Expected 4-dimensional input for 4-dimensional weight [64, 20, 7, 7], but got input of size [30, 9] instead

Long shot but can anyone please tell me what I am doing wrong? Thank you.

I am using the slightly modified version of: https://github.com/jeffreyhuang1/two-stream-action-recognition

Everything worked perfectly fine (albeit bad performance) with random crop for training and centercrop for testing/val. Now, I am trying to get fivecrops and I have modified the batch sizes and I get an error that I don’t really understand because my conv input should be the (ncropsxbatchsize, channel, height, width) - [5x6,20,224,224]

I get this error:

Traceback (most recent call last):
File “motion_cnn.py”, line 282, in
main()
File “motion_cnn.py”, line 73, in main
model.run()
File “motion_cnn.py”, line 123, in run
self.train_1epoch()
File “motion_cnn.py”, line 172, in train_1epoch
output = self.model(result)
File “/media/d/DATA_2/two-stream-action-recognition-master/venv/local/lib/python2.7/site-packages/torch/nn/modules/module.py”, line 477, in call
result = self.forward(*input, **kwargs)
File “/media/d/DATA_2/two-stream-action-recognition-master/network.py”, line 145, in forward
x = self.conv1_custom(x)
File “/media/d/DATA_2/two-stream-action-recognition-master/venv/local/lib/python2.7/site-packages/torch/nn/modules/module.py”, line 477, in call
result = self.forward(*input, **kwargs)
File “/media/d/DATA_2/two-stream-action-recognition-master/venv/local/lib/python2.7/site-packages/torch/nn/modules/conv.py”, line 301, in forward
self.padding, self.dilation, self.groups)
RuntimeError: Expected 4-dimensional input for 4-dimensional weight [64, 20, 7, 7], but got input of size [30, 9] instead

This is how my motion_cnn looks:

import numpy as np
import pickle
from PIL import Image
import time
import tqdm
import shutil
from random import randint
import argparse

from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as transforms
import torchvision.models as models
import torch.nn as nn
import torch
import torch.backends.cudnn as cudnn
import torch.cuda as cuda
from torch.autograd import Variable
from torch.optim.lr_scheduler import StepLR, ReduceLROnPlateau 

from utils import *
from network import *
import dataloader


os.environ["CUDA_VISIBLE_DEVICES"] = "0"
cuda.set_device(0)

parser = argparse.ArgumentParser(description='UCF101 motion stream on resnet101')
parser.add_argument('--epochs', default=1000, type=int, metavar='N', help='number of total epochs')
parser.add_argument('--batch-size', default=6, type=int, metavar='N', help='mini-batch size (default: 4)')
parser.add_argument('--lr', default=1e-2, type=float, metavar='LR', help='initial learning rate')#best is 2
parser.add_argument('--evaluate', dest='evaluate', action='store_true', help='evaluate model on validation set')
parser.add_argument('--resume', default='80', type=str, metavar='PATH', help='path to latest checkpoint (default: none)')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N', help='manual epoch number (useful on restarts)')

def main():
    global arg
    arg = parser.parse_args()
    print arg

    #Prepare DataLoader
    data_loader = dataloader.Motion_DataLoader(
                        BATCH_SIZE=arg.batch_size,
                        num_workers=8,
                        path='/media/d/DATA_2/two-stream-action-recognition-master/J/flow/',
                        ucf_list='/media/d/DATA_2/two-stream-action-recognition-master/J_list/',
                        ucf_split='01',
                        in_channel=10,
                        )
    
    train_loader,test_loader, test_video = data_loader.run()

    #print len(train_loader.dataset)
    

    #Model 
    model = Motion_CNN(
                        # Data Loader
                        train_loader=train_loader,
                        test_loader=test_loader,
                        # Utility
                        start_epoch=arg.start_epoch,
                        resume=arg.resume,
                        evaluate=arg.evaluate,
                        # Hyper-parameter
                        nb_epochs=arg.epochs,
                        lr=arg.lr,
                        batch_size=arg.batch_size,
                        channel = 10*2,
                        test_video=test_video
                        )
    #Training
    model.run()

class Motion_CNN():
    def __init__(self, nb_epochs, lr, batch_size, resume, start_epoch, evaluate, train_loader, test_loader, channel,test_video):
        self.nb_epochs=nb_epochs
        self.lr=lr
        self.batch_size=batch_size
        self.resume=resume
        self.start_epoch=start_epoch
        self.evaluate=evaluate
        self.train_loader=train_loader
        self.test_loader=test_loader
        self.best_prec1=0
        self.channel=channel
        self.test_video=test_video

