I am using torch.utils.save_image() to save an image that has 5 channels. The image is formed by concatenating 12 images together across dim 3. The current dimensions of the image are (16, 5, 128, 1536) with 16 being the batch size.
Code:
r = self.denorm(x_concat.data.cpu())
save_image(r, sample_path, nrow=1, padding=0)
The code is giving this error:
Traceback (most recent call last):
File "main.py", line 137, in <module>
main(config)
File "main.py", line 48, in main
solver.train()
File "/solver.py", line 366, in train
save_image(r, sample_path, nrow=1, padding=0)
File "/torchvision/utils.py", line 129, in save_image
im = Image.fromarray(ndarr)
File "/PIL/Image.py", line 2751, in fromarray
raise TypeError("Cannot handle this data type: %s, %s" % typekey) from e
TypeError: Cannot handle this data type: (1, 1, 5), |u1
pytorch version = 1.7.0
python version = 3.7.10
I am assuming the error is due to the channels being 5 since this worked previously for a set of images that had channels = 3.
Related
I'm trying to train a Detectron2 model with a COCO dataset. My dataset seems to load correctly. But when I try to train the model using the DefaultTrainer I get
TypeError: Caught TypeError in DataLoader worker process 1.
This is my setup:
from detectron2.engine import DefaultTrainer
# TOTAL_NUM_IMAGES = 10531
cfg = get_cfg()
cfg.OUTPUT_DIR = os.path.join('./output')
cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
cfg.DATASETS.TRAIN = ("my_dataset_train",)
cfg.DATASETS.TEST = ()
cfg.DATALOADER.NUM_WORKERS = 2
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml") # Let training initialize from model zoo
cfg.SOLVER.IMS_PER_BATCH = 2
cfg.SOLVER.BASE_LR = 0.00025 # pick a good LR
# single_iteration = cfg.SOLVER.IMS_PER_BATCH
# iterations_for_one_epoch = TOTAL_NUM_IMAGES / single_iteration
# cfg.SOLVER.MAX_ITER = int(iterations_for_one_epoch) * 20
cfg.SOLVER.STEPS = [] # do not decay learning rate
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1 # only has one class (person). (see https://detectron2.readthedocs.io/tutorials/datasets.html#update-the-config-for-new-datasets)
# NOTE: this config means the number of classes, but a few popular unofficial tutorials incorrect uses num_classes+1 here.
os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)
trainer = DefaultTrainer(cfg)
trainer.resume_or_load(resume=False)
trainer.train()
And I get this error after a couple of iterations:
[01/06 15:14:00 d2.utils.events]: eta: 11:25:20 iter: 125 total_loss: 0.9023 loss_cls: 0.1827 loss_box_reg: 0.1385 loss_mask: 0.5601 loss_rpn_cls: 0.009945 loss_rpn_loc: 0.0023 time: 0.5232 data_time: 0.3085 lr: 3.1219e-05 max_mem: 3271M
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-17-8c48e6e17647> in <module>()
26 trainer = DefaultTrainer(cfg)
27 trainer.resume_or_load(resume=False)
---> 28 trainer.train()
8 frames
/usr/local/lib/python3.7/dist-packages/torch/_utils.py in reraise(self)
432 # instantiate since we don't know how to
433 raise RuntimeError(msg) from None
--> 434 raise exception
435
436
TypeError: Caught TypeError in DataLoader worker process 1.
