Legacy autograd function with non-static forward method is deprecated. Please use new-style autograd function with static forward method. (Example: https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function)

huynth: <p>I have some problems when i try to run inference.py. I tried to fix this error following similar topics but it’s still get this error. This is my code</p>
<pre><code class="lang-auto">from lib import *
from l2_norm import L2Norm
from default_box import DefBox
import torch.nn.functional as F

def create_vgg():
    layers = []
    in_channels = 3

    cfgs = [64, 64, 'M', 128, 128, 'M',
            256, 256, 256, 'MC', 512, 512, 512, 'M',
            512, 512, 512]

    for cfg in cfgs:
        if cfg == 'M':  # floor
            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
        elif cfg == 'MC':  # ceiling
            layers += [nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)]
        else:
            conv2d = nn.Conv2d(in_channels, cfg, kernel_size=3, padding=1)

            layers += [conv2d, nn.ReLU(inplace=True)]
            in_channels = cfg

    pool5 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
    conv6 = nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6)
    conv7 = nn.Conv2d(1024, 1024, kernel_size=1)
    layers += [pool5, conv6, nn.ReLU(inplace=True), conv7, nn.ReLU(inplace=True)]

    return nn.ModuleList(layers)


def create_extras():
    layers = []
    in_channels = 1024
    cfgs = [256, 512, 128, 256, 128, 256, 128, 256]

    layers += [nn.Conv2d(in_channels, cfgs[0], kernel_size=1)]
    layers += [nn.Conv2d(cfgs[0], cfgs[1], kernel_size=3, stride=2, padding=1)]
    layers += [nn.Conv2d(cfgs[1], cfgs[2], kernel_size=1)]
    layers += [nn.Conv2d(cfgs[2], cfgs[3], kernel_size=3, stride=2, padding=1)]
    layers += [nn.Conv2d(cfgs[3], cfgs[4], kernel_size=1)]
    layers += [nn.Conv2d(cfgs[4], cfgs[5], kernel_size=3)]
    layers += [nn.Conv2d(cfgs[5], cfgs[6], kernel_size=1)]
    layers += [nn.Conv2d(cfgs[6], cfgs[7], kernel_size=3)]

    return nn.ModuleList(layers)


def create_loc_conf(num_classes=1,bbox_aspect_num=[4, 6, 6, 6, 4, 4]):
    loc_layers = []
    conf_layers = []

    # source1
    # loc
    loc_layers += [nn.Conv2d(512, bbox_aspect_num[0] * 4, kernel_size = 3, padding=1)]
    # conf
    conf_layers += [nn.Conv2d(512, bbox_aspect_num[0] * num_classes, kernel_size=3, padding=1)]

    # source2
    # loc
    loc_layers += [nn.Conv2d(1024, bbox_aspect_num[1] * 4, kernel_size=3, padding=1)]
    # conf
    conf_layers += [nn.Conv2d(1024, bbox_aspect_num[1] * num_classes, kernel_size=3, padding=1)]

    # source3
    # loc
    loc_layers += [nn.Conv2d(512, bbox_aspect_num[2] * 4, kernel_size=3, padding=1)]
    # conf
    conf_layers += [nn.Conv2d(512, bbox_aspect_num[2] * num_classes, kernel_size=3, padding=1)]

    # source4
    # loc
    loc_layers += [nn.Conv2d(256, bbox_aspect_num[3] * 4, kernel_size=3, padding=1)]
    # conf
    conf_layers += [nn.Conv2d(256, bbox_aspect_num[3] * num_classes, kernel_size=3, padding=1)]

    # source5
    # loc
    loc_layers += [nn.Conv2d(256, bbox_aspect_num[4] * 4, kernel_size=3, padding=1)]
    # conf
    conf_layers += [nn.Conv2d(256, bbox_aspect_num[4] * num_classes, kernel_size=3, padding=1)]

