Compare commits
6 Commits
31c87bc5b7
...
53539df2fd
| Author | SHA1 | Date |
|---|---|---|
Sergey Revyakin
|
53539df2fd | 1 week ago |
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Sergey Revyakin
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d1eecb9d7d | 1 week ago |
|
Sergey Revyakin
|
7845d908ac | 1 week ago |
|
Sergey Revyakin
|
0d23ffd045 | 1 week ago |
|
Sergey Revyakin
|
d9b50ffa69 | 1 week ago |
|
Sergey Revyakin
|
7dccb2acef | 1 week ago |
@ -1,197 +1 @@
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from torchvision import models
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import torch.nn as nn
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import matplotlib
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import numpy as np
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import torch
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import cv2
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import gc
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import io
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def _render_plot(values, figsize=(16, 16), dpi=16):
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import matplotlib.pyplot as plt
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fig = plt.figure(figsize=figsize)
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plt.axes(ylim=(-1, 1))
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plt.plot(values, color="black")
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plt.gca().set_axis_off()
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plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
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plt.margins(0, 0)
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buf = io.BytesIO()
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fig.savefig(buf, format="png", dpi=dpi)
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buf.seek(0)
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img_arr = np.frombuffer(buf.getvalue(), dtype=np.uint8)
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buf.close()
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img = cv2.imdecode(img_arr, 1)
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if img is None:
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raise RuntimeError("failed to decode plot image")
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plt.clf()
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plt.cla()
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plt.close()
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plt.close(fig)
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return np.asarray(cv2.split(img), dtype=np.float32)
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def pre_func_ensemble(data=None, src="", ind_inference=0):
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try:
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import matplotlib.pyplot as plt
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matplotlib.use("Agg")
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plt.ioff()
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real = np.asarray(data[0], dtype=np.float32)
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imag = np.asarray(data[1], dtype=np.float32)
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signal = real + 1j * imag
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img_real = _render_plot(signal.real)
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img_mag = _render_plot(np.abs(signal))
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cv2.destroyAllWindows()
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gc.collect()
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print("Подготовка данных завершена")
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print()
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return [img_real, img_mag]
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except Exception as exc:
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print(str(exc))
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return None
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def build_func_ensemble(file_model="", file_config="", num_classes=None):
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try:
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import matplotlib.pyplot as plt
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matplotlib.use("Agg")
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plt.ioff()
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torch.cuda.empty_cache()
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num_classes = 2
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model1 = models.resnet18(pretrained=False)
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model2 = models.resnet50(pretrained=False)
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model1.fc = nn.Linear(model1.fc.in_features, num_classes)
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model2.fc = nn.Linear(model2.fc.in_features, num_classes)
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class Ensemble(nn.Module):
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def __init__(self, model1, model2):
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super().__init__()
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self.model1 = model1
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self.model2 = model2
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self.fc = nn.Linear(2 * num_classes, num_classes)
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def forward(self, x):
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if isinstance(x, (list, tuple)):
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x1 = x[0]
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x2 = x[1] if len(x) > 1 else x[0]
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else:
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x1 = x
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x2 = x
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y1 = self.model1(x1)
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y2 = self.model2(x2)
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y = torch.cat((y1, y2), dim=1)
