
        try:
            mean_loss = 0
            for i, (inputs, targets) in enumerate(tl):
                inputs = cuda(inputs)

                with torch.no_grad():
                    targets = cuda(targets)

                outputs = model(inputs)
                #print(outputs.shape, targets.shape)
                loss = criterion(outputs, targets)
                optimizer.zero_grad()
                batch_size = inputs.size(0)
                loss.backward()
                optimizer.step()
                step += 1
                tq.update(batch_size)
                losses.append(loss.item())
                mean_loss = np.mean(losses[-report_each:])
                tq.set_postfix(loss='{:.5f}'.format(mean_loss))
                if i and i % report_each == 0:
                    write_event(log, step, loss=mean_loss)
            write_event(log, step, loss=mean_loss)
            tq.close()
            save(epoch + 1)
            valid_metrics = validation(model, criterion, valid_loader, num_classes)
            write_event(log, step, **valid_metrics)
            valid_loss = valid_metrics['valid_loss']
            valid_losses.append(valid_loss)
        except KeyboardInterrupt:
            tq.close()
            print('Ctrl+C, saving snapshot')
            save(epoch)
            print('done.')
            return
</code></pre>
<p>Here is the code at ‘unet_transfer.py’:</p>
<pre><code class="lang-auto">from torch import nn
from torch.nn import functional as F
import torch
from torchvision import models
import torchvision

input_size = (448, 448)

class Interpolate(nn.Module):
    def __init__(self, size=None, scale_factor=None, mode='nearest', align_corners=False):
        super(Interpolate, self).__init__()
        self.interp = nn.functional.interpolate
        self.size = size
        self.mode = mode
        self.scale_factor = scale_factor
        self.align_corners = align_corners

    def forward(self, x):
        x = self.interp(x, size=self.size, scale_factor=self.scale_factor,
                        mode=self.mode, align_corners=self.align_corners)
        return x

def conv3x3(in_, out):
    return nn.Conv2d(in_, out, 3, padding=1)


class ConvRelu(nn.Module):
    def __init__(self, in_, out):
        super().__init__()
        self.conv = conv3x3(in_, out)
        self.activation = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.conv(x)
        x = self.activation(x)
        return x


class DecoderBlockV2(nn.Module):
    def __init__(self, in_channels, middle_channels, out_channels, is_deconv=True):
        super(DecoderBlockV2, self).__init__()
        self.in_channels = in_channels

        if is_deconv:
            """
                Paramaters for Deconvolution were chosen to avoid artifacts, following
                link https://distill.pub/2016/deconv-checkerboard/
            """

            #self.block = nn.ModuleList(
            self.block = nn.Sequential(
                ConvRelu(in_channels, middle_channels),
                nn.ConvTranspose2d(middle_channels, out_channels, kernel_size=4, stride=2,
                                   padding=1),
                nn.ReLU(inplace=True)
            )
        else:
            self.block = nn.Sequential(
                Interpolate(scale_factor=2, mode='bilinear'),
                ConvRelu(in_channels, middle_channels),
                ConvRelu(middle_channels, out_channels),
            )

    def forward(self, x):
        return self.block(x)

class UNet16(nn.Module):
    def __init__(self, num_classes=1, num_filters=32, pretrained=False, is_deconv=False):
    #def __init__(self, num_classes=8, num_filters=32, pretrained=False, is_deconv=False):
        """
        :param num_classes:
        :param num_filters:
        :param pretrained:
            False - no pre-trained network used
            True - encoder pre-trained with VGG16
        :is_deconv:
            False: bilinear interpolation is used in decoder
            True: deconvolution is used in decoder
        """
        super().__init__()
        self.num_classes = num_classes

        self.pool = nn.MaxPool2d(2, 2)

