Category : gpu

@guvectorize([‘void(float64[:, :], int64[:, :, :], float64[:, :])’], ‘(m, n), (g, h, m) ->(g, h)’, target=’cuda’, nopython=True) def das(data1, k_value, image1): for i in range(image1.shape[0]) : for j in range(image1.shape[1]) : sum = 0. for k in range(data1.shape[0]): k_num = k_value[i, j, k] if k_num < data1.shape[1] : sum += data1[k, k_num] image1[i, j] = sum ..

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When I use wmi module in Python3, I found that the GPU Memory is wrong, My graphics card is RTX3070 Laptop with 8GB RAM Task manager and dxdiag.exe: screenshot here is my code: import wmi wmi_o = wmi.WMI() for gpu_item in wmi_o.Win32_VideoController(): print(gpu_item) and here is the output: instance of Win32_VideoController { AdapterCompatibility = "NVIDIA"; ..

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I use Cuda 10.0, torch 1.2.0 nvcc says Cuda compilation tools, release 10.0, V10.0.130 NVIDIA-SMI 496.13 Driver Version: 496.13 CUDA Version: 11.5 There is Runtime Error Traceback (most recent call last): File "demo_imitator.py", line 111, in <module> main() File "demo_imitator.py", line 102, in main generate_actor_result(test_opt, src_img_path) File "demo_imitator.py", line 44, in generate_actor_result imitator = Imitator(test_opt) ..

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Hi I am a beginner in DL and tensorflow, I created a CNN (you can see the model below) model = tf.keras.Sequential() model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=7, activation="relu", input_shape=[512, 640, 3])) model.add(tf.keras.layers.MaxPooling2D(2)) model.add(tf.keras.layers.Conv2D(filters=128, kernel_size=3, activation="relu")) model.add(tf.keras.layers.Conv2D(filters=128, kernel_size=3, activation="relu")) model.add(tf.keras.layers.MaxPooling2D(2)) model.add(tf.keras.layers.Conv2D(filters=256, kernel_size=3, activation="relu")) model.add(tf.keras.layers.Conv2D(filters=256, kernel_size=3, activation="relu")) model.add(tf.keras.layers.MaxPooling2D(2)) model.add(tf.keras.layers.Flatten()) model.add(tf.keras.layers.Dense(128, activation=’relu’)) model.add(tf.keras.layers.Dropout(0.5)) model.add(tf.keras.layers.Dense(64, activation=’relu’)) model.add(tf.keras.layers.Dropout(0.5)) model.add(tf.keras.layers.Dense(2, activation=’softmax’)) optimizer = tf.keras.optimizers.SGD(learning_rate=0.2) ..

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In the Pillow library, the Image class has a wonderful function – alpha_composite. If you slip it two Image in RGBA profile, it will glue two images together very correctly, for example: If both images had transparent areas – then the resulting image will also be transparent. If the one on top has a semi-transparent ..

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I have built OpenCV with Cuda for Python and am using the following lines to use GPU. net = cv2.dnn.readNetFromCaffe(proto_file, weights_file) net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA) net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA) However, my FPS with GPU is about 1.8 while it is about 0.8 with CPU. In the task manager, my GPU utilization is displayed as about 5%. My GPU is Nvidia GeForce ..

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