70e4855f5608c4481dfffd5f762e310d631d06c3,test_model_CAM.py,,,#,68

Before Change

modelID = 1

// load category
with open("category_momentsv1.txt") as f:
    categories = [line.rstrip() for line in f.readlines()]



// load the labels
model = load_model(modelID, categories)

// load the model
features_blobs = []

// load the transformer
tf = returnTF() // image transformer

// get the softmax weight
params = list(model.parameters())
weight_softmax = params[-2].data.numpy()
weight_softmax[weight_softmax<0] = 0

// load the test image
if os.path.exists("test.jpg"):
    os.remove("test.jpg")
img_url = "http://places2.csail.mit.edu/imgs/demo/IMG_5970.JPG"
os.system("wget %s -q -O test.jpg" % img_url)
img = Image.open("test.jpg")
input_img = V(tf(img).unsqueeze(0), volatile=True)

// forward pass
logit = model.forward(input_img)
h_x = F.softmax(logit, 1).data.squeeze()
probs, idx = h_x.sort(0, True)

print("RESULT ON " + img_url)


// output the prediction of action category
print("--Top Actions:")
for i in range(0, 5):
    print("{:.3f} -> {}".format(probs[i], categories[idx[i]]))

// generate class activation mapping
print("Class activation map is saved as cam.jpg")
CAMs = returnCAM(features_blobs[0], weight_softmax, [idx[0]])

// render the CAM and output
img = cv2.imread("test.jpg")
height, width, _ = img.shape
heatmap = cv2.applyColorMap(cv2.resize(CAMs[0],(width, height)), cv2.COLORMAP_JET)
result = heatmap * 0.4 + img * 0.5
cv2.imwrite("cam.jpg", result)

After Change

        return [line.rstrip() for line in f.readlines()]


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="test on a single image")
    parser.add_argument("--multi", dest="multi", action="store_true")
    args = parser.parse_args()

    // load categories and model
    if args.multi:
      categories = load_categories("category_multi_momentsv2.txt")
      model = load_model(categories, "multi_moments_v2_RGB_resnet50_imagenetpretrained.pth.tar")
    else:
      categories = load_categories("category_momentsv2.txt")
      model = load_model(categories, "moments_v2_RGB_resnet50_imagenetpretrained.pth.tar")

    // load the model
    features_blobs = []

    // load the transformer
    tf = returnTF() // image transformer

    // get the softmax weight
    params = list(model.parameters())
    weight_softmax = params[-2].data
    weight_softmax[weight_softmax<0] = 0

    // load the test image
    if os.path.exists("test.jpg"):
        os.remove("test.jpg")
    img_url = "http://places2.csail.mit.edu/imgs/demo/IMG_5970.JPG"
    os.system("wget %s -q -O test.jpg" % img_url)
    img = Image.open("test.jpg")
    input_img = tf(img).unsqueeze(0)

    // forward pass
    logit = model.forward(input_img)
    h_x = F.softmax(logit, 1).data.squeeze()
    probs, idx = h_x.sort(0, True)

    print("RESULT ON " + img_url)


    // output the prediction of action category
    print("--Top Actions:")
    for i in range(0, 5):
        print("{:.3f} -> {}".format(probs[i], categories[idx[i]]))

    // generate class activation mapping
    print("Class activation map is saved as cam.jpg")
    CAMs = returnCAM(features_blobs[0], weight_softmax, [idx[0]])

    // render the CAM and output
    img = cv2.imread("test.jpg")
    height, width, _ = img.shape
    heatmap = cv2.applyColorMap(cv2.resize(CAMs[0],(width, height)), cv2.COLORMAP_JET)
    result = heatmap * 0.4 + img * 0.5
    cv2.imwrite("cam.jpg", result)

Instances