    def build_model(self):
        print ('==> Build model and setup loss and optimizer')
        #build model
        self.model = resnet101(pretrained= True, channel=self.channel).cuda()
        #print self.model
        #Loss function and optimizer
        self.criterion = nn.CrossEntropyLoss().cuda()
        self.optimizer = torch.optim.SGD(self.model.parameters(), self.lr, momentum=0.9, weight_decay=1e-2)# torch.optim.Adam(self.model.parameters(), self.lr, eps=1e-2, amsgrad=True)  #torch.optim.Adagrad(self.model.parameters(), self.lr, weight_decay=1e-2)  #torch.optim.SGD(self.model.parameters(), self.lr, weight_decay=1e-2, nesterov=True)  
        #self.scheduler =  ReduceLROnPlateau(self.optimizer, 'min', patience=5, verbose=True)   #StepLR(self.optimizer, step_size=10, gamma=0.25)  

    def resume_and_evaluate(self):
        if self.resume:
            if os.path.isfile(self.resume):
                print("==> loading checkpoint '{}'".format(self.resume))
                checkpoint = torch.load(self.resume)
                self.start_epoch = checkpoint['epoch']
                self.best_prec1 = checkpoint['best_prec1']
                self.model.load_state_dict(checkpoint['state_dict'])
                self.optimizer.load_state_dict(checkpoint['optimizer'])
                print("==> loaded checkpoint '{}' (epoch {}) (best_prec1 {})"
                  .format(self.resume, checkpoint['epoch'], self.best_prec1))
            else:
                print("==> no checkpoint found at '{}'".format(self.resume))
        if self.evaluate:
            self.epoch=0
            prec1, val_loss = self.validate_1epoch()
            return
    
    def run(self):
        self.build_model()
        self.resume_and_evaluate()
        cudnn.benchmark = True
        
        for self.epoch in range(self.start_epoch, self.nb_epochs):
            self.train_1epoch()
            prec1, val_loss = self.validate_1epoch()
            is_best = prec1 > self.best_prec1
            #lr_scheduler
            self.scheduler.step(val_loss)
            # save model
            if is_best:
                self.best_prec1 = prec1
                with open('record/motion/motion_video_preds.pickle','wb') as f:
                    pickle.dump(self.dic_video_level_preds,f)
                f.close() 
            
            save_checkpoint({
                'epoch': self.epoch,
                'state_dict': self.model.state_dict(),
                'best_prec1': self.best_prec1,
                'optimizer' : self.optimizer.state_dict()
            },is_best,'record/motion/checkpoint.pth.tar','record/motion/model_best.pth.tar')

    def train_1epoch(self):
        print('==> Epoch:[{0}/{1}][training stage]'.format(self.epoch, self.nb_epochs))

        batch_time = AverageMeter()
        data_time = AverageMeter()
        losses = AverageMeter()
        top1 = AverageMeter()
        top5 = AverageMeter()
        #switch to train mode
        self.model.train()    
        end = time.time()
        # mini-batch training
        progress = tqdm(self.train_loader)
        for i, (data,label) in enumerate(progress):

            # measure data loading time
            data_time.update(time.time() - end)
            
            label = label.cuda(async=True)
            input_var = Variable(data).cuda()
            
            #print len(input_var)
            target_var = Variable(label).cuda()
 
            bs, ncrops, c, h, w = input_var.size()
            result = self.model(input_var.view(-1, c, h, w)) # fuse batch size and ncrops



            # compute output
            output = self.model(result)
            loss = self.criterion(output, target_var)

            # measure accuracy and record loss
            prec1, prec5 = accuracy(output.data, label, topk=(1, 5))
            losses.update(loss.data[0], data.size(0))
            top1.update(prec1[0], data.size(0))
            top5.update(prec5[0], data.size(0))

            # compute gradient and do SGD step
            self.optimizer.zero_grad()
            loss.backward()
            #torch.nn.utils.clip_grad_norm(self.model.parameters(), 0.25)
            self.optimizer.step()