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return self.fc(y)
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model = Ensemble(model1, model2)
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device = "cuda" if torch.cuda.is_available() else "cpu"
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if device != "cpu":
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model = model.to(device)
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model.load_state_dict(torch.load(file_model, map_location=device))
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model.eval()
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cv2.destroyAllWindows()
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gc.collect()
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print("Инициализация модели завершена")
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print()
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return model
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except Exception as exc:
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print(str(exc))
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return None
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def inference_func_ensemble(data=None, model=None, mapping=None, shablon=""):
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try:
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cv2.destroyAllWindows()
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gc.collect()
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torch.cuda.empty_cache()
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device = "cuda" if torch.cuda.is_available() else "cpu"
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if isinstance(data, (list, tuple)) and len(data) >= 2:
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inputs = [
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torch.unsqueeze(torch.tensor(data[0]).cpu(), 0).to(device).float(),
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torch.unsqueeze(torch.tensor(data[1]).cpu(), 0).to(device).float(),
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]
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else:
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tensor = torch.unsqueeze(torch.tensor(data).cpu(), 0).to(device).float()
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inputs = [tensor, tensor]
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with torch.no_grad():
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output = model(inputs)
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_, predict = torch.max(output.data, 1)
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prediction = mapping[int(np.asarray(predict.cpu())[0])]
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print("PREDICTION" + shablon + ": " + prediction)
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output = output.cpu()
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label = np.asarray(np.argmax(output, axis=1))[0]
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output = np.asarray(torch.squeeze(output, 0))
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expon = np.exp(output - np.max(output))
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probability = round((expon / expon.sum())[label], 2)
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cv2.destroyAllWindows()
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gc.collect()
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print("Уверенность" + shablon + " в предсказании: " + str(probability))
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print("Инференс завершен")
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print()
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return [prediction, probability]
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except Exception as exc:
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print(str(exc))
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return None
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def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inference=0, data=None):
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try:
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import matplotlib.pyplot as plt
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matplotlib.use("Agg")
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plt.ioff()
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if int(ind_inference) <= 100 and isinstance(data, (list, tuple)) and len(data) >= 2:
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fig, ax = plt.subplots()
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ax.imshow(np.moveaxis(data[0], 0, -1))
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plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_real_" + str(model_id) + "_" + model_type + ".png")
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plt.clf()
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plt.cla()
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plt.close(fig)
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cv2.destroyAllWindows()
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gc.collect()
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fig, ax = plt.subplots()
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ax.imshow(np.moveaxis(data[1], 0, -1))
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plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_mod_" + str(model_id) + "_" + model_type + ".png")
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plt.clf()
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plt.cla()
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plt.close(fig)
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cv2.destroyAllWindows()
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gc.collect()
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plt.clf()
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plt.cla()
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plt.close()
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cv2.destroyAllWindows()
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gc.collect()
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print("Постобработка завершена")
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print()