        #print(torchvision.models.vgg16(pretrained=pretrained))

        self.encoder = torchvision.models.vgg16(pretrained=pretrained).features
        #self.encoder = torchvision.models.vgg16(pretrained=False).features

        self.relu = nn.ReLU(inplace=True)

        self.conv1 = nn.Sequential(self.encoder[0],
                                   self.relu,
                                   self.encoder[2],
                                   self.relu)

        self.conv2 = nn.Sequential(self.encoder[5],
                                   self.relu,
                                   self.encoder[7],
                                   self.relu)

        self.conv3 = nn.Sequential(self.encoder[10],
                                   self.relu,
                                   self.encoder[12],
                                   self.relu,
                                   self.encoder[14],
                                   self.relu)

        self.conv4 = nn.Sequential(self.encoder[17],
                                   self.relu,
                                   self.encoder[19],
                                   self.relu,
                                   self.encoder[21],
                                   self.relu)

        self.conv5 = nn.Sequential(self.encoder[24],
                                   self.relu,
                                   self.encoder[26],
                                   self.relu,
                                   self.encoder[28],
                                   self.relu)

        self.center = DecoderBlockV2(512, num_filters * 8 * 2, num_filters * 8, is_deconv)

        self.dec5 = DecoderBlockV2(512 + num_filters * 8, num_filters * 8 * 2, num_filters * 8, is_deconv)
        self.dec4 = DecoderBlockV2(512 + num_filters * 8, num_filters * 8 * 2, num_filters * 8, is_deconv)
        self.dec3 = DecoderBlockV2(256 + num_filters * 8, num_filters * 4 * 2, num_filters * 2, is_deconv)
        self.dec2 = DecoderBlockV2(128 + num_filters * 2, num_filters * 2 * 2, num_filters, is_deconv)
        self.dec1 = ConvRelu(64 + num_filters, num_filters)
        self.final = nn.Conv2d(num_filters, num_classes, kernel_size=1)

    def forward(self, x):
        conv1 = self.conv1(x)
        conv2 = self.conv2(self.pool(conv1))
        conv3 = self.conv3(self.pool(conv2))
        conv4 = self.conv4(self.pool(conv3))
        conv5 = self.conv5(self.pool(conv4))

        center = self.center(self.pool(conv5))

        dec5 = self.dec5(torch.cat([center, conv5], 1))

        dec4 = self.dec4(torch.cat([dec5, conv4], 1))
        dec3 = self.dec3(torch.cat([dec4, conv3], 1))
        dec2 = self.dec2(torch.cat([dec3, conv2], 1))
        dec1 = self.dec1(torch.cat([dec2, conv1], 1))

        if self.num_classes &gt; 1:
            x_out = F.log_softmax(self.final(dec1), dim=1)
        else:
            x_out = self.final(dec1)
            #x_out = F.sigmoid(x_out)

        return x_out

class UNetResNet(nn.Module):

    def __init__(self, encoder_depth, num_classes, num_filters=32, dropout_2d=0.2,
                 pretrained=False, is_deconv=False):
        super().__init__()
        self.num_classes = num_classes
        self.dropout_2d = dropout_2d

        if encoder_depth == 34:
            self.encoder = torchvision.models.resnet34(pretrained=pretrained)
            bottom_channel_nr = 512
        elif encoder_depth == 101:
            self.encoder = torchvision.models.resnet101(pretrained=pretrained)
            bottom_channel_nr = 2048
        elif encoder_depth == 152:
            self.encoder = torchvision.models.resnet152(pretrained=pretrained)
            bottom_channel_nr = 2048
        else:
            raise NotImplementedError('only 34, 101, 152 version of Resnet are implemented')

        self.pool = nn.MaxPool2d(2, 2)

        self.relu = nn.ReLU(inplace=True)

        #self.conv1 = nn.Sequential(self.encoder.conv1,
        #                           self.encoder.bn1,
        #                           self.encoder.relu,
        #                           self.pool)

        self.conv1 = nn.Sequential(nn.Conv2d(1,64,kernel_size=(7,7),stride=(2,2),padding=(3,3),bias=False), # 1 Here is for grayscale images, replace by 3 if you need RGB/BGR
                                   nn.BatchNorm2d(64),
                                   nn.ReLU(),
                                   self.pool
                                )
        
