Resample=None, url=None, data=None, **kwargs) Interpolation_stage=None, filternorm=True, filterrad=4.0, Vmin=None, vmax=None, origin=None, extent=None, Norm=None, aspect=None, interpolation=None, alpha=None, Show_image show_image (im, ax=None, figsize=None, title=None, ctx=None, cmap=None, suptitle, sharex, sharey, squeeze, subplot_kw and gridspec_kw are all passed down to plt.subplots. suptitle provides a way to create a figure title for all images. Sharey=False, squeeze=True, subplot_kw=None, Show_images show_images (ims, nrows=1, ncols=None, titles=None, figsize:'tuple'=None, If you use suptitle, constrained_layout is used unless you set constrained_layout to False. Title to be set to returned figure passed to subplots Size (in inches) of images that will be displayed in the returned figure passed to subplots Width, height in inches of the returned figure passed to subplots Number of columns in returned axes grid passed to subplots Number of rows in returned axes grid passed to subplots Show all images ims as subplots with rows using titles. To_detach to_detach (b, cpu=True, gather=True) Gather copies of x on axis (if training is distributed) T = tensor() unsqueeze_(t, n = 2) test_eq(t, tensor().view( 1, 1, 1))Īpply func recursively to x, passing on args Recursively detach lists of tensors in b put them on the CPU if cpu=True. Gather only applies during distributed training and the result tensor will be the one gathered across processes if gather=True (as a result, the batch size will be multiplied by the number of processes). Tensor.pca Tensor.pca (x:torch.Tensor, k=2) TensorImageBW TensorImageBW (x, **kwargs) TensorImageBase TensorImageBase (x, **kwargs)Ī Tensor which support subclass pickling, and maintains metadata when casting or after methods T = tensor(,]) t.img_size = 1 t2 = cast(t, TensorBase) test_eq(t2.img_size, t.img_size) x = retain_type(tensor(), t2) test_eq(x.img_size, t.img_size) t3 = TensorBase(,], img_size = 1) test_eq(t3.img_size, t.img_size) t4 = t2 + 1 t4.img_size = 2 test_eq(t2.img_size, 1) test_eq(t4.img_size, 2) # this will fail with `Tensor` but works with `TensorBase` test_eq(pickle.loads(pickle.dumps(t2)).img_size, t2.img_size) Return or set default device use_cuda: -1 - CUDA/mps if available True - error if not available False - CPU Return or set default device use_cuda: None - CUDA if available True - error if not available False - CPU Recursively map lists of int tensors in b to float.ĭefault_device default_device (use_cuda=-1) Recursively map lists of tensors in b to FP16. Return the number of processes in distributed training (if applicable). Return the distributed rank of this process (if applicable). Path.save_array Path.save_array (p:pathlib.Path, o, complib='lz4', lvl=3) Place a synchronization barrier in distributed trainingĪfter calling this, ALL sub-processes in the pytorch process group must arrive here before proceeding. Plt.subplot(int(bz**0.5),int(np.ceil(bz/int(bz**0.Path.load_array Path.load_array (p:pathlib.Path) Save numpy array to a compressed pytables file, using compression level lvlĬompression lib can be any of: blosclz, lz4, lz4hc, snappy, zlib or zstd. Raise Exception("unsupported type! "+str(img.size())) Raise Exception("unsupported type! " + str(img.size())) Print('warning: more than 3 channels! only channels 0,1,2 are preserved!')Įlif bz > 1 and c = 1: # multiple grayscale imagesĮlif bz > 1 and c = 3: # multiple RGB imagesĮlif bz > 1 and c > 3: # multiple feature maps If bz=1 and c=1: # single grayscale imageĮlif bz=1 and c > 3: # multiple feature maps Show(x,y,z) produces three windows, displaying x, y, z respectively, where x,y,z can be in any form described above. If x is a 2D tensor, it will be shown as grayscale map If x is a 3D tensor, this function shows first 3 channels at most (in RGB format) If x is a 4D tensor (like image batch with the size of b(atch)*c(hannel)*h(eight)*w(eight), this function splits x in batch dimension, showing b subplots in total, where each subplot displays first 3 channels (3*h*w) at most. Show(x) gives the visualization of x, where x should be a torch.Tensor Input imgs can be single or multiple tensor(s), this function uses matplotlib to visualize. I've written a simple function to visualize the pytorch tensor using matplotlib. # If you try to plot image with shape (C, H, W) Tensor_image = tensor_image.view(tensor_image.shape, tensor_image.shape, tensor_image.shape) Print(type(tensor_image), tensor_image.shape) But PyTorch Tensors ("Image tensors") are channel first, so to use them with matplotlib you need to reshape it: As you can see matplotlib works fine even without conversion to numpy array.
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