noise return gauss_noise * 255 def save_image(image, path): """ image: saved image, numpy array, dtype=float path: saving path """ # The type of the image is float, and range of the image might not be in [0, 255] # Thus, before saving the image, the image needs to be ...
完整代码如下: import wx import os import cv2 import numpy class MyWindow(wx.Frame): """docstring for MyWindow""" def __init__(self,*args,**kw): super(MyWindow, self).__init__(*args,**kw) self.makePanel() self.Centre() self.Show() def makePanel(self): #总框架 self.abox=...
"import numpy as np\n", "from collections import defaultdict\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from itertools import combinations\n", "from tqdm import tqdm\n", "from ite.cost.x_factory import co_factory\n", "import random\n", "import scipy\n",...
to_edge(UP))) self.wait(2) # 3b. Optimal Map T_1 (Grid Warping) # Create grid using lines grid = VGroup() # Vertical lines for x in np.arange(-3, 3.1, 0.5): grid.add(Line(np.array([x, -3, 0]), np.array([x, 3, 0]), stroke_width=1, color=GRAY)) # Horizontal ...
runtime.DeviceArray: if tensor.dtype == torch.bfloat16: tensor_as_int16 = tensor.view(dtype=torch.int16) device_array_as_int16 = iree.runtime.asdevicearray( device, unbox_tensor(tensor_as_int16).to("cpu").numpy() ) buffer_view = iree.runtime.HalBufferView( buffer=device_array_as...
image_tensor = torch.from_numpy(image_array).to(dtype=torch.float32) / 127.5 - 1.0 return image_tensor.to(device)def do_img2img( img, model, sampler, value_dict, num_samples, force_uc_zero_embeddings=[], additional_kwargs={}, offset_noise_level: float = 0.0, ...
mask = lens_to_mask(lens, length=seq_len) # useless here, as collate_fn will pad to max length in batch # get a random span to mask out for training conditionally frac_lengths = torch.zeros((batch,), device = self.device).float().uniform_(*self.frac_lengths_mask) frac_lengths =...
random.random(3), np.array([0.6])], axis=0) 95 + else: 96 + color = np.array([30/255, 144/255, 255/255, 0.6]) 97 + h, w = mask.shape[-2:] 98 + mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1) 99 + ax.imshow(mask_image) 100 + 101...
array, torch.Tensor]) -> List[Image.Image]: if isinstance(images, List) and isinstance(images[0], Image.Image): return images if isinstance(images, torch.Tensor): images = (images * 255).round().clamp(0, 255).to(torch.uint8).cpu().numpy() images = images.transpose(0, 2, 3, ...
random.rand(1, 4, 64, 64) t = np.array([999]) prompt_embeds=np.random.rand(1, 77, 1024) return latent,t,prompt_embeds def convert_into_torch(args): return [torch.from_numpy(arg).to(torch.float32) for arg in args] @@ -35,7 +44,10 @@ def export_unet(): unet = unet....