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except Exception as exc:
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print(str(exc))
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return None
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from Models.ensemble_2_pic import *
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@ -1,197 +1 @@
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from torchvision import models
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import torch.nn as nn
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import matplotlib
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import numpy as np
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import torch
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import cv2
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import gc
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import io
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def _render_plot(values, figsize=(16, 16), dpi=16):
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import matplotlib.pyplot as plt
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fig = plt.figure(figsize=figsize)
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plt.axes(ylim=(-1, 1))
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plt.plot(values, color="black")
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plt.gca().set_axis_off()
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plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
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plt.margins(0, 0)
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buf = io.BytesIO()
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fig.savefig(buf, format="png", dpi=dpi)
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buf.seek(0)
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img_arr = np.frombuffer(buf.getvalue(), dtype=np.uint8)
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buf.close()
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img = cv2.imdecode(img_arr, 1)
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if img is None:
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raise RuntimeError("failed to decode plot image")
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plt.clf()
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plt.cla()
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plt.close()
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plt.close(fig)
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return np.asarray(cv2.split(img), dtype=np.float32)
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def pre_func_ensemble(data=None, src="", ind_inference=0):
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try:
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import matplotlib.pyplot as plt
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matplotlib.use("Agg")
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plt.ioff()
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real = np.asarray(data[0], dtype=np.float32)
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imag = np.asarray(data[1], dtype=np.float32)
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signal = real + 1j * imag
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img_real = _render_plot(signal.real)
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img_mag = _render_plot(np.abs(signal))
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cv2.destroyAllWindows()
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gc.collect()
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print("Подготовка данных завершена")
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print()
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return [img_real, img_mag]
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except Exception as exc:
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print(str(exc))
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return None
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def build_func_ensemble(file_model="", file_config="", num_classes=None):
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try:
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import matplotlib.pyplot as plt
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matplotlib.use("Agg")
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plt.ioff()
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torch.cuda.empty_cache()
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num_classes = 2
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model1 = models.resnet18(pretrained=False)
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model2 = models.resnet50(pretrained=False)
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model1.fc = nn.Linear(model1.fc.in_features, num_classes)
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model2.fc = nn.Linear(model2.fc.in_features, num_classes)
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class Ensemble(nn.Module):
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def __init__(self, model1, model2):
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super().__init__()
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self.model1 = model1
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self.model2 = model2
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self.fc = nn.Linear(2 * num_classes, num_classes)
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def forward(self, x):
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if isinstance(x, (list, tuple)):
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x1 = x[0]
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x2 = x[1] if len(x) > 1 else x[0]
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else:
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x1 = x
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x2 = x
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y1 = self.model1(x1)
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y2 = self.model2(x2)
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y = torch.cat((y1, y2), dim=1)
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return self.fc(y)
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model = Ensemble(model1, model2)