            # measure elapsed time
            batch_time.update(time.time() - end)
            end = time.time()
        
        info = {'Epoch':[self.epoch],
                'Batch Time':[round(batch_time.avg,3)],
                'Data Time':[round(data_time.avg,3)],
                'Loss':[round(losses.avg,5)],
                'Prec@1':[round(top1.avg,4)],
                'Prec@5':[round(top5.avg,4)],
                'lr': self.optimizer.param_groups[0]['lr']
                }
        record_info(info, 'record/motion/opf_train.csv','train')

    def validate_1epoch(self):
        print('==> Epoch:[{0}/{1}][validation stage]'.format(self.epoch, self.nb_epochs))

        batch_time = AverageMeter()
        losses = AverageMeter()
        top1 = AverageMeter()
        top5 = AverageMeter()
        # switch to evaluate mode
        self.model.eval()
        self.dic_video_level_preds={}
        end = time.time()
        progress = tqdm(self.test_loader)
        for i, (keys,data,label) in enumerate(progress):
            
            #data = data.sub_(127.353346189).div_(14.971742063)
            label = label.cuda(async=True)
            data_var = Variable(data, volatile=True).cuda(async=True)
            label_var = Variable(label, volatile=True).cuda(async=True)

            bs, ncrops, c, h, w = data_var.size()
            result = self.model(data_var.view(-1, c, h, w)) # fuse batch size and ncrops
            result_avg = result.view(bs, ncrops, -1).mean(1) # avg over crops

            # compute output
            output = self.model(result_avg)

            # measure elapsed time
            batch_time.update(time.time() - end)
            end = time.time()
            #Calculate video level prediction
            preds = output.data.cpu().numpy()
            nb_data = preds.shape[0]
            for j in range(nb_data):
                videoName = keys[j].split('-',1)[0] # ApplyMakeup_g01_c01
                if videoName not in self.dic_video_level_preds.keys():
                    self.dic_video_level_preds[videoName] = preds[j,:]
                else:
                    self.dic_video_level_preds[videoName] += preds[j,:]
                    
        #Frame to video level accuracy
        video_top1, video_top5, video_loss = self.frame2_video_level_accuracy()
        info = {'Epoch':[self.epoch],
                'Batch Time':[round(batch_time.avg,3)],
                'Loss':[round(video_loss,5)],
                'Prec@1':[round(video_top1,3)],
                'Prec@5':[round(video_top5,3)]
                }
        record_info(info, 'record/motion/opf_test.csv','test')
        return video_top1, video_loss

    def frame2_video_level_accuracy(self):
     
        correct = 0
        video_level_preds = np.zeros((len(self.dic_video_level_preds),9)) #6
        video_level_labels = np.zeros(len(self.dic_video_level_preds))
        ii=0
        for key in sorted(self.dic_video_level_preds.keys()):
            name = key.split('-',1)[0]

            preds = self.dic_video_level_preds[name]
            label = int(self.test_video[name])-1
                
            video_level_preds[ii,:] = preds
            video_level_labels[ii] = label
            ii+=1         
            if np.argmax(preds) == (label):
                correct+=1

        #top1 top5
        video_level_labels = torch.from_numpy(video_level_labels).long()
        video_level_preds = torch.from_numpy(video_level_preds).float()

        loss = self.criterion(Variable(video_level_preds).cuda(), Variable(video_level_labels).cuda())    
        top1,top5 = accuracy(video_level_preds, video_level_labels, topk=(1,5))     
                            
        top1 = float(top1.numpy())
        top5 = float(top5.numpy())
            
        return top1,top5,loss.data.cpu().numpy()

if __name__=='__main__':
    main()

And this is how my dataloader looks:

import numpy as np
import pickle
from PIL import Image
import time
import shutil
import random
import argparse

from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as transforms
import torchvision.models as models
import torch.nn as nn
import torch
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
from torch.optim.lr_scheduler import ReduceLROnPlateau

from split_train_test_video import *
 
class motion_dataset(Dataset):  
    def __init__(self, dic, in_channel, root_dir, mode, transform=None):
        #Generate a 16 Frame clip
        self.keys=dic.keys()
        self.values=dic.values()
        self.root_dir = root_dir
        self.transform = transform
        self.mode=mode
        self.in_channel = in_channel
        self.img_rows=224
        self.img_cols=224
        self.fiveCrops = True

    def stackopf(self,video_name, clip_idx, nb_clips=None):
        name = self.video
        u = self.root_dir+ 'u/' + name
        v = self.root_dir+ 'v/'+ name