        self.conv2 = self.encoder.layer1

        self.conv3 = self.encoder.layer2

        self.conv4 = self.encoder.layer3

        self.conv5 = self.encoder.layer4

        self.center = DecoderBlockV2(bottom_channel_nr, num_filters * 8 * 2, num_filters * 8, is_deconv)
        self.dec5 = DecoderBlockV2(bottom_channel_nr + num_filters * 8, num_filters * 8 * 2, num_filters * 8, is_deconv)
        self.dec4 = DecoderBlockV2(bottom_channel_nr // 2 + num_filters * 8, num_filters * 8 * 2, num_filters * 8,
                                   is_deconv)
        self.dec3 = DecoderBlockV2(bottom_channel_nr // 4 + num_filters * 8, num_filters * 4 * 2, num_filters * 2,
                                   is_deconv)
        self.dec2 = DecoderBlockV2(bottom_channel_nr // 8 + num_filters * 2, num_filters * 2 * 2, num_filters * 2 * 2,
                                   is_deconv)
        self.dec1 = DecoderBlockV2(num_filters * 2 * 2, num_filters * 2 * 2, num_filters, is_deconv)
        self.dec0 = ConvRelu(num_filters, num_filters)
        self.final = nn.Conv2d(num_filters, num_classes, kernel_size=1)
        #self.final = nn.Conv2d(num_filters, 1, kernel_size=1)

    def forward(self, x):
        conv1 = self.conv1(x)
        conv2 = self.conv2(conv1)
        conv3 = self.conv3(conv2)
        conv4 = self.conv4(conv3)
        conv5 = self.conv5(conv4)

        pool = self.pool(conv5)
        center = self.center(pool)

        dec5 = self.dec5(torch.cat([center, conv5], 1))

        dec4 = self.dec4(torch.cat([dec5, conv4], 1))
        dec3 = self.dec3(torch.cat([dec4, conv3], 1))
        dec2 = self.dec2(torch.cat([dec3, conv2], 1))
        dec1 = self.dec1(dec2)
        dec0 = self.dec0(dec1)
        
        return self.final(F.dropout2d(dec0, p=self.dropout_2d))

'''
class UNetResNet(nn.Module):
    """PyTorch U-Net model using ResNet(34, 101 or 152) encoder.
    UNet: https://arxiv.org/abs/1505.04597
    ResNet: https://arxiv.org/abs/1512.03385
    Proposed by Alexander Buslaev: https://www.linkedin.com/in/al-buslaev/
    Args:
            encoder_depth (int): Depth of a ResNet encoder (34, 101 or 152).
            num_classes (int): Number of output classes.
            num_filters (int, optional): Number of filters in the last layer of decoder. Defaults to 32.
            dropout_2d (float, optional): Probability factor of dropout layer before output layer. Defaults to 0.2.
            pretrained (bool, optional):
                False - no pre-trained weights are being used.
                True  - ResNet encoder is pre-trained on ImageNet.
                Defaults to False.
            is_deconv (bool, optional):
                False: bilinear interpolation is used in decoder.
                True: deconvolution is used in decoder.
                Defaults to False.
    """

    def __init__(self, encoder_depth, num_classes, num_filters=32, dropout_2d=0.2,
                 pretrained=False, is_deconv=False):
        super().__init__()
        self.num_classes = num_classes
        self.dropout_2d = dropout_2d

        if encoder_depth == 34:
            self.encoder = torchvision.models.resnet34(pretrained=pretrained)
            bottom_channel_nr = 512
        elif encoder_depth == 101:
            self.encoder = torchvision.models.resnet101(pretrained=pretrained)
            bottom_channel_nr = 2048
        elif encoder_depth == 152:
            self.encoder = torchvision.models.resnet152(pretrained=pretrained)
            bottom_channel_nr = 2048
        else:
            raise NotImplementedError('only 34, 101, 152 version of Resnet are implemented')

        self.pool = nn.MaxPool2d(2, 2)

        self.relu = nn.ReLU(inplace=True)

        self.conv1 = nn.Sequential(self.encoder.conv1,
                                   self.encoder.bn1,
                                   self.encoder.relu,
                                   self.pool)

