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import os
from typing import List, Tuple, Optional, Dict
import argparse
from PIL import Image
import numpy as np
import torch
import torch.nn.functional
from matplotlib import pyplot as plt
from atoms_detection.dataset import CoordinatesDataset
from atoms_detection.image_preprocessing import dl_prepro_image
from atoms_detection.model import BasicCNN
from utils.constants import ModelArgs, Split
from utils.paths import ACTIVATIONS_VIS_PATH
class ConvLayerVisualizer:
CONV_0 = 'Conv0'
CONV_3 = 'Conv3'
CONV_6 = 'Conv6'
def __init__(self, model_name: ModelArgs, ckpt_filename: str):
self.model_name = model_name
self.ckpt_filename = ckpt_filename
self.device = self.get_torch_device()
self.batch_size = 64
self.stride = 1
self.padding = 10
self.window_size = (21, 21)
@staticmethod
def get_torch_device():
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
return device
def sliding_window(self, image: np.ndarray) -> Tuple[int, int, np.ndarray]:
# slide a window across the image
x_to_center = self.window_size[0] // 2 - 1 if self.window_size[0] % 2 == 0 else self.window_size[0] // 2
y_to_center = self.window_size[1] // 2 - 1 if self.window_size[1] % 2 == 0 else self.window_size[1] // 2
for y in range(0, image.shape[0] - self.window_size[1]+1, self.stride):
for x in range(0, image.shape[1] - self.window_size[0]+1, self.stride):
# yield the current window
center_x = x + x_to_center
center_y = y + y_to_center
yield center_x, center_y, image[y:y + self.window_size[1], x:x + self.window_size[0]]
def padding_image(self, img: np.ndarray) -> np.ndarray:
image_padded = np.zeros((img.shape[0] + self.padding*2, img.shape[1] + self.padding*2))
image_padded[self.padding:-self.padding, self.padding:-self.padding] = img
return image_padded
def images_to_torch_input(self, image: np.ndarray) -> torch.Tensor:
expanded_img = np.expand_dims(image, axis=(0, 1))
input_tensor = torch.from_numpy(expanded_img).float()
input_tensor = input_tensor.to(self.device)
return input_tensor
def load_model(self) -> BasicCNN:
checkpoint = torch.load(self.ckpt_filename, map_location=self.device)
model = BasicCNN(num_classes=2).to(self.device)
model.load_state_dict(checkpoint['state_dict'])
model.eval()
return model
@staticmethod
def center_to_slice(x_center: int, y_center: int, width: int, height: int) -> Tuple[slice, slice]:
x_to_center = width // 2 - 1 if width % 2 == 0 else width // 2
y_to_center = height // 2 - 1 if height % 2 == 0 else height // 2
x = x_center - x_to_center
y = y_center - y_to_center
return slice(x, x + width), slice(y, y + height)
def get_prediction_map(self, padded_image: np.ndarray) -> Dict[str, np.ndarray]:
_shape = padded_image.shape
convs_activations_dict = {
self.CONV_0: (np.zeros(_shape), np.zeros(_shape)),
self.CONV_3: (np.zeros(_shape), np.zeros(_shape)),
self.CONV_6: (np.zeros(_shape), np.zeros(_shape))
}
model = self.load_model()
for x, y, image_crop in self.sliding_window(padded_image):
torch_input = self.images_to_torch_input(image_crop)
conv_outputs = self.get_conv_activations(torch_input, model)
for conv_layer_key, activations_blob in conv_outputs.items():
activation_map = self.sum_channels(activations_blob)
h, w = activation_map.shape
x_slice, y_slice = self.center_to_slice(x, y, w, h)
convs_activations_dict[conv_layer_key][0][y_slice, x_slice] += 1
convs_activations_dict[conv_layer_key][1][y_slice, x_slice] += activation_map
activations_dict = {}
for conv_layer_key, (counting_map, output_map) in convs_activations_dict.items():
zero_rows = np.sum(counting_map, axis=1)
zero_cols = np.sum(counting_map, axis=0)
output_map = np.delete(output_map, np.where(zero_rows == 0), axis=0)
clean_output_map = np.delete(output_map, np.where(zero_cols == 0), axis=1)
counting_map = np.delete(counting_map, np.where(zero_rows == 0), axis=0)
clean_counting_map = np.delete(counting_map, np.where(zero_cols == 0), axis=1)
activations_dict[conv_layer_key] = clean_output_map / clean_counting_map
return activations_dict
def get_conv_activations(self, input_image: torch.Tensor, model: BasicCNN) -> Dict[str, np.ndarray]:
conv_activations = {}
activations = input_image
for i, layer in enumerate(model.features):
activations = layer(activations)
if i == 0:
conv_activations[self.CONV_0] = activations.squeeze(0).detach().cpu().numpy()
elif i == 3:
conv_activations[self.CONV_3] = activations.squeeze(0).detach().cpu().numpy()
elif i == 6:
conv_activations[self.CONV_6] = activations.squeeze(0).detach().cpu().numpy()
return conv_activations
@staticmethod
def sum_channels(activations: np.ndarray):
aggregated_activations = np.sum(activations, axis=0)
return aggregated_activations
def image_to_pred_map(self, img: np.ndarray) -> Dict[str, np.ndarray]:
preprocessed_img = dl_prepro_image(img)
padded_image = self.padding_image(preprocessed_img)
activations_dict = self.get_prediction_map(padded_image)
return activations_dict
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"architecture",
type=ModelArgs,
choices=ModelArgs,
help="Architecture name"
)
parser.add_argument(
"ckpt_filename",
type=str,
help="Path to model checkpoint"
)
parser.add_argument(
"coords_csv",
type=str,
help="Coordinates CSV file to use as input"
)
return parser.parse_args()
if __name__ == "__main__":
args = get_args()
print(args)
conv_visualizer = ConvLayerVisualizer(
model_name=args.architecture,
ckpt_filename=args.ckpt_filename
)
coordinates_dataset = CoordinatesDataset(args.coords_csv)
for image_path, coordinates_path in coordinates_dataset.iterate_data(Split.TEST):
img = Image.open(image_path)
np_img = np.array(img)
activations_dict = conv_visualizer.image_to_pred_map(np_img)
img_name = os.path.splitext(os.path.basename(image_path))[0]
output_folder = os.path.join(ACTIVATIONS_VIS_PATH, f"{img_name}")
if not os.path.exists(output_folder):
os.makedirs(output_folder)
for conv_layer_key, activation_map in activations_dict.items():
fig = plt.figure()
plt.title(f"{conv_layer_key} -- {img_name}")
plt.imshow(activation_map)
output_path = os.path.join(output_folder, f"{conv_layer_key}_{img_name}.png")
plt.savefig(output_path, bbox_inches='tight')
plt.close(fig)
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