Spaces:
Running
on
Zero
Running
on
Zero
File size: 7,546 Bytes
79e0f15 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 |
import warnings
warnings.filterwarnings("ignore")
import os
import glob
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
from torchvision import models, transforms
from thop import profile
is_flop_cal = False
# get the activation
def get_activation(model, layer, input_img_data):
model.eval()
activations = []
inputs = []
def hook(module, input, output):
activations.append(output)
inputs.append(input[0])
hook_handle = layer.register_forward_hook(hook)
with torch.no_grad():
model(input_img_data)
hook_handle.remove()
return activations, inputs
def get_activation_map(frame, layer_name, resnet50, device):
# image pre-processing
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
# Apply the transformations (resize and normalize)
frame_tensor = transform(frame)
# adding index 0 changes the original [C, H, W] shape to [1, C, H, W]
if frame_tensor.dim() == 3:
frame_tensor = frame_tensor.unsqueeze(0)
# print(f'Image dimension: {frame_tensor.shape}')
# getting the activation of a given layer
conv_idx = layer_name
layer_obj = eval(conv_idx)
activations, inputs = get_activation(resnet50, layer_obj, frame_tensor)
activated_img = activations[0][0]
activation_array = activated_img.cpu().numpy()
# calculate FLOPs for layer
if is_flop_cal == True:
flops, params = profile(layer_obj, inputs=(inputs[0],), verbose=False)
if params == 0 and isinstance(layer_obj, torch.nn.Conv2d):
params = layer_obj.in_channels * layer_obj.out_channels * layer_obj.kernel_size[0] * layer_obj.kernel_size[1]
if layer_obj.bias is not None:
params += layer_obj.out_channels
# print(f"FLOPs for {layer_name}: {flops}, Params: {params}")
else:
flops, params = None, None
return activated_img, activation_array, flops, params
def process_video_frame(video_name, frame, frame_number, layer_name, resnet50, device):
# create a dictionary to store activation arrays for each layer
activations_dict = {}
total_flops = 0
total_params = 0
fig_name = f"resnet50_feature_map_layer_{layer_name}"
combined_name = f"resnet50_feature_map"
activated_img, activation_array, flops, params = get_activation_map(frame, layer_name, resnet50, device)
if is_flop_cal == True:
total_flops += flops
total_params += params
# save activation maps as png
# png_path = f'../visualisation/resnet50/{video_name}/frame_{frame_number}/'
# npy_path = f'../features/resnet50/{video_name}/frame_{frame_number}/'
# os.makedirs(png_path, exist_ok=True)
# os.makedirs(npy_path, exist_ok=True)
# get_activation_png(png_path, fig_name, activated_img)
# save activation features as pny
# get_activation_npy(npy_path, fig_name, activation_array)
# print(f"total FLOPs for Resnet50 layerstack: {total_flops}, Params: {total_params}")
frame_npy_path = f'../features/resnet50/{video_name}/frame_{frame_number}_{combined_name}.npy'
return activated_img, frame_npy_path, total_flops, total_params
def get_activation_png(png_path, fig_name, activated_img, n=8):
fig = plt.figure(figsize=(10, 10))
# visualise activation map for 64 channels
for i in range(n):
for j in range(n):
idx = (n * i) + j
if idx >= activated_img.shape[0]:
break
ax = fig.add_subplot(n, n, idx + 1)
ax.imshow(activated_img[idx].cpu().numpy(), cmap='viridis')
