FC

Dockerfile

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+FROM pytorch/pytorch:1.12.0-cuda11.3-cudnn8-runtime
+
+ARG USER
+ARG GROUP
+ARG UID
+ARG GID
+
+RUN groupadd -g ${GID} ${GROUP}
+RUN useradd -u ${UID} -g ${GROUP} -s /bin/bash -m ${USER} 
+
+RUN mkdir /wd
+RUN chown ${USER}:${GROUP} /wd
+WORKDIR /wd
+
+USER ${UID}:${GID}
+
+#RUN conda init bash
+#RUN conda create -n jupyter-env jupyterlab -y
+#RUN echo "conda activate jupyter-env" >> /home/${USER}/.bashrc
+
+RUN pip install jupyterlab matplotlib einops scikit-learn
+
+EXPOSE 9000
+
+SHELL ["/bin/bash", "--login", "-i", "-c"]
+ENV SHELL=/bin/bash
+
+CMD jupyter lab --ip 0.0.0.0 --port 9000

build.sh

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+#/bin/bash
+
+docker build . \
+	-t ${USER}_pytorch \
+	--build-arg USER=${USER} \
+	--build-arg GROUP=${USER} \
+	--build-arg UID=$(id -u ${USER}) \
+	--build-arg GID=$(id -g ${USER})

start.sh

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+docker run -d --gpus all -p 9000:9000 -v $(pwd):/wd ${USER}_pytorch

utils.py

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+import torch
+import matplotlib.pyplot as plt
+
+def imshow(tensor, figsize=None, title="", **args):
+    figsize = figsize if figsize else (13*0.8,5*0.8)
+    
+    if type(tensor) is list:
+        for idx, el in enumerate(tensor):
+            imshow(el, figsize=figsize, title=title, **args)
+            plt.suptitle("{} {}".format(idx, title))
+        return
+    if len(tensor.shape)==4:
+        for idx, el in enumerate(torch.squeeze(tensor, dim=1)):
+            imshow(el, figsize=figsize, title=title, **args)
+            plt.suptitle("{} {}".format(idx, title))
+        return
+    
+    tensor = tensor.detach().cpu() if type(tensor) == torch.Tensor else tensor
+    if tensor.dtype == torch.complex64:
+        f, ax = plt.subplots(1, 5, figsize=figsize, gridspec_kw={'width_ratios': [46.5,3,1,46.5,3]})
+        real_im = ax[0].imshow(tensor.real, **args)
+        imag_im = ax[3].imshow(tensor.imag, **args)
+        box = ax[1].get_position()
+        box.x0 = box.x0 - 0.02
+        box.x1 = box.x1 - 0.03
+        ax[1].set_position(box)
+        box = ax[4].get_position()
+        box.x0 = box.x0 - 0.02
+        box.x1 = box.x1 - 0.03
+        ax[4].set_position(box)
+        ax[0].set_title("real");
+        ax[3].set_title("imag");
+        f.colorbar(real_im, ax[1]);
+        f.colorbar(imag_im, ax[4]);
+        f.suptitle(title)
+        ax[2].remove()
+        return f, ax
+    else:
+        f, ax = plt.subplots(1, 2, gridspec_kw={'width_ratios': [95,5]}, figsize=figsize)
+        im = ax[0].imshow(tensor, **args)
+        f.colorbar(im, ax[1])
+        f.suptitle(title)
+        return f, ax
+    
+def perm_roll(im, axis, amount):
+  permutation = torch.roll(torch.arange(im.shape[axis], device=im.device), amount, dims=0)
+  return torch.index_select(im, axis, permutation)
+
+def shift_left(im):
+  tt = perm_roll(im, axis=-2, amount=-(im.shape[-2]+1)//2)
+  tt = perm_roll(tt, axis=-1, amount=-(im.shape[-1]+1)//2)
+  return tt
+
+def shift_right(im):
+  tt = perm_roll(im, axis=-2, amount=(im.shape[-2]+1)//2)
+  tt = perm_roll(tt, axis=-1, amount=(im.shape[-1]+1)//2)
+  return tt
+
+
+def pad_zeros(input, size):
+  h, w = input.shape[-2:]
+  th, tw = size
+    
+  if len(input.shape) == 2:
+    gg = torch.zeros(size, device=input.device)
+    x, y = int(th/2 - h/2), int(tw/2 - w/2)
+    gg[x:int(th/2 + h/2),y:int(tw/2 + w/2)] = input[:,:]
+    
+  if len(input.shape) == 4:
+    gg = torch.zeros(input.shape[:2] + size, device=input.device)
+    x, y = int(th/2 - h/2), int(tw/2 - w/2)
+    gg[:,:,x:int(th/2 + h/2),y:int(tw/2 + w/2)] = input[:,:,:,:]
+
+  return gg
+
+def unpad_zeros(input, size):
+  h, w = input.shape[-2:]
+  th, tw = size
+  dx,dy = h-th, w-tw
+    
+  if len(input.shape) == 2: 
+    gg = input[int(h/2 - th/2):int(th/2 + h/2), int(w/2 - tw/2):int(tw/2 + w/2)]
+    
+  if len(input.shape) == 4:
+    gg = input[:,:,dx//2:dx//2+th, dy//2:dy//2+tw]
+  return gg
+
+def circular_aperture(h, w, r=None, is_inv=False):
+    if r is None:
+        r = min(h//2, w//2)
+    x, y = torch.meshgrid(torch.arange(-h//2, h//2), torch.arange(-w//2, w//2), indexing='ij')
+    circle_dist = torch.sqrt(x**2 + y**2)
+    if is_inv:
+        circle_aperture = torch.where(circle_dist<r, torch.zeros_like(circle_dist), torch.ones_like(circle_dist))
+    else:
+        circle_aperture = torch.where(circle_dist<r, torch.ones_like(circle_dist), torch.zeros_like(circle_dist))
+    return circle_aperture
+
+def to_class_labels(softmax_distibutions):
+    return torch.argmax(softmax_distibutions, dim=1).cpu()