        if self.fiveCrops:
            self.ncrops = 5
        else:
            self.ncrops = 1
        
        flow = torch.FloatTensor(self.ncrops,2*self.in_channel,self.img_rows,self.img_cols)
        #i = int(self.clips_idx)
        i = int(clip_idx)


        for j in range(self.in_channel):
            idx = i + j
            if self.mode == 'train':
                if idx >= nb_clips+1:
                    idx = nb_clips+1
            idx = str(idx)
            frame_idx = 'frame'+ idx.zfill(6) #6zeros for frame name
            h_image = u +'/' + frame_idx +'.jpg'
            v_image = v +'/' + frame_idx +'.jpg'
            
            imgH=(Image.open(h_image))
            imgV=(Image.open(v_image))

            H = self.transform(imgH)
            V = self.transform(imgV)

            
            if self.fiveCrops:
                flow[:, 2 * (j - 1), :, :] = H.squeeze()
                flow[:, 2 * (j - 1) + 1, :, :] = V.squeeze()
            else:
                flow[:, 2 * (j - 1), :, :] = H
                flow[:, 2 * (j - 1) + 1, :, :] = V
   
            imgH.close()
            imgV.close()  

        return flow.squeeze()

    def __len__(self):
        return len(self.keys)

    def __getitem__(self, idx):
        #print ('mode:',self.mode,'calling Dataset:__getitem__ @ idx=%d'%idx)
        nb_clips=0
         
        if self.mode == 'train':
            self.video, nb_clips = self.keys[idx].split('-')
            self.clips_idx = random.randint(1,int(nb_clips))
        elif self.mode == 'val':
            self.video,self.clips_idx = self.keys[idx].split('-')
        else:
            raise ValueError('There are only train and val mode')

        label = self.values[idx]
        label = int(label)-1 
        data = self.stackopf(self.video, self.clips_idx, int(nb_clips))
        #len(data)

        if self.mode == 'train':
            sample = (data,label)
        elif self.mode == 'val':
            sample = (self.video,data,label)
        else:
            raise ValueError('There are only train and val mode')
        return sample





class Motion_DataLoader():
    def __init__(self, BATCH_SIZE, num_workers, in_channel,  path, ucf_list, ucf_split):

        self.BATCH_SIZE=BATCH_SIZE
        self.num_workers = num_workers
        self.frame_count={}
        self.in_channel = in_channel
        self.data_path=path
        # split the training and testing videos
        splitter = UCF101_splitter(path=ucf_list,split=ucf_split)
        self.train_video, self.test_video = splitter.split_video()
        
    def load_frame_count(self):
        #print '==> Loading frame number of each video'
        with open('/media/d/DATA_2/two-stream-action-recognition-master/dataloader/dic/frame_count_j.pickle','rb') as file:
            dic_frame = pickle.load(file)
        file.close()

        for line in dic_frame :  #'v_Lunges_g07_c01.avi'
            #videoname = line.split('_',1)[1].split('.',1)[0]  #Lunges_g07_c01
            #n,g = videoname.split('_',1)
            #if n == 'HandStandPushups':
            #    videoname = 'HandstandPushups_'+ g
            self.frame_count[line]=dic_frame[line] 

    def run(self):
        self.load_frame_count()
        self.get_training_dic()
        self.val_sample19()
        train_loader = self.train()
        print len(train_loader.dataset)
        val_loader = self.val()

        return train_loader, val_loader, self.test_video
            
    def val_sample19(self):
        self.dic_test_idx = {}
        #print len(self.test_video)
        for video in self.test_video:   #Knot_Tying_D001_000041_000170     #ApplyEyeMakeup_g01_c01
            if self.frame_count[video]>27 and self.frame_count[video]<1200: # CHANGE
                #n,g = video.split('_',1)    #v_ApplyEyeMakeup_g01_c01.avi

                sampling_interval = int((self.frame_count[video]-10+1)/19)
                for index in range(19):
                    clip_idx = index*sampling_interval
                    key = video + '-' + str(clip_idx+1)
                    self.dic_test_idx[key] = self.test_video[video]
             
    def get_training_dic(self):
        self.dic_video_train={}
        for video in self.train_video:
            if self.frame_count[video]>27 and self.frame_count[video]<1200: # CHANGE!
                nb_clips = self.frame_count[video]-10+1
                key = video +'-' + str(nb_clips)
                self.dic_video_train[key] = self.train_video[video] 
                            