Original Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
data = fetcher.fetch(index)
File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py", line 32, in fetch
data.append(next(self.dataset_iter))
File "/usr/local/lib/python3.7/dist-packages/detectron2/data/common.py", line 201, in __iter__
yield self.dataset[idx]
File "/usr/local/lib/python3.7/dist-packages/detectron2/data/common.py", line 90, in __getitem__
data = self._map_func(self._dataset[cur_idx])
File "/usr/local/lib/python3.7/dist-packages/detectron2/utils/serialize.py", line 26, in __call__
return self._obj(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/detectron2/data/dataset_mapper.py", line 189, in __call__
self._transform_annotations(dataset_dict, transforms, image_shape)
File "/usr/local/lib/python3.7/dist-packages/detectron2/data/dataset_mapper.py", line 128, in _transform_annotations
for obj in dataset_dict.pop("annotations")
File "/usr/local/lib/python3.7/dist-packages/detectron2/data/dataset_mapper.py", line 129, in <listcomp>
if obj.get("iscrowd", 0) == 0
File "/usr/local/lib/python3.7/dist-packages/detectron2/data/detection_utils.py", line 297, in transform_instance_annotations
p.reshape(-1) for p in transforms.apply_polygons(polygons)
File "/usr/local/lib/python3.7/dist-packages/fvcore/transforms/transform.py", line 297, in <lambda>
return lambda x: self._apply(x, name)
File "/usr/local/lib/python3.7/dist-packages/fvcore/transforms/transform.py", line 291, in _apply
x = getattr(t, meth)(x)
File "/usr/local/lib/python3.7/dist-packages/fvcore/transforms/transform.py", line 150, in apply_polygons
return [self.apply_coords(p) for p in polygons]
File "/usr/local/lib/python3.7/dist-packages/fvcore/transforms/transform.py", line 150, in <listcomp>
return [self.apply_coords(p) for p in polygons]
File "/usr/local/lib/python3.7/dist-packages/detectron2/data/transforms/transform.py", line 150, in apply_coords
coords[:, 0] = coords[:, 0] * (self.new_w * 1.0 / self.w)
TypeError: can't multiply sequence by non-int of type 'float'
Turns out some of the id's in "annotations" where written in scientific notation resulting in some id's with type float. Converting these to integers solved the problem.
I'm trying to reproduce meshed memory transformer https://github.com/aimagelab/meshed-memory-transformer, but I get this error: RuntimeError: gather_out_cuda(): Expected dtype int64 for index.
The detailed error information are as follows:
Traceback (most recent call last):
File "/home/ai/data/meshed-memory-transformer/train.py", line 252, in <module>
scores = evaluate_metrics(model, dict_dataloader_val, text_field)
File "/home/ai/data/meshed-memory-transformer/train.py", line 55, in evaluate_metrics
out, _ = model.beam_search(images, 20, text_field.vocab.stoi['<eos>'], 5, out_size=1)
File "/home/ai/data/meshed-memory-transformer/models/captioning_model.py", line 70, in beam_search
return bs.apply(visual, out_size, return_probs, **kwargs)
File "/home/ai/data/meshed-memory-transformer/models/beam_search/beam_search.py", line 82, in apply
visual, outputs = self.iter(t, visual, outputs, return_probs, **kwargs)
File "/home/ai/data/meshed-memory-transformer/models/beam_search/beam_search.py", line 132, in iter
self.model.apply_to_states(self._expand_state(selected_beam, cur_beam_size))
File "/home/ai/data/meshed-memory-transformer/models/containers.py", line 30, in apply_to_states
self._buffers[name] = fn(self._buffers[name])
File "/home/ai/data/meshed-memory-transformer/models/beam_search/beam_search.py", line 38, in fn
beam.expand(*([self.b_s, self.beam_size] + shape[1:])))
RuntimeError: gather_out_cuda(): Expected dtype int64 for index
GPU version:RTX3090
Cuda compilation tools, release 11.2, V11.2.142
Build cuda_11.2.r11.2/compiler.29558016_0
PyTorch version:1.9.1 build:py3.7_cuda11.1_cudnn8.0.5_0
I do transformations on images as below (which works with RandCrop): (it is from this dataloader script: https://github.com/jeffreyhuang1/two-stream-action-recognition/blob/master/dataloader/motion_dataloader.py)
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.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
But when I do try to get Five Crops, I get this error:
Traceback (most recent call last):
File “motion_cnn.py”, line 267, in
main()
File “motion_cnn.py”, line 51, in main
train_loader,test_loader, test_video = data_loader.run()
File “/media/d/DATA_2/two-stream-action-recognition-master/dataloader/motion_dataloader.py”, line 120, in run