ax.axis('off')
# save figures
fig_path = f'{png_path}{fig_name}.png'
print(fig_path)
print("----------------" + '\n')
plt.savefig(fig_path)
plt.close()
def get_activation_npy(npy_path, fig_name, activation_array):
np.save(f'{npy_path}{fig_name}.npy', activation_array)
if __name__ == '__main__':
device_name = "gpu"
if device_name == "gpu":
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
else:
device = torch.device("cpu")
print(f"Running on {'GPU' if device.type == 'cuda' else 'CPU'}")
# pre-trained ResNet-50 model to device
resnet50 = models.resnet50(pretrained=True).to(device)
for idx, (name, layer) in enumerate(resnet50.named_children()):
print(f"Index: {idx}, Layer Name: {name}, Layer Type: {type(layer)}")
layer_name = 'layer4.2.conv2'
video_type = 'test'
# Test
if video_type == 'test':
metadata_path = "../../metadata/test_videos.csv"
# NR:
elif video_type == 'resolution_ugc':
resolution = '360P'
metadata_path = f"../../metadata/YOUTUBE_UGC_{resolution}_metadata.csv"
else:
metadata_path = f'../../metadata/{video_type.upper()}_metadata.csv'
ugcdata = pd.read_csv(metadata_path)
for i in range(len(ugcdata)):
video_name = ugcdata['vid'][i]
sampled_frame_path = os.path.join('../..', 'video_sampled_frame', 'sampled_frame', f'{video_name}')
print(f"Processing video: {video_name}")
image_paths = glob.glob(os.path.join(sampled_frame_path, f'{video_name}_*.png'))
frame_number = 0
for image in image_paths:
print(f"{image}")
frame_number += 1
process_video_frame(video_name, image, frame_number, layer_name, resnet50, device)
# # ResNet-50 layers to visualize
# layers_to_visualize_resnet50 = {
# 'conv1': 0,
# 'layer1.0.conv1': 2,
# 'layer1.0.conv2': 3,
# 'layer1.1.conv1': 5,
# 'layer1.1.conv2': 6,
# 'layer1.2.conv1': 8,
# 'layer1.2.conv2': 9,
# 'layer2.0.conv1': 11,
# 'layer2.0.conv2': 12,
# 'layer2.1.conv1': 14,
# 'layer2.1.conv2': 15,
# 'layer2.2.conv1': 17,
# 'layer2.2.conv2': 18,
# 'layer2.3.conv1': 20,
# 'layer2.3.conv2': 21,
# 'layer3.0.conv1': 23,
# 'layer3.0.conv2': 24,
# 'layer3.0.downsample.0': 25,
# 'layer3.1.conv1': 27,
# 'layer3.1.conv2': 28,
# 'layer3.2.conv1': 30,
# 'layer3.2.conv2': 31,
# 'layer3.3.conv1': 33,
# 'layer3.3.conv2': 34,
# 'layer4.0.conv1': 36,
# 'layer4.0.conv2': 37,
# 'layer4.0.downsample.0': 38,
# 'layer4.1.conv1': 40,
# 'layer4.1.conv2': 41,
# 'layer4.2.conv1': 43,
# 'layer4.2.conv2': 44,
# }
# Index: 0, Layer Name: conv1, Layer Type: <class 'torch.nn.modules.conv.Conv2d'>
# Index: 1, Layer Name: bn1, Layer Type: <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
# Index: 2, Layer Name: relu, Layer Type: <class 'torch.nn.modules.activation.ReLU'>
# Index: 3, Layer Name: maxpool, Layer Type: <class 'torch.nn.modules.pooling.MaxPool2d'>
# Index: 4, Layer Name: layer1, Layer Type: <class 'torch.nn.modules.container.Sequential'>
# Index: 5, Layer Name: layer2, Layer Type: <class 'torch.nn.modules.container.Sequential'>
# Index: 6, Layer Name: layer3, Layer Type: <class 'torch.nn.modules.container.Sequential'>
# Index: 7, Layer Name: layer4, Layer Type: <class 'torch.nn.modules.container.Sequential'>
# Index: 8, Layer Name: avgpool, Layer Type: <class 'torch.nn.modules.pooling.AdaptiveAvgPool2d'>
# Index: 9, Layer Name: fc, Layer Type: <class 'torch.nn.modules.linear.Linear'>
|