    # source6
    # loc
    loc_layers += [nn.Conv2d(256, bbox_aspect_num[5] * 4, kernel_size=3, padding=1)]
    # conf
    conf_layers += [nn.Conv2d(256, bbox_aspect_num[5] * num_classes, kernel_size=3, padding=1)]

    return nn.ModuleList(loc_layers), nn.ModuleList(conf_layers)

cfg = {
    "num_classes" : 2, #we only have 1 class: fire
    "input_size" : 300, #SSD 300
    "bbox_aspect_num" : [4, 6, 6, 6, 4, 4], # ty le cho source 1 -&gt; 6
    "feature_maps" : [38, 19, 10, 5, 3, 1],
    "steps" : [8, 16, 32, 64, 100, 300], # Size of default box
    "min_size" : [30, 60, 111, 162, 213, 264],
    "max_size" : [60, 111, 162, 213, 264, 315],
    "aspect_ratios" : [[2], [2, 3], [2, 3], [2, 3], [2], [2]]
}

class SSD(nn.Module):
    def __init__(self, phase, cfg):
        super(SSD, self).__init__()
        self.phase = phase
        self.num_classes = cfg['num_classes']

        # Create main modules
        self.vgg = create_vgg()
        self.extras = create_extras()
        self.loc, self.conf = create_loc_conf(cfg['num_classes'], cfg['bbox_aspect_num'])
        self.L2Norm = L2Norm()

        # Create default box
        dbox = DefBox(cfg)
        self.dbox_list = dbox.create_defbox()

        if phase == "inference":
            self.detect = Detect()

    def forward(self, x):
        sources = list()
        loc = list()
        conf = list()

        for k in range(23):
            x = self.vgg[k](x)

        # source 1
        source1 = self.L2Norm(x)
        sources.append(source1)

        for k in range(23, len(self.vgg)):
            x = self.vgg[k](x)

         # source 2
        sources.append(x)

        # source 3-6
        for k, v in enumerate(self.extras):
            x = F.relu(v(x), inplace = True)
            if k % 2 == 1:
                sources.append(x)

        for (x, l, c) in zip(sources, self.loc, self.conf):
            # Data có dạng (batch_size, 4*aspect_ratio_num, featuremap_height, featuremap_width)
            # aspect_ratio_num = 4, 6, ...
            # -&gt; (batch_size, featuremap_height, featuremap_width, 4*aspect_ratio_num)
            loc.append(l(x).permute(0, 2, 3, 1).contiguous())
            conf.append(c(x).permute(0, 2, 3, 1).contiguous())

        loc = torch.cat([o.view(o.size(0), -1) for o in loc], 1)            #(batch_size, 34928)
        conf = torch.cat([o.view(o.size(0), -1) for o in conf], 1)          # (btach_size, 8732)

        loc = loc.view(loc.size(0), -1, 4)      # (batch_size, 8732, 4)
        conf = conf.view(conf.size(0), -1, self.num_classes)        # (batch_size, 8732, 2)

        output = (loc, conf, self.dbox_list)

        if self.phase == "inference":
            return self.detect(output[0], output[1], output[2])
        else:
            return output

def decode(loc, defbox_list):
    '''
    loc: [8732, 4]               (delta_x, delta_y, delta_w, delta_h)
    defbox_list: [8732, 4]      (cx_d, cy_d, w_d, h_d)
    returns: boxes[xmin, ymin, xmax, ymax]
    '''
    boxes = torch.cat((defbox_list[:, :2] + loc[:, :2]*defbox_list[:, 2:]),
    defbox_list[:, 2:] * torch.exp(loc[:, 2:] * 0.2), dim = 1)

    boxes[:, :2] -= boxes[:, 2:] / 2 # calculate x_min, y_min
    boxes[:, 2:] += boxes[:, :2] / 2 # calculate x_max, y_max

    return boxes

def nms(boxes, scores, overlap = 0.45, top_k = 200):
    """
    boxes: [num_box, 4] # có 8732 num_box
    scores: [num_box]
    """
    count = 0
    keep = scores.new(scores.size(0)).zero_().long()

