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import cv2 # opencv | |
import numpy as np | |
font_scale = 1.5 | |
font = cv2.FONT_HERSHEY_PLAIN | |
# set the rectangle background to white | |
rectangle_bgr = (255, 255, 255) | |
# make a black image | |
img = np.zeros((500, 500)) |
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mkdir coco | |
cd coco | |
mkdir images | |
cd images | |
wget http://images.cocodataset.org/zips/train2017.zip | |
wget http://images.cocodataset.org/zips/val2017.zip | |
wget http://images.cocodataset.org/zips/test2017.zip | |
wget http://images.cocodataset.org/zips/unlabeled2017.zip |
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# Created by .ignore support plugin (hsz.mobi) | |
### Python template | |
# Byte-compiled / optimized / DLL files | |
__pycache__/ | |
*.py[cod] | |
*$py.class | |
# C extensions | |
*.so |
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class AttendDiagnose(nn.Module): | |
def __init__(self, number_measurements, filter_number): | |
super().__init__() | |
self.d_model = filter_number*number_measurements | |
self.embedding_conv = nn.Conv1d(number_measurements, filter_number*number_measurements, 1) | |
self.pe = PositionalEncoding(filter_number*number_measurements) | |
# embed_dim and attention_heads | |
self.masked_attn = nn.modules.activation.MultiheadAttention(filter_number*number_measurements, 8) | |
self.norm = nn.modules.normalization.LayerNorm(self.d_model) | |
self.final_layer = nn.Linear(self.d_model, 1) |
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#https://medium.com/@acpanjan/download-google-drive-files-using-wget-3c2c025a8b99 | |
# change FILEID | |
# https://drive.google.com/file/d/1UibyVC_C2hoT_XEw15gPEwPW4yFyJFeOEA | |
# FILEID = 1UibyVC_C2hoT_XEw15gPEwPW4yFyJFeOEA | |
# tools | |
https://gdrive-wget.glitch.me/ | |
# small file |