Source code for tensorboardX.utils

# Functions for converting
[docs]def figure_to_image(figures, close=True): """Render matplotlib figure to numpy format. Note that this requires the ``matplotlib`` package. Args: figure (matplotlib.pyplot.figure) or list of figures: figure or a list of figures close (bool): Flag to automatically close the figure Returns: numpy.array: image in [CHW] order """ import numpy as np try: import matplotlib.pyplot as plt import matplotlib.backends.backend_agg as plt_backend_agg except ModuleNotFoundError: print('please install matplotlib') def render_to_rgb(figure): canvas = plt_backend_agg.FigureCanvasAgg(figure) canvas.draw() data = np.frombuffer(canvas.buffer_rgba(), dtype=np.uint8) w, h = figure.canvas.get_width_height() image_hwc = data.reshape([h, w, 4])[:, :, 0:3] image_chw = np.moveaxis(image_hwc, source=2, destination=0) if close: plt.close(figure) return image_chw if isinstance(figures, list): images = [render_to_rgb(figure) for figure in figures] return np.stack(images) else: image = render_to_rgb(figures) return image
def graphviz_to_image(): pass def _prepare_video(V): import numpy as np b, t, c, h, w = V.shape if V.dtype == np.uint8: V = np.float32(V) / 255. def is_power2(num): return num != 0 and ((num & (num - 1)) == 0) # pad to nearest power of 2, all at once if not is_power2(V.shape[0]): len_addition = int(2**V.shape[0].bit_length() - V.shape[0]) V = np.concatenate( (V, np.zeros(shape=(len_addition, c, t, h, w))), axis=0) n_rows = 2**((b.bit_length() - 1) // 2) n_cols = b // n_rows V = np.reshape(V, newshape=(n_rows, n_cols, t, c, h, w)) V = np.transpose(V, axes=(2, 0, 4, 1, 5, 3)) V = np.reshape(V, newshape=(t, n_rows * h, n_cols * w, c)) return V def make_grid(I, ncols=8): # I: N1HW or N3HW import numpy as np assert isinstance( I, np.ndarray), 'plugin error, should pass numpy array here' if I.shape[1] == 1: I = np.concatenate([I, I, I], 1) assert I.ndim == 4 and I.shape[1] == 3 nimg = I.shape[0] H = I.shape[2] W = I.shape[3] ncols = min(nimg, ncols) nrows = int(np.ceil(float(nimg) / ncols)) canvas = np.zeros((3, H * nrows, W * ncols)) i = 0 for y in range(nrows): for x in range(ncols): if i >= nimg: break canvas[:, y * H:(y + 1) * H, x * W:(x + 1) * W] = I[i] i = i + 1 return canvas # if modality == 'IMG': # if x.dtype == np.uint8: # x = x.astype(np.float32) / 255.0 def convert_to_HWC(tensor, input_format): # tensor: numpy array import numpy as np assert(len(set(input_format)) == len(input_format)), "You can not use the same dimension shordhand twice." assert(len(tensor.shape) == len(input_format)), "size of input tensor and input format are different" input_format = input_format.upper() if len(input_format) == 4: index = [input_format.find(c) for c in 'NCHW'] tensor_NCHW = tensor.transpose(index) tensor_CHW = make_grid(tensor_NCHW) return tensor_CHW.transpose(1, 2, 0) if len(input_format) == 3: index = [input_format.find(c) for c in 'HWC'] tensor_HWC = tensor.transpose(index) if tensor_HWC.shape[2] == 1: tensor_HWC = np.concatenate([tensor_HWC, tensor_HWC, tensor_HWC], 2) return tensor_HWC if len(input_format) == 2: index = [input_format.find(c) for c in 'HW'] tensor = tensor.transpose(index) tensor = np.stack([tensor, tensor, tensor], 2) return tensor