yacwc

old_files/test.py

@@ -0,0 +1,49 @@
+# %%
+import torchvision
+from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
+from collections import defaultdict as ddict
+import json
+import torch
+from torchvision import datasets, transforms as T
+import cv2
+import numpy as np
+import os
+import sys
+
+
+sys.path.append(r"K:\Designs\ML\inaturalist_models\data_aug")
+sys.path.append(r"K:\Designs\ML\inaturalist_models\vision")
+
+
+model = torchvision.models.detection.fasterrcnn_resnet50_fpn(num_classes = 2)
+
+model.load_state_dict(torch.load('K:\Designs\ML\inaturalist_models\model_weights_start_9.pth'))
+model.eval()
+model.to('cuda')
+
+
+import random
+rtdir = r'J:\hummingbird_imagenet\hummingbird'
+
+ff = os.listdir(rtdir)
+
+# %%
+img = os.path.join(rtdir,random.choice(ff))
+image = cv2.imread(img)[:,:,::-1].copy()
+o = T.ToTensor()(image).cuda()
+img = o[None, :, :, :]
+
+ou = model(img)
+from torchvision.utils import draw_bounding_boxes
+import torch as t
+oimage = t.tensor(image, dtype=t.uint8).permute([2,0,1])
+import matplotlib.pyplot as plt
+ox = draw_bounding_boxes(oimage, ou[0]['boxes'], width=1)
+plt.imshow(ox.permute([1,2,0]))
+# %%
+from data import iNaturalistDataset
+sd = iNaturalistDataset(validation=True)
+
+
+
+# %%