yacwc

test.py

@@ -1,50 +0,0 @@
-# %%
-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 = 3)
-
-model.load_state_dict(torch.load('K:\Designs\ML\inaturalist_models\model_weights_start_1.pth'))
-model.eval()
-model.to('cuda')
-
-#img = r'D:\ishan\ml\inaturalist\test2017\00a903fa1d23b2f8f28248e81bc1c4a4.jpg'
-#img = r'J:\hummingbird_imagenet\hummingbird\Hummingbird_01_20210617093423.mp4_023.jpg'
-img = r'J:\hummingbird_imagenet\hummingbird\Hummingbird_01_20210609095848.mp4_133.jpg'
-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)
-# # %%
-
-
-
-# %%