    def train(self):
        training_set = motion_dataset(dic=self.dic_video_train, in_channel=self.in_channel, root_dir=self.data_path,
            mode='train',
            transform = transforms.Compose([
            transforms.Resize([256,256]),
            transforms.FiveCrop([224, 224]),
            transforms.Lambda(lambda crops: torch.stack([transforms.ToTensor()(crop) for crop in crops]))
            #transforms.RandomCrop([224, 224]),
            #transforms.ToTensor(),
            #transforms.Normalize([0.5], [0.5])
            ]))
        print '==> Training data :',len(training_set),' videos',training_set[1][0].size()

        train_loader = DataLoader(
            dataset=training_set, 
            batch_size=self.BATCH_SIZE,
            shuffle=True,
            num_workers=self.num_workers,
            pin_memory=True
            )
        return train_loader

    def val(self):
        validation_set = motion_dataset(dic= self.dic_test_idx, in_channel=self.in_channel, root_dir=self.data_path ,
            mode ='val',
            transform = transforms.Compose([
            transforms.Resize([256,256]),
	    #transforms.CenterCrop([224, 224]),
            #transforms.ToTensor(),
            transforms.FiveCrop([224, 224]),
            transforms.Lambda(lambda crops: torch.stack([transforms.ToTensor()(crop) for crop in crops]))
            #transforms.Normalize([0.5], [0.5])
            ]))
        print '==> Validation data :',len(validation_set),' frames',validation_set[1][1].size()
        #print validation_set[1]

        val_loader = DataLoader(
            dataset=validation_set, 
            batch_size=self.BATCH_SIZE, 
            shuffle=True,
            num_workers=self.num_workers)

        return val_loader

if __name__ == '__main__':
    data_loader =Motion_DataLoader(BATCH_SIZE=1,num_workers=1,in_channel=10,
                                        path='/media/d/DATA_2/two-stream-action-recognition-master/J/flow/',
                                        ucf_list='/media/d/DATA_2/two-stream-action-recognition-master/J_list',
                                        ucf_split='01'
                                        )
    train_loader,val_loader,test_video = data_loader.run()
    #print train_loader,val_loader
1 Like

I’m sorry but I can’t really understand what is happening here. I would advise to to print the size of the data tensors along the way. Print it in the dataset, before the transform, after the transform, before going into the network and you’ll see where it messes up

So awkward, I try to print those (see below) but the program completely skips the prints, I use size because this is a line from the official document here (Fivecrop part: https://pytorch.org/docs/stable/torchvision/transforms.html

         label = label.cuda(async=True)
            input_var = Variable(data).cuda()
            
            #print len(input_var)
            target_var = Variable(label).cuda()
 
            bs, ncrops, c, h, w = input_var.size()
            print input_var.size
            result = self.model(input_var.view(-1, c, h, w)) # fuse batch size and ncrops
            print result.size

            # compute output
            output = self.model(result)
            print output.size
            loss = self.criterion(output, target_var)

Though this is the shape I get from the dataloader (no batch) = five crops, channels, dim, dim

==> Training data : 437 videos (5, 20, 224, 224)
==> Validation data : 2964 frames (5, 20, 224, 224)

If it skips the prints, I don’t think that specific code is running. You seemed to have more than one entry point but I’m not sure.

But where it fails: output = self.model(result) is after the prints, that is why I thought it is weird

Yeah, that is weird. Could you try to debug without the async=True?

Just curious here. What’s the benefit of doing another forward pass on the result?

result = self.model(input_var.view(-1, c, h, w)) # fuse batch size and ncrops
output = self.model(result)
1 Like

Still doesn’t print anything, when I skip the async parameter and when set I set it to False.

Try to run just crash the program by putting in some weird letters, like erbgniberg. (Or use a debugger :p). Might be running the validation or something? I don’t know man, strange if that code runs but no print output. Maybe the tqdm progressbar is eating it?

1 Like

Ahh, thank you, that is exactly where I fail! I haven’t noticed I was passing them to the model when I reshaped (sorry, new to pytorch)

Of course now I have another -unrelated- error but let me see :slight_smile: thanks

1 Like

np, good luck :slight_smile: Feel free to post again if you run into another problem

1 Like