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""" |
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Different utilities such as orthogonalization of weights, initialization of |
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loggers, etc |
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Copyright (C) 2018, Matias Tassano <matias.tassano@parisdescartes.fr> |
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This program is free software: you can use, modify and/or |
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redistribute it under the terms of the GNU General Public |
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License as published by the Free Software Foundation, either |
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version 3 of the License, or (at your option) any later |
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version. You should have received a copy of this license along |
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this program. If not, see <http://www.gnu.org/licenses/>. |
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""" |
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import numpy as np |
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import cv2 |
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def variable_to_cv2_image(varim): |
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r"""Converts a torch.autograd.Variable to an OpenCV image |
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Args: |
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varim: a torch.autograd.Variable |
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""" |
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nchannels = varim.size()[1] |
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if nchannels == 1: |
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res = (varim.data.cpu().numpy()[0, 0, :]*255.).clip(0, 255).astype(np.uint8) |
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elif nchannels == 3: |
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res = varim.data.cpu().numpy()[0] |
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res = cv2.cvtColor(res.transpose(1, 2, 0), cv2.COLOR_RGB2BGR) |
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res = (res*255.).clip(0, 255).astype(np.uint8) |
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else: |
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raise Exception('Number of color channels not supported') |
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return res |
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def normalize(data): |
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return np.float32(data/255.) |
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def remove_dataparallel_wrapper(state_dict): |
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r"""Converts a DataParallel model to a normal one by removing the "module." |
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wrapper in the module dictionary |
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Args: |
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state_dict: a torch.nn.DataParallel state dictionary |
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""" |
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from collections import OrderedDict |
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new_state_dict = OrderedDict() |
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for k, vl in state_dict.items(): |
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name = k[7:] |
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new_state_dict[name] = vl |
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return new_state_dict |
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def is_rgb(im_path): |
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r""" Returns True if the image in im_path is an RGB image |
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""" |
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from skimage.io import imread |
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rgb = False |
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im = imread(im_path) |
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if (len(im.shape) == 3): |
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if not(np.allclose(im[...,0], im[...,1]) and np.allclose(im[...,2], im[...,1])): |
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rgb = True |
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print("rgb: {}".format(rgb)) |
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print("im shape: {}".format(im.shape)) |
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return rgb |
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