    # boxes:
    x1 = boxes[:, 0]
    y1 = boxes[:, 1]
    x2 = boxes[:, 2]
    y2 = boxes[:, 3]

    # boxes area
    area = torch.mul(x2 - x1, y2 - y1)

    tmp_x1 = boxes.new()
    tmp_y1 = boxes.new()
    tmp_x2 = boxes.new()
    tmp_y2 = boxes.new()
    tmp_w = boxes.new()
    tmp_h = boxes.new()

    value, idx = scores.sort(0)
    idx = idx[-top_k:]      # lấy 200 cái bdbox có độ tự tin cao nhất

    while idx.numel() &gt; 0:
        i = idx[-1]         # id của box có độ tự tin cao nhất
        keep[count] = i
        count += 1

        if id.size(0) == 1:
            break
        idx = idx[:, -1]    # id của các boxes ngoại trừ box có độ tự tin cao nhất

        #information boxes
        torch.index_select(x1, 0, idx, out = tmp_x1)       # lấy ra những thằng có gt trong khoảng idx trong x1
        torch.index_select(y1, 0, idx, out = tmp_y1)
        torch.index_select(x2, 0, idx, out = tmp_x2)
        torch.index_select(y2, 0, idx, out = tmp_y2)

        tmp_x1 = torch.clamp(tmp_x1, min = x1[i])           # = x1[i] nếu tmp_x1 &lt; x[i]
        tmp_y1 = torch.clamp(tmp_y1, min = y1[i])
        tmp_x2 = torch.clamp(tmp_x2, max = x2[i])
        tmp_y2 = torch.clamp(tmp_y2, max = y2[i])           # = y2[i] nếu tmp_y2 &gt; y2[i]

        # chuyển về tensor có size sao cho index giảm đi 1
        tmp_w.resize_as_(tmp_x2)
        tmp_h.resize_as_(tmp_y2)

        tmp_w = tmp_x2 - tmp_x1
        tmp_h = tmp_y2 - tmp_y1

        tmp_w = torch.clamp(tmp_w, min = 0.0)               # đưa phần tử &lt; 0 về 0
        tmp_h = torch.clamp(tmp_h, min = 0.0)

        # dien tich overlap
        inter = tmp_w * tmp_h                               # dien tich phan trung nhau
        others_area = torch.index_select(area, 0, idx)      # dien tich cua cac bbox con lai
        union = area[i] + others_area - inter

        iou = inter / union

        idx = idx[iou.le(overlap)]                          # giu cac box co idx nho hon 0.45

    return keep, count

class Detect(Function):
    def __init__(self, conf_thresh = 0.01, top_k = 200, nms_thresh = 0.45):
        self.softmax = nn.Softmax(dim = -1)
        self.conf_thresh = conf_thresh
        self.top_k = top_k
        self.nms_thresh = nms_thresh

    @staticmethod
    def forward(self, loc_data, conf_data, dbox_list):
        num_batch = loc_data.size(0)
        num_dbox = loc_data.size(1)                     # tra ve 8732 dbox
        num_class = conf_data.size(2)                   # tra ve so class la 1

        conf_data = self.softmax(conf_data)             # tinh xac suat, dang co dinh dang (btach_num, 8732, 1)
        conf_preds = conf_data.transpose(2,1)            # thanh (batch_num, num_class, num_dbox)

        output = torch.zeros(num_batch, num_class, self.top_k, 5)
        #xu li tung anh trong 1 batch
        for i in range(num_batch):
            # tinh bbox tu offset information va default box
            decode_boxes = decode(loc_data[i], dbox_list)