train_loader = self.train()
File “/media/d/DATA_2/two-stream-action-recognition-master/dataloader/motion_dataloader.py”, line 156, in train
print ‘==> Training data :’,len(training_set),’ videos’,training_set[1][0].size()
File “/media/d/DATA_2/two-stream-action-recognition-master/dataloader/motion_dataloader.py”, line 77, in getitem
data = self.stackopf()
File “/media/d/DATA_2/two-stream-action-recognition-master/dataloader/motion_dataloader.py”, line 51, in stackopf
H = self.transform(imgH)
File “/media/d/DATA_2/two-stream-action-recognition-master/venv/local/lib/python2.7/site-packages/torchvision/transforms/transforms.py”, line 60, in call
img = t(img)
File “/media/d/DATA_2/two-stream-action-recognition-master/venv/local/lib/python2.7/site-packages/torchvision/transforms/transforms.py”, line 91, in call
return F.to_tensor(pic)
File “/media/d/DATA_2/two-stream-action-recognition-master/venv/local/lib/python2.7/site-packages/torchvision/transforms/functional.py”, line 50, in to_tensor
raise TypeError(‘pic should be PIL Image or ndarray. Got {}’.format(type(pic)))
TypeError: pic should be PIL Image or ndarray. Got <type ‘tuple’>
Getting 5 random crops, I should handle a tuple of images instead of a PIL image - so I use Lambda, but then I get the error, at line 55, in stackopf
flow[2*(j),:,:] = H
RuntimeError: expand(torch.FloatTensor{[5, 1, 224, 224]}, size=[224,
224]): the number of sizes provided (2) must be greater or equal to
the number of dimensions in the tensor (4)
and when I try to set flow = torch.FloatTensor(5, 2*self.in_channel,self.img_rows,self.img_cols)
I get motion_dataloader.py", line 55, in stackopf
flow[:,2*(j),:,:] = H
RuntimeError: expand(torch.FloatTensor{[5, 1, 224, 224]}, size=[5,
224, 224]): the number of sizes provided (3) must be greater or equal
to the number of dimensions in the tensor (4)
when I multiply the train batchsize by 5 that is returned, I also get the same error.
I installed anaconda and all the packages I needed. I tried to run this mnist example from keras It works fine. Now I'm trying to visualize the Graph with keras manual. but it does not work.
When I run the mnist example I receive this error Message:
Traceback (most recent call last):
File "dummy.py", line 77, in <module>
plot_model(model, to_file='model.png')
File "C:\Users\***\AppData\Local\Continuum\anaconda3\lib\site-packages\keras\utils\vis_utils.py", line 132, in plot_model
dot = model_to_dot(model, show_shapes, show_layer_names, rankdir)
File "C:\Users\***\AppData\Local\Continuum\anaconda3\lib\site-packages\keras\utils\vis_utils.py", line 55, in model_to_dot
_check_pydot()
File "C:\Users\***\AppData\Local\Continuum\anaconda3\lib\site-packages\keras\utils\vis_utils.py", line 26, in _check_pydot
pydot.Dot.create(pydot.Dot())
File "C:\Users\***\AppData\Local\Continuum\anaconda3\lib\site-packages\pydot.py", line 1885, in create
assert p.returncode == 0, p.returncode
AssertionError: 1
I also tried the convnet_drawer. It failed too.
Any Ideas?
I was trying to build a 3D convolutional layer using keras. It works fine, but when I added a subsample parameter it crashed. The code:
l_1 = Convolution3D(2, 10,10,10,
border_mode='same',
name = 'l_1',
activation='relu',
subsample = (5,5,5)
)(inputs)
the error is:
Traceback (most recent call last):
File "image_proc_09.py", line 244, in <module>
)(inputs)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 572, in __call__
self.add_inbound_node(inbound_layers, node_indices, tensor_indices)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 635, in add_inbound_node
Node.create_node(self, inbound_layers, node_indices, tensor_indices)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 166, in create_node
output_tensors = to_list(outbound_layer.call(input_tensors[0], mask=input_masks[0]))
File "/usr/local/lib/python2.7/dist-packages/keras/layers/convolutional.py", line 1234, in call
filter_shape=self.W_shape)
File "/usr/local/lib/python2.7/dist-packages/keras/backend/theano_backend.py", line 1627, in conv3d
dim_ordering, volume_shape, filter_shape)
File "/usr/local/lib/python2.7/dist-packages/keras/backend/theano_backend.py", line 1686, in _old_theano_conv3d
assert(strides == (1, 1, 1))
AssertionError
I am using theano 0.8.2.
Thanks
You cannot use the subsample parameter with border_mode='same'. Use 'valid' or 'full'
Check out the line of code where the assertion error happens