        self.conv2 = self.encoder.layer1

        self.conv3 = self.encoder.layer2

        self.conv4 = self.encoder.layer3

        self.conv5 = self.encoder.layer4

        self.center = DecoderBlockV2(bottom_channel_nr, num_filters * 8 * 2, num_filters * 8, is_deconv)
        self.dec5 = DecoderBlockV2(bottom_channel_nr + num_filters * 8, num_filters * 8 * 2, num_filters * 8, is_deconv)
        self.dec4 = DecoderBlockV2(bottom_channel_nr // 2 + num_filters * 8, num_filters * 8 * 2, num_filters * 8,
                                   is_deconv)
        self.dec3 = DecoderBlockV2(bottom_channel_nr // 4 + num_filters * 8, num_filters * 4 * 2, num_filters * 2,
                                   is_deconv)
        self.dec2 = DecoderBlockV2(bottom_channel_nr // 8 + num_filters * 2, num_filters * 2 * 2, num_filters * 2 * 2,
                                   is_deconv)
        self.dec1 = DecoderBlockV2(num_filters * 2 * 2, num_filters * 2 * 2, num_filters, is_deconv)
        self.dec0 = ConvRelu(num_filters, num_filters)
        self.final = nn.Conv2d(num_filters, num_classes, kernel_size=1)

    def forward(self, x):
        self.conv1 = torch.nn.Conv2d(1, 64, (7, 7), (2, 2), (3, 3), bias=False)
        conv1 = self.conv1(x)
        conv2 = self.conv2(conv1)
        conv3 = self.conv3(conv2)
        conv4 = self.conv4(conv3)
        conv5 = self.conv5(conv4)

        pool = self.pool(conv5)
        center = self.center(pool)

        dec5 = self.dec5(torch.cat([center, conv5], 1))

        dec4 = self.dec4(torch.cat([dec5, conv4], 1))
        dec3 = self.dec3(torch.cat([dec4, conv3], 1))
        dec2 = self.dec2(torch.cat([dec3, conv2], 1))
        dec1 = self.dec1(dec2)
        dec0 = self.dec0(dec1)

        return self.final(F.dropout2d(dec0, p=self.dropout_2d))
'''
</code></pre>

ptrblck: <p>The file is downloaded into your <code>.cache</code> folder as shown in the download log. Are you deleting this folder afterwards? If so, then a new download would be expected.</p>

hedeya1980: <p>I want to use this model in a real-time application. I don’t want this file to be downloaded during the application. How can it be incorporated from the beginning, so that it doesn’t need to be downloaded while the application is running.</p>

ptrblck: <p>You would need to download it once at least. If you don’t want to download it in the inference script, you could write a “warmup” script which would download the model into the <code>cache</code> folder.</p>

hedeya1980: <p>I’m just wondering, why do I need that. The model is already trained and I have a .pt file. So, why do I need the pre-trained weights to be downloaded. I understand that the pre-trained weights are just needed for initialization. Why can’t I just rely on the latest .pt model? Shouldn’t it include all the weights?<br>
I found another topic here: <a href="https://discuss.pytorch.org/t/resent-50-model-downloading-every-time-i-load-my-trained-model/115300" class="inline-onebox">Resent-50 model downloading every time I load my trained model</a>, with a suggestion about setting pretrained_backbone to False. However, I can’t find pretrained_backbone in the above codes.</p>

ptrblck: <p>You are explicitly setting <code>pretrained=True</code> <a href="https://github.com/khanhha/crack_segmentation/blob/e924b6a3632134848b993c68e7295b1aae92ce28/unet/unet_transfer.py#L85">here</a> so that you are forcing <code>torchvision</code> to download the pretrained <code>state_dict</code>. If you don’t need it and are loading a custom pretrained <code>state_dict</code> pass <code>pretrained=False</code> and <code>torchvision</code> will not download anything.</p>

TTAyanlade: <pre><code>    model = models.vgg16()

    model_path = "vgg16-397923af.pth"
    
    model.load_state_dict(torch.load(model_path))
</code></pre>
<p>you can download it and use it as shown above</p>