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device = "cuda" if torch.cuda.is_available() else "cpu"
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if device != "cpu":
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model = model.to(device)
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model.load_state_dict(torch.load(file_model, map_location=device))
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model.eval()
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cv2.destroyAllWindows()
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gc.collect()
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print("Инициализация модели завершена")
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print()
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return model
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except Exception as exc:
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print(str(exc))
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return None
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def inference_func_ensemble(data=None, model=None, mapping=None, shablon=""):
|
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try:
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cv2.destroyAllWindows()
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gc.collect()
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torch.cuda.empty_cache()
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|
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device = "cuda" if torch.cuda.is_available() else "cpu"
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if isinstance(data, (list, tuple)) and len(data) >= 2:
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inputs = [
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torch.unsqueeze(torch.tensor(data[0]).cpu(), 0).to(device).float(),
|
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torch.unsqueeze(torch.tensor(data[1]).cpu(), 0).to(device).float(),
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]
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else:
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tensor = torch.unsqueeze(torch.tensor(data).cpu(), 0).to(device).float()
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inputs = [tensor, tensor]
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with torch.no_grad():
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output = model(inputs)
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_, predict = torch.max(output.data, 1)
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prediction = mapping[int(np.asarray(predict.cpu())[0])]
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print("PREDICTION" + shablon + ": " + prediction)
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output = output.cpu()
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label = np.asarray(np.argmax(output, axis=1))[0]
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output = np.asarray(torch.squeeze(output, 0))
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expon = np.exp(output - np.max(output))
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probability = round((expon / expon.sum())[label], 2)
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cv2.destroyAllWindows()
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gc.collect()
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print("Уверенность" + shablon + " в предсказании: " + str(probability))
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print("Инференс завершен")
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print()
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return [prediction, probability]
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except Exception as exc:
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print(str(exc))
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return None
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def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inference=0, data=None):
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try:
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import matplotlib.pyplot as plt
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matplotlib.use("Agg")
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plt.ioff()
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if int(ind_inference) <= 100 and isinstance(data, (list, tuple)) and len(data) >= 2:
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fig, ax = plt.subplots()
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ax.imshow(np.moveaxis(data[0], 0, -1))
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plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_real_" + str(model_id) + "_" + model_type + ".png")
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plt.clf()
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plt.cla()
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plt.close(fig)
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cv2.destroyAllWindows()
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gc.collect()
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fig, ax = plt.subplots()
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ax.imshow(np.moveaxis(data[1], 0, -1))
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plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_mod_" + str(model_id) + "_" + model_type + ".png")
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plt.clf()
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plt.cla()
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plt.close(fig)
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cv2.destroyAllWindows()
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gc.collect()
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plt.clf()
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plt.cla()
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plt.close()
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cv2.destroyAllWindows()
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gc.collect()
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print("Постобработка завершена")
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print()