            # copy conf score cua anh thu i
            conf_scores = conf_preds[i].clone()

            for cl in range(1, num_class):
                c_mask = conf_preds[cl].gt(self.conf_thresh)     # chi lay nhung conf &gt; 0.01
                scores = conf_preds[cl][c_mask]                  # CHỖ NÀY CẦN XEM LẠI
                if scores.nelement() == 0: # numel
                    continue

                l_mask = c_mask.unsqueeze(1).expand_as(decode_boxes)     # để đưa chiều về giống chiều của decode_box

                boxes = decode_boxes[l_mask].view(-1, 4)
                ids, count = nms(boxes, scores, self.nms_thresh, self.top_k)

                output[i, cl, :count] = torch.cat((scores[ids[:count]].unsqueeze(1), boxes[ids[:count]]), 1)

        return output


if __name__ == "__main__":
#    vgg = create_vgg()
#    print(vgg)
     extras = create_extras()
     print(extras)
     loc, conf = create_loc_conf()
     print(loc)
     print(conf)

     ssd = SSD(phase="train", cfg=cfg)
     print(ssd)

</code></pre>
<p>and here’re inference.py</p>
<pre><code class="lang-auto">from lib import *
from model import SSD
from transform import DataTransform


classes = ["fire"]

cfg = {
    "num_classes": 2, #VOC data include 20 class + 1 background class
    "input_size": 300, #SSD300
    "bbox_aspect_num": [4, 6, 6, 6, 4, 4], # Tỷ lệ khung hình cho source1-&gt;source6`
    "feature_maps": [38, 19, 10, 5, 3, 1],
    "steps": [8, 16, 32, 64, 100, 300], # Size of default box
    "min_size": [30, 60, 111, 162, 213, 264], # Size of default box
    "max_size": [60, 111, 162, 213, 264, 315], # Size of default box
    "aspect_ratios": [[2], [2,3], [2,3], [2,3], [2], [2]]
}

net = SSD(phase="inference", cfg=cfg)
net_weights = torch.load("./weights/ssd300_20.pth", map_location={"cuda:0":"cpu"})
net.load_state_dict(net_weights)

def predict(img_file_path):
    img = cv2.imread(img_file_path)

    color_mean = (8, 17, 32)
    input_size = 300
    transform = DataTransform(input_size, color_mean)

    phase = "val"
    img_tranformed, boxes, labels = transform(img, phase, "", "")
    img_tensor = torch.from_numpy(img_tranformed[:,:,(2,1,0)]).permute(2,0,1)

    net.eval()
    inputs = img_tensor.unsqueeze(0) #(1, 3, 300, 300)
    output = net(inputs)

    plt.figure(figsize=(10, 10))
    colors = [(255,0,0), (0,255,0), (0,0,255)]
    font = cv2.FONT_HERSHEY_SIMPLEX

    detections = output.data #(1, 21, 200, 5) 5: score, cx, cy, w, h
    scale = torch.Tensor(img.shape[1::-1]).repeat(2)

    for i in range(detections.size(1)):
        j = 0
        while detections[0, i, j, 0] &gt;= 0.6:
            score = detections[0, i, j, 0]
            pt = (detections[0, i, j, 1:]*scale).cpu().numpy()
            cv2.rectangle(img,
                          (int(pt[0]), int(pt[1])),
                          (int(pt[2]), int(pt[3])),
                          colors[i%3], 2
                          )
            display_text = "%s: %.2f"%(classes[i-1], score)
            cv2.putText(img, display_text, (int(pt[0]), int(pt[1])),
                font, 0.5, (255, 255, 255), 1, cv2.LINE_AA)
            j += 1
    
    cv2.imshow("Result", img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()


if __name__ == "__main__":
    img_file_path = "/home/huynth/ImageProcessing/result/Median.jpg"
    predict(img_file_path)
</code></pre>
<p>Please help me T.T Thank you.</p>

albanD: <p>Hi,</p>
<p>I don’t think you want to use a Function subclass here since you don’t implement the backward for it. You can just make this a regular python class/function.</p>

huynth: <aside class="quote no-group" data-username="albanD" data-post="2" data-topic="135731">
<div class="title">
<div class="quote-controls"></div>
<img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/alband/48/215_2.png" class="avatar"> albanD:</div>
<blockquote>
<p>I don’t think you want to use a Function subclass here since you don’t implement the backward for it. You can just make this a regular python class/function.</p>
</blockquote>
</aside>
<p>Thank you. I will try it</p>