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except Exception as exc:
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print(str(exc))
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return None
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from Models.ensemble_2_pic import *
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|
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@ -0,0 +1,197 @@
|
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from torchvision import models
|
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import torch.nn as nn
|
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import matplotlib
|
||||
import numpy as np
|
||||
import torch
|
||||
import cv2
|
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import gc
|
||||
import io
|
||||
|
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|
||||
def _render_plot(values, figsize=(16, 16), dpi=16):
|
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import matplotlib.pyplot as plt
|
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|
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fig = plt.figure(figsize=figsize)
|
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plt.axes(ylim=(-1, 1))
|
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plt.plot(values, color="black")
|
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plt.gca().set_axis_off()
|
||||
plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
|
||||
plt.margins(0, 0)
|
||||
|
||||
buf = io.BytesIO()
|
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fig.savefig(buf, format="png", dpi=dpi)
|
||||
buf.seek(0)
|
||||
img_arr = np.frombuffer(buf.getvalue(), dtype=np.uint8)
|
||||
buf.close()
|
||||
|
||||
img = cv2.imdecode(img_arr, 1)
|
||||
if img is None:
|
||||
raise RuntimeError("failed to decode plot image")
|
||||
|
||||
plt.clf()
|
||||
plt.cla()
|
||||
plt.close()
|
||||
plt.close(fig)
|
||||
|
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return np.asarray(cv2.split(img), dtype=np.float32)
|
||||
|
||||
|
||||
def pre_func_ensemble(data=None, src="", ind_inference=0):
|
||||
try:
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
matplotlib.use("Agg")
|
||||
plt.ioff()
|
||||
|
||||
real = np.asarray(data[0], dtype=np.float32)
|
||||
imag = np.asarray(data[1], dtype=np.float32)
|
||||
signal = real + 1j * imag
|
||||
|
||||
img_real = _render_plot(signal.real)
|
||||
img_mag = _render_plot(np.abs(signal))
|
||||
|
||||
cv2.destroyAllWindows()
|
||||
gc.collect()
|
||||
|
||||
print("Подготовка данных завершена")
|
||||
print()
|
||||
return [img_real, img_mag]
|
||||
|
||||
except Exception as exc:
|
||||
print(str(exc))
|
||||
return None
|
||||
|
||||
|
||||
def build_func_ensemble(file_model="", file_config="", num_classes=None):
|
||||
try:
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
matplotlib.use("Agg")
|
||||
plt.ioff()
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
num_classes = 2
|
||||
model1 = models.resnet18(pretrained=False)
|
||||
model2 = models.resnet50(pretrained=False)
|
||||
|
||||
model1.fc = nn.Linear(model1.fc.in_features, num_classes)
|
||||
model2.fc = nn.Linear(model2.fc.in_features, num_classes)
|
||||
|
||||
class Ensemble(nn.Module):
|
||||
def __init__(self, model1, model2):
|
||||
super().__init__()
|
||||
self.model1 = model1
|
||||
self.model2 = model2
|
||||
self.fc = nn.Linear(2 * num_classes, num_classes)
|
||||
|
||||
def forward(self, x):
|
||||
if isinstance(x, (list, tuple)):
|
||||
x1 = x[0]
|
||||
x2 = x[1] if len(x) > 1 else x[0]
|
||||
else:
|
||||
x1 = x
|
||||
x2 = x
|
||||
y1 = self.model1(x1)
|
||||
y2 = self.model2(x2)
|
||||
y = torch.cat((y1, y2), dim=1)
|
||||
return self.fc(y)
|
||||
|
||||
model = Ensemble(model1, model2)
|
||||
|
||||
device = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
if device != "cpu":
|
||||
model = model.to(device)
|
||||
model.load_state_dict(torch.load(file_model, map_location=device))
|
||||
model.eval()
|
||||
|
||||
cv2.destroyAllWindows()
|
||||
gc.collect()
|
||||
|
||||
print("Инициализация модели завершена")
|
||||
print()
|
||||
return model
|
||||
|
||||
except Exception as exc:
|
||||
print(str(exc))
|
||||
return None
|
||||
|
||||
|
||||
def inference_func_ensemble(data=None, model=None, mapping=None, shablon=""):
|
||||
try:
|
||||
cv2.destroyAllWindows()
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
device = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
if isinstance(data, (list, tuple)) and len(data) >= 2:
|
||||
inputs = [
|
||||
torch.unsqueeze(torch.tensor(data[0]).cpu(), 0).to(device).float(),
|
||||
torch.unsqueeze(torch.tensor(data[1]).cpu(), 0).to(device).float(),
|
||||
]
|
||||
else:
|
||||
tensor = torch.unsqueeze(torch.tensor(data).cpu(), 0).to(device).float()
|
||||
inputs = [tensor, tensor]
|
||||
|
||||
with torch.no_grad():
|
||||
output = model(inputs)
|
||||
_, predict = torch.max(output.data, 1)
|
||||
|
||||
prediction = mapping[int(np.asarray(predict.cpu())[0])]
|
||||
print("PREDICTION" + shablon + ": " + prediction)
|
||||
|
||||
output = output.cpu()
|
||||
label = np.asarray(np.argmax(output, axis=1))[0]
|
||||
output = np.asarray(torch.squeeze(output, 0))
|
||||
expon = np.exp(output - np.max(output))
|
||||
probability = round((expon / expon.sum())[label], 2)
|
||||
|
||||
cv2.destroyAllWindows()
|
||||
gc.collect()
|
||||
print("Уверенность" + shablon + " в предсказании: " + str(probability))
|
||||
print("Инференс завершен")
|
||||
print()
|
||||
return [prediction, probability]
|
||||
|
||||
except Exception as exc:
|
||||
print(str(exc))
|
||||
return None
|
||||
|
||||
|
||||
def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inference=0, data=None):
|
||||
try:
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
matplotlib.use("Agg")
|
||||
plt.ioff()
|
||||
|
||||
if int(ind_inference) <= 100 and isinstance(data, (list, tuple)) and len(data) >= 2:
|
||||
fig, ax = plt.subplots()
|
||||
ax.imshow(np.moveaxis(data[0], 0, -1))
|
||||
plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_real_" + str(model_id) + "_" + model_type + ".png")
|
||||
plt.clf()
|
||||
plt.cla()
|
||||
plt.close(fig)
|
||||
cv2.destroyAllWindows()
|
||||
gc.collect()
|
||||
|
||||
fig, ax = plt.subplots()
|
||||
ax.imshow(np.moveaxis(data[1], 0, -1))
|
||||
plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_mod_" + str(model_id) + "_" + model_type + ".png")
|
||||
plt.clf()
|
||||
plt.cla()
|
||||
plt.close(fig)
|
||||
cv2.destroyAllWindows()
|
||||
gc.collect()
|
||||
|
||||
plt.clf()
|
||||
plt.cla()
|
||||
plt.close()
|
||||
cv2.destroyAllWindows()
|
||||
gc.collect()
|
||||
|
||||
print("Постобработка завершена")
|
||||
print()
|
||||
|
||||
except Exception as exc:
|
||||
print(str(exc))
|
||||
return None
|
||||
@ -1,197 +1 @@
|
||||
from torchvision import models
|
||||
import torch.nn as nn
|
||||
import matplotlib
|
||||
import numpy as np
|
||||
import torch
|
||||
import cv2
|
||||
import gc
|
||||
import io
|
||||
|
||||
|
||||
def _render_plot(values, figsize=(16, 16), dpi=16):
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
fig = plt.figure(figsize=figsize)
|
||||
plt.axes(ylim=(-1, 1))
|
||||
plt.plot(values, color="black")
|
||||
plt.gca().set_axis_off()
|
||||
plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
|
||||
plt.margins(0, 0)
|
||||
|
||||
buf = io.BytesIO()
|
||||
fig.savefig(buf, format="png", dpi=dpi)
|
||||
buf.seek(0)
|
||||
img_arr = np.frombuffer(buf.getvalue(), dtype=np.uint8)
|
||||
buf.close()
|
||||
|
||||
img = cv2.imdecode(img_arr, 1)
|
||||
if img is None:
|
||||
raise RuntimeError("failed to decode plot image")
|
||||
|
||||
plt.clf()
|
||||
plt.cla()
|
||||
plt.close()
|
||||
plt.close(fig)
|
||||
|
||||
return np.asarray(cv2.split(img), dtype=np.float32)
|
||||
|
||||
|
||||
def pre_func_ensemble(data=None, src="", ind_inference=0):
|
||||
try:
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
matplotlib.use("Agg")
|
||||
plt.ioff()
|
||||
|
||||
real = np.asarray(data[0], dtype=np.float32)
|
||||
imag = np.asarray(data[1], dtype=np.float32)
|
||||
signal = real + 1j * imag
|
||||
|
||||
img_real = _render_plot(signal.real)
|
||||
img_mag = _render_plot(np.abs(signal))
|
||||
|
||||
cv2.destroyAllWindows()
|
||||
gc.collect()
|
||||
|
||||
print("Подготовка данных завершена")
|
||||
print()
|
||||
return [img_real, img_mag]
|
||||
|
||||
except Exception as exc:
|
||||
print(str(exc))
|
||||
return None
|
||||
|
||||
|
||||
def build_func_ensemble(file_model="", file_config="", num_classes=None):
|
||||
try:
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
matplotlib.use("Agg")
|
||||
plt.ioff()
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
num_classes = 2
|
||||
model1 = models.resnet18(pretrained=False)
|
||||
model2 = models.resnet50(pretrained=False)
|
||||
|
||||
model1.fc = nn.Linear(model1.fc.in_features, num_classes)
|
||||
model2.fc = nn.Linear(model2.fc.in_features, num_classes)
|
||||
|
||||
class Ensemble(nn.Module):
|
||||
def __init__(self, model1, model2):
|
||||
super().__init__()
|
||||
self.model1 = model1
|
||||
self.model2 = model2
|
||||
self.fc = nn.Linear(2 * num_classes, num_classes)
|
||||
|
||||
def forward(self, x):
|
||||
if isinstance(x, (list, tuple)):
|
||||
x1 = x[0]
|
||||
x2 = x[1] if len(x) > 1 else x[0]
|
||||
else:
|
||||
x1 = x
|
||||
x2 = x
|
||||
y1 = self.model1(x1)
|
||||
y2 = self.model2(x2)
|
||||
y = torch.cat((y1, y2), dim=1)
|
||||
return self.fc(y)
|
||||
|
||||
model = Ensemble(model1, model2)
|
||||
|
||||
device = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
if device != "cpu":
|
||||
model = model.to(device)
|
||||
model.load_state_dict(torch.load(file_model, map_location=device))
|
||||
model.eval()
|
||||
|
||||
cv2.destroyAllWindows()
|
||||
gc.collect()
|
||||
|
||||
print("Инициализация модели завершена")
|
||||
print()
|
||||
return model
|
||||
|
||||
except Exception as exc:
|
||||
print(str(exc))
|
||||
return None
|
||||
|
||||
|
||||
def inference_func_ensemble(data=None, model=None, mapping=None, shablon=""):
|
||||
try:
|
||||
cv2.destroyAllWindows()
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
device = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
if isinstance(data, (list, tuple)) and len(data) >= 2:
|
||||
inputs = [
|
||||
torch.unsqueeze(torch.tensor(data[0]).cpu(), 0).to(device).float(),
|
||||
torch.unsqueeze(torch.tensor(data[1]).cpu(), 0).to(device).float(),
|
||||
]
|
||||
else:
|
||||
tensor = torch.unsqueeze(torch.tensor(data).cpu(), 0).to(device).float()
|
||||
inputs = [tensor, tensor]
|
||||
|
||||
with torch.no_grad():
|
||||
output = model(inputs)
|
||||
_, predict = torch.max(output.data, 1)
|
||||
|
||||
prediction = mapping[int(np.asarray(predict.cpu())[0])]
|
||||
print("PREDICTION" + shablon + ": " + prediction)
|
||||
|
||||
output = output.cpu()
|
||||
label = np.asarray(np.argmax(output, axis=1))[0]
|
||||
output = np.asarray(torch.squeeze(output, 0))
|
||||
expon = np.exp(output - np.max(output))
|
||||
probability = round((expon / expon.sum())[label], 2)
|
||||
|
||||
cv2.destroyAllWindows()
|
||||
gc.collect()
|
||||
print("Уверенность" + shablon + " в предсказании: " + str(probability))
|
||||
print("Инференс завершен")
|
||||
print()
|
||||
return [prediction, probability]
|
||||
|
||||
except Exception as exc:
|
||||
print(str(exc))
|
||||
return None
|
||||
|
||||
|
||||
def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inference=0, data=None):
|
||||
try:
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
matplotlib.use("Agg")
|
||||
plt.ioff()
|
||||
|
||||
if int(ind_inference) <= 100 and isinstance(data, (list, tuple)) and len(data) >= 2:
|
||||
fig, ax = plt.subplots()
|
||||
ax.imshow(np.moveaxis(data[0], 0, -1))
|
||||
plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_real_" + str(model_id) + "_" + model_type + ".png")
|
||||
plt.clf()
|
||||
plt.cla()
|
||||
plt.close(fig)
|
||||
cv2.destroyAllWindows()
|
||||
gc.collect()
|
||||
|
||||
fig, ax = plt.subplots()
|
||||
ax.imshow(np.moveaxis(data[1], 0, -1))
|
||||
plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_mod_" + str(model_id) + "_" + model_type + ".png")
|
||||
plt.clf()
|
||||
plt.cla()
|
||||
plt.close(fig)
|
||||
cv2.destroyAllWindows()
|
||||
gc.collect()
|
||||
|
||||
plt.clf()
|
||||
plt.cla()
|
||||
plt.close()
|
||||
cv2.destroyAllWindows()
|
||||
gc.collect()
|
||||
|
||||
print("Постобработка завершена")
|
||||
print()
|
||||
|
||||
except Exception as exc:
|
||||
print(str(exc))
|
||||
return None
|
||||
from Models.ensemble_2_pic import *
|
||||
|
||||
@ -1,3 +0,0 @@
|
||||
SHARED_VECTOR_LEN = 4096
|
||||
SHARED_868_ADDR = 'tcp://127.0.0.1:35068'
|
||||
SHARED_915_ADDR = 'tcp://127.0.0.1:35069'
|
||||
Loading…